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Smiles WJ, Ovens AJ, Oakhill JS, Kofler B. The metabolic sensor AMPK: Twelve enzymes in one. Mol Metab 2024; 90:102042. [PMID: 39362600 PMCID: PMC11752127 DOI: 10.1016/j.molmet.2024.102042] [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: 08/02/2024] [Revised: 09/12/2024] [Accepted: 09/27/2024] [Indexed: 10/05/2024] Open
Abstract
BACKGROUND AMP-activated protein kinase (AMPK) is an evolutionarily conserved regulator of energy metabolism. AMPK is sensitive to acute perturbations to cellular energy status and leverages fundamental bioenergetic pathways to maintain cellular homeostasis. AMPK is a heterotrimer comprised of αβγ-subunits that in humans are encoded by seven individual genes (isoforms α1, α2, β1, β2, γ1, γ2 and γ3), permitting formation of at least 12 different complexes with personalised biochemical fingerprints and tissue expression patterns. While the canonical activation mechanisms of AMPK are well-defined, delineation of subtle, as well as substantial, differences in the regulation of heterogenous AMPK complexes remain poorly defined. SCOPE OF REVIEW Here, taking advantage of multidisciplinary findings, we dissect the many aspects of isoform-specific AMPK function and links to health and disease. These include, but are not limited to, allosteric activation by adenine nucleotides and small molecules, co-translational myristoylation and post-translational modifications (particularly phosphorylation), governance of subcellular localisation, and control of transcriptional networks. Finally, we delve into current debate over whether AMPK can form novel protein complexes (e.g., dimers lacking the α-subunit), altogether highlighting opportunities for future and impactful research. MAJOR CONCLUSIONS Baseline activity of α1-AMPK is higher than its α2 counterpart and is more sensitive to synergistic allosteric activation by metabolites and small molecules. α2 complexes however, show a greater response to energy stress (i.e., AMP production) and appear to be better substrates for LKB1 and mTORC1 upstream. These differences may explain to some extent why in certain cancers α1 is a tumour promoter and α2 a suppressor. β1-AMPK activity is toggled by a 'myristoyl-switch' mechanism that likely precedes a series of signalling events culminating in phosphorylation by ULK1 and sensitisation to small molecules or endogenous ligands like fatty acids. β2-AMPK, not entirely beholden to this myristoyl-switch, has a greater propensity to infiltrate the nucleus, which we suspect contributes to its oncogenicity in some cancers. Last, the unique N-terminal extensions of the γ2 and γ3 isoforms are major regulatory domains of AMPK. mTORC1 may directly phosphorylate this region in γ2, although whether this is inhibitory, especially in disease states, is unclear. Conversely, γ3 complexes might be preferentially regulated by mTORC1 in response to physical exercise.
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Affiliation(s)
- William J Smiles
- Research Program for Receptor Biochemistry and Tumour Metabolism, Department of Paediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria; Metabolic Signalling Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia.
| | - Ashley J Ovens
- Protein Engineering in Immunity & Metabolism, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia
| | - Jonathan S Oakhill
- Metabolic Signalling Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Melbourne, Australia; Department of Medicine, University of Melbourne, Parkville, Australia
| | - Barbara Kofler
- Research Program for Receptor Biochemistry and Tumour Metabolism, Department of Paediatrics, University Hospital of the Paracelsus Medical University, Salzburg, Austria
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Anwardeen NR, Naja K, Elrayess MA. Advancements in precision medicine: multi-omics approach for tailored metformin treatment in type 2 diabetes. Front Pharmacol 2024; 15:1506767. [PMID: 39669200 PMCID: PMC11634602 DOI: 10.3389/fphar.2024.1506767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2024] [Accepted: 11/20/2024] [Indexed: 12/14/2024] Open
Abstract
Metformin has become the frontline treatment in addressing the significant global health challenge of type 2 diabetes due to its proven effectiveness in lowering blood glucose levels. However, the reality is that many patients struggle to achieve their glycemic targets with the medication and the cause behind this variability has not been investigated thoroughly. While genetic factors account for only about a third of this response variability, the potential influence of metabolomics and the gut microbiome on drug efficacy opens new avenues for investigation. This review explores the different molecular signatures to uncover how the complex interplay between genetics, metabolic profiles, and gut microbiota can shape individual responses to metformin. By highlighting the insights from recent studies and identifying knowledge gaps regarding metformin-microbiota interplay, we aim to highlight the path toward more personalized and effective diabetes management strategies and moving beyond the one-size-fits-all approach.
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Affiliation(s)
| | - Khaled Naja
- Biomedical Research Center, Qatar University, Doha, Qatar
| | - Mohamed A. Elrayess
- Biomedical Research Center, Qatar University, Doha, Qatar
- College of Medicine, QU Health, Qatar University, Doha, Qatar
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3
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Morales-Rivera MI, Alemón-Medina R, Martínez-Hernández A, Contreras-Cubas C, Altamirano-Bustamante NF, Gómez-Garduño J, Mendoza-Caamal EC, Nuñez-González JO, García-Álvarez R, Revilla-Monsalve C, Valcarcel-Gamiño JA, Villafan-Bernal JR, Centeno-Cruz F, García-Ortiz H, Barajas-Olmos F, Orozco L. Exome Sequence Data of Eight SLC Transporters Reveal That SLC22A1 and SLC22A3 Variants Alter Metformin Pharmacokinetics and Glycemic Control. Pharmaceuticals (Basel) 2024; 17:1385. [PMID: 39459024 PMCID: PMC11510168 DOI: 10.3390/ph17101385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Revised: 10/02/2024] [Accepted: 10/15/2024] [Indexed: 10/28/2024] Open
Abstract
Background: Type 2 diabetes (T2D) is one of the leading causes of mortality and is a public health challenge worldwide. Metformin is the first-choice treatment for T2D; its pharmacokinetics (PK) is facilitated by members of the solute carrier (SLC) superfamily of transporters, it is not metabolized, and it is excreted by the kidney. Although interindividual variability in metformin pharmacokinetics is documented in the Mexican population, its pharmacogenomics is still underexplored. We aimed to identify variants in metformin SLC transporter genes associated with metformin PK and response in Mexican patients. Methods: Using exome data from 2217 Mexican adults, we identified 86 biallelic SNVs in the eight known genes encoding SLC transporters, with a minor allele frequency ≥ 1%, which were analyzed in an inadequate glycemic control (IGC) association study in T2D metformin treated patients. Metformin PK was evaluated in a pediatric cohort and the effect of associated SNVs was correlated. Results: Functional annotation classified two SNVs as pathogenic. The association study revealed two blocks associated with IGC. These haplotypes comprise rs622591, rs4646272, rs4646273, and rs4646276 in SLC22A1; and rs1810126 and rs668871 in SLC22A3. PK profiles revealed that homozygotes of the SLC22A1 haplotype reached lower plasma metformin concentrations 2 h post administration than the other groups. Conclusions: Our findings highlight the potential of pharmacogenomics studies to enhance precision medicine, which may involve dosage adjustments or the exploration of alternative therapeutic options. These hold significant implications for public health, particularly in populations with a high susceptibility to develop metabolic diseases, such as Latin Americans.
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Affiliation(s)
- Monserrat I. Morales-Rivera
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
- Postdoctoral Researcher, Consejo Nacional de Humanidades Ciencias y Tecnologías, Mexico City 14610, Mexico
| | - Radamés Alemón-Medina
- Pharmacology Laboratory, Instituto Nacional de Pediatría, SSA, Mexico City 04530, Mexico; (R.A.-M.); (J.G.-G.); (R.G.-Á.)
| | - Angélica Martínez-Hernández
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Cecilia Contreras-Cubas
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | | | - Josefina Gómez-Garduño
- Pharmacology Laboratory, Instituto Nacional de Pediatría, SSA, Mexico City 04530, Mexico; (R.A.-M.); (J.G.-G.); (R.G.-Á.)
| | - Elvia C. Mendoza-Caamal
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - J. Orlando Nuñez-González
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Raquel García-Álvarez
- Pharmacology Laboratory, Instituto Nacional de Pediatría, SSA, Mexico City 04530, Mexico; (R.A.-M.); (J.G.-G.); (R.G.-Á.)
| | - Cristina Revilla-Monsalve
- Medical Research Unit in Metabolic Diseases, UMAE Hospital de Cardiología, Centro Médico Nacional Siglo XXI, IMSS, Mexico City 06720, Mexico;
| | - José Antonio Valcarcel-Gamiño
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - José Rafael Villafan-Bernal
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Federico Centeno-Cruz
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Humberto García-Ortiz
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Francisco Barajas-Olmos
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
| | - Lorena Orozco
- Immunogenomics and Metabolic Diseases Laboratory, Instituto Nacional de Medicina Genómica, SS, Mexico City 14610, Mexico; (M.I.M.-R.); (A.M.-H.); (C.C.-C.); (E.C.M.-C.); (J.A.V.-G.); (J.R.V.-B.); (F.C.-C.); (H.G.-O.)
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Mohammadi Jouabadi S, Peymani P, Nekouei Shahraki M, van Rooij JGJ, Broer L, Roks AJM, Stricker BH, Ahmadizar F. Effects and interaction of single nucleotide polymorphisms at the pharmacokinetic/pharmacodynamic site: insights from the Rotterdam study into metformin clinical response and dose titration. THE PHARMACOGENOMICS JOURNAL 2024; 24:31. [PMID: 39375343 DOI: 10.1038/s41397-024-00352-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 09/23/2024] [Accepted: 10/01/2024] [Indexed: 10/09/2024]
Abstract
Our study investigated the impact of genetic variations on metformin glycemic response in a cohort from the Rotterdam Study, comprising 14,926 individuals followed for up to 27 years. Among 1285 metformin users of European ancestry, using linear mixed models, we analyzed the association of single nucleotide polymorphisms (SNPs) and a Polygenic Risk Score (PRS) with glycemic response, measured by changes in metformin dosage or HbA1c levels. While individual genetic variants showed no significant association, rs622342 on SLC2A1 correlated with increased glycemic response only in metformin monotherapy patients (β = -2.09, P-value < 0.001). The collective effect of variants, as represented by PRS, weakly correlated with changes in metformin dosage (β = 0.023, P-value = 0.027). Synergistic interaction was observed between rs7124355 and rs8192675. Our findings suggest that while higher PRS correlates with increased metformin dosage, its modest effect size limits clinical utility, emphasizing the need for future research in diverse populations to refine genetic risk models.
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Affiliation(s)
- Soroush Mohammadi Jouabadi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Division of Vascular Disease and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
| | - Payam Peymani
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- College of pharmacy, University of Manitoba, Winnipeg, MB, Canada
| | - Mitra Nekouei Shahraki
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jeroen G J van Rooij
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Linda Broer
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Anton J M Roks
- Division of Vascular Disease and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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5
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Damarov IS, Korbolina EE, Rykova EY, Merkulova TI. Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response. Int J Mol Sci 2024; 25:9297. [PMID: 39273245 PMCID: PMC11394919 DOI: 10.3390/ijms25179297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.
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Affiliation(s)
- Igor S Damarov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elena E Korbolina
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elena Y Rykova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Department of Engineering Problems of Ecology, Novosibirsk State Technical University, 630087 Novosibirsk, Russia
| | - Tatiana I Merkulova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
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Billings LK, Jablonski KA, Pan Q, Florez JC, Franks PW, Goldberg RB, Hivert MF, Kahn SE, Knowler WC, Lee CG, Merino J, Huerta-Chagoya A, Mercader JM, Raghavan S, Shi Z, Srinivasan S, Xu J, Udler MS. Increased Genetic Risk for β-Cell Failure Is Associated With β-Cell Function Decline in People With Prediabetes. Diabetes 2024; 73:1352-1360. [PMID: 38758294 PMCID: PMC11262049 DOI: 10.2337/db23-0761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 μU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Liana K. Billings
- Division of Endocrinology, Department of Medicine, NorthShore University HealthSystem/Endeavor Health, Skokie, IL
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL
| | | | - Qing Pan
- Biostatistics Center, George Washington University, Washington, DC
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Program in Metabolism and Program in Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle
| | - William C. Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Christine G. Lee
- Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - Jordi Merino
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Program in Metabolism and Program in Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Alicia Huerta-Chagoya
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Program in Metabolism and Program in Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Program in Metabolism and Program in Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sridharan Raghavan
- Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL
| | - Shylaja Srinivasan
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Program in Metabolism and Program in Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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Chaudhary S, Kulkarni A. Metformin: Past, Present, and Future. Curr Diab Rep 2024; 24:119-130. [PMID: 38568468 DOI: 10.1007/s11892-024-01539-1] [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] [Accepted: 03/12/2024] [Indexed: 05/12/2024]
Abstract
PURPOSE OF REVIEW This review provides the most recent update of metformin, a biguanide oral antihyperglycemic drug used as a first-line treatment in type 2 diabetes mellitus. RECENT FINDINGS Metformin continues to dominate in the world of antidiabetics, and its use will continue to rise because of its high efficiency and easy availability. Apart from type 2 diabetes, research is exploring its potential in other conditions such as cancer, memory loss, bone disorders, immunological diseases, and aging. Metformin is the most prescribed oral antidiabetic worldwide. It has been in practical use for the last six decades and continues to be the preferred drug for newly diagnosed type 2 diabetes mellitus. It reduces glucose levels by decreasing hepatic glucose production, reducing intestinal glucose absorption, and increasing insulin sensitivity. It can be used as monotherapy or combined with other antidiabetics like sulfonylureas, DPP-4 inhibitors, SGLT-2 inhibitors, or insulin, improving its efficacy. Metformin can be used once or twice daily, depending on requirements. Prolonged usage of metformin may lead to abdominal discomfort, deficiency of Vitamin B12, or lactic acidosis. It should be used carefully in patients with renal impairment. Recent studies have explored additional benefits of metformin in polycystic ovarian disease, gestational diabetes mellitus, cognitive disorders, and immunological diseases. However, more extensive studies are needed to confirm these additional benefits.
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Le J, Chen Y, Yang W, Chen L, Ye J. Metabolic basis of solute carrier transporters in treatment of type 2 diabetes mellitus. Acta Pharm Sin B 2024; 14:437-454. [PMID: 38322335 PMCID: PMC10840401 DOI: 10.1016/j.apsb.2023.09.004] [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: 05/26/2023] [Revised: 07/10/2023] [Accepted: 08/09/2023] [Indexed: 02/08/2024] Open
Abstract
Solute carriers (SLCs) constitute the largest superfamily of membrane transporter proteins. These transporters, present in various SLC families, play a vital role in energy metabolism by facilitating the transport of diverse substances, including glucose, fatty acids, amino acids, nucleotides, and ions. They actively participate in the regulation of glucose metabolism at various steps, such as glucose uptake (e.g., SLC2A4/GLUT4), glucose reabsorption (e.g., SLC5A2/SGLT2), thermogenesis (e.g., SLC25A7/UCP-1), and ATP production (e.g., SLC25A4/ANT1 and SLC25A5/ANT2). The activities of these transporters contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). Notably, SLC5A2 has emerged as a valid drug target for T2DM due to its role in renal glucose reabsorption, leading to groundbreaking advancements in diabetes drug discovery. Alongside SLC5A2, multiple families of SLC transporters involved in the regulation of glucose homeostasis hold potential applications for T2DM therapy. SLCs also impact drug metabolism of diabetic medicines through gene polymorphisms, such as rosiglitazone (SLCO1B1/OATP1B1) and metformin (SLC22A1-3/OCT1-3 and SLC47A1, 2/MATE1, 2). By consolidating insights into the biological activities and clinical relevance of SLC transporters in T2DM, this review offers a comprehensive update on their roles in controlling glucose metabolism as potential drug targets.
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Affiliation(s)
- Jiamei Le
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yilong Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Wei Yang
- Metabolic Disease Research Center, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
| | - Ligong Chen
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Jianping Ye
- Metabolic Disease Research Center, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, China
- Research Center for Basic Medicine, Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450052, China
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Bodhini D, Morton RW, Santhakumar V, Nakabuye M, Pomares-Millan H, Clemmensen C, Fitzpatrick SL, Guasch-Ferre M, Pankow JS, Ried-Larsen M, Franks PW, Tobias DK, Merino J, Mohan V, Loos RJF. Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:133. [PMID: 37794109 PMCID: PMC10551013 DOI: 10.1038/s43856-023-00363-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D. METHODS We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier. RESULTS The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. CONCLUSIONS We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.
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Affiliation(s)
| | - Robert W Morton
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton, ON, Canada
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark
| | - Vanessa Santhakumar
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mariam Nakabuye
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hugo Pomares-Millan
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie L Fitzpatrick
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Marta Guasch-Ferre
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Mathias Ried-Larsen
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
- Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Deirdre K Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Merino
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Chennai, India
- Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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10
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Lyssenko V, Vaag A. Genetics of diabetes-associated microvascular complications. Diabetologia 2023; 66:1601-1613. [PMID: 37452207 PMCID: PMC10390394 DOI: 10.1007/s00125-023-05964-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/27/2023] [Indexed: 07/18/2023]
Abstract
Diabetes is associated with excess morbidity and mortality due to both micro- and macrovascular complications, as well as a range of non-classical comorbidities. Diabetes-associated microvascular complications are those considered most closely related to hyperglycaemia in a causal manner. However, some individuals with hyperglycaemia (even those with severe hyperglycaemia) do not develop microvascular diseases, which, together with evidence of co-occurrence of microvascular diseases in families, suggests a role for genetics. While genome-wide association studies (GWASs) produced firm evidence of multiple genetic variants underlying differential susceptibility to type 1 and type 2 diabetes, genetic determinants of microvascular complications are mostly suggestive. Identified susceptibility variants of diabetic kidney disease (DKD) in type 2 diabetes mirror variants underlying chronic kidney disease (CKD) in individuals without diabetes. As for retinopathy and neuropathy, reported risk variants currently lack large-scale replication. The reported associations between type 2 diabetes risk variants and microvascular complications may be explained by hyperglycaemia. More extensive phenotyping, along with adjustments for unmeasured confounding, including both early (fetal) and late-life (hyperglycaemia, hypertension, etc.) environmental factors, are urgently needed to understand the genetics of microvascular complications. Finally, genetic variants associated with reduced glycolysis, mitochondrial dysfunction and DNA damage and sustained cell regeneration may protect against microvascular complications, illustrating the utility of studies in individuals who have escaped these complications.
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Affiliation(s)
- Valeriya Lyssenko
- Department of Clinical Science, Mohn Research Center for Diabetes Precision Medicine, University of Bergen, Bergen, Norway.
- Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Lund, Sweden.
| | - Allan Vaag
- Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Lund, Sweden
- Steno Diabetes Center Copenhagen, Herlev, Denmark
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11
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Schweighofer N, Strasser M, Obermayer A, Trummer O, Sourij H, Sourij C, Obermayer-Pietsch B. Identification of Novel Intronic SNPs in Transporter Genes Associated with Metformin Side Effects. Genes (Basel) 2023; 14:1609. [PMID: 37628660 PMCID: PMC10454417 DOI: 10.3390/genes14081609] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Metformin is a widely used and effective medication in type 2 diabetes (T2DM) as well as in polycystic ovary syndrome (PCOS). Single nucleotide polymorphisms (SNPs) contribute to the occurrence of metformin side effects. The aim of the present study was to identify intronic genetic variants modifying the occurrence of metformin side effects and to replicate them in individuals with T2DM and in women with PCOS. We performed Next Generation Sequencing (Illumina Next Seq) of 115 SNPs in a discovery cohort of 120 metformin users and conducted a systematic literature review. Selected SNPs were analysed in two independent cohorts of individuals with either T2DM or PCOS, using 5'-3'exonucleaseassay. A total of 14 SNPs in the organic cation transporters (OCTs) showed associations with side effects in an unadjusted binary logistic regression model, with eight SNPs remaining significantly associated after appropriate adjustment in the discovery cohort. Five SNPs were confirmed in a combined analysis of both replication cohorts but showed different association patterns in subgroup analyses. In an unweighted polygenic risk score (PRS), the risk for metformin side effects increased with the number of risk alleles. Intronic SNPs in the OCT cluster contribute to the development of metformin side effects in individuals with T2DM and in women with PCOS and are therefore of interest for personalized therapy options.
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Affiliation(s)
- Natascha Schweighofer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Center for Biomarker Research in Medicine, CBmed, 8010 Graz, Austria
| | - Moritz Strasser
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Department of Health Studies, Institute of Biomedical, FH Joanneum University of Applied Sciences, 8020 Graz, Austria
| | - Anna Obermayer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Interdisciplinary Metabolic Medicine Trials Unit, Medical University of Graz, 8036 Graz, Austria
| | - Olivia Trummer
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
| | - Harald Sourij
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
- Interdisciplinary Metabolic Medicine Trials Unit, Medical University of Graz, 8036 Graz, Austria
| | - Caren Sourij
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria;
| | - Barbara Obermayer-Pietsch
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria; (N.S.); (M.S.); (A.O.); (H.S.); barbar (B.O.-P.)
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12
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Bodhini D, Morton RW, Santhakumar V, Nakabuye M, Pomares-Millan H, Clemmensen C, Fitzpatrick SL, Guasch-Ferre M, Pankow JS, Ried-Larsen M, Franks PW, Tobias DK, Merino J, Mohan V, Loos RJF. Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.03.23289433. [PMID: 37205385 PMCID: PMC10187453 DOI: 10.1101/2023.05.03.23289433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.
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13
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Damanhouri ZA, Alkreathy HM, Alharbi FA, Abualhamail H, Ahmad MS. A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes. Int J Med Sci 2023; 20:142-150. [PMID: 36619226 PMCID: PMC9812811 DOI: 10.7150/ijms.77206] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 12/02/2022] [Indexed: 01/06/2023] Open
Abstract
Metformin is the most often prescribed drug for people with type 2 diabetes (T2D). More than 120 million patients with T2D use metformin worldwide. However, monotherapy fails to achieve glycemic control in a third of the treated patients. Genetics contribute to some of the inter-individual variations in glycemic response to metformin. Numerous pharmacogenetic studies have demonstrated that variations in genes related to pharmacokinetics and pharmacodynamics of metformin's encoding transporters are mainly associated with metformin response. The goal of this review is to evaluate the current state of metformin pharmacogenetics and metabolomics research, discuss the clinical and scientific issues that need to be resolved in order to increase our knowledge of patient response variability to metformin, and how to improve patient outcomes. Metformin's hydrophilic nature and absorption as well as its action mechanism and effectiveness on T2D initiation are discussed. The impacts of variations associated with various genes are analysed to identify and evaluate the effect of genetic polymorphisms on the therapeutic activity of metformin. The metabolic pattern of T2D and metformin is also indicated. This is to emphasise that studies of pharmacogenetics and metabolomics could expand our knowledge of metformin response in T2D.
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Affiliation(s)
- Zoheir A Damanhouri
- Pharmacology Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Huda M Alkreathy
- Pharmacology Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fawaz A Alharbi
- Pharmacology Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Abualhamail
- Pharmacology Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad S Ahmad
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
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14
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Vohra M, Sharma AR, Mallya S, Prabhu NB, Jayaram P, Nagri SK, Umakanth S, Rai PS. Implications of genetic variations, differential gene expression, and allele-specific expression on metformin response in drug-naïve type 2 diabetes. J Endocrinol Invest 2022; 46:1205-1218. [PMID: 36528847 DOI: 10.1007/s40618-022-01989-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE Metformin is widely used to treat type 2 diabetes mellitus (T2DM) individuals. Clinically, inter-individual variability of metformin response is of significant concern and is under interrogation. In this study, a targeted exome and whole transcriptome analysis were performed to identify predictive biomarkers of metformin response in drug-naïve T2DM individuals. METHODS The study followed a prospective study design. Drug-naïve T2DM individuals (n = 192) and controls (n = 223) were enrolled. T2DM individuals were administered with metformin monotherapy and defined as responders and non-responders based on their glycated haemoglobin change over three months. 146 T2DM individuals were used for the final analysis and remaining samples were lost during the follow-up. Target exome sequencing and RNA-seq was performed to analyze genetic and transcriptome profile. The selected SNPs were validated by genotyping and allele specific gene expression using the TaqMan assay. The gene prioritization, enrichment analysis, drug-gene interactions, disease-gene association, and correlation analysis were performed using various tools and databases. RESULTS rs1050152 and rs272893 in SLC22A4 were associated with improved response to metformin. The copy number loss was observed in PPARGC1A in the non-responders. The expression analysis highlighted potential differentially expressed targets for predicting metformin response (n = 35) and T2DM (n = 14). The expression of GDF15, TWISTNB, and RPL36A genes showed a maximum correlation with the change in HbA1c levels. The disease-gene association analysis highlighted MAGI2 rs113805659 to be linked with T2DM. CONCLUSION The results provide evidence for the genetic variations, perturbed transcriptome, allele-specific gene expression, and pathways associated with metformin drug response in T2DM.
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Affiliation(s)
- M Vohra
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - A R Sharma
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - S Mallya
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - N B Prabhu
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - P Jayaram
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - S K Nagri
- Department of Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India
| | - S Umakanth
- Department of Medicine, Dr. T.M.A. Pai Hospital, Manipal Academy of Higher Education, Manipal, India
| | - P S Rai
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India.
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15
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Flowers E, Aouizerat BE, Kanaya AM, Florez JC, Gong X, Zhang L. MicroRNAs Associated with Incident Diabetes in the Diabetes Prevention Program. J Clin Endocrinol Metab 2022; 108:e306-e312. [PMID: 36477577 DOI: 10.1210/clinem/dgac714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE MicroRNAs (miRs) are short (i.e., 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. The purpose of this study was to assess whether miRs are predictive of incident T2D in the Diabetes Prevention Program (DPP) trial. RESEARCH DESIGN AND METHODS This was a secondary analysis (n = 1,000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso to identify the optimal miR predictors and cox proportional hazards to model time to T2D overall and within intervention arms. RESULTS We identified five miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazards ratio 2.81 (95% confidence interval (CI) 2.05, 3.87); p < 0.001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared to the lowest quartile risk group was 5.91 (95% CI (2.02, 17.3); p < 0.001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI (2.50, 5.18); p < 0.001) and the metformin (HR 2.72; 95% CI (1.47, 5.00); p = 0.001) groups compared to placebo. Of the five miRs identified, one targets a gene with prior known associations with risk for T2D. DISCUSSION We identified five miRs that are optimal predictors of incident T2D in the DPP cohort. Future directions include validation of this finding in an independent sample in order to determine whether this risk score may have potential clinical utility for risk stratification of individuals with prediabetes, and functional analysis of the potential genes and pathways targeted by the miRs that were included in the risk score.
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Affiliation(s)
- Elena Flowers
- University of California, San Francisco, Department of Physiological Nursing, San Francisco, CA
- University of California, San Francisco, Institute for Human Genetics, San Francisco, CA
| | - Bradley E Aouizerat
- New York University, Bluestone Center for Clinical Research, New York, NY
- New York University, Department of Oral and Maxillofacial Surgery, New York, NY
| | - Alka M Kanaya
- University of California, San Francisco, Department of Medicine, Division of General Internal Medicine, San Francisco, CA
- University of California, San Francisco, Department of Epidemiology and Biostatistics, San Francisco, CA
| | - Jose C Florez
- Center for Genomic Medicine and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Xingyue Gong
- University of California, San Francisco, Department of Physiological Nursing, San Francisco, CA
| | - Li Zhang
- University of California, San Francisco, Department of Epidemiology and Biostatistics, San Francisco, CA
- University of California, San Francisco, Department of Medicine, Division of Hematology and Oncology, San Francisco, CA
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Abstract
Data generated over nearly two decades clearly demonstrate the importance of epigenetic modifications and mechanisms in the pathogenesis of type 2 diabetes. However, the role of pharmacoepigenetics in type 2 diabetes is less well established. The field of pharmacoepigenetics covers epigenetic biomarkers that predict response to therapy, therapy-induced epigenetic alterations as well as epigenetic therapies including inhibitors of epigenetic enzymes. Not all individuals with type 2 diabetes respond to glucose-lowering therapies in the same way, and there is therefore a need for clinically useful biomarkers that discriminate responders from non-responders. Blood-based epigenetic biomarkers may be useful for this purpose. There is also a need for a better understanding of whether existing glucose-lowering therapies exert their function partly through therapy-induced epigenetic alterations. Finally, epigenetic enzymes may be drug targets for type 2 diabetes. Here, I discuss whether pharmacoepigenetics is clinically relevant for type 2 diabetes based on studies addressing this topic.
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Affiliation(s)
- Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.
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17
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Chen P, Cao Y, Chen S, Liu Z, Chen S, Guo Y. Association of SLC22A1, SLC22A2, SLC47A1, and SLC47A2 Polymorphisms with Metformin Efficacy in Type 2 Diabetic Patients. Biomedicines 2022; 10:2546. [PMID: 36289808 PMCID: PMC9599747 DOI: 10.3390/biomedicines10102546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/03/2022] [Accepted: 10/10/2022] [Indexed: 02/05/2023] Open
Abstract
Response to metformin, first-line therapy for type 2 diabetes mellitus (T2DM), exists interindividual variation. Considering that transporters belonging to the solute carrier (SLC) superfamily are determinants of metformin pharmacokinetics, we evaluated the effects of promoter variants in organic cation transporter 1 (OCT1) (SLC22A1 rs628031), OCT2 (SLC22A2 rs316019), multidrug and toxin extrusion protein 1 (MATE1) (SLC47A1 rs2289669), and MATE2 (SLC47A2 rs12943590) on the variation in metformin response. The glucose-lowering effects and improvement of insulin resistance of metformin were assessed in newly diagnosed, treatment-naive type 2 diabetic patients of Han nationality in Chaoshan China (n = 93) receiving metformin. Fasting plasma glucose (FPG), fasting insulin (FINS), glycated hemoglobin A1 (HbA1C), homeostasis model assessment-insulin sensitivity (HOMA-IS), and homeostasis model assessment-insulin resistance (HOMA-IR) were the main metformin efficacy measurements. There were significant correlations between both SLC47A1 rs2289669 and SLC47A2 rs12943590 and the efficacy of metformin in individuals with T2DM. In normal weight T2DM patients, significant associations between the AA and GG genotypes of the rs2289669 variant of SLC47A1 and a greater reduction in FINS and HOMA-IR were detected. A significant correlation was observed between the AG genotype of the rs12943590 polymorphism of SLC47A2 and a greater reduction in HOMA-IR. Gene-environment interaction analysis showed that in the FINS interaction model, the second-order of dose30_g-SLC47A2 rs12943590 was statistically significant. The variants of SLC47A1 rs2289669 and SLC47A2 rs12943590 could be predictors of insulin resistance in type 2 diabetic patients treated with metformin. The second-order interaction of dose30_g-SLC47A2 rs12943590 may have a significant effect on FINS in patients with T2DM on metformin treatment. These findings suggest that promoter variants of SLC47A1 and SLC47A2 are important determinants of metformin transport and response in type 2 diabetes mellitus.
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Affiliation(s)
- Peixian Chen
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China
| | - Yumin Cao
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China
| | - Shenren Chen
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China
| | - Zhike Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Shiyi Chen
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China
| | - Yali Guo
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China
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18
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Kim H, Bae S, Yoon HY, Yee J, Gwak HS. Association of the SLC47A1 Gene Variant With Responses to Metformin Monotherapy in Drug-naive Patients With Type 2 Diabetes. J Clin Endocrinol Metab 2022; 107:2684-2690. [PMID: 35639991 DOI: 10.1210/clinem/dgac333] [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: 12/19/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Although metformin is the first-line treatment for type 2 diabetes, the blood sugar-lowering effect of metformin varies among populations. SLC47A1 plays an important role in metformin pharmacokinetics and pharmacodynamics. OBJECTIVE We performed a systematic review and meta-analysis to investigate the association between SLC47A1 rs2289669 (G > A) and the metformin response in drug-naive patients with type 2 diabetes. METHODS Studies published until January 27, 2022, were retrieved from Cochrane CENTRAL, Embase, PubMed, and Web of Science. Two reviewers independently screened titles, abstracts, and full-text articles. Studies conducted in newly diagnosed or drug-naive patients with type 2 diabetes who received metformin monotherapy were included. A total of 6 studies involving 953 patients were included in this meta-analysis. We extracted the study characteristics and changes in glycated hemoglobin (HbA1c) levels before and after treatment according to the SLC47A1 rs2289669 genotype. Changes in HbA1c levels were analyzed using mean differences (MDs) and 95% CIs. SLC47A1 rs2289669 was associated with changes in HbA1c levels (A carrier vs GG; MD = -0.55; 95% CI, -0.91 to - 0.20; I² = 63%). The sensitivity analysis yielded similar results to the main analysis (MD range, -0.64 to -0.37). When comparing all 3 genotypes, there were significant differences in HbA1c level changes between AA vs GG and GA vs GG, but not in GA vs AA. CONCLUSION This meta-analysis showed that SLC47A1 rs2289669 is associated with the glycemic response to metformin in drug-naive patients with type 2 diabetes.
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Affiliation(s)
- Hamin Kim
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Suhyun Bae
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Ha Young Yoon
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Jeong Yee
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Hye Sun Gwak
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
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Nasykhova YA, Barbitoff YA, Tonyan ZN, Danilova MM, Nevzorov IA, Komandresova TM, Mikhailova AA, Vasilieva TV, Glavnova OB, Yarmolinskaya MI, Sluchanko EI, Glotov AS. Genetic and Phenotypic Factors Affecting Glycemic Response to Metformin Therapy in Patients with Type 2 Diabetes Mellitus. Genes (Basel) 2022; 13:genes13081310. [PMID: 35893047 PMCID: PMC9330240 DOI: 10.3390/genes13081310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 12/10/2022] Open
Abstract
Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist–hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.
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Affiliation(s)
- Yulia A. Nasykhova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Yury A. Barbitoff
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
- St. Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Ziravard N. Tonyan
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Maria M. Danilova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Ivan A. Nevzorov
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Anastasiia A. Mikhailova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Olga B. Glavnova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | - Maria I. Yarmolinskaya
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
| | | | - Andrey S. Glotov
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Y.A.B.); (Z.N.T.); (M.M.D.); (I.A.N.); (A.A.M.); (O.B.G.); (M.I.Y.)
- Correspondence: ; Tel.: +7-9117832003
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From pharmacogenetics to pharmaco-omics: Milestones and future directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Marie C. Sadler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
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21
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Nonnecke EB, Castillo PA, Johansson MEV, Hollox EJ, Shen B, Lönnerdal B, Bevins CL. Human intelectin-2 (ITLN2) is selectively expressed by secretory Paneth cells. FASEB J 2022; 36:e22200. [PMID: 35182405 PMCID: PMC9262044 DOI: 10.1096/fj.202101870r] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/20/2022] [Accepted: 01/26/2022] [Indexed: 01/04/2023]
Abstract
Intelectins (intestinal lectins) are highly conserved across chordate evolution and have been implicated in various human diseases, including Crohn's disease (CD). The human genome encodes two intelectin genes, intelectin-1 (ITLN1) and intelectin-2 (ITLN2). Other than its high sequence similarity with ITLN1, little is known about ITLN2. To address this void in knowledge, we report that ITLN2 exhibits discrete, yet notable differences from ITLN1 in primary structure, including a unique amino terminus, as well as changes in amino acid residues associated with the glycan-binding activity of ITLN1. We identified that ITLN2 is a highly abundant Paneth cell-specific product, which localizes to secretory granules, and is expressed as a multimeric protein in the small intestine. In surgical specimens of ileal CD, ITLN2 mRNA levels were reduced approximately five-fold compared to control specimens. The ileal expression of ITLN2 was unaffected by previously reported disease-associated variants in ITLN2 and CD-associated variants in neighboring ITLN1 as well as NOD2 and ATG16L1. ITLN2 mRNA expression was undetectable in control colon tissue; however, in both ulcerative colitis (UC) and colonic CD, metaplastic Paneth cells were found to express ITLN2. Together, the data reported establish the groundwork for understanding ITLN2 function(s) in the intestine, including its possible role in CD.
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Affiliation(s)
- Eric B Nonnecke
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, California, USA
| | - Patricia A Castillo
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, California, USA
| | - Malin E V Johansson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Edward J Hollox
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Bo Shen
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Bo Lönnerdal
- Department of Nutrition, University of California, Davis, Davis, California, USA
| | - Charles L Bevins
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, California, USA
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Chen P, Cao Y, Guo Y, Xu Q, Wang X, Zhang L, Liu Z, Chen D, Chen S, Chen S. Association of SLC22A1 rs622342 and ATM rs11212617 polymorphisms with metformin efficacy in patients with type 2 diabetes. Pharmacogenet Genomics 2022; 32:67-71. [PMID: 34545025 DOI: 10.1097/fpc.0000000000000454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Metformin is the first-choice oral anti-hyperglycemic drug for type 2 diabetes mellitus (T2DM) patients. There are controversies about the association of SLC22A1 rs622342, which was not reported in the Chinese population, and ataxia-telangiectasia mutated (ATM) rs11212617 polymorphisms with metformin efficacy in T2DM. Our study was to investigate the effects of the two single nucleotide polymorphisms on the efficacy of metformin in T2DM of Han nationality in Chaoshan China. After enrollment, 82 newly diagnosed T2DM patients went on 2-month metformin monotherapy. According to BMI before treatment, the patients were divided into a normal weight group (≥18.5 and <25 kg/m2) and an overweight group (BMI ≥ 25 and <30 kg/m2). T-test, Pearson χ2 test, and regression analysis, which adjusted for age, BMI, sex, the dose of metformin, education, tea drink, smoking, and sweet, were used to evaluate the effects of rs622342 and rs11212617 on several variables, such as fasting plasma glucose (FPG). Compared with the AA or CC genotype, patients with AC genotype of rs622342 achieved greater reduction in Δ60FPG and Δ(60-30)FPG (P = 0.00820, 0.00089, respectively). For 11212617, the reduction in Δ30FPG and Δ60FPG was significantly different among patients with the AC genotype (P = 0.00026, 0.00820, respectively). Our results indicated that common variants of SLC22A1 rs622342 and ATM rs11212617 were associated with the efficacy of metformin in T2DM of Han nationality in Chaoshan China.
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Affiliation(s)
- Peixian Chen
- Department of Endocrinology
- Department of Infection Control
| | - Yumin Cao
- Department of Neurology, Meizhou People's Hospital, Meizhou, Guangdong Province
| | - Yali Guo
- Department of Endocrinology, Central Hospital of Shenzhen Guangming New District, Shenzhen
| | - Qi Xu
- Department of Endocrinology, the Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province
| | - Xiaozhu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, People's Republic of China
| | - Liuwei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, People's Republic of China
| | - Zhike Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, People's Republic of China
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, People's Republic of China
| | - Shiyi Chen
- Department of Endocrinology, the Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province
| | - Shenren Chen
- Department of Endocrinology, the Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province
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23
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Yu H, Hu W, Lin C, Xu L, Liu H, Luo L, Chen R, Huang J, Chen W, Yang C, Kong D, Ding Y. Polymorphisms analysis for association between ADIPO signaling pathway and genetic susceptibility to T2DM in Chinese han population. Adipocyte 2021; 10:463-474. [PMID: 34641739 PMCID: PMC8525967 DOI: 10.1080/21623945.2021.1978728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The aim of the present study is to explored the relationship between ADIPO signalling pathway and T2DM, to provide clues for further study of the pathogenesis of T2DM and to determine the possible drug targets. This study employed a case-control study design. Twenty-three single nucleotide polymorphisms (SNPs) of 13 genes in the selected ADIPO signalling pathway were genotyped by SNPscanTM kit. All statistical analysis was performed by SPSS 25.0, PLINK 1.07, R 2.14.2, Haploview 4.2, SNPstats, and other statistical software packages. In the association analysis based on a single SNPs, rs1044471 had statistical significance in the overdominant model without adjusting covariates. Rs1042531 had statistical significance in the overdominant model. Rs12718444 had statistical significance in the recessive model. There was a linkage disequilibrium between the loci within 9 genes, and the two loci in RXRA gene did not form blocks. Four kernel functions were used for SNPs set analysis based on ADIPO signalling pathway showed that there was no statistical significance whether covariates were added or not, P>0.05.According to our research results, it is found that some single nucleotide polymorphisms (ADIPOR2 rs1044471, PCK1 rs1042531, GLUT1 rs12718444) in the adiponectin signalling pathway may be associated with T2DM
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Affiliation(s)
- Haibing Yu
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Wei Hu
- Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China
| | - Chunwen Lin
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Lin Xu
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Hao Liu
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Ling Luo
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Rong Chen
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Jialu Huang
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Weiying Chen
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Chen Yang
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
| | - Danli Kong
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Yuanlin Ding
- Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
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24
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Guo M, Ren Q, Yang F, Han T, Du W, Zhao F, Li W, Li J, Feng Y, Zhang Y, Wang S, Wu W. Association between AMPKα1 gene polymorphisms and gestational diabetes in the Chinese population: A case-control study. Diabet Med 2021; 38:e14613. [PMID: 34053110 DOI: 10.1111/dme.14613] [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: 10/27/2020] [Accepted: 05/25/2021] [Indexed: 12/01/2022]
Abstract
AIM The aim is to examine the association between seven candidate single nucleotide polymorphisms in AMPKα1 and gestational diabetes in Chinese people. METHOD We used a matched nested case-control study design, individuals including 334 participants with gestational diabetes and 334 healthy pregnant women. Confirmed 334 gestational diabetes cases and maternal age and district of residence matched controls (1:1) were enrolled. We examined seven candidate single nucleotide polymorphisms in AMPKα1 gene and the risk of gestational diabetes. The associations were estimated in Co-dominant, Dominant, Recessive, and Alleles models. The odds ratios (ORs) and their 95% confidence intervals (95% CI) were estimated by unconditional logistical regression as a measure of the associations between genotypes and gestational diabetes adjusting for maternal age, prepregnancy body mass index (BMI), fetal sex and parity. RESULT At the gene level, we found that AMPKα1 was associated with gestational diabetes (p = 0.008). After adjusting the covariates and multiple comparison correction, AMPKα1 (rsc1002424, rs10053664, rs13361707) polymorphisms were associated with the risk of gestational diabetes. In addition, gestational diabetes was related to the AAGGA haplotype comprising rs1002424, rs2570091, rs10053664, rs13361707 and rs3805486 in the haplotype models (p = 0.011). CONCLUSIONS This study provides evidence that the AMPKα1 genotypes (rs1002424 G/A, rs10053664 A/G, rs13361707 A/G) and the haplotype (AAGGA) are relevant genetic factors in a Chinese population with gestational diabetes.
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Affiliation(s)
- Mengzhu Guo
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Qingwen Ren
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Feifei Yang
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Tianbi Han
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Wenqiong Du
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Feng Zhao
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Wangjun Li
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Jinbo Li
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - YongLiang Feng
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Yawei Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Suping Wang
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
| | - Weiwei Wu
- Department of Epidemiology, School of Public Health, Center of Clinical Epidemiology and Evidence Based Medicine, Shanxi Medical University, Taiyuan, China
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Abstract
We have conducted a narrative review based on a structured search strategy, focusing on the effects of metformin on the progression of non-diabetic hyperglycemia to clinical type 2 diabetes mellitus. The principal trials that demonstrated a significantly lower incidence of diabetes in at-risk populations randomized to metformin (mostly with impaired glucose tolerance [IGT]) were published mainly from 1999 to 2012. Metformin reduced the 3-year risk of diabetes by -31% in the randomized phase of the Diabetes Prevention Program (DPP), vs. -58% for intensive lifestyle intervention (ILI). Metformin was most effective in younger, heavier subjects. Diminishing but still significant reductions in diabetes risk for subjects originally randomized to these groups were present in the trial's epidemiological follow-up, the DPP Outcomes Study (DPPOS) at 10 years (-18 and -34%, respectively), 15 years (-18 and -27%), and 22 years (-18 and -25%). Long-term weight loss was also seen in both groups, with better maintenance under metformin. Subgroup analyses from the DPP/DPPOS have shed important light on the actions of metformin, including a greater effect in women with prior gestational diabetes, and a reduction in coronary artery calcium in men that might suggest a cardioprotective effect. Improvements in long-term clinical outcomes with metformin in people with non-diabetic hyperglycemia ("prediabetes") have yet to be demonstrated, but cardiovascular and microvascular benefits were seen for those in the DPPOS who did not vs. did develop diabetes. Multiple health economic analyses suggest that either metformin or ILI is cost-effective in a community setting. Long-term diabetes prevention with metformin is feasible and is supported in influential guidelines for selected groups of subjects. Future research will demonstrate whether intervention with metformin in people with non-diabetic hyperglycemia will improve long-term clinical outcomes.
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Affiliation(s)
- Ulrike Hostalek
- Global Medical Affairs, Merck Healthcare KGaA, Darmstadt, Germany
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Kalamajski S, Huang M, Dalla-Riva J, Keller M, Dawed AY, Hansson O, Pearson ER, Mulder H, Franks PW. Genomic editing of metformin efficacy-associated genetic variants in SLC47A1 does not alter SLC47A1 expression. Hum Mol Genet 2021; 31:491-498. [PMID: 34505146 PMCID: PMC8863414 DOI: 10.1093/hmg/ddab266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/05/2022] Open
Abstract
Several pharmacogenetics studies have identified an association between a greater metformin-dependent reduction in HbA1c levels and the minor A allele at rs2289669 in intron 10 of SLC47A1, encoding multidrug and toxin extrusion 1 (MATE1), a presumed metformin transporter. It is currently unknown if the rs2289669 locus is a cis-eQTL, which would validate its role as predictor of metformin efficacy. We looked at association between common genetic variants in the SLC47A1 gene region and HbA1c reduction after metformin treatment using locus-wise meta-analysis from the MetGen consortium. CRISPR-Cas9 was applied to perform allele editing of, or genomic deletion around, rs2289669 and of the closely linked rs8065082 in HepG2 cells. The genome-edited cells were evaluated for SLC47A1 expression and splicing. None of the common variants including rs2289669 showed significant association with metformin response. Genomic editing of either rs2289669 or rs8065082 did not alter SLC47A1 expression or splicing. Experimental and in silico analyses show that the rs2289669-containing haploblock does not appear to carry genetic variants that could explain its previously reported association with metformin efficacy.
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Affiliation(s)
- Sebastian Kalamajski
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden
| | - Mi Huang
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden
| | - Jonathan Dalla-Riva
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden
| | - Maria Keller
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden.,IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany
| | - Adem Y Dawed
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, DD2 1UB, Scotland, UK
| | - Ola Hansson
- Department of Clinical Sciences, Genomics, Diabetes and Endocrinology, Lund University, Malmö, 20502, Sweden.,Finnish Institute for Molecular Medicine, Helsinki University, Helsinki, 00014, Finland
| | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, DD2 1UB, Scotland, UK
| | | | - Hindrik Mulder
- Department of Clinical Sciences, Unit of Molecular Metabolism, Lund University, Malmö, 20502, Sweden
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, 20502, Sweden.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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27
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Virginia DM, Wahyuningsih MSH, Nugrahaningsih DAA. Association between Three Variants in the PRKAA2 gene, rs2796498, rs9803799, and rs2746342, with 10-year ASCVD Risk on Newly Diagnosed T2DM in Yogyakarta, Indonesia. Open Access Maced J Med Sci 2021. [DOI: 10.3889/oamjms.2021.6213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: AMPK has pivotal roles in glucose and lipid metabolism, including AMPKa2, which PRKAA2 encodes. Metformin as an anti-hyperglycemia agent acts through AMPK. Poor glycemia control among patients with type 2 diabetes mellitus (T2DM) could increase atherosclerosis cardiovascular disease (ASCVD) risk. Therefore, PRKAA2 genetic variation might contribute to 10-year ASCVD risk in patients with newly diagnosed T2DM receiving monotherapy metformin.
AIM: The study aimed to detect an association between PRKAA2 genetic variation with 10 year-ASCVD risk among newly diagnosed T2DM patients prescribed monotherapy metformin.
METHODS: This present study was a case-control study involving 107 participants. Analysis of PRKAA2 genetic variation was performed using the TaqMan assay.
RESULTS: A total of 91 participants who fulfilled our criteria enrolled in this study. Most of the participants were female, with mean age 54.40±7.75 years old, mean HbA1c level of 8.35±1.31%, and the lipid profile indicated normal conditions. There was a significant difference in age (p<0.01), HbA1c level (p=0.04), sex (p<0.01), and smoking status (p<0.01) between low-risk and high-risk groups. The GT genotype of rs9803799 had 187.86 times higher possibility for high-risk of 10-year ASCVD risk than TT genotype (OR=187.86, 95%CI:2.98–11863.51). The dominant model of rs9803799 showed that GT+GG had 94.33 times higher possibility for high-risk of 10-year ASCVD risk than TT genotype (OR=94.33; 95%CI:2.32–3841.21). Other results showed that G allele of rs980377 had 20.48 times higher possibility for high-risk of 10-year ASCVD risk than T allele (OR = 20.48; 95%CI:1.48–283.30). These associations were found after multivariate analysis.
CONCLUSION: Our findings indicated that rs9803799 as one of PRKAA2 genetic variations might impact the 10-year ASCVD risk among newly diagnosed T2DM patients receiving monotherapy metformin. After considering non-genetic factors, patient assessment should include potential genetic factors in cases with hyperglycemia involving treatment affecting glucose and lipid metabolism such as monotherapy metformin.
Keywords: PRKAA2, genetic variation, atherosclerosis cardiovascular disease, type 2 diabetes mellitus, metformin, Indonesia
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Virginia DM, Wahyuningsih MSH, Nugrahaningsih DAA. PRKAA2 variation and the clinical characteristics of patients newly diagnosed with type 2 diabetes mellitus in Yogyakarta, Indonesia. ASIAN BIOMED 2021; 15:161-170. [PMID: 37551330 PMCID: PMC10388783 DOI: 10.2478/abm-2021-0021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background Adenosine monophosphate (AMP)-activated protein kinase (AMPK; EC 2.7.11.31) enzymes play a pivotal role in cell metabolism. They are involved in type 2 diabetes mellitus (T2DM) pathogenesis. Genetic variation of PRKAA2 coding for the AMPK α2 catalytic subunit (AMPKα2) is reported to be associated with susceptibility for T2DM. Objectives To determine the association between PRKAA2 genetic variations (rs2796498, rs9803799, and rs2746342) with clinical characteristics in patients newly diagnosed with T2DM. Methods We performed a cross-sectional study including 166 T2DM patients from 10 primary health care centers in Yogyakarta, Indonesia. We measured fasting plasma glucose, hemoglobin A1c, serum creatinine, glomerular filtration rate, blood pressure, and body mass index as clinical characteristics. PRKAA2 genetic variations were determined by TaqMan SNP genotyping assay. Hardy-Weinberg equilibrium was calculated using χ2 tests. Results There was no difference in clinical characteristics for genotypes rs2796498, rs9803799, or rs2746342 (P > 0.05). No significant association was found between PRKAA2 genetic variations and any clinical feature observed. Further subgroup analysis adjusting for age, sex, and waist circumference did not detect any significant association of PRKAA2 genetic variations with clinical characteristics (P > 0.05). Conclusion PRKAA2 genetic variation is not associated with the clinical characteristics of Indonesian patients with newly diagnosed T2DM.
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Affiliation(s)
- Dita Maria Virginia
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
- Faculty of Pharmacy, Universitas Sanata Dharma, Yogyakarta552181, Indonesia
| | - Mae Sri Hartati Wahyuningsih
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
| | - Dwi Aris Agung Nugrahaningsih
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
- Center of Genetic Study, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
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Xiao D, Liu JY, Zhang SM, Liu RR, Yin JY, Han XY, Li X, Zhang W, Chen XP, Zhou HH, Ji LN, Liu ZQ. A Two-Stage Study Identifies Two Novel Polymorphisms in PRKAG2 Affecting Metformin Response in Chinese Type 2 Diabetes Patients. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:745-755. [PMID: 34188521 PMCID: PMC8236263 DOI: 10.2147/pgpm.s305020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/25/2021] [Indexed: 12/29/2022]
Abstract
Objective Individual differences in glycemic response to metformin in antidiabetic treatment exist widely. Although some associated genetic variations have been discovered, they still cannot accurately predict metformin response. In the current study, we set out to investigate novel genetic variants affecting metformin response in Chinese type 2 diabetes (T2D) patients. Methods A two-stage study enrolled 500 T2D patients who received metformin, glibenclamide or a combination of both were recruited from 2009 to 2012 in China. Change of HbA1c, adjusted by clinical covariates, was used to evaluate glycemic response to metformin. Selected single nucleotide polymorphisms (SNPs) were genotyped using the Infinium iSelect and/or Illumina GoldenGate genotyping platform. A linear regression model was used to evaluate the association between SNPs and response. Results A total of 3739 SNPs were screened in Stage 1, of which 50 were associated with drug response. Except for one genetic variant preferred to affect glibenclamide, the remaining SNPs were subsequently verified in Stage 2, and two SNPs were successfully validated. These were PRKAG2 rs2727528 (discovery group: β=−0.212, P=0.046; validation group: β=−0.269, P=0.028) and PRKAG2 rs1105842 (discovery group: β=0.205, P=0.048; validation group: β=0.273, P=0.025). C allele carriers of rs2727528 and C allele carriers of rs1105842 would have a larger difference of HbA1c level when using metformin. Conclusion Two variants rs2727528 and rs1105842 in PRKAG2, encoding γ2 subunit of AMP-activated protein kinase (AMPK), were found to be associated with metformin response in Chinese T2D patients. These findings may provide some novel information for personalized pharmacotherapy of metformin in China.
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Affiliation(s)
- Di Xiao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Department of pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Jun-Yan Liu
- Department of orthopaedics, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Si-Min Zhang
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Rang-Ru Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Key Laboratory of Tropical Diseases and Translational Medicine of the Ministry of Education & Hainan Provincial Key Laboratory of Tropical Medicine, Hainan Medical College, Haikou, People's Republic of China
| | - Ji-Ye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Xue-Yao Han
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Xiao-Ping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Li-Nong Ji
- Department of Endocrinology and Metabolism, The People's Hospital of Peking University, Beijing, People's Republic of China
| | - Zhao-Qian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, People's Republic of China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
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Nonnecke EB, Castillo PA, Dugan AE, Almalki F, Underwood MA, De La Motte CA, Yuan W, Lu W, Shen B, Johansson MEV, Kiessling LL, Hollox EJ, Lönnerdal B, Bevins CL. Human intelectin-1 (ITLN1) genetic variation and intestinal expression. Sci Rep 2021; 11:12889. [PMID: 34145348 PMCID: PMC8213764 DOI: 10.1038/s41598-021-92198-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/07/2021] [Indexed: 12/13/2022] Open
Abstract
Intelectins are ancient carbohydrate binding proteins, spanning chordate evolution and implicated in multiple human diseases. Previous GWAS have linked SNPs in ITLN1 (also known as omentin) with susceptibility to Crohn's disease (CD); however, analysis of possible functional significance of SNPs at this locus is lacking. Using the Ensembl database, pairwise linkage disequilibrium (LD) analyses indicated that several disease-associated SNPs at the ITLN1 locus, including SNPs in CD244 and Ly9, were in LD. The alleles comprising the risk haplotype are the major alleles in European (67%), but minor alleles in African superpopulations. Neither ITLN1 mRNA nor protein abundance in intestinal tissue, which we confirm as goblet-cell derived, was altered in the CD samples overall nor when samples were analyzed according to genotype. Moreover, the missense variant V109D does not influence ITLN1 glycan binding to the glycan β-D-galactofuranose or protein-protein oligomerization. Taken together, our data are an important step in defining the role(s) of the CD-risk haplotype by determining that risk is unlikely to be due to changes in ITLN1 carbohydrate recognition, protein oligomerization, or expression levels in intestinal mucosa. Our findings suggest that the relationship between the genomic data and disease arises from changes in CD244 or Ly9 biology, differences in ITLN1 expression in other tissues, or an alteration in ITLN1 interaction with other proteins.
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Affiliation(s)
- Eric B Nonnecke
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, CA, 95616, USA.
| | - Patricia A Castillo
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, CA, 95616, USA
- Elanco Animal Health, Fort Dodge, IA, 50501, USA
| | - Amanda E Dugan
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Faisal Almalki
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Almadinah Almunwarah, Saudi Arabia
| | - Mark A Underwood
- Department of Pediatrics, School of Medicine, University of California, Davis, Sacramento, CA, 95817, USA
| | - Carol A De La Motte
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Weirong Yuan
- Institute of Human Virology, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Wuyuan Lu
- Institute of Human Virology, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Fudan University, Shanghai, China
| | - Bo Shen
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Surgery, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA
| | - Malin E V Johansson
- Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Gothenburg, Sweden
| | - Laura L Kiessling
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Edward J Hollox
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Bo Lönnerdal
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Charles L Bevins
- Department of Microbiology and Immunology, School of Medicine, University of California, Davis, Davis, CA, 95616, USA.
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Masilela C, Pearce B, Ongole JJ, Adeniyi OV, Benjeddou M. Single Nucleotide Polymorphisms Associated with Metformin and Sulphonylureas' Glycaemic Response among South African Adults with Type 2 Diabetes Mellitus. J Pers Med 2021; 11:104. [PMID: 33561991 PMCID: PMC7914534 DOI: 10.3390/jpm11020104] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/25/2021] [Accepted: 02/01/2021] [Indexed: 12/31/2022] Open
Abstract
AIMS To examine the association of polymorphisms belonging to SLC22A1, SP1, PRPF31, NBEA, SCNN1B, CPA6 and CAPN10 genes with glycaemic response to metformin and sulphonylureas (SU) combination therapy among South African adults with diabetes mellitus type 2 (T2DM). METHODS A total of 128 individuals of Swati (n = 22) and Zulu (n = 106) origin attending chronic care for T2DM were recruited. Nine SNPs previously associated with metformin and SUs were selected and genotyped using MassArray. Uncontrolled T2DM was defined as HbA1c > 7%. The association between genotypes, alleles and glycaemic response to treatment was determined using multivariate logistic regression model analysis. RESULTS About 85.93% (n = 110) of the study participants were female and 77.34% (n = 99) had uncontrolled T2DM (HbA1c > 7%). In the multivariate (adjusted) logistic regression model analysis, the CC genotype of rs2162145 (CPA6), GG and GA genotypes of rs889299 (SCNN1B) were significantly associated with uncontrolled T2DM. On the other hand, the C allele of rs254271 (PRPF31) and the GA genotype of rs3792269 (CAPN10) were associated with controlled T2DM. A significant interaction between rs2162145 and rs889299 in response to metformin and SU combination therapy was observed. CONCLUSIONS In this study, we reported the association of rs2162145 (CC) and rs889299 (GG and GA) with uncontrolled T2DM. We also reported the association of rs254271 (C) and rs3792269 (GA) with controlled T2DM in response to metformin and SU combination therapy. Furthermore, an interaction between rs2162145 and rs889299 was established, where the genotype combination GA (rs889299) and TT (rs2162145) was associated with uncontrolled T2DM.
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Affiliation(s)
- Charity Masilela
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
| | - Brendon Pearce
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
| | - Joven Jebio Ongole
- Center for Teaching and Learning, Department of Family Medicine, Piet Retief Hospital, Mkhondo 2380, South Africa;
| | | | - Mongi Benjeddou
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (B.P.); (M.B.)
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Sarah EH, El Omri N, Ibrahimi A, El Jaoudi R. Metabolic and genetic studies of glimepiride and metformin and their association with type 2 diabetes. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wang C, Chen B, Feng Q, Nie C, Li T. Clinical perspectives and concerns of metformin as an anti-aging drug. Aging Med (Milton) 2020; 3:266-275. [PMID: 33392433 PMCID: PMC7771567 DOI: 10.1002/agm2.12135] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 12/27/2022] Open
Abstract
As percentages of elderly people rise in many societies, age-related diseases have become more prevalent than ever. Research interests have been shifting to delaying age-related diseases by delaying or reversing aging itself. We use metformin as an entry point to talk about the important molecular and genetic longevity-regulating mechanisms that have been extensively studied with it. Then we review a number of observational studies, animal studies, and clinical trials to reflect the clinical potentials of the mechanisms in lifespan extension, cardiovascular diseases, tumors, and neurodegeneration. Finally, we highlight remaining concerns that are related to metformin and future anti-aging research.
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Affiliation(s)
- Chuyao Wang
- BGI‐ShenzhenBeishan Industrial ZoneShenzhenChina
- Department of Biomedical EngineeringUniversity of RochesterRochesterNYUSA
| | - Bangwei Chen
- BGI‐ShenzhenBeishan Industrial ZoneShenzhenChina
- School of Biology and Biological EngineeringSouth China University of TechnologyGuangzhouChina
| | - Qian Feng
- BGI‐ShenzhenBeishan Industrial ZoneShenzhenChina
- China National GeneBankBGI‐ShenzhenShenzhenChina
| | - Chao Nie
- BGI‐ShenzhenBeishan Industrial ZoneShenzhenChina
- China National GeneBankBGI‐ShenzhenShenzhenChina
| | - Tao Li
- BGI‐ShenzhenBeishan Industrial ZoneShenzhenChina
- China National GeneBankBGI‐ShenzhenShenzhenChina
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Jones VC, Dietze EC, Jovanovic-Talisman T, McCune JS, Seewaldt VL. Metformin and Chemoprevention: Potential for Heart-Healthy Targeting of Biologically Aggressive Breast Cancer. Front Public Health 2020; 8:509714. [PMID: 33194937 PMCID: PMC7658387 DOI: 10.3389/fpubh.2020.509714] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 09/24/2020] [Indexed: 12/14/2022] Open
Abstract
Currently, tamoxifen is the only drug approved for reduction of breast cancer risk in premenopausal women. The significant cardiovascular side effects of tamoxifen, coupled with lack of a survival benefit, potential for genotoxicity, and failure to provide a significant risk-reduction for estrogen receptor-negative breast cancer, all contribute to the low acceptance of tamoxifen chemoprevention in premenopausal women at high-risk for breast cancer. While other prevention options exist for postmenopausal women, there is a search for well-tolerated prevention agents that can simultaneously reduce risk of breast cancers, cardiovascular disease, and type-2 diabetes. Metformin is a well-tolerated oral biguanide hypoglycemic agent that is prescribed worldwide to over 120 million individuals with type-2 diabetes. Metformin is inexpensive, safe during pregnancy, and the combination of metformin, healthy lifestyle, and exercise has been shown to be effective in preventing diabetes. There is a growing awareness that prevention drugs and interventions should make the “whole woman healthy.” To this end, current efforts have focused on finding low toxicity alternatives, particularly repurposed drugs for chemoprevention of breast cancer, including metformin. Metformin's mechanisms of actions are complex but clearly involve secondary lowering of circulating insulin. Signaling pathways activated by insulin also drive biologically aggressive breast cancer and predict poor survival in women with breast cancer. The mechanistic rationale for metformin chemoprevention is well-supported by the scientific literature. Metformin is cheap, safe during pregnancy, and has the potential to provide heart-healthy breast cancer prevention. On-going primary and secondary prevention trials will provide evidence whether metformin is effective in preventing breast cancer.
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Affiliation(s)
- Veronica C Jones
- City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Eric C Dietze
- City of Hope Comprehensive Cancer Center, Duarte, CA, United States
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Nasykhova YA, Tonyan ZN, Mikhailova AA, Danilova MM, Glotov AS. Pharmacogenetics of Type 2 Diabetes-Progress and Prospects. Int J Mol Sci 2020; 21:ijms21186842. [PMID: 32961860 PMCID: PMC7555942 DOI: 10.3390/ijms21186842] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022] Open
Abstract
Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.
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Affiliation(s)
- Yulia A. Nasykhova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Ziravard N. Tonyan
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
| | - Anastasiia A. Mikhailova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Maria M. Danilova
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
| | - Andrey S. Glotov
- Department of Genomic Medicine, D.O. Ott’s Institute of Obstetrics, Gynecology and Reproductology, 199034 Saint-Petersburg, Russia; (Y.A.N.); (Z.N.T.); (A.A.M.); (M.M.D.)
- Laboratory of Biobanking and Genomic Medicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
- Correspondence: ; Tel.: +7-9117832003
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García-Calzón S, Perfilyev A, Martinell M, Ustinova M, Kalamajski S, Franks PW, Bacos K, Elbere I, Pihlajamäki J, Volkov P, Vaag A, Groop L, Maziarz M, Klovins J, Ahlqvist E, Ling C. Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes. Sci Transl Med 2020; 12:12/561/eaaz1803. [DOI: 10.1126/scitranslmed.aaz1803] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 01/27/2020] [Accepted: 08/24/2020] [Indexed: 12/14/2022]
Abstract
Metformin is the first-line pharmacotherapy for managing type 2 diabetes (T2D). However, many patients with T2D do not respond to or tolerate metformin well. Currently, there are no phenotypes that successfully predict glycemic response to, or tolerance of, metformin. We explored whether blood-based epigenetic markers could discriminate metformin response and tolerance by analyzing genome-wide DNA methylation in drug-naïve patients with T2D at the time of their diagnosis. DNA methylation of 11 and 4 sites differed between glycemic responders/nonresponders and metformin-tolerant/intolerant patients, respectively, in discovery and replication cohorts. Greater methylation at these sites associated with a higher risk of not responding to or not tolerating metformin with odds ratios between 1.43 and 3.09 per 1-SD methylation increase. Methylation risk scores (MRSs) of the 11 identified sites differed between glycemic responders and nonresponders with areas under the curve (AUCs) of 0.80 to 0.98. MRSs of the 4 sites associated with future metformin intolerance generated AUCs of 0.85 to 0.93. Some of these blood-based methylation markers mirrored the epigenetic pattern in adipose tissue, a key tissue in diabetes pathogenesis, and genes to which these markers were annotated to had biological functions in hepatocytes that altered metformin-related phenotypes. Overall, we could discriminate between glycemic responders/nonresponders and participants tolerant/intolerant to metformin at diagnosis by measuring blood-based epigenetic markers in drug-naïve patients with T2D. This epigenetics-based tool may be further developed to help patients with T2D receive optimal therapy.
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Affiliation(s)
- Sonia García-Calzón
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
- Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Mats Martinell
- Department of Public Health and Caring Sciences, Uppsala University, 751 22 Uppsala, Sweden
| | - Monta Ustinova
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Karl Bacos
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, Internal Medicine, University of Eastern Finland, 70211 Kuopio, Finland
- Clinical Nutrition and Obesity Center, Kuopio University Hospital, 70210 Kuopio, Finland
| | - Petr Volkov
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Allan Vaag
- Type 2 Diabetes Biology Research, Steno Diabetes Center, 2820 Gentofte, Denmark
| | - Leif Groop
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Marlena Maziarz
- Bioinformatics Unit, Department of Clinical Sciences, Lund University Diabetes Centre, 214 28 Malmö, Sweden
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Rātsupītes Street 1, k-1, Riga LV-1067, Latvia
- Faculty of Biology, University of Latvia, Riga LV-1004, Latvia
| | - Emma Ahlqvist
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 214 28 Malmö, Sweden
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Zazuli Z, Duin NJCB, Jansen K, Vijverberg SJH, Maitland-van der Zee AH, Masereeuw R. The Impact of Genetic Polymorphisms in Organic Cation Transporters on Renal Drug Disposition. Int J Mol Sci 2020; 21:ijms21186627. [PMID: 32927790 PMCID: PMC7554776 DOI: 10.3390/ijms21186627] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 12/18/2022] Open
Abstract
A considerable number of drugs and/or their metabolites are excreted by the kidneys through glomerular filtration and active renal tubule secretion via transporter proteins. Uptake transporters in the proximal tubule are part of the solute carrier (SLC) superfamily, and include the organic cation transporters (OCTs). Several studies have shown that specific genetic polymorphisms in OCTs alter drug disposition and may lead to nephrotoxicity. Multiple single nucleotide polymorphisms (SNPs) have been reported for the OCT genes (SLC22A1, SLC22A2 and SLC22A3), which can influence the proteins’ structure and expression levels and affect their transport function. A gain-in-function mutation may lead to accumulation of drugs in renal proximal tubule cells, eventually leading to nephrotoxicity. This review illustrates the impact of genetic polymorphisms in OCTs on renal drug disposition and kidney injury, the clinical significances and how to personalize therapies to minimize the risk of drug toxicity.
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Affiliation(s)
- Zulfan Zazuli
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
- Department of Pharmacology-Clinical Pharmacy, School of Pharmacy, Bandung Institute of Technology, Jawa Barat 40132, Indonesia
- Correspondence: (Z.Z.); (R.M.)
| | - Naut J. C. B. Duin
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
| | - Katja Jansen
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
| | - Susanne J. H. Vijverberg
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
| | - Anke H. Maitland-van der Zee
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (S.J.H.V.); (A.H.M.-v.d.Z.)
| | - Rosalinde Masereeuw
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands; (N.J.C.B.D.); (K.J.)
- Correspondence: (Z.Z.); (R.M.)
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Mazur II, Drozdovska S, Andrieieva O, Vinnichuk Y, Polishchuk A, Dosenko V, Andreev I, Pickering C, Ahmetov II. PPARGC1A gene polymorphism is associated with exercise-induced fat loss. Mol Biol Rep 2020; 47:7451-7457. [PMID: 32910289 DOI: 10.1007/s11033-020-05801-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 09/03/2020] [Indexed: 11/26/2022]
Abstract
Obesity is a widespread problem within modern society, serving to increase the risk of cardiovascular, metabolic, and neurodegenerative disorders. Peroxisome proliferator-activated receptor gamma (PPARγ) and PPARγ coactivator 1 α (PGC1α) play a key role in the regulation of cellular energy metabolism and is implicated in the pathology of these diseases. This study examined the association between polymorphisms of the PPARG and PPARGC1A genes and individual variability in weight loss in response to physical activity intervention. 39 obese Ukrainian women (44.4 ± 7.5 years, BMI > 30.0 kg/m2) undertook a 3-month fitness program whilst following a hypocaloric diet (~ 1500 cal). Anthropometric and biochemical measurements took place before and after the program. Single nucleotide polymorphisms within or near PPARG (n = 94) and PPARGC1A (n = 138) were identified and expression of PPARG mRNA was measured via reverse transcription and amplification. The association between DNA polymorphisms and exercise-induced weight loss, initial body mass, biochemistry and PPARG expression was determined using one-way analysis of variance (ANOVA). The present intervention induced significant fat loss in all participants (total fat: 40.3 ± 5.3 vs 36.4 ± 5.7%; P < 0.00001). Only one polymorphism (rs17650401 C/T) within the PPARGC1A gene was found to be associated with fat loss efficiency after correction for multiple testing, with T allele carriers showing the greatest reduction in body fat percentage (2.5-fold; P = 0.00013) compared to non-carriers. PPARGC1A (rs17650401) is associated with fat loss efficiency of the fitness program in obese women. Further studies are warranted to test whether this variation is associated with fat oxidation.
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Affiliation(s)
- Iuliia Iu Mazur
- Medical and Biology Department, National University of Physical Education and Sport of Ukraine, 1 Fizkul'tury st., Kyiv, 03150, Ukraine.
| | - Svitlana Drozdovska
- Medical and Biology Department, National University of Physical Education and Sport of Ukraine, 1 Fizkul'tury st., Kyiv, 03150, Ukraine
| | - Olena Andrieieva
- Medical and Biology Department, National University of Physical Education and Sport of Ukraine, 1 Fizkul'tury st., Kyiv, 03150, Ukraine
| | - Yulia Vinnichuk
- Medical and Biology Department, National University of Physical Education and Sport of Ukraine, 1 Fizkul'tury st., Kyiv, 03150, Ukraine
| | - Anna Polishchuk
- Medical and Biology Department, National University of Physical Education and Sport of Ukraine, 1 Fizkul'tury st., Kyiv, 03150, Ukraine
| | - Victor Dosenko
- Bogomoletz Institute of Physiology, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Igor Andreev
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Craig Pickering
- Institute of Coaching and Performance, School of Sport and Wellbeing, University of Central Lancashire, Preston, UK
| | - Ildus I Ahmetov
- Laboratory of Molecular Genetics, Kazan State Medical University, Kazan, Russia
- Department of Physical Education, Plekhanov Russian University of Economics, Moscow, Russia
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
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Chung WK, Erion K, Florez JC, Hattersley AT, Hivert MF, Lee CG, McCarthy MI, Nolan JJ, Norris JM, Pearson ER, Philipson L, McElvaine AT, Cefalu WT, Rich SS, Franks PW. Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia 2020; 63:1671-1693. [PMID: 32556613 PMCID: PMC8185455 DOI: 10.1007/s00125-020-05181-w] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The convergence of advances in medical science, human biology, data science and technology has enabled the generation of new insights into the phenotype known as 'diabetes'. Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment) and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e. monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realise its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.
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Affiliation(s)
- Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Karel Erion
- American Diabetes Association, Arlington, VA, USA
| | - Jose C Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Marie-France Hivert
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Christine G Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - John J Nolan
- School of Medicine, Trinity College, Dublin, Ireland
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, Scotland, UK
| | - Louis Philipson
- Department of Medicine, University of Chicago, Chicago, IL, USA
- Department of Pediatrics, University of Chicago, Chicago, IL, USA
| | | | - William T Cefalu
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, CRC, Skåne University Hospital - Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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40
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Chung WK, Erion K, Florez JC, Hattersley AT, Hivert MF, Lee CG, McCarthy MI, Nolan JJ, Norris JM, Pearson ER, Philipson L, McElvaine AT, Cefalu WT, Rich SS, Franks PW. Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2020; 43:1617-1635. [PMID: 32561617 PMCID: PMC7305007 DOI: 10.2337/dci20-0022] [Citation(s) in RCA: 215] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.
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Affiliation(s)
- Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Karel Erion
- American Diabetes Association, Arlington, VA
| | - Jose C Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, U.K
| | - Marie-France Hivert
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Christine G Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K
| | - John J Nolan
- School of Medicine, Trinity College, Dublin, Ireland
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, Scotland, U.K
| | - Louis Philipson
- Department of Medicine, University of Chicago, Chicago, IL
- Department of Pediatrics, University of Chicago, Chicago, IL
| | | | - William T Cefalu
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmo, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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Jiang G, Luk AO, Tam CHT, Lau ES, Ozaki R, Chow EYK, Kong APS, Lim CKP, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JYY, Tsang MW, Kam G, Lau IT, Li JK, Yeung VT, Lau E, Lo S, Fung SKS, Cheng YL, Chow CC, Pearson ER, So WY, Chan JCN, Ma RCW. Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank. PLoS Med 2020; 17:e1003209. [PMID: 32722720 PMCID: PMC7386560 DOI: 10.1371/journal.pmed.1003209] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/22/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. METHODS AND FINDINGS In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. CONCLUSIONS Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.
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Affiliation(s)
- Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Andrea O. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Risa Ozaki
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Elaine Y. K. Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alice P. S. Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Cadmon K. P. Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka Fai Lee
- Department of Medicine and Geriatrics, Kwong Wah Hospital, Hong Kong, China
| | - Shing Chung Siu
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Grace Hui
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Chiu Chi Tsang
- Diabetes and Education Centre, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | | | - Jenny Y. Y. Leung
- Department of Medicine and Geriatrics, Ruttonjee Hospital, Hong Kong, China
| | - Man-wo Tsang
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Grace Kam
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Ip Tim Lau
- Tseung Kwan O Hospital, Hong Kong, China
| | - June K. Li
- Department of Medicine, Yan Chai Hospital, Hong Kong, China
| | - Vincent T. Yeung
- Centre for Diabetes Education and Management, Our Lady of Maryknoll Hospital, Hong Kong, China
| | - Emmy Lau
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Stanley Lo
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Samuel K. S. Fung
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong, China
| | - Yuk Lun Cheng
- Department of Medicine, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | - Chun Chung Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Ewan R. Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland, United Kingdom
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
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Li Z, Ye CY, Zhao TY, Yang L. Model of genetic and environmental factors associated with type 2 diabetes mellitus in a Chinese Han population. BMC Public Health 2020; 20:1024. [PMID: 32600448 PMCID: PMC7325035 DOI: 10.1186/s12889-020-09130-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 06/16/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a metabolic disorder which accounts for high morbidity and mortality due to complications like renal failure, amputations, cardiovascular disease, and cerebrovascular events. METHODS We collected medical reports, lifestyle details, and blood samples of individuals and used the polymerase chain reaction-ligase detection reaction method to genotype the SNPs, and a visit was conducted in August 2016 to obtain the incidence of Type 2 diabetes in the 2113 eligible people. To explore which genes and environmental factors are associated with type 2 diabetes mellitus in a Chinese Han population, we used elastic net to build a model, which is to explain which variables are strongly associated with T2DM, rather than predict the occurrence of T2DM. RESULT The genotype of the additive of rs964184, together with the history of hypertension, regular intake of meat and waist circumference, increased the risk of T2DM (adjusted OR = 2.38, p = 0.042; adjusted OR = 3.31, p < 0.001; adjusted OR = 1.05, p < 0.001). The TT genotype of the additive and recessive models of rs12654264, the CC genotype of the additive and dominant models of rs2065412, the TT genotype of the additive and dominant models of rs4149336, together with the degree of education, regular exercise, reduced the risk of T2DM (adjusted OR = 0.46, p = 0.017; adjusted OR = 0.53, p = 0.021; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.50, p = 0.007; adjusted OR = 0.80, p = 0.032) . CONCLUSION Eventually we identified a set of SNPs and environmental factors: rs5805 in the SLC12A3, rs12654264 in the HMGCR, rs2065412 and rs414936 in the ABCA1, rs96418 in the ZPR1 gene, waistline, degree of education, exercise frequency, hypertension, and the intake of meat. Although there was no interaction between these variables, people with two risk factors had a higher risk of T2DM than those only having one factor. These results provide the theoretical basis for gene and other risk factors screening to prevent T2DM.
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Affiliation(s)
- Zheng Li
- Medical School, Hangzhou Normal University, 2318 Yuhangtang Rd, Hangzhou, 310000 Zhejiang China
| | - Cheng-yin Ye
- Medical School, Hangzhou Normal University, 2318 Yuhangtang Rd, Hangzhou, 310000 Zhejiang China
| | - Tian-Yu Zhao
- Medical School, Hangzhou Normal University, 2318 Yuhangtang Rd, Hangzhou, 310000 Zhejiang China
- Medical School, Shihezi University, Shihezi, 832000 China
| | - Lei Yang
- Medical School, Hangzhou Normal University, 2318 Yuhangtang Rd, Hangzhou, 310000 Zhejiang China
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Radouani F, Zass L, Hamdi Y, Rocha JD, Sallam R, Abdelhak S, Ahmed S, Azzouzi M, Benamri I, Benkahla A, Bouhaouala-Zahar B, Chaouch M, Jmel H, Kefi R, Ksouri A, Kumuthini J, Masilela P, Masimirembwa C, Othman H, Panji S, Romdhane L, Samtal C, Sibira R, Ghedira K, Fadlelmola F, Kassim SK, Mulder N. A review of clinical pharmacogenetics Studies in African populations. Per Med 2020; 17:155-170. [PMID: 32125935 PMCID: PMC8093600 DOI: 10.2217/pme-2019-0110] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Effective interventions and treatments for complex diseases have been implemented globally, however, coverage in Africa has been comparatively lower due to lack of capacity, clinical applicability and knowledge on the genetic contribution to disease and treatment. Currently, there is a scarcity of genetic data on African populations, which have enormous genetic diversity. Pharmacogenomics studies have the potential to revolutionise treatment of diseases, therefore, African populations are likely to benefit from these approaches to identify likely responders, reduce adverse side effects and optimise drug dosing. This review discusses clinical pharmacogenetics studies conducted in African populations, focusing on studies that examined drug response in complex diseases relevant to healthcare. Several pharmacogenetics associations have emerged from African studies, as have gaps in knowledge.
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Affiliation(s)
- Fouzia Radouani
- Research Department, Chlamydiae & Mycoplasmas Laboratory, Institut Pasteur du Maroc, Casablanca 20360, Morocco
| | - Lyndon Zass
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Yosr Hamdi
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, 13, Place Pasteur BP 74, 1002 Tunis, Belvédère, Tunisie
| | - Jorge da Rocha
- Sydney Brenner Institute for Molecular Bioscience, University of The Witwatersrand, Johannesburg, South Africa
| | - Reem Sallam
- Medical Biochemistry & Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo 11381, Egypt
| | - Sonia Abdelhak
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, 13, Place Pasteur BP 74, 1002 Tunis, Belvédère, Tunisie
| | - Samah Ahmed
- Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, 321 Khartoum, Sudan.,Faculty of Clinical & Industrial Pharmacy, National University, Khartoum, Sudan
| | - Maryame Azzouzi
- Research Department, Chlamydiae & Mycoplasmas Laboratory, Institut Pasteur du Maroc, Casablanca 20360, Morocco
| | - Ichrak Benamri
- Research Department, Chlamydiae & Mycoplasmas Laboratory, Institut Pasteur du Maroc, Casablanca 20360, Morocco.,Systems & Data Engineering Team, National School of Applied Sciences of Tangier, Morocco
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics & Biostatistics LR 16 IPT 09, Institute Pasteur de Tunis, Tunisia
| | - Balkiss Bouhaouala-Zahar
- Laboratory of Venoms & Therapeutic Molecules, Pasteur Institute of Tunis, 13 Place Pasteur, BP74, Tunis Belvedere- University of Tunis El Manar, Tunisia
| | - Melek Chaouch
- Laboratory of Bioinformatics, Biomathematics & Biostatistics LR 16 IPT 09, Institute Pasteur de Tunis, Tunisia
| | - Haifa Jmel
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, 13, Place Pasteur BP 74, 1002 Tunis, Belvédère, Tunisie
| | - Rym Kefi
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, 13, Place Pasteur BP 74, 1002 Tunis, Belvédère, Tunisie
| | - Ayoub Ksouri
- Laboratory of Bioinformatics, Biomathematics & Biostatistics LR 16 IPT 09, Institute Pasteur de Tunis, Tunisia.,Laboratory of Venoms & Therapeutic Molecules, Pasteur Institute of Tunis, 13 Place Pasteur, BP74, Tunis Belvedere- University of Tunis El Manar, Tunisia
| | - Judit Kumuthini
- H3ABioNet, Bioinformatics Department, Centre for Proteomic & Genomic Research, Cape Town, South Africa
| | - Phumlani Masilela
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Collen Masimirembwa
- Sydney Brenner Institute for Molecular Bioscience, University of The Witwatersrand, Johannesburg, South Africa.,DMPK Department, African Institute of Biomedical Science & Technology, Harare, Zimbabwe
| | - Houcemeddine Othman
- Sydney Brenner Institute for Molecular Bioscience, University of The Witwatersrand, Johannesburg, South Africa
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Lilia Romdhane
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, 13, Place Pasteur BP 74, 1002 Tunis, Belvédère, Tunisie.,Département des Sciences de la Vie, Faculté des Sciences de Bizerte, Université Carthage, 7021 Jarzouna, BP 21, Tunisie
| | - Chaimae Samtal
- Biotechnology Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco.,Department of Biology, University of Mohammed Premier, Oujda, Morocco.,Department of Biology Faculty of Sciences, University of Sidi Mohamed Ben Abdellah, Fez, Morocco
| | - Rania Sibira
- Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, 321 Khartoum, Sudan.,Department of Neurosurgery, National Center For Neurological Sciences, Khartoum, Sudan
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics & Biostatistics LR 16 IPT 09, Institute Pasteur de Tunis, Tunisia
| | - Faisal Fadlelmola
- Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, 321 Khartoum, Sudan
| | - Samar Kamal Kassim
- Medical Biochemistry & Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo 11381, Egypt
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
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Khatami F, Mohajeri-Tehrani MR, Tavangar SM. The Importance of Precision Medicine in Type 2 Diabetes Mellitus (T2DM): From Pharmacogenetic and Pharmacoepigenetic Aspects. Endocr Metab Immune Disord Drug Targets 2020; 19:719-731. [PMID: 31122183 DOI: 10.2174/1871530319666190228102212] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 10/18/2018] [Accepted: 11/21/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Type 2 Diabetes Mellitus (T2DM) is a worldwide disorder as the most important challenges of health-care systems. Controlling the normal glycaemia greatly profit long-term prognosis and gives explanation for early, effective, constant, and safe intervention. MATERIAL AND METHODS Finding the main genetic and epigenetic profile of T2DM and the exact molecular targets of T2DM medications can shed light on its personalized management. The comprehensive information of T2DM was earned through the genome-wide association study (GWAS) studies. In the current review, we represent the most important candidate genes of T2DM like CAPN10, TCF7L2, PPAR-γ, IRSs, KCNJ11, WFS1, and HNF homeoboxes. Different genetic variations of a candidate gene can predict the efficacy of T2DM personalized strategy medication. RESULTS SLCs and AMPK variations are considered for metformin, CYP2C9, KATP channel, CDKAL1, CDKN2A/2B and KCNQ1 for sulphonylureas, OATP1B, and KCNQ1 for repaglinide and the last but not the least ADIPOQ, PPAR-γ, SLC, CYP2C8, and SLCO1B1 for thiazolidinediones response prediction. CONCLUSION Taken everything into consideration, there is an extreme need to determine the genetic status of T2DM patients in some known genetic region before planning the medication strategies.
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Affiliation(s)
- Fatemeh Khatami
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad R Mohajeri-Tehrani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed M Tavangar
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Department of Pathology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
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Randrianarisoa E, Lehn-Stefan A, Krier J, Böhm A, Heni M, Hrabě De Angelis M, Fritsche A, Häring HU, Stefan N, Staiger H. AMPK Subunits Harbor Largely Nonoverlapping Genetic Determinants for Body Fat Mass, Glucose Metabolism, and Cholesterol Metabolism. J Clin Endocrinol Metab 2020; 105:5568228. [PMID: 31512724 DOI: 10.1210/clinem/dgz020] [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: 06/07/2019] [Revised: 07/29/2019] [Accepted: 09/04/2019] [Indexed: 02/13/2023]
Abstract
CONTEXT Adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a heterotrimeric enzyme and central regulator of cellular energy metabolism. The impact of single nucleotide polymorphisms (SNPs) in all 7 AMPK subunit genes on adiposity, glucose metabolism, and lipid metabolism has not yet been systematically studied. OBJECTIVE To analyze the associations of common SNPs in all AMPK genes, and of different scores thereof, with adiposity, insulin sensitivity, insulin secretion, blood glucose, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, total cholesterol, and triglycerides. STUDY DESIGN AND METHODS A cohort of 2789 nondiabetic participants from the Tübingen Family study of type 2 diabetes, metabolically characterized by oral glucose tolerance test and genotyped by genome-wide SNP array, was analyzed. RESULTS We identified 6 largely nonoverlapping SNP sets across 4 AMPK genes (PRKAA1, PRKAA2, PRKAG2, PRKAG3) associated with adiposity, insulin sensitivity, insulin secretion, blood glucose, total/LDL cholesterol, or HDL cholesterol, respectively. A genetic score of body-fat-increasing alleles revealed per-allele effect sizes on body mass index (BMI) of +0.22 kg/m2 (P = 2.3 × 10-7), insulin sensitivity of -0.12 × 1019 L2/mol2 (P = 9.9 × 10-6) and 2-hour blood glucose of +0.02 mmol/L (P = 0.0048). Similar effects on blood glucose were observed with scores of insulin-sensitivity-reducing, insulin-secretion-reducing and glucose-raising alleles, respectively. A genetic cholesterol score increased total and LDL cholesterol by 1.17 mg/dL per allele (P = 0.0002 and P = 3.2 × 10-5, respectively), and a genetic HDL score decreased HDL cholesterol by 0.32 mg/dL per allele (P = 9.1 × 10-6). CONCLUSIONS We describe largely nonoverlapping genetic determinants in AMPK genes for diabetes-/atherosclerosis-related traits, which reflect the metabolic pathways controlled by the enzyme. Formation of trait-specific genetic scores revealed additivity of allele effects, with body-fat-raising alleles reaching a marked effect size. (J Clin Endocrinol Metab XX: 0-0, 2019).
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Affiliation(s)
- Elko Randrianarisoa
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
| | - Angela Lehn-Stefan
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
| | - Johannes Krier
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
| | - Anja Böhm
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology and Nephrology, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Martin Heni
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology and Nephrology, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Martin Hrabě De Angelis
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Institute of Experimental Genetics, Helmholtz Center Munich, 85764 Neuherberg, Germany
- Chair of Experimental Genetics, Technical University Munich, 85764 Neuherberg, Germany
| | - Andreas Fritsche
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology and Nephrology, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Hans-Ulrich Häring
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology and Nephrology, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Norbert Stefan
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Department of Internal Medicine, Division of Endocrinology, Diabetology and Nephrology, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
| | - Harald Staiger
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, 72076 Tübingen, Germany
- German Center for Diabetes Research (DZD), 72076 Tübingen, Germany
- Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
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Au Yeung SL, Schooling CM. Impact of glycemic traits, type 2 diabetes and metformin use on breast and prostate cancer risk: a Mendelian randomization study. BMJ Open Diabetes Res Care 2019; 7:e000872. [PMID: 31908803 PMCID: PMC6936416 DOI: 10.1136/bmjdrc-2019-000872] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 11/12/2019] [Accepted: 12/03/2019] [Indexed: 12/12/2022] Open
Abstract
Objectives Observational studies suggest glycemic traits and type 2 diabetes are positively associated, and metformin inversely associated with breast and prostate cancer risk. However, observational studies are susceptible to unmeasured confounding while studies of metformin use are also vulnerable to immortal time bias. The use of Mendelian randomization may reduce confounding due to random allocation of relevant genetic markers at birth, and may reduce immortal time bias (for metformin-related variants analysis) since the start of exposure is at birth. Research design and methods We identified strong genetic predictors of fasting glucose, glycated hemoglobin, and type 2 diabetes from the Meta-Analyses of Glucose and Insulin-related traits Consortium and Diabetes Genetics Replication And Meta-analysis Consortium (n=140 595 for glucose; n=123 665 for HbA1c; n=898 130 for type 2 diabetes) and of AMPK-instrumented HbA1c reduction as a proxy of metformin and applied them to large genome-wide association studies of breast cancer (Breast Cancer Association Consortium; BCAC) and prostate cancer (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome; PRACTICAL). We used inverse variance weighting to obtain estimates. Sensitivity analyses included use of MR-Egger, weighted median, exclusion of pleiotropic instruments, and validation using UK Biobank (breast cancer only). Results There was no association of fasting glucose (OR 1.03 per mmol/L, 95% CI 0.85 to 1.25), HbA1c (OR 1.02 per %, 95% CI 0.73 to 1.45), or type 2 diabetes (OR 0.98 per log odds, 95% CI 0.95 to 1.01) with breast cancer in BCAC, with similar findings from UK Biobank. There was no association of fasting glucose (OR 0.93 per mmol/L, 95% CI 0.73 to 1.17), HbA1c (OR 0.90 per %, 95% CI 0.58 to 1.40) or type 2 diabetes (OR 1.02 per log odds, 95% CI 0.97 to 1.07) with prostate cancer in PRACTICAL. No strong evidence was observed for AMPK-instrumented HbA1c reduction on cancer risk. Conclusion Glycemic traits and type 2 diabetes unlikely cause breast and prostate cancer. Whether metformin can be repurposed for cancer prevention remains unclear.
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Affiliation(s)
- Shiu Lun Au Yeung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Catherine Mary Schooling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Graduate School of Public Health and Health Policy, City University of New York, New York City, New York, USA
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47
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Nagaraj SB, Sidorenkov G, van Boven JFM, Denig P. Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms. Diabetes Obes Metab 2019; 21:2704-2711. [PMID: 31453664 PMCID: PMC6899933 DOI: 10.1111/dom.13860] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 07/30/2019] [Accepted: 08/20/2019] [Indexed: 01/04/2023]
Abstract
AIM To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow-up of 2 years. Short- and long-term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine-learning algorithms was compared for the prediction of short- and long-term responses and the area under the receiver-operating characteristic curve (AUC) was used to assess the performance of the prediction models. RESULTS The elastic net regularization-based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short- and long-term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78-0.83) and 0.81 (95% CI 0.79-0.84), respectively, and outperformed the other machine-learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65-0.73) and 0.72 (95% CI 0.66-0.75) was obtained for predicting short-term and long-term response, respectively. CONCLUSIONS Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables.
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Affiliation(s)
- Sunil B. Nagaraj
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Centre GroningenGroningenThe Netherlands
| | - Grigory Sidorenkov
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Centre GroningenGroningenThe Netherlands
- Department of Epidemiology, University of GroningenUniversity Medical Centre GroningenGroningenThe Netherlands
| | - Job F. M. van Boven
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Centre GroningenGroningenThe Netherlands
| | - Petra Denig
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Centre GroningenGroningenThe Netherlands
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Briviba M, Silamikelis I, Kalnina I, Ansone L, Rovite V, Elbere I, Radovica-Spalvina I, Fridmanis D, Aladyeva J, Konrade I, Pirags V, Klovins J. Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals. PLoS One 2019; 14:e0224835. [PMID: 31703101 PMCID: PMC6839856 DOI: 10.1371/journal.pone.0224835] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/22/2019] [Indexed: 01/22/2023] Open
Abstract
Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin's action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.
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Affiliation(s)
- Monta Briviba
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | - Ineta Kalnina
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Laura Ansone
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Vita Rovite
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | | | | | | | - Valdis Pirags
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Riga, Latvia
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Dietrich S, Jacobs S, Zheng JS, Meidtner K, Schwingshackl L, Schulze MB. Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review. Obes Rev 2019; 20:1557-1571. [PMID: 31478326 PMCID: PMC8650574 DOI: 10.1111/obr.12921] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/26/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022]
Abstract
The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.
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Affiliation(s)
- Stefan Dietrich
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, Germany
| | - Simone Jacobs
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, Germany
| | - Ju-Sheng Zheng
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,School of Life Sciences, Westlake University, Hangzhou, China
| | - Karina Meidtner
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Lukas Schwingshackl
- Institute for Evidence in Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany.,University of Potsdam, Institute of Nutritional Sciences, Nuthetal, Germany
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50
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Foretz M, Guigas B, Viollet B. Understanding the glucoregulatory mechanisms of metformin in type 2 diabetes mellitus. Nat Rev Endocrinol 2019; 15:569-589. [PMID: 31439934 DOI: 10.1038/s41574-019-0242-2] [Citation(s) in RCA: 396] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/11/2019] [Indexed: 02/07/2023]
Abstract
Despite its position as the first-line drug for treatment of type 2 diabetes mellitus, the mechanisms underlying the plasma glucose level-lowering effects of metformin (1,1-dimethylbiguanide) still remain incompletely understood. Metformin is thought to exert its primary antidiabetic action through the suppression of hepatic glucose production. In addition, the discovery that metformin inhibits the mitochondrial respiratory chain complex 1 has placed energy metabolism and activation of AMP-activated protein kinase (AMPK) at the centre of its proposed mechanism of action. However, the role of AMPK has been challenged and might only account for indirect changes in hepatic insulin sensitivity. Various mechanisms involving alterations in cellular energy charge, AMP-mediated inhibition of adenylate cyclase or fructose-1,6-bisphosphatase 1 and modulation of the cellular redox state through direct inhibition of mitochondrial glycerol-3-phosphate dehydrogenase have been proposed for the acute inhibition of gluconeogenesis by metformin. Emerging evidence suggests that metformin could improve obesity-induced meta-inflammation via direct and indirect effects on tissue-resident immune cells in metabolic organs (that is, adipose tissue, the gastrointestinal tract and the liver). Furthermore, the gastrointestinal tract also has a major role in metformin action through modulation of glucose-lowering hormone glucagon-like peptide 1 and the intestinal bile acid pool and alterations in gut microbiota composition.
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Affiliation(s)
- Marc Foretz
- INSERM, U1016, Institut Cochin, Paris, France
- CNRS, UMR8104, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Bruno Guigas
- Department of Parasitology, Leiden University Medical Centre, Leiden, Netherlands
| | - Benoit Viollet
- INSERM, U1016, Institut Cochin, Paris, France.
- CNRS, UMR8104, Paris, France.
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
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