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Warmuzińska N, Łuczykowski K, Stryjak I, Wojtal E, Woderska-Jasińska A, Masztalerz M, Włodarczyk Z, Bojko B. Metabolomic and Lipidomic Profiling for Pre-Transplant Assessment of Delayed Graft Function Risk Using Chemical Biopsy with Microextraction Probes. Int J Mol Sci 2024; 25:13502. [PMID: 39769265 PMCID: PMC11728147 DOI: 10.3390/ijms252413502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 12/13/2024] [Accepted: 12/15/2024] [Indexed: 01/16/2025] Open
Abstract
Organ shortage remains a significant challenge in transplantology, prompting efforts to maximize the use of available organs and expand the donor pool, including through extended criteria donors (ECDs). However, ECD kidney recipients often face poorer outcomes, including a higher incidence of delayed graft function (DGF), which is linked to worse graft performance, reduced long-term survival, and an increased need for interventions like dialysis. This underscores the urgent need for strategies to improve early DGF risk assessment and optimize post-transplant management for high-risk patients. This study conducted multi-time point metabolomic and lipidomic analyses of donor kidney tissue and recipient plasma to identify compounds predicting DGF risk and assess the translational potential of solid-phase microextraction (SPME) for graft evaluation and early complication detection. The SPME-based chemical biopsy enabled a direct kidney analysis, while thin-film microextraction facilitated high-throughput plasma preparation. Following high-performance liquid chromatography coupled with a mass spectrometry analysis, the random forest algorithm was applied to identify compounds with predictive potential for assessing DGF risk before transplantation. Additionally, a comparison of metabolomic and lipidomic profiles of recipient plasma during the early post-operative days identified metabolites that distinguish between DGF and non-DGF patients. The selected compounds primarily included amino acids and their derivatives, nucleotides, organic acids, peptides, and lipids, particularly phospholipids and triacylglycerols. In conclusion, this study highlights the significant translational potential of chemical biopsies and plasma metabolite analyses for risk assessments and the non-invasive monitoring of DGF. The identified metabolites provide a foundation for developing a comprehensive DGF assessment and monitoring method, with potential integration into routine clinical practice.
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Affiliation(s)
- Natalia Warmuzińska
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089 Bydgoszcz, Poland
| | - Kamil Łuczykowski
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089 Bydgoszcz, Poland
| | - Iga Stryjak
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089 Bydgoszcz, Poland
| | - Emilia Wojtal
- Department of Transplantology and General Surgery, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-094 Bydgoszcz, Poland
| | - Aleksandra Woderska-Jasińska
- Department of Transplantology and General Surgery, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-094 Bydgoszcz, Poland
| | - Marek Masztalerz
- Department of Transplantology and General Surgery, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-094 Bydgoszcz, Poland
| | - Zbigniew Włodarczyk
- Department of Transplantology and General Surgery, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-094 Bydgoszcz, Poland
| | - Barbara Bojko
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089 Bydgoszcz, Poland
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Zhu J, Nie G, Dai X, Wang D, Li S, Zhang C. Activating PPARβ/δ-Mediated Fatty Acid β-Oxidation Mitigates Mitochondrial Dysfunction Co-induced by Environmentally Relevant Levels of Molybdenum and Cadmium in Duck Kidneys. Biol Trace Elem Res 2024:10.1007/s12011-024-04450-8. [PMID: 39546187 DOI: 10.1007/s12011-024-04450-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Accepted: 11/09/2024] [Indexed: 11/17/2024]
Abstract
Cadmium (Cd) and high molybdenum (Mo) pose deleterious effects on health. Prior studies have indicated that exposure to Mo and Cd leads to damage in duck kidneys, but limited studies have explored this damage from the perspective of fatty acid metabolism. In this study, 40 healthy 8-day-old ducks were randomly assigned to four groups and fed a basic diet containing Cd (4 mg/kg Cd) or Mo (100 mg/kg Mo) or both. Kidney tissues were harvested on the 16th week. Results demonstrated that Cd and/or Mo inhibited mitochondrial fatty acid β-oxidation and disrupted mitochondrial dynamics, along with significant suppression of peroxisome proliferator-activated receptor β/δ (PPARβ/δ) protein in duck kidneys. In vitro study, duck renal tubular epithelial cells were exposed for 12 h to either Mo (480 μM Mo), Cd (2.5 μM Cd), and GW0742 (0.3 μM, a potent agonist of PPARβ/δ) alone or in combination. The results demonstrated that Cd and/or Mo led to marked fatty acid oxidation deficiency and mitochondrial dysfunction and that PPARβ/δ protein was involved in the process. Altogether, this study found that activating PPARβ/δ-mediated fatty acid β-oxidation mitigates mitochondrial dysfunction co-induced by Mo and Cd in duck kidneys.
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Affiliation(s)
- Jiamei Zhu
- Jiangxi Provincial Key Laboratory for Animal Health, Institute of Animal Population Health, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Gaohui Nie
- Jiangxi Hongzhou Vocational College, Fengcheng, Jiangxi, China
| | - Xueyan Dai
- Jiangxi Provincial Key Laboratory for Animal Health, Institute of Animal Population Health, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Dianyun Wang
- Jiangxi Provincial Key Laboratory for Animal Health, Institute of Animal Population Health, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - ShanXin Li
- Jiangxi Provincial Key Laboratory for Animal Health, Institute of Animal Population Health, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Caiying Zhang
- Jiangxi Provincial Key Laboratory for Animal Health, Institute of Animal Population Health, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi, China.
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Zheng K, Qian Y, Wang H, Song D, You H, Hou B, Han F, Zhu Y, Feng F, Lam SM, Shui G, Li X. Withdrawn: Combinatorial lipidomics and proteomics underscore erythrocyte lipid membrane aberrations in the development of adverse cardio-cerebrovascular complications in maintenance hemodialysis patients. Redox Biol 2024; 76:103295. [PMID: 39159596 PMCID: PMC11378344 DOI: 10.1016/j.redox.2024.103295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/31/2024] [Indexed: 08/21/2024] Open
Abstract
This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). The authors reached out to the Publisher to alert the Publisher to incorrect text published in the article. After investigating the situation, the journal came to the conclusion that the wrong version of the file was sent by the authors to the production team during the proof stage and the misplaced text was not noticed by the authors when they approved the final version. After consulting with the Editor-in-Chief of the journal, the decision was made to withdraw the current version of the article.
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Affiliation(s)
- Ke Zheng
- Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yujun Qian
- Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China; Department of Nephrology, Jiangsu Province Hospital/The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Haiyun Wang
- Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Dan Song
- Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fei Han
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yicheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Sin Man Lam
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
| | - Xuemei Li
- Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
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Muglia L, Di Dio M, Filicetti E, Greco GI, Volpentesta M, Beccacece A, Fabbietti P, Lattanzio F, Corsonello A, Gembillo G, Santoro D, Soraci L. Biomarkers of chronic kidney disease in older individuals: navigating complexity in diagnosis. Front Med (Lausanne) 2024; 11:1397160. [PMID: 39055699 PMCID: PMC11269154 DOI: 10.3389/fmed.2024.1397160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Chronic kidney disease (CKD) in older individuals is a matter of growing concern in the field of public health across the globe. Indeed, prevalence of kidney function impairment increases with advancing age and is often exacerbated by age-induced modifications of kidney function, presence of chronic diseases such as diabetes, hypertension, and cardiovascular disorders, and increased burden related to frailty, cognitive impairment and sarcopenia. Accurate assessment of CKD in older individuals is crucial for timely intervention and management and relies heavily on biomarkers for disease diagnosis and monitoring. However, the interpretation of these biomarkers in older patients may be complex due to interplays between CKD, aging, chronic diseases and geriatric syndromes. Biomarkers such as serum creatinine, estimated glomerular filtration rate (eGFR), and albuminuria can be significantly altered by systemic inflammation, metabolic changes, and medication use commonly seen in this population. To overcome the limitations of traditional biomarkers, several innovative proteins have been investigated as potential, in this review we aimed at consolidating the existing data concerning the geriatric aspects of CKD, describing the challenges and considerations in using traditional and innovative biomarkers to assess CKD in older patients, highlighting the need for integration of the clinical context to improve biomarkers' accuracy.
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Affiliation(s)
- Lucia Muglia
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, Italian National Research Center on Aging (IRCCS INRCA), Ancona and Cosenza, Italy
| | - Michele Di Dio
- Unit of Urology, Department of Surgery, Annunziata Hospital, Cosenza, Italy
| | - Elvira Filicetti
- Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza, Italy
| | - Giada Ida Greco
- Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza, Italy
| | - Mara Volpentesta
- Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza, Italy
| | - Alessia Beccacece
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, Italian National Research Center on Aging (IRCCS INRCA), Ancona and Cosenza, Italy
| | - Paolo Fabbietti
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, Italian National Research Center on Aging (IRCCS INRCA), Ancona and Cosenza, Italy
| | - Fabrizia Lattanzio
- Scientific Direction, Italian National Research Center on Aging (IRCCS INRCA), Ancona, Italy
| | - Andrea Corsonello
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, Italian National Research Center on Aging (IRCCS INRCA), Ancona and Cosenza, Italy
- Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza, Italy
- Department of Pharmacy, Health and Nutritional Sciences, School of Medicine and Digital Technologies, University of Calabria, Arcavacata di Rende, Italy
| | - Guido Gembillo
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Domenico Santoro
- Unit of Nephrology and Dialysis, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Luca Soraci
- Unit of Geriatric Medicine, Italian National Research Center on Aging (IRCCS INRCA), Cosenza, Italy
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An TF, Zhang ZP, Xue JT, Luo WM, Li Y, Fang ZZ, Zong GW. Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients. Front Endocrinol (Lausanne) 2024; 15:1279034. [PMID: 38915893 PMCID: PMC11194401 DOI: 10.3389/fendo.2024.1279034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/17/2024] [Indexed: 06/26/2024] Open
Abstract
Objective The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. Methods We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. Results For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Conclusion Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.
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Affiliation(s)
- Tian-Feng An
- Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zhi-Peng Zhang
- Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Jun-Tang Xue
- Department of Surgery, Peking University Third Hospital, Beijing, China
| | - Wei-Ming Luo
- Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yang Li
- Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zhong-Ze Fang
- Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
| | - Guo-Wei Zong
- Department of Mathematics, School of Public Health, Tianjin Medical University, Tianjin, China
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Li T, Grams ME, Inker LA, Chen J, Rhee EP, Warady BA, Levey AS, Denburg MR, Furth SL, Ramachandran VS, Kimmel PL, Coresh J. Consistency of metabolite associations with measured glomerular filtration rate in children and adults. Clin Kidney J 2024; 17:sfae108. [PMID: 38859934 PMCID: PMC11163224 DOI: 10.1093/ckj/sfae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Indexed: 06/12/2024] Open
Abstract
Background There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation. Methods We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI). Results A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race. Conclusions The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.
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Affiliation(s)
- Taibo Li
- MD-PhD Program, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Morgan E Grams
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, NYU Grossman School of Medicine, New York City, NY, USA
- Department of Medicine and Department of Epidemiology, NYU Grossman School of Medicine, New York City, NY, USA
| | - Lesley A Inker
- Division of Nephrology, Department of Medicine, Tufts Medical Center, Boston, MA, USA
| | - Jingsha Chen
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Bradley A Warady
- Department of Pediatrics, Children's Mercy Hospital, Kansas City, MO, USA
| | - Andrew S Levey
- Division of Nephrology, Department of Medicine, Tufts Medical Center, Boston, MA, USA
| | - Michelle R Denburg
- Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Division of Nephrology, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania and Children's Hospital of Philadelphia (CHOP), Philadelphia, PA, USA
| | - Susan L Furth
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Division of Nephrology, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania and Children's Hospital of Philadelphia (CHOP), Philadelphia, PA, USA
| | - Vasan S Ramachandran
- Department of Population Health Sciences, University of Texas School of Public Health San Antonio, San Antonio, TX, USA
| | - Paul L Kimmel
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Josef Coresh
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, NYU Grossman School of Medicine, New York City, NY, USA
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Nusinovici S, Li H, Chong C, Yu M, Sørensen IMH, Bisgaard LS, Christoffersen C, Bro S, Liu S, Liu JJ, Chi LS, Wong TY, Tan GSW, Cheng CY, Sabanayagam C. Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease. J Nephrol 2024; 37:1007-1016. [PMID: 38308753 DOI: 10.1007/s40620-023-01872-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 12/26/2023] [Indexed: 02/05/2024]
Abstract
BACKGROUND The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction. METHODS Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance. RESULTS Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors. CONCLUSION The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.
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Affiliation(s)
- Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore.
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore.
| | - Hengtong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
| | - Crystal Chong
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
| | | | - Line Stattau Bisgaard
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christina Christoffersen
- Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Susanne Bro
- Department of Nephrology, Rigshospitalet University Hospital, Copenhagen, Denmark
| | - Sylvia Liu
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Jian-Jun Liu
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Lim Su Chi
- Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore
- Saw Swee Hock School of Public Heath, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Eye-ACP, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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8
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van der Burgh AC, Geurts S, Ahmad S, Ikram MA, Chaker L, Ferraro PM, Ghanbari M. Circulating metabolites associated with kidney function decline and incident CKD: a multi-platform population-based study. Clin Kidney J 2024; 17:sfad286. [PMID: 38213486 PMCID: PMC10783258 DOI: 10.1093/ckj/sfad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Indexed: 01/13/2024] Open
Abstract
Background Investigation of circulating metabolites associated with kidney function and chronic kidney disease (CKD) risk could enhance our understanding of underlying pathways and identify new biomarkers for kidney function. Methods We selected participants from the population-based Rotterdam Study with data on circulating metabolites and estimated glomerular filtration rate based on serum creatinine (eGFRcreat) available at the same time point. Data on eGFR based on serum cystatin C (eGFRcys) and urine albumin-to-creatinine ratio (ACR) were also included. CKD was defined as eGFRcreat <60 ml/min per 1.73 m2. Data on circulating metabolites (ntotal = 1381) was obtained from the Nightingale and Metabolon platform. Linear regression, linear mixed, and Cox proportional-hazards regression analyses were conducted to study the associations between metabolites and kidney function. We performed bidirectional two-sample Mendelian randomization analyses to investigate causality of the identified associations. Results We included 3337 and 1540 participants with data from Nightingale and Metabolon, respectively. A total of 1381 metabolites (243 from Nightingale and 1138 from Metabolon) were included in the analyses. A large number of metabolites were significantly associated with eGFRcreat, eGFRcys, ACR, and CKD, including 16 metabolites that were associated with all four outcomes. Among these, C-glycosyltryptophan (HR 1.50, 95%CI 1.31;1.71) and X-12026 (HR 1.46, 95%CI 1.26;1.68) were most strongly associated with CKD risk. We revealed sex differences in the associations of 11-ketoetiocholanolone glucuronide and 11-beta-glucuronide with the kidney function assessments. No causal associations between the identified metabolites and kidney function were observed. Conclusion Our study indicates that several circulating metabolites are associated with kidney function which are likely to have potential as biomarkers, rather than as molecules involved in the pathophysiology of kidney function decline.
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Affiliation(s)
- Anna C van der Burgh
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sven Geurts
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Layal Chaker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Pietro Manuel Ferraro
- Division of Nephrology, Department of Medicine, Università degli Studi di Verona, Verona, Italy
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Hassan MH, Galal O, Sakhr HM, Kamaleldeen EB, Zekry NF, Fateen E, Toghan R. Profile of plasma free amino acids, carnitine and acylcarnitines, and JAK2 v617f mutation as potential metabolic markers in children with type 1 diabetic nephropathy. Biomed Chromatogr 2023; 37:e5747. [PMID: 37728037 DOI: 10.1002/bmc.5747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/21/2023]
Abstract
Fifty diabetic nephropathy (DN) children with type 1 diabetes mellitus (T1DM) and 50 healthy matched controls were included. Chromatographic assays of 14 amino acids, free carnitine and 27 carnitine esters using high-performance liquid chromatography/electrospray ionization-mass spectroscopy, and genetic testing for JAK2v617f mutation using real-time PCR were performed. Patients had significantly lower levels of tyrosine, branched-chain amino acids (BCAAs), and BCAA/AAA (aromatic chain amino acids) ratios, glycine, arginine, ornithine, free carnitine and some carnitine esters (C5, 6, 12 and 16) and higher phenylalanine, phenylalanine/tyrosine ratio and C18 compared with the controls and in the macro-albuminuria vs. the microalbuminuria group (p < 0.05 for all) except for free carnitine. Plasma carnitine was negatively correlated with eGFR (r = -0.488, p = 0.000). There were significant positive correlations between tyrosine with UACR ratio (r = 0.296, p = 0.037). The plasma BCAA/AAA ratio showed significant negative correlations with UACR (r = -0.484, p = 0.000). There was a significantly higher frequency of the JAK2V617F gene mutation in diabetic nephropathy patients compared with the control group and in macro-albuminuria than the microalbuminuria group (p = 0.000) for both. When monitoring children with T1DM, plasma free amino acids and acylcarnitine profiles should be considered, especially if they have tested positive for JAK2V617F for the early diagnosis of DN.
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Affiliation(s)
- Mohammed H Hassan
- Department of Medical Biochemistry, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Omyma Galal
- Medical Physiology Department, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Hala M Sakhr
- Department of Pediatrics, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Eman B Kamaleldeen
- Department of Pediatrics, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Nadia Farouk Zekry
- Medical Physiology Department, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Ekram Fateen
- Department of Biochemical Genetics, National Research Center, Cairo, Egypt
| | - Rana Toghan
- Medical Physiology Department, Faculty of Medicine, South Valley University, Qena, Egypt
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10
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Downie ML, Desjarlais A, Verdin N, Woodlock T, Collister D. Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience. Can J Kidney Health Dis 2023; 10:20543581231209012. [PMID: 37920777 PMCID: PMC10619345 DOI: 10.1177/20543581231209012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/10/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose of review Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review. Sources of information Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics." Methods A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience. Key findings Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy. Limitations There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory. Implications Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.
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Affiliation(s)
- Mallory L. Downie
- McGill University Health Center Research Institute, Montreal, QC, Canada
| | - Arlene Desjarlais
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - Nancy Verdin
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - Tania Woodlock
- Kidney Research Scientist Core Education and National Training Program, Montreal, QC, Canada
| | - David Collister
- Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
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11
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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12
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Rhee J, Loftfield E, Albanes D, Layne TM, Stolzenberg-Solomon R, Liao LM, Playdon MC, Berndt SI, Sampson JN, Freedman ND, Moore SC, Purdue MP. A metabolomic investigation of serum perfluorooctane sulfonate and perfluorooctanoate. ENVIRONMENT INTERNATIONAL 2023; 180:108198. [PMID: 37716341 PMCID: PMC10591812 DOI: 10.1016/j.envint.2023.108198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/10/2023] [Accepted: 09/07/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND Exposures to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), environmentally persistent chemicals detectable in the blood of most Americans, have been associated with several health outcomes. To offer insight into their possible biologic effects, we evaluated the metabolomic correlates of circulating PFOS and PFOA among 3,647 participants in eight nested case-control serum metabolomic profiling studies from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. METHODS Metabolomic profiling was conducted by Metabolon Inc., using ultra high-performance liquid chromatography/tandem accurate mass spectrometry. We conducted study-specific multivariable linear regression analyses estimating the associations of metabolite levels with levels of PFOS or PFOA. For metabolites measured in at least 3 of 8 nested case-control studies, random effects meta-analysis was used to summarize study-specific results (1,038 metabolites in PFOS analyses and 1,100 in PFOA analyses). RESULTS The meta-analysis identified 51 and 38 metabolites associated with PFOS and PFOA, respectively, at a Bonferroni-corrected significance level (4.8x10-5 and 4.6x10-5, respectively). For both PFOS and PFOA, the most common types of associated metabolites were lipids (sphingolipids, fatty acid metabolites) and xenobiotics (xanthine metabolites, chemicals). Positive associations were commonly observed with lipid metabolites sphingomyelin (d18:1/18:0) (P = 2.0x10-10 and 2.0x10-8, respectively), 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (P = 2.7x10-15, 1.1x10-17), and lignoceroylcarnitine (C24) (P = 2.6x10-8, 6.2x10-6). The strongest positive associations were observed for chemicals 3,5-dichloro-2,6-dihydroxybenzoic acid (P = 3.0x10-112 and 6.8x10-13, respectively) and 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (P = 1.6x10-14, 2.3x10-6). Other metabolites positively associated with PFOS included D-glucose (carbohydrate), carotene diol (vitamin A metabolism), and L-alpha-aminobutyric acid (glutathione metabolism), while uric acid (purine metabolite) was positively associated with PFOA. PFOS associations were consistent even after adjusting for PFOA as a covariate, while PFOA associations were greatly attenuated with PFOS adjustment. CONCLUSIONS In this large metabolomic study, we observed robust positive associations with PFOS for several molecules. Further investigation of these metabolites may offer insight into PFOS-related biologic effects.
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Affiliation(s)
- Jongeun Rhee
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Tracy M Layne
- Department of Obstetrics, Gynecology, and Reproductive Science, and Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachael Stolzenberg-Solomon
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Linda M Liao
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Mary C Playdon
- Department of Nutrition and Integrative Physiology, University of Utah and Cancer Control and Population Sciences Program, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Sonja I Berndt
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Joshua N Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Steven C Moore
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Mark P Purdue
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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13
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Belghasem M, Yin W, Lotfollahzadeh S, Yang X, Meyer RD, Napoleon MA, Sellinger IE, Vazirani A, Metrikova E, Jose A, Zhebrun A, Whelan SA, Lee N, Rahimi N, Chitalia VC. Tryptophan Metabolites Target Transmembrane and Immunoglobulin Domain-Containing 1 Signaling to Augment Renal Tubular Injury. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:1501-1516. [PMID: 37676196 PMCID: PMC10548275 DOI: 10.1016/j.ajpath.2023.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/18/2023] [Accepted: 06/29/2023] [Indexed: 09/08/2023]
Abstract
Chronic kidney disease (CKD) is characterized by the accumulation of uremic toxins and renal tubular damage. Tryptophan-derived uremic toxins [indoxyl sulfate (IS) and kynurenine (Kyn)] are well-characterized tubulotoxins. Emerging evidence suggests that transmembrane and immunoglobulin domain-containing 1 (TMIGD1) protects tubular cells and promotes survival. However, the direct molecular mechanism(s) underlying how these two opposing pathways crosstalk remains unknown. We posited that IS and Kyn mediate tubular toxicity through TMIGD1 and the loss of TMIGD1 augments tubular injury. Results from the current study showed that IS and Kyn suppressed TMIGD1 transcription in tubular cells in a dose-dependent manner. The wild-type CCAAT enhancer-binding protein β (C/EBPβ) enhanced, whereas a dominant-negative C/EBPβ suppressed, TMIGD1 promoter activity. IS down-regulated C/EBPβ in primary human renal tubular cells. The adenine-induced CKD, unilateral ureteric obstruction, and deoxycorticosterone acetate salt unilateral nephrectomy models showed reduced TMIGD1 expression in the renal tubules, which correlated with C/EBPβ expression. C/EBPβ levels negatively correlated with the IS and Kyn levels. Inactivation of TMIGD1 in mice significantly lowered acetylated tubulin, decreased tubular cell proliferation, caused severe tubular damage, and worsened renal function. Thus, the current results demonstrate that TMIGD1 protects renal tubular cells from renal injury in different models of CKD and uncovers a novel mechanism of tubulotoxicity of tryptophan-based uremic toxins.
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Affiliation(s)
- Mostafa Belghasem
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Wenqing Yin
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Saran Lotfollahzadeh
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Xiaosheng Yang
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Rosana D Meyer
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Marc A Napoleon
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Isaac E Sellinger
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Aniket Vazirani
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts; Department of Surgery, Boston University School of Medicine, Boston, Massachusetts
| | - Elena Metrikova
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Asha Jose
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Anna Zhebrun
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Stephen A Whelan
- Department of Surgery, Boston University School of Medicine, Boston, Massachusetts; Chemistry Instrumentation Core, School of Chemistry, Boston University, Boston, Massachusetts
| | - Norman Lee
- Department of Surgery, Boston University School of Medicine, Boston, Massachusetts; Chemistry Instrumentation Core, School of Chemistry, Boston University, Boston, Massachusetts
| | - Nader Rahimi
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Vipul C Chitalia
- Renal Section, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts; Veterans Affairs Boston Healthcare System, Boston, Massachusetts; Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts; Center of Cross-Organ Vascular Pathology, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts.
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14
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Trischitta V, Mastroianno M, Scarale MG, Prehn C, Salvemini L, Fontana A, Adamski J, Schena FP, Cosmo SD, Copetti M, Menzaghi C. Circulating metabolites improve the prediction of renal impairment in patients with type 2 diabetes. BMJ Open Diabetes Res Care 2023; 11:e003422. [PMID: 37734903 PMCID: PMC10514631 DOI: 10.1136/bmjdrc-2023-003422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
INTRODUCTION Low glomerular filtration rate (GFR) is a leading cause of reduced lifespan in type 2 diabetes. Unravelling biomarkers capable to identify high-risk patients can help tackle this burden. We investigated the association between 188 serum metabolites and kidney function in type 2 diabetes and then whether the associated metabolites improve two established clinical models for predicting GFR decline in these patients. RESEARCH DESIGN AND METHODS Two cohorts comprising 849 individuals with type 2 diabetes (discovery and validation samples) and a follow-up study of 575 patients with estimated GFR (eGFR) decline were analyzed. RESULTS Ten metabolites were independently associated with low eGFR in the discovery sample, with nine of them being confirmed also in the validation sample (ORs range 1.3-2.4 per 1SD, p values range 1.9×10-2-2.5×10-9). Of these, five metabolites were also associated with eGFR decline (ie, tiglylcarnitine, decadienylcarnitine, total dimethylarginine, decenoylcarnitine and kynurenine) (β range -0.11 to -0.19, p values range 4.8×10-2 to 3.0×10-3). Indeed, tiglylcarnitine and kynurenine, which captured all the information of the other three markers, improved discrimination and reclassification (all p<0.01) of two clinical prediction models of GFR decline in people with diabetes. CONCLUSIONS Further studies are needed to validate our findings in larger cohorts of different clinical, environmental and genetic background.
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Affiliation(s)
- Vincenzo Trischitta
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
- Experimental Medicine, University of Rome La Sapienza, Rome, Italy
| | - Mario Mastroianno
- Scientific Direction, Fondazione IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - Maria Giovanna Scarale
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Cornelia Prehn
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Lucia Salvemini
- Research Unit of Diabetes and Endocrine Diseases, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Andrea Fontana
- Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Biochemistry, National University Singapore Yong Loo Lin School of Medicine, Singapore
| | | | - Salvatore De Cosmo
- Unit of Internal Medicine, IRCCS Casa Sollievo della Sofferenza San Giovanni Rotondo, Foggia, Italy
| | - Massimiliano Copetti
- Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Claudia Menzaghi
- Research Unit of Diabetes and Endocrine Diseases, Istituti di Ricovero e Cura a Carattere Scientifico Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
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15
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Li X, Miao Y, Fang Z, Zhang Q. The association and prediction value of acylcarnitine on diabetic nephropathy in Chinese patients with type 2 diabetes mellitus. Diabetol Metab Syndr 2023; 15:130. [PMID: 37330521 DOI: 10.1186/s13098-023-01058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND Acylcarnitines play a role in type 2 diabetes mellitus (T2DM), but the relationship between acylcarnitine and diabetic nephropathy was unclear. We aimed to explore the association of acylcarnitine metabolites with diabetic nephropathy and estimate the predictive value of acylcarnitine for diabetic nephropathy. METHODS A total of 1032 (mean age: 57.24 ± 13.82) T2DM participants were derived from Liaoning Medical University First Affiliated Hospital. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Diabetic nephropathy was ascertained based on the medical records. Factor analysis was used to reduce the dimensions and extract factors of the 25 acylcarnitine metabolites. Logistic regression was used to estimate the relationship between factors extracted from the 25 acylcarnitine metabolites and diabetic nephropathy. Receiver operating characteristic curves were used to test the predictive values of acylcarnitine factors for diabetic nephropathy. RESULTS Among all T2DM participants, 138 (13.37%) patients had diabetic nephropathy. Six factors were extracted from 25 acylcarnitines, which account for 69.42% of the total variance. In multi-adjusted logistic regression models, the odds ratio (OR, 95% confidence interval [CI]) of diabetic nephropathy on factor 1 (including butyrylcarnitine/glutaryl-carnitine/hexanoylcarnitine/octanoylcarnitine/decanoylcarnitine/lauroylcarnitine/tetradecenoylcarnitine), factor 2 (including propionylcarnitine/palmitoylcarnitine/hydroxypalmitoleyl-carnitine/octadecanoylcarnitine/arachidiccarnitine), and factor 3 (including tetradecanoyldiacylcarnitine/behenic carnitine/tetracosanoic carnitine/hexacosanoic carnitine) were 1.33 (95%CI 1.12-1.58), 0.76 (95%CI 0.62-0.93), and 1.24 (95%CI 1.05-1.47), respectively. The area under the curve for diabetic nephropathy prediction was significantly increased after the complement of factors 1, 2, and 3 in traditional factors model (P < 0.01). CONCLUSIONS Some plasma acylcarnitine metabolites extracted in factors 1 and 3 were higher in diabetic nephropathy, while factor 2 was lower in diabetic nephropathy among T2DM patients. The addition of acylcarnitine to traditional factors model improved the predictive value for diabetic nephropathy.
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Affiliation(s)
- Xuerui Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Anshan Road 154, Heping district, Tianjin, 300052, China
| | - Yuyang Miao
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Anshan Road 154, Heping district, Tianjin, 300052, China
| | - Zhongze Fang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Qixiangtai Road 22, Heping district, Tianjin, 300070, China.
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China.
| | - Qiang Zhang
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Anshan Road 154, Heping district, Tianjin, 300052, China.
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Mohandes S, Doke T, Hu H, Mukhi D, Dhillon P, Susztak K. Molecular pathways that drive diabetic kidney disease. J Clin Invest 2023; 133:165654. [PMID: 36787250 PMCID: PMC9927939 DOI: 10.1172/jci165654] [Citation(s) in RCA: 140] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Abstract
Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney disease (DKD) is responsible for close to half of all chronic kidney disease cases. DKD usually develops in a genetically susceptible individual as a result of poor metabolic (glycemic) control. Molecular and genetic studies indicate the key role of podocytes and endothelial cells in driving albuminuria and early kidney disease in diabetes. Proximal tubule changes show a strong association with the glomerular filtration rate. Hyperglycemia represents a key cellular stress in the kidney by altering cellular metabolism in endothelial cells and podocytes and by imposing an excess workload requiring energy and oxygen for proximal tubule cells. Changes in metabolism induce early adaptive cellular hypertrophy and reorganization of the actin cytoskeleton. Later, mitochondrial defects contribute to increased oxidative stress and activation of inflammatory pathways, causing progressive kidney function decline and fibrosis. Blockade of the renin-angiotensin system or the sodium-glucose cotransporter is associated with cellular protection and slowing kidney function decline. Newly identified molecular pathways could provide the basis for the development of much-needed novel therapeutics.
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Affiliation(s)
- Samer Mohandes
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Tomohito Doke
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hailong Hu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dhanunjay Mukhi
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Poonam Dhillon
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine;,Institute for Diabetes, Obesity, and Metabolism;,Department of Genetics; and,Kidney Innovation Center; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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17
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Wruck W, Boima V, Erichsen L, Thimm C, Koranteng T, Kwakyi E, Antwi S, Adu D, Adjaye J. Urine-Based Detection of Biomarkers Indicative of Chronic Kidney Disease in a Patient Cohort from Ghana. J Pers Med 2022; 13:jpm13010038. [PMID: 36675700 PMCID: PMC9863148 DOI: 10.3390/jpm13010038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/07/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Chronic kidney disease (CKD) is a global health burden with a continuously increasing prevalence associated with an increasing incidence of diabetes and hypertension in aging populations. CKD is characterized by low glomerular filtration rate (GFR) and other renal impairments including proteinuria, thus implying that multiple factors may contribute to the etiology this disease. While there are indications of ethnic differences, it is hard to disentangle these from confounding social factors. Usually, CKD is detected in later stages of the disease when irreversible renal damage has already occurred, thus suggesting a need for early non-invasive diagnostic markers. In this study, we explored the urine secretome of a CKD patient cohort from Ghana with 40 gender-matched patients and 40 gender-matched healthy controls employing a kidney injury and a more general cytokine assay. We identified panels of kidney-specific cytokine markers, which were also gender-specific, and a panel of gender-independent cytokine markers. The gender-specific markers are IL10 and MME for male and CLU, RETN, AGER, EGFR and VEGFA for female. The gender-independent cytokine markers were APOA1, ANGPT2, C5, CFD, GH1, ICAM1, IGFBP2, IL8, KLK4, MMP9 and SPP1 (up-regulated) and FLT3LG, CSF1, PDGFA, RETN and VEGFA (down-regulated). APOA1-the major component of HDL particles-was up-regulated in Ghanaian CKD patients and its co-occurrence with APOL1 in a subpopulation of HDL particles may point to specific CKD-predisposing APOL1 haplotypes in patients of African descent-this, however, needs further investigation. The identified panels, though preliminary, lay down the foundation for the development of robust CKD-diagnostic assays.
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Affiliation(s)
- Wasco Wruck
- Institute for Stem Cell Research and Regenerative Medicine, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225 Düsseldorf, Germany
| | - Vincent Boima
- Department of Medicine & Therapeutics, University of Ghana Medical School, College of Health Sciences, Box 4236, University of Ghana, Accra P.O. Box LG 1181, Ghana
| | - Lars Erichsen
- Institute for Stem Cell Research and Regenerative Medicine, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225 Düsseldorf, Germany
| | - Chantelle Thimm
- Institute for Stem Cell Research and Regenerative Medicine, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225 Düsseldorf, Germany
| | - Theresa Koranteng
- NHS-Clover Health Centre, Equitable House, 10 Woolich New Road, Woolich, London SE18 6AB, UK
| | - Edward Kwakyi
- Department of Medicine & Therapeutics, University of Ghana Medical School, College of Health Sciences, Box 4236, University of Ghana, Accra P.O. Box LG 1181, Ghana
| | - Sampson Antwi
- Department of Child Health, School of Medical Sciences, Kwame Nkrumah University of Science and Technology, Komfo Anokye Teaching Hospital, Kumasi P.O. Box KS 9265, Ghana
| | - Dwomoa Adu
- Department of Medicine & Therapeutics, University of Ghana Medical School, College of Health Sciences, Box 4236, University of Ghana, Accra P.O. Box LG 1181, Ghana
| | - James Adjaye
- Institute for Stem Cell Research and Regenerative Medicine, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225 Düsseldorf, Germany
- EGA Institute for Women’s Health, University College London, 86-96 Chenies Mews, London WC1E 6HX, UK
- Correspondence:
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18
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Verissimo T, Faivre A, Sgardello S, Naesens M, de Seigneux S, Criton G, Legouis D. Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function. Metabolites 2022; 12:57. [PMID: 35050179 PMCID: PMC8778290 DOI: 10.3390/metabo12010057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/26/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023] Open
Abstract
Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.
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Affiliation(s)
- Thomas Verissimo
- Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland; (T.V.); (A.F.); (S.d.S.)
| | - Anna Faivre
- Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland; (T.V.); (A.F.); (S.d.S.)
| | - Sebastian Sgardello
- Department of Surgery, University Hospital of Geneva, 1205 Geneva, Switzerland;
| | - Maarten Naesens
- Service of Nephrology, University Hospitals of Leuven, 3000 Leuven, Belgium;
| | - Sophie de Seigneux
- Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland; (T.V.); (A.F.); (S.d.S.)
- Service of Nephrology, Department of Internal Medicine Specialties, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Gilles Criton
- Geneva School of Economics and Management, University of Geneva, 1205 Geneva, Switzerland;
| | - David Legouis
- Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland; (T.V.); (A.F.); (S.d.S.)
- Division of Intensive Care, Department of Acute Medicine, University hospital of Geneva, 1205 Geneva, Switzerland
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19
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Yan Z, Wang G, Shi X. Advances in the Progression and Prognosis Biomarkers of Chronic Kidney Disease. Front Pharmacol 2022; 12:785375. [PMID: 34992536 PMCID: PMC8724575 DOI: 10.3389/fphar.2021.785375] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/30/2021] [Indexed: 12/29/2022] Open
Abstract
Chronic kidney disease (CKD) is one of the increasingly serious public health concerns worldwide; the global burden of CKD is increasingly due to high morbidity and mortality. At present, there are three key problems in the clinical treatment and management of CKD. First, the current diagnostic indicators, such as proteinuria and serum creatinine, are greatly interfered by the physiological conditions of patients, and the changes in the indicator level are not synchronized with renal damage. Second, the established diagnosis of suspected CKD still depends on biopsy, which is not suitable for contraindication patients, is also traumatic, and is not sensitive to early progression. Finally, the prognosis of CKD is affected by many factors; hence, it is ineviatble to develop effective biomarkers to predict CKD prognosis and improve the prognosis through early intervention. Accurate progression monitoring and prognosis improvement of CKD are extremely significant for improving the clinical treatment and management of CKD and reducing the social burden. Therefore, biomarkers reported in recent years, which could play important roles in accurate progression monitoring and prognosis improvement of CKD, were concluded and highlighted in this review article that aims to provide a reference for both the construction of CKD precision therapy system and the pharmaceutical research and development.
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Affiliation(s)
- Zhonghong Yan
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Guanran Wang
- Heilongjiang University of Chinese Medicine, Harbin, China.,Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xingyang Shi
- Heilongjiang University of Chinese Medicine, Harbin, China
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20
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Hassan L, Efremov L, Großkopf A, Kartschmit N, Medenwald D, Schott A, Schmidt-Pokrzywniak A, Lacruz ME, Tiller D, Kraus FB, Greiser KH, Haerting J, Werdan K, Sedding D, Simm A, Nuding S, Kluttig A, Mikolajczyk R. Cardiovascular risk factors, living and ageing in Halle: the CARLA study. Eur J Epidemiol 2022; 37:103-116. [PMID: 34978665 PMCID: PMC8791893 DOI: 10.1007/s10654-021-00824-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022]
Abstract
The CARLA study (Cardiovascular Disease, Living and Ageing in Halle) is a longitudinal population-based cohort study of the general population of the city of Halle (Saale), Germany. The primary aim of the cohort was to investigate risk factors for cardiovascular diseases based on comprehensive cardiological phenotyping of study participants and was extended to study factors associated with healthy ageing. In total, 1779 probands (812 women and 967 men, aged 45–83 years) were examined at baseline (2002–2005), with a first and second follow-up performed 4 and 8 years later. The response proportion at baseline was 64.1% and the reparticipation proportion for the first and second follow-up was 86% and 77% respectively. Sixty-four percent of the study participants were in retirement while 25% were full- or partially-employed and 11% were unemployed at the time of the baseline examination. The currently running third follow-up focuses on the assessment of physical and mental health, with an intensive 4 h examination program, including measurement of cardiovascular, neurocognitive, balance and gait parameters. The data collected in the CARLA Study resulted in answering various research questions in over 80 publications, of which two thirds were pooled analyses with other similar population-based studies. Due to the extensiveness of information on risk factors, subclinical conditions and evident diseases, the biobanking concept for the biosamples, the cohort representativeness of an elderly population, and the high level of quality assurance, the CARLA cohort offers a unique platform for further research on important indicators for healthy ageing.
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Affiliation(s)
- Lamiaa Hassan
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Ljupcho Efremov
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Department of Radiation Oncology, University Hospital Halle (Saale), Halle (Saale), Germany
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Anne Großkopf
- University Clinic and Outpatient Clinic for Cardiac Surgery, Middle German Heart Centre at the University Hospital Halle, Halle, Germany
| | - Nadja Kartschmit
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Medenwald
- Department of Radiation Oncology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Artjom Schott
- Department of Internal Medicine III, University Hospital, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Andrea Schmidt-Pokrzywniak
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Maria E Lacruz
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Tiller
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Clinical Computing Center - Data Integration Center, University Hospital Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | | | - Karin H Greiser
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Johannes Haerting
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Karl Werdan
- Department of Internal Medicine III, University Hospital, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Sedding
- Department of Internal Medicine III, University Hospital, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Andreas Simm
- University Clinic and Outpatient Clinic for Cardiac Surgery, Middle German Heart Centre at the University Hospital Halle, Halle, Germany
| | - Sebastian Nuding
- Department of Internal Medicine III, University Hospital, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexander Kluttig
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
| | - Rafael Mikolajczyk
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
- Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
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21
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Lin BM, Zhang Y, Yu B, Boerwinkle E, Thygarajan B, Yunes M, Daviglus ML, Qi Q, Kaplan R, Lash J, Cai J, Sofer T, Franceschini N. Metabolome-wide association study of estimated glomerular filtration rates in Hispanics. Kidney Int 2022; 101:144-151. [PMID: 34774559 PMCID: PMC8741745 DOI: 10.1016/j.kint.2021.09.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/17/2021] [Accepted: 09/24/2021] [Indexed: 01/03/2023]
Abstract
Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m2. We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.
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Affiliation(s)
- Bridget M Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Bharat Thygarajan
- Division of Molecular Pathology and Genomics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Milagros Yunes
- Department of Medicine, Division of Nephrology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago College of Medicine, Chicago, Illinois, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - James Lash
- Department of Medicine, Division of Nephrology, University of Illinois, Chicago, Illinois, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA; Department of Medicine, Harvard University, Boston, Massachusetts, USA; Department of Biostatistics, Harvard University, Boston, Massachusetts, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA.
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22
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Baek J, He C, Afshinnia F, Michailidis G, Pennathur S. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat Rev Nephrol 2022; 18:38-55. [PMID: 34616096 PMCID: PMC9146017 DOI: 10.1038/s41581-021-00488-2] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 01/03/2023]
Abstract
Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.
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Affiliation(s)
- Judy Baek
- Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Chenchen He
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Farsad Afshinnia
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Subramaniam Pennathur
- Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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23
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Zeng T, Liang Y, Chen J, Cao G, Yang Z, Zhao X, Tian J, Xin X, Lei B, Cai Z. Urinary metabolic characterization with nephrotoxicity for residents under cadmium exposure. ENVIRONMENT INTERNATIONAL 2021; 154:106646. [PMID: 34049269 DOI: 10.1016/j.envint.2021.106646] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/04/2021] [Accepted: 05/13/2021] [Indexed: 06/12/2023]
Abstract
Cadmium is a well-known hazardous pollutant that mainly comes from dietary, tobacco and occupational exposure, posing threat to kidney. However, there is still a lack of systematic study on metabolic pathways and urinary biomarkers related to its nephrotoxicity under cadmium exposure for both females and males. In this study, a mass spectrometry-based metabolomics investigation of a cohort of 144 volunteers was conducted to explore sex-specific metabolic alteration and to screen biomarkers related to cadmium-induced nephrotoxicity. When the concentration of urinary cadmium increased, creatine pathway, amino acid metabolism especially the tryptophan metabolism, aminoacyl-tRNA biosynthesis, and purine metabolism were primarily influenced regardless of the gender. Also, the most specific biomarkers linked with nephrotoxicity based on the statistical analysis were detected including creatine, creatinine, l-tryptophan, adenine and uric acid. The study outcome might provide information to reflect the body burden and help improve health policy for risk assessment.
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Affiliation(s)
- Ting Zeng
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai 519087, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Yanshan Liang
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai 519087, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Jinyao Chen
- Department of Nutrition, Food Safety and Toxicology, West China School of Public Health, Sichuan University, Sichuan, Chengdu 610041, China
| | - Guodong Cao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Zhu Yang
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Xingchen Zhao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Jinglin Tian
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai 519087, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region
| | - Xiong Xin
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai 519087, China
| | - Bo Lei
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai 519087, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong Special Administrative Region.
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24
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Wu Z, Jankowski V, Jankowski J. Irreversible post-translational modifications - Emerging cardiovascular risk factors. Mol Aspects Med 2021; 86:101010. [PMID: 34404548 DOI: 10.1016/j.mam.2021.101010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 12/23/2022]
Abstract
Despite the introduction of lipid-lowering drugs, antihypertensives, antiplatelet and anticoagulation therapies for primary prevention of cardiovascular and heart diseases (CVD), it remains the number one cause of death globally, raising the question for novel/further essential factors besides traditional risk factors such as cholesterol, blood pressure and coagulation. With continuous identification and characterization of non-enzymatic post-translationally modified isoforms of proteins and lipoproteins, it is becoming increasingly clear that irreversible non-enzymatic post-translational modifications (nPTMs) alter the biological functions of native proteins and lipoproteins thereby transforming innate serum components into CVD mediators. In particular renal insufficiency and metabolic imbalance are major contributors to the systemically increased concentration of reactive metabolites and thus increased frequency of nPTMs, promoting multi-morbid disease development centering around cardiovascular disease. nPTMs are significantly involved in the onset and progression of cardiovascular disease and represent a significant and novel risk factor. These insights represent potentially new avenues for risk assessment, prevention and therapy. This review chapter summarizes all forms of nPTMs found in CKD and under metabolic imbalance and discusses the biochemical connections between molecular alterations and the pathological impact on increased cardiovascular risk, novel nPTM-associated non-traditional cardiovascular risk factors, and clinical implication of nPTM in cardiovascular disease.
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Affiliation(s)
- Zhuojun Wu
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Vera Jankowski
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Joachim Jankowski
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, Maastricht, the Netherlands.
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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Esmati P, Najjar N, Emamgholipour S, Hosseinkhani S, Arjmand B, Soleimani A, Kakaii A, Razi F. Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy. J Diabetes Metab Disord 2021; 20:591-599. [PMID: 34222079 PMCID: PMC8212236 DOI: 10.1007/s40200-021-00786-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/20/2021] [Indexed: 10/24/2022]
Abstract
BACKGROUND Amino acids (AAs) and acylcarnitines play a key role in metabolic disease and can be used as biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases, therefore, designing an accurate and simple laboratory method that simultaneously measure both groups of substances, could improve the process of analytes quantification. In this research, a flow injection tandem mass spectrometry (FI-MS/MS) method for simultaneous measurement of AAs and acylcarnitines in addition to results of validation is explained. METHODS Samples were mixed with internal standards and after derivatization (with butanolic-HCL), AAs, and acylcarnitines were quantified by tandem mass spectrometry (SCIEX API 3200). Analytical performance studies were designed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines including precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Samples from patients with T2D in different stages of kidney disease were also analyzed to ensure the clinical usage of the method. RESULTS Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision (reported as a coefficient variation) for AAs and acylcarnitines were less than 8.7% and 12.3%, the estimated mean bias was below 8.8% and 10.2% respectively. LOD of analytes ranged between 0.6-10 μmol per liter (μmol/L) for AAs and 0.02-1 μmol/L for acylcarnitines. LOQ analytes showed a range of 2-25 μmol/L and 0.05-5 μmol/L for AAs and carnitine/acylcarnitines respectively. In diabetic patients sample analysis, a significant increase in acylcarnitines (C2, C4, C5DC, C6, C8, C10, C14) and citrulline with a significant decrease in valine were seen in patients with severely increased albuminuria. CONCLUSION FI-MS/MS method with pre-injection derivatization with butanolic-HCL can be used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time and it satisfies the analytical performance requirements. This method is applied for AAs and carnitine/acylcarnitines measurement in patient with T2DM and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s40200-021-00786-3.
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Affiliation(s)
- Parsa Esmati
- Department of mechanical engineering, University of Bristol, Bristol, UK
- Metabolomics and genomics research center, Endocrinology and metabolism molecular-cellular sciences institute, Tehran University of medical sciences, Tehran, Iran
| | - Niloufar Najjar
- Metabolomics and genomics research center, Endocrinology and metabolism molecular-cellular sciences institute, Tehran University of medical sciences, Tehran, Iran
| | - Solaleh Emamgholipour
- Department of Clinical Biochemistry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Shaghayegh Hosseinkhani
- Department of Clinical Biochemistry, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Babak Arjmand
- Metabolomics and genomics research center, Endocrinology and metabolism molecular-cellular sciences institute, Tehran University of medical sciences, Tehran, Iran
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amin Soleimani
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular -Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Kakaii
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farideh Razi
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Beneath Dr. Shariati Hospital, Gomnam Highway, Tehran, Iran
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Long-Chain Acylcarnitines and Monounsaturated Fatty Acids Discriminate Heart Failure Patients According to Pulmonary Hypertension Status. Metabolites 2021; 11:metabo11040196. [PMID: 33810372 PMCID: PMC8066759 DOI: 10.3390/metabo11040196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 11/17/2022] Open
Abstract
Defects in fatty acid (FA) utilization have been well described in group 1 pulmonary hypertension (PH) and in heart failure (HF), yet poorly studied in group 2 PH. This study was to assess whether the metabolomic profile of patients with pulmonary hypertension (PH) due HF, classified as group 2 PH, differs from those without PH. We conducted a proof-of-principle cross-sectional analysis of 60 patients with chronic HF with reduced ejection fraction and 72 healthy controls in which the circulating level of 71 energy-related metabolites was measured using various methods. Echocardiography was used to classify HF patients as noPH-HF (n = 27; mean pulmonary artery pressure [mPAP] 21 mmHg) and PH-HF (n = 33; mPAP 35 mmHg). The profile of circulating metabolites among groups was compared using principal component analysis (PCA), analysis of covariance (ANCOVA), and Pearson’s correlation tests. Patients with noPH-HF and PH-HF were aged 64 ± 11 and 68 ± 10 years, respectively, with baseline left ventricular ejection fractions of 27 ± 7% and 26 ± 7%. Principal component analysis segregated groups, more markedly for PH-HF, with long-chain acylcarnitines, acetylcarnitine, and monounsaturated FA carrying the highest loading scores. After adjustment for age, sex, kidney function, insulin resistance, and N-terminal pro-brain natriuretic peptide (NT-proBNP), 5/15 and 8/15 lipid-related metabolite levels were significantly different from controls in noPH-HF and PH-HF subjects, respectively. All metabolites for which circulating levels interacted between group and NT-proBNP significantly correlated with NT-proBNP in HF-PH, but none with HF-noPH. FA-related metabolites were differently affected in HF with or without PH, and may convey adverse outcomes given their distinct correlation with NT-proBNP in the setting of PH.
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Gui T, Li Y, Zhang S, Alecu I, Chen Q, Zhao Y, Hornemann T, Kullak-Ublick GA, Gai Z. Oxidative stress increases 1-deoxysphingolipid levels in chronic kidney disease. Free Radic Biol Med 2021; 164:139-148. [PMID: 33450378 DOI: 10.1016/j.freeradbiomed.2021.01.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 12/22/2020] [Accepted: 01/06/2021] [Indexed: 12/16/2022]
Abstract
Chronic kidney disease (CKD) leads to deep changes in lipid metabolism and obvious dyslipidemia. The dysregulation of lipid metabolism in turn results in CKD progression and the complications of cardiovascular diseases. To obtain a profound insight into the associated dyslipidemia in CKD, we performed lipidomic analysis to measure lipid metabolites in the serum from a rat 5/6 nephrectomy (5/6 Nx) model of CKD as well as in the serum from CKD patients. HK-2 cells were also used to examine oxidative stress-induced sphingolipid changes. Totally 182 lipid species were identified in 5/6 Nx rats. We found glycerolipids, total free fatty acids, and sphingolipids levels were significantly upregulated in 5/6 Nx rats. The atypical sphingolipids, 1-deoxysphingolipids, were significantly altered in both CKD animals and human CKD patients. The levels of 1-deoxysphingolipids directly relevant to the level of oxidative stress in vivo and in vitro. These results demonstrate that 1-deoxysphingolipid levels are increased in CKD and this increase directly correlates with increased kidney oxidative stress.
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Affiliation(s)
- Ting Gui
- Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan, 250355, PR China
| | - Yunlun Li
- Innovation Research Institute of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, PR China; The Third Department of Cardiovascular Diseases, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250000, PR China
| | - Shijun Zhang
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, PR China; Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Irina Alecu
- Neural Regeneration Laboratory, Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Ottawa, ON, Canada; Department of Cellular and Molecular Medicine, UOttawa Brain and Mind Research Institute, Ottawa, ON, Canada; Department of Chemistry and Biomolecular Sciences, Centre for Catalysis and Research Innovation, University of Ottawa, Ottawa, ON, Canada
| | - Qingfa Chen
- Institute for Tissue Engineering and Regenerative Medicine, Liaocheng University/Liaocheng People's Hospital, Liaocheng, Shandong, PR China
| | - Ying Zhao
- Department of Basic Biology, Institute of Biological Sciences, Jining Medical University, Jining, PR China
| | - Thorsten Hornemann
- Department of Clinical Chemistry, University Hospital Zurich, University of Zurich, Switzerland
| | - Gerd A Kullak-Ublick
- Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Mechanistic Safety, CMO & Patient Safety, Global Drug Development, Novartis Pharma, Basel, Switzerland.
| | - Zhibo Gai
- Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan, 250355, PR China; Innovation Research Institute of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, PR China; Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Revealing metabolic pathways relevant to prediabetes based on metabolomics profiling analysis. Biochem Biophys Res Commun 2020; 533:188-194. [DOI: 10.1016/j.bbrc.2020.09.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 12/26/2022]
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Cheng Y, Li Y, Benkowitz P, Lamina C, Köttgen A, Sekula P. The relationship between blood metabolites of the tryptophan pathway and kidney function: a bidirectional Mendelian randomization analysis. Sci Rep 2020; 10:12675. [PMID: 32728058 PMCID: PMC7391729 DOI: 10.1038/s41598-020-69559-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
Blood metabolites of the tryptophan pathway were found to be associated with kidney function and disease in observational studies. In order to evaluate causal relationship and direction, we designed a study using a bidirectional Mendelian randomization approach. The analyses were based on published summary statistics with study sizes ranging from 1,960 to 133,413. After correction for multiple testing, results provided no evidence of an effect of metabolites of the tryptophan pathway on estimated glomerular filtration rate (eGFR). Conversely, lower eGFR was related to higher levels of four metabolites: C-glycosyltryptophan (effect estimate = − 0.16, 95% confidence interval [CI] (− 0.22; − 0.1); p = 9.2e−08), kynurenine (effect estimate = − 0.18, 95% CI (− 0.25; − 0.11); p = 1.1e−06), 3-indoxyl sulfate (effect estimate = − 0.25, 95% CI (− 0.4; − 0.11); p = 6.3e−04) and indole-3-lactate (effect estimate = − 0.26, 95% CI (− 0.38; − 0.13); p = 5.4e−05). Our study supports that lower eGFR causes higher blood metabolite levels of the tryptophan pathway including kynurenine, C-glycosyltryptophan, 3-indoxyl sulfate, and indole-3-lactate. These findings aid the notion that metabolites of the tryptophan pathway are a consequence rather than a cause of reduced eGFR. Further research is needed to specifically examine relationships with respect to chronic kidney disease (CKD) progression among patients with existing CKD.
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Affiliation(s)
- Yurong Cheng
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yong Li
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Paula Benkowitz
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Claudia Lamina
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Köttgen
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany
| | - Peggy Sekula
- Department of Biometry, Epidemiology and Medical Bioinformatics, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.
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Lee H, Jang HB, Yoo MG, Park SI, Lee HJ. Amino Acid Metabolites Associated with Chronic Kidney Disease: An Eight-Year Follow-Up Korean Epidemiology Study. Biomedicines 2020; 8:biomedicines8070222. [PMID: 32708997 PMCID: PMC7399801 DOI: 10.3390/biomedicines8070222] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
The discovery of metabolomics-based biomarkers has been a focus of recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic kidney disease (CKD) in the general population using a cross-sectional study design. At baseline, 6.5% of subjects had CKD. Pearson correlation analysis showed that 28 metabolites were significantly associated with estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, 4 biogenic amines, 1 phosphatidylcholine, and 1 sphingolipid were associated with CKD (p < 0.05). After eight years, 13.5% of subjects had CKD. Three amino acid metabolites were positively associated with new-onset CKD: citrulline [odds ratio (OR): 2.41, 95% confidence interval (CI): 1.26–4.59], kynurenine (OR: 1.98, 95% CI: 1.05–3.73), and phenylalanine (OR: 2.68, 95% CI: 1.00–7.16). The kynurenine:tryptophan ratio was also associated with CKD (OR: 3.20; 95% CI: 1.57–6.51). The addition of multiple metabolites significantly improved the CKD prediction by C statistics (0.756–0.85, p < 0.0001), and the net reclassification improvement was 0.84 (95% CI: 0.72–0.96). Elevated hs-C reactive protein (CRP) was associated with new-onset CKD (OR: 1.045, 95% CI: 1.005–1.086); however, this association disappeared following adjustment with the kynurenine:tryptophan ratio. The levels of citrulline and kynurenine and their ratio to tryptophan in CKD patients with proteinuria were worse than those with one or neither characteristic. Together, the results of this study demonstrate that amino acid metabolites are associated with CKD eight years after initial metabolite assessment. These results could improve the identification of subjects at high risk of CKD who have modified amino acid metabolism.
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Affiliation(s)
| | | | | | | | - Hye-Ja Lee
- Correspondence: ; Tel.: +82-43-719-8692; Fax: +82-43-719-8702
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Epidemiology research to foster improvement in chronic kidney disease care. Kidney Int 2020; 97:477-486. [DOI: 10.1016/j.kint.2019.11.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 11/24/2022]
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Abstract
Metabolomics has been increasingly applied to study renal and related cardiometabolic diseases, including diabetes and cardiovascular diseases. These studies span cross-sectional studies correlating metabolites with specific phenotypes, longitudinal studies to identify metabolite predictors of future disease, and physiologic/interventional studies to probe underlying causal relationships. This chapter provides a description of how metabolomic profiling is being used in these contexts, with an emphasis on study design considerations as a practical guide for investigators who are new to this area. Research in kidney diseases is underlined to illustrate key principles. The chapter concludes by discussing the future potential of metabolomics in the study of renal and cardiometabolic diseases.
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Guo Y, Yu H, Chen D, Zhao YY. Machine learning distilled metabolite biomarkers for early stage renal injury. Metabolomics 2019; 16:4. [PMID: 31807893 DOI: 10.1007/s11306-019-1624-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 11/28/2019] [Indexed: 12/21/2022]
Abstract
INTRODUCTION With chronic kidney disease (CKD), kidney becomes damaged overtime and fails to clean blood. Around 15% of US adults have CKD and nine in ten adults with CKD do not know they have it. OBJECTIVE Early prediction and accurate monitoring of CKD may improve care and decrease the frequent progression to end-stage renal disease. There is an urgent demand to discover specific biomarkers that allow for monitoring of early-stage CKD, and response to treatment. METHOD To discover such biomarkers, shotgun high throughput was applied to the detection of serum metabolites biomarker discovery for early stages of CKD from 703 participants. Ultra performance liquid chromatography coupled with high-definition mass spectrometry (UPLC-HDMS)-based metabolomics was used for the determination of 703 fasting serum samples from five stages of CKD patients and age-matched healthy controls. RESULTS AND CONCLUSION We discovered a set of metabolite biomarkers using a series of classic and neural network based machine learning techniques. This set of metabolites can separate early CKD stage patents from normal subjects with high accuracy. Our study illustrates the power of machine learning methods in metabolite biomarker study.
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Affiliation(s)
- Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.
| | - Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Danqian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Ministry of Education, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Ying-Yong Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, School of Life Sciences, Ministry of Education, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China.
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Takaya H, Namisaki T, Kitade M, Shimozato N, Kaji K, Tsuji Y, Nakanishi K, Noguchi R, Fujinaga Y, Sawada Y, Saikawa S, Sato S, Kawaratani H, Moriya K, Akahane T, Yoshiji H. Acylcarnitine: Useful biomarker for early diagnosis of hepatocellular carcinoma in non-steatohepatitis patients. World J Gastrointest Oncol 2019; 11:887-897. [PMID: 31662827 PMCID: PMC6815927 DOI: 10.4251/wjgo.v11.i10.887] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 09/03/2019] [Accepted: 09/10/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Early diagnosis of hepatocellular carcinoma (HCC) is necessary to improve the prognosis of patients. However, the currently available tumor biomarkers are insufficient for the early detection of HCC. Acylcarnitine is essential in fatty acid metabolic pathways. A recent study reported that a high level of acylcarnitine may serve as a useful biomarker for the early diagnosis of HCC in steatohepatitis (SH) patients. In contrast, another study reported that the level of acetylcarnitine (AC2) - one of the acylcarnitine species - in non-SH patients with HCC was decreased vs that reported in those without HCC.
AIM To investigate the usefulness of acylcarnitine as a biomarker for the early diagnosis of HCC in non-SH patients.
METHODS Thirty-three non-SH patients (14 with HCC and 19 without HCC) were enrolled in this study. Blood samples were obtained from patients at the time of admission. The levels of acylcarnitine and AC2 in the serum were determined through tandem mass spectrometry. The levels of vascular endothelial growth factor (VEGF) and VEGF receptor 2 (VEGFR-2) were determined by enzyme-linked immunosorbent assay. Univariate and multivariate analyses were used to determine early diagnostic factors of HCC.
RESULTS The level of acylcarnitine was significantly lower in non-SH patients with HCC vs those without HCC (P < 0.05). In contrast, the level of lens culinaris agglutinin-reactive fraction of α-fetoprotein (AFP) - AFP-L3% - was significantly higher in non-SH patients with HCC vs those without HCC (P < 0.05). However, the levels of total carnitine, free carnitine, AFP, des-γ-carboxy prothrombin, VEGF, and VEGFR-2 were not different between patients with and without HCC. The multivariate analysis showed that a low level of acylcarnitine was the only independent factor for the early diagnosis of HCC. The patients with a low level of AC2 had a significantly higher level of VEGF vs those with a high level of AC2 (P < 0.05).
CONCLUSION The metabolic pathways of fatty acids may differ between SH HCC and non-SH HCC. Further studies are warranted to investigate these differences.
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Affiliation(s)
- Hiroaki Takaya
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Tadashi Namisaki
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Mitsuteru Kitade
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Naotaka Shimozato
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Kosuke Kaji
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Yuki Tsuji
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Keisuke Nakanishi
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Ryuichi Noguchi
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Yukihisa Fujinaga
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Yasuhiko Sawada
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Soichiro Saikawa
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Shinya Sato
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Hideto Kawaratani
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Kei Moriya
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Takemi Akahane
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
| | - Hitoshi Yoshiji
- Third Department of Internal Medicine, Nara Medical University, Kashihara, Nara 6348522, Japan
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Yang T, Richards EM, Pepine CJ, Raizada MK. The gut microbiota and the brain-gut-kidney axis in hypertension and chronic kidney disease. Nat Rev Nephrol 2019; 14:442-456. [PMID: 29760448 DOI: 10.1038/s41581-018-0018-2] [Citation(s) in RCA: 466] [Impact Index Per Article: 77.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Crosstalk between the gut microbiota and the host has attracted considerable attention owing to its involvement in diverse diseases. Chronic kidney disease (CKD) is commonly associated with hypertension and is characterized by immune dysregulation, metabolic disorder and sympathetic activation, which are all linked to gut dysbiosis and altered host-microbiota crosstalk. In this Review, we discuss the complex interplay between the brain, the gut, the microbiota and the kidney in CKD and hypertension and explain our brain-gut-kidney axis hypothesis for the pathogenesis of these diseases. Consideration of the role of the brain-gut-kidney axis in the maintenance of normal homeostasis and of dysregulation of this axis in CKD and hypertension could lead to the identification of novel therapeutic targets. In addition, the discovery of unique microbial communities and their associated metabolites and the elucidation of brain-gut-kidney signalling are likely to fill fundamental knowledge gaps leading to innovative research, clinical trials and treatments for CKD and hypertension.
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Affiliation(s)
- Tao Yang
- Department of Physiology and Functional Genomics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Elaine M Richards
- Department of Physiology and Functional Genomics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Carl J Pepine
- Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Mohan K Raizada
- Department of Physiology and Functional Genomics, College of Medicine, University of Florida, Gainesville, FL, USA.
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Fest J, Vijfhuizen LS, Goeman JJ, Veth O, Joensuu A, Perola M, Männistö S, Ness-Jensen E, Hveem K, Haller T, Tonisson N, Mikkel K, Metspalu A, van Duijn CM, Ikram A, Stricker BH, Ruiter R, van Eijck CHJ, van Ommen GJB, ʼt Hoen PAC. Search for Early Pancreatic Cancer Blood Biomarkers in Five European Prospective Population Biobanks Using Metabolomics. Endocrinology 2019; 160:1731-1742. [PMID: 31125048 PMCID: PMC6594461 DOI: 10.1210/en.2019-00165] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/17/2019] [Indexed: 02/06/2023]
Abstract
Most patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. Prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between 1 month and 17 years after sampling (N = 356) and age- and sex-matched controls (N = 887) were collected from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian Biobank, Rotterdam Study). We applied proton nuclear magnetic resonance-based metabolomics on the Nightingale platform. Logistic regression identified two interesting hits: glutamine (P = 0.011) and histidine (P = 0.012), with Westfall-Young family-wise error rate adjusted P values of 0.43 for both. Stratification in quintiles showed a 1.5-fold elevated risk for the lowest 20% of glutamine and a 2.2-fold increased risk for the lowest 20% of histidine. Stratification by time to diagnosis suggested glutamine to be involved in an earlier process (2 to 5 years before diagnosis), and histidine in a process closer to the actual onset (<2 years). Our data did not support the branched-chain amino acids identified earlier in several US cohorts as potential biomarkers for pancreatic cancer. Thus, although we identified glutamine and histidine as potential biomarkers of biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.
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Affiliation(s)
- Jesse Fest
- Department of Surgery, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Olga Veth
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Anni Joensuu
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Markus Perola
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Satu Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Eivind Ness-Jensen
- HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Neeme Tonisson
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Clinical Genetics, Tartu University Hospital, Tartu, Estonia
| | - Kairit Mikkel
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | | | - Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Rikje Ruiter
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Gert-Jan B van Ommen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Peter A C ʼt Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
- Correspondence: Peter A. C. ’t Hoen, PhD, Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Route 260, P.O. Box 9101, 6500 HB Nijmegen, Netherlands. E-mail:
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Kordalewska M, Macioszek S, Wawrzyniak R, Sikorska-Wiśniewska M, Śledziński T, Chmielewski M, Mika A, Markuszewski MJ. Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease. J Chromatogr B Analyt Technol Biomed Life Sci 2019; 1117:49-57. [DOI: 10.1016/j.jchromb.2019.04.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 01/18/2019] [Accepted: 04/01/2019] [Indexed: 12/24/2022]
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Velenosi TJ, Thomson BKA, Tonial NC, RaoPeters AAE, Mio MA, Lajoie GA, Garg AX, House AA, Urquhart BL. Untargeted metabolomics reveals N, N, N-trimethyl-L-alanyl-L-proline betaine (TMAP) as a novel biomarker of kidney function. Sci Rep 2019; 9:6831. [PMID: 31048706 PMCID: PMC6497643 DOI: 10.1038/s41598-019-42992-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 04/08/2019] [Indexed: 01/19/2023] Open
Abstract
The diagnosis and prognosis of chronic kidney disease (CKD) currently relies on very few circulating small molecules, which can vary by factors unrelated to kidney function. In end-stage renal disease (ESRD), these same small molecules are used to determine dialysis dose and dialytic clearance. Therefore, we aimed to identify novel plasma biomarkers to estimate kidney function in CKD and dialytic clearance in ESRD. Untargeted metabolomics was performed on plasma samples from patients with a single kidney, non-dialysis CKD, ESRD and healthy controls. For ESRD patients, pre- and post-dialysis plasma samples were obtained from several dialysis modalities. Metabolomics analysis revealed over 400 significantly different features in non-dialysis CKD and ESRD plasma compared to controls while less than 35 features were significantly altered in patients with a single kidney. N,N,N-trimethyl-L-alanyl-L-proline betaine (TMAP, AUROC = 0.815) and pyrocatechol sulfate (AUROC = 0.888) outperformed creatinine (AUROC = 0.745) in accurately identifying patients with a single kidney. Several metabolites accurately predicted ESRD; however, when comparing pre-and post-hemodialysis, TMAP was the most robust biomarker of dialytic clearance for all modalities (AUROC = 0.993). This study describes TMAP as a novel potential biomarker of kidney function and dialytic clearance across several hemodialysis modalities.
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Affiliation(s)
- Thomas J Velenosi
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Benjamin K A Thomson
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Nicholas C Tonial
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Adrien A E RaoPeters
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Megan A Mio
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Gilles A Lajoie
- Department of Biochemistry, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Amit X Garg
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada.,Lawson Health Research Institute, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada
| | - Andrew A House
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada.,Lawson Health Research Institute, London, Canada
| | - Bradley L Urquhart
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada. .,Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, The University of Western Ontario, Ontario, Canada. .,Lawson Health Research Institute, London, Canada.
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40
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Zhang F, Wu C, Jia C, Gao K, Wang J, Zhao H, Wang W, Chen J. Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome. J Affect Disord 2019; 250:380-390. [PMID: 30877861 DOI: 10.1016/j.jad.2019.03.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 02/13/2019] [Accepted: 03/03/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to one type of disease. Unveiling the association between depressive and the fatigue diseases provides a great opportunity to bridge the modern medicine with the traditional Chinese medicine. METHODS In this work, 295 general participants were recruited to complete Zung Self-Rating Depression Scales and Chalder Fatigue Scales, and meanwhile, to donate plasma and urine samples for 1H NMR-metabolic profiling. Artificial intelligence methods was used to analysis the underlying association between DD and CFS. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyze the metabolic profiles with respect to gender and age. Variable importance in projection and t-test were employed in conjunction with the PLS-DA models to identify the metabolite biomarkers. Considering the asymmetry and complexity of the data, convolutional neural networks (CNN) model, an artificial intelligence method, was built to analyze the data characteristics between each groups. RESULTS The results showed the gender- and age-related differences for the candidate biomarkers of the DD and the CFS diseases, and indicated the same and different biomarkers of the two diseases. PCA analysis for the data characteristics reflected that DD and CFS was separated completely in plasma metabolite. However, DD and CFS was merged into one group. LIMITATION Lack of transcriptomic analysis limits the understanding of the association of the DD and the CFS diseases on gene level. CONCLUSION The unmasked candidate biomarkers provide reliable evidence to explore the commonality and differences of the depressive and the fatigue diseases, and thereby, bridge over the traditional Chinese medicine with the modern medicine.
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Affiliation(s)
- Feilong Zhang
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Chuanhong Wu
- The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266071, China; State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Caixia Jia
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Kuo Gao
- Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China
| | - Jinping Wang
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Huihui Zhao
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Wei Wang
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jianxin Chen
- Beijing University of Chinese Medicine, Beijing 100029, China.
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Coresh J, Inker LA, Sang Y, Chen J, Shafi T, Post WS, Shlipak MG, Ford L, Goodman K, Perichon R, Greene T, Levey AS. Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study. Nephrol Dial Transplant 2019; 34:825-833. [PMID: 29718360 PMCID: PMC6503300 DOI: 10.1093/ndt/gfy094] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. METHODS We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction. RESULTS Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6% (P = 0.02) and 9.1% (P < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7% (P > 0.05) and 9.1% (P < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4% (P = 0.11) and 3.4% (P < 0.001) in the AASK and MESA] versus eGFRcr. CONCLUSIONS Multimetabolite panels can enable accurate GFR estimation. Metabolomic equations, preferably excluding creatinine and demographic characteristics, should be tested for robustness and generalizability as a potential confirmatory test when eGFRcr is unreliable.
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Affiliation(s)
- Josef Coresh
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Lesley A Inker
- Department of Nephrology, Tufts Medical Center, Boston, MA, USA
| | - Yingying Sang
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Jingsha Chen
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Tariq Shafi
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Wendy S Post
- Departments of Epidemiology, Biostatistics and Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Michael G Shlipak
- Department of General Internal Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Lisa Ford
- Metabolon, Inc., Morrisville, NC, USA
| | | | | | - Tom Greene
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Andrew S Levey
- Department of Nephrology, Tufts Medical Center, Boston, MA, USA
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Onderwater GLJ, Ligthart L, Bot M, Demirkan A, Fu J, van der Kallen CJH, Vijfhuizen LS, Pool R, Liu J, Vanmolkot FHM, Beekman M, Wen KX, Amin N, Thesing CS, Pijpers JA, Kies DA, Zielman R, de Boer I, van Greevenbroek MMJ, Arts ICW, Milaneschi Y, Schram MT, Dagnelie PC, Franke L, Ikram MA, Ferrari MD, Goeman JJ, Slagboom PE, Wijmenga C, Stehouwer CDA, Boomsma DI, van Duijn CM, Penninx BW, 't Hoen PAC, Terwindt GM, van den Maagdenberg AMJM. Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine. Neurology 2019; 92:e1899-e1911. [PMID: 30944236 PMCID: PMC6550500 DOI: 10.1212/wnl.0000000000007313] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 12/21/2018] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To identify a plasma metabolomic biomarker signature for migraine. METHODS Plasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a 1H-NMR-based metabolomics platform, to quantify 146 individual metabolites (e.g., lipids, fatty acids, and lipoproteins) and 79 metabolite ratios. Metabolite measures associated with migraine were obtained after single-metabolite logistic regression combined with a random-effects meta-analysis performed in a nonstratified and sex-stratified manner. Next, a global test analysis was performed to identify sets of related metabolites associated with migraine. The Holm procedure was applied to control the family-wise error rate at 5% in single-metabolite and global test analyses. RESULTS Decreases in the level of apolipoprotein A1 (β -0.10; 95% confidence interval [CI] -0.16, -0.05; adjusted p = 0.029) and free cholesterol to total lipid ratio present in small high-density lipoprotein subspecies (HDL) (β -0.10; 95% CI -0.15, -0.05; adjusted p = 0.029) were associated with migraine status. In addition, only in male participants, a decreased level of omega-3 fatty acids (β -0.24; 95% CI -0.36, -0.12; adjusted p = 0.033) was associated with migraine. Global test analysis further supported that HDL traits (but not other lipoproteins) were associated with migraine status. CONCLUSIONS Metabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs.
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Affiliation(s)
- Gerrit L J Onderwater
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Lannie Ligthart
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Mariska Bot
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Ayse Demirkan
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Jingyuan Fu
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Carla J H van der Kallen
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Lisanne S Vijfhuizen
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - René Pool
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Jun Liu
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Floris H M Vanmolkot
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Marian Beekman
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Ke-Xin Wen
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Najaf Amin
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Carisha S Thesing
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Judith A Pijpers
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Dennis A Kies
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Ronald Zielman
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Irene de Boer
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Marleen M J van Greevenbroek
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Ilja C W Arts
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Yuri Milaneschi
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Miranda T Schram
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Pieter C Dagnelie
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Lude Franke
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - M Arfan Ikram
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Michel D Ferrari
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Jelle J Goeman
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - P Eline Slagboom
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Cisca Wijmenga
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Coen D A Stehouwer
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Dorret I Boomsma
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Cornelia M van Duijn
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Brenda W Penninx
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Peter A C 't Hoen
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Gisela M Terwindt
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Arn M J M van den Maagdenberg
- From the Departments of Neurology (G.L.J.O., J.A.P., D.A.K., R.Z., I.d.B., M.D.F., G.M.T., A.M.J.M.v.d.M.), Human Genetics (A.D., L.S.V., P.A.C.'tH., A.M.J.M.v.d.M.), Molecular Epidemiology (M.B., P.E.S.), Radiology (D.A.K.), and Medical Statistics (J.J.G.), Leiden University Medical Centre; Department of Biological Psychology (L.L., R.P., D.I.B.), Vrije Universiteit Amsterdam; Amsterdam Public Health Institute (L.L.); Amsterdam Neuroscience and Amsterdam Public Health (M.B., C.S.T., Y.M., D.I.B., B.W.P.); Department of Psychiatry (M.B., C.S.T., Y.M., B.W.P.), VU University Medical Centre/GGZ inGeest, Amsterdam; Departments of Epidemiology (A.D., J.L., K.-x.W., N.A., M.A.I., C.M.v.D.) and Neurology (M.A.I.), Erasmus Medical Centre, Rotterdam; Departments of Genetics (J.F., L.F., C.W.) and Pediatrics (J.F.), University Medical Centre Groningen; Department of Internal Medicine (C.J.H.v.d.K., F.H.M.V., M.M.J.v.G., M.T.S., C.D.A.S.) and Heart and Vascular Center (M.T.S.), Maastricht University Medical Centre; CARIM School for Cardiovascular Diseases (C.J.H.v.d.K., M.M.J.v.G., I.C.W.A., M.T.S., P.C.D., C.D.A.S.), Department of Epidemiology (I.C.W.A.), MaCSBio Maastricht Centre for Systems Biology (I.C.W.A.), and Department of Epidemiology (P.C.D.), Maastricht University; Department of Radiology (M.A.I.), Erasmus MC University Medical Centre, Rotterdam; Leiden Academic Centre in Drug Research, Faculty Science (C.M.v.D.), Leiden University; and Centre for Molecular and Biomolecular Informatics (P.A.C.'tH.), Radboud University Medical Centre Nijmegen, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands.
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Chen DQ, Cao G, Chen H, Argyopoulos CP, Yu H, Su W, Chen L, Samuels DC, Zhuang S, Bayliss GP, Zhao S, Yu XY, Vaziri ND, Wang M, Liu D, Mao JR, Ma SX, Zhao J, Zhang Y, Shang YQ, Kang H, Ye F, Cheng XH, Li XR, Zhang L, Meng MX, Guo Y, Zhao YY. Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan. Nat Commun 2019; 10:1476. [PMID: 30931940 PMCID: PMC6443780 DOI: 10.1038/s41467-019-09329-0] [Citation(s) in RCA: 185] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 03/06/2019] [Indexed: 12/27/2022] Open
Abstract
Early detection and accurate monitoring of chronic kidney disease (CKD) could improve care and retard progression to end-stage renal disease. Here, using untargeted metabolomics in 2155 participants including patients with stage 1–5 CKD and healthy controls, we identify five metabolites, including 5-methoxytryptophan (5-MTP), whose levels strongly correlate with clinical markers of kidney disease. 5-MTP levels decrease with progression of CKD, and in mouse kidneys after unilateral ureteral obstruction (UUO). Treatment with 5-MTP ameliorates renal interstitial fibrosis, inhibits IκB/NF-κB signaling, and enhances Keap1/Nrf2 signaling in mice with UUO or ischemia/reperfusion injury, as well as in cultured human kidney cells. Overexpression of tryptophan hydroxylase-1 (TPH-1), an enzyme involved in 5-MTP synthesis, reduces renal injury by attenuating renal inflammation and fibrosis, whereas TPH-1 deficiency exacerbates renal injury and fibrosis by activating NF-κB and inhibiting Nrf2 pathways. Together, our results suggest that TPH-1 may serve as a target in the treatment of CKD. Accurate monitoring of chronic kidney disease (CKD) progression is essential for efficient disease management. Here Chen et al. identify five serum metabolites in patients with stage 1–5 CKD whose levels associate with disease progression, and find that 5-methoxytryptophan and its regulatory enzyme TPH-1 exert anti-fibrotic effects in mouse models of kidney injury.
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Affiliation(s)
- Dan-Qian Chen
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Gang Cao
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang, 310053, China
| | - Hua Chen
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Christos P Argyopoulos
- Department of Internal Medicine, University of New Mexico, 1700 Lomas Blvd NE, Albuquerque, New Mexico, 87131, USA
| | - Hui Yu
- Department of Internal Medicine, University of New Mexico, 1700 Lomas Blvd NE, Albuquerque, New Mexico, 87131, USA
| | - Wei Su
- Department of Nephrology, Baoji Central Hospital, No. 8 Jiangtan Road, Baoji, Shaanxi, 721008, China
| | - Lin Chen
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - David C Samuels
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, Tennessee, 37232, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, 1211 Medical Center Dr, Nashville, Tennessee, 37232, USA
| | - Shougang Zhuang
- Department of Nephrology, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Shanghai, 200120, China.,Department of Medicine, Rhode Island Hospital and Alpert Medical School, Brown University, 593 Eddy St, Providence, Rhode Island, 02903, USA
| | - George P Bayliss
- Department of Medicine, Rhode Island Hospital and Alpert Medical School, Brown University, 593 Eddy St, Providence, Rhode Island, 02903, USA
| | - Shilin Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, Tennessee, 37232, USA
| | - Xiao-Yong Yu
- Department of Nephrology, Affiliated Hospital of Shaanxi Institute of Traditional Chinese Medicine, No. 2 Xihuamen, Xi'an, Shaanxi, 710003, China
| | - Nosratola D Vaziri
- Division of Nephrology and Hypertension, School of Medicine, University of California Irvine, 1001 Health Sciences Rd, Irvine, California, 92897, USA
| | - Ming Wang
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Dan Liu
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Jia-Rong Mao
- Department of Nephrology, Affiliated Hospital of Shaanxi Institute of Traditional Chinese Medicine, No. 2 Xihuamen, Xi'an, Shaanxi, 710003, China
| | - Shi-Xing Ma
- Department of Nephrology, Baoji Central Hospital, No. 8 Jiangtan Road, Baoji, Shaanxi, 721008, China
| | - Jin Zhao
- Department of Nephrology, Xi'an No. 4 Hospital, No. 21 Jiefang Road, Xi'an, 710004, China
| | - Yuan Zhang
- Department of Nephrology, Xi'an No. 4 Hospital, No. 21 Jiefang Road, Xi'an, 710004, China
| | - You-Quan Shang
- Department of Nephrology, Baoji Central Hospital, No. 8 Jiangtan Road, Baoji, Shaanxi, 721008, China
| | - Huining Kang
- Department of Internal Medicine, University of New Mexico, 1700 Lomas Blvd NE, Albuquerque, New Mexico, 87131, USA
| | - Fei Ye
- Department of Biomedical Informatics, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, Tennessee, 37232, USA
| | - Xiao-Hong Cheng
- Department of Nephrology, Affiliated Hospital of Shaanxi Institute of Traditional Chinese Medicine, No. 2 Xihuamen, Xi'an, Shaanxi, 710003, China
| | - Xiang-Ri Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 North Third Ring Road, Beijing, 100029, China
| | - Li Zhang
- Department of Nephrology, Xi'an No. 4 Hospital, No. 21 Jiefang Road, Xi'an, 710004, China
| | - Mei-Xia Meng
- Department of Nephrology, Xi'an No. 4 Hospital, No. 21 Jiefang Road, Xi'an, 710004, China
| | - Yan Guo
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China. .,Department of Internal Medicine, University of New Mexico, 1700 Lomas Blvd NE, Albuquerque, New Mexico, 87131, USA.
| | - Ying-Yong Zhao
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China.
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Metabolomics biomarkers and the risk of overall mortality and ESRD in CKD: Results from the Progredir Cohort. PLoS One 2019; 14:e0213764. [PMID: 30883578 PMCID: PMC6422295 DOI: 10.1371/journal.pone.0213764] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 02/28/2019] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION Studies on metabolomics and CKD have primarily addressed CKD incidence defined as a decline on eGFR or appearance of albuminuria in the general population, with very few evaluating hard outcomes. In the present study, we investigated the association between metabolites and mortality and ESRD in a CKD cohort. SETTING AND METHODS Data on 454 participants of the Progredir Cohort Study, Sao Paulo, Brazil were used. Metabolomics was performed by GC-MS (Agilent MassHunter) and metabolites were identified using Agilent Fiehn GC/MS and NIST libraries. After excluding metabolites present in <50% of participants, 293 metabolites were analyzed. An FDR q value <0.05 criteria was applied in Cox models on the composite outcome (mortality or incident renal replacement therapy) adjusted for batch effect, resulting in 34 metabolites associated with the outcome. Multivariable-adjusted Cox models were then built for the composite outcome, death, and ESRD incident events. Competing risk analysis was also performed for ESRD. RESULTS Mean age was 68±12y, mean eGFR-CKDEPI was 38.4±14.6 ml/min/1.73m2 and 57% were diabetic. After adjustments (GC-MS batch, sex, age, DM and eGFR), 18 metabolites remained significantly associated with the composite outcome. Nine metabolites were independently associated with death: D-malic acid (HR 1.84, 95%CI 1.32-2.56, p = 0.0003), acetohydroxamic acid (HR 1.90, 95%CI 1.30-2.78, p = 0.0008), butanoic acid (HR 1.59, 95%CI 1.17-2.15, p = 0.003), and docosahexaenoic acid (HR 0.58, 95%CI 0.39-0.88, p = 0.009), among the top associations. Lactose (SHR 1.49, 95%CI 1.04-2.12, p = 0.03), 2-O-glycerol-α-D-galactopyranoside (SHR 1.76, 95%CI 1.06-2.92, p = 0.03), and tyrosine (SHR 0.52, 95%CI 0.31-0.88, p = 0.02) were associated to ESRD risk, while D-threitol, mannitol and myo-inositol presented strong borderline associations. CONCLUSION Our results identify specific metabolites related to hard outcomes in a CKD population. These findings point to the need of further exploration of these metabolites as biomarkers in CKD and the understanding of the underlying biological mechanisms related to the observed associations.
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Cañadas-Garre M, Anderson K, McGoldrick J, Maxwell AP, McKnight AJ. Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease. J Proteomics 2019; 193:93-122. [PMID: 30292816 DOI: 10.1016/j.jprot.2018.09.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/20/2018] [Accepted: 09/30/2018] [Indexed: 12/15/2022]
Abstract
Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.
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Affiliation(s)
- M Cañadas-Garre
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - K Anderson
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - J McGoldrick
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A P Maxwell
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A J McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
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Abstract
The measurement of select circulating metabolites such as creatinine, glucose, and cholesterol are integral to clinical medicine, with implications for diagnosis, prognosis, and treatment. Metabolomics studies in nephrology research seek to build on this paradigm, with the goal to identify novel markers and causal participants in the pathogenesis of kidney disease and its complications. This article reviews three themes pertinent to this goal. Each is rooted in long-established principles of human physiology, with recent updates enabled by metabolomics and other tools. First, the kidney has a broad and heterogeneous impact on circulating metabolites, with progressive loss of kidney function resulting in a multitude of small molecule alterations. Second, an increasing number of circulating metabolites have been shown to possess functional roles, in some cases acting as ligands for specific G-protein-coupled receptors. Third, circulating metabolites traffic through varied, and sometimes complex, interorgan circuits. Taken together, these themes emphasize the importance of viewing renal metabolomics at the systems level, recognizing the diverse origins and physiologic effects of blood metabolites. However, how to synthesize these themes and how to establish clinical relevance remain uncertain and will require further investigation.
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Affiliation(s)
- Eugene P Rhee
- Nephrology and Endocrinology Divisions, Massachusetts General Hospital, Boston, MA.
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Köttgen A, Raffler J, Sekula P, Kastenmüller G. Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology. Semin Nephrol 2019; 38:151-174. [PMID: 29602398 DOI: 10.1016/j.semnephrol.2018.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.
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Affiliation(s)
- Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
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Increased Plasma Acetylcarnitine in Sepsis Is Associated With Multiple Organ Dysfunction and Mortality. Crit Care Med 2019; 47:210-218. [DOI: 10.1097/ccm.0000000000003517] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Wiesenhofer FM, Herzog R, Boehm M, Wagner A, Unterwurzacher M, Kasper DC, Alper SL, Vychytil A, Aufricht C, Kratochwill K. Targeted Metabolomic Profiling of Peritoneal Dialysis Effluents Shows Anti-oxidative Capacity of Alanyl-Glutamine. Front Physiol 2019; 9:1961. [PMID: 30719009 PMCID: PMC6348277 DOI: 10.3389/fphys.2018.01961] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 12/28/2018] [Indexed: 01/25/2023] Open
Abstract
Readily available peritoneal dialysis (PD) effluents from PD patients in the course of renal replacement therapy are a potentially rich source for molecular markers for predicting clinical outcome, monitoring the therapy, and therapeutic interventions. The complex clinical phenotype of PD patients might be reflected in the PD effluent metabolome. Metabolomic analysis of PD effluent might allow quantitative detection and assessment of candidate PD biomarkers for prognostication and therapeutic monitoring. We therefore subjected peritoneal equilibration test effluents from 20 stable PD patients, obtained in a randomized controlled trial (RCT) to evaluate cytoprotective effects of standard PD solution (3.86% glucose) supplemented with 8 mM alanyl-glutamine (AlaGln) to targeted metabolomics analysis. One hundred eighty eight pre-defined metabolites, including free amino acids, acylcarnitines, and glycerophospholipids, as well as custom metabolic indicators calculated from these metabolites were surveyed in a high-throughput assay requiring only 10 μl of PD effluent. Metabolite profiles of effluents from the cross-over trial were analyzed with respect to AlaGln status and clinical parameters such as duration of PD therapy and history of previous episodes of peritonitis. This targeted approach detected and quantified 184 small molecules in PD effluent, a larger number of detected metabolites than in all previous metabolomic studies in PD effluent combined. Metabolites were clustered within substance classes regarding concentrations after a 4-h dwell. PD effluent metabolic profiles were differentiated according to PD patient sub-populations, revealing novel changes in small molecule abundance during PD therapy. AlaGln supplementation of PD fluid altered levels of specific metabolites, including increases in alanine and glutamine but not glutamate, and reduced levels of small molecule indicators of oxidative stress, such as methionine sulfoxide. Our study represents the first application of targeted metabolomics to PD effluents. The observed metabolomic changes in PD effluent associated with AlaGln-supplementation during therapy suggested an anti-oxidant effect, and were consistent with the restoration of important stress and immune processes previously noted in the RCT. High-throughput detection of PD effluent metabolomic signatures and their alterations by therapeutic interventions offers new opportunities for metabolome-clinical correlation in PD and for prescription of personalized PD therapy.
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Affiliation(s)
- Florian M Wiesenhofer
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Rebecca Herzog
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Michael Boehm
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Anja Wagner
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Markus Unterwurzacher
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | | | - Seth L Alper
- Division of Nephrology and Vascular Biology Research Center, Beth Israel Deaconess Medical Center, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Andreas Vychytil
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Vienna, Austria
| | - Christoph Aufricht
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Klaus Kratochwill
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
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Xia FY, Zhu L, Xu C, Wu QQ, Chen WJ, Zeng R, Deng YY. Plasma acylcarnitines could predict prognosis and evaluate treatment of IgA nephropathy. Nutr Metab (Lond) 2019; 16:2. [PMID: 30631376 PMCID: PMC6323753 DOI: 10.1186/s12986-018-0328-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 12/17/2018] [Indexed: 12/19/2022] Open
Abstract
Background Effective evaluation or prediction of therapy response could be helpful for treatment of chronic kidney disease (CKD), which may rely on accurate biomarkers. Acylcarnitines are involved with lipid metabolism and mitochondrial function. The relation of acylcarnitines with treatment response in patients with CKD is unknown. The purpose of this study is to investigate the association of plasma acylcarnitines with renal function and its alteration by intervention in patients with IgA nephropathy (IgAN). Methods A retrospective study was performed in 81 IgAN patients with treatment by traditional Chinese medicine (TCM). Multivariate linear regression analyses were performed to identify the association of acylcarnitines with baseline estimated glomerular filtration rate (eGFR) and eGFR changes after treatment. Results Twenty-seven acylcarnitines were measured at baseline and after 1-year TCM intervention. Certain short-chain and median-chain acylcarnitines were independently associated with baseline eGFR and eGFR alterations after 1 year treatment. Particularly, patients with high C5:1(β = − 0.42), C8:1(β = − 0.49), C3DC(β = − 0.5), C10:1(β = − 0.36) and C5DC(β = − 0.64)at baseline would have worse prognosis and treatment response. Moreover, certain acylcarnitines could be changed along with the eGFR alteration after 1-year TCM treatment. Conclusions The findings indicate a significant association between plasma acylcarnitines with prognosis and treatment responses in patients with IgAN, which suggest its role as a potential penal of biomarker for IgAN. Electronic supplementary material The online version of this article (10.1186/s12986-018-0328-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fang-Ying Xia
- 1Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 25 South Wanping Road, Shanghai, 200032 China.,2CAS Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031 China
| | - Li Zhu
- 2CAS Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031 China
| | - Chao Xu
- 1Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 25 South Wanping Road, Shanghai, 200032 China
| | - Qing-Qing Wu
- 2CAS Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031 China
| | - Wan-Jia Chen
- 1Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 25 South Wanping Road, Shanghai, 200032 China
| | - Rong Zeng
- 2CAS Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031 China
| | - Yue-Yi Deng
- 1Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 25 South Wanping Road, Shanghai, 200032 China
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