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Li CL, Liu YK, Lan YY, Wang ZS. Association of education with cholelithiasis and mediating effects of cardiometabolic factors: A Mendelian randomization study. World J Clin Cases 2024; 12:4272-4288. [PMID: 39015929 PMCID: PMC11235540 DOI: 10.12998/wjcc.v12.i20.4272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/10/2024] [Accepted: 06/03/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Education, cognition, and intelligence are associated with cholelithiasis occurrence, yet which one has a prominent effect on cholelithiasis and which cardiometabolic risk factors mediate the causal relationship remain unelucidated. AIM To explore the causal associations between education, cognition, and intelligence and cholelithiasis, and the cardiometabolic risk factors that mediate the associations. METHODS Applying genome-wide association study summary statistics of primarily European individuals, we utilized two-sample multivariable Mendelian randomization to estimate the independent effects of education, intelligence, and cognition on cholelithiasis and cholecystitis (FinnGen study, 37041 and 11632 patients, respectively; n = 486484 participants) and performed two-step Mendelian randomization to evaluate 21 potential mediators and their mediating effects on the relationships between each exposure and cholelithiasis. RESULTS Inverse variance weighted Mendelian randomization results from the FinnGen consortium showed that genetically higher education, cognition, or intelligence were not independently associated with cholelithiasis and cholecystitis; when adjusted for cholelithiasis, higher education still presented an inverse effect on cholecystitis [odds ratio: 0.292 (95%CI: 0.171-0.501)], which could not be induced by cognition or intelligence. Five out of 21 cardiometabolic risk factors were perceived as mediators of the association between education and cholelithiasis, including body mass index (20.84%), body fat percentage (40.3%), waist circumference (44.4%), waist-to-hip ratio (32.9%), and time spent watching television (41.6%), while time spent watching television was also a mediator from cognition (20.4%) and intelligence to cholelithiasis (28.4%). All results were robust to sensitivity analyses. CONCLUSION Education, cognition, and intelligence all play crucial roles in the development of cholelithiasis, and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.
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Affiliation(s)
- Chang-Lei Li
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Yu-Kun Liu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Ying-Ying Lan
- Department of Oncology Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266002, Shandong Province, China
| | - Zu-Sen Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
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Camaya I, Hill M, Sais D, Tran N, O'Brien B, Donnelly S. The Parasite-Derived Peptide, FhHDM-1, Selectively Modulates miRNA Expression in β-Cells to Prevent Apoptotic Pathways Induced by Proinflammatory Cytokines. J Diabetes Res 2024; 2024:8555211. [PMID: 39022651 PMCID: PMC11254460 DOI: 10.1155/2024/8555211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/04/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
We have previously identified a parasite-derived peptide, FhHDM-1, that prevented the progression of diabetes in nonobese diabetic (NOD) mice. Disease prevention was mediated by the activation of the PI3K/Akt pathway to promote β-cell survival and metabolism without inducing proliferation. To determine the molecular mechanisms driving the antidiabetogenic effects of FhHDM-1, miRNA:mRNA interactions and in silico predictions of the gene networks were characterised in β-cells, which were exposed to the proinflammatory cytokines that mediate β-cell destruction in Type 1 diabetes (T1D), in the presence and absence of FhHDM-1. The predicted gene targets of miRNAs differentially regulated by FhHDM-1 mapped to the biological pathways that regulate β-cell biology. Six miRNAs were identified as important nodes in the regulation of PI3K/Akt signaling. Additionally, IGF-2 was identified as a miRNA gene target that mediated the beneficial effects of FhHDM-1 on β-cells. The findings provide a putative mechanism by which FhHDM-1 positively impacts β-cells to permanently prevent diabetes. As β-cell death/dysfunction underlies diabetes development, FhHDM-1 opens new therapeutic avenues.
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Affiliation(s)
- Inah Camaya
- The School of Life SciencesUniversity of Technology Sydney, Ultimo, New South Wales, Australia
| | - Meredith Hill
- School of Biomedical EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology Sydney, Ultimo, New South Wales, Australia
| | - Dayna Sais
- School of Biomedical EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology Sydney, Ultimo, New South Wales, Australia
| | - Nham Tran
- School of Biomedical EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology Sydney, Ultimo, New South Wales, Australia
| | - Bronwyn O'Brien
- The School of Life SciencesUniversity of Technology Sydney, Ultimo, New South Wales, Australia
| | - Sheila Donnelly
- The School of Life SciencesUniversity of Technology Sydney, Ultimo, New South Wales, Australia
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Schulze MB, Stefan N. Metabolically healthy obesity: from epidemiology and mechanisms to clinical implications. Nat Rev Endocrinol 2024:10.1038/s41574-024-01008-5. [PMID: 38937638 DOI: 10.1038/s41574-024-01008-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 06/29/2024]
Abstract
The concept of metabolic health, particularly in obesity, has attracted a lot of attention in the scientific community, and is being increasingly used to determine the risk of cardiovascular diseases and diabetes mellitus-related complications. This Review assesses the current understanding of metabolically healthy obesity (MHO). First, we present the historical evolution of the concept. Second, we discuss the evidence for and against its existence, the usage of different definitions of MHO over the years and the efforts made to provide novel definitions of MHO. Third, we highlight epidemiological data with regard to cardiovascular risk in MHO, which is estimated to be moderately elevated using widely used definitions of MHO when compared with individuals with metabolically healthy normal weight, but potentially not elevated using a novel definition of MHO. Fourth, we discuss novel findings about the physiological mechanisms involved in MHO and how such knowledge helps to identify and characterize both people with MHO and those with metabolically unhealthy normal weight. Finally, we address how the concept of MHO can be used for risk stratification and treatment in clinical practice.
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Affiliation(s)
- Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- German Center for Diabetes Research, Neuherberg, Germany.
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany.
| | - Norbert Stefan
- German Center for Diabetes Research, Neuherberg, Germany
- Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Centre Munich, Tübingen, Germany
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Yuan C, Shu X, Hu Z, Jie Z. Genetic prediction of the relationship between metabolic syndrome and colorectal cancer risk: a Mendelian randomization study. Diabetol Metab Syndr 2024; 16:109. [PMID: 38773583 PMCID: PMC11110320 DOI: 10.1186/s13098-024-01351-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/15/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Despite a growing body of observational studies indicating a potential link between metabolic syndrome and colorectal cancer, a definitive causal relationship has yet to be established. This study aimed to elucidate the causal relationship between metabolic syndrome and colorectal cancer through Mendelian randomization. METHODS We screened for instrumental variables associated with metabolic syndrome and its diagnostic components and with colorectal cancer through the use of a genome-wide association study database, and conducted a preliminary Mendelian randomization analysis. To corroborate the dependability of our conclusions, an additional dataset was used for replication analysis in a Mendelian randomization method, which was further integrated with a meta-analysis. RESULTS Preliminary analysis using the inverse variance weighted method revealed positive correlations between metabolic syndrome (OR [95% CI] = 1.37[1.15-1.63], P = 5.02 × 10-4) and waist circumference (OR [95% CI] = 1.39[1.21-1.61], P = 7.38 × 10-6) and the risk of colorectal cancer. Replication analysis also revealed the same results: metabolic syndrome (OR [95% CI] = 1.24[1.02-1.51], P = 0.030) and waist circumference (OR [95% CI] = 1.23[1.05-1.45], P = 0.013). The meta-analysis results further confirmed the associations between metabolic syndrome (OR [95% CI] = 1.31[1.15-1.49], P < 0.001) and waist circumference (OR [95% CI] = 1.32[1.18-1.47], P < 0.001) and colorectal cancer. CONCLUSION Our study indicated that metabolic syndrome increases the risk of CRC, particularly in patients with abdominal obesity.
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Affiliation(s)
- Chendong Yuan
- Department of General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xufeng Shu
- Department of General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Zhenzhen Hu
- Department of Anesthesiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Zhigang Jie
- Department of General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
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Zhao Y, Pang J, Fang X, Yan Z, Yang H, Deng Q, Ma T, Lv M, Li Y, Tu Z, Zou L. Causal relationships between modifiable risk factors and polycystic ovary syndrome: a comprehensive Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1348368. [PMID: 38779450 PMCID: PMC11109383 DOI: 10.3389/fendo.2024.1348368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Background Polycystic Ovary Syndrome (PCOS) is a heritable condition with an as yet unclear etiology. Various factors, such as genetics, lifestyle, environment, inflammation, insulin resistance, hyperandrogenism, iron metabolism, and gut microbiota, have been proposed as potential contributors to PCOS. Nevertheless, a systematic assessment of modifiable risk factors and their causal effects on PCOS is lacking. This study aims to establish a comprehensive profile of modifiable risk factors for PCOS by utilizing a two-sample Mendelian Randomization (MR) framework. Methods After identifying over 400 modifiable risk factors, we employed a two-sample MR approach, including the Inverse Variance Weighted (IVW) method, Weighted Median method, and MR-Egger, to investigate their causal associations with PCOS. The reliability of our estimates underwent rigorous examination through sensitivity analyses, encompassing Cochran's Q test, MR-Egger intercept analysis, leave-one-out analysis, and funnel plots. Results We discovered that factors such as smoking per day, smoking initiation, body mass index, basal metabolic rate, waist-to-hip ratio, whole body fat mass, trunk fat mass, overall health rating, docosahexaenoic acid (DHA) (22:6n-3) in blood, monounsaturated fatty acids, other polyunsaturated fatty acids apart from 18:2 in blood, omega-3 fatty acids, ratio of bisallylic groups to double bonds, omega-9 and saturated fatty acids, total lipids in medium VLDL, phospholipids in medium VLDL, phospholipids in very large HDL, triglycerides in very large HDL, the genus Oscillibacter, the genus Alistipes, the genus Ruminiclostridium 9, the class Mollicutes, and the phylum Tenericutes, showed a significant effect on heightening genetic susceptibility of PCOS. In contrast, factors including fasting insulin interaction with body mass index, sex hormone-binding globulin, iron, ferritin, SDF1a, college or university degree, years of schooling, household income, the genus Enterorhabdus, the family Bifidobacteriaceae, the order Bifidobacteriales, the class Actinobacteria, and the phylum Actinobacteria were determined to reduce risk of PCOS. Conclusion This study innovatively employs the MR method to assess causal relationships between 400 modifiable risk factors and the susceptibility of PCOS risk. It supports causal links between factors like smoking, BMI, and various blood lipid levels and PCOS. These findings offer novel insights into potential strategies for the management and treatment of PCOS.
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Affiliation(s)
- Yuheng Zhao
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
- The First School of Clinical Medicine, Graduate School of Guangdong Medical University, Zhanjiang, China
| | - Jinglin Pang
- The First School of Clinical Medicine, Graduate School of Guangdong Medical University, Zhanjiang, China
- Department of Anorectal Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xingyi Fang
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zhaohua Yan
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Haili Yang
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Qinghua Deng
- Department of Gynecology, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Tianzhong Ma
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Mengqi Lv
- Department of Pathology, Southwest Hospital of Army Medical University, Chongqing, China
| | - Yingying Li
- The First School of Clinical Medicine, Graduate School of Guangdong Medical University, Zhanjiang, China
- Department of Obstetrics and Gynecology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Ziying Tu
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
- The First School of Clinical Medicine, Graduate School of Guangdong Medical University, Zhanjiang, China
| | - Lin Zou
- Department of Reproductive Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
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Zammit M, Agius R, Fava S, Vassallo J, Pace NP. Association between a polygenic lipodystrophy genetic risk score and diabetes risk in the high prevalence Maltese population. Acta Diabetol 2024; 61:555-564. [PMID: 38280973 DOI: 10.1007/s00592-023-02230-9] [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: 09/11/2023] [Accepted: 12/23/2023] [Indexed: 01/29/2024]
Abstract
BACKGROUND Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic complications and is typically associated with obesity. A group of common variants at eleven loci are associated with insulin resistance and risk of both type 2 diabetes and coronary artery disease. These variants describe a polygenic correlate of lipodystrophy, with a high metabolic disease risk despite a low BMI. OBJECTIVES In this cross-sectional study, we sought to investigate the association of a polygenic risk score composed of eleven lipodystrophy variants with anthropometric, glycaemic and metabolic traits in an island population characterised by a high prevalence of both obesity and type 2 diabetes. METHODS 814 unrelated adults (n = 477 controls and n = 337 T2DM cases) of Maltese-Caucasian ethnicity were genotyped and associations with phenotypes explored. RESULTS A higher polygenic lipodystrophy risk score was correlated with lower adiposity indices (lower waist circumference and body mass index measurements) and higher HOMA-IR, atherogenic dyslipidaemia and visceral fat dysfunction as assessed by the visceral adiposity index in the DM group. In crude and covariate-adjusted models, individuals in the top quartile of polygenic risk had a higher T2DM risk relative to individuals in the first quartile of the risk score distribution. CONCLUSION This study consolidates the association between polygenic lipodystrophy risk alleles, metabolic syndrome parameters and T2DM risk particularly in normal-weight individuals. Our findings demonstrate that polygenic lipodystrophy risk alleles drive insulin resistance and diabetes risk independent of an increased BMI.
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Affiliation(s)
- Maria Zammit
- Department of Pathology, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Rachel Agius
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Stephen Fava
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Josanne Vassallo
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Nikolai Paul Pace
- Department of Anatomy, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta.
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Room 325, Msida, MSD2080, Malta.
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Wang SH, Huang YC, Cheng CW, Chang YW, Liao WL. Impact of the trans-ancestry polygenic risk score on type 2 diabetes risk, onset age and progression among population in Taiwan. Am J Physiol Endocrinol Metab 2024; 326:E547-E554. [PMID: 38363735 DOI: 10.1152/ajpendo.00252.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 02/18/2024]
Abstract
Type 2 diabetes (T2D) prevalence in adults at a younger age has increased but the disease status may go unnoticed. This study aimed to determine whether the onset age and subsequent diabetic complications can be attributed to the polygenic architecture of T2D in the Taiwan Han population. A total of 9,627 cases with T2D and 85,606 controls from the Taiwan Biobank were enrolled. Three diabetic polygenic risk scores (PRSs), PRS_EAS and PRS_EUR, and a trans-ancestry PRS (PRS_META), calculated using summary statistic from East Asian and European populations. The onset age was identified by linking to the National Taiwan Insurance Research Database, and the incidence of different diabetic complications during follow-up was recorded. PRS_META (7.4%) explained a higher variation for T2D status. And the higher percentile of PRS is also correlated with higher percentage of T2D family history and prediabetes status. More, the PRS was negatively associated with onset age (β = -0.91 yr), and this was more evident among males (β = -1.11 vs. -0.76 for males and females, respectively). The hazard ratio of diabetic retinopathy (DR) and diabetic foot were significantly associated with PRS_EAS and PRS_META, respectively. However, the PRS was not associated with other diabetic complications, including diabetic nephropathy, cardiovascular disease, and hypertension. Our findings indicated that diabetic PRS which combined susceptibility variants from cross-population could be used as a tool for early screening of T2D, especially for high-risk populations, such as individuals with high genetic risk, and may be associated with the risk of complications in subjects with T2D. NEW & NOTEWORTHY Our findings indicated that diabetic polygenic risk score (PRS) which combined susceptibility variants from Asian and European population affect the onset age of type 2 diabetes (T2D) and could be used as a tool for early screening of T2D, especially for individuals with high genetic risk, and may be associated with the risk of diabetic complications among people in Taiwan.
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Affiliation(s)
- Shi-Heng Wang
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan
- Department of Public Health, China Medical University, Taichung, Taiwan
| | - Yu-Chuen Huang
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chun-Wen Cheng
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Clinical Laboratory, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
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Lingyu M, Hongguang L, Mingdong Z, Na L, Yahui L. Aminotransferases as causal factors for metabolic syndrome: A bidirectional Mendelian randomization study. PLoS One 2024; 19:e0302209. [PMID: 38662679 PMCID: PMC11045112 DOI: 10.1371/journal.pone.0302209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Circulating aminotransferases (ALT and AST) have been used as biomarkers for liver injury. The causal relationships between aminotransferases and metabolic syndrome remain ambiguous. METHODS We conducted bidirectional and multivariable Mendelian randomization (MR) analyses between aminotransferases and traits related to metabolic syndrome using genetic variants obtained from genome-wide association studies (GWASs). MR-PRESSO tests were adopted to remove outliers and eliminate pleiotropy. MR steiger tests were conducted to ensure the correct direction of the causal effects. RESULTS Both aminotransferases were risk factors for essential hypertension. ALT is a risk factor for type 2 diabetes. The bidirectional causal relationship between ALT and hyperglycemia, serum lipids, and obesity was demonstrated. The effect of fasting glucose on AST was demonstrated, while type 2 diabetes did not affect AST. The effect of HDL-C on ALT and the effect of triglycerides on AST were found in multivariable MR analyses. CONCLUSIONS Our bidirectional MR analyses suggest that ALT and AST are causally associated with several metabolic syndrome-related traits, especially hypertension and type 2 diabetes. These findings highlight the potential role of aminotransferases as biomarkers and therapeutic targets for metabolic syndrome.
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Affiliation(s)
- Meng Lingyu
- Hepatobiliary and Pancreatic Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Li Hongguang
- Office of Hospital Infection Control, The Third Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China
| | - Zhang Mingdong
- Faculty of Rehabilitation Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, China
| | - Li Na
- Beihua University, Changchun, Jilin, China
| | - Liu Yahui
- Hepatobiliary and Pancreatic Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
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Wong THT, Mo JMY, Zhou M, Zhao JV, Schooling CM, He B, Luo S, Au Yeung SL. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits. Commun Biol 2024; 7:293. [PMID: 38459184 PMCID: PMC10923832 DOI: 10.1038/s42003-024-05977-1] [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] [Received: 01/02/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.
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Affiliation(s)
- Tommy H T Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jacky M Y Mo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mingqi Zhou
- Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, CA, USA
- Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Jiang Y, Zhang W, Wei M, Yin D, Tang Y, Jia W, Wang C, Guo J, Li A, Gong Y. Associations between type 1 diabetes and pulmonary tuberculosis: a bidirectional mendelian randomization study. Diabetol Metab Syndr 2024; 16:60. [PMID: 38443967 PMCID: PMC10913601 DOI: 10.1186/s13098-024-01296-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/20/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Type 1 diabetes mellitus (T1DM) has been associated with higher pulmonary tuberculosis (PTB) risk in observational studies. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect between T1DM and PTB using bidirectional Mendelian randomization (MR) analysis. METHODS Single nucleotide polymorphisms (SNPs) of T1DM and PTB were extracted from the public genetic variation summary database. In addition, GWAS data were collected to explore the causal relationship between PTB and relevant clinical traits of T1DM, including glycemic traits, lipids, and obesity. The inverse variance weighting method (IVW), weighted median method, and MR‒Egger regression were used to evaluate the causal relationship. To ensure the stability of the results, sensitivity analyses assess the robustness of the results by estimating heterogeneity and pleiotropy. RESULTS IVW showed that T1DM increased the risk of PTB (OR = 1.07, 95% CI: 1.03-1.12, P < 0.001), which was similar to the results of MR‒Egger and weighted median analyses. Moreover, we found that high-density lipoprotein cholesterol (HDL-C; OR = 1.28, 95% CI: 1.03-1.59, P = 0.026) was associated with PTB. There was no evidence of an effect of glycemic traits, remaining lipid markers, or obesity on the risk of PTB. In the reverse MR analysis, no causal relationships were detected for PTB on T1DM and its relevant clinical traits. CONCLUSION This study supported that T1DM and HDL-C were risk factors for PTB. This implies the effective role of treating T1DM and managing HDL-C in reducing the risk of PTB, which provides an essential basis for the prevention and comanagement of concurrent T1DM and PTB in clinical practice.
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Affiliation(s)
- Yijia Jiang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Wenhua Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Maoying Wei
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Dan Yin
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Yiting Tang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Weiyu Jia
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Churan Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Jingyi Guo
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Aijing Li
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China
| | - Yanbing Gong
- Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700, Beijing, China.
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11
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Bi Y, Gao Y, Xie Y, Zhou M, Liu Z, Tian S, Sun C. White blood cells and type 2 diabetes: A Mendelian randomization study. PLoS One 2024; 19:e0296701. [PMID: 38427644 PMCID: PMC10906821 DOI: 10.1371/journal.pone.0296701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/17/2023] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Observational studies have demonstrated an association between white blood cells (WBC) subtypes and type 2 diabetes (T2D) risk. However, it is unknown whether this relationship is causal. We used Mendelian randomization (MR) to investigate the causal effect of WBC subtypes on T2D and glycemic traits. METHODS The summary data for neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were extracted from a recent genome-wide association study (n = 173,480). The DIAGRAM and MAGIC consortia offered summary data pertaining to T2D and glycemic characteristics, including fasting glucose (FG) (n = 133,010), glycosylated hemoglobin (HbA1c) (n = 46,368), and homeostatic model assessment-estimated insulin resistance (HOMA-IR) (n = 37,037). A series of MR analyses (univariable MR, multivariable MR, and reverse MR) were used to investigate the causal association of different WBC subtypes with T2D and glycemic traits. RESULTS Using the inverse-variance weighted method, we found one standard deviation increases in genetically determined neutrophil [odd ratio (OR): 1.086, 95% confidence interval (CI): 0.877-1.345], lymphocyte [0.878 (0.766-1.006)], monocyte [1.010 (0.906-1.127)], eosinophil [0.995 (0.867-1.142)], and basophil [0.960 (0.763-1.207)] were not causally associated with T2D risk. These findings were consistent with the results of three pleiotropy robust methods (MR-Egger, weighted median, and mode-based estimator) and multivariable MR analyses. Reverse MR analysis provided no evidence for the reverse causation of T2D on WBC subtypes. The null causal effects of WBC subtypes on FG, HbA1c, and HOMA-IR were also identified. CONCLUSIONS WBCs play no causal role in the development of insulin resistance and T2D. The observed association between these factors may be explained by residual confounding.
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Affiliation(s)
- Yaru Bi
- Department of Endocrinology and Metabolism, First Hospital of Jilin University, Changchun, China
| | - Yuan Gao
- Department of Endocrinology and Metabolism, First Hospital of Jilin University, Changchun, China
| | - Yao Xie
- Department of Clinical Nutrition, First Hospital of Jilin University, Changchun, China
| | - Meng Zhou
- Department of Obstetrics and Gynecology, Shengli Oilfield Central Hospital, Dongying, China
| | - Zhiyuan Liu
- Department of Clinical Medicine, Yanbian University, Yanji, China
| | - Suyan Tian
- Division of Clinical Research, First Hospital of Jilin University, Changchun, China
| | - Chenglin Sun
- Department of Endocrinology and Metabolism, First Hospital of Jilin University, Changchun, China
- Department of Clinical Nutrition, First Hospital of Jilin University, Changchun, China
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12
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Jun SY, Cho S, Kim MJ, Park JW, Ryoo SB, Jeong SY, Park KJ, Shin A. Glycemic traits and colorectal cancer survival in a cohort of South Korean patients: A Mendelian randomization analysis. Cancer Med 2024; 13:e7084. [PMID: 38477501 DOI: 10.1002/cam4.7084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/13/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Clinical diabetic traits have been reported to be associated with increased colorectal cancer (CRC) risk in observational studies. Using the Mendelian randomization (MR) analysis method, we examined the causal association between glycemic traits, such as fasting glucose (FG), fasting insulin (FI), and glycosylated hemoglobin A1c (HbA1c), and survival in a cohort of CRC patients. METHODS We conducted a two-sample MR analysis among a cohort of patients with locally advanced CRC at Seoul National University Hospital. Single-nucleotide polymorphisms robustly associated (p < 5 × 10-8 ) with the three glycemic traits were obtained from the Meta-Analyses of Glucose and Insulin-related traits Consortium, Asian Genetic Epidemiology Network, and Korea Biobank Array. Three-year and 5-year overall survival (OS) and progression-free survival (PFS) were used as outcomes. Survival analysis was conducted using subgroup analysis by cancer stage and subsite in a multivariate Cox proportional hazards model adjusted for age and sex to examine whether glycemic traits affected survival. RESULTS A total of 509 patients were included in our final analysis. MR analysis showed that HbA1c levels were associated with poor 3-year OS (β = 4.20, p = 0.02). Sensitivity analyses did not show evidence of any violations of the MR assumptions. In the cancer subgroup analysis of the Cox proportional hazards model, pooled hazard ratios for FG were significantly associated with poor 3-year OS and PFS regardless of cancer stage. FI was not significantly associated with any 3-year survival endpoints. Among Stage III patients, three glycemic traits were significantly associated with both 5-year OS and PFS. Location-specific subgroup analysis showed a significant association between three glycemic traits and 5-year PFS in patients with left-sided colon cancer. FG was associated with poor 3-year survival for colon cancer but not rectal cancer. CONCLUSIONS Our results suggest that FG and HbA1c could be used to predict prognosis in CRC patients. Lifestyle and/or pharmacological interventions targeting glycemic traits could help improve survival for CRC patients.
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Affiliation(s)
- So Yon Jun
- Department of Preventative Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sooyoung Cho
- Department of Preventative Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Medical Research Center, Genomic Medicine Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Min Jung Kim
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Ji Won Park
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Seung-Bum Ryoo
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Yong Jeong
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Kyu Joo Park
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Aesun Shin
- Department of Preventative Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
- Medical Research Center, Genomic Medicine Institute, Seoul National University College of Medicine, Seoul, South Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
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13
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Emanuelsson F, Afzal S, Jørgensen NR, Nordestgaard BG, Benn M. Hyperglycaemia, diabetes and risk of fragility fractures: observational and Mendelian randomisation studies. Diabetologia 2024; 67:301-311. [PMID: 38095658 PMCID: PMC10789835 DOI: 10.1007/s00125-023-06054-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/12/2023] [Indexed: 01/16/2024]
Abstract
AIMS/HYPOTHESIS Fragility fractures may be a complication of diabetes, partly caused by chronic hyperglycaemia. We hypothesised that: (1) individuals with hyperglycaemia and diabetes have increased risk of fragility fracture; (2) hyperglycaemia is causally associated with increased risk of fragility fracture; and (3) diabetes and fragility fracture jointly associate with the highest risk of all-cause mortality. METHODS In total, 117,054 individuals from the Copenhagen City Heart Study and the Copenhagen General Population Study (the Copenhagen studies) and 390,374 individuals from UK Biobank were included for observational and one-sample Mendelian randomisation (MR) analyses. Fragility fractures were defined as fractures at the hip, spine and arm (humerus/wrist), collected from national health registries. Summary data for fasting glucose and HbA1c concentrations from 196,743 individuals in the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) were combined with data on fragility fractures from the Copenhagen studies in two-sample MR analyses. RESULTS Higher fasting and non-fasting glucose and HbA1c concentrations were associated with higher risk of any fragility fracture (p<0.001). Individuals with vs without diabetes had HRs for fragility fracture of 1.50 (95% CI 1.19, 1.88) in type 1 diabetes and 1.22 (1.13, 1.32) in type 2 diabetes. One-sample MR supported a causal association between high non-fasting glucose concentrations and increased risk of arm fracture in the Copenhagen studies and UK Biobank combined (RR 1.41 [1.11, 1.79], p=0.004), with similar results for fasting glucose and HbA1c in two-sample MR analyses (ORs 1.50 [1.03, 2.18], p=0.03; and 2.79 [1.12, 6.93], p=0.03, respectively). The corresponding MR estimates for any fragility fracture were 1.18 (1.00, 1.41), p=0.06; 1.36 (0.89, 2.09), p=0.15; and 2.47 (0.95, 6.43), p=0.06, respectively. At age 80 years, cumulative death was 27% in individuals with fragility fracture only, 54% in those with diabetes only, 67% in individuals with both conditions and 17% in those with neither. CONCLUSIONS/INTERPRETATION Hyperglycaemia and diabetes are risk factors for fragility fracture and one- and two-sample MR analyses supported a causal effect of hyperglycaemia on arm fractures. Diabetes and previous fragility fracture jointly conferred the highest risk of death in the general population.
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Affiliation(s)
- Frida Emanuelsson
- Department of Clinical Biochemistry, Copenhagen University Hospital Rigshospitalet, Centre of Diagnostic Investigation, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Shoaib Afzal
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Herlev and Gentofte, Herlev, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital Herlev and Gentofte, Herlev, Denmark
| | - Niklas R Jørgensen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Rigshospitalet, Centre of Diagnostic Investigation, Glostrup, Denmark
| | - Børge G Nordestgaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Herlev and Gentofte, Herlev, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital Herlev and Gentofte, Herlev, Denmark
| | - Marianne Benn
- Department of Clinical Biochemistry, Copenhagen University Hospital Rigshospitalet, Centre of Diagnostic Investigation, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- The Copenhagen General Population Study, Copenhagen University Hospital Herlev and Gentofte, Herlev, Denmark.
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14
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Oliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P, Chen VL, Halligan BD, Speliotes EK. Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank. Nat Genet 2024; 56:212-221. [PMID: 38200128 PMCID: PMC10923176 DOI: 10.1038/s41588-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/28/2023] [Indexed: 01/12/2024]
Abstract
Insulin resistance (IR) is a well-established risk factor for metabolic disease. The ratio of triglycerides to high-density lipoprotein cholesterol (TG:HDL-C) is a surrogate marker of IR. We conducted a genome-wide association study of the TG:HDL-C ratio in 402,398 Europeans within the UK Biobank. We identified 369 independent SNPs, of which 114 had a false discovery rate-adjusted P value < 0.05 in other genome-wide studies of IR making them high-confidence IR-associated loci. Seventy-two of these 114 loci have not been previously associated with IR. These 114 loci cluster into five groups upon phenome-wide analysis and are enriched for candidate genes important in insulin signaling, adipocyte physiology and protein metabolism. We created a polygenic-risk score from the high-confidence IR-associated loci using 51,550 European individuals in the Michigan Genomics Initiative. We identified associations with diabetes, hyperglyceridemia, hypertension, nonalcoholic fatty liver disease and ischemic heart disease. Collectively, this study provides insight into the genes, pathways, tissues and subtypes critical in IR.
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Affiliation(s)
- Antonino Oliveri
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Ryan J Rebernick
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Annapurna Kuppa
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Asmita Pant
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Yanhua Chen
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Xiaomeng Du
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Kelly C Cushing
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Hannah N Bell
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Chinmay Raut
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Ponnandy Prabhu
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Vincent L Chen
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Brian D Halligan
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Elizabeth K Speliotes
- Division of Gastroenterology and Hepatology, University of Michigan Health System, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
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15
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Duan YY, Ke X, Wu H, Yao S, Shi W, Han JZ, Zhu RJ, Wang JH, Jia YY, Yang TL, Li M, Guo Y. Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes. Diabetes Obes Metab 2024; 26:135-147. [PMID: 37779362 DOI: 10.1111/dom.15298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023]
Abstract
AIM Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study. MATERIALS AND METHODS We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis. RESULTS We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk. CONCLUSIONS Our study provided new insights into IR aetiology and avenues for therapeutic development.
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Affiliation(s)
- Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ji-Zhou Han
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ren-Jie Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jia-Hao Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ying-Ying Jia
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Meng Li
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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16
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Xu Z, Shi Y, Wei C, Li T, Wen J, Du W, Yu Y, Zhu T. Causal relationship between glycemic traits and bone mineral density in different age groups and skeletal sites: a Mendelian randomization analysis. J Bone Miner Metab 2024; 42:90-98. [PMID: 38157037 DOI: 10.1007/s00774-023-01480-5] [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: 07/31/2023] [Accepted: 10/25/2023] [Indexed: 01/03/2024]
Abstract
INTRODUCTION Previous research has confirmed that patients with type 2 diabetes mellitus tend to have higher bone mineral density (BMD), but it is unknown whether this pattern holds true for individuals without diabetes. This Mendelian randomization (MR) study aims to investigate the potential causal relationship between various glycemic trait (including fasting glucose, fasting insulin, 2-h postprandial glucose, and glycated hemoglobin) and BMD in non-diabetic individuals. The investigation focuses on different age groups (15-30, 30-45, 45-60, and 60 + years) and various skeletal sites (forearm, lumbar spine, and hip). MATERIALS AND METHODS We utilized genome-wide association study data from large population-based cohorts to identify robust instrumental variables for each glycemic traits parameter. Our primary analysis employed the inverse-variance weighted method, with sensitivity analyses conducted using MR-Egger, weighted median, MR-PRESSO, and multivariable MR methods to assess the robustness and potential horizontal pleiotropy of the study results. RESULTS Fasting insulin showed a negative modulating relationship on both lumbar spine and forearm. However, these associations were only nominally significant. No significant causal association was observed between blood glucose traits and BMD across the different age groups. The direction of fasting insulin's causal effects on BMD showed inconsistency between genders, with potentially decreased BMD in women with high fasting insulin levels and an increasing trend in BMD in men. CONCLUSIONS In the non-diabetic population, currently available evidence does not support a causal relationship between glycemic traits and BMD. However, further investigation is warranted considering the observed gender differences.
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Affiliation(s)
- Zhangmeng Xu
- Department of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Chengdu, Sichuan, China
- Department-2 of Neck Shoulder Back and Leg Pain, Department of Preventive Treatment, Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China
| | - Yushan Shi
- Department of Medical Laboratory, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Changhong Wei
- Department of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Chengdu, Sichuan, China
| | - Tao Li
- Department-2 of Neck Shoulder Back and Leg Pain, Department of Preventive Treatment, Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China
| | - Jiang Wen
- Department-2 of Neck Shoulder Back and Leg Pain, Department of Preventive Treatment, Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China
| | - Wanli Du
- Department-2 of Neck Shoulder Back and Leg Pain, Department of Preventive Treatment, Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China
| | - Yaming Yu
- Department-2 of Neck Shoulder Back and Leg Pain, Department of Preventive Treatment, Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China.
- Department of preventive treatment, Sichuan Province Orthopaedic Hospital, No. 132 West 1st Section, 1st Ring Road in Chengdu, Chengdu, Sichuan, China.
| | - Tianmin Zhu
- Department of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Chengdu, Sichuan, China.
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Ansari MA, Chauhan W, Shoaib S, Alyahya SA, Ali M, Ashraf H, Alomary MN, Al-Suhaimi EA. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. Int J Obes (Lond) 2023; 47:1179-1199. [PMID: 37696926 DOI: 10.1038/s41366-023-01369-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/04/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023]
Abstract
Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.
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Affiliation(s)
- Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia.
| | - Waseem Chauhan
- Department of Hematology, Duke University, Durham, NC, 27710, USA
| | - Shoaib Shoaib
- Department of Biochemistry, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Sami A Alyahya
- Wellness and Preventive Medicine Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia
| | - Mubashshir Ali
- USF Health Byrd Alzheimer's Center and Neuroscience Institute, Department of Molecular Medicine, Tampa, FL, USA
| | - Hamid Ashraf
- Rajiv Gandhi Center for Diabetes and Endocrinology, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Mohammad N Alomary
- Advanced Diagnostic and Therapeutic Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia.
| | - Ebtesam A Al-Suhaimi
- King Abdulaziz & his Companions Foundation for Giftedness & Creativity, Riyadh, Saudi Arabia.
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18
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Bassyouni M, Mysara M, Wohlers I, Busch H, Saber-Ayad M, El-Hadidi M. A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions. Sci Rep 2023; 13:20517. [PMID: 37993469 PMCID: PMC10665412 DOI: 10.1038/s41598-023-46844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/06/2023] [Indexed: 11/24/2023] Open
Abstract
Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations' variations, supported by genotypes' principal component analysis. Results show that the Egyptian's reference metabolic genes were clustered together with the Europeans', Ad-Mixed Americans', and South-Asians'. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations.
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Affiliation(s)
- Mahmoud Bassyouni
- Bioinformatics Group, Center for Informatics Sciences (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
- Bioscience Research Laboratories Department, MARC for Medical Services and Scientific Research, 6th of October, Jiza, Egypt
| | - Mohamed Mysara
- Bioinformatics Group, Center for Informatics Sciences (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
- Microbiology unit, Belgian Nuclear Research Centre (SCK CEN), Mol, Belgium
| | - Inken Wohlers
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology, and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Biomolecular Data Science in Pneumology, Research Center Borstel, 23845, Borstel, Germany
| | - Hauke Busch
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology, and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- University Cancer Center Schleswig-Holstein, University Hospital of Schleswig-Holstein, Campus Lübeck, 23538, Lübeck, Germany
| | - Maha Saber-Ayad
- Department of Clinical Sciences, College of Medicine, University of Sharjah, 27272, Sharjah, UAE.
- Pharmacology Department, College of Medicine, Cairo University, Cairo, 12613, Egypt.
| | - Mohamed El-Hadidi
- Bioinformatics Group, Center for Informatics Sciences (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt.
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham Dubai Campus, Dubai, United Arab Emirates.
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19
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Reim PK, Engelbrechtsen L, Gybel-Brask D, Schnurr TM, Kelstrup L, Høgdall EV, Hansen T. The influence of insulin-related genetic variants on fetal growth, fetal blood flow, and placental weight in a prospective pregnancy cohort. Sci Rep 2023; 13:19638. [PMID: 37949941 PMCID: PMC10638310 DOI: 10.1038/s41598-023-46910-6] [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: 05/26/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023] Open
Abstract
The fetal insulin hypothesis proposes that low birthweight and type 2 diabetes (T2D) in adulthood may be two phenotypes of the same genotype. In this study we aimed to explore this theory further by testing the effects of GWAS-identified genetic variants related to insulin release and sensitivity on fetal growth and blood flow from week 20 of gestation to birth and on placental weight at birth. We calculated genetic risk scores (GRS) of first phase insulin release (FPIR), fasting insulin (FI), combined insulin resistance and dyslipidaemia (IR + DLD) and insulin sensitivity (IS) in a study population of 665 genotyped newborns. Two-dimensional ultrasound measurements with estimation of fetal weight and blood flow were carried out at week 20, 25, and 32 of gestation in all 665 pregnancies. Birthweight and placental weight were registered at birth. Associations between the GRSs and fetal growth, blood flow and placental weight were investigated using linear mixed models. The FPIR GRS was directly associated with fetal growth from week 20 to birth, and both the FI GRS, IR + DLD GRS, and IS GRS were associated with placental weight at birth. Our findings indicate that insulin-related genetic variants might primarily affect fetal growth via the placenta.
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Affiliation(s)
- Pauline K Reim
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Maersk Tower, Blegdamsvej 3B, 8th Floor, 2200, Copenhagen, Denmark
| | - Line Engelbrechtsen
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Maersk Tower, Blegdamsvej 3B, 8th Floor, 2200, Copenhagen, Denmark
- Department of Gynaecology and Obstetrics, Herlev Hospital, University of Copenhagen, Herlev, Denmark
| | - Dorte Gybel-Brask
- Psycotherapeutic Outpatient Clinic, Department of Psychiatry, Ballerup Hospital, Ballerup, Denmark
| | - Theresia M Schnurr
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Maersk Tower, Blegdamsvej 3B, 8th Floor, 2200, Copenhagen, Denmark
| | - Louise Kelstrup
- Department of Gynaecology and Obstetrics, Herlev Hospital, University of Copenhagen, Herlev, Denmark
| | - Estrid V Høgdall
- Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Herlev, Denmark
| | - Torben Hansen
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Maersk Tower, Blegdamsvej 3B, 8th Floor, 2200, Copenhagen, Denmark.
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20
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Zhu X, Cheng D, Ruan K, Shen M, Ye Y. Causal relationships between type 2 diabetes, glycemic traits and keratoconus. Front Med (Lausanne) 2023; 10:1264061. [PMID: 38020157 PMCID: PMC10658005 DOI: 10.3389/fmed.2023.1264061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Purpose The relationship between diabetes mellitus and keratoconus remains controversial. This study aimed to assess the potential causal relationships among type 2 diabetes, glycemic traits, and the risk of keratoconus. Methods We used a two-sample Mendelian randomization (MR) design based on genome-wide association summary statistics. Fasting glucose, proinsulin levels, adiponectin, hemoglobin A1c (HbA1c) and type 2 diabetes with and without body mass index (BMI) adjustment were used as exposures and keratoconus was used as the outcome. MR analysis was performed using the inverse-variance weighted method, MR-Egger regression method, weighted-mode method, weighted median method and the MR-pleiotropy residual sum and outlier test (PRESSO). Results Results showed that genetically predicted lower fasting glucose were significantly associated with a higher risk of keratoconus [IVW: odds ratio (OR) = 0.382; 95% confidence interval (CI) = 0.261-0.560; p = 8.162 × 10-7]. Genetically predicted lower proinsulin levels were potentially linked to a higher risk of keratoconus (IVW: OR = 0.739; 95% CI = 0.568-0.963; p = 0.025). In addition, genetically predicted type 2 diabetes negatively correlated with keratoconus (IVW: BMI-unadjusted: OR = 0.869; 95% CI = 0.775-0.974, p = 0.016; BMI-adjusted: OR = 0.880, 95% CI = 0.789-0.982, p = 0.022). These associations were further corroborated by the evidence from all sensitivity analyses. Conclusion These findings provide genetic evidence that higher fasting glucose levels are associated with a lower risk of keratoconus. However, further studies are required to confirmed this hypothesis and to understand the mechanisms underlying this putative causative relationship.
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Affiliation(s)
| | | | | | | | - Yufeng Ye
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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21
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Kasai S, Kokubu D, Mizukami H, Itoh K. Mitochondrial Reactive Oxygen Species, Insulin Resistance, and Nrf2-Mediated Oxidative Stress Response-Toward an Actionable Strategy for Anti-Aging. Biomolecules 2023; 13:1544. [PMID: 37892226 PMCID: PMC10605809 DOI: 10.3390/biom13101544] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/12/2023] [Accepted: 10/15/2023] [Indexed: 10/29/2023] Open
Abstract
Reactive oxygen species (ROS) are produced mainly by mitochondrial respiration and function as signaling molecules in the physiological range. However, ROS production is also associated with the pathogenesis of various diseases, including insulin resistance (IR) and type 2 diabetes (T2D). This review focuses on the etiology of IR and early events, especially mitochondrial ROS (mtROS) production in insulin-sensitive tissues. Importantly, IR and/or defective adipogenesis in the white adipose tissues (WAT) is thought to increase free fatty acid and ectopic lipid deposition to develop into systemic IR. Fatty acid and ceramide accumulation mediate coenzyme Q reduction and mtROS production in IR in the skeletal muscle, while coenzyme Q synthesis downregulation is also involved in mtROS production in the WAT. Obesity-related IR is associated with the downregulation of mitochondrial catabolism of branched-chain amino acids (BCAAs) in the WAT, and the accumulation of BCAA and its metabolites as biomarkers in the blood could reliably indicate future T2D. Transcription factor NF-E2-related factor 2 (Nrf2), which regulates antioxidant enzyme expression in response to oxidative stress, is downregulated in insulin-resistant tissues. However, Nrf2 inducers, such as sulforaphane, could restore Nrf2 and target gene expression and attenuate IR in multiple tissues, including the WAT.
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Affiliation(s)
- Shuya Kasai
- Department of Stress Response Science, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Japan;
| | - Daichi Kokubu
- Department of Vegetable Life Science, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Japan;
- Diet & Well-being Research Institute, KAGOME CO., LTD., 17 Nishitomiyama, Nasushiobara 329-2762, Japan
| | - Hiroki Mizukami
- Department of Pathology and Molecular Medicine, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Japan;
| | - Ken Itoh
- Department of Stress Response Science, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Japan;
- Department of Vegetable Life Science, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki 036-8562, Japan;
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22
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Mehlig K, Foraita R, Nagrani R, Wright MN, De Henauw S, Molnár D, Moreno LA, Russo P, Tornaritis M, Veidebaum T, Lissner L, Kaprio J, Pigeot I. Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort. Diabetologia 2023; 66:1914-1924. [PMID: 37420130 PMCID: PMC10473990 DOI: 10.1007/s00125-023-05957-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/27/2023] [Indexed: 07/09/2023]
Abstract
AIMS/HYPOTHESIS There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution. METHODS Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin. RESULTS A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10-8). Two variants associated with low z-insulin (P15, p value <5×10-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin. CONCLUSIONS/INTERPRETATION The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.
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Affiliation(s)
- Kirsten Mehlig
- School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Rajini Nagrani
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Marvin N Wright
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Stefaan De Henauw
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Dénes Molnár
- Department of Paediatrics, Medical School, University of Pécs, Pécs, Hungary
| | - Luis A Moreno
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Zaragoza, Spain
- Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Paola Russo
- Institute of Food Sciences, National Research Council, Avellino, Italy
| | | | | | - Lauren Lissner
- School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Iris Pigeot
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
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23
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Reddy RK, Ardissino M, Ng FS. Type 2 Diabetes and Atrial Fibrillation: Evaluating Causal and Pleiotropic Pathways Using Mendelian Randomization. J Am Heart Assoc 2023; 12:e030298. [PMID: 37609985 PMCID: PMC10547336 DOI: 10.1161/jaha.123.030298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023]
Abstract
Background Observational associations between type 2 diabetes (T2D) and atrial fibrillation (AF) have been established, but causality remains undetermined. We performed Mendelian randomization (MR) to study causal effects of genetically predicted T2D on AF risk, independent of cardiometabolic risk factors. Methods and Results Instrumental variables included 182 uncorrelated single nucleotide polymorphisms associated with T2D at genome-wide significance (P <5×10-8). Genetic association estimates for cardiometabolic exposures were obtained from genome-wide association studies including 188 577 individuals for low-density lipoprotein-C, 694 649 individuals for body mass index, and 757 601 for systolic blood pressure. Two-sample, inverse-variance weighted MR formed the primary analyses. The MR-TRYX approach was used to dissect potential pleiotropic pathways, with multivariable MR performed to investigate cardiometabolic mediation. Genetically predicted T2D associated with increased AF liability in univariable MR (odds ratio [OR], 1.08 [95% CI, 1.02-1.13], P=0.003). Sensitivity analyses indicated potential pleiotropy, with radial MR identifying 4 outlier single nucleotide polymorphisms that were likely contributors. Phenomic scanning on MR-base and subsequent least absolute shrinkage and selection operator regression allowed prioritization of 7 candidate traits. The outlier-adjusted effect estimate remained consistent with the original inverse-variance weighted estimate (OR, 1.07 [95% CI, 1.02-1.12], P=0.008). On multivariable MR, T2D remained associated with increased AF liability after adjustment for low-density lipoprotein-C and body mass index. Following adjustment for systolic blood pressure, the relationship between T2D and AF became nonsignificant (OR, 1.04 [95% CI, 0.95-1.13], P=0.40). Conclusions These data provide novel genetic evidence that while T2D likely causally associates with AF, mediation via systolic blood pressure exists. Endeavoring to lower systolic blood pressure alongside achieving normoglycemia may provide particular benefit on AF risk in patients with T2D.
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Affiliation(s)
- Rohin K. Reddy
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
| | - Maddalena Ardissino
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
- Papworth Hospital, Cambridge Biomedical CampusCambridgeUnited Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
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24
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Lagou V, Jiang L, Ulrich A, Zudina L, González KSG, Balkhiyarova Z, Faggian A, Maina JG, Chen S, Todorov PV, Sharapov S, David A, Marullo L, Mägi R, Rujan RM, Ahlqvist E, Thorleifsson G, Gao Η, Εvangelou Ε, Benyamin B, Scott RA, Isaacs A, Zhao JH, Willems SM, Johnson T, Gieger C, Grallert H, Meisinger C, Müller-Nurasyid M, Strawbridge RJ, Goel A, Rybin D, Albrecht E, Jackson AU, Stringham HM, Corrêa IR, Farber-Eger E, Steinthorsdottir V, Uitterlinden AG, Munroe PB, Brown MJ, Schmidberger J, Holmen O, Thorand B, Hveem K, Wilsgaard T, Mohlke KL, Wang Z, Shmeliov A, den Hoed M, Loos RJF, Kratzer W, Haenle M, Koenig W, Boehm BO, Tan TM, Tomas A, Salem V, Barroso I, Tuomilehto J, Boehnke M, Florez JC, Hamsten A, Watkins H, Njølstad I, Wichmann HE, Caulfield MJ, Khaw KT, van Duijn CM, Hofman A, Wareham NJ, Langenberg C, Whitfield JB, Martin NG, Montgomery G, Scapoli C, Tzoulaki I, Elliott P, Thorsteinsdottir U, Stefansson K, Brittain EL, McCarthy MI, Froguel P, Sexton PM, Wootten D, Groop L, Dupuis J, Meigs JB, Deganutti G, Demirkan A, Pers TH, Reynolds CA, Aulchenko YS, Kaakinen MA, Jones B, Prokopenko I. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification. Nat Genet 2023; 55:1448-1461. [PMID: 37679419 PMCID: PMC10484788 DOI: 10.1038/s41588-023-01462-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 06/27/2023] [Indexed: 09/09/2023]
Abstract
Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.
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Affiliation(s)
- Vasiliki Lagou
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Anna Ulrich
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Liudmila Zudina
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Karla Sofia Gutiérrez González
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Molecular Diagnostics, Clinical Laboratory, Clinica Biblica Hospital, San José, Costa Rica
| | - Zhanna Balkhiyarova
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
| | - Alessia Faggian
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
- Laboratory for Artificial Biology, Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Jared G Maina
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
- UMR 8199-EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France
| | - Shiqian Chen
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, UK
| | - Petar V Todorov
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Sodbo Sharapov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics SD RAS, Novosibirsk, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Alessia David
- Centre for Bioinformatics and System Biology, Department of Life Sciences, Imperial College London, London, UK
| | - Letizia Marullo
- Department of Evolutionary Biology, Genetic Section, University of Ferrara, Ferrara, Italy
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Roxana-Maria Rujan
- Centre for Sports, Exercise and Life Sciences, Coventry University, Conventry, UK
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | | | - Ηe Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Εvangelos Εvangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Aaron Isaacs
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases and Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
- Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Jing Hua Zhao
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sara M Willems
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Toby Johnson
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christa Meisinger
- Epidemiology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany
| | - Rona J Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Denis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Eva Albrecht
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | - Eric Farber-Eger
- Vanderbilt Institute for Clinical and Translational Research and Vanderbilt Translational and Clinical Cardiovascular Research Center, Nashville, TN, USA
| | | | - André G Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Morris J Brown
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Julian Schmidberger
- Department of Internal Medicine I, Ulm University Medical Centre, Ulm, Germany
| | - Oddgeir Holmen
- Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim, Norway
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Kristian Hveem
- K G Jebsen Centre for Genetic Epdiemiology, Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tom Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
- Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Aleksey Shmeliov
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Wolfgang Kratzer
- Department of Internal Medicine I, Ulm University Medical Centre, Ulm, Germany
| | - Mark Haenle
- Department of Internal Medicine I, Ulm University Medical Centre, Ulm, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Bernhard O Boehm
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore and Department of Endocrinology, Tan Tock Seng Hospital, Singapore City, Singapore
| | - Tricia M Tan
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, UK
| | - Alejandra Tomas
- Section of Cell Biology and Functional Genomics, Imperial College London, London, UK
| | - Victoria Salem
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Unit, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jose C Florez
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Inger Njølstad
- Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
- Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
| | - H-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Leiden, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Netherlands Consortium for Healthy Ageing, the Hague, the Netherlands
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Grant Montgomery
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
- National Institute for Health Research Imperial College London Biomedical Research Centre, Imperial College London, London, UK
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Evan L Brittain
- Vanderbilt University Medical Center and the Vanderbilt Translational and Clinical Cardiovascular Research Center, Nashville, TN, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Philippe Froguel
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UMR 8199-EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France
| | - Patrick M Sexton
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- ARC Centre for Cryo-Electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Denise Wootten
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- ARC Centre for Cryo-Electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Leif Groop
- Lund University Diabetes Centre, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Finnish Institute for Molecular Medicine (FIMM), Helsinki University, Helsinki, Finland
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Giuseppe Deganutti
- Centre for Sports, Exercise and Life Sciences, Coventry University, Conventry, UK
| | - Ayse Demirkan
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Tune H Pers
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Christopher A Reynolds
- Centre for Sports, Exercise and Life Sciences, Coventry University, Conventry, UK
- School of Life Sciences, University of Essex, Colchester, UK
| | - Yurii S Aulchenko
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Laboratory of Glycogenomics, Institute of Cytology and Genetics SD RAS, Novosibirsk, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Marika A Kaakinen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK.
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK.
| | - Ben Jones
- Section of Endocrinology and Investigative Medicine, Imperial College London, London, UK.
| | - Inga Prokopenko
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Guildford, UK.
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK.
- UMR 8199-EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille, France.
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25
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Duan YY, Chen XF, Zhu RJ, Jia YY, Huang XT, Zhang M, Yang N, Dong SS, Zeng M, Feng Z, Zhu DL, Wu H, Jiang F, Shi W, Hu WX, Ke X, Chen H, Liu Y, Jing RH, Guo Y, Li M, Yang TL. High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes. Am J Hum Genet 2023; 110:1266-1288. [PMID: 37506691 PMCID: PMC10432149 DOI: 10.1016/j.ajhg.2023.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.
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Affiliation(s)
- Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Ren-Jie Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Ying-Ying Jia
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Xiao-Ting Huang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Meng Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Ning Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Mengqi Zeng
- Frontier Institute of Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Zhihui Feng
- Frontier Institute of Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Wei-Xin Hu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Hao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Rui-Hua Jing
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710000, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Meng Li
- Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
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26
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Vámos A, Arianti R, Vinnai BÁ, Alrifai R, Shaw A, Póliska S, Guba A, Csősz É, Csomós I, Mocsár G, Lányi C, Balajthy Z, Fésüs L, Kristóf E. Human abdominal subcutaneous-derived active beige adipocytes carrying FTO rs1421085 obesity-risk alleles exert lower thermogenic capacity. Front Cell Dev Biol 2023; 11:1155673. [PMID: 37416800 PMCID: PMC10321670 DOI: 10.3389/fcell.2023.1155673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/26/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction: White adipocytes store lipids, have a large lipid droplet and few mitochondria. Brown and beige adipocytes, which produce heat, are characterized by high expression of uncoupling protein (UCP) 1, multilocular lipid droplets, and large amounts of mitochondria. The rs1421085 T-to-C single-nucleotide polymorphism (SNP) of the human FTO gene interrupts a conserved motif for ARID5B repressor, resulting in adipocyte type shift from beige to white. Methods: We obtained abdominal subcutaneous adipose tissue from donors carrying FTO rs1421085 TT (risk-free) or CC (obesity-risk) genotypes, isolated and differentiated their preadipocytes into beige adipocytes (driven by the PPARγ agonist rosiglitazone for 14 days), and activated them with dibutyryl-cAMP for 4 hours. Then, either the same culture conditions were applied for additional 14 days (active beige adipocytes) or it was replaced by a white differentiation medium (inactive beige adipocytes). White adipocytes were differentiated by their medium for 28 days. Results and Discussion: RNA-sequencing was performed to investigate the gene expression pattern of adipocytes carrying different FTO alleles and found that active beige adipocytes had higher brown adipocyte content and browning capacity compared to white or inactive beige ones when the cells were obtained from risk-free TT but not from obesity-risk CC genotype carriers. Active beige adipocytes carrying FTO CC had lower thermogenic gene (e.g., UCP1, PM20D1, CIDEA) expression and thermogenesis measured by proton leak respiration as compared to TT carriers. In addition, active beige adipocytes with CC alleles exerted lower expression of ASC-1 neutral amino acid transporter (encoded by SLC7A10) and less consumption of Ala, Ser, Cys, and Gly as compared to risk-free carriers. We did not observe any influence of the FTO rs1421085 SNP on white and inactive beige adipocytes highlighting its exclusive and critical effect when adipocytes were activated for thermogenesis.
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Affiliation(s)
- Attila Vámos
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Molecular Cell and Immune Biology, University of Debrecen, Debrecen, Hungary
| | - Rini Arianti
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Universitas Muhammadiyah Bangka Belitung, Pangkalanbaru, Indonesia
| | - Boglárka Ágnes Vinnai
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Molecular Cell and Immune Biology, University of Debrecen, Debrecen, Hungary
| | - Rahaf Alrifai
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Molecular Cell and Immune Biology, University of Debrecen, Debrecen, Hungary
| | - Abhirup Shaw
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Szilárd Póliska
- Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Andrea Guba
- Doctoral School of Molecular Cell and Immune Biology, University of Debrecen, Debrecen, Hungary
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Éva Csősz
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - István Csomós
- Department of Biophysics and Cell Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Gábor Mocsár
- Department of Biophysics and Cell Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | | | - Zoltán Balajthy
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - László Fésüs
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Endre Kristóf
- Laboratory of Cell Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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27
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Martínez-Pinna J, Sempere-Navarro R, Medina-Gali RM, Fuentes E, Quesada I, Sargis RM, Trasande L, Nadal A. Endocrine disruptors in plastics alter β-cell physiology and increase the risk of diabetes mellitus. Am J Physiol Endocrinol Metab 2023; 324:E488-E505. [PMID: 37134142 PMCID: PMC10228669 DOI: 10.1152/ajpendo.00068.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/05/2023]
Abstract
Plastic pollution breaks a planetary boundary threatening wildlife and humans through its physical and chemical effects. Of the latter, the release of endocrine disrupting chemicals (EDCs) has consequences on the prevalence of human diseases related to the endocrine system. Bisphenols (BPs) and phthalates are two groups of EDCs commonly found in plastics that migrate into the environment and make low-dose human exposure ubiquitous. Here we review epidemiological, animal, and cellular studies linking exposure to BPs and phthalates to altered glucose regulation, with emphasis on the role of pancreatic β-cells. Epidemiological studies indicate that exposure to BPs and phthalates is associated with diabetes mellitus. Studies in animal models indicate that treatment with doses within the range of human exposure decreases insulin sensitivity and glucose tolerance, induces dyslipidemia, and modifies functional β-cell mass and serum levels of insulin, leptin, and adiponectin. These studies reveal that disruption of β-cell physiology by EDCs plays a key role in impairing glucose homeostasis by altering the mechanisms used by β-cells to adapt to metabolic stress such as chronic nutrient excess. Studies at the cellular level demonstrate that BPs and phthalates modify the same biochemical pathways involved in adaptation to chronic excess fuel. These include changes in insulin biosynthesis and secretion, electrical activity, expression of key genes, and mitochondrial function. The data summarized here indicate that BPs and phthalates are important risk factors for diabetes mellitus and support a global effort to decrease plastic pollution and human exposure to EDCs.
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Affiliation(s)
- Juan Martínez-Pinna
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- Departamento de Fisiología, Genética y Microbiología, Universidad de Alicante, Alicante, Spain
| | - Roberto Sempere-Navarro
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Regla M Medina-Gali
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Esther Fuentes
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Ivan Quesada
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Robert M Sargis
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States
| | - Leonardo Trasande
- Department of Pediatrics, New York University Grossman School of Medicine, New York, New York, United States
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, United States
- Wagner School of Public Service, New York University, New York, New York, United States
| | - Angel Nadal
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Elche, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
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28
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Tan Q, Yang S, Wang B, Wang M, Yu L, Liang R, Liu W, Song J, Guo Y, Zhou M, Chen W. Gene-environment interaction in long-term effects of polychlorinated biphenyls exposure on glucose homeostasis and type 2 diabetes: The modifying effects of genetic risk and lifestyle. JOURNAL OF HAZARDOUS MATERIALS 2023; 457:131757. [PMID: 37276697 DOI: 10.1016/j.jhazmat.2023.131757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 06/07/2023]
Abstract
The longitudinal relationships of polychlorinated biphenyls (PCBs) exposure with glucose homeostasis and type 2 diabetes (T2D) risk among Chinese population have not been assessed, and interactions of PCB exposure with genetic susceptibility and lifestyle are unclear. In this prospective cohort study, fasting plasma glucose (FPG) and insulin (FPI) and seven serum indicator-PCBs were measured for each participant. We constructed polygenic risk score (PRS) of T2D and healthy lifestyle score. Each 1-unit increment of ln-transformed PCB-118 was related with a 0.141 mmol/L, 11.410 pmol/L, 0.661, and 74.5% increase in FPG, FPI, homeostasis model assessment of insulin resistance, and incident T2D risk over 6 years, respectively. Each 1-unit increment in T2D-PRS was related with a 0.169 mmol/L elevation of FPG and 65.5% elevation of incident T2D risk during 6 years. Compared with participants who had low T2D-PRS and low PCB-118, participants with high T2D-PRS and high PCB-118 showed a significant increase in FPG (0.162 mmol/L; P for interaction <0.001) and incident T2D risk [hazard ratio (HR)= 2.222]. Participants with low PCB-118, low PRS, and healthy lifestyle had the lowest incident T2D risk (HR=0.232). Our findings highlighted the significance of reducing PCB exposure and improvement in lifestyle for T2D prevention and management, especially for individuals with higher genetic risk of T2D.
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Affiliation(s)
- Qiyou Tan
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Shijie Yang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, Hubei, China
| | - Bin Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Mengyi Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Linling Yu
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Ruyi Liang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Wei Liu
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Jiahao Song
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Yanjun Guo
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Min Zhou
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Weihong Chen
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
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29
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Williamson A, Norris DM, Yin X, Broadaway KA, Moxley AH, Vadlamudi S, Wilson EP, Jackson AU, Ahuja V, Andersen MK, Arzumanyan Z, Bonnycastle LL, Bornstein SR, Bretschneider MP, Buchanan TA, Chang YC, Chuang LM, Chung RH, Clausen TD, Damm P, Delgado GE, de Mello VD, Dupuis J, Dwivedi OP, Erdos MR, Fernandes Silva L, Frayling TM, Gieger C, Goodarzi MO, Guo X, Gustafsson S, Hakaste L, Hammar U, Hatem G, Herrmann S, Højlund K, Horn K, Hsueh WA, Hung YJ, Hwu CM, Jonsson A, Kårhus LL, Kleber ME, Kovacs P, Lakka TA, Lauzon M, Lee IT, Lindgren CM, Lindström J, Linneberg A, Liu CT, Luan J, Aly DM, Mathiesen E, Moissl AP, Morris AP, Narisu N, Perakakis N, Peters A, Prasad RB, Rodionov RN, Roll K, Rundsten CF, Sarnowski C, Savonen K, Scholz M, Sharma S, Stinson SE, Suleman S, Tan J, Taylor KD, Uusitupa M, Vistisen D, Witte DR, Walther R, Wu P, Xiang AH, Zethelius B, Ahlqvist E, Bergman RN, Chen YDI, Collins FS, Fall T, Florez JC, Fritsche A, Grallert H, Groop L, Hansen T, Koistinen HA, Komulainen P, Laakso M, Lind L, Loeffler M, März W, Meigs JB, Raffel LJ, Rauramaa R, Rotter JI, Schwarz PEH, Stumvoll M, Sundström J, Tönjes A, Tuomi T, Tuomilehto J, Wagner R, Barroso I, Walker M, Grarup N, Boehnke M, Wareham NJ, Mohlke KL, Wheeler E, O'Rahilly S, Fazakerley DJ, Langenberg C. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake. Nat Genet 2023; 55:973-983. [PMID: 37291194 PMCID: PMC7614755 DOI: 10.1038/s41588-023-01408-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 04/26/2023] [Indexed: 06/10/2023]
Abstract
Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.
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Affiliation(s)
- Alice Williamson
- MRC Epidemiology Unit Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Dougall M Norris
- Metabolic Research Laboratories Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Emma P Wilson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne U Jackson
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Vasudha Ahuja
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Zorayr Arzumanyan
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lori L Bonnycastle
- Center for Precision Health Research National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stefan R Bornstein
- Department of Internal Medicine III, Metabolic and Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, Germany
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Maxi P Bretschneider
- Department of Internal Medicine III, Metabolic and Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, Germany
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine USC, Los Angeles, CA, USA
| | - Yi-Cheng Chang
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei City, Taiwan
- Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | - Lee-Ming Chuang
- Department of Internal Medicine, Division of Endocrinology and Metabolism, National Taiwan University Hospital, Taipei City, Taiwan
| | - Ren-Hua Chung
- Institute of Population Health Sciences, National Health Research Institutes, Toufen, Taiwan
| | - Tine D Clausen
- Department of Gynecology and Obstetrics, Nordsjaellands Hospital, Hillerød, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Damm
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Pregnant Women with Diabetes, Rigshospitalet, Copenhagen, Denmark
- Department of Obstetrics, Rigshospitalet, Copenhagen, Denmark
| | - Graciela E Delgado
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Vanessa D de Mello
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
| | - Om P Dwivedi
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Michael R Erdos
- Center for Precision Health Research National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Christian Gieger
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Gad Hatem
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Sandra Herrmann
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- Department of Internal Medicine III, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - Kurt Højlund
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Katrin Horn
- Medical Faculty Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, Medical Faculty, Leipzig, Germany
| | - Willa A Hsueh
- Internal Medicine, Endocrinology, Diabetes and Metabolism, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Yi-Jen Hung
- Institute of Preventive Medicine, National Defense Medical Center, New Taipei City, Taiwan
| | - Chii-Min Hwu
- Department of Medicine Section of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei City, Taiwan
| | - Anna Jonsson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line L Kårhus
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Marcus E Kleber
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Marie Lauzon
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Department of Internal Medicine Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung City, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung City, Taiwan
| | - Cecilia M Lindgren
- Big Data Institute Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK
- Broad Institute, Cambridge, MA, USA
| | | | - Allan Linneberg
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Dina Mansour Aly
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Elisabeth Mathiesen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Pregnant Women with Diabetes, Rigshospitalet, Copenhagen, Denmark
- Department of Endocrinology Rigshospitalet, Copenhagen, Denmark
| | - Angela P Moissl
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany
- Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD) Halle-Jena, Jena, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Narisu Narisu
- Center for Precision Health Research National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nikolaos Perakakis
- Department of Internal Medicine III, Metabolic and Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, Germany
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Rashmi B Prasad
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Roman N Rodionov
- Department of Internal Medicine III, University Center for Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, Germany
- College of Medicine and Public Health, Flinders University and Flinders Medical Centre, Adelaide, Australia
| | - Kathryn Roll
- Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Carsten F Rundsten
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center, Houston, TX, USA
| | - Kai Savonen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Markus Scholz
- Medical Faculty Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, Medical Faculty, Leipzig, Germany
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising-Weihenstephan, München, Germany
| | - Sara E Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sufyan Suleman
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jingyi Tan
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kent D Taylor
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matti Uusitupa
- Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Dorte Vistisen
- Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Daniel R Witte
- Steno Diabetes Center Aarhus, Aarhus, Denmark
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Romy Walther
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- Department of Internal Medicine III, Pathobiochemistry, Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Anny H Xiang
- Research and Evaluation, Division of Biostatistics, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Björn Zethelius
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Emma Ahlqvist
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Francis S Collins
- Center for Precision Health Research National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andreas Fritsche
- Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany
| | - Harald Grallert
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Leif Groop
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Lund, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Heikki A Koistinen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Markus Loeffler
- Medical Faculty Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, Medical Faculty, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany
| | - James B Meigs
- Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany
- Clinical Sciences Malmö, Genomics, Diabetes and Endocrinology, Lund University, Lund, Sweden
- Department of Medicine Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Leslie J Raffel
- Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, CA, USA
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Peter E H Schwarz
- Helmholtz Zentrum München Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
- Department of Internal Medicine III, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Johan Sundström
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Jaakko Tuomilehto
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Robert Wagner
- Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
| | - Eleanor Wheeler
- MRC Epidemiology Unit Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Stephen O'Rahilly
- Metabolic Research Laboratories Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK.
| | - Daniel J Fazakerley
- Metabolic Research Laboratories Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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30
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Hanyuda A, Goto A, Katagiri R, Koyanagi YN, Nakatochi M, Sutoh Y, Nakano S, Oze I, Ito H, Yamaji T, Sawada N, Iwagami M, Kadota A, Koyama T, Katsuura-Kamano S, Ikezaki H, Tanaka K, Takezaki T, Imoto I, Suzuki M, Momozawa Y, Takeuchi K, Narita A, Hozawa A, Kinoshita K, Shimizu A, Tanno K, Matsuo K, Tsugane S, Wakai K, Sasaki M, Yamamoto M, Iwasaki M. Investigating the association between glycaemic traits and colorectal cancer in the Japanese population using Mendelian randomisation. Sci Rep 2023; 13:7052. [PMID: 37120602 PMCID: PMC10148817 DOI: 10.1038/s41598-023-33966-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 04/21/2023] [Indexed: 05/01/2023] Open
Abstract
Observational studies suggest that abnormal glucose metabolism and insulin resistance contribute to colorectal cancer; however, the causal association remains unknown, particularly in Asian populations. A two-sample Mendelian randomisation analysis was performed to determine the causal association between genetic variants associated with elevated fasting glucose, haemoglobin A1c (HbA1c), and fasting C-peptide and colorectal cancer risk. In the single nucleotide polymorphism (SNP)-exposure analysis, we meta-analysed study-level genome-wide associations of fasting glucose (~ 17,289 individuals), HbA1c (~ 52,802 individuals), and fasting C-peptide (1,666 individuals) levels from the Japanese Consortium of Genetic Epidemiology studies. The odds ratios of colorectal cancer were 1.01 (95% confidence interval [CI], 0.99-1.04, P = 0.34) for fasting glucose (per 1 mg/dL increment), 1.02 (95% CI, 0.60-1.73, P = 0.95) for HbA1c (per 1% increment), and 1.47 (95% CI, 0.97-2.24, P = 0.06) for fasting C-peptide (per 1 log increment). Sensitivity analyses, including Mendelian randomisation-Egger and weighted-median approaches, revealed no significant association between glycaemic characteristics and colorectal cancer (P > 0.20). In this study, genetically predicted glycaemic characteristics were not significantly related to colorectal cancer risk. The potential association between insulin resistance and colorectal cancer should be validated in further studies.
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Grants
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- 28-A-19 and 31-A-18 National Cancer Center Research and Development Fund
- No. 16H06277[CoBia] Japan Society for the Promotion of Science (JSPS) KAKENHI Grant
- No. 16H06277[CoBia] Japan Society for the Promotion of Science (JSPS) KAKENHI Grant
- No. 16H06277[CoBia] Japan Society for the Promotion of Science (JSPS) KAKENHI Grant
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- JP20km0105001, JP20km0105002, JP20km0105003, JP20km0105004 Japan Agency for Medical Research and Development
- 15ck0106095h0002, 16ck0106095h0003, and 17ck0106266h001 Japan Agency for Medical Research and Development
- a Grant-in-Aid for Cancer Research Ministry of Health, Labour and Welfare
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Affiliation(s)
- Akiko Hanyuda
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan
| | - Atsushi Goto
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
- Department of Health Data Science, Graduate School of Data Science, Yokohama City University, 22-2 Seto, Kanazawa-Ku, Yokohama, 236-0027, Japan.
| | - Ryoko Katagiri
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Yuriko N Koyanagi
- Division of Cancer Information and Control, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank, Morioka, Iwate, Japan
| | - Shiori Nakano
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center, Nagoya, Aichi, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Masao Iwagami
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Aya Kadota
- NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Shiga, Japan
| | - Teruhide Koyama
- Department of Epidemiology for Community Health and Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Keitaro Tanaka
- Department of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Toshiro Takezaki
- Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Issei Imoto
- Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Midori Suzuki
- Core Facilities, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Kenji Takeuchi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Akira Narita
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Atsushi Hozawa
- Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Kengo Kinoshita
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank, Morioka, Iwate, Japan
| | - Kozo Tanno
- Division of Clinical Research and Epidemiology, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Morioka, Iwate, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center, Nagoya, Aichi, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
- National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Morioka, Iwate, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
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31
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Vejrazkova D, Vankova M, Lukasova P, Hill M, Vcelak J, Tura A, Chocholova D, Bendlova B. The Glycemic Curve during the Oral Glucose Tolerance Test: Is It Only Indicative of Glycoregulation? Biomedicines 2023; 11:biomedicines11051278. [PMID: 37238949 DOI: 10.3390/biomedicines11051278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/17/2023] [Accepted: 04/22/2023] [Indexed: 05/28/2023] Open
Abstract
The shape of the glycemic curve during the oral glucose tolerance test (OGTT), interpreted in the correct context, can predict impaired glucose homeostasis. Our aim was to reveal information inherent in the 3 h glycemic trajectory that is of physiological relevance concerning the disruption of glycoregulation and complications beyond, such as components of metabolic syndrome (MS). METHODS In 1262 subjects (1035 women, 227 men) with a wide range of glucose tolerance, glycemic curves were categorized into four groups: monophasic, biphasic, triphasic, and multiphasic. The groups were then monitored in terms of anthropometry, biochemistry, and timing of the glycemic peak. RESULTS Most curves were monophasic (50%), then triphasic (28%), biphasic (17.5%), and multiphasic (4.5%). Men had more biphasic curves than women (33 vs. 14%, respectively), while women had more triphasic curves than men (30 vs. 19%, respectively) (p < 0.01). Monophasic curves were more frequent in people with impaired glucose regulation and MS compared to bi-, tri-, and multiphasic ones. Peak delay was the most common in monophasic curves, in which it was also most strongly associated with the deterioration of glucose tolerance and other components of MS. CONCLUSION The shape of the glycemic curve is gender dependent. A monophasic curve is associated with an unfavorable metabolic profile, especially when combined with a delayed peak.
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Affiliation(s)
| | | | - Petra Lukasova
- Institute of Endocrinology, 110 00 Prague, Czech Republic
| | - Martin Hill
- Institute of Endocrinology, 110 00 Prague, Czech Republic
| | - Josef Vcelak
- Institute of Endocrinology, 110 00 Prague, Czech Republic
| | - Andrea Tura
- Institute of Neuroscience, National Research Council (CNR), 351 22 Padova, Italy
| | - Denisa Chocholova
- Faculty of Science, Charles University, 128 00 Prague, Czech Republic
| | - Bela Bendlova
- Institute of Endocrinology, 110 00 Prague, Czech Republic
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32
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Lin Z, Xue H, Pan W. Robust multivariable Mendelian randomization based on constrained maximum likelihood. Am J Hum Genet 2023; 110:592-605. [PMID: 36948188 PMCID: PMC10119150 DOI: 10.1016/j.ajhg.2023.02.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/27/2023] [Indexed: 03/24/2023] Open
Abstract
Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haoran Xue
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
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33
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Nguyen A, Khafagy R, Gao Y, Meerasa A, Roshandel D, Anvari M, Lin B, Cherney DZI, Farkouh ME, Shah BR, Paterson AD, Dash S. Association Between Obesity and Chronic Kidney Disease: Multivariable Mendelian Randomization Analysis and Observational Data From a Bariatric Surgery Cohort. Diabetes 2023; 72:496-510. [PMID: 36657976 DOI: 10.2337/db22-0696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/10/2023] [Indexed: 01/21/2023]
Abstract
Obesity is postulated to independently increase chronic kidney disease (CKD), even after adjusting for type 2 diabetes (T2D) and hypertension. Dysglycemia below T2D thresholds, frequently seen with obesity, also increases CKD risk. Whether obesity increases CKD independent of dysglycemia and hypertension is unknown and likely influences the optimal weight loss (WL) needed to reduce CKD. T2D remission rates plateau with 20-25% WL after bariatric surgery (BS), but further WL increases normoglycemia and normotension. We undertook bidirectional inverse variance weighted Mendelian randomization (IVWMR) to investigate potential independent causal associations between increased BMI and estimated glomerular filtration rate (eGFR) in CKD (CKDeGFR) (<60 mL/min/1.73 m2) and microalbuminuria (MA). In 5,337 BS patients, we assessed whether WL influences >50% decline in eGFR (primary outcome) or CKD hospitalization (secondary outcome), using <20% WL as a comparator. IVWMR results suggest that increased BMI increases CKDeGFR (b = 0.13, P = 1.64 × 10-4; odds ratio [OR] 1.14 [95% CI 1.07, 1.23]) and MA (b = 0.25; P = 2.14 × 10-4; OR 1.29 [1.13, 1.48]). After adjusting for hypertension and fasting glucose, increased BMI did not significantly increase CKDeGFR (b = -0.02; P = 0.72; OR 0.98 [0.87, 1.1]) or MA (b = 0.19; P = 0.08; OR 1.21 [0.98, 1.51]). Post-BS WL significantly reduced the primary outcome with 30 to <40% WL (hazard ratio [HR] 0.53 [95% CI 0.32, 0.87]) but not 20 to <30% WL (HR 0.72 [0.44, 1.2]) and ≥40% WL (HR 0.73 [0.41, 1.30]). For CKD hospitalization, progressive reduction was seen with increased WL, which was significant for 30 to <40% WL (HR 0.37 [0.17, 0.82]) and ≥40% WL (HR 0.24 [0.07, 0.89]) but not 20 to <30% WL (HR 0.60 [0.29, 1.23]). The data suggest that obesity is likely not an independent cause of CKD. WL thresholds previously associated with normotension and normoglycemia, likely causal mediators, may reduce CKD after BS.
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Affiliation(s)
- Anthony Nguyen
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Rana Khafagy
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Yiding Gao
- Division of Endocrinology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Ameena Meerasa
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Mehran Anvari
- Department of Surgery, St. Joseph's Hospital, McMaster University, Hamilton, Ontario, Canada
| | - Boxi Lin
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - David Z I Cherney
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Michael E Farkouh
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Baiju R Shah
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
| | - Andrew D Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Satya Dash
- Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada
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34
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Mattis KK, Krentz NAJ, Metzendorf C, Abaitua F, Spigelman AF, Sun H, Ikle JM, Thaman S, Rottner AK, Bautista A, Mazzaferro E, Perez-Alcantara M, Manning Fox JE, Torres JM, Wesolowska-Andersen A, Yu GZ, Mahajan A, Larsson A, MacDonald PE, Davies B, den Hoed M, Gloyn AL. Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression. Diabetologia 2023; 66:674-694. [PMID: 36633628 PMCID: PMC9947029 DOI: 10.1007/s00125-022-05856-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/17/2022] [Indexed: 01/13/2023]
Abstract
AIMS/HYPOTHESIS Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycaemic traits. However, little is known about the function of the zinc finger transcription factor Ras-responsive element binding protein 1 (RREB1) in glucose homeostasis or how changes in its expression and/or function influence diabetes risk. METHODS A zebrafish model lacking rreb1a and rreb1b was used to study the effect of RREB1 loss in vivo. Using transcriptomic and cellular phenotyping of a human beta cell model (EndoC-βH1) and human induced pluripotent stem cell (hiPSC)-derived beta-like cells, we investigated how loss of RREB1 expression and activity affects pancreatic endocrine cell development and function. Ex vivo measurements of human islet function were performed in donor islets from carriers of RREB1 type 2 diabetes risk alleles. RESULTS CRISPR/Cas9-mediated loss of rreb1a and rreb1b function in zebrafish supports an in vivo role for the transcription factor in beta cell mass, beta cell insulin expression and glucose levels. Loss of RREB1 also reduced insulin gene expression and cellular insulin content in EndoC-βH1 cells and impaired insulin secretion under prolonged stimulation. Transcriptomic analysis of RREB1 knockdown and knockout EndoC-βH1 cells supports RREB1 as a novel regulator of genes involved in insulin secretion. In vitro differentiation of RREB1KO/KO hiPSCs revealed dysregulation of pro-endocrine cell genes, including RFX family members, suggesting that RREB1 also regulates genes involved in endocrine cell development. Human donor islets from carriers of type 2 diabetes risk alleles in RREB1 have altered glucose-stimulated insulin secretion ex vivo, consistent with a role for RREB1 in regulating islet cell function. CONCLUSIONS/INTERPRETATION Together, our results indicate that RREB1 regulates beta cell function by transcriptionally regulating the expression of genes involved in beta cell development and function.
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Affiliation(s)
- Katia K Mattis
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Nicole A J Krentz
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Christoph Metzendorf
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Fernando Abaitua
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Aliya F Spigelman
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Han Sun
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Jennifer M Ikle
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Swaraj Thaman
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Antje K Rottner
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Austin Bautista
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Eugenia Mazzaferro
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | | | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Jason M Torres
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Grace Z Yu
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Anders Larsson
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Benjamin Davies
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Marcel den Hoed
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA.
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK.
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35
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Zulueta M, Gallardo-Rincón H, Martinez-Juarez LA, Lomelin-Gascon J, Ortega-Montiel J, Montoya A, Mendizabal L, Arregi M, Martinez-Martinez MDLA, Camarillo Romero EDS, Mendieta Zerón H, Garduño García JDJ, Simón L, Tapia-Conyer R. Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms. BMJ Open Diabetes Res Care 2023; 11:11/2/e003046. [PMID: 37085278 PMCID: PMC10124192 DOI: 10.1136/bmjdrc-2022-003046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/01/2023] [Indexed: 04/23/2023] Open
Abstract
INTRODUCTION Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables. RESEARCH DESIGN AND METHODS Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort. RESULTS Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively. CONCLUSIONS We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.
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Affiliation(s)
- Mirella Zulueta
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Héctor Gallardo-Rincón
- Health Sciences University Center, University of Guadalajara, Guadalajara, Mexico
- Operative Solutions, Carlos Slim Foundation, Mexico City, Mexico
| | | | | | | | | | - Leire Mendizabal
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Maddi Arregi
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | | | | | - Hugo Mendieta Zerón
- Faculty of Medicine, Autonomous University of the State of Mexico, Toluca, Mexico
| | | | - Laureano Simón
- Research and Development Department, Patia Europe, San Sebastian, Spain
| | - Roberto Tapia-Conyer
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
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36
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Mina T, Yew YW, Ng HK, Sadhu N, Wansaicheong G, Dalan R, Low DYW, Lam BCC, Riboli E, Lee ES, Ngeow J, Elliott P, Griva K, Loh M, Lee J, Chambers J. Adiposity impacts cognitive function in Asian populations: an epidemiological and Mendelian Randomization study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 33:100710. [PMID: 36851942 PMCID: PMC9957736 DOI: 10.1016/j.lanwpc.2023.100710] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/19/2023] [Accepted: 01/26/2023] [Indexed: 02/15/2023]
Abstract
Background Obesity and related metabolic disturbances including diabetes, hypertension and hyperlipidemia predict future cognitive decline. Asia has a high prevalence of both obesity and metabolic disease, potentially amplifying the future burden of dementia in the region. We aimed to investigate the impact of adiposity and metabolic risk on cognitive function in Asian populations, using an epidemiological analysis and a two-sample Mendelian Randomization (MR) study. Methods The Health for Life in Singapore (HELIOS) Study is a population-based cohort of South-East-Asian men and women in Singapore, aged 30-84 years. We analyzed 8769 participants with metabolic and cognitive data collected between 2018 and 2021. Whole-body fat mass was quantified with Dual X-Ray Absorptiometry (DEXA). Cognition was assessed using a computerized cognitive battery. An index of general cognition ' g ' was derived through factor analysis. We tested the relationship of fat mass indices and metabolic measures with ' g ' using regression approaches. We then performed inverse-variance-weighted MR of adiposity and metabolic risk factors on ' g ', using summary statistics for genome-wide association studies of BMI, visceral adipose tissue (VAT), waist-hip-ratio (WHR), blood pressure, HDL cholesterol, triglycerides, fasting glucose, HbA1c, and general cognition. Findings Participants were 58.9% female, and aged 51.4 (11.3) years. In univariate analysis, all 29 adiposity and metabolic measures assessed were associated with ' g ' at P < 0.05. In multivariable analyses, reduced ' g ' was consistently associated with increased visceral fat mass index and lower HDL cholesterol (P < 0.001), but not with blood pressure, triglycerides, or glycemic indices. The reduction in ' g ' associated with 1SD higher visceral fat, or 1SD lower HDL cholesterol, was equivalent to a 0.7 and 0.9-year increase in chronological age respectively (P < 0.001). Inverse variance MR analyses showed that reduced ' g ' is associated with genetically determined elevation of VAT, BMI and WHR (all P < 0.001). In contrast, MR did not support a causal role for blood pressure, lipid, or glycemic indices on cognition. Interpretation We show an independent relationship between adiposity and cognition in a multi-ethnic Asian population. MR analyses suggest that both visceral adiposity and raised BMI are likely to be causally linked to cognition. Our findings have important implications for preservation of cognitive health, including further motivation for action to reverse the rising burden of obesity in the Asia-Pacific region. Funding The Nanyang Technological University-the Lee Kong Chian School of Medicine, National Healthcare Group, National Medical Research Council, Ministry of Education, Singapore.
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Affiliation(s)
- Theresia Mina
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Yik Weng Yew
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,National Skin Centre, Research Division, 1 Mandalay Rd, 308205, Singapore
| | - Hong Kiat Ng
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Nilanjana Sadhu
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Gervais Wansaicheong
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Diagnostic Radiology, Tan Tock Seng Hospital (TTSH), 11 Jalan Tan Tock Seng, 308433, Singapore
| | - Rinkoo Dalan
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Endocrinology, TTSH, Singapore
| | - Dorrain Yan Wen Low
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Benjamin Chih Chiang Lam
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Khoo Teck Puat Hospital, Integrated Care for Obesity & Diabetes, 90 Yishun Central, 768828, Singapore
| | - Elio Riboli
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
| | - Eng Sing Lee
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Clinical Research Unit, National Healthcare Group Polyclinic, 3 Fusionopolis Link, Nexus@one-north, #05-10, 138543, Singapore
| | - Joanne Ngeow
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Division of Medical Oncology, National Cancer Centre, 11 Hospital Drive, 169610, Singapore
| | - Paul Elliott
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
| | - Konstadina Griva
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Marie Loh
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,National Skin Centre, Research Division, 1 Mandalay Rd, 308205, Singapore
| | - Jimmy Lee
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Research Division, Institute of Mental Health, 539747, Singapore
| | - John Chambers
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
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37
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Lowe WL. Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes. Int J Mol Sci 2023; 24:6047. [PMID: 37047019 PMCID: PMC10094577 DOI: 10.3390/ijms24076047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Gestational diabetes (GDM) is one of the most common complications of pregnancy, affecting as many as one in six pregnancies. It is associated with both short- and long-term adverse outcomes for the mother and fetus and has important implications for the life course of affected women. Advances in genetics and epigenetics have not only provided new insight into the pathophysiology of GDM but have also provided new approaches to identify women at high risk for progression to postpartum cardiometabolic disease. GDM and type 2 diabetes share similarities in their pathophysiology, suggesting that they also share similarities in their genetic architecture. Candidate gene and genome-wide association studies have identified susceptibility genes that are shared between GDM and type 2 diabetes. Despite these similarities, a much greater effect size for MTNR1B in GDM compared to type 2 diabetes and association of HKDC1, which encodes a hexokinase, with GDM but not type 2 diabetes suggest some differences in the genetic architecture of GDM. Genetic risk scores have shown some efficacy in identifying women with a history of GDM who will progress to type 2 diabetes. The association of epigenetic changes, including DNA methylation and circulating microRNAs, with GDM has also been examined. Targeted and epigenome-wide approaches have been used to identify DNA methylation in circulating blood cells collected during early, mid-, and late pregnancy that is associated with GDM. DNA methylation in early pregnancy had some ability to identify women who progressed to GDM, while DNA methylation in blood collected at 26-30 weeks gestation improved upon the ability of clinical factors alone to identify women at risk for progression to abnormal glucose tolerance post-partum. Finally, circulating microRNAs and long non-coding RNAs that are present in early or mid-pregnancy and associated with GDM have been identified. MicroRNAs have also proven efficacious in predicting both the development of GDM as well as its long-term cardiometabolic complications. Studies performed to date have demonstrated the potential for genetic and epigenetic technologies to impact clinical care, although much remains to be done.
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Affiliation(s)
- William L Lowe
- Department of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Rubloff 12, 420 E. Superior Street, Chicago, IL 60611, USA
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Pan Y, Sun X, Mi X, Huang Z, Hsu Y, Hixson JE, Munzy D, Metcalf G, Franceschini N, Tin A, Köttgen A, Francis M, Brody JA, Kestenbaum B, Sitlani CM, Mychaleckyj JC, Kramer H, Lange LA, Guo X, Hwang SJ, Irvin MR, Smith JA, Yanek LR, Vaidya D, Chen YDI, Fornage M, Lloyd-Jones DM, Hou L, Mathias RA, Mitchell BD, Peyser PA, Kardia SLR, Arnett DK, Correa A, Raffield LM, Vasan RS, Cupple LA, Levy D, Kaplan RC, North KE, Rotter JI, Kooperberg C, Reiner AP, Psaty BM, Tracy RP, Gibbs RA, Morrison AC, Feldman H, Boerwinkle E, He J, Kelly TN. Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease. Hum Mol Genet 2023; 32:1048-1060. [PMID: 36444934 PMCID: PMC9990994 DOI: 10.1093/hmg/ddac290] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022] Open
Abstract
Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.
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Affiliation(s)
- Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Xiao Sun
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Xuenan Mi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Zhijie Huang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Yenchih Hsu
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James E Hixson
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Donna Munzy
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nora Franceschini
- Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Adrienne Tin
- University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center – University of Freiburg, Freiburg 79106, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Michael Francis
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | | | - Jennifer A Brody
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Bryan Kestenbaum
- University of Washington, Department of Medicine, Division of Nephrology, Kidney Research Institute, Seattle, WA 98195, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA 22903, USA
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL 60153, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver, Aurora, CO 80045, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL 35233, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dhananjay Vaidya
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Braxton D Mitchell
- Department of Medicine, 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
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY 40506, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27516, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - L Adrienne Cupple
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA 01702, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Kari E North
- Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Centre, Torrance, CA 90502, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Department of Health Services, University of Washington, Seattle, WA 98195, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Harold Feldman
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiang He
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
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Li FF, Zhu MC, Shao YL, Lu F, Yi QY, Huang XF. Causal Relationships Between Glycemic Traits and Myopia. Invest Ophthalmol Vis Sci 2023; 64:7. [PMID: 36867130 PMCID: PMC9988699 DOI: 10.1167/iovs.64.3.7] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
Purpose Little is known about whether sugar intake is a risk factor for myopia, and the influence of glycemic control remains unclear, with inconsistent results reported. This study aimed to clarify this uncertainty by evaluating the link between multiple glycemic traits and myopia. Methods We employed a two-sample Mendelian randomization (MR) design using summary statistics from independent genome-wide association studies. A total of six glycemic traits, including adiponectin, body mass index, fasting blood glucose, fasting insulin, hemoglobin A1c (HbA1c), and proinsulin levels, were used as exposures, and myopia was used as the outcome. The inverse-variance-weighted (IVW) method was the main applied analytic tool and was complemented with comprehensive sensitivity analyses. Results Out of the six glycemic traits studied, we found that adiponectin was significantly associated with myopia. The genetically predicted level of adiponectin was consistently negatively associated with myopia incidence: IVW (odds ratio [OR] = 0.990; P = 2.66 × 10-3), MR Egger (OR = 0.983; P = 3.47 × 10-3), weighted median method (OR = 0.989; P = 0.01), and weighted mode method (OR = 0.987; P = 0.01). Evidence from all sensitivity analyses further supported these associations. In addition, a higher HbA1c level was associated with a greater risk of myopia: IVW (OR = 1.022; P = 3.06 × 10-5). Conclusions Genetic evidence shows that low adiponectin levels and high HbA1c are associated with an increased risk of myopia. Given that physical activity and sugar intake are controllable variables in blood glycemia treatment, these findings provide new insights into potential strategies to delay myopia onset.
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Affiliation(s)
- Fen-Fen Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.,State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Meng-Chao Zhu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.,State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yi-Lei Shao
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.,State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Fan Lu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.,State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Quan-Yong Yi
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, Zhejiang, China
| | - Xiu-Feng Huang
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, Zhejiang, China
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40
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Kon T, Sasaki Y, Abe Y, Onozato Y, Yagi M, Mizumoto N, Sakai T, Umehara M, Ito M, Nakamura S, Goto H, Ueno Y. Modulation of AMPK/ TET2/ 5-hmC axis in response to metabolic alterations as a novel pathway for obesity-related colorectal cancer development. Sci Rep 2023; 13:2858. [PMID: 36806702 PMCID: PMC9938119 DOI: 10.1038/s41598-023-29958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
Obesity is a major risk factor for colorectal cancer (CRC). Sustained hyperglycemia destabilizes tumor suppressor ten-eleven translocation (TET) 2, which is a substrate of AMPK, thereby dysregulating 5-hydroxymethylcytosine (5-hmC). However, the role played by this novel pathway in the development of obesity-related CRC is unclear. In this study, we aimed to evaluate the expression levels of TET2 and 5-hmC in obesity-related CRC and the effects of TET2 expression on the proliferation of CRC cells. To this end, surgically resected CRC samples from seven obese patients (Ob-CRC) and seven non-obese patients (nOb-CRC) were analyzed, and expression levels of the TET family and 5-hmC were compared between the groups. A decrease was observed in TET2 mRNA levels and 5-hmC levels in Ob-CRC compared to that in nOb-CRC. Furthermore, we used CRC cell lines to investigate the relationship between insulin, proliferation, and TET expression and AMPK. In cell lines, glucose and insulin treatments suppressed the expression of TET2 and increased cell proliferation. Downregulation of TET2 using siRNA also induced cell proliferation. An AMPK activator inhibited insulin- or glucose-stimulated cell proliferation and restored TET2 expression. We propose the AMPK-TET2-5-hmC axis as a novel pathway and potential therapeutic target in obesity-related CRC development.
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Affiliation(s)
- Takashi Kon
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Yu Sasaki
- Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585, Japan.
| | - Yasuhiko Abe
- grid.413006.00000 0004 7646 9307Division of Endoscopy, Yamagata University Hospital, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Yusuke Onozato
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Makoto Yagi
- grid.413006.00000 0004 7646 9307Division of Endoscopy, Yamagata University Hospital, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Naoko Mizumoto
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Takayuki Sakai
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Matsuki Umehara
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Minami Ito
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Shuhei Nakamura
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Hiroki Goto
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
| | - Yoshiyuki Ueno
- grid.268394.20000 0001 0674 7277Faculty of Medicine, Department of Gastroenterology, Faculty of Medicine, Yamagata University, 2-2-2 Iida-Nishi, Yamagata, 990-9585 Japan
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Kwak J, Shin D. Gene-Nutrient Interactions in Obesity: COBLL1 Genetic Variants Interact with Dietary Fat Intake to Modulate the Incidence of Obesity. Int J Mol Sci 2023; 24:ijms24043758. [PMID: 36835164 PMCID: PMC9959357 DOI: 10.3390/ijms24043758] [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] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/26/2023] [Accepted: 02/02/2023] [Indexed: 02/16/2023] Open
Abstract
The COBLL1 gene is associated with leptin, a hormone important for appetite and weight maintenance. Dietary fat is a significant factor in obesity. This study aimed to determine the association between COBLL1 gene, dietary fat, and incidence of obesity. Data from the Korean Genome and Epidemiology Study were used, and 3055 Korean adults aged ≥ 40 years were included. Obesity was defined as a body mass index ≥ 25 kg/m2. Patients with obesity at baseline were excluded. The effects of the COBLL1 rs6717858 genotypes and dietary fat on incidence of obesity were evaluated using multivariable Cox proportional hazard models. During an average follow-up period of 9.2 years, 627 obesity cases were documented. In men, the hazard ratio (HR) for obesity was higher in CT, CC carriers (minor allele carriers) in the highest tertile of dietary fat intake than for men with TT carriers in the lowest tertile of dietary fat intake (Model 1: HR: 1.66, 95% confidence interval [CI]: 1.07-2.58; Model 2: HR: 1.63, 95% CI: 1.04-2.56). In women, the HR for obesity was higher in TT carriers in the highest tertile of dietary fat intake than for women with TT carriers in the lowest tertile of dietary fat intake (Model 1: HR: 1.49, 95% CI: 1.08-2.06; Model 2: HR: 1.53, 95% CI: 1.10-2.13). COBLL1 genetic variants and dietary fat intake had different sex-dependent effects in obesity. These results imply that a low-fat diet may protect against the effects of COBLL1 genetic variants on future obesity risk.
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Qiao Z, Sidorenko J, Revez JA, Xue A, Lu X, Pärna K, Snieder H, Visscher PM, Wray NR, Yengo L. Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose. Nat Commun 2023; 14:451. [PMID: 36707517 PMCID: PMC9883484 DOI: 10.1038/s41467-023-36013-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 01/12/2023] [Indexed: 01/29/2023] Open
Abstract
The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h2 = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.
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Affiliation(s)
- Zhen Qiao
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Joana A Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Angli Xue
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, Guangdong, China
| | - Katri Pärna
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
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Nguyen A, Khafagy R, Hashemy H, Kuo KHM, Roshandel D, Paterson AD, Dash S. Investigating the association between fasting insulin, erythrocytosis and HbA1c through Mendelian randomization and observational analyses. Front Endocrinol (Lausanne) 2023; 14:1146099. [PMID: 37008938 PMCID: PMC10064082 DOI: 10.3389/fendo.2023.1146099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Insulin resistance (IR) with associated compensatory hyperinsulinemia (HI) are early abnormalities in the etiology of prediabetes (preT2D) and type 2 diabetes (T2D). IR and HI also associate with increased erythrocytosis. Hemoglobin A1c (HbA1c) is commonly used to diagnose and monitor preT2D and T2D, but can be influenced by erythrocytosis independent of glycemia. METHODS We undertook bidirectional Mendelian randomization (MR) in individuals of European ancestry to investigate potential causal associations between increased fasting insulin adjusted for BMI (FI), erythrocytosis and its non-glycemic impact on HbA1c. We investigated the association between the triglyceride-glucose index (TGI), a surrogate measure of IR and HI, and glycation gap (difference between measured HbA1c and predicted HbA1c derived from linear regression of fasting glucose) in people with normoglycemia and preT2D. RESULTS Inverse variance weighted MR (IVWMR) suggested that increased FI increases hemoglobin (Hb, b=0.54 ± 0.09, p=2.7 x 10-10), red cell count (RCC, b=0.54 ± 0.12, p=5.38x10-6) and reticulocyte (RETIC, b=0.70 ± 0.15, p=2.18x10-6). Multivariable MR indicated that increased FI did not impact HbA1c (b=0.23 ± 0.16, p=0.162) but reduced HbA1c after adjustment for T2D (b=0.31 ± 0.13, p=0.016). Increased Hb (b=0.03 ± 0.01, p=0.02), RCC (b=0.02 ± 0.01, p=0.04) and RETIC (b=0.03 ± 0.01, p=0.002) might modestly increase FI. In the observational cohort, increased TGI associated with decreased glycation gap, (i.e., measured HbA1c was lower than expected based on fasting glucose, (b=-0.09 ± 0.009, p<0.0001)) in people with preT2D but not in those with normoglycemia (b=0.02 ± 0.007, p<0.0001). CONCLUSIONS MR suggests increased FI increases erythrocytosis and might potentially decrease HbA1c by non-glycemic effects. Increased TGI, a surrogate measure of increased FI, associates with lower-than-expected HbA1c in people with preT2D. These findings merit confirmatory studies to evaluate their clinical significance.
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Affiliation(s)
- Anthony Nguyen
- Department of Medicine, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Rana Khafagy
- Department of Medicine, University Health Network, University of Toronto, Toronto, ON, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Habiba Hashemy
- Department of Medicine, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Kevin H. M. Kuo
- Division of Medical Oncology and Haematology, Department of Medicine, University Health Network, Toronto, ON, Canada
- Division of Haematology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Andrew D. Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Satya Dash
- Department of Medicine, University Health Network, University of Toronto, Toronto, ON, Canada
- *Correspondence: Satya Dash,
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Rangaswamaiah H, Somashekar P, Gutlur Nagarajaiah Setty R, Ganesh V. Urinary nephrin linked nephropathy in type-2 diabetes mellitus. Bioinformation 2022; 18:1131-1135. [PMID: 37701508 PMCID: PMC10492904 DOI: 10.6026/973206300181131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/20/2022] [Accepted: 12/31/2022] [Indexed: 09/14/2023] Open
Abstract
Diabetic nephropathy is a one of the risk factor for end stage renal disease in patients with type 2 diabetes mellitus. Urinary micr oalbumin currently marker using for diagnosis of nephropathy in type 2 diabetes mellitus due to its larger variability and less specificity. Urinary nephrin is podocyte protein can be act as a early predictable and prognostic marker than micro albumin for nephropathy in type 2 diabetes mellitus. This case-control prospective observational study included 60 type 2 diabetes mellitus and 30 controls after applying criteria. Basic biochemical, clinical and experimental data were measured and recorded. There was a significantly elevated level of urinary nephrin in type 2 diabetes mellitus patients when compared to controls. The urinary nephrin significantly elevated in normo albuminuria group only when compared to urinary ACR and it is positive association with kidney damage. This study concluded significantly elevated levels of urinary nephrin might be used for early diagnosis and prognostic marker for nephropathy in type 2 diabetes mellitus.
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Affiliation(s)
- Hareesh Rangaswamaiah
- Department of General Medicine, Akash Institute of Medical Sciences and Research Centre, Bangalore - 562110, Karnataka, India
| | - Pallavi Somashekar
- Department of General Medicine, Akash Institute of Medical Sciences and Research Centre, Bangalore - 562110, Karnataka, India
| | | | - Veluri Ganesh
- Department of Biochemistry, Akash Institute of Medical Sciences and Research Centre, Bangalore - 562110, Karnataka, India
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45
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Sommer C, Vangberg KG, Moen GH, Evans DM, Lee-Ødegård S, Blom-Høgestøl IK, Sletner L, Jenum AK, Drevon CA, Gulseth HL, Birkeland KI. Insulin and body mass index decrease serum soluble leptin receptor levels in humans. J Clin Endocrinol Metab 2022; 108:1110-1119. [PMID: 36459457 PMCID: PMC10099165 DOI: 10.1210/clinem/dgac699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/30/2022] [Accepted: 11/30/2022] [Indexed: 12/04/2022]
Abstract
PURPOSE Test how serum soluble leptin receptor (sOb-R) is influenced by glucose, insulin, body fat, body mass index (BMI), food intake and physical activity. METHODS We performed an epidemiological triangulation combining cross-sectional, interventional and Mendelian randomization study designs. In five independent clinical studies (n = 24-823), sOb-R was quantified in serum or plasma by commercial ELISA kits using monoclonal antibodies. We performed mixed models regression and two-sample Mendelian randomization. RESULTS In pooled, cross-sectional data, levelling on study, sOb-R associated inversely with body mass index (BMI) (beta [95% CI] -0.19 [-0.21 to -0.17]), body fat (-0.12 [-0.14 to -0.10) and fasting C-peptide (-2.04 [-2.46 to -1.62]). sOb-R decreased in response to acute hyperinsulinaemia during euglycaemic glucose clamp in two independent clinical studies (-0.5 [-0.7 to -0.4] and -0.5 [-0.6 to -0.3]), and immediately increased in response to intensive exercise (0.18 [0.04 to 0.31]) and food intake (0.20 [0.06 to 0.34]). In two-sample Mendelian randomization, higher fasting insulin and higher BMI were causally linked to lower sOb-R levels (inverse variance weighted, -1.72 [-2.86 to -0.58], and -0.20 [-0.36 to -0.04], respectively). The relationship between hyperglycaemia and sOb-R were inconsistent in cross-sectional studies, non-significant in intervention studies, and two-sample Mendelian randomization suggested no causal effect of fasting glucose on sOb-R. MAIN CONCLUSION Both BMI and insulin causally decreased serum sOb-R levels. Conversely, intensive exercise and food intake acutely increased sOb-R. Our results suggest that sOb-R is involved in short-term regulation of leptin signalling, either directly or indirectly, and that hyperinsulinaemia may reduce leptin signalling.
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Affiliation(s)
- Christine Sommer
- Dept. of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Kjersti G Vangberg
- Dept. of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Dept. of Nutrition, Inst. Basic Medical Sciences, Faculty Medicine, University of Oslo, Oslo, Norway
| | - Gunn-Helen Moen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - David M Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Sindre Lee-Ødegård
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Dept. of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Ingvild K Blom-Høgestøl
- Dept. of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Line Sletner
- Dept. of Child and Adolescents Medicine, Akershus University Hospital, Norway
| | - Anne K Jenum
- General Practice Research Unit (AFE), Dept. of General Practice, Inst. of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Christian A Drevon
- Dept. of Nutrition, Inst. Basic Medical Sciences, Faculty Medicine, University of Oslo, Oslo, Norway
- Vitas AS, Oslo Science Park, Oslo, Norway
| | - Hanne L Gulseth
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Kåre I Birkeland
- Dept. of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Dept. of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
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46
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Siddiqui MK, Hall C, Cunningham SG, McCrimmon R, Morris A, Leese GP, Pearson ER. Using Data to Improve the Management of Diabetes: The Tayside Experience. Diabetes Care 2022; 45:2828-2837. [PMID: 36288800 DOI: 10.2337/dci22-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/12/2022] [Indexed: 02/03/2023]
Abstract
Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service (NHS) Tayside health board, which provides health care services to the population. In Tayside, Scotland, a comprehensive informatics network for diabetes care and research has been established for over 25 years. This has expanded more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information - Diabetes (SCI-Diabetes). This has enabled improved diabetes screening and integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all of these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, but also the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. In this article we describe how health informatics has been used to improve care of people with diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and comorbidities.
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Affiliation(s)
- Moneeza K Siddiqui
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
| | - Christopher Hall
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
| | - Scott G Cunningham
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
| | - Rory McCrimmon
- Division of Systems Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
| | - Andrew Morris
- Usher Institute, College of Medicine and Veterinary Medicine, Edinburgh, U.K
| | - Graham P Leese
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
| | - Ewan R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K
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47
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Jääskeläinen T, Klemetti MM. Genetic Risk Factors and Gene-Lifestyle Interactions in Gestational Diabetes. Nutrients 2022; 14:nu14224799. [PMID: 36432486 PMCID: PMC9694797 DOI: 10.3390/nu14224799] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
Paralleling the increasing trends of maternal obesity, gestational diabetes (GDM) has become a global health challenge with significant public health repercussions. In addition to short-term adverse outcomes, such as hypertensive pregnancy disorders and fetal macrosomia, in the long term, GDM results in excess cardiometabolic morbidity in both the mother and child. Recent data suggest that women with GDM are characterized by notable phenotypic and genotypic heterogeneity and that frequencies of adverse obstetric and perinatal outcomes are different between physiologic GDM subtypes. However, as of yet, GDM treatment protocols do not differentiate between these subtypes. Mapping the genetic architecture of GDM, as well as accurate phenotypic and genotypic definitions of GDM, could potentially help in the individualization of GDM treatment and assessment of long-term prognoses. In this narrative review, we outline recent studies exploring genetic risk factors of GDM and later type 2 diabetes (T2D) in women with prior GDM. Further, we discuss the current evidence on gene-lifestyle interactions in the development of these diseases. In addition, we point out specific research gaps that still need to be addressed to better understand the complex genetic and metabolic crosstalk within the mother-placenta-fetus triad that contributes to hyperglycemia in pregnancy.
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Affiliation(s)
- Tiina Jääskeläinen
- Department of Food and Nutrition, University of Helsinki, P.O. Box 66, 00014 Helsinki, Finland
- Department of Medical and Clinical Genetics, University of Helsinki, P.O. Box 63, 00014 Helsinki, Finland
- Correspondence:
| | - Miira M. Klemetti
- Department of Medical and Clinical Genetics, University of Helsinki, P.O. Box 63, 00014 Helsinki, Finland
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, P.O. Box 140, 00029 Helsinki, Finland
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48
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Wang K, Shi M, Yang A, Fan B, Tam CHT, Lau E, Luk AOY, Kong APS, Ma RCW, Chan JCN, Chow E. GCKR and GCK polymorphisms are associated with increased risk of end-stage kidney disease in Chinese patients with type 2 diabetes: The Hong Kong Diabetes Register (1995-2019). Diabetes Res Clin Pract 2022; 193:110118. [PMID: 36243233 DOI: 10.1016/j.diabres.2022.110118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 11/26/2022]
Abstract
AIMS Glucokinase (GCK) and glucokinase regulatory protein (GKRP) regulate glucose and lipid metabolism. We investigated the associations of GCKR and GCK polymorphisms with kidney outcomes. METHODS Analyses were performed in a prospective cohort who were enrolled in the Hong Kong Diabetes Register between 1995 and 2017. The associations of GCKR rs1260326 and GCK rs1799884 polymorphisms with incident end-stage kidney disease (ESKD), albuminuria and rapid eGFR decline were analysed by Cox regression or logistic regression with adjustment. RESULTS 6072 patients (baseline mean age 57.4 years; median diabetes duration 6.0 years; 54.5 % female) were included, with a median follow-up of 15.5 years. The GCKR rs1260326 [HR (95 %CI) 1.23 (1.05-1.44) for CT; HR 1.23 (1.02-1.48) for TT] and GCK rs1799884 T alleles [HR 1.73 (1.24-2.40) for TT] were independently associated with increased risk of ESKD versus their respective CC genotypes. GCKR rs1260326 T allele was also associated with albuminuria [OR 1.18 (1.05-1.33) for CT; OR 1.34 (1.16-1.55) for TT] and rapid eGFR decline. CONCLUSIONS In Chinese patients with type 2 diabetes, T allele carriers of GCKR rs1260326 and GCK rs1799884 were at high risk for ESKD. These genetic markers may be used to identify high risk patients for early intensive management for renoprotection.
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Affiliation(s)
- Ke Wang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Aimin Yang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Baoqi Fan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Eric Lau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Andrea O Y Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Alice P S Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
| | - Elaine Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
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49
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Long-term Outcomes Among Young Adults With Type 2 Diabetes Based on Durability of Glycemic Control: Results From the TODAY Cohort Study. Diabetes Care 2022; 45:2689-2697. [PMID: 36190810 PMCID: PMC9679266 DOI: 10.2337/dc22-0784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/19/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To examine the effect of different patterns of durable glycemic control on the development of comorbidities among youth with type 2 diabetes (T2D) and to assess the impact of fasting glucose (FG) variability on the clinical course of T2D. RESEARCH DESIGN AND METHODS From the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study, 457 participants (mean age, 14 years) with mean diabetes duration <2 years at entry and a minimum study follow-up of 10 years were included in these analyses. HbA1c, FG concentrations, and β-cell function estimates from oral glucose tolerance tests were measured longitudinally. Prevalence of comorbidities by glycemic control status after 10 years in the TODAY study was assessed. RESULTS Higher baseline HbA1c concentration, lower β-cell function, and maternal history of diabetes were strongly associated with loss of glycemic control in youth with T2D. Higher cumulative HbA1c concentration over 4 years and greater FG variability over a year within 3 years of diagnosis were related to higher prevalence of dyslipidemia, nephropathy, and retinopathy progression over the subsequent 10 years. A coefficient of variability in FG ≥8.3% predicted future loss of glycemic control and development of comorbidities. CONCLUSIONS Higher baseline HbA1c concentration and FG variability during year 1 accurately predicted youth with T2D who will experience metabolic decompensation and comorbidities. These values may be useful tools for clinicians when considering early intensification of therapy.
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50
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Carrasco-Zanini J, Pietzner M, Lindbohm JV, Wheeler E, Oerton E, Kerrison N, Simpson M, Westacott M, Drolet D, Kivimaki M, Ostroff R, Williams SA, Wareham NJ, Langenberg C. Proteomic signatures for identification of impaired glucose tolerance. Nat Med 2022; 28:2293-2300. [PMID: 36357677 PMCID: PMC7614638 DOI: 10.1038/s41591-022-02055-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/27/2022] [Indexed: 11/12/2022]
Abstract
The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Joni V Lindbohm
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
- The Klarman Cell Observatory, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Erin Oerton
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nicola Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | | | | | - Mika Kivimaki
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | | | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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