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Zang K, Brossard M, Wilson T, Ali SA, Espin-Garcia O. A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100540. [PMID: 39640910 PMCID: PMC11617925 DOI: 10.1016/j.ocarto.2024.100540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 10/31/2024] [Indexed: 12/07/2024] Open
Abstract
Background Genetic colocalization analysis is a statistical method that evaluates whether two traits (e.g., osteoarthritis [OA] risk and microRNA [miRNA] expression levels) share the same or distinct genetic association signals in a locus typically identified in genome-wide association studies (GWAS). This method is useful for providing insights into the biological relevance of genetic association signals, particularly in intergenic regions, which can help to elucidate disease mechanisms in OA and other complex traits. Objectives To review the existing literature on genetic colocalization methods, assess their suitability for studying OA, and investigate their capacity to integrate miRNA data, while bearing in view their statistical assumptions. Design We followed scoping review methodology and used Covidence software for data management. Search terms for colocalization, GWAS, and genetic or statistical models were used in the databases MEDLINE and EMBASE, searched till March 4, 2024. Results Our search returned 546 peer-reviewed papers, of which 96 were included following title/abstract and full-text screening. Based on both cumulative and annual publication counts, the most cited method for colocalization analysis was coloc. Four papers examined OA-related phenotypes, and none examined miRNA. An approach to colocalization analysis using miRNA was postulated based on further hand-searching. Conclusions Colocalization analysis is a largely unexplored method in OA. Many of the approaches to colocalization analysis identified in this review, including the integration of GWAS and miRNA data, may help to elucidate genetic and epigenetic factors implicated in OA and other complex traits.
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Affiliation(s)
- Kathleen Zang
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
| | - Myriam Brossard
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Thomas Wilson
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
| | - Shabana Amanda Ali
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA
| | - Osvaldo Espin-Garcia
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Department of Biostatistics, Krembil Research Institute and Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
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Shen J, Jiang C. Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:113. [PMID: 39609470 PMCID: PMC11605010 DOI: 10.1038/s41537-024-00533-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2024] [Accepted: 11/15/2024] [Indexed: 11/30/2024]
Abstract
The comorbidity between cardiovascular diseases (CVD) and schizophrenia (SCZ) has attracted widespread attention from researchers, with shared genetic causes potentially providing important insights into their association. This study conducted a comprehensive analysis of genetic data from 17 types of CVD and SCZ using genome-wide multi-trait association studies (GWAS), employing statistical methods such as LDSC, MTAG, LAVA, and bidirectional Mendelian randomization to explore global and local genetic correlations and identify pleiotropic single nucleotide variants (SNVs). The analysis revealed a significant genetic correlation between CVD and SCZ, identifying 842 potential pleiotropic single nucleotide variants (SNVs) and multiple associated biological pathways. Notably, genes such as TRIM27, CENPM, and MYH7B played critical roles in the shared genetic variations of both types of diseases. This study reveals the complex genetic relationship between CVD and SCZ, highlighting potential shared biological mechanisms involving immune responses, metabolic factors, and neurodevelopmental processes, thereby providing new directions for future interventions and treatments.
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Affiliation(s)
- Jing Shen
- The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Chuang Jiang
- The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.
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Alver M, Kasela S, Haring L, Luitva LB, Fischer K, Möls M, Milani L. Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank. THE LANCET REGIONAL HEALTH. EUROPE 2024; 41:100914. [PMID: 38707868 PMCID: PMC11066665 DOI: 10.1016/j.lanepe.2024.100914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 05/07/2024]
Abstract
Background Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited. Methods To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n = 677) to age- and sex-matched controls (n = 2708). Findings SCZ cases exhibited higher genetic predisposition to SCZ (OR = 1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR = 0.88, 95% CI 0.88-0.96) and C-reactive protein (OR = 0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases. Interpretation These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data. Funding EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.
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Affiliation(s)
- Maris Alver
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
| | - Liina Haring
- Department of Psychiatry, Institute of Clinical Medicine, University of Tartu, Raja 31, Tartu, 50417, Estonia
- Psychiatry Clinic of Tartu University Hospital, Raja 31, Tartu, 50417, Estonia
| | - Laura Birgit Luitva
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
| | | | | | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia
| | - Märt Möls
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia
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Caspi A, Shireby G, Mill J, Moffitt TE, Sugden K, Hannon E. Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies. Biol Psychiatry 2024; 95:1038-1047. [PMID: 37924924 PMCID: PMC11063120 DOI: 10.1016/j.biopsych.2023.10.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/29/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging. METHODS We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years. RESULTS We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication. CONCLUSIONS Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.
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Affiliation(s)
- Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina; Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom; PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Gemma Shireby
- Centre of Longitudinal Studies, University College London, Exeter, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina; Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom; PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
| | - Karen Sugden
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
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Vessels T, Strayer N, Lee H, Choi KW, Zhang S, Han L, Morley TJ, Smoller JW, Xu Y, Ruderfer DM. Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100297. [PMID: 38645405 PMCID: PMC11033077 DOI: 10.1016/j.bpsgos.2024.100297] [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: 11/22/2023] [Revised: 02/07/2024] [Accepted: 02/11/2024] [Indexed: 04/23/2024] Open
Abstract
Background Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations. Methods Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks. Results Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes. Conclusions This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.
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Affiliation(s)
- Tess Vessels
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Nicholas Strayer
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hyunjoon Lee
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Karmel W. Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lide Han
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Theodore J. Morley
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jordan W. Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Douglas M. Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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Varden Gjerde K, Bartz-Johannessen C, Steen VM, Andreassen OA, Steen NE, Ueland T, Lekva T, Rettenbacher M, Joa I, Reitan SK, Johnsen E, Kroken RA. Cellular adhesion molecules in drug-naïve and previously medicated patients with schizophrenia-spectrum disorders. Schizophr Res 2024; 267:223-229. [PMID: 38574562 DOI: 10.1016/j.schres.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/08/2024] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Endothelial inflammation may be involved in the pathogenesis of schizophrenia, and cellular adhesion molecules (CAMs) on endothelial cells may facilitate leukocyte binding and transendothelial migration of cells and inflammatory factors. The aim of the present study was to assess levels of soluble cellular adhesion molecules, including intercellular adhesion molecule (ICAM)-1, vascular adhesion molecule (VCAM)-1, mucosal addressin cell adhesion molecule (MADCAM), junctional adhesion molecule (JAM-A) and neural cadherin (N-CAD) in patients with schizophrenia compared to healthy controls. METHODS The study population consists of 138 patients with schizophrenia-spectrum disorder, of whom 54 were drug-naïve, compared to 317 general population controls. The potential confounders age, gender, smoking and body mass index (BMI) were adjusted for in linear regression models. RESULTS The total patient group showed significantly higher levels of ICAM-1 (p < 0.001) and VCAM-1 (p < 0.001) compared to controls. Previously medicated patients showed higher ICAM-1 levels compared to drug-naïve patients (p = 0.042) and controls (p < 0.001), and elevated VCAM-1 levels compared to controls (p < 0.001). Drug-naive patients had elevated levels of VCAM-1 (p = 0.031) compared to controls. CONCLUSIONS In our study, patients with schizophrenia - including the drug-naïve - have higher levels of soluble CAMs compared to healthy controls. These findings suggest activation of the endothelial system as in inflammation.
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Affiliation(s)
- Kristian Varden Gjerde
- NKS Olaviken Gerontopsychiatric Hospital, Erdal, Norway; Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway; NORMENT Centre of Excellence, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.
| | | | - Vidar Martin Steen
- NORMENT Centre of Excellence, Department of Clinical Science (K2), University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre of Excellence, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT Centre of Excellence, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Clinical Medicine, Thrombosis Research Center, UiT - The Arctic University of Norway, Tromsø, Norway; Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Tove Lekva
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Maria Rettenbacher
- Medical University of Innsbruck; Department of Psychiatry, Psychotherapy and Psychosomatics, Innsbruck, Austria
| | - Inge Joa
- TIPS Center for Clinical Research in Psychosis, Division of Psychiatry, Stavanger University Hospital, Stavanger, Norway; Department of Public Health, Faculty of Health Science, University of Stavanger, Stavanger, Norway
| | - Solveig Klæbo Reitan
- St. Olav University Hospital, Nidelv community mental health centre, Trondheim, Norway; Norwegian University of Science and Technology, Department of Mental Health, Trondheim, Norway
| | - Erik Johnsen
- Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway; NORMENT Centre of Excellence, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Rune Andreas Kroken
- Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway; NORMENT Centre of Excellence, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
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Zhang Y, Bharadhwaj VS, Kodamullil AT, Herrmann C. A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders. DISCOVER MENTAL HEALTH 2024; 4:11. [PMID: 38573526 PMCID: PMC10994898 DOI: 10.1007/s44192-024-00063-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/28/2024] [Indexed: 04/05/2024]
Abstract
The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case-control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms involving activation of inflammatory response auxiliarily interconnecting angiogenesis, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.
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Affiliation(s)
- Youcheng Zhang
- Institute of Pharmacy and Molecular Biotechnology (IPMB) & BioQuant, Universität Heidelberg, 69120, Heidelberg, Germany
| | - Vinay S Bharadhwaj
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
| | - Alpha T Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
| | - Carl Herrmann
- Institute of Pharmacy and Molecular Biotechnology (IPMB) & BioQuant, Universität Heidelberg, 69120, Heidelberg, Germany.
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Kiltschewskij DJ, Reay WR, Geaghan MP, Atkins JR, Xavier A, Zhang X, Watkeys OJ, Carr VJ, Scott RJ, Green MJ, Cairns MJ. Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk. Biol Psychiatry 2024; 95:647-661. [PMID: 37480976 DOI: 10.1016/j.biopsych.2023.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored. METHODS We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores. RESULTS Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia. CONCLUSIONS Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.
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Affiliation(s)
- Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Michael P Geaghan
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alexandre Xavier
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Xiajie Zhang
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Oliver J Watkeys
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia; Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Melissa J Green
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.
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Arruda AL, Khandaker GM, Morris AP, Smith GD, Huckins LM, Zeggini E. Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:22. [PMID: 38383672 PMCID: PMC10881980 DOI: 10.1038/s41537-024-00445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify putative effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Munich School for Data Science, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Technical University of Munich (TUM), TUM School of Medicine and Health, Graduate School of Experimental Medicine, Munich, 81675, Germany
| | - Golam M Khandaker
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, 81675, Germany.
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10
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Rødevand L, Rahman Z, Hindley GFL, Smeland OB, Frei O, Tekin TF, Kutrolli G, Bahrami S, Hoseth EZ, Shadrin A, Lin A, Djurovic S, Dale AM, Steen NE, Andreassen OA. Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. Am J Psychiatry 2023; 180:815-826. [PMID: 37752828 PMCID: PMC11780279 DOI: 10.1176/appi.ajp.20220660] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge. METHODS Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci. RESULTS Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells. CONCLUSIONS These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.
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Affiliation(s)
- Linn Rødevand
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Zillur Rahman
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Guy F L Hindley
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Olav B Smeland
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Oleksandr Frei
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Tahir Filiz Tekin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Gleda Kutrolli
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Shahram Bahrami
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Eva Z Hoseth
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Alexey Shadrin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Aihua Lin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Srdjan Djurovic
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Anders M Dale
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Nils Eiel Steen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
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11
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Vessels T, Strayer N, Choi KW, Lee H, Zhang S, Han L, Morley TJ, Smoller JW, Xu Y, Ruderfer DM. Identifying modifiable comorbidities of schizophrenia by integrating electronic health records and polygenic risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.01.23290057. [PMID: 37333378 PMCID: PMC10274978 DOI: 10.1101/2023.06.01.23290057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Patients with schizophrenia have substantial comorbidity contributing to reduced life expectancy of 10-20 years. Identifying which comorbidities might be modifiable could improve rates of premature mortality in this population. We hypothesize that conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore potentially modifiable. To test this hypothesis, we calculated phenome-wide comorbidity from electronic health records (EHR) in 250,000 patients in each of two independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham) and association with schizophrenia polygenic risk scores (PRS) across the same phenotypes (phecodes) in linked biobanks. Comorbidity with schizophrenia was significantly correlated across institutions (r = 0.85) and consistent with prior literature. After multiple test correction, there were 77 significant phecodes comorbid with schizophrenia. Overall, comorbidity and PRS association were highly correlated (r = 0.55, p = 1.29×10-118), however, 36 of the EHR identified comorbidities had significantly equivalent schizophrenia PRS distributions between cases and controls. Fifteen of these lacked any PRS association and were enriched for phenotypes known to be side effects of antipsychotic medications (e.g., "movement disorders", "convulsions", "tachycardia") or other schizophrenia related factors such as from smoking ("bronchitis") or reduced hygiene (e.g., "diseases of the nail") highlighting the validity of this approach. Other phenotypes implicated by this approach where the contribution from shared common genetic risk with schizophrenia was minimal included tobacco use disorder, diabetes, and dementia. This work demonstrates the consistency and robustness of EHR-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies comorbidities with an absence of shared genetic risk indicating other causes that might be more modifiable and where further study of causal pathways could improve outcomes for patients.
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Affiliation(s)
- Tess Vessels
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
| | - Nicholas Strayer
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville TN
| | - Karmel W. Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Hyunjoon Lee
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
| | - Lide Han
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
| | - Theodore J. Morley
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
| | - Jordan W. Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
| | - Douglas M. Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
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12
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Wagner SK, Cortina-Borja M, Silverstein SM, Zhou Y, Romero-Bascones D, Struyven RR, Trucco E, Mookiah MRK, MacGillivray T, Hogg S, Liu T, Williamson DJ, Pontikos N, Patel PJ, Balaskas K, Alexander DC, Stuart KV, Khawaja AP, Denniston AK, Rahi JS, Petzold A, Keane PA. Association Between Retinal Features From Multimodal Imaging and Schizophrenia. JAMA Psychiatry 2023; 80:478-487. [PMID: 36947045 PMCID: PMC10034669 DOI: 10.1001/jamapsychiatry.2023.0171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/23/2023] [Indexed: 03/23/2023]
Abstract
Importance The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts. Objective To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population. Design, Setting, and Participants This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022. Main Outcomes and Measures Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers. Results A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 μm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 μm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 μm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes. Conclusions and Relevance In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.
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Affiliation(s)
- Siegfried K. Wagner
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Mario Cortina-Borja
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Steven M. Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York
- Center for Visual Science, University of Rochester, Rochester, New York
| | - Yukun Zhou
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - David Romero-Bascones
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
| | - Robbert R. Struyven
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Emanuele Trucco
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Muthu R. K. Mookiah
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Tom MacGillivray
- VAMPIRE Project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen Hogg
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Timing Liu
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Dominic J. Williamson
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Nikolas Pontikos
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Praveen J. Patel
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Konstantinos Balaskas
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Kelsey V. Stuart
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Anthony P. Khawaja
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Alastair K. Denniston
- University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, United Kingdom
| | - Jugnoo S. Rahi
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
- Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
- Ulverscroft Vision Research Group, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital, London, United Kingdom
| | - Axel Petzold
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Pearse A. Keane
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
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13
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Shamsutdinova D, Das-Munshi J, Ashworth M, Roberts A, Stahl D. Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population. Int J Med Inform 2023; 172:105019. [PMID: 36787689 DOI: 10.1016/j.ijmedinf.2023.105019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/20/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
BACKGROUND AND AIMS Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. METHODS We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. RESULTS Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes. CONCLUSIONS Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.
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Affiliation(s)
- Diana Shamsutdinova
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Jayati Das-Munshi
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom; ESRC Centre for Society and Mental Health, King's College London, London, United Kingdom; South London and Maudsley NHS Trust, London, United Kingdom
| | - Mark Ashworth
- ESRC Centre for Society and Mental Health, King's College London, London, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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14
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Omori NE, Malys MK, Woo G, Mansor L. Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders. Front Psychiatry 2023; 14:1142682. [PMID: 37139329 PMCID: PMC10149735 DOI: 10.3389/fpsyt.2023.1142682] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.
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Affiliation(s)
- Naomi Elyse Omori
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
- *Correspondence: Naomi Elyse Omori,
| | - Mantas Kazimieras Malys
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom
| | - Geoffrey Woo
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
| | - Latt Mansor
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
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15
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Dadras Z, Molaei B, Aghamohammadi M. The relationship between personality profile and self-care among patients with type 2 diabetes. Front Psychol 2022; 13:1030911. [PMID: 36457923 PMCID: PMC9706217 DOI: 10.3389/fpsyg.2022.1030911] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 08/15/2023] Open
Abstract
Background As a chronic disease, diabetes needs special self-care behaviors until the end of life. Personality traits are considered to be effective psychological factors in controlling diabetes and self-care in patients with diabetes. The present study was conducted to determine the relationship between personality profile and self-care among people with type 2 diabetes. Methods In this descriptive-correlational study conducted in 2021, 160 patients with type 2 diabetes referred to the diabetes clinic of Imam Khomeini Educational and Medical Center in Ardabil were selected by convenience sampling method. The data collection tools included the Diabetes Self-Care Activities questionnaire (SDSCA) and the short form of the Millon Multi-Axis Clinical Test (MCMI-3), which were completed through interviews with patients. Data were analyzed by SPSS software using descriptive statistics (mean, SD, and frequency) and inferential statistics (Pearson correlation coefficient and linear regression). Results Based on the results, apart from the obsessive personality disorder, which had a positive relationship with self-care behaviors, a significant negative correlation was observed between schizoid, avoidant, depressed, dependent, antisocial, self-harming, borderline, and paranoid personality disorders with self-care behaviors (p < 0.01). Conclusion The results showed that there is a significant negative relationship between personality profile and self-care status of patients with type 2 diabetes. In other words, a person's personality profile can predict self-care behaviors. Accordingly, personality traits can be considered as one of the influencing factors on self-care in the educational programs of diabetic patients. Holding educational classes to empower patients using psychological interventions and teaching effective solutions can be an effective step toward increasing the level of mental-physical health and self-care of patients with type 2 diabetes.
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Affiliation(s)
- Zahra Dadras
- Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Behnam Molaei
- Department of Family Health, Social Determinants of Health Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
- Department of Psychiatry, Faculty of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Masoumeh Aghamohammadi
- Department of Emergency Nursing, School of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil, Iran
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16
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Evidence that the pituitary gland connects type 2 diabetes mellitus and schizophrenia based on large-scale trans-ethnic genetic analyses. J Transl Med 2022; 20:501. [PMID: 36329495 PMCID: PMC9632150 DOI: 10.1186/s12967-022-03704-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Previous studies on European (EUR) samples have obtained inconsistent results regarding the genetic correlation between type 2 diabetes mellitus (T2DM) and Schizophrenia (SCZ). A large-scale trans-ethnic genetic analysis may provide additional evidence with enhanced power. OBJECTIVE We aimed to explore the genetic basis for both T2DM and SCZ based on large-scale genetic analyses of genome-wide association study (GWAS) data from both East Asian (EAS) and EUR subjects. METHODS A range of complementary approaches were employed to cross-validate the genetic correlation between T2DM and SCZ at the whole genome, autosomes (linkage disequilibrium score regression, LDSC), loci (Heritability Estimation from Summary Statistics, HESS), and causal variants (MiXeR and Mendelian randomization, MR) levels. Then, genome-wide and transcriptome-wide cross-trait/ethnic meta-analyses were performed separately to explore the effective shared organs, cells and molecular pathways. RESULTS A weak genome-wide negative genetic correlation between SCZ and T2DM was found for the EUR (rg = - 0.098, P = 0.009) and EAS (rg =- 0.053 and P = 0.032) populations, which showed no significant difference between the EUR and EAS populations (P = 0.22). After Bonferroni correction, the rg remained significant only in the EUR population. Similar results were obtained from analyses at the levels of autosomes, loci and causal variants. 25 independent variants were firstly identified as being responsible for both SCZ and T2DM. The variants associated with the two disorders were significantly correlated to the gene expression profiles in the brain (P = 1.1E-9) and pituitary gland (P = 1.9E-6). Then, 61 protein-coding and non-coding genes were identified as effective genes in the pituitary gland (P < 9.23E-6) and were enriched in metabolic pathways related to glutathione mediated arsenate detoxification and to D-myo-inositol-trisphosphate. CONCLUSION Here, we show that a negative genetic correlation exists between SCZ and T2DM at the whole genome, autosome, locus and causal variant levels. We identify pituitary gland as a common effective organ for both diseases, in which non-protein-coding effective genes, such as lncRNAs, may be responsible for the negative genetic correlation. This highlights the importance of molecular metabolism and neuroendocrine modulation in the pituitary gland, which may be responsible for the initiation of T2DM in SCZ patients.
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17
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Sormunen E, Saarinen MM, Salokangas RKR, Hutri-Kähönen N, Viikari J, Raitakari OT, Hietala J. Metabolic trajectories in childhood and adolescence: Effects on risk for schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:82. [PMID: 36220836 PMCID: PMC9553975 DOI: 10.1038/s41537-022-00282-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 09/03/2022] [Indexed: 11/09/2022]
Abstract
Abnormal glucose and lipid metabolism is common in antipsychotic-naive first-episode patients with schizophrenia, but it is unclear whether these changes can already be seen in premorbid or prodromal period, before the first psychotic episode. We examined insulin, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglyceride trajectories in children and adolescents (9-18 years old), who were later diagnosed with schizophrenia, any non-affective psychosis (NAP) or affective disorder (AD). The study population consisted of a general population-based cohort "The Cardiovascular Risk in Young Finns Study", started in 1980 (n = 3596). Psychiatric diagnoses were derived from the Health Care Register up to the year 2018. Multivariate statistical analysis indicated no significant differences in insulin or lipid levels in children and adolescents who later developed schizophrenia (n = 41) compared to the cohort control group (n = 3202). In addition, no changes in these parameters were seen in the NAP (n = 74) or AD (n = 156) groups compared to the controls, but lower triglyceride levels in childhood/adolescence associated with earlier diagnosis of psychotic disorder in the NAP group. Taken together, our results do not support any gross-level insulin or lipid changes during childhood and adolescence in individuals with later diagnosis of schizophrenia-spectrum disorder. Since changes in glucose and lipid metabolism can be observed in neuroleptic-naive patients with schizophrenia, we hypothesize that the more marked metabolic changes develop during the prodrome closer to the onset of the first psychotic episode. The findings have relevance for studies on developmental hypotheses of schizophrenia.
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Affiliation(s)
- Elina Sormunen
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Maiju M. Saarinen
- grid.1374.10000 0001 2097 1371Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Raimo K. R. Salokangas
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Nina Hutri-Kähönen
- grid.502801.e0000 0001 2314 6254Tampere Centre for Skills Training and Simulation, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jorma Viikari
- grid.410552.70000 0004 0628 215XDepartment of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland
| | - Olli T. Raitakari
- grid.1374.10000 0001 2097 1371Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland ,grid.1374.10000 0001 2097 1371Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland ,grid.410552.70000 0004 0628 215XDepartment of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Jarmo Hietala
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
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18
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Greco LA, Reay WR, Dayas CV, Cairns MJ. Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance. Transl Psychiatry 2022; 12:403. [PMID: 36151087 PMCID: PMC9508072 DOI: 10.1038/s41398-022-02186-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 12/04/2022] Open
Abstract
Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (rg = 0.368, SE = 0.076, P = 1.61 × 10-6), cannabis use disorder (rg = 0.309, SE = 0.033, P = 1.97 × 10-20) and nicotine (rg = 0.117, SE = 0.043, P = 7.0 × 10-3), as well as drinks per week (rg = 0.087, SE = 0.021, P = 6.36 × 10-5), cigarettes per day (rg = 0.11, SE = 0.024, P = 4.93 × 10-6) and life-time cannabis use (rg = 0.234, SE = 0.029, P = 3.74 × 10-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.
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Affiliation(s)
- Laura A Greco
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Christopher V Dayas
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
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Tissink E, de Lange SC, Savage JE, Wightman DP, de Leeuw CA, Kelly KM, Nagel M, van den Heuvel MP, Posthuma D. Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health. Commun Biol 2022; 5:710. [PMID: 35842455 PMCID: PMC9288439 DOI: 10.1038/s42003-022-03672-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.
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Affiliation(s)
- Elleke Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Kristen M Kelly
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Mats Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.
- Department of Child and Adolescent Psychiatry, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.
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20
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Goh KK, Chen CYA, Wu TH, Chen CH, Lu ML. Crosstalk between Schizophrenia and Metabolic Syndrome: The Role of Oxytocinergic Dysfunction. Int J Mol Sci 2022; 23:ijms23137092. [PMID: 35806096 PMCID: PMC9266532 DOI: 10.3390/ijms23137092] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 02/01/2023] Open
Abstract
The high prevalence of metabolic syndrome in persons with schizophrenia has spurred investigational efforts to study the mechanism beneath its pathophysiology. Early psychosis dysfunction is present across multiple organ systems. On this account, schizophrenia may be a multisystem disorder in which one organ system is predominantly affected and where other organ systems are also concurrently involved. Growing evidence of the overlapping neurobiological profiles of metabolic risk factors and psychiatric symptoms, such as an association with cognitive dysfunction, altered autonomic nervous system regulation, desynchrony in the resting-state default mode network, and shared genetic liability, suggest that metabolic syndrome and schizophrenia are connected via common pathways that are central to schizophrenia pathogenesis, which may be underpinned by oxytocin system dysfunction. Oxytocin, a hormone that involves in the mechanisms of food intake and metabolic homeostasis, may partly explain this piece of the puzzle in the mechanism underlying this association. Given its prosocial and anorexigenic properties, oxytocin has been administered intranasally to investigate its therapeutic potential in schizophrenia and obesity. Although the pathophysiology and mechanisms of oxytocinergic dysfunction in metabolic syndrome and schizophrenia are both complex and it is still too early to draw a conclusion upon, oxytocinergic dysfunction may yield a new mechanistic insight into schizophrenia pathogenesis and treatment.
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Affiliation(s)
- Kah Kheng Goh
- Department of Psychiatry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (K.K.G.); (C.Y.-A.C.); (C.-H.C.)
- Psychiatric Research Center, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan;
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Cynthia Yi-An Chen
- Department of Psychiatry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (K.K.G.); (C.Y.-A.C.); (C.-H.C.)
- Psychiatric Research Center, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan;
| | - Tzu-Hua Wu
- Psychiatric Research Center, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan;
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| | - Chun-Hsin Chen
- Department of Psychiatry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (K.K.G.); (C.Y.-A.C.); (C.-H.C.)
- Psychiatric Research Center, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan;
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Mong-Liang Lu
- Department of Psychiatry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (K.K.G.); (C.Y.-A.C.); (C.-H.C.)
- Psychiatric Research Center, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan;
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Correspondence:
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21
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Abstract
BACKGROUND Treatment with antipsychotics is associated with an increased risk of type 2 diabetes mellitus (T2D), and increased levels of inflammatory biomarkers are present in patients with T2D. We previously demonstrated that the glucagon-like peptide-1 receptor agonist liraglutide significantly reduced glucometabolic disturbances and body weight in prediabetic, overweight/obese schizophrenia-spectrum disorder patients treated with clozapine or olanzapine. This study aims to assess the involvement of cytokines in the therapeutic effects of liraglutide. METHODS Serum concentrations of 10 cytokines (interferon-γ [IFN-γ], tumor necrosis factor-α, interleukin 1β [IL-1β], IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, and IL-13) from fasting prediabetic and normal glucose-tolerant (NGT) patients with schizophrenia-spectrum disorders were measured using multiplexed immunoassays. Prediabetic patients were randomized to 16 weeks of treatment with liraglutide or placebo, and cytokines were measured again at the end of the treatment. RESULTS IFN-γ (1.98 vs 1.17 pg/ml, P = .001), IL-4 (0.02 vs 0.01 pg/ml, P < .001), and IL-6 (0.73 vs 0.46 pg/ml, P < .001) were significantly higher in prediabetic (n = 77) vs NGT patients (n = 31). No significant changes in cytokine levels following treatment with liraglutide (n = 37) vs placebo (n = 40) were found. CONCLUSION Prediabetic vs NGT patients with schizophrenia treated with clozapine or olanzapine had increased serum levels of several proinflammatory cytokines, further substantiating the link between inflammation and T2D. Treatment with liraglutide did not affect the investigated cytokines. Further testing of these findings in larger numbers of individuals is needed.
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22
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Henkel ND, Wu X, O'Donovan SM, Devine EA, Jiron JM, Rowland LM, Sarnyai Z, Ramsey AJ, Wen Z, Hahn MK, McCullumsmith RE. Schizophrenia: a disorder of broken brain bioenergetics. Mol Psychiatry 2022; 27:2393-2404. [PMID: 35264726 DOI: 10.1038/s41380-022-01494-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 02/07/2023]
Abstract
A substantial and diverse body of literature suggests that the pathophysiology of schizophrenia is related to deficits of bioenergetic function. While antipsychotics are an effective therapy for the management of positive psychotic symptoms, they are not efficacious for the complete schizophrenia symptom profile, such as the negative and cognitive symptoms. In this review, we discuss the relationship between dysfunction of various metabolic pathways across different brain regions in relation to schizophrenia. We contend that several bioenergetic subprocesses are affected across the brain and such deficits are a core feature of the illness. We provide an overview of central perturbations of insulin signaling, glycolysis, pentose-phosphate pathway, tricarboxylic acid cycle, and oxidative phosphorylation in schizophrenia. Importantly, we discuss pharmacologic and nonpharmacologic interventions that target these pathways and how such interventions may be exploited to improve the symptoms of schizophrenia.
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Affiliation(s)
- Nicholas D Henkel
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
| | - Xiajoun Wu
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Sinead M O'Donovan
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Emily A Devine
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Jessica M Jiron
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zoltan Sarnyai
- Laboratory of Psychiatric Neuroscience, Australian Institute for Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
| | - Amy J Ramsey
- Department of Pharmacology and Toxicology, Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Margaret K Hahn
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Robert E McCullumsmith
- Department of Neurosciences, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
- Neurosciences Institute, ProMedica, Toledo, OH, USA
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23
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Association between schizophrenia and prostate cancer risk: Results from a pool of cohort studies and Mendelian randomization analysis. Compr Psychiatry 2022; 115:152308. [PMID: 35303584 DOI: 10.1016/j.comppsych.2022.152308] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Observational studies analyzing the risk of prostate cancer in schizophrenia patients have generated mixed results. We performed a meta-analysis and a Mendelian randomization (MR) analysis to evaluate the relationship and causality between schizophrenia and the risk of prostate cancer. METHODS A comprehensive and systematic search of cohort studies was conducted, and a random-effects model meta-analysis was performed to calculate the standardized incidence ratios (SIRs) for prostate cancer incidence among schizophrenia patients versus the general population. To investigate the correlation between genetically-predicted schizophrenia and prostate cancer risk, we used summary statistics from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium (61,106 controls and 79,148 cases), and 75 schizophrenia-associated single nucleotide polymorphisms (SNP) from European descent as the instrumental variable. RESULTS In the meta-analysis of 13 cohort studies with 218,076 men involved, a decreased risk of prostate cancer was observed among schizophrenia patients [SIR 0.610; 95% confidence interval (CI) 0.500-0.740; p < 0.001] with significant heterogeneity (I2 = 83.3%; p < 0.001). However, MR analysis did not sustain the link between genetically-predicted schizophrenia and prostate cancer [odds ratio (OR) 1.033; 95% CI 0.998-1.069; p = 0.065]. The result was robust against extensive sensitivity analyses. CONCLUSIONS Our study indicated a decreased risk of prostate cancer in schizophrenia patients through meta-analysis, while MR analysis did not support the connection between schizophrenia and prostate cancer. Due to the interaction of genetic variants between binary exposures, we need to be cautious in interpreting and presenting causal associations. Moreover, further research is needed to investigate underlying factors that might link schizophrenia to the risk of prostate cancer.
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Bellomo TR, Bone WP, Chen BY, Gawronski KAB, Zhang D, Park J, Levin M, Tsao N, Klarin D, Lynch J, Assimes TL, Gaziano JM, Wilson PW, Cho K, Vujkovic M, O’Donnell CJ, Chang KM, Tsao PS, Rader DJ, Ritchie MD, Damrauer SM, Voight BF. Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci. Front Genet 2022; 12:787545. [PMID: 35186008 PMCID: PMC8847690 DOI: 10.3389/fgene.2021.787545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.
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Affiliation(s)
- Tiffany R. Bellomo
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - William P. Bone
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Brian Y. Chen
- School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David Zhang
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States
| | - Joseph Park
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael Levin
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
| | - Noah Tsao
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
| | - Derek Klarin
- VA Boston Healthcare System, Boston, MA, United States
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, United States
- Department of Surgery, Massachusetts General Hospital, Boston, MA, United States
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Julie Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, United States
- University of Massachusetts College of Nursing and Health Sciences, Boston, MA, United States
| | - Themistocles L. Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Medicine, Stanford University, Stanford, CA, United States
| | - J. Michael Gaziano
- VA Boston Healthcare System, Boston, MA, United States
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Brigham Women’s Hospital, Boston, MA, United States
| | - Peter W. Wilson
- Atlanta VA Medical Center, Decatur, GA, United States
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA, United States
| | - Kelly Cho
- VA Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Brigham Women’s Hospital, Boston, MA, United States
| | - Marijana Vujkovic
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher J. O’Donnell
- VA Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Brigham Women’s Hospital, Boston, MA, United States
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip S. Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Medicine, Stanford University, Stanford, CA, United States
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, United States
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Precision Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Scott M. Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Kelava I, Chiaradia I, Pellegrini L, Kalinka AT, Lancaster MA. Androgens increase excitatory neurogenic potential in human brain organoids. Nature 2022; 602:112-116. [PMID: 35046577 PMCID: PMC7612328 DOI: 10.1038/s41586-021-04330-4] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 12/09/2021] [Indexed: 12/15/2022]
Abstract
The biological basis of male-female brain differences has been difficult to elucidate in humans. The most notable morphological difference is size, with male individuals having on average a larger brain than female individuals1,2, but a mechanistic understanding of how this difference arises remains unknown. Here we use brain organoids3 to show that although sex chromosomal complement has no observable effect on neurogenesis, sex steroids-namely androgens-lead to increased proliferation of cortical progenitors and an increased neurogenic pool. Transcriptomic analysis and functional studies demonstrate downstream effects on histone deacetylase activity and the mTOR pathway. Finally, we show that androgens specifically increase the neurogenic output of excitatory neuronal progenitors, whereas inhibitory neuronal progenitors are not increased. These findings reveal a role for androgens in regulating the number of excitatory neurons and represent a step towards understanding the origin of sex-related brain differences in humans.
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Affiliation(s)
- Iva Kelava
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
| | - Ilaria Chiaradia
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Laura Pellegrini
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Alex T Kalinka
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
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The Role of Ketogenic Metabolic Therapy on the Brain in Serious Mental Illness: A Review. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2022; 7:e220009. [PMID: 36483840 PMCID: PMC9728807 DOI: 10.20900/jpbs.20220009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In search of interventions targeting brain dysfunction and underlying cognitive impairment in schizophrenia, we look at the brain and beyond to the potential role of dysfunctional systemic metabolism on neural network instability and insulin resistance in serious mental illness. We note that disrupted insulin and cerebral glucose metabolism are seen even in medication-naïve first-episode schizophrenia, suggesting that people with schizophrenia are at risk for Type 2 diabetes and cardiovascular disease, resulting in a shortened life span. Although glucose is the brain's default fuel, ketones are a more efficient fuel for the brain. We highlight evidence that a ketogenic diet can improve both the metabolic and neural stability profiles. Specifically, a ketogenic diet improves mitochondrial metabolism, neurotransmitter function, oxidative stress/inflammation, while also increasing neural network stability and cognitive function. To reverse the neurodegenerative process, increasing the brain's access to ketone bodies may be needed. We describe evidence that metabolic, neuroprotective, and neurochemical benefits of a ketogenic diet potentially provide symptomatic relief to people with schizophrenia while also improving their cardiovascular or metabolic health. We review evidence for KD side effects and note that although high in fat it improves various cardiovascular and metabolic risk markers in overweight/obese individuals. We conclude by calling for controlled clinical trials to confirm or refute the findings from anecdotal and case reports to address the potential beneficial effects of the ketogenic diet in people with serious mental illness.
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Abstract
Depression and psychosis have a developmental component to their origin. Epidemiologic evidence, which we synthesize in this nonsystematic review, suggests that early-life infection, inflammation, and metabolic alterations could play a role in the etiology of these psychiatric disorders. The risk of depression and psychosis is associated with prenatal maternal and childhood infections, which could be mediated by impaired neurodevelopment. Evidence suggests linear dose-response associations between elevated concentrations of circulating inflammatory markers in childhood, particularly the inflammatory cytokine interleukin 6, and the risk for depression and psychosis subsequently in early adulthood. Childhood inflammatory markers are also associated with persistence of depressive symptoms subsequently in adolescence and early adulthood. Developmental trajectories reflecting persistently high insulin levels during childhood and adolescence are associated with a higher risk of psychosis in adulthood, whereas increased adiposity during and after puberty is associated with the risk of depression. Together, these findings suggest that higher levels of infection, inflammation, and metabolic alterations commonly seen in people with depression and psychosis could be a cause for, rather than simply a consequence of, these disorders. Therefore, early-life immuno-metabolic alterations, as well as factors influencing these alterations such as adversity or maltreatment, could represent targets for prevention of these psychiatric disorders. Inflammation could also be an important treatment target for depression and psychosis. The field requires further research to examine sensitive periods when exposure to such immuno-metabolic alterations is most harmful. Interventional studies are also needed to test the potential usefulness of targeting early-life immuno-metabolic alterations for preventing adult depression and psychosis.
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Perry BI, Bowker N, Burgess S, Wareham NJ, Upthegrove R, Jones PB, Langenberg C, Khandaker GM. Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses. SCHIZOPHRENIA BULLETIN OPEN 2022; 3:sgac001. [PMID: 35156041 PMCID: PMC8827407 DOI: 10.1093/schizbullopen/sgac001] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology. METHODS We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology. RESULTS We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways. CONCLUSIONS LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.
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Affiliation(s)
- Benjamin I Perry
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Nicholas Bowker
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Golam M Khandaker
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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Wang Y, Zeng L, Chen L, Zhou X, Huo L, Wang T, Zhou Y, Zhang X. The prevalence and clinical characteristics of diabetes mellitus in Chinese inpatients with chronic schizophrenia: a multicenter cross-sectional study. PeerJ 2021; 9:e12553. [PMID: 34909279 PMCID: PMC8641481 DOI: 10.7717/peerj.12553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 11/05/2021] [Indexed: 11/20/2022] Open
Abstract
Background Diabetes mellitus (DM) is common among patients with schizophrenia. However, information on patients comorbid DM and schizophrenia is limited in China. The present study investigated the prevalence of DM and its clinical characteristics in Chinese inpatients with chronic schizophrenia. Methods A cross-sectional study was performed in Chinese inpatients with chronic schizophrenia. Diagnosis of Diabetes was established using World Health Organization diagnostic criteria for diabetes mellitus (persistent fasting glucose levels ≥ 126 mg/dl or 2-h plasma glucose ≥ 200 mg/dL after a 75-g Oral Glucose Tolerance Test). Patients were also measured height, weight, waist circumference, hip circumference, triglyceride level, and cholesterol level. Patients’ psychiatric symptoms were measured by the Positive and Negative Syndrome Scale (PANSS). Binary logistic regression analysis was performed to examine the associated demographic and clinical variables in chronic schizophrenia. Results A total of 988 inpatients (64.6% male, average age of 47.19 ± 12.55) was recruited. The prevalence of DM in Chinese patients with chronic schizophrenia was 13.8% (95% CI [11.6–15.9]%). Logistic regression analysis showed that overweight (OR = 1.90, 95% CI [1.20–3.03], p = 0.006), obesity (OR = 1.85, 95% CI [1.07–3.21], p = 0.028), comorbid hypertension (OR = 2.14, 95% CI [1.34–3.42], p = 0.002), and course of schizophrenia (OR = 1.03, 95% CI [1.01–1.06], p = 0.040) were significantly associated with the DM risk in patients with schizophrenia. Conclusion The findings indicated that diabetes mellitus was non-negligible in patients with chronic schizophrenia. Patients with schizophrenia should be regularly monitored for DM. Overweight/obesity, long duration of schizophrenia, and comorbid hypertension possibly were risk factors for diabetes.
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Affiliation(s)
- Yanni Wang
- Department of Maternal, Child and Adolescent Health, School of Public Health, Lanzhou, China
| | - Lingyun Zeng
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China
| | - Lijuan Chen
- School of Literature, Journalism & Communication, South-Central University for Nationalities, Wuhan, China
| | - Xin Zhou
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China
| | - Lijuan Huo
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tingwei Wang
- Department of Maternal, Child and Adolescent Health, School of Public Health, Lanzhou, China
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
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Chan JKN, Wong CSM, Or PCF, Chen EYH, Chang WC. Diabetes complication burden and patterns and risk of mortality in people with schizophrenia and diabetes: A population-based cohort study with 16-year follow-up. Eur Neuropsychopharmacol 2021; 53:79-88. [PMID: 34481187 DOI: 10.1016/j.euroneuro.2021.08.263] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/29/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
Schizophrenia is associated with increased prevalence of diabetes. However, risk of diabetes complications as well as the impact of complication burden and patterns on subsequent mortality risk in schizophrenia patients with co-existing diabetes is understudied. This population-based, propensity-score matched (1:10) cohort study identified 6991 patients with incident diabetes and pre-existing schizophrenia and 68,682 patients with incident diabetes only (comparison group) between 2001 and 2016 in Hong Kong, using territory-wide medical-record database of public healthcare services. Complications were measured by Diabetes Complications Severity Index (DCSI), which stratified complication burden into 6 levels (DCSI score=0, 1, 2, 3, 4, or ≥5). Associations of diabetes complications, in terms of DCSI scores (complication burden), specific types and two-way combinations of complications (complication patterns), with all-cause mortality rate in schizophrenia were evaluated using Cox proportional-hazards models. Schizophrenia group had comparable macrovascular (adjusted OR 0.99 [95% CI 0.92-1.06]) and lower microvascular (0.79 [0.73-0.86]) complication rates relative to comparison group. Mortality risk ratio for schizophrenia was elevated at all complication burden levels, which conferred incremental impact on excess mortality in both groups. Cardiovascular diseases (1.60 [1.45-1.77]) and cerebrovascular-metabolic diseases (2.74 [1.25-5.99]) were associated with the highest differential mortality in schizophrenia among various specific complications and complication combinations, respectively. Our results indicate that schizophrenia patients with co-existing diabetes are at increased risk of excess mortality relative to those with diabetes alone, regardless of complication burden levels. Implementation of multilevel, targeted interventions is needed to improve diabetes-related outcomes and reduce mortality gap in this vulnerable population.
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Affiliation(s)
- Joe Kwun Nam Chan
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong
| | - Corine Sau Man Wong
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong
| | - Philip Chi Fai Or
- Department of Psychiatry, Queen Mary Hospital, Hospital Authority, Hong Kong
| | - Eric Yu Hai Chen
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong
| | - Wing Chung Chang
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong.
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Tkachev AI, Stekolshchikova EA, Morozova AY, Anikanov NA, Zorkina YA, Alekseyeva PN, Khobta EB, Andreyuk DS, Zozulya SA, Barkhatova AN, Klyushnik TP, Reznik AM, Kostyuk GP, Khaitovich PE. Ceramides: Shared Lipid Biomarkers of Cardiovascular Disease and Schizophrenia. CONSORTIUM PSYCHIATRICUM 2021; 2:35-43. [PMID: 39044755 PMCID: PMC11262249 DOI: 10.17816/cp101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
INTRODUCTION Schizophrenia, although a debilitating mental illness, greatly affects individuals' physical health as well. One of the leading somatic comorbidities associated with schizophrenia is cardiovascular disease, which has been estimated to be one of the leading causes of excess mortality in patients diagnosed with schizophrenia. Although the shared susceptibility to schizophrenia and cardiovascular disease is well established, the mechanisms linking these two disorders are not well understood. Genetic studies have hinted toward shared lipid metabolism abnormalities co-occurring in the two disorders, while lipid compounds have emerged as prognostic markers for cardiovascular disease. In particular, three ceramide species in the blood plasma, Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1), have been robustly linked to the latter disorder. AIM We aimed to assess the differences in abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in the blood plasma of schizophrenia patients compared to healthy controls. METHODS We measured the abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in a cohort of 82 patients with schizophrenia and 138 controls without a psychiatric diagnosis and validated the results using an independent cohort of 26 patients with schizophrenia, 55 control individuals, and 19 patients experiencing a first psychotic episode. RESULTS We found significant alterations for all three ceramide species Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) and a particularly strong difference in concentrations between psychiatric patients and controls for the ceramide species Cer(d18:1/18:0). CONCLUSIONS The alteration of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) levels in the blood plasma might be a manifestation of metabolic abnormalities common to both schizophrenia and cardiovascular disease.
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Chang SC, Goh KK, Lu ML. Metabolic disturbances associated with antipsychotic drug treatment in patients with schizophrenia: State-of-the-art and future perspectives. World J Psychiatry 2021; 11:696-710. [PMID: 34733637 PMCID: PMC8546772 DOI: 10.5498/wjp.v11.i10.696] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/16/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Metabolic disturbances and obesity are major cardiovascular risk factors in patients with schizophrenia, resulting in a higher mortality rate and shorter life expectancy compared with those in the general population. Although schizophrenia and metabolic disturbances may share certain genetic or pathobiological risks, antipsychotics, particularly those of second generation, may further increase the risk of weight gain and metabolic disturbances in patients with schizophrenia. This review included articles on weight gain and metabolic disturbances related to antipsychotics and their mechanisms, monitoring guidelines, and interventions. Nearly all antipsychotics are associated with weight gain, but the degree of the weight gain varies considerably. Although certain neurotransmitter receptor-binding affinities and hormones are correlated with weight gain and specific metabolic abnormalities, the precise mechanisms underlying antipsychotic-induced weight gain and metabolic disturbances remain unclear. Emerging evidence indicates the role of genetic polymorphisms associated with antipsychotic-induced weight gain and antipsychotic-induced metabolic disturbances. Although many guidelines for screening and monitoring antipsychotic-induced metabolic disturbances have been developed, they are not routinely implemented in clinical care. Numerous studies have also investigated strategies for managing antipsychotic-induced metabolic disturbances. Thus, patients and their caregivers must be educated and motivated to pursue a healthier life through smoking cessation and dietary and physical activity programs. If lifestyle intervention fails, switching to another antipsychotic drug with a lower metabolic risk or adding adjunctive medication to mitigate weight gain should be considered. Antipsychotic medications are essential for schizophrenia treatment, hence clinicians should monitor and manage the resulting weight gain and metabolic disturbances.
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Affiliation(s)
- Shen-Chieh Chang
- Department of Psychiatry, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Kah Kheng Goh
- Department of Psychiatry, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 116, Taiwan
| | - Mong-Liang Lu
- Department of Psychiatry, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 116, Taiwan
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Lindekilde N, Rutters F, Erik Henriksen J, Lasgaard M, Schram MT, Rubin KH, Kivimäki M, Nefs G, Pouwer F. Psychiatric disorders as risk factors for type 2 diabetes: An umbrella review of systematic reviews with and without meta-analyses. Diabetes Res Clin Pract 2021; 176:108855. [PMID: 33965448 DOI: 10.1016/j.diabres.2021.108855] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/19/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022]
Abstract
Having a psychiatric disorder may increase the risk of developing type 2 diabetes[T2D] and this umbrella review aims to determine whether people with a psychiatric disorder have an increased risk of developing T2D and to investigate potential underlying mechanisms. A literature search was performed to identify systematic reviews of longitudinal studies investigating different psychiatric disorders as risk factors for incident T2D in humans (≥18 years). A total of 8612 abstracts were identified, 180 full-text articles were read, and 25 systematic reviews were included. Six categories of psychiatric disorders were identified. Except for eating disorders, all psychiatric disorders were associated with increased risk of incident T2D ranging from RR = 1.18 [95% CI 1.12-1.24] to RR = 1.60 [95% CI 1.37-1.88] for depression; from RR = 1.27 [95% CI 1.19-1.35] to OR = 1.50 [95% CI 1.08-2.10] for use of antidepressant medication; from OR = 1.93 [1.37-2.73] to OR = 1.94 [1.34-2.80] for use of antipsychotic medication; from RR = 1.55 [95% CI 1.21-1.99] to RR = 1.74 [95% CI 1.30-2.34] for insomnia, and finally showed OR = 1.47 [95% CI 1.23-1.75] for anxiety disorders. Plausible underlying mechanisms were discussed, but in most reviews corrections for mechanisms did not explain the association. Notable, only 16% of the systematic reviews had a high methodological quality.
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Affiliation(s)
- Nanna Lindekilde
- Department of Psychology, University of Southern Denmark, Odense, Denmark.
| | - Femke Rutters
- Department Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands.
| | - Jan Erik Henriksen
- STENO Diabetes Centre Odense, Odense University Hospital, Odense, Denmark.
| | - Mathias Lasgaard
- DEFACTUM - Public Health and Health Services Research, Central Denmark Region, Aarhus, Denmark.
| | - Miranda T Schram
- Department of Internal Medicine, CARIM School for Cardiovascular Diseases, MHeNs School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Katrine Hass Rubin
- OPEN - Open Patient data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark.
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University of College London, London, United Kingdom; Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Giesje Nefs
- Department of Medical and Clinical Psychology, Center of Research on Psychological and Somatic disorders (CoRPS), Tilburg University, Tilburg, the Netherlands; Diabeter, National treatment and research center for children, adolescents and young adults with type 1 diabetes, Rotterdam, the Netherlands; Radboud university medical center, Radboud Institute for Health Sciences, Department of Medical Psychology, Nijmegen, the Netherlands.
| | - Frans Pouwer
- Department of Psychology, University of Southern Denmark, Odense, Denmark; STENO Diabetes Centre Odense, Odense University Hospital, Odense, Denmark; School of Psychology, Deakin University, Geelong, Australia.
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Chen Q, Cao T, Li N, Zeng C, Zhang S, Wu X, Zhang B, Cai H. Repurposing of Anti-Diabetic Agents as a New Opportunity to Alleviate Cognitive Impairment in Neurodegenerative and Neuropsychiatric Disorders. Front Pharmacol 2021; 12:667874. [PMID: 34108878 PMCID: PMC8182376 DOI: 10.3389/fphar.2021.667874] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/07/2021] [Indexed: 12/16/2022] Open
Abstract
Cognitive impairment is a shared abnormality between type 2 diabetes mellitus (T2DM) and many neurodegenerative and neuropsychiatric disorders, such as Alzheimer’s disease (AD) and schizophrenia. Emerging evidence suggests that brain insulin resistance plays a significant role in cognitive deficits, which provides the possibility of anti-diabetic agents repositioning to alleviate cognitive deficits. Both preclinical and clinical studies have evaluated the potential cognitive enhancement effects of anti-diabetic agents targeting the insulin pathway. Repurposing of anti-diabetic agents is considered to be promising for cognitive deficits prevention or control in these neurodegenerative and neuropsychiatric disorders. This article reviewed the possible relationship between brain insulin resistance and cognitive deficits. In addition, promising therapeutic interventions, especially current advances in anti-diabetic agents targeting the insulin pathway to alleviate cognitive impairment in AD and schizophrenia were also summarized.
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Affiliation(s)
- Qian Chen
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Ting Cao
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - NaNa Li
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Cuirong Zeng
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Shuangyang Zhang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Xiangxin Wu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Bikui Zhang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Hualin Cai
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacy, Central South University, Changsha, China
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Mizuki Y, Sakamoto S, Okahisa Y, Yada Y, Hashimoto N, Takaki M, Yamada N. Mechanisms Underlying the Comorbidity of Schizophrenia and Type 2 Diabetes Mellitus. Int J Neuropsychopharmacol 2021; 24:367-382. [PMID: 33315097 PMCID: PMC8130204 DOI: 10.1093/ijnp/pyaa097] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 11/29/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
The mortality rate of patients with schizophrenia is high, and life expectancy is shorter by 10 to 20 years. Metabolic abnormalities including type 2 diabetes mellitus (T2DM) are among the main reasons. The prevalence of T2DM in patients with schizophrenia may be epidemiologically frequent because antipsychotics induce weight gain as a side effect and the cognitive dysfunction of patients with schizophrenia relates to a disordered lifestyle, poor diet, and low socioeconomic status. Apart from these common risk factors and risk factors unique to schizophrenia, accumulating evidence suggests the existence of common susceptibility genes between schizophrenia and T2DM. Functional proteins translated from common genetic susceptibility genes are known to regulate neuronal development in the brain and insulin in the pancreas through several common cascades. In this review, we discuss common susceptibility genes, functional cascades, and the relationship between schizophrenia and T2DM. Many genetic and epidemiological studies have reliably associated the comorbidity of schizophrenia and T2DM, and it is probably safe to think that common cascades and mechanisms suspected from common genes' functions are related to the onset of both schizophrenia and T2DM. On the other hand, even when genetic analyses are performed on a relatively large number of comorbid patients, the results are sometimes inconsistent, and susceptibility genes may carry only a low or moderate risk. We anticipate future directions in this field.
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Affiliation(s)
- Yutaka Mizuki
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
- Shimonoseki Hospital
| | - Shinji Sakamoto
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
| | - Yuko Okahisa
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
| | - Yuji Yada
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
- Okayama Psychiatric Medical Center
| | - Nozomu Hashimoto
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
- Okayama Psychiatric Medical Center
| | - Manabu Takaki
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
| | - Norihito Yamada
- Department of Neuropsychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Adams DM, Reay WR, Geaghan MP, Cairns MJ. Investigation of glycaemic traits in psychiatric disorders using Mendelian randomisation revealed a causal relationship with anorexia nervosa. Neuropsychopharmacology 2021; 46:1093-1102. [PMID: 32920595 PMCID: PMC8115098 DOI: 10.1038/s41386-020-00847-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/02/2020] [Accepted: 08/24/2020] [Indexed: 12/22/2022]
Abstract
Data from observational studies have suggested an involvement of abnormal glycaemic regulation in the pathophysiology of psychiatric illness. This may be an attractive target for clinical intervention as glycaemia can be modulated by both lifestyle factors and pharmacological agents. However, observational studies are inherently confounded, and therefore, causal relationships cannot be reliably established. We employed genetic variants rigorously associated with three glycaemic traits (fasting glucose, fasting insulin, and glycated haemoglobin) as instrumental variables in a two-sample Mendelian randomisation analysis to investigate the causal effect of these measures on the risk for eight psychiatric disorders. A significant protective effect of a natural log transformed pmol/L increase in fasting insulin levels was observed for anorexia nervosa after the application of multiple testing correction (OR = 0.48 [95% CI: 0.33-0.71]-inverse-variance weighted estimate). There was no consistently strong evidence for a causal effect of glycaemic factors on the other seven psychiatric disorders considered. The relationship between fasting insulin and anorexia nervosa was supported by a suite of sensitivity analyses, with no statistical evidence of instrument heterogeneity or horizontal pleiotropy. Further investigation is required to explore the relationship between insulin levels and anorexia.
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Affiliation(s)
- Danielle M Adams
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Michael P Geaghan
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia.
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Perry BI, Stochl J, Upthegrove R, Zammit S, Wareham N, Langenberg C, Winpenny E, Dunger D, Jones PB, Khandaker GM. Longitudinal Trends in Childhood Insulin Levels and Body Mass Index and Associations With Risks of Psychosis and Depression in Young Adults. JAMA Psychiatry 2021; 78:416-425. [PMID: 33439216 PMCID: PMC7807390 DOI: 10.1001/jamapsychiatry.2020.4180] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
IMPORTANCE Cardiometabolic disorders often occur concomitantly with psychosis and depression, contribute to high mortality rates, and are detectable from the onset of the psychiatric disorders. However, it is unclear whether longitudinal trends in cardiometabolic traits from childhood are associated with risks for adult psychosis and depression. OBJECTIVE To examine whether specific developmental trajectories of fasting insulin (FI) levels and body mass index (BMI) from early childhood were longitudinally associated with psychosis and depression in young adults. DESIGN, SETTING, AND PARTICIPANTS A cohort study from the Avon Longitudinal Study of Parents and Children, a prospective study including a population-representative British cohort of 14 975 individuals, was conducted using data from participants aged 1 to 24 years. Body mass index and FI level data were used for growth mixture modeling to delineate developmental trajectories, and associations with psychosis and depression were assessed. The study was conducted between July 15, 2019, and March 24, 2020. EXPOSURES Fasting insulin levels were measured at 9, 15, 18, and 24 years, and BMI was measured at 1, 2, 3, 4, 7, 9, 10, 11, 12, 15, 18, and 24 years. Data on sex, race/ethnicity, paternal social class, childhood emotional and behavioral problems, and cumulative scores of sleep problems, average calorie intake, physical activity, smoking, and alcohol and substance use in childhood and adolescence were examined as potential confounders. MAIN OUTCOMES AND MEASURES Psychosis risk (definite psychotic experiences, psychotic disorder, at-risk mental state status, and negative symptom score) depression risk (measured using the computerized Clinical Interview Schedule-Revised) were assessed at 24 years. RESULTS From data available on 5790 participants (3132 [54.1%] female) for FI levels and data available on 10 463 participants (5336 [51.0%] female) for BMI, 3 distinct trajectories for FI levels and 5 distinct trajectories for BMI were noted, all of which were differentiated by mid-childhood. The persistently high FI level trajectory was associated with a psychosis at-risk mental state (adjusted odds ratio [aOR], 5.01; 95% CI, 1.76-13.19) and psychotic disorder (aOR, 3.22; 95% CI, 1.29-8.02) but not depression (aOR, 1.38; 95% CI, 0.75-2.54). A puberty-onset major increase in BMI was associated with depression (aOR, 4.46; 95% CI, 2.38-9.87) but not psychosis (aOR, 1.98; 95% CI, 0.56-7.79). CONCLUSIONS AND RELEVANCE The cardiometabolic comorbidity of psychosis and depression may have distinct, disorder-specific early-life origins. Disrupted insulin sensitivity could be a shared risk factor for comorbid cardiometabolic disorders and psychosis. A puberty-onset major increase in BMI could be a risk factor or risk indicator for adult depression. These markers may represent targets for prevention and treatment of cardiometabolic disorders in individuals with psychosis and depression.
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Affiliation(s)
- Benjamin I. Perry
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Jan Stochl
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom,Department of Kinanthropology, Charles University, Prague, Czechia
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Stan Zammit
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom,MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Eleanor Winpenny
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - David Dunger
- Department of Paediatrics, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Peter B. Jones
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Golam M. Khandaker
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom,Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom,MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Rahman MR, Islam T, Nicoletti F, Petralia MC, Ciurleo R, Fisicaro F, Pennisi M, Bramanti A, Demirtas TY, Gov E, Islam MR, Mussa BM, Moni MA, Fagone P. Identification of Common Pathogenetic Processes between Schizophrenia and Diabetes Mellitus by Systems Biology Analysis. Genes (Basel) 2021; 12:genes12020237. [PMID: 33562405 PMCID: PMC7916024 DOI: 10.3390/genes12020237] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive symptoms (i.e., psychosis) and negative symptoms (such as apathy, anhedonia, and poverty of speech). Epidemiological data show a high likelihood of early onset of type 2 diabetes mellitus (T2DM) in SCZ patients. However, the molecular processes that could explain the epidemiological association between SCZ and T2DM have not yet been characterized. Therefore, in the present study, we aimed to identify underlying common molecular pathogenetic processes and pathways between SCZ and T2DM. To this aim, we analyzed peripheral blood mononuclear cell (PBMC) transcriptomic data from SCZ and T2DM patients, and we detected 28 differentially expressed genes (DEGs) commonly modulated between SCZ and T2DM. Inflammatory-associated processes and membrane trafficking pathways as common biological processes were found to be in common between SCZ and T2DM. Analysis of the putative transcription factors involved in the regulation of the DEGs revealed that STAT1 (Signal Transducer and Activator of Transcription 1), RELA (v-rel reticuloendotheliosis viral oncogene homolog A (avian)), NFKB1 (Nuclear Factor Kappa B Subunit 1), and ERG (ETS-related gene) are involved in the expression of common DEGs in SCZ and T2DM. In conclusion, we provide core molecular signatures and pathways that are shared between SCZ and T2DM, which may contribute to the epidemiological association between them.
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Affiliation(s)
- Md Rezanur Rahman
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh;
- Department of Biochemistry and Biotechnology, Khwaja Yunus Ali University, Enayetpur, Sirajganj 6751, Bangladesh;
| | - Tania Islam
- Department of Biochemistry and Biotechnology, Khwaja Yunus Ali University, Enayetpur, Sirajganj 6751, Bangladesh;
| | - Ferdinando Nicoletti
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy; (F.F.); (M.P.); (P.F.)
- Correspondence:
| | - Maria Cristina Petralia
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.C.P.); (R.C.); (A.B.)
| | - Rosella Ciurleo
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.C.P.); (R.C.); (A.B.)
| | - Francesco Fisicaro
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy; (F.F.); (M.P.); (P.F.)
| | - Manuela Pennisi
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy; (F.F.); (M.P.); (P.F.)
| | - Alessia Bramanti
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.C.P.); (R.C.); (A.B.)
| | - Talip Yasir Demirtas
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana 01250, Turkey; (T.Y.D.); (E.G.)
| | - Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana 01250, Turkey; (T.Y.D.); (E.G.)
| | - Md Rafiqul Islam
- School of Biomedical Sciences, Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- Department of Pharmacy, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | - Bashair M. Mussa
- Basic Medical Sciences Department, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates;
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, Sydney, NSW 2052, Australia;
| | - Paolo Fagone
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy; (F.F.); (M.P.); (P.F.)
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Mayén-Lobo YG, Martínez-Magaña JJ, Pérez-Aldana BE, Ortega-Vázquez A, Genis-Mendoza AD, Dávila-Ortiz de Montellano DJ, Soto-Reyes E, Nicolini H, López-López M, Monroy-Jaramillo N. Integrative Genomic-Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis. Pharmaceuticals (Basel) 2021; 14:118. [PMID: 33557049 PMCID: PMC7913835 DOI: 10.3390/ph14020118] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 02/07/2023] Open
Abstract
Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein-protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study.
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Affiliation(s)
- Yerye Gibrán Mayén-Lobo
- Department of Biological Systems, Metropolitan Autonomous University-Xochimilco, Mexico City 04960, Mexico; (Y.G.M.-L.); (B.E.P.-A.); (A.O.-V.); (M.L.-L.)
- Department of Genetics, National Institute of Neurology and Neurosurgery, “Manuel Velasco Suárez”, Mexico City 14269, Mexico;
| | - José Jaime Martínez-Magaña
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, Instituto Nacional de Medicina Genómica, SSA, Mexico City 14610, Mexico; (J.J.M.-M.); (A.D.G.-M.); (H.N.)
| | - Blanca Estela Pérez-Aldana
- Department of Biological Systems, Metropolitan Autonomous University-Xochimilco, Mexico City 04960, Mexico; (Y.G.M.-L.); (B.E.P.-A.); (A.O.-V.); (M.L.-L.)
| | - Alberto Ortega-Vázquez
- Department of Biological Systems, Metropolitan Autonomous University-Xochimilco, Mexico City 04960, Mexico; (Y.G.M.-L.); (B.E.P.-A.); (A.O.-V.); (M.L.-L.)
| | - Alma Delia Genis-Mendoza
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, Instituto Nacional de Medicina Genómica, SSA, Mexico City 14610, Mexico; (J.J.M.-M.); (A.D.G.-M.); (H.N.)
| | | | - Ernesto Soto-Reyes
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City 05348, Mexico;
| | - Humberto Nicolini
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, Instituto Nacional de Medicina Genómica, SSA, Mexico City 14610, Mexico; (J.J.M.-M.); (A.D.G.-M.); (H.N.)
- Grupo de Estudios Médicos y Familiares Carracci, Mexico City 03740, Mexico
| | - Marisol López-López
- Department of Biological Systems, Metropolitan Autonomous University-Xochimilco, Mexico City 04960, Mexico; (Y.G.M.-L.); (B.E.P.-A.); (A.O.-V.); (M.L.-L.)
| | - Nancy Monroy-Jaramillo
- Department of Genetics, National Institute of Neurology and Neurosurgery, “Manuel Velasco Suárez”, Mexico City 14269, Mexico;
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Galderisi S, De Hert M, Del Prato S, Fagiolini A, Gorwood P, Leucht S, Maggioni AP, Mucci A, Arango C. Identification and management of cardiometabolic risk in subjects with schizophrenia spectrum disorders: A Delphi expert consensus study. Eur Psychiatry 2021; 64:e7. [PMID: 33413701 PMCID: PMC8057390 DOI: 10.1192/j.eurpsy.2020.115] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Patients with schizophrenia spectrum disorders (SSD) have worse physical health and reduced life expectancy compared to the general population. In 2009, the European Psychiatric Association, the European Society of Cardiology and the European Association for the Study of Diabetes published a position paper aimed to improve cardiovascular and diabetes care in patients with severe mental illnesses. However, the initiative did not produce the expected results. Experts in SSD or in cardiovascular and metabolic diseases convened to identify main issues relevant to management of cardiometabolic risk factors in schizophrenia patients and to seek consensus through the Delphi method. METHODS The steering committee identified four topics: 1) cardiometabolic risk factors in schizophrenia patients; 2) cardiometabolic risk factors related to antipsychotic treatment; 3) differences in antipsychotic cardiometabolic profiles; 4) management of cardiometabolic risk. Twelve key statements were included in a Delphi questionnaire delivered to a panel of expert European psychiatrists. RESULTS Consensus was reached for all statements with positive agreement higher than 85% in the first round. European psychiatrists agreed on: 1) high cardiometabolic risk in patients with SSD, 2) importance of correct risk management of cardiometabolic diseases, from lifestyle modification to treatment of risk factors, including the choice of antipsychotic drugs with a favourable cardiometabolic profile. The expert panel identified the psychiatrist as the central coordinating figure of management, possibly assisted by other specialists and general practitioners. CONCLUSIONS This study demonstrates high level of agreement among European psychiatrists regarding the importance of cardiovascular risk assessment and management in subjects with SSD.
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Affiliation(s)
- Silvana Galderisi
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Marc De Hert
- University Psychiatric Centre—KU Leuven, Kortenberg, Belgium
- Department of Neuroscience, KU Leuven, Kortenberg, Belgium
- Antwerp Health Law and Ethics Chair, AHLEC University Antwerpen, Antwerp, Belgium
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, Section of Metabolic Diseases and Diabetes, University of Pisa, Pisa, Italy
| | - Andrea Fagiolini
- Department of Molecular Medicine, Division of Psychiatry, University of Siena, Siena, Italy
| | - Philip Gorwood
- INSERM U1266, Institute of Psychiatry and Neurosciences of Paris (IPNP) & GHU Paris Psychiatrie et Neurosciences (CMME, Sainte-Anne Hospital), Université de Paris, Paris, France
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, School of Medicine, Technische Universität München, Munich, Germany
| | | | - Armida Mucci
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Gregorio Marañón, IiSGM, School of Medicine, Universidad Complutense, CIBERSAM, Madrid, Spain
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Diabetes in late-life schizophrenia: Prevalence, factors, and association with clinical symptoms. J Psychiatr Res 2021; 132:44-49. [PMID: 33038565 DOI: 10.1016/j.jpsychires.2020.09.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 08/25/2020] [Accepted: 09/26/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The prevalence of diabetes mellitus has been found to be higher in patients with schizophrenia. Older patients are the fastest-growing segment of the schizophrenia population. However, few studies have explored diabetes in older patients with schizophrenia. Therefore, this study aimed to determine the prevalence and characteristics of factors associated with diabetes in Chinese patients with late-life schizophrenia (LLS), which has not been reported in previous studies. METHODS A total of 289 inpatients aged 60 or above who met the DSM-IV criteria for schizophrenia were recruited. The severity of psychopathology was assessed by the Positive and Negative Syndrome Scale (PANSS). Diabetes was diagnosed by fasting blood glucose tests, or oral glucose tolerance tests. RESULTS The overall prevalence of diabetes in LLS patients was 25.3%. The prevalence of diabetes in female patients was significantly higher than that in male patients (35% vs. 21.53%). Other factors associated with diabetes included higher BMI, greater waistline (only for males), higher levels of triglyceride, and more severe positive symptoms. CONCLUSION These results indicate that the prevalence of diabetes in LLS patients is similar to that in the age-matched general population. Female gender, excess weight and abdominal obesity, dyslipidemia, and clinical symptoms can be potential risk factors of diabetes in the LLS patient group.
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Van L, Heung T, Malecki SL, Fenn C, Tyrer A, Sanches M, Chow EW, Boot E, Corral M, Dash S, George SR, Bassett AS. 22q11.2 microdeletion and increased risk for type 2 diabetes. EClinicalMedicine 2020; 26:100528. [PMID: 33089125 PMCID: PMC7565196 DOI: 10.1016/j.eclinm.2020.100528] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND The 22q11.2 microdeletion is the pathogenic copy number variation (CNV) associated with 22q11.2 deletion syndrome (22q11.2DS, formerly known as DiGeorge syndrome). Familiar endocrinological manifestations include hypoparathyroidism and hypothyroidism, with recent elucidation of elevated risk for obesity in adults. In this study, we aimed to determine whether adults with 22q11.2DS have an increased risk of developing type 2 diabetes (T2D). METHODS We studied the effect of the 22q11.2 microdeletion on risk for T2D, defined by history and glycosylated hemoglobin (HbA1c), using weighted survey data from the adult Canadian population (based on n = 11,874) and from a clinical cohort of adults with 22q11.2DS (n = 314), aged 17-69 years. Binomial logistic regression models accounted for age, sex, non-European ethnicity, family history of T2D, obesity, and antipsychotic medication use. FINDINGS The 22q11.2 microdeletion was a significant independent risk factor for T2D (OR 2·44, 95% CI 1·39-4·31), accounting for other factors (p < 0·0001). All factors except sex were also significant within 22q11.2DS. The median age at diagnosis of T2D was significantly younger in 22q11.2DS than in the Canadian population sample (32 vs 50 years, p < 0·0001). In adults without T2D, HbA1c was significantly higher in 22q11.2DS than the population (p = 0·042), after accounting for younger age of the 22q11.2DS group. INTERPRETATION The results support the 22q11.2 microdeletion as a novel independent risk factor and potential model for early onset T2D. The findings complement emerging evidence that rare CNVs may contribute to risk for T2D. The results have implications for precision medicine and research into the underlying pathogenesis of T2D.
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Affiliation(s)
- Lily Van
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Tracy Heung
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- The Dalglish Family 22q Clinic, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Sarah L. Malecki
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Christian Fenn
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Tyrer
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Marcos Sanches
- Biostatistical Consulting Service, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Eva W.C. Chow
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Erik Boot
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- The Dalglish Family 22q Clinic, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
- Advisium, ’s Heeren Loo Zorggroep, Amersfoort, the Netherlands
| | - Maria Corral
- The Dalglish Family 22q Clinic, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Satya Dash
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Banting & Best Diabetes Center, University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Susan R. George
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
- Department of Pharmacology, University of Toronto, Toronto, Ontario, Canada
| | - Anne S. Bassett
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- The Dalglish Family 22q Clinic, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
- Toronto Congenital Cardiac Centre for Adults, and Division of Cardiology, Department of Medicine, University Health Network, Toronto, Ontario, Canada
- Toronto General Research Institute and Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Corresponding author at: The Dalglish Family 22q Clinic, Toronto General Hospital, 200 Elizabeth Street, 8NU-802 Toronto, ON M4G 2C5, Canada.
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Lis M, Stańczykiewicz B, Liśkiewicz P, Misiak B. Impaired hormonal regulation of appetite in schizophrenia: A narrative review dissecting intrinsic mechanisms and the effects of antipsychotics. Psychoneuroendocrinology 2020; 119:104744. [PMID: 32534330 DOI: 10.1016/j.psyneuen.2020.104744] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/25/2020] [Accepted: 05/30/2020] [Indexed: 12/14/2022]
Abstract
Cardiometabolic diseases are the main contributor of reduced life expectancy in patients with schizophrenia. It is now widely accepted that antipsychotic treatment plays an important role in the development of obesity and its consequences. However, some intrinsic mechanisms need to be taken into consideration. One of these mechanisms might be related to impaired hormonal regulation of appetite in this group of patients. In this narrative review, we aimed to dissect impairments of appetite-regulating hormones attributable to intrinsic mechanisms and those related to medication effects. Early hormonal alterations that might be associated with intrinsic mechanisms include low levels of leptin and glucagon-like peptide-1 (GLP-1) together with elevated insulin levels in first-episode psychosis (FEP) patients. However, evidence regarding low GLP-1 levels in FEP patients is based on one large study. In turn, multiple-episode schizophrenia patients show elevated levels of insulin, leptin and orexin A together with decreased levels of adiponectin. In addition, patients receiving olanzapine may present with low ghrelin levels. Post mortem studies have also demonstrated reduced number of neuropeptide Y neurons in the prefrontal cortex of patients with schizophrenia. Treatment with certain second-generation antipsychotics may also point to these alterations. Although our understanding of hormonal regulation of appetite in schizophrenia has largely been improved, several limitations and directions for future studies need to be addressed. This is of particular importance since several novel pharmacological interventions for obesity and diabetes have already been developed and translation of these developments to the treatment of cardiometabolic comorbidities in schizophrenia patients is needed.
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Affiliation(s)
- Michał Lis
- Clinical Department of Internal Diseases, Endocrinology and Diabetology, The Central Clinical Hospital of the Ministry of the Interior in Warsaw, Wołoska 137 Street, 02-507 Warsaw, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Nervous System Diseases, Wroclaw Medical University, Bartla 5 Street, 51-618, Wroclaw, Poland
| | - Paweł Liśkiewicz
- Department of Psychiatry, Pomeranian Medical University, Broniewskiego 26 Street, 71-460, Szczecin, Poland
| | - Błażej Misiak
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1 Street, 50-368 Wroclaw, Poland.
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Perry BI, Jones HJ, Richardson TG, Zammit S, Wareham NJ, Lewis G, Jones PB, Khandaker GM. Common mechanisms for type 2 diabetes and psychosis: Findings from a prospective birth cohort. Schizophr Res 2020; 223:227-235. [PMID: 32828613 PMCID: PMC7758839 DOI: 10.1016/j.schres.2020.08.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 04/15/2020] [Accepted: 08/11/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Psychosis and type 2 diabetes mellitus (T2DM) are commonly comorbid and may share pathophysiologic mechanisms. To investigate shared genetic variation and inflammation as potential common mechanisms, we tested: (i) associations between genetic predisposition for T2DM and psychotic experiences and psychotic disorder in young adults; (ii) the association between genetic predisposition for schizophrenia and insulin resistance (IR), a precursor of T2DM; and (iii) whether these associations are mediated by childhood inflammation. METHODS Psychotic experiences (PEs), psychotic disorder and IR were assessed at age 18. Polygenic risk scores (PRS) for T2DM and schizophrenia were derived based on large genome-wide association studies. Associations between PRS and psychotic/IR outcomes were assessed using regression analysis based on 3768 ALSPAC birth cohort participants with complete data. Inflammatory markers C-reactive protein (CRP) and interleukin 6 (IL-6) measured at age 9 were used in regression and mediation analyses. RESULTS Genetic predisposition for T2DM was associated with PEs (adjusted OR = 1.21; 95% CI, 1.01-1.45) and psychotic disorder (adjusted OR = 1.51; 95% CI, 1.04-2.03) at age 18 in a linear dose-response fashion. Genetic predisposition for schizophrenia was weakly associated with IR (adjusted OR = 1.10; 95% C·I, 0.99-1.22) at age 18. The association between genetic risk for T2DM and PEs was partly mediated by childhood CRP (p = .040). CONCLUSIONS Comorbidity between psychosis and T2DM may be partly underpinned by shared genes and inflammation. A summation of minor genetic variation representing lifetime risk for T2DM at conception may predispose individuals to psychosis in adulthood by influencing physiologic changes, such as low-grade inflammation, detectable as early as childhood.
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Affiliation(s)
- Benjamin I Perry
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, England, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England, UK.
| | - Hannah J Jones
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England, UK; NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England, UK
| | - Stan Zammit
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England, UK; NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, England, UK
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, England, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, England, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England, UK
| | - Golam M Khandaker
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, England, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England, UK
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Misiak B, Wiśniewski M, Lis M, Samochowiec J, Stańczykiewicz B. Glucose homeostasis in unaffected first-degree relatives of schizophrenia patients: A systematic review and meta-analysis. Schizophr Res 2020; 223:2-8. [PMID: 32739343 DOI: 10.1016/j.schres.2020.07.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/28/2020] [Accepted: 07/19/2020] [Indexed: 12/15/2022]
Abstract
It has been proposed that type 2 diabetes and schizophrenia-spectrum disorders share overlapping genetic backgrounds. Therefore, we aimed to perform a systematic review and meta-analysis of studies comparing fasting levels of glucose and insulin, the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), glucose levels during the oral glucose tolerance test (OGTT) and the levels of glycated hemoglobin (HbA1c) in unaffected first-degree relatives of patients with schizophrenia and controls. Online searches covered the publication period from database inception until May 8th 2020. Meta-analyses were performed using random-effects models with Hedges' g as the effect size estimate. Out of 2556 records identified, 12 studies representing 672 relatives of schizophrenia patients and 6446 controls were found to be eligible. There were no significant differences in fasting levels of glucose (g = 0.54, 95%CI = -0.26 to 1.35, p = 0.188) and insulin (g = 0.07, 95%CI = -0.14 to 0.29, p = 0.491), HOMA-IR (g = 0.12, 95%CI = -0.19 to 0.43, p = 0.433), and the levels of HbA1c (g = 0.38, 95%CI = -0.02 to 0.77, p = 0.061) between relatives of schizophrenia patients and controls. Two studies demonstrated significantly higher 2-hour glucose levels during OGTT in relatives of patients with schizophrenia (g = 0.90, 95%CI = 0.49 to 1.31, p < 0.001). Our findings do not support the hypothesis that familial liability to psychosis is related to altered fasting parameters of glucose homeostasis. However, this population might show impaired glucose tolerance. More studies are needed to confirm these observations.
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Affiliation(s)
- Błażej Misiak
- Department of Genetics, Wroclaw Medical University, Marcinkowskiego 1 Street, 50-368 Wroclaw, Poland.
| | - Michał Wiśniewski
- First Department of Psychiatry, Institute of Psychiatry & Neurology, Sobieskiego 9 Street, 02-957 Warsaw, Poland
| | - Michał Lis
- Clinical Department of Internal Diseases, Endocrinology and Diabetology, The Central Clinical Hospital of the Ministry of the Interior in Warsaw, Wołoska 137 Street, 02-507 Warsaw, Poland
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Broniewskiego 26 Street, 71-460 Szczecin, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Nervous System Diseases, Wroclaw Medical University, Bartla 5 Street, 51-618 Wroclaw, Poland
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Garcia-Rizo C, Bitanihirwe BKY. Implications of early life stress on fetal metabolic programming of schizophrenia: A focus on epiphenomena underlying morbidity and early mortality. Prog Neuropsychopharmacol Biol Psychiatry 2020; 101:109910. [PMID: 32142745 DOI: 10.1016/j.pnpbp.2020.109910] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/16/2020] [Accepted: 03/03/2020] [Indexed: 12/19/2022]
Abstract
The fetal origin of adult disease hypothesis postulates that a stressful in utero environment can have deleterious consequences on fetal programming, potentially leading to chronic disease in later life. Factors known to impact fetal programming include the timing, intensity, duration and nature of the external stressor during pregnancy. As such, dynamic modulation of fetal programming is heavily involved in shaping health throughout the life course, possibly by influencing metabolic parameters including insulin action, hypothalamic-pituitary-adrenal activity and immune function. The ability of prenatal insults to program adult disease is likely to occur as a result of reduced functional capacity in key organs-a "thrifty" phenotype-where more resources are re-allocated to preserve critical organs such as the brain. Notably, it has been postulated that the manifestation of neuropsychiatric disorders in individuals priorly exposed to prenatal stress may arise from the interaction between hereditary factors and the intrauterine environment, which together precipitate disease onset by disrupting the trajectory of normal brain development. In this review we discuss the evidence linking prenatal programming to neuropsychiatric disorders, mainly schizophrenia, via a "Thrifty psychiatric phenotype" concept. We start by outlining the conception of the thrifty psychiatric phenotype. Next, we discuss the convergence of potential mechanistic pathways through which prenatal insults may trigger epigenetic changes that contribute to the increased morbidity and early mortality observed in neuropsychiatric disorders. Finally, we touch on the public health importance of fetal programming for these disorders. We conclude by providing a brief outlook on the future of this evolving field of research.
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Affiliation(s)
- Clemente Garcia-Rizo
- Barcelona Clinic Schizophrenia Unit, Neuroscience Institute, Hospital Clinic, Barcelona, Spain; Institute of Biomedical Research Agusti Pi iSunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Department of Medicine, University of Barcelona, Barcelona, Spain.
| | - Byron K Y Bitanihirwe
- Centre for Global Health, Trinity College Dublin, Dublin, Ireland; Department of Psychology, Trinity College Dublin, Dublin, Ireland; School of Medicine, Trinity College Dublin, Dublin, Ireland
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Leppert B, Millard LAC, Riglin L, Davey Smith G, Thapar A, Tilling K, Walton E, Stergiakouli E. A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank. PLoS Genet 2020; 16:e1008185. [PMID: 32392212 PMCID: PMC7274459 DOI: 10.1371/journal.pgen.1008185] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/05/2020] [Accepted: 02/11/2020] [Indexed: 12/14/2022] Open
Abstract
Psychiatric disorders are highly heritable and associated with a wide variety of social adversity and physical health problems. Using genetic liability (rather than phenotypic measures of disease) as a proxy for psychiatric disease risk can be a useful alternative for research questions that would traditionally require large cohort studies with long-term follow up. Here we conducted a hypothesis-free phenome-wide association study in about 330,000 participants from the UK Biobank to examine associations of polygenic risk scores (PRS) for five psychiatric disorders (major depression (MDD), bipolar disorder (BP), schizophrenia (SCZ), attention-deficit/ hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)) with 23,004 outcomes in UK Biobank, using the open-source PHESANT software package. There was evidence after multiple testing (p<2.55x10-06) for associations of PRSs with 294 outcomes, most of them attributed to associations of PRSMDD (n = 167) and PRSSCZ (n = 157) with mental health factors. Among others, we found strong evidence of association of higher PRSADHD with 1.1 months younger age at first sexual intercourse [95% confidence interval [CI]: -1.25,-0.92] and a history of physical maltreatment; PRSASD with 0.01% lower erythrocyte distribution width [95%CI: -0.013,-0.007]; PRSSCZ with 0.95 lower odds of playing computer games [95%CI:0.95,0.96]; PRSMDD with a 0.12 points higher neuroticism score [95%CI:0.111,0.135] and PRSBP with 1.03 higher odds of having a university degree [95%CI:1.02,1.03]. We were able to show that genetic liabilities for five major psychiatric disorders associate with long-term aspects of adult life, including socio-demographic factors, mental and physical health. This is evident even in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis. Psychiatric disorders are associated with a wide range of adverse health, social and economic problems. Our study investigated the association of genetic risk for five common psychiatric disorders with socio- demographics, lifestyle and health of about 330,000 participants in the UK Biobank using a systematic, hypothesis-free approach. We found that genetic risk for attention deficit/hyperactivity disorder (ADHD) and bipolar disorder were most strongly associated with lifestyle factors, such as time of first sexual intercourse and educational attainment. Genetic risks for autism spectrum disorder and schizophrenia were associated with altered blood cell counts and decreased risk of playing computer games, respectively. Increased genetic risk for depression was associated with other mental health outcomes such as neuroticism and irritability. In general, our results suggest that genetic risk for psychiatric disorders associates with a range of health and lifestyle traits that were measured in adulthood, in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis. However, it is important to note that these associations are not necessary causal but can also represent genetic correlation or be influenced by other factors, such as socio-economic factors and selection into the cohort. The findings should inform future research using causally informative designs.
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Affiliation(s)
- Beate Leppert
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- * E-mail: (BL); (ES)
| | - Louise A. C. Millard
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Intelligent Systems Laboratory, University of Bristol, Bristol, United Kingdom
| | - Lucy Riglin
- Division of Psychological Medicine and Clinical Neurosciences; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Anita Thapar
- Division of Psychological Medicine and Clinical Neurosciences; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Kate Tilling
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Esther Walton
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Evie Stergiakouli
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- * E-mail: (BL); (ES)
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Habtewold TD, Islam MA, Liemburg EJ, Bruggeman R, Alizadeh BZ. Polygenic risk score for schizophrenia was not associated with glycemic level (HbA1c) in patients with non-affective psychosis: Genetic Risk and Outcome of Psychosis (GROUP) cohort study. J Psychosom Res 2020; 132:109968. [PMID: 32169752 DOI: 10.1016/j.jpsychores.2020.109968] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 02/13/2020] [Accepted: 02/13/2020] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a common comorbidity in patients with schizophrenia (SCZ). The underlying pathophysiologic mechanisms are yet to be fully elucidated, although it can be argued that shared genes, environmental factors or their interaction effect are involved. This study investigated the association between polygenic risk score of SCZ (PRSSCZ) and glycated haemoglobin (HbA1c) while adjusting for polygenic risk score of T2D (PRST2D), and clinical and demographic covariables. METHODS Genotype, clinical and demographic data of 1129 patients with non-affective psychosis were extracted from Genetic Risk and Outcome of Psychosis (GROUP) cohort study. The glycated haemoglobin (HbA1c) was the outcome. PRS was calculated using standard methods. Univariable and multivariable linear regression analyses were applied to estimate associations. Additionally, sensitivity analysis based on multiple imputation was done. After correction for multiple testing, a two-sided p-value ≤.003 was considered to discover evidence for an association. RESULTS Of 1129 patients, 75.8% were male with median age of 29 years. The mean (standard deviation) HbA1c level was 35.1 (5.9) mmol/mol. There was no evidence for an association between high HbA1c level and increased PRSSCZ (adjusted regression coefficient (aβ) = 0.69, standard error (SE) = 0.77, p-value = .37). On the other hand, there was evidence for an association between high HbA1c level and increased PRST2D (aβ = 0.93, SE = 0.32, p-value = .004), body mass index (aβ = 0.20, SE = 0.08, p-value = .01), diastolic blood pressure (aβ = 0.08, SE = 0.04, p-value = .03), late age of first psychosis onset (aβ = 0.19, SE = 0.05, p-value = .0004) and male gender (aβ = 1.58, SE = 0.81, p-value = .05). After multiple testing correction, there was evidence for an association between high HbA1c level and late age of first psychosis onset. Evidence for interaction effect between PRSscz and antipsychotics was not observed. The multiple imputation-based sensitivity analysis provided consistent results with complete case analysis. CONCLUSIONS Glycemic dysregulation in patients with SCZ was not associated with PRSSCZ. This suggests that the mechanisms of hyperglycemia or diabetes are at least partly independent from genetic predisposition to SCZ. Our findings show that the change in HbA1c level can be caused by at least in part due to PRST2D, late age of illness onset, male gender, and increased body mass index and diastolic blood pressure.
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Affiliation(s)
- Tesfa Dejenie Habtewold
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, the Netherlands.
| | - Md Atiqul Islam
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands; Shahjalal University of Science and Technology, Department of Statistics, Sylhet, Bangladesh
| | - Edith J Liemburg
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Department of Neuroscience, Groningen, the Netherlands
| | - Richard Bruggeman
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, the Netherlands; University of Groningen, Department of Clinical and Developmental Neuropsychology, Groningen, the Netherlands.
| | - Behrooz Z Alizadeh
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, the Netherlands
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Liu H, Sun Y, Zhang X, Li S, Hu D, Xiao L, Chen Y, He L, Wang DW. Integrated Analysis of Summary Statistics to Identify Pleiotropic Genes and Pathways for the Comorbidity of Schizophrenia and Cardiometabolic Disease. Front Psychiatry 2020; 11:256. [PMID: 32425817 PMCID: PMC7212438 DOI: 10.3389/fpsyt.2020.00256] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 03/17/2020] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified abundant risk loci associated with schizophrenia (SCZ), cardiometabolic disease (CMD) including body mass index, coronary artery diseases, type 2 diabetes, low- and high-density lipoprotein, total cholesterol, and triglycerides. Although recent studies have suggested that genetic risk shared between these disorders, the pleiotropic genes and biological pathways shared between them are still vague. Here we integrated comprehensive multi-dimensional data from GWAS, expression quantitative trait loci (eQTL), and gene set database to systematically identify potential pleiotropic genes and biological pathways shared between SCZ and CMD. By integrating the results from different approaches including FUMA, Sherlock, SMR, UTMOST, FOCUS, and DEPICT, we revealed 21 pleiotropic genes that are likely to be shared between SCZ and CMD. These genes include VRK2, SLC39A8, NT5C2, AMBRA1, ARL6IP4, OGFOD2, PITPNM2, CDK2AP1, C12orf65, ABCB9, SETD8, MPHOSPH9, FES, FURIN, INO80E, YPEL3, MAPK3, SREBF1, TOM1L2, GATAD2A, and TM6SF2. In addition, we also performed the gene-set enrichment analysis using the software of GSA-SNP2 and MAGMA with GWAS summary statistics and identified three biological pathways (MAPK-TRK signaling, growth hormone signaling, and regulation of insulin secretion signaling) shared between them. Our study provides insights into the pleiotropic genes and biological pathways underlying mechanisms for the comorbidity of SCZ and CMD. However, further genetic and functional studies are required to validate the role of these potential pleiotropic genes and pathways in the etiology of the comorbidity of SCZ and CMD, which should provide potential targets for future diagnostics and therapeutics.
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Affiliation(s)
- Hao Liu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai, China
| | - Yang Sun
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Xinxin Zhang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai, China
| | - Shiyang Li
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Dong Hu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Lei Xiao
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Yanghui Chen
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai, China
| | - Dao Wen Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
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Huo L, Zhang G, Du XD, Jia Q, Qian ZK, Chen D, Xiu M, Wu F, Soares JC, Huang X, Cassidy RM, Ning Y, Zhang XY. The prevalence, risk factors and clinical correlates of diabetes mellitus in Chinese patients with schizophrenia. Schizophr Res 2020; 218:262-266. [PMID: 31987695 DOI: 10.1016/j.schres.2019.12.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 12/21/2019] [Indexed: 01/19/2023]
Abstract
Diabetes is one of the most common comorbid diseases in patients with schizophrenia. The present study examined the prevalence of diabetes and its clinical correlates in a large sample of Chinese patients with schizophrenia, which has not been examined systemically. In this cross-sectional study, a total of 1189 patients (males/females = 938/251; average age: 48.51 ± 10.09 years) were recruited. Fasting blood samples were collected to diagnose diabetes. Psychiatric symptoms were measured with the Positive and Negative Syndrome Scale (PANSS). The prevalence of diabetes was 12.53% with a significant gender difference (males: 10.87% versus females: 18.73%). Compared to patients without diabetes, those with diabetes were older, had a later age of onset, had a higher BMI, had higher positive symptom scores and had higher level of metabolic indices, including triglyceride, cholesterol and HDL cholesterol. After stepwise binary logistic regression analysis, age, BMI, and triglyceride level remained significantly associated with diabetes. This study suggests that diabetes occur with high prevalence in Chinese schizophrenia patients. In addition, age, BMI, and triglyceride level possibly are useful markers predicting an increased risk for diabetes.
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Affiliation(s)
- Lijuan Huo
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Guangya Zhang
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Xiang-Dong Du
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Qiaqiufang Jia
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Zheng-Kang Qian
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Dachun Chen
- Beijing HuilongGuan Hosptial, Beijing, China
| | - Meihong Xiu
- Beijing HuilongGuan Hosptial, Beijing, China
| | - Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Jair C Soares
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xingbing Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Ryan M Cassidy
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
| | - Xiang Yang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China; Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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