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Jindal M, Chhetri A, Ludhiadch A, Singh P, Peer S, Singh J, Brar RS, Munshi A. Neuroimaging Genomics a Predictor of Major Depressive Disorder (MDD). Mol Neurobiol 2024; 61:3427-3440. [PMID: 37989980 DOI: 10.1007/s12035-023-03775-0] [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: 06/01/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023]
Abstract
Depression is a complex psychiatric disorder influenced by various genetic and environmental factors. Strong evidence has established the contribution of genetic factors in depression through twin studies and the heritability rate for depression has been reported to be 37%. Genetic studies have identified genetic variations associated with an increased risk of developing depression. Imaging genetics is an integrated approach where imaging measures are combined with genetic information to explore how specific genetic variants contribute to brain abnormalities. Neuroimaging studies allow us to examine both structural and functional abnormalities in individuals with depression. This review has been designed to study the correlation of the significant genetic variants with different regions of neural activity, connectivity, and structural alteration in the brain as detected by imaging techniques to understand the scope of biomarkers in depression. This might help in developing novel therapeutic interventions targeting specific genetic pathways or brain circuits and the underlying pathophysiology of depression based on this integrated approach can be established at length.
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Affiliation(s)
- Manav Jindal
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bathinda, India
| | - Aakash Chhetri
- Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, 151401, India
| | - Abhilash Ludhiadch
- Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, 151401, India
| | - Paramdeep Singh
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bathinda, India
| | - Sameer Peer
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bathinda, India
| | - Jawahar Singh
- Department of Psychiatry, All India Institute of Medical Sciences, Bathinda, India
| | - Rahatdeep Singh Brar
- Department of Diagnostic and Interventional Radiology, Homi Bhabha Cancer Hospital & Research Center, Mohali, India
| | - Anjana Munshi
- Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, 151401, India.
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Li J, Huang Y, Yang X, Cai Y, Wang Y, Dai W, Jiang L, Wang C, Wen Z. Tyrosine-phosphorylated DNER sensitizes insulin signaling in hepatic gluconeogenesis by inducing proteasomal degradation of TRB3. Mol Metab 2024; 83:101927. [PMID: 38553003 PMCID: PMC10999696 DOI: 10.1016/j.molmet.2024.101927] [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: 10/23/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVE Hepatic insulin resistance, which leads to increased hepatic gluconeogenesis, is a major contributor to fasting hyperglycemia in type 2 diabetes mellitus (T2DM). However, the mechanism of impaired insulin-dependent suppression of hepatic gluconeogenesis remains elusive. Delta/Notch-like epidermal growth factor (EGF)-related receptor (DNER), firstly described as a neuron-specific Notch ligand, has been recently identified as a susceptibility gene for T2DM through genome-wide association studies. We herein investigated whether DNER regulates hepatic gluconeogenesis and whether this is mediated by enhanced insulin signaling. METHODS The association between DNER, tribbles homolog 3 (TRB3) and Akt signaling was evaluated in C57BL/6J, ob/ob and db/db mice by western blot analysis. DNER loss-of-function and gain-of-function in hepatic gluconeogenesis were analyzed by western blot analysis, quantitative real-time PCR, glucose uptake and output assay in AML-12 cells and partially validated in primary mouse hepatocytes. Hepatic DNER knockdown mice were generated by tail vein injection of adenovirus to confirm the effects of DNER in vivo. The interaction between DNER and TRB3 was investigated by rescue experiments, cycloheximide chase analysis, co-immunoprecipitation and immunofluorescence. The potential insulin-stimulated phosphorylation sites of DNER were determined by co-immunoprecipitation, LC-MS/MS analysis and site-specific mutagenesis. RESULTS Here we show that DNER enhanced hepatic insulin signaling in gluconeogenesis by inhibiting TRB3, an endogenous Akt inhibitor, through the ubiquitin-proteasome degradation pathway. In AML-12 hepatocytes, insulin-stimulated activation of Akt and suppression of gluconeogenesis are attenuated by DNER knockdown, but potentiated by DNER over-expression. In C57BL/6J mice, hepatic DNER knockdown is accompanied by impaired glucose and pyruvate tolerance. Furthermore, the in vitro effects of DNER knockdown or over-expression on both Akt activity and hepatic gluconeogenesis can be rescued by TRB3 knockdown or over-expression, respectively. In response to insulin stimulation, DNER interacted directly with insulin receptor and was phosphorylated at Tyr677. This site-specific phosphorylation is essential for DNER to upregulate Akt activity and then downregulate G6Pase and PEPCK expression, by interacting with TRB3 directly and inducing TRB3 proteasome-dependent degradation. CONCLUSIONS Taken together, the crosstalk between insulin-Akt and DNER-TRB3 pathways represents a previously unrecognized mechanism by which insulin regulates hepatic gluconeogenesis.
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Affiliation(s)
- Junfeng Li
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yan Huang
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinyu Yang
- Department of Pathology & Pathophysiology, Wuhan University School of Basic Medical Sciences, Wuhan 430071, China
| | - Yuli Cai
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Ye Wang
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Wenling Dai
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Liu Jiang
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Changhua Wang
- Department of Pathology & Pathophysiology, Wuhan University School of Basic Medical Sciences, Wuhan 430071, China
| | - Zhongyuan Wen
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan 430060, China
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Ruiz-Otero N, Kuruvilla R. Role of Delta/Notch-like EGF-related receptor in blood glucose homeostasis. Front Endocrinol (Lausanne) 2023; 14:1161085. [PMID: 37223028 PMCID: PMC10200888 DOI: 10.3389/fendo.2023.1161085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/18/2023] [Indexed: 05/25/2023] Open
Abstract
Cell-cell interactions are necessary for optimal endocrine functions in the pancreas. β-cells, characterized by the expression and secretion of the hormone insulin, are a major constituent of functional micro-organs in the pancreas known as islets of Langerhans. Cell-cell contacts between β-cells are required to regulate insulin production and glucose-stimulated insulin secretion, which are key determinants of blood glucose homeostasis. Contact-dependent interactions between β-cells are mediated by gap junctions and cell adhesion molecules such as E-cadherin and N-CAM. Recent genome-wide studies have implicated Delta/Notch-like EGF-related receptor (Dner) as a potential susceptibility locus for Type 2 Diabetes in humans. DNER is a transmembrane protein and a proposed Notch ligand. DNER has been implicated in neuron-glia development and cell-cell interactions. Studies herein demonstrate that DNER is expressed in β-cells with an onset during early postnatal life and sustained throughout adulthood in mice. DNER loss in adult β-cells in mice (β-Dner cKO mice) disrupted islet architecture and decreased the expression of N-CAM and E-cadherin. β-Dner cKO mice also exhibited impaired glucose tolerance, defects in glucose- and KCl-induced insulin secretion, and decreased insulin sensitivity. Together, these studies suggest that DNER plays a crucial role in mediating islet cell-cell interactions and glucose homeostasis.
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Affiliation(s)
- Nelmari Ruiz-Otero
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rejji Kuruvilla
- Department of Biology, Johns Hopkins University, Baltimore, MD, United States
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Wang Z, Chen H, Peng L, He Y, Wei J, Zhang X. DNER and GNL2 are differentially m6A methylated in periodontitis in comparison with periodontal health revealed by m6A microarray of human gingival tissue and transcriptomic analysis. J Periodontal Res 2023; 58:529-543. [PMID: 36941720 DOI: 10.1111/jre.13117] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE This study aims to investigate the differences in the epigenomic patterns of N6-methyladenosine (m6A) methylation in gingival tissues between patients with periodontitis (PD) and healthy controls, identifying potential biomarkers. BACKGROUND As a multifactorial disease, PD involves multiple genetic and environmental effects. The m6A modification is the most prevalent internal mRNA modification and linked to various inflammatory diseases. However, the m6A modification pattern and m6A-related signatures in PD remain unclear. MATERIALS AND METHODS An m6A microarray of human gingival tissues was conducted in eight subjects: four diagnosed with PD and four healthy controls. Microarray analysis was performed to identify the differentially m6A methylated mRNAs (DMGs) and the differentially expressed mRNAs (DEGs). The differentially methylated and expressed mRNAs (DMEGs) were subjected to functional enrichment analysis by Metascape. The weighted gene co-expression network analysis (WGCNA) algorithm, the least absolute shrinkage and selection operator (LASSO) regression, and univariate logistic regression were performed to identify potential biomarkers. The cell type localization of the target genes was determined using single-cell RNA-seq (scRNA-seq) analysis. The m6A methylation level and gene expression of hub genes were subsequently verified by m6A methylated RNA immunoprecipitation (MeRIP) and quantitative real-time PCR (qRT-PCR). RESULTS In total, 458 DMGs, 750 DEGs, and 279 DMEGs were identified based on our microarray. Pathway analyses conducted for the DMEGs revealed that biological functions were mainly involved in the regulation of stem cell differentiation, ossification, circadian rhythm, and insulin secretion pathways. Besides, the genes involved in crucial biological processes were mainly expressed in fibroblast and epithelial cells. Furthermore, the m6A methylation and expression levels of two hub biomarkers (DNER and GNL2) were validated. CONCLUSION The current study exhibited a distinct m6A epitranscriptome, identified and verified two PD-related biomarkers (DNER and GNL2), which may provide novel insights into revealing the new molecular mechanisms and latent targets of PD.
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Affiliation(s)
- Zhenxiang Wang
- College of Stomatology, Chongqing Medical University, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Hang Chen
- College of Stomatology, Chongqing Medical University, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Limin Peng
- College of Stomatology, Chongqing Medical University, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Yujuan He
- Department of Laboratory Medicine, Key Laboratory of Diagnostic Medicine (Ministry of Education), Chongqing Medical University, Chongqing, China
| | - Jingjing Wei
- College of Stomatology, Chongqing Medical University, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
| | - Xiaonan Zhang
- College of Stomatology, Chongqing Medical University, Chongqing, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1305] [Impact Index Per Article: 1305.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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A conserved YAP/Notch/REST network controls the neuroendocrine cell fate in the lungs. Nat Commun 2022; 13:2690. [PMID: 35577801 PMCID: PMC9110333 DOI: 10.1038/s41467-022-30416-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 04/20/2022] [Indexed: 12/30/2022] Open
Abstract
The Notch pathway is a conserved cell-cell communication pathway that controls cell fate decisions. Here we sought to determine how Notch pathway activation inhibits the neuroendocrine cell fate in the lungs, an archetypal process for cell fate decisions orchestrated by Notch signaling that has remained poorly understood at the molecular level. Using intratumoral heterogeneity in small-cell lung cancer as a tractable model system, we uncovered a role for the transcriptional regulators REST and YAP as promoters of the neuroendocrine to non-neuroendocrine transition. We further identified the specific neuroendocrine gene programs repressed by REST downstream of Notch in this process. Importantly, we validated the importance of REST and YAP in neuroendocrine to non-neuroendocrine cell fate switches in both developmental and tissue repair processes in the lungs. Altogether, these experiments identify conserved roles for REST and YAP in Notch-driven inhibition of the neuroendocrine cell fate in embryonic lungs, adult lungs, and lung cancer.
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7
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Generation of Isogenic hiPSCs with Targeted Edits at Multiple Intronic SNPs to Study the Effects of the Type 2 Diabetes Associated KCNQ1 Locus in American Indians. Cells 2022; 11:cells11091446. [PMID: 35563754 PMCID: PMC9102014 DOI: 10.3390/cells11091446] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/11/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The top genetic association signal for type 2 diabetes (T2D) in Southwestern American Indians maps to intron 15 of KCNQ1, an imprinted gene. We aim to understand the biology whereby variation at this locus affects T2D specifically in this genomic background. To do so, we obtained human induced pluripotent stem cells (hiPSC) derived from American Indians. Using these iPSCs, we show that imprinting of KCNQ1 and CDKN1C during pancreatic islet-like cell generation from iPSCs is consistent with known imprinting patterns in fetal pancreas and adult islets and therefore is an ideal model system to study this locus. In this report, we detail the use of allele-specific guide RNAs and CRISPR to generate isogenic hiPSCs that differ only at multiple T2D associated intronic SNPs at this locus which can be used to elucidate their functional effects. Characterization of these isogenic hiPSCs identified a few aberrant cell lines; namely cell lines with large hemizygous deletions in the putative functional region of KCNQ1 and cell lines hypomethylated at the KCNQ1OT1 promoter. Comparison of an isogenic cell line with a hemizygous deletion to the parental cell line identified CDKN1C and H19 as differentially expressed during the endocrine progenitor stage of pancreatic-islet development.
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van der Heide V, Jangra S, Cohen P, Rathnasinghe R, Aslam S, Aydillo T, Geanon D, Handler D, Kelley G, Lee B, Rahman A, Dawson T, Qi J, D'Souza D, Kim-Schulze S, Panzer JK, Caicedo A, Kusmartseva I, Posgai AL, Atkinson MA, Albrecht RA, García-Sastre A, Rosenberg BR, Schotsaert M, Homann D. Limited extent and consequences of pancreatic SARS-CoV-2 infection. Cell Rep 2022; 38:110508. [PMID: 35247306 PMCID: PMC8858708 DOI: 10.1016/j.celrep.2022.110508] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/17/2021] [Accepted: 02/16/2022] [Indexed: 02/05/2023] Open
Abstract
Concerns that infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), may cause new-onset diabetes persist in an evolving research landscape, and precise risk assessment is hampered by, at times, conflicting evidence. Here, leveraging comprehensive single-cell analyses of in vitro SARS-CoV-2-infected human pancreatic islets, we demonstrate that productive infection is strictly dependent on the SARS-CoV-2 entry receptor ACE2 and targets practically all pancreatic cell types. Importantly, the infection remains highly circumscribed and largely non-cytopathic and, despite a high viral burden in infected subsets, promotes only modest cellular perturbations and inflammatory responses. Similar experimental outcomes are also observed after islet infection with endemic coronaviruses. Thus, the limits of pancreatic SARS-CoV-2 infection, even under in vitro conditions of enhanced virus exposure, challenge the proposition that in vivo targeting of β cells by SARS-CoV-2 precipitates new-onset diabetes. Whether restricted pancreatic damage and immunological alterations accrued by COVID-19 increase cumulative diabetes risk, however, remains to be evaluated.
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Affiliation(s)
- Verena van der Heide
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonia Jangra
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Phillip Cohen
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raveen Rathnasinghe
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sadaf Aslam
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Teresa Aydillo
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel Geanon
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diana Handler
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Geoffrey Kelley
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Lee
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adeeb Rahman
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Travis Dawson
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jingjing Qi
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Darwin D'Souza
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Julia K Panzer
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alejandro Caicedo
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Irina Kusmartseva
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL, USA
| | - Amanda L Posgai
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL, USA
| | - Mark A Atkinson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL, USA; Department of Pediatrics, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Randy A Albrecht
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brad R Rosenberg
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Schotsaert
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Dirk Homann
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Diabetes Obesity & Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Lubberding AF, Juhl CR, Skovhøj EZ, Kanters JK, Mandrup‐Poulsen T, Torekov SS. Celebrities in the heart, strangers in the pancreatic beta cell: Voltage-gated potassium channels K v 7.1 and K v 11.1 bridge long QT syndrome with hyperinsulinaemia as well as type 2 diabetes. Acta Physiol (Oxf) 2022; 234:e13781. [PMID: 34990074 PMCID: PMC9286829 DOI: 10.1111/apha.13781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 12/20/2021] [Accepted: 01/02/2022] [Indexed: 12/13/2022]
Abstract
Voltage‐gated potassium (Kv) channels play an important role in the repolarization of a variety of excitable tissues, including in the cardiomyocyte and the pancreatic beta cell. Recently, individuals carrying loss‐of‐function (LoF) mutations in KCNQ1, encoding Kv7.1, and KCNH2 (hERG), encoding Kv11.1, were found to exhibit post‐prandial hyperinsulinaemia and episodes of hypoglycaemia. These LoF mutations also cause the cardiac disorder long QT syndrome (LQTS), which can be aggravated by hypoglycaemia. Interestingly, patients with LQTS also have a higher burden of diabetes compared to the background population, an apparent paradox in relation to the hyperinsulinaemic phenotype, and KCNQ1 has been identified as a type 2 diabetes risk gene. This review article summarizes the involvement of delayed rectifier K+ channels in pancreatic beta cell function, with emphasis on Kv7.1 and Kv11.1, using the cardiomyocyte for context. The functional and clinical consequences of LoF mutations and polymorphisms in these channels on blood glucose homeostasis are explored using evidence from pre‐clinical, clinical and genome‐wide association studies, thereby evaluating the link between LQTS, hyperinsulinaemia and type 2 diabetes.
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Affiliation(s)
- Anniek F. Lubberding
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Christian R. Juhl
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Emil Z. Skovhøj
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Jørgen K. Kanters
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Thomas Mandrup‐Poulsen
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
| | - Signe S. Torekov
- Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
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10
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2499] [Impact Index Per Article: 1249.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Su H, Xue H, Gao S, Yan B, Wang R, Tan G, Xu Z, Zeng L. Effect of Rhizoma Drynariae on differential gene expression in ovariectomized rats with osteoporosis based on transcriptome sequencing. Front Endocrinol (Lausanne) 2022; 13:930912. [PMID: 35983515 PMCID: PMC9380231 DOI: 10.3389/fendo.2022.930912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Osteoporosis is increasingly becoming a serious problem affecting the quality of life of the older population. Several experimental studies have shown that Chinese medicine has a definite effect on improving osteoporosis. Based on transcriptome sequencing, we analyzed the differential gene expression and mechanism of the related signaling pathways. Fifteen rats were randomly divided into an experimental group, a model group, and a sham surgery group. The rat model for menopausal osteoporosis was established using an ovariectomy method. One week after modeling, the experimental group was administered(intragastric administration)8.1 g/kg of Rhizoma drynariae, whereas the model and sham groups received 0.9% saline solution twice daily for 12 weeks. Subsequently, the rats were sacrificed, and the left femur of each group was removed for computerized tomography testing, while right femurs were used for hematoxylin and eosin staining. High-throughput RNA sequencing and functional and pathway enrichment analyses were performed. Comparing the gene expression between the experimental and model groups, 149 differential genes were identified, of which 44 were downregulated and 105 were upregulated. The criteria for statistical significance were |log2 Fold Change| > 1 and P < 0.05. Gene ontology analysis showed that the differentially expressed genes were enriched in cell component terms such as cell part and outer cell membrane part, and the genes were associated with cell process, biological regulation, metabolic processes, DNA transcription, and catalytic activity. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways showed significantly enriched pathways associated with systemic lupus erythematosus, herpes simplex infection, circadian rhythm, vascular smooth muscle contraction, the AGE-RAGE signaling pathway in diabetic complications, and the TNF, Apelin, and Ras signaling pathways. Our results revealed that the Npas2, Dbp, Rt1, Arntl, Grem2, H2bc9, LOC501233, Pla2g2c, Hpgd, Pde6c, and Dner genes, and the circadian rhythm, lipid metabolism, inflammatory signaling pathway, and immune pathways may be the key targets and pathways for traditional Chinese medicine therapy of Rhizoma Drynariae in osteoporosis.
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Affiliation(s)
- Hui Su
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Haipeng Xue
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Shang Gao
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Binghan Yan
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruochong Wang
- College of traditional Chinese medicine, Beijing University of Traditional Chinese Medicine, Beijing, China
| | - Guoqing Tan
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- *Correspondence: Guoqing Tan, ; Zhanwang Xu, ; Lingfeng Zeng,
| | - Zhanwang Xu
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- *Correspondence: Guoqing Tan, ; Zhanwang Xu, ; Lingfeng Zeng,
| | - Lingfeng Zeng
- The 2nd Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Guoqing Tan, ; Zhanwang Xu, ; Lingfeng Zeng,
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12
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Liu C, Sun YV. Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics. Endocrinol Metab Clin North Am 2021; 50:559-574. [PMID: 34399961 DOI: 10.1016/j.ecl.2021.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.
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Affiliation(s)
- Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA; Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033, USA.
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13
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Kim HI, Ye B, Gosalia N, Köroğlu Ç, Hanson RL, Hsueh WC, Knowler WC, Baier LJ, Bogardus C, Shuldiner AR, Van Hout CV, Van Hout CV. Characterization of Exome Variants and Their Metabolic Impact in 6,716 American Indians from the Southwest US. Am J Hum Genet 2020; 107:251-264. [PMID: 32640185 DOI: 10.1016/j.ajhg.2020.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 06/10/2020] [Indexed: 12/21/2022] Open
Abstract
Applying exome sequencing to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from a Southwestern American Indian (SWAI) population with well-characterized metabolic traits. We found that the SWAI population has distinct allelic architecture compared to populations of European and East Asian ancestry, and there were many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in the SWAI population. We used pLOF and nonsynonymous variants in the SWAI population to evaluate gene-burden associations of candidate genes from European genome-wide association studies (GWASs) for type 2 diabetes, body mass index, and four major plasma lipids. We found 19 significant gene-burden associations for 11 genes, providing additional evidence for prioritizing candidate effector genes of GWAS signals. Interestingly, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in the SWAI population. Particularly, we found four pLOF or nonsynonymous variants in APOB, APOE, PCSK9, and TM6SF2 that are private or enriched in the SWAI population and associated with low-density lipoprotein (LDL) cholesterol levels. Their large estimated effects on LDL cholesterol levels suggest strong impacts on protein function and potential clinical implications of these variants in cardiovascular health. In summary, our study illustrates the utility and potential of exome sequencing in genetically unique populations, such as the SWAI population, to prioritize candidate effector genes within GWAS loci and to find additional variants in known disease genes with potential clinical impact.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Cristopher V Van Hout
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc., Tarrytown, NY 10591, USA.
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Li Y, Shen K, Li C, Yang Y, Yang M, Tao W, He S, Shi L, Yao Y. Identifying the association between single nucleotide polymorphisms in KCNQ1, ARAP1, and KCNJ11 and type 2 diabetes mellitus in a Chinese population. Int J Med Sci 2020; 17:2379-2386. [PMID: 32922204 PMCID: PMC7484634 DOI: 10.7150/ijms.48072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/21/2020] [Indexed: 12/27/2022] Open
Abstract
Background: Type 2 diabetes mellitus (T2DM) has a high global prevalence, and insufficient insulin secretion is one of the major reasons for its development. Therefore, investigating the association between T2DM and the single nucleotide polymorphisms (SNPs) in genes associated with insulin secretion is necessary. Methods: T2DM (1,194) and nondiabetic (NDM) (1,292) subjects were enrolled and the ten single nucleotide polymorphisms (SNPs) in KCNQ1, ARAP1, and KCNJ11 associated with insulin secretion were genotyped in a Chinese population. Results: Our data revealed that the rs2237897T allele in KCNQ1 is the protective allele for T2DM (P<0.001, OR=0.793; 95%CI: 0.705-0.893). However, the A allele of rs1552224 in ARAP1 may be a risk factor for T2DM (P=0.002, OR=12.070; 95% CI: 1.578-92.337). The haplotype analysis revealed that rs151290-rs2237892CC and rs2237895-rs2237897CC in KCNQ1 constitute the risk haplotype in T2DM development (P=0.010, OR=1.160; 95% CI: 1.037-1.299 and P=0.004, OR=1.192; 95% CI: 1.057-1.344). Moreover, rs2237895-rs2237897AT in KCNQ1 constitutes the protective haplotype in T2DM (P=0.001, OR=0.819; 95% CI: 0.727-0.923). In the inheritance models analysis, the rs2283228 (C/A-C/C) genotype is the protective factor compared to the A/A genotype (P=0.005, OR=0.79; 95% CI: 0.68-0.93). For rs2237897, the C/T-T/T genotype is the protective factor compared to the C/C genotype (P<0.001, OR=0.74; 95% CI: 0.63-0.87). Furthermore, when compared with the rs2237897 (C/T-T/T) genotype, rs2237897C/C genotype showed higher HbA1C levels (8.731±2.697 vs 9.282±2.921, P=0.001). Conclusion: Our results revealed that genetic variations in KCNQ1 and ARAP1 were associated with T2DM susceptibility in a Chinese population.
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Affiliation(s)
- Yiping Li
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Keyu Shen
- Department of Medicine, Dentistry and Healthy Science, The University of Melbourne, Melbourne VIC3010, Australia
| | - Chuanyin Li
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
| | - Ying Yang
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Man Yang
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Wenyu Tao
- Department of Endocrinology and Metabolism, The Second People's Hospital of Yunnan Province & The Fourth Affiliated Hospital of Kunming Medical University, Kunming 650021, Yunnan, China
| | - Siqi He
- School of Clinical Medicine, Dali University, Dali 671000, Yunnan, China
| | - Li Shi
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
| | - Yufeng Yao
- Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming 650118, Yunnan, China
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15
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Samaha G, Beatty J, Wade CM, Haase B. The Burmese cat as a genetic model of type 2 diabetes in humans. Anim Genet 2019; 50:319-325. [PMID: 31179570 DOI: 10.1111/age.12799] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2019] [Indexed: 12/16/2022]
Abstract
The recent extension of genetic tools to the domestic cat, together with the serendipitous consequences of selective breeding, have been essential to the study of the genetic diseases that affect them. Cats are increasingly presented for veterinary surveillance and share many of human's heritable diseases, allowing them to serve as natural models of these conditions. Feline diabetes mellitus is a common condition in domestic cats that bears close pathological and clinical resemblance to type 2 diabetes in humans, including pancreatic β-cell dysfunction and peripheral insulin resistance. In Australia, New Zealand and Europe, diabetes mellitus is almost four times more common in cats of the Burmese breed than in other breeds. This geographically based breed predisposition parallels familial and population clustering of type 2 diabetes in humans. As a genetically isolated population, the Australian Burmese breed provides a spontaneous, naturally occurring genetic model of type 2 diabetes. Genetically isolated populations typically exhibit extended linkage disequilibrium and increased opportunity for deleterious variants to reach high frequencies over many generations due to genetic drift. Studying complex diseases in such populations allows for tighter control of confounding factors including environmental heterogeneity, allelic frequencies and population stratification. The homogeneous genetic background of Australian Burmese cats may provide a unique opportunity to either refine genetic signals previously associated with type 2 diabetes or identify new risk factors for this disease.
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Affiliation(s)
- G Samaha
- Sydney School of Veterinary Science, University of Sydney, Sydney, NSW, 2006, Australia
| | - J Beatty
- Sydney School of Veterinary Science, Valentine Charlton Cat Centre, University of Sydney, Sydney, NSW, 2006, Australia
| | - C M Wade
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| | - B Haase
- Sydney School of Veterinary Science, University of Sydney, Sydney, NSW, 2006, Australia
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16
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Goto A, Yamaji T, Sawada N, Momozawa Y, Kamatani Y, Kubo M, Shimazu T, Inoue M, Noda M, Tsugane S, Iwasaki M. Diabetes and cancer risk: A Mendelian randomization study. Int J Cancer 2019; 146:712-719. [PMID: 30927373 PMCID: PMC6916579 DOI: 10.1002/ijc.32310] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 03/03/2019] [Accepted: 03/13/2019] [Indexed: 12/28/2022]
Abstract
Earlier cohort studies using conventional regression models have consistently shown an increased cancer risk among individuals with type 2 diabetes. However, reverse causality and residual confounding due to common risk factors could exist, and it remains unclear whether diabetes per se contributes to cancer development. Mendelian randomization analyses might clarify the true association between diabetes and cancer risk. We conducted a case-cohort study with 10,536 subcohort subjects and 3,541 newly diagnosed cancer cases derived from 32,949 eligible participants aged 40-69 years within the Japan Public Health Center-based Prospective Study. With 29 known type 2 diabetes susceptibility variants, we used an inverse variance-weighted method to estimate hazard ratios for the associations of diabetes with risks of total and site-specific cancers. The hazard ratios of cancer per doubling of the probability of diabetes were 1.03 (95% confidence interval [CI], 0.92-1.15) overall, 1.08 (95% CI: 0.73-1.59) for the pancreas, 0.80 (95% CI: 0.57-1.14) for the liver and 0.90 (95% CI: 0.74-1.10) for the colorectum. Additional analyses, using publicly available large-scale genome-wide association study data on colorectal cancer in Japan, resulted in a narrower CI (hazard ratio: 1.00; 95% CI: 0.93-1.07). In this prospective Mendelian randomization study with a large number of incident cancer cases, we found no strong evidence to support associations between diabetes and overall and site-specific cancer risks. Our findings suggest that there is little evidence to support the genetic role of type 2 diabetes in cancer development in the Japanese population.
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Affiliation(s)
- Atsushi Goto
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Taiki Yamaji
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Norie Sawada
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Yukihide Momozawa
- Laboratory for Genotyping DevelopmentRIKEN Center for Integrative Medical SciencesYokohamaJapan
| | - Yoichiro Kamatani
- Laboratory for Genotyping DevelopmentRIKEN Center for Integrative Medical SciencesYokohamaJapan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical SciencesYokohamaJapan
| | - Taichi Shimazu
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Manami Inoue
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Mitsuhiko Noda
- Department of Endocrinology and DiabetesSaitama Medical UniversitySaitamaJapan
| | - Shoichiro Tsugane
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
| | - Motoki Iwasaki
- Epidemiology and Prevention Group, Center for Public Health SciencesNational Cancer CenterTokyoJapan
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Herder C, Kannenberg JM, Carstensen-Kirberg M, Strom A, Bönhof GJ, Rathmann W, Huth C, Koenig W, Heier M, Krumsiek J, Peters A, Meisinger C, Roden M, Thorand B, Ziegler D. A Systemic Inflammatory Signature Reflecting Cross Talk Between Innate and Adaptive Immunity Is Associated With Incident Polyneuropathy: KORA F4/FF4 Study. Diabetes 2018; 67:2434-2442. [PMID: 30115651 DOI: 10.2337/db18-0060] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 08/06/2018] [Indexed: 11/13/2022]
Abstract
Prospective analyses of biomarkers of inflammation and distal sensorimotor polyneuropathy (DSPN) are scarce and limited to innate immunity. We therefore aimed to assess associations between biomarkers reflecting multiple aspects of immune activation and DSPN. The study was based on 127 case subjects with incident DSPN and 386 noncase subjects from the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort (follow-up 6.5 years). Proximity extension assay technology was used to measure serum levels of biomarkers of inflammation. Of 71 biomarkers assessed, 26 were associated with incident DSPN. After adjustment for multiple testing, higher levels of six biomarkers remained related to incident DSPN. Three of these proteins (MCP-3/CCL7, MIG/CXCL9, IP-10/CXCL10) were chemokines, and the other three (DNER, CD40, TNFRSF9) were soluble forms of transmembrane receptors. The chemokines had neurotoxic effects on neuroblastoma cells in vitro. Addition of all six biomarkers improved the C statistic of a clinical risk model from 0.748 to 0.783 (P = 0.011). Pathway analyses indicated that multiple cell types from innate and adaptive immunity are involved in the development of DSPN. We thus identified novel associations between biomarkers of inflammation and incident DSPN pointing to a complex cross talk between innate and adaptive immunity in the pathogenesis of the disease.
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Affiliation(s)
- Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julia M Kannenberg
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Maren Carstensen-Kirberg
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Alexander Strom
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Gidon J Bönhof
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Cornelia Huth
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Munich Heart Alliance, Munich, Germany
- Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany
| | - Margit Heier
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Annette Peters
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München am UNIKA-T Augsburg, Augsburg, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dan Ziegler
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Richter B, Hemmingsen B, Metzendorf M, Takwoingi Y. Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia. Cochrane Database Syst Rev 2018; 10:CD012661. [PMID: 30371961 PMCID: PMC6516891 DOI: 10.1002/14651858.cd012661.pub2] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts. OBJECTIVES To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia. SEARCH METHODS We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining. SELECTION CRITERIA We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only. DATA COLLECTION AND ANALYSIS One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework. MAIN RESULTS We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018. AUTHORS' CONCLUSIONS Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.
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Affiliation(s)
- Bernd Richter
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Bianca Hemmingsen
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Maria‐Inti Metzendorf
- Institute of General Practice, Medical Faculty of the Heinrich‐Heine‐University DüsseldorfCochrane Metabolic and Endocrine Disorders GroupPO Box 101007DüsseldorfGermany40001
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchEdgbastonBirminghamUKB15 2TT
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Balakrishnan P, Vaidya D, Voruganti VS, Haack K, Kent JW, North KE, Laston S, Howard BV, Umans JG, Lee ET, Best LG, MacCluer JW, Cole SA, Navas-Acien A, Franceschini N. Genetic Variants Related to Cardiometabolic Traits Are Associated to B Cell Function, Insulin Resistance, and Diabetes Among AmeriCan Indians: The Strong Heart Family Study. Front Genet 2018; 9:466. [PMID: 30369944 PMCID: PMC6194194 DOI: 10.3389/fgene.2018.00466] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/24/2018] [Indexed: 01/03/2023] Open
Abstract
Background: Genetic research may inform underlying mechanisms for disparities in the burden of type 2 diabetes mellitus among American Indians. Our objective was to assess the association of genetic variants in cardiometabolic candidate genes with B cell dysfunction via HOMA-B, insulin resistance via HOMA-IR, and type 2 diabetes mellitus in the Strong Heart Family Study (SHFS). Methods and Results: We examined the association of variants, previously associated with cardiometabolic traits (∼200,000 from Illumina Cardio MetaboChip), using mixed models of HOMA-B residuals corrected for HOMA-IR (cHOMA-B), log transformed HOMA-IR, and incident diabetes, adjusted for age, sex, population stratification, and familial relatedness. Center-specific estimates were combined using fixed effect meta-analyses. We used Bonferroni correction to account for multiple testing (P < 4.13 × 10−7). We also assessed the association between variants in candidate diabetes genes with these metabolic traits. We explored the top SNPs in an independent, replication sample from Southwestern Arizona. We identified significant associations with cHOMA-B for common variants at 26 loci of which 8 were novel (PRSS7, FCRL5, PEL1, LRP12, IGLL1, ARHGEF10, PARVA, FLJ16686). The most significant variant association with cHOMA-B was observed on chromosome 5 for an intergenic variant near PARP8 (rs2961831, P = 6.39 × 10−9). In the replication study, we found a signal at rs4607517 near GCK/YKT6 (P = 0.01). Variants near candidate diabetes genes (especially GCK and KCNQ1) were also nominally associated with HOMA-IR and cHOMA-B. Conclusion: We identified variants at novel loci and confirmed those at known candidate diabetes loci associations for cHOMA-B. This study also provided evidence for association of variants at KCNQ2, CTNAA2, and KCNQ1with cHOMA-B among American Indians. Further studies are needed to account for the high heritability of diabetes among the American Indian participants of the SHFS cohort.
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Affiliation(s)
- Poojitha Balakrishnan
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Dhananjay Vaidya
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.,Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - V Saroja Voruganti
- Department of Nutrition, UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Karin Haack
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Jack W Kent
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sandra Laston
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Barbara V Howard
- MedStar Health Research Institute, Hyattsville, MD, United States
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville, MD, United States.,Georgetown and Howard Universities Center for Clinical and Translational Science, Washington, DC, United States
| | - Elisa T Lee
- Center for American Indian Health Research, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Lyle G Best
- Missouri Breaks Industries Research, Inc., Eagle Butte, SD, United States
| | - Jean W MacCluer
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Shelley A Cole
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, United States
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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20
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Liang Y, Luo H, Zhang H, Dong Y, Bao Y. Oncogene Delta/Notch-Like EGF-Related Receptor Promotes Cell Proliferation, Invasion, and Migration in Hepatocellular Carcinoma and Predicts a Poor Prognosis. Cancer Biother Radiopharm 2018; 33:380-386. [PMID: 30235011 DOI: 10.1089/cbr.2018.2460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To determine the expression and function of Delta/Notch-like EGF-related receptor (DNER) in hepatocellular carcinoma (HCC). METHODS The expression of DNER in 84 HCC tissue samples and matched adjacent noncancerous specimens, as well as HCC cells, were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Survival analysis was evaluated using Kaplan-Meier method. For experiments in vitro, cell viability was measured by Cell Counting Kit-8 Assay and Colony Formation Assay. Furthermore, cell invasion and migration assays were performed with Transwell Assay. RESULTS The results showed that DNER was overexpressed in the tissues and cell lines of HCC (all, p < 0.05), and the upregulated expression of DNER was significantly correlated with advanced pathologic stage (p = 0.013) and pathologic-M1 (p = 0.012) in HCC patients. Survival analysis revealed that patients with high DNER levels had worse overall survival (OS) than those with low DNER levels (p = 0.004). More importantly, DNER could be an independent predictor of prognosis for OS (HR = 2.582, 95% CI 1.239-5.380, p = 0.011). In vitro, knockdown of DNER significantly suppressed cell proliferation, colony formation, cell invasion, and cell migration in HepG2 cells. Moreover, inhibition of DNER inactivated PI3K/AKT signaling pathway by downregulating the expression of p-PI3K, p-AKT, and p-70s6k. CONCLUSIONS Taken together, DNER could promote proliferation, migration, and invasion of HCC cells by regulating the activation of PI3K/AKT pathway, and it might act as a potential prognostic biomarker for HCC.
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Affiliation(s)
- Yan Liang
- 1 Department of Gastroenterology, Renmin Hospital of Wuhan University , Wuhan, China .,2 Department of Oncology, Xiangyang No.1 People's Hospital, Hubei University of Medicine , XiangYang, Hubei, China
| | - Hesheng Luo
- 1 Department of Gastroenterology, Renmin Hospital of Wuhan University , Wuhan, China
| | - Haidong Zhang
- 2 Department of Oncology, Xiangyang No.1 People's Hospital, Hubei University of Medicine , XiangYang, Hubei, China
| | - Youhong Dong
- 2 Department of Oncology, Xiangyang No.1 People's Hospital, Hubei University of Medicine , XiangYang, Hubei, China
| | - Ying Bao
- 2 Department of Oncology, Xiangyang No.1 People's Hospital, Hubei University of Medicine , XiangYang, Hubei, China
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21
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Dziewulska A, Dobosz AM, Dobrzyn A. High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes. Genes (Basel) 2018; 9:E374. [PMID: 30050001 PMCID: PMC6115814 DOI: 10.3390/genes9080374] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 07/20/2018] [Accepted: 07/23/2018] [Indexed: 12/20/2022] Open
Abstract
Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.
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Affiliation(s)
- Anna Dziewulska
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
| | - Aneta M Dobosz
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
| | - Agnieszka Dobrzyn
- Laboratory of Cell Signaling and Metabolic Disorders, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093 Warsaw, Poland.
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22
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Hsueh WC, Nair AK, Kobes S, Chen P, Göring HHH, Pollin TI, Malhotra A, Knowler WC, Baier LJ, Hanson RL. Identity-by-Descent Mapping Identifies Major Locus for Serum Triglycerides in Amerindians Largely Explained by an APOC3 Founder Mutation. ACTA ACUST UNITED AC 2018; 10:CIRCGENETICS.117.001809. [PMID: 29237685 DOI: 10.1161/circgenetics.117.001809] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 10/03/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Identity-by-descent mapping using empirical estimates of identity-by-descent allele sharing may be useful for studies of complex traits in founder populations, where hidden relationships may augment the inherent genetic information that can be used for localization. METHODS AND RESULTS Through identity-by-descent mapping, using ≈400 000 single-nucleotide polymorphisms (SNPs), of serum lipid profiles, we identified a major linkage signal for triglycerides in 1007 Pima Indians (LOD=9.23; P=3.5×10-11 on chromosome 11q). In subsequent fine-mapping and replication association studies in ≈7500 Amerindians, we determined that this signal reflects effects of a loss-of-function Ala43Thr substitution in APOC3 (rs147210663) and 3 established functional SNPs in APOA5. The association with rs147210663 was particularly strong; each copy of the Thr allele conferred 42% lower triglycerides (β=-0.92±0.059 SD unit; P=9.6×10-55 in 4668 Pimas and 2793 Southwest Amerindians combined). The Thr allele is extremely rare in most global populations but has a frequency of 2.5% in Pimas. We further demonstrated that 3 APOA5 SNPs with established functional impact could explain the association with the most well-replicated SNP (rs964184) for triglycerides identified by genome-wide association studies. Collectively, these 4 SNPs account for 6.9% of variation in triglycerides in Pimas (and 4.1% in Southwest Amerindians), and their inclusion in the original linkage model reduced the linkage signal to virtually null. CONCLUSIONS APOC3/APOA5 constitutes a major locus for serum triglycerides in Amerindians, especially the Pimas, and these results provide an empirical example for the concept that population-based linkage analysis is a useful strategy to identify complex trait variants.
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Affiliation(s)
- Wen-Chi Hsueh
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.).
| | - Anup K Nair
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Sayuko Kobes
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Peng Chen
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Harald H H Göring
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Toni I Pollin
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Alka Malhotra
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - William C Knowler
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Leslie J Baier
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
| | - Robert L Hanson
- From the Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, AZ (W.-C.H., A.K.N., S.K., P.C., A.M., W.C.K., L.J.B., R.L.H.); South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, San Antonio (H.H.H.G.); Departments of Medicine and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore (T.I.P.); and Illumina Inc, San Diego, CA (A.M.)
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Hsueh WC, Bennett PH, Esparza-Romero J, Urquidez-Romero R, Valencia ME, Ravussin E, Williams RC, Knowler WC, Baier LJ, Schulz LO, Hanson RL. Analysis of type 2 diabetes and obesity genetic variants in Mexican Pima Indians: Marked allelic differentiation among Amerindians at HLA. Ann Hum Genet 2018; 82:287-299. [PMID: 29774533 DOI: 10.1111/ahg.12252] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Revised: 02/11/2018] [Accepted: 03/08/2018] [Indexed: 01/21/2023]
Abstract
Prevalence of diabetes and obesity in Mexican Pima Indians is low, while prevalence in US Pima Indians is high. Although lifestyle likely accounts for much of the difference, the role of genetic factors is not well explored. To examine this, we genotyped 359 single nucleotide polymorphisms, including established type 2 diabetes and obesity variants from genome-wide association studies (GWAS) and 96 random markers, in 342 Mexican Pimas. A multimarker risk score of obesity variants was associated with body mass index (BMI; β = 0.81 kg/m2 per SD, P = 0.0066). The mean value of the score was lower in Mexican Pimas than in US Pimas (P = 4.3 × 10-11 ), and differences in allele frequencies at established loci could account for approximately 7% of the population difference in BMI; however, the difference in risk scores was consistent with evolutionary neutrality given genetic distance. To identify loci potentially under recent natural selection, allele frequencies at 283 variants were compared between US and Mexican Pimas, accounting for genetic distance. The largest differences were seen at HLA markers (e.g., rs9271720, difference = 0.75, P = 8.7 × 10-9 ); genetic distances at HLA were greater than at random markers (P = 1.6 × 10-46 ). Analyses of GWAS data in 937 US Pimas also showed sharing of alleles identical by descent at HLA that exceeds its genomic expectation (P = 7.0 × 10-10 ). These results suggest that, in addition to the widely recognized balancing selection at HLA, recent directional selection may also occur, resulting in marked allelic differentiation between closely related populations.
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Affiliation(s)
- Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Peter H Bennett
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Julian Esparza-Romero
- Departamento de Nutrición Pública y Salud, Coordinación de Nutrición, Centro de Investigación en Alimentación y Desarrollo, Hermosillo, Sonora, México
| | - Rene Urquidez-Romero
- Instituto de Ciencias Biomédicas, Departamento de Ciencias de la Salud, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, México
| | - Mauro E Valencia
- Departamento de Nutrición Pública y Salud, Coordinación de Nutrición, Centro de Investigación en Alimentación y Desarrollo, Hermosillo, Sonora, México
| | - Eric Ravussin
- Pennington Biomedical Research Center, Louisiana State University Systems, Baton Rouge, LA, USA
| | - Robert C Williams
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Leslie O Schulz
- College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, USA
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
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Admixture mapping in the Hispanic Community Health Study/Study of Latinos reveals regions of genetic associations with blood pressure traits. PLoS One 2017; 12:e0188400. [PMID: 29155883 PMCID: PMC5695820 DOI: 10.1371/journal.pone.0188400] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 11/06/2017] [Indexed: 01/11/2023] Open
Abstract
Admixture mapping can be used to detect genetic association regions in admixed populations, such as Hispanics/Latinos, by estimating associations between local ancestry allele counts and the trait of interest. We performed admixture mapping of the blood pressure traits systolic and diastolic blood pressure (SBP, DBP), mean arterial pressure (MAP), and pulse pressure (PP), in a dataset of 12,116 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Hispanics/Latinos have three predominant ancestral populations (European, African, and Amerindian), for each of which we separately tested local ancestry intervals across the genome. We identified four regions that were significantly associated with a blood pressure trait at the genome-wide admixture mapping level. A 6p21.31 Amerindian ancestry association region has multiple known associations, but none explained the admixture mapping signal. We identified variants that completely explained this signal. One of these variants had p-values of 0.02 (MAP) and 0.04 (SBP) in replication testing in Pima Indians. A 11q13.4 Amerindian ancestry association region spans a variant that was previously reported (p-value = 0.001) in a targeted association study of Blood Pressure (BP) traits and variants in the vitamin D pathway. There was no replication evidence supporting an association in the identified 17q25.3 Amerindian ancestry association region. For a region on 6p12.3, associated with African ancestry, we did not identify any candidate variants driving the association. It may be driven by rare variants. Whole genome sequence data may be necessary to fine map these association signals, which may contribute to disparities in BP traits between diverse populations.
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Wood AR, Jonsson A, Jackson AU, Wang N, van Leewen N, Palmer ND, Kobes S, Deelen J, Boquete-Vilarino L, Paananen J, Stančáková A, Boomsma DI, de Geus EJC, Eekhoff EMW, Fritsche A, Kramer M, Nijpels G, Simonis-Bik A, van Haeften TW, Mahajan A, Boehnke M, Bergman RN, Tuomilehto J, Collins FS, Mohlke KL, Banasik K, Groves CJ, McCarthy MI, Pearson ER, Natali A, Mari A, Buchanan TA, Taylor KD, Xiang AH, Gjesing AP, Grarup N, Eiberg H, Pedersen O, Chen YD, Laakso M, Norris JM, Smith U, Wagenknecht LE, Baier L, Bowden DW, Hansen T, Walker M, Watanabe RM, 't Hart LM, Hanson RL, Frayling TM. A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants. Diabetes 2017; 66:2296-2309. [PMID: 28490609 PMCID: PMC5521867 DOI: 10.2337/db16-1452] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 05/02/2017] [Indexed: 01/19/2023]
Abstract
Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose-raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose-raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide-based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.
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Affiliation(s)
- Andrew R Wood
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Anna Jonsson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
| | - Nan Wang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Nienke van Leewen
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Joris Deelen
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Lorena Boquete-Vilarino
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K
| | - Jussi Paananen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Alena Stančáková
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | - Elisabeth M W Eekhoff
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Andreas Fritsche
- Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Department of Internal Medicine IV, University of Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich, University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD e.V.), Tübingen, Germany
| | - Mark Kramer
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Giel Nijpels
- EMGO+ Institute for Health and Care Research, VU University Medical Center, Department of General Practice, Amsterdam, the Netherlands
| | - Annemarie Simonis-Bik
- Diabetes Center, Internal Medicine Unit, VU University Medical Center, Amsterdam, the Netherlands
| | - Timon W van Haeften
- Department of Internal Medicine, Utrecht University Medical Center, Utrecht, the Netherlands
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
| | - Richard N Bergman
- Diabetes & Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Jaakko Tuomilehto
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
- Dasman Diabetes Institute, Dasman, Kuwait
- Department of Clinical Neurosciences and Preventive Medicine, Danube University Krems, Krems, Austria
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Karina Banasik
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, U.K
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, U.K
- Oxford Biomedical Research Centre, National Institute for Health Research, Churchill Hospital, Oxford, U.K
| | | | - Andrea Natali
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, Italy
| | - Thomas A Buchanan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
- Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA
| | - Anny H Xiang
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans Eiberg
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yii-Derr Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO
| | - Ulf Smith
- Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Leslie Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle, U.K.
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Diabetes & Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Leen M 't Hart
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
- Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Timothy M Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K.
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26
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Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, Kutalik Z, Luan J, Malkin I, Ried JS, Smith AV, Thorleifsson G, Vandenput L, Hua Zhao J, Zhang W, Aghdassi A, Åkesson K, Amin N, Baier LJ, Barroso I, Bennett DA, Bertram L, Biffar R, Bochud M, Boehnke M, Borecki IB, Buchman AS, Byberg L, Campbell H, Campos Obanda N, Cauley JA, Cawthon PM, Cederberg H, Chen Z, Cho NH, Jin Choi H, Claussnitzer M, Collins F, Cummings SR, De Jager PL, Demuth I, Dhonukshe-Rutten RAM, Diatchenko L, Eiriksdottir G, Enneman AW, Erdos M, Eriksson JG, Eriksson J, Estrada K, Evans DS, Feitosa MF, Fu M, Garcia M, Gieger C, Girke T, Glazer NL, Grallert H, Grewal J, Han BG, Hanson RL, Hayward C, Hofman A, Hoffman EP, Homuth G, Hsueh WC, Hubal MJ, Hubbard A, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson JO, Jordan JM, Jula A, Karlsson M, Khaw KT, Kilpeläinen TO, Klopp N, Kloth JSL, Koistinen HA, Kraus WE, Kritchevsky S, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Launer LJ, Lee JY, Lerch MM, Lewis JR, Lind L, Lindgren C, Liu Y, Liu T, Liu Y, Ljunggren Ö, Lorentzon M, Luben RN, Maixner W, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellström D, Melov S, Michaëlsson K, Mitchell BD, Morris AP, Mosekilde L, Newman A, Nielson CM, O'Connell JR, Oostra BA, Orwoll ES, Palotie A, Parker SCJ, Peacock M, Perola M, Peters A, Polasek O, Prince RL, Räikkönen K, Ralston SH, Ripatti S, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schadt EE, Schipf S, Scott L, Sehmi J, Shen J, Soo Shin C, Sigurdsson G, Smith S, Soranzo N, Stančáková A, Steinhagen-Thiessen E, Streeten EA, Styrkarsdottir U, Swart KMA, Tan ST, Tarnopolsky MA, Thompson P, Thomson CA, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Tuomilehto J, van Schoor NM, Verma A, Vollenweider P, Völzke H, Wactawski-Wende J, Walker M, Weedon MN, Welch R, Wichmann HE, Widen E, Williams FMK, Wilson JF, Wright NC, Xie W, Yu L, Zhou Y, Chambers JC, Döring A, van Duijn CM, Econs MJ, Gudnason V, Kooner JS, Psaty BM, Spector TD, Stefansson K, Rivadeneira F, Uitterlinden AG, Wareham NJ, Ossowski V, Waterworth D, Loos RJF, Karasik D, Harris TB, Ohlsson C, Kiel DP. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 2017; 8:80. [PMID: 28724990 PMCID: PMC5517526 DOI: 10.1038/s41467-017-00031-7] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 05/02/2017] [Indexed: 12/25/2022] Open
Abstract
Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10-8) or suggestively genome wide (p < 2.3 × 10-6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.
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Affiliation(s)
- M Carola Zillikens
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Serkalem Demissie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Yi-Hsiang Hsu
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Laura M Yerges-Armstrong
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Wen-Chi Chou
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Gregory Livshits
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Linda Broer
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Toby Johnson
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Daniel L Koller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Ida Malkin
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Janina S Ried
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | | | - Liesbeth Vandenput
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Weihua Zhang
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Ali Aghdassi
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Kristina Åkesson
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
- Department of Orthopedics, Skåne University Hospital, Malmö, S-205 02, Sweden
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
- NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 OQQ, UK
- Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, CB2 OQQ, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Experimental & Integrative Genomics, University of Lübeck, Lübeck, 23562, Germany
- School of Public Health, Faculty of Medicine, Imperial College London, London, W6 8RP, UK
| | - Rainer Biffar
- Centre of Oral Health, Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University of Greifswald, Greifswald, 17489, Germany
| | - Murielle Bochud
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
- Division of Biostatistics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Liisa Byberg
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Harry Campbell
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | | | - Jane A Cauley
- Department of Epidemiology Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Peggy M Cawthon
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Henna Cederberg
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Zhao Chen
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Nam H Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Youngtong-Gu, Suwon, 16499, Korea
| | - Hyung Jin Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
- Department of Internal Medicine, Chungbuk National University Hospital, Cheongju Si, Korea
| | - Melina Claussnitzer
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139, USA
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
| | - Francis Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Philip L De Jager
- Harvard Medical School, Boston, MA, 02115, USA
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
| | - Ilja Demuth
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
- Institute of Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, 13353, Germany
| | | | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, H3A 0G1, Canada
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | | | - Anke W Enneman
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Mike Erdos
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, 00014, Finland
- Unit of General Practice, Helsinki University Central Hospital, Helsinki, 00014, Finland
- Folkhalsan Research Centre, Helsinki, 00250, Finland
- Vasa Central Hospital, Vasa, 65130, Finland
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Joel Eriksson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Karol Estrada
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Mao Fu
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Melissa Garcia
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Nicole L Glazer
- Departments of Medicine and Epidemiology, Boston University School of Medicine and Public Health, Boston, MA, 02118, USA
| | - Harald Grallert
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- CCG Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- CCG Nutrigenomics and Type 2 Diabetes. Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Jagvir Grewal
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Bok-Ghee Han
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Albert Hofman
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Eric P Hoffman
- Department of Pharmaceutical Sciences, SUNY Binghamton, Binghamton, NY, 13902, USA
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, 17487, Germany
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Monica J Hubal
- Department of Exercise and Nutrition Sciences, George Washington University, Washington, DC, 20052, USA
- Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, 20052, USA
| | - Alan Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Kim M Huffman
- Division of Rheumatology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Lise B Husted
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Human Genetics, Hannover Medical School, Hannover, 30625, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | - Erik Ingelsson
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Till Ittermann
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - John-Olov Jansson
- Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE 405 30, Sweden
| | - Joanne M Jordan
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Antti Jula
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Magnus Karlsson
- Department of Clinical Sciences and Orthopaedics, Lund University, Skåne University Hospital SUS, Malmö, 22362, Sweden
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Tuomas O Kilpeläinen
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen, 2100, Denmark
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Norman Klopp
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | | | - Heikki A Koistinen
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Department of Health, National Institute for Health and Welfare, Helsinki, 00271, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, 00290, Finland
| | - William E Kraus
- Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Stephen Kritchevsky
- Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Teemu Kuulasmaa
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Jari Lahti
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Thomas Lang
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Bente L Langdahl
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Jong-Young Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Markus M Lerch
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Joshua R Lewis
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Centre for Kidney Research, School of Public Health, University of Sydney, Sydney, 2006, Australia
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27517, USA
| | - Tian Liu
- Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
- Max Planck Institute for Human Development, Berlin, 14195, Germany
| | - Youfang Liu
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Östen Ljunggren
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Mattias Lorentzon
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Robert N Luben
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - William Maixner
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Fiona E McGuigan
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Thomas Meitinger
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Håkan Melhus
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Dan Mellström
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Simon Melov
- Buck Institute for Research on Aging, Novato, CA, 94945, USA
- Leonard Davis School of Gerontology, University of Southern California, LA, CA, 90089, USA
| | - Karl Michaëlsson
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Leif Mosekilde
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Anne Newman
- Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | | | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ben A Oostra
- Department of Clinical Genetics, Erasmus MC, Rotterdam, 300 CA, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Eric S Orwoll
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, FI00014, Finland
| | - Stephen C J Parker
- Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Munro Peacock
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, FI00014, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Ozren Polasek
- Faculty of Medicine, Department of Public Health, University of Split, Split, 21000, Croatia
| | - Richard L Prince
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gardiner Hospital, Perth, 6009, Australia
| | - Katri Räikkönen
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Stuart H Ralston
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Hjelt Institute, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - John A Robbins
- Department of Medicine, University of California at Davis, Sacramento, CA, 95817, USA
| | - Jerome I Rotter
- Institute for Translational Genomic and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor UCLA Medical Center, Torrance, CA, 90502, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Suzanne Satterfield
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Science, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sabine Schipf
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Laura Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Joban Sehmi
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Jian Shen
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, Reykjavik, 101, Iceland
| | - Shad Smith
- Center for Translational Pain Medicine, Department of Anesthiology, Duke University Medical Center, Durham, NC, 27110, USA
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Alena Stančáková
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Elisabeth Steinhagen-Thiessen
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
| | - Elizabeth A Streeten
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatric Research and Education Clinical Center (GRECC) - Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | | | - Karin M A Swart
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Sian-Tsung Tan
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Mark A Tarnopolsky
- Department of Medicine, McMaster University Medical Center, Hamilton, ON, Canada, L8N 3Z5
| | - Patricia Thompson
- Department of Pathology, Stony Brook School of Medicine, Stony Brook, NY, 11794, USA
| | - Cynthia A Thomson
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Emmi Tikkanen
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Gregory J Tranah
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Jaakko Tuomilehto
- Vasa Central Hospital, Vasa, 65130, Finland
- Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, 3500, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, 12589, Saudi Arabia
- Dasman Diabetes Institute, Dasman, 15462, Kuwait
| | - Natasja M van Schoor
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Arjun Verma
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Peter Vollenweider
- Department of Medicine and Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, CH-1011, Switzerland
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - H-Erich Wichmann
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, 81377, Germany
- Institute of Medical Statistics and Epidemiology, Technical University, Munich, 81675, Germany
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - James F Wilson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Nicole C Wright
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Weijia Xie
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - John C Chambers
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust and Imperial College, London, SW3 6NP, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Angela Döring
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Michael J Econs
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Jaspal S Kooner
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
- Kaiser Permanente Washington Health Research Institute, Washington, Seattle, WA, 98101, USA
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Vicky Ossowski
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Dawn Waterworth
- Medical Genetics, GlaxoSmithKline, Philadelphia, PA, 19112, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Institute of Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Genetics of Obesity and Related Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David Karasik
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, 1311502, Israel
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Claes Ohlsson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Douglas P Kiel
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
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Brown LA, Sofer T, Stilp AM, Baier LJ, Kramer HJ, Masindova I, Levy D, Hanson RL, Moncrieft AE, Redline S, Rosas SE, Lash JP, Cai J, Laurie CC, Browning S, Thornton T, Franceschini N. Admixture Mapping Identifies an Amerindian Ancestry Locus Associated with Albuminuria in Hispanics in the United States. J Am Soc Nephrol 2017; 28:2211-2220. [PMID: 28137830 PMCID: PMC5491288 DOI: 10.1681/asn.2016091010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 12/29/2016] [Indexed: 11/03/2022] Open
Abstract
Increased urine albumin excretion is highly prevalent in Hispanics/Latinos. Previous studies have found an association between urine albumin excretion and Amerindian ancestry in Hispanic/Latino populations. Admixture between racial/ethnic groups creates long-range linkage disequilibrium between variants with different allelic frequencies in the founding populations and it can be used to localize genes. Hispanic/Latino genomes are an admixture of European, African, and Amerindian ancestries. We leveraged this admixture to identify associations between urine albumin excretion (urine albumin-to-creatinine ratio [UACR]) and genomic regions harboring variants with highly differentiated allele frequencies among the ancestral populations. Admixture mapping analysis of 12,212 Hispanic Community Health Study/Study of Latinos participants, using a linear mixed model, identified three novel genome-wide significant signals on chromosomes 2, 11, and 16. The admixture mapping signal identified on chromosome 2, spanning q11.2-14.1 and not previously reported for UACR, is driven by a difference between Amerindian ancestry and the other two ancestries (P<5.7 × 10-5). Within this locus, two common variants located at the proapoptotic BCL2L11 gene associated with UACR: rs116907128 (allele frequency =0.14; P=1.5 × 10-7) and rs586283 (C allele frequency =0.35; P=4.2 × 10-7). In a secondary analysis, rs116907128 accounted for most of the admixture mapping signal observed in the region. The rs116907128 variant is common among full-heritage Pima Indians (A allele frequency =0.54) but is monomorphic in the 1000 Genomes European and African populations. In a replication analysis using a sample of full-heritage Pima Indians, rs116907128 significantly associated with UACR (P=0.01; n=1568). Our findings provide evidence for the presence of Amerindian-specific variants influencing the variation of urine albumin excretion in Hispanics/Latinos.
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Affiliation(s)
- Lisa A Brown
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Tamar Sofer
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Leslie J Baier
- Epidemiology and Clinical Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
| | - Holly J Kramer
- Department of Public Health Sciences and Medicine, Division of Nephrology and Hypertension, Loyola University Chicago, Maywood, Illinois
| | - Ivica Masindova
- Epidemiology and Clinical Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
| | - Daniel Levy
- The Framingham Heart Study, Framingham, Massachusetts, and Population Sciences Branch, National Heart, Lung, and Blood Institute, US National Institutes of Health, Bethesda, Maryland
| | - Robert L Hanson
- Epidemiology and Clinical Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
| | | | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Sylvia E Rosas
- Department of Medicine, Division of Nephrology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - James P Lash
- Department of Medicine, Division of Nephrology and Institute for Minority Health Research, University of Illinois at Chicago, Chicago, Illinois
| | - Jianwen Cai
- Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; and
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Sharon Browning
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Timothy Thornton
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
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28
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Muller YL, Piaggi P, Chen P, Wiessner G, Okani C, Kobes S, Knowler WC, Bogardus C, Hanson RL, Baier LJ. Assessing variation across 8 established East Asian loci for type 2 diabetes mellitus in American Indians: Suggestive evidence for new sex-specific diabetes signals in GLIS3 and ZFAND3. Diabetes Metab Res Rev 2017; 33. [PMID: 27862917 DOI: 10.1002/dmrr.2869] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 10/24/2016] [Accepted: 10/29/2016] [Indexed: 01/20/2023]
Abstract
BACKGROUND Eight new loci for type 2 diabetes mellitus (T2DM) were identified in an East Asian genome-wide association study meta-analysis. We assess tag SNPs across these loci for associations with T2DM in American Indians. METHODS A total of 435 SNPs that tag (R2 ≥ .85) common variation across the 8 loci were analyzed for association with T2DM (n = 7710), early onset T2DM (n = 1060), body mass index (n = 6839), insulin sensitivity (n = 555), and insulin secretion (n = 298). RESULTS Tag SNPs within FITM2-R3HDML-HNF4A, GLIS3, KCNK16, and ZFAND3 associated with T2DM after accounting for locus-wide multiple testing. The T2DM association in FITM2-R3HDML-HNF4A (rs3212183; P = .0002; OR = 1.19 [1.09-1.30]) was independent from the East Asian lead SNP (rs6017317), which did not associate with T2DM in American Indians. The top signals in GLIS3 (rs7875253; P = .0004; OR = 1.23 [1.10-1.38]) and KCNK16 (rs1544050; P = .002; OR = 1.16 [1.06-1.27]) were attenuated after adjustment for the East Asian lead SNPs (rs7041847 in GLIS3; rs1535500 in KCNK16), both of which also associated with T2DM in American Indians (P = .02; OR = 1.11 [1.01-1.21]; P = .007; OR = 1.19 [1.05-1.36] respectively). The top SNP in ZFAND3 (rs9470794; P = .002; OR = 1.43 [1.14-1.80]) was the identical East Asian lead SNP. Additional SNPs in GLIS3 (rs180867004) and ZFAND3 (rs4714120 and rs9470701) had significant genotype × sex interactions (P ≤ .008). The GLIS3 SNP (rs180867004) associated with T2DM only in men (P = .00006, OR = 1.94 [1.40-2.68]). The ZFAND3 SNPs (rs4714120 and rs9470701) associated with T2DM only in women (P = .0002, OR = 1.35 [1.16-1.59]; P = .0003, OR = 1.37 [1.16-1.63] respectively). CONCLUSIONS Replication of lead T2DM SNPs in GLIS3, KCNK16, and ZFAND3 was observed in American Indians. Sex-specific T2DM signals in GLIS3 and ZFAND3, which are distinct from the East Asian GWAS signals, were also identified.
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Affiliation(s)
- Yunhua L Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Paolo Piaggi
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Peng Chen
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Gregory Wiessner
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Chidinma Okani
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Clifton Bogardus
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, Arizona, USA
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Chen G, Zhang Z, Adebamowo SN, Liu G, Adeyemo A, Zhou Y, Doumatey AP, Wang C, Zhou J, Yan W, Shriner D, Tekola-Ayele F, Bentley AR, Jiang C, Rotimi CN. Common and rare exonic MUC5B variants associated with type 2 diabetes in Han Chinese. PLoS One 2017; 12:e0173784. [PMID: 28346466 PMCID: PMC5367689 DOI: 10.1371/journal.pone.0173784] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 02/27/2017] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies have identified over one hundred common genetic risk variants associated with type 2 diabetes (T2D). However, most of the heritability of T2D has not been accounted for. In this study, we investigated the contribution of rare and common variants to T2D susceptibility by analyzing exome array data in 1,908 Han Chinese genotyped with Affymetrix Axiom® Exome Genotyping Arrays. Based on the joint common and rare variants analysis of 57,704 autosomal SNPs within 12,244 genes using Sequence Kernel Association Tests (SKAT), we identified significant associations between T2D and 25 variants (9 rare and 16 common) in MUC5B, p-value 1.01×10−14. This finding was replicated (p = 0.0463) in an independent sample that included 10,401 unrelated individuals. Sixty-six of 1,553 possible haplotypes based on 25 SNPs within MUC5B showed significant association with T2D (Bonferroni corrected p values < 3.2×10−5). The expression level of MUC5B is significantly higher in pancreatic tissues of persons with T2D compared to those without T2D (p-value = 5×10−5). Our findings suggest that dysregulated MUC5B expression may be involved in the pathogenesis of T2D. As a strong candidate gene for T2D, MUC5B may play an important role in the mechanisms underlying T2D etiology and its complications.
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Affiliation(s)
- Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (CNR); (GC)
| | | | - Sally N. Adebamowo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | | | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Yanxun Zhou
- Suizhou Central Hospital, Suizhou, Hubei, China
| | - Ayo P. Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | | | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | | | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Fasil Tekola-Ayele
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | | | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (CNR); (GC)
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Choi S, Bae S, Park T. Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes. Genomics Inform 2016; 14:138-148. [PMID: 28154504 PMCID: PMC5287117 DOI: 10.5808/gi.2016.14.4.138] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 12/05/2016] [Accepted: 12/05/2016] [Indexed: 12/31/2022] Open
Abstract
The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.
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Affiliation(s)
- Sungkyoung Choi
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Sunghwan Bae
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Department of Statistics, Seoul National University, Seoul 08826, Korea
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Andersen MK, Pedersen CET, Moltke I, Hansen T, Albrechtsen A, Grarup N. Genetics of Type 2 Diabetes: the Power of Isolated Populations. Curr Diab Rep 2016; 16:65. [PMID: 27189761 DOI: 10.1007/s11892-016-0757-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.
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Affiliation(s)
- Mette Korre Andersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark
| | - Casper-Emil Tingskov Pedersen
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Ida Moltke
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, J.B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Anders Albrechtsen
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark.
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Duan P, Tu P, Si L, Hu W, Liu M, Liu J, Xue Y. Gene Polymorphisms in the RANKL/RANK/OPG Pathway Are Associated with Type 2 Diabetes Mellitus in Southern Han Chinese Women. Genet Test Mol Biomarkers 2016; 20:285-90. [PMID: 27171030 DOI: 10.1089/gtmb.2015.0306] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Peng Duan
- Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Ping Tu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Lian Si
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Wan Hu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Meng Liu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Jia Liu
- Department of Endocrinology and Metabolism, The Third Hospital of Nanchang, Nanchang, China
| | - Yaoming Xue
- Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Hanson RL, Leti F, Tsinajinnie D, Kobes S, Puppala S, Curran JE, Almasy L, Lehman DM, Blangero J, Duggirala R, DiStefano JK. The Arg59Trp variant in ANGPTL8 (betatrophin) is associated with total and HDL-cholesterol in American Indians and Mexican Americans and differentially affects cleavage of ANGPTL3. Mol Genet Metab 2016; 118:128-37. [PMID: 27117576 PMCID: PMC4880492 DOI: 10.1016/j.ymgme.2016.04.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 04/15/2016] [Accepted: 04/15/2016] [Indexed: 11/19/2022]
Abstract
We previously identified a locus linked to total cholesterol (TC) concentration in Pima Indians on chromosome 19p. To characterize this locus, we genotyped >2000 SNPs in 1838 Pimas and assessed association with log(TC). We observed evidence for association with log(TC) with rs2278426 (3.5% decrease/copy of the T allele; P=5.045×10(-6)) in the ANGPTL8 (angiopoietin-like 8) gene. We replicated this association in 2413 participants of the San Antonio Mexican American Family Study (SAMAFS: 2.0% decrease per copy of the T allele; P=0.005842). In a meta-analysis of the combined data, we found the strongest estimated effect with rs2278426 (P=2.563×10(-7)). The variant T allele at rs2278426 predicts an Arg59Trp substitution and has previously been associated with LDL-C and HDL-C. In Pimas and SAMAFS participants, the T allele of rs2278426 was associated with reduced HDL-C levels (P=0.000741 and 0.00002, respectively), and the combined estimated effect for the two cohorts was -3.8% (P=8.526×10(-8)). ANGPTL8 transcript and protein levels increased in response to both glucose and insulin. The variant allele was associated with increased levels of cleaved ANGPTL3. We conclude that individuals with the variant allele may have lower TC and HDL-C levels due to increased activation of ANGPTL3 by ANGPTL8.
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MESH Headings
- Adult
- Alleles
- Amino Acid Substitution
- Angiopoietin-Like Protein 3
- Angiopoietin-Like Protein 8
- Angiopoietin-like Proteins/genetics
- Angiopoietin-like Proteins/metabolism
- Arginine/genetics
- Blood Glucose/metabolism
- Cholesterol, HDL/blood
- Cholesterol, HDL/genetics
- Cholesterol, HDL/metabolism
- Cholesterol, LDL/blood
- Cholesterol, LDL/genetics
- Cholesterol, LDL/metabolism
- Chromosomes, Human, Pair 19/genetics
- Cohort Studies
- Coronary Disease/blood
- Coronary Disease/genetics
- Diabetes Mellitus/genetics
- Female
- Genome-Wide Association Study
- Hep G2 Cells
- Humans
- Indians, North American/genetics
- Insulin/metabolism
- Male
- Mexican Americans/genetics
- Middle Aged
- Peptide Hormones/genetics
- Peptide Hormones/metabolism
- Polymorphism, Single Nucleotide
- Tryptophan/genetics
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Affiliation(s)
- Robert L Hanson
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 East Indian School Road, Phoenix, AZ 85014, United States
| | - Fatjon Leti
- Center for Genes, Environment, and Health, Department of Biomedical Research, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States
| | - Darwin Tsinajinnie
- Diabetes, Cardiovascular and Metabolic Diseases Division, Translational Genomics Research Institute, 445 Fifth Street, Phoenix, AZ 85004, United States
| | - Sayuko Kobes
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 East Indian School Road, Phoenix, AZ 85014, United States
| | - Sobha Puppala
- South Texas Diabetes and Obesity Institute (STDOI), University of Texas Rio Grande Valley (UTRGV) School of Medicine, 2700 E. Jackson St. Brownsville, TX 78520; 1214 W. Schunior Street, Edinburgh, TX 78541; 3463 Magic Drive San Antonio, TX 78229, United States
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute (STDOI), University of Texas Rio Grande Valley (UTRGV) School of Medicine, 2700 E. Jackson St. Brownsville, TX 78520; 1214 W. Schunior Street, Edinburgh, TX 78541; 3463 Magic Drive San Antonio, TX 78229, United States
| | - Laura Almasy
- South Texas Diabetes and Obesity Institute (STDOI), University of Texas Rio Grande Valley (UTRGV) School of Medicine, 2700 E. Jackson St. Brownsville, TX 78520; 1214 W. Schunior Street, Edinburgh, TX 78541; 3463 Magic Drive San Antonio, TX 78229, United States
| | - Donna M Lehman
- Departments of Medicine and Cellular & Structural Biology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, United States
| | - John Blangero
- South Texas Diabetes and Obesity Institute (STDOI), University of Texas Rio Grande Valley (UTRGV) School of Medicine, 2700 E. Jackson St. Brownsville, TX 78520; 1214 W. Schunior Street, Edinburgh, TX 78541; 3463 Magic Drive San Antonio, TX 78229, United States
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute (STDOI), University of Texas Rio Grande Valley (UTRGV) School of Medicine, 2700 E. Jackson St. Brownsville, TX 78520; 1214 W. Schunior Street, Edinburgh, TX 78541; 3463 Magic Drive San Antonio, TX 78229, United States
| | - Johanna K DiStefano
- Center for Genes, Environment, and Health, Department of Biomedical Research, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States; Diabetes, Cardiovascular and Metabolic Diseases Division, Translational Genomics Research Institute, 445 Fifth Street, Phoenix, AZ 85004, United States.
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34
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Williams RC, Elston RC, Kumar P, Knowler WC, Abboud HE, Adler S, Bowden DW, Divers J, Freedman BI, Igo RP, Ipp E, Iyengar SK, Kimmel PL, Klag MJ, Kohn O, Langefeld CD, Leehey DJ, Nelson RG, Nicholas SB, Pahl MV, Parekh RS, Rotter JI, Schelling JR, Sedor JR, Shah VO, Smith MW, Taylor KD, Thameem F, Thornley-Brown D, Winkler CA, Guo X, Zager P, Hanson RL. Selecting SNPs informative for African, American Indian and European Ancestry: application to the Family Investigation of Nephropathy and Diabetes (FIND). BMC Genomics 2016; 17:325. [PMID: 27142425 PMCID: PMC4855449 DOI: 10.1186/s12864-016-2654-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 04/22/2016] [Indexed: 01/06/2023] Open
Abstract
Background The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. Results A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. Conclusions The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2654-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert C Williams
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, 85014, USA.
| | - Robert C Elston
- Genetic Analysis and Data Coordinating Center, Case Western Reserve University, Cleveland, OH, 44104, USA
| | - Pankaj Kumar
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, 85014, USA
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, 85014, USA
| | - Hanna E Abboud
- Division of Nephrology, The University of Texas Health Science Center, San Antonio, TX, 78229, USA
| | - Sharon Adler
- Department of Nephrology, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Donald W Bowden
- Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Jasmin Divers
- Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | | | - Robert P Igo
- Genetic Analysis and Data Coordinating Center, Case Western Reserve University, Cleveland, OH, 44104, USA
| | - Eli Ipp
- Department of Nephrology, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Sudha K Iyengar
- Genetic Analysis and Data Coordinating Center, Case Western Reserve University, Cleveland, OH, 44104, USA
| | - Paul L Kimmel
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 20892, USA
| | - Michael J Klag
- Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, MD, 21205, USA
| | - Orly Kohn
- The University of Chicago Medical Center, Chicago, IL, 60637, USA
| | | | - David J Leehey
- Loyola University Medical Center, Chicago, IL, 60153, USA
| | - Robert G Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, 85014, USA
| | - Susanne B Nicholas
- Divisions of Nephrology and Endocrinology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Madeleine V Pahl
- Division of Nephrology and Hypertension, Department of Medicine, UC Irvine School of Medicine, University of California, Orange, 92868, CA, USA
| | - Rulan S Parekh
- Hospital for Sick Children, University Health Network and the University of Toronto, Ontario, M5G1X8, Canada
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Jeffrey R Schelling
- Departments of Medicine and Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, 44104, USA
| | - John R Sedor
- Departments of Medicine and Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, 44104, USA
| | - Vallabh O Shah
- The University of New Mexico, Albuquerque, NM, 87131, USA
| | - Michael W Smith
- National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Farook Thameem
- Division of Nephrology, The University of Texas Health Science Center, San Antonio, TX, 78229, USA.,Department of Biochemistry, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | | | - Cheryl A Winkler
- Center for Cancer Research, National Cancer Institute, NIH, Leidos Biomedical, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Phillip Zager
- The University of New Mexico, Albuquerque, NM, 87131, USA
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, 85014, USA
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35
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Association between single nucleotide polymorphisms of delta/notch-like epidermal growth factor (EGF)-related receptor (DNER) and Delta-like 1 Ligand (DLL 1) with the risk of type 2 diabetes mellitus in a Chinese Han population. Cell Biochem Biophys 2016; 71:331-5. [PMID: 25300688 DOI: 10.1007/s12013-014-0202-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The aim of the study was to investigate the association between single nucleotide polymorphisms (SNPs), rs1861612 of delta/notch-like Epidermal Growth Factor (EGF)-related receptor (DNER) and rs1884190 in the Delta-like 1 ligand (DLL1) gene and type 2 diabetes mellitus (T2DM) susceptibility in a Chinese Han population. DNER rs1861612 and DLL1 rs1884190 polymorphisms were genotyped in patients with T2DM and age- and sex-matched T2DM-free controls from a Chinese Han population. A total of 298 patients with T2DM and 500 controls were enrolled in this study. We found that TC and TT genotypes of rs1861612 and variant T were associated with a significantly increased risk of T2DM. In contrast, the AG and AA genotypes of rs1884190 were not significantly associated with the risk of T2DM, even after further stratification analysis based on age or sex. Our results showed that DNER rs1861612 C to T change and variant T genotype may contribute to T2DM in a Chinese Han population.
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Nair AK, Baier LJ. Complex Genetics of Type 2 Diabetes and Effect Size: What have We Learned from Isolated Populations? Rev Diabet Stud 2016; 12:299-319. [PMID: 27111117 DOI: 10.1900/rds.2015.12.299] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Genetic studies in large outbred populations have documented a complex, highly polygenic basis for type 2 diabetes (T2D). Most of the variants currently known to be associated with T2D risk have been identified in large studies that included tens of thousands of individuals who are representative of a single major ethnic group such as European, Asian, or African. However, most of these variants have only modest effects on the risk for T2D; identification of definitive 'causal variant' or 'causative loci' is typically lacking. Studies in isolated populations offer several advantages over outbred populations despite being, on average, much smaller in sample size. For example, reduced genetic variability, enrichment of rare variants, and a more uniform environment and lifestyle, which are hallmarks of isolated populations, can reduce the complexity of identifying disease-associated genes. To date, studies in isolated populations have provided valuable insight into the genetic basis of T2D by providing both a deeper understanding of previously identified T2D-associated variants (e.g. demonstrating that variants in KCNQ1 have a strong parent-of-origin effect) or providing novel variants (e.g. ABCC8 in Pima Indians, TBC1D4 in the Greenlandic population, HNF1A in Canadian Oji-Cree). This review summarizes advancements in genetic studies of T2D in outbred and isolated populations, and provides information on whether the difference in the prevalence of T2D in different populations (Pima Indians vs. non-Hispanic Whites and non-Hispanic Whites vs. non-Hispanic Blacks) can be explained by the difference in risk allele frequencies of established T2D variants.
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Affiliation(s)
- Anup K Nair
- Diabetes Molecular Genetics Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85004, USA
| | - Leslie J Baier
- Diabetes Molecular Genetics Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85004, USA
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37
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Affiliation(s)
- Ewan R Pearson
- Division of Cardiovascular & Diabetes Medicine, School of Medicine, University of Dundee, Dundee, Scotland
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38
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Baier LJ, Muller YL, Remedi MS, Traurig M, Piaggi P, Wiessner G, Huang K, Stacy A, Kobes S, Krakoff J, Bennett PH, Nelson RG, Knowler WC, Hanson RL, Nichols CG, Bogardus C. ABCC8 R1420H Loss-of-Function Variant in a Southwest American Indian Community: Association With Increased Birth Weight and Doubled Risk of Type 2 Diabetes. Diabetes 2015; 64:4322-32. [PMID: 26246406 PMCID: PMC4657583 DOI: 10.2337/db15-0459] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 08/03/2015] [Indexed: 12/21/2022]
Abstract
Missense variants in KCNJ11 and ABCC8, which encode the KIR6.2 and SUR1 subunits of the β-cell KATP channel, have previously been implicated in type 2 diabetes, neonatal diabetes, and hyperinsulinemic hypoglycemia of infancy (HHI). To determine whether variation in these genes affects risk for type 2 diabetes or increased birth weight as a consequence of fetal hyperinsulinemia in Pima Indians, missense and common noncoding variants were analyzed in individuals living in the Gila River Indian Community. A R1420H variant in SUR1 (ABCC8) was identified in 3.3% of the population (N = 7,710). R1420H carriers had higher mean birth weights and a twofold increased risk for type 2 diabetes with a 7-year earlier onset age despite being leaner than noncarriers. One individual homozygous for R1420H was identified; retrospective review of his medical records was consistent with HHI and a diagnosis of diabetes at age 3.5 years. In vitro studies showed that the R1420H substitution decreases KATP channel activity. Identification of this loss-of-function variant in ABCC8 with a carrier frequency of 3.3% affects clinical care as homozygous inheritance and potential HHI will occur in 1/3,600 births in this American Indian population.
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Affiliation(s)
- Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Yunhua Li Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Maria Sara Remedi
- Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO
| | - Michael Traurig
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Paolo Piaggi
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Gregory Wiessner
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Ke Huang
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Alyssa Stacy
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Jonathan Krakoff
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Peter H Bennett
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Robert G Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Colin G Nichols
- Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO
| | - Clifton Bogardus
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
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Comparative Genome of GK and Wistar Rats Reveals Genetic Basis of Type 2 Diabetes. PLoS One 2015; 10:e0141859. [PMID: 26529237 PMCID: PMC4631338 DOI: 10.1371/journal.pone.0141859] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 10/14/2015] [Indexed: 12/12/2022] Open
Abstract
The Goto-Kakizaki (GK) rat, which has been developed by repeated inbreeding of glucose-intolerant Wistar rats, is the most widely studied rat model for Type 2 diabetes (T2D). However, the detailed genetic background of T2D phenotype in GK rats is still largely unknown. We report a survey of T2D susceptible variations based on high-quality whole genome sequencing of GK and Wistar rats, which have generated a list of GK-specific variations (228 structural variations, 2660 CNV amplification and 2834 CNV deletion, 1796 protein affecting SNVs or indels) by comparative genome analysis and identified 192 potential T2D-associated genes. The genes with variants are further refined with prior knowledge and public resource including variant polymorphism of rat strains, protein-protein interactions and differential gene expression. Finally we have identified 15 genetic mutant genes which include seven known T2D related genes (Tnfrsf1b, Scg5, Fgb, Sell, Dpp4, Icam1, and Pkd2l1) and eight high-confidence new candidate genes (Ldlr, Ccl2, Erbb3, Akr1b1, Pik3c2a, Cd5, Eef2k, and Cpd). Our result reveals that the T2D phenotype may be caused by the accumulation of multiple variations in GK rat, and that the mutated genes may affect biological functions including adipocytokine signaling, glycerolipid metabolism, PPAR signaling, T cell receptor signaling and insulin signaling pathways. We present the genomic difference between two closely related rat strains (GK and Wistar) and narrow down the scope of susceptible loci. It also requires further experimental study to understand and validate the relationship between our candidate variants and T2D phenotype. Our findings highlight the importance of sequenced-based comparative genomics for investigating disease susceptibility loci in inbreeding animal models.
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Chung SJ, Kim MJ, Kim J, Ryu HS, Kim YJ, Kim SY, Lee JH. Association of type 2 diabetes GWAS loci and the risk of Parkinson's and Alzheimer's diseases. Parkinsonism Relat Disord 2015; 21:1435-40. [PMID: 26499758 DOI: 10.1016/j.parkreldis.2015.10.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 09/30/2015] [Accepted: 10/07/2015] [Indexed: 12/18/2022]
Abstract
BACKGROUND Insulin and insulin-like growth factor contribute to normal brain function. Recent experimental and clinical studies showed that type 2 diabetes mellitus (T2DM) and Parkinson's disease (PD) or Alzheimer's disease (AD) share several dysregulated pathways. OBJECTIVE We aimed to investigate whether genome-wide significant loci of T2DM are associated with the risk of PD and AD as well as the severity of cognitive impairment. METHODS Study subjects were 500 PD patients, 400 AD patients, and 500 unrelated controls. We selected 32 genetic variants from 11 genes (CDC123, CDKAL1, CDKN2B, FTO, GLIS3, HHEX, IGF2BP2, KCNJ11, KCNQ1, SLC30A8, and TCF7L2) and intergenic regions based on results of recent genome-wide association studies (GWAS) in T2DM. These variants were reported to be T2DM-susceptibility loci and have been replicated in other independent studies. All association analyses were performed using logistic regression models, adjusting for age and sex. RESULTS KCNQ1 SNP rs163182 showed the strongest association with AD, but it was not significant after Bonferroni correction (OR = 1.30, 95% CI = 1.07-1.59, Pcorrected = 0.32). In PD patients, CDC123 SNP rs11257655 showed modest association with MMSE score <26, and CDKN2B SNPs (rs2383208, rs10965250, and rs10811661) showed modest association with MoCA score <26, which were not significant after Bonferroni correction. Other genetic variants had no association with the risk of PD or AD and the severity of cognitive impairment. CONCLUSIONS Our results suggest that genome-wide significant loci of T2DM play no major role in the risk and cognitive impairment of PD and AD.
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Affiliation(s)
- Sun Ju Chung
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Mi-Jung Kim
- Department of Neurology, Bobath Memorial Hospital, Seongnam, Republic of Korea
| | - Juyeon Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Ho-Sung Ryu
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young Jin Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Seong Yoon Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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Muller YL, Hanson RL, Wiessner G, Nieboer L, Kobes S, Piaggi P, Abdussamad M, Okani C, Knowler WC, Bogardus C, Baier LJ. Assessing FOXO1A as a potential susceptibility locus for type 2 diabetes and obesity in American Indians. Obesity (Silver Spring) 2015; 23:1960-5. [PMID: 26337673 PMCID: PMC4586407 DOI: 10.1002/oby.21236] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 07/03/2015] [Indexed: 01/17/2023]
Abstract
OBJECTIVE A prior genome-wide association study (GWAS) in Pima Indians identified variation within FOXO1A that modestly associated with early-onset (onset age < 25 years) type 2 diabetes (T2D). FOXO1A encodes the forkhead transcription factor involved in pancreatic β-cell growth and hypothalamic energy balance; therefore, FOXO1A was analyzed as a candidate gene for T2D and obesity in a population-based sample of 7,710 American Indians. METHODS Tag SNPs in/near FOXO1A (minor allele frequency ≥ 0.05) were analyzed for association with T2D at early onset (n = 1,060) and all ages (n = 7,710) and with insulin secretion (n = 298). SNPs were also analyzed for association with maximum body mass index (BMI) in adulthood (n = 5,918), maximum BMI z-score in childhood (n = 5,350), and % body fat (n = 555). RESULTS An intronic SNP rs2297627 associated with early-onset T2D [OR = 1.34 (1.13-1.58), P = 8.7 × 10(-4)] and T2D onset at any age [OR = 1.19 (1.09-1.30), P = 1 × 10(-4) ]. The T2D risk allele also associated with lower acute insulin secretion (β = 0.88, as a multiplier, P = 0.02). Another intronic SNP (rs1334241, D' = 0.99, r(2) = 0.49 with rs2297627) associated with maximum adulthood BMI (β = 1.02, as a multiplier, P = 3 × 10(-5)), maximum childhood BMI z-score (β = 0.08, P = 3 × 10(-4)), and % body fat (β = 0.83%, P = 0.04). CONCLUSIONS Common variation in FOXO1A may modestly affect risk for T2D and obesity in American Indians.
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Affiliation(s)
- Yunhua L Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Gregory Wiessner
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Lori Nieboer
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Paolo Piaggi
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Mahdi Abdussamad
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Chidinma Okani
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Clifton Bogardus
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes Of Health, Phoenix, Arizona, USA
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Siddiqui K, Tyagi S. Genetics, genomics and personalized medicine in Type 2 diabetes: a perspective on the Arab region. Per Med 2015; 12:417-431. [DOI: 10.2217/pme.15.11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Type 2 diabetes (T2D) is a wide-spread, chronic metabolic disorder, affecting millions of people worldwide. The epidemic of diabetes has placed a huge strain on public health, longevity and economy. T2D occurs as a result of both genetic and environmental factors and is heterogeneous in its presentation across individuals. This review gives an overview of the genetic variations identified by genome-wide association studies which predispose individuals to T2D and those which are responsible for variable drug response across patients, and the necessity to adopt a personalized approach to diabetes management. We also include a perspective on diabetes in Arabs, given the high incidence of T2D and consanguineous marriages, and the need to understand associated genetic components in this vulnerable population.
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Affiliation(s)
- Khalid Siddiqui
- Strategic Center for Diabetes Research, College of Medicine, King Saud University, P.O. Box 245, Riyadh 11411, Kingdom of Saudi Arabia
| | - Shivani Tyagi
- Freelance writer, Al Rajhi Street, Sulaimaniyah District, Riyadh, Kingdom of Saudi Arabia
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Mohlke KL, Boehnke M. Recent advances in understanding the genetic architecture of type 2 diabetes. Hum Mol Genet 2015; 24:R85-92. [PMID: 26160912 DOI: 10.1093/hmg/ddv264] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 07/06/2015] [Indexed: 12/18/2022] Open
Abstract
Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c). At the end of 2011, established loci (P < 5 × 10(-8)) totaled 55 for T2D and 32 for glycemic traits. Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05] variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations. Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci. Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered. Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.
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Affiliation(s)
- Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA and
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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Hanson RL, Rong R, Kobes S, Muller YL, Weil EJ, Curtis JM, Nelson RG, Baier LJ. Role of Established Type 2 Diabetes-Susceptibility Genetic Variants in a High Prevalence American Indian Population. Diabetes 2015; 64:2646-57. [PMID: 25667308 PMCID: PMC4477349 DOI: 10.2337/db14-1715] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 02/03/2015] [Indexed: 01/08/2023]
Abstract
Several single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM) have been identified, but there is little information on their role in populations at high risk for T2DM. We genotyped SNPs at 63 T2DM loci in 3,421 individuals from a high-risk American Indian population. Nominally significant (P < 0.05) associations were observed at nine SNPs in a direction consistent with the established association. A genetic risk score derived from all loci was strongly associated with T2DM (odds ratio 1.05 per risk allele, P = 6.2 × 10(-6)) and, in 292 nondiabetic individuals, with lower insulin secretion (by 4% per copy, P = 4.1 × 10(-6)). Genetic distances between American Indians and HapMap populations at T2DM markers did not differ significantly from genomic expectations. Analysis of U.S. national survey data suggested that 66% of the difference in T2DM prevalence between African Americans and European Americans, but none of the difference between American Indians and European Americans, was attributable to allele frequency differences at these loci. These analyses suggest that, in general, established T2DM loci influence T2DM in American Indians and that risk is mediated in part through an effect on insulin secretion. However, differences in allele frequencies do not account for the high population prevalence of T2DM.
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Affiliation(s)
- Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Rong Rong
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Yunhua Li Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - E Jennifer Weil
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jeffrey M Curtis
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Robert G Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
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Yracheta JM, Lanaspa MA, Le MT, Abdelmalak MF, Alfonso J, Sánchez-Lozada LG, Johnson RJ. Diabetes and Kidney Disease in American Indians: Potential Role of Sugar-Sweetened Beverages. Mayo Clin Proc 2015; 90:813-23. [PMID: 26046414 DOI: 10.1016/j.mayocp.2015.03.018] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Revised: 03/19/2015] [Accepted: 03/24/2015] [Indexed: 10/23/2022]
Abstract
Since the early 20th century, a marked increase in obesity, diabetes, and chronic kidney disease has occurred in the American Indian population, especially the Pima Indians of the Southwest. Here, we review the current epidemic and attempt to identify remediable causes. A search was performed using PubMed and the search terms American Indian and obesity, American Indian and diabetes, American Indian and chronic kidney disease, and American Indian and sugar or fructose, Native American, Alaska Native, First Nations, Aboriginal, Amerind, and Amerindian for American Indian for articles linking American Indians with diabetes, obesity, chronic kidney disease, and sugar; additional references were identified in these publications traced to 1900 and articles were reviewed if they were directly discussing these topics. Multiple factors are involved in the increased risk for diabetes and kidney disease in the American Indian population, including poverty, overnutrition, poor health care, high intake of sugar, and genetic mechanisms. Genetic factors may be especially important in the Pima, as historical records suggest that this group was predisposed to obesity before exposure to Western culture and diet. Exposure to sugar-sweetened beverages may also be involved in the increased risk for chronic kidney disease. In these small populations in severe health crisis, we recommend further studies to investigate the role of excess added sugar, especially sugar-sweetened beverages, as a potentially remediable risk factor.
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Affiliation(s)
- Joseph M Yracheta
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA.
| | - Miguel A Lanaspa
- Division of Renal Diseases and Hypertension, University of Colorado, Aurora, CO; Colorado Research Partners LLC, Aurora, CO
| | - MyPhuong T Le
- Division of Renal Diseases and Hypertension, University of Colorado, Aurora, CO; Colorado Research Partners LLC, Aurora, CO
| | - Manal F Abdelmalak
- Division of Gastroenterology and Hepatology, Duke University, Durham, NC
| | - Javier Alfonso
- Division of Renal Diseases and Hypertension, University of Colorado, Aurora, CO
| | - Laura G Sánchez-Lozada
- Colorado Research Partners LLC, Aurora, CO; Laboratory of Renal Physiopathology and Department of Nephrology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Richard J Johnson
- Division of Renal Diseases and Hypertension, University of Colorado, Aurora, CO; Colorado Research Partners LLC, Aurora, CO
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Yin Z, Xia K, Chung W, Sullivan PF, Zou F. Fast eQTL Analysis for Twin Studies. Genet Epidemiol 2015; 39:357-65. [PMID: 25865703 DOI: 10.1002/gepi.21900] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Revised: 01/17/2015] [Accepted: 02/23/2015] [Indexed: 12/29/2022]
Abstract
Twin data are commonly used for studying complex psychiatric disorders, and mixed effects models are one of the most popular tools for modeling dependence structures between twin pairs. However, for eQTL (expression quantitative trait loci) data where associations between thousands of transcripts and millions of single nucleotide polymorphisms need to be tested, mixed effects models are computationally inefficient and often impractical. In this paper, we propose a fast eQTL analysis approach for twin eQTL data where we randomly split twin pairs into two groups, so that within each group the samples are unrelated, and we then apply a multiple linear regression analysis separately to each group. A score statistic that automatically adjusts the (hidden) correlation between the two groups is constructed for combining the results from the two groups. The proposed method has well-controlled type I error. Compared to mixed effects models, the proposed method has similar power but drastically improved computational efficiency. We demonstrate the computational advantage of the proposed method via extensive simulations. The proposed method is also applied to a large twin eQTL data from the Netherlands Twin Register.
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Affiliation(s)
- Zhaoyu Yin
- Department of Biostatistics, University of North Carolina, Chapel Hill, North, Carolina, United States of America
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Wonil Chung
- School of Public Health, Harvard, Boston, Massachusetts, United States of America
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North, Carolina, United States of America
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Abstract
Type 2 diabetes (T2D) had long been referred to as the "geneticist's nightmare." Genome-wide association studies have fully confirmed the polygenic nature of T2D, demonstrating the role of many genes in T2D risk. The increasingly busier picture of T2D genetics is quite difficult to understand for the diabetes research community, which can create misunderstandings with geneticists, and can eventually limit both basic research and translational outcomes of these genetic discoveries. The present review wishes to lift the fog around genetics of T2D with the hope that it will foster integrated diabetes modeling approaches from genetic defects to personalized medicine.
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Affiliation(s)
- Amélie Bonnefond
- CNRS-UMR8199, Lille Pasteur Institute, Lille 59000, France; Lille University, Lille 59000, France; European Genomic Institute for Diabetes (EGID), Lille 59000, France
| | - Philippe Froguel
- CNRS-UMR8199, Lille Pasteur Institute, Lille 59000, France; Lille University, Lille 59000, France; European Genomic Institute for Diabetes (EGID), Lille 59000, France; Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London W12 0NN, UK.
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48
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Muller YL, Piaggi P, Hanson RL, Kobes S, Bhutta S, Abdussamad M, Leak-Johnson T, Kretzler M, Huang K, Weil EJ, Nelson RG, Knowler WC, Bogardus C, Baier LJ. A cis-eQTL in PFKFB2 is associated with diabetic nephropathy, adiposity and insulin secretion in American Indians. Hum Mol Genet 2015; 24:2985-96. [PMID: 25662186 DOI: 10.1093/hmg/ddv040] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Accepted: 02/02/2015] [Indexed: 01/13/2023] Open
Abstract
A prior genome-wide association study (GWAS) in Pima Indians identified a variant within PFKFB2 (rs17258746) associated with body mass index (BMI). PFKFB2 encodes 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase isoform 2, which plays a role in glucose metabolism. To follow-up on the GWAS, tag SNPs across PFKFB2 were genotyped in American Indians (AI) who had longitudinal data on BMI (n = 6839), type 2 diabetes (T2D; n = 7710), diabetic nephropathy (DN; n = 2452), % body fat (n = 555) and insulin secretion (n = 298). Two SNPs were further genotyped in urban AI to assess replication for DN (n = 864). PFKFB2 expression was measured in 201 adipose biopsies using real-time RT-PCR and 61 kidney biopsies using the Affymetrix U133 array. Two SNPs (rs17258746 and rs11120137), which capture the same signal, were associated with maximum BMI in adulthood (β = 1.02 per risk allele, P = 7.3 × 10(-4)), maximum BMI z-score in childhood (β = 0.079, P = 0.03) and % body fat in adulthood (β = 3.4%, P = 3 × 10(-7)). The adiposity-increasing allele correlated with lower PFKFB2 adipose expression (β = 0.81, P = 9.4 × 10(-4)). Lower expression of PFKFB2 further correlated with higher % body fat (r = -0.16, P = 0.02) and BMI (r = -0.17, P = 0.02). This allele was also associated with increased risk for DN in both cohorts of AI [odds ratio = 1.64 (1.32-2.02), P = 5.8 × 10(-6)], and similarly correlated with lower PFKFB2 expression in kidney glomeruli (β = 0.87, P = 0.03). The same allele was also associated with lower insulin secretion assessed by acute insulin response (β = 0.78, P = 0.03) and 30-min plasma insulin concentrations (β = 0.78, P = 1.1 × 10(-4)). Variation in PFKFB2 appears to reduce PFKFB2 expression in adipose and kidney tissues, and thereby increase risk for adiposity and DN.
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Affiliation(s)
- Yunhua L Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Paolo Piaggi
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Shujera Bhutta
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Maryam Abdussamad
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Tennille Leak-Johnson
- Department of Internal Medicine and Computational Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Matthias Kretzler
- Department of Internal Medicine and Computational Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ke Huang
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - E Jennifer Weil
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Robert G Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Clifton Bogardus
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health, Phoenix, AZ, USA and
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Laston SL, Voruganti VS, Haack K, Shah VO, Bobelu A, Bobelu J, Ghahate D, Harford AM, Paine SS, Tentori F, Cole SA, MacCluer JW, Comuzzie AG, Zager PG. Genetics of kidney disease and related cardiometabolic phenotypes in Zuni Indians: the Zuni Kidney Project. Front Genet 2015; 6:6. [PMID: 25688259 PMCID: PMC4311707 DOI: 10.3389/fgene.2015.00006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 01/08/2015] [Indexed: 12/31/2022] Open
Abstract
The objective of this study is to identify genetic factors associated with chronic kidney disease (CKD) and related cardiometabolic phenotypes among participants of the Genetics of Kidney Disease in Zuni Indians study. The study was conducted as a community-based participatory research project in the Zuni Indians, a small endogamous tribe in rural New Mexico. We recruited 998 members from 28 extended multigenerational families, ascertained through probands with CKD who had at least one sibling with CKD. We used the Illumina Infinium Human1M-Duo version 3.0 BeadChips to type 1.1 million single nucleotide polymorphisms (SNPs). Prevalence estimates for CKD, hyperuricemia, diabetes, and hypertension were 24%, 30%, 17% and 34%, respectively. We found a significant (p < 1.58 × 10-7) association for a SNP in a novel gene for serum creatinine (PTPLAD2). We replicated significant associations for genes with serum uric acid (SLC2A9), triglyceride levels (APOA1, BUD13, ZNF259), and total cholesterol (PVRL2). We found novel suggestive associations (p < 1.58 × 10-6) for SNPs in genes with systolic (OLFML2B), and diastolic blood pressure (NFIA). We identified a series of genes associated with CKD and related cardiometabolic phenotypes among Zuni Indians, a population with a high prevalence of kidney disease. Illuminating genetic variations that modulate the risk for these disorders may ultimately provide a basis for novel preventive strategies and therapeutic interventions.
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Affiliation(s)
- Sandra L Laston
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas at San Antonio Harlingen, TX, USA
| | - V Saroja Voruganti
- Department of Nutrition, University of North Carolina at Chapel Hill Kannapolis, NC, USA ; University of North Carolina Nutrition Research Institute, University of North Carolina at Chapel Hill Kannapolis, NC, USA
| | - Karin Haack
- Department of Genetics, Texas Biomedical Research Institute San Antonio, TX, USA
| | - Vallabh O Shah
- Department of Biochemistry, University of New Mexico School of Medicine Albuquerque, NM, USA
| | - Arlene Bobelu
- Department of Biochemistry, University of New Mexico School of Medicine Albuquerque, NM, USA
| | - Jeanette Bobelu
- Department of Biochemistry, University of New Mexico School of Medicine Albuquerque, NM, USA
| | - Donica Ghahate
- Department of Biochemistry, University of New Mexico School of Medicine Albuquerque, NM, USA
| | - Antonia M Harford
- Department of Biochemistry, University of New Mexico School of Medicine Albuquerque, NM, USA
| | | | | | - Shelley A Cole
- Department of Genetics, Texas Biomedical Research Institute San Antonio, TX, USA
| | - Jean W MacCluer
- Department of Genetics, Texas Biomedical Research Institute San Antonio, TX, USA
| | - Anthony G Comuzzie
- Department of Genetics, Texas Biomedical Research Institute San Antonio, TX, USA ; Southwest National Primate Research Center San Antonio, TX, USA
| | - Philip G Zager
- Dialysis Clinic, Inc., Albuquerque, NM USA ; Department of Medicine, Division of Nephrology, University of New Mexico School of Medicine Albuquerque, NM, USA
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Nair AK, Muller YL, McLean NA, Abdussamad M, Piaggi P, Kobes S, Weil EJ, Curtis JM, Nelson RG, Knowler WC, Hanson RL, Baier LJ. Variants associated with type 2 diabetes identified by the transethnic meta-analysis study: assessment in American Indians and evidence for a new signal in LPP. Diabetologia 2014; 57:2334-8. [PMID: 25112377 PMCID: PMC4180905 DOI: 10.1007/s00125-014-3351-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 07/22/2014] [Indexed: 10/29/2022]
Abstract
AIM/HYPOTHESIS A recent genome-wide trans-ancestry meta-analysis identified seven new loci associated with type 2 diabetes. We assessed the replication of the seven lead single nucleotide polymorphisms (SNPs) and evaluated these loci for additional signals in American Indians. METHODS Seven SNPs were genotyped in 7,710 individuals from a longitudinally studied American Indian population, and associations with type 2 diabetes, BMI and related phenotypes were assessed. Previous genome-wide association study (GWAS) data from these individuals were used to screen for additional type 2 diabetes signals at these loci. A variant independent of the trans-ancestry meta-analysis was identified within LPP, and its replication was assessed in an additional 3,106 urban American Indians. RESULTS SNP rs6813195 near to TMEM154 was nominally associated with type 2 diabetes (p = 0.01, OR 1.12 [95% CI 1.03, 1.22]) and adiposity: the type 2 diabetes risk allele was associated with a lower percentage body fat (β = -1.451%, p = 4.8 × 10(-4)). Another SNP, rs3130501 near to POU5F1-TCF19, was associated with BMI (β = -0.012, p = 0.004), type 2 diabetes adjusted for BMI (p = 0.02, OR 1.11 [95% CI 1.02, 1.22]), 2 h glucose concentrations (β = 0.080 mmol/l, p = 0.02) and insulin resistance estimated by homeostatic model (β = 0.039, p = 0.009). The independent variant identified at the LPP locus in our American Indian GWAS for type 2 diabetes was replicated in the additional samples (all American Indian meta-analysis, p = 8.9 × 10(-6), OR 1.29 [95% CI 1.15, 1.45]). CONCLUSIONS/INTERPRETATION For two of the seven newly identified variants, there was nominal evidence for association with type 2 diabetes and related traits in American Indians. Identification of an independent variant at the LPP locus suggests the existence of more than one type 2 diabetes signal at this locus.
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Affiliation(s)
- Anup K. Nair
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Yunhua Li Muller
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Nellie A. McLean
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Maryam Abdussamad
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Paolo Piaggi
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - E. Jennifer Weil
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Jeffrey M. Curtis
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Robert G. Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - William C. Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Robert L. Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
| | - Leslie J. Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 North 5th Street, Phoenix, AZ 85004 USA
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