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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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Lee HA, Park H, Park B. Genetic predisposition, lifestyle inflammation score, food-based dietary inflammatory index, and the risk for incident diabetes: Findings from the KoGES data. Nutr Metab Cardiovasc Dis 2024; 34:642-650. [PMID: 38161120 DOI: 10.1016/j.numecd.2023.10.028] [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: 05/23/2023] [Revised: 09/21/2023] [Accepted: 10/22/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND AIMS We investigated whether genetic predisposition, the Lifestyle Inflammation Score (LIS), or the Food-based Dietary Inflammatory Index (FDII) were associated with diabetes incidence and whether these factors interact. METHODS AND RESULTS The study was conducted using population-based cohort data derived from the Korean Genome and Epidemiology Study, which included 6568 people aged 40-69 years. Based on 25 genetic variants related to diabetes, genetic risk scores (GRSs) were determined and LISs and FDIIs were calculated and stratified into quartiles. We investigated the effects of gene-lifestyle interactions on the incident diabetes. The multivariate Cox proportional hazard model was used to generate hazard ratios with 95 % CIs. During the 16-year follow-up period, diabetes incidence was 13.6 per 1000 person-years. A dose-response association with diabetes was observed for both GRS and LIS quartiles but not for FDII quartiles. The GRS and LIS were also independently associated with diabetes incidence in a multivariate model. Compared to the bottom quartile, the top LIS quartile and the top GRS quartile had a 2.4-fold (95 % CI, 2.0-2.8) and a 1.4-fold (95 % CI, 1.2-1.7) higher diabetes risk, respectively. However, the FDII exhibited null association. When each genetic variant was evaluated, the top versus bottom LIS quartiles exhibited heterogeneous diabetes risks for rs560887 within G6PC2, rs7072268 within HK1, and rs837763 within CDT1; however, these differences were not statistically significant in multiple comparison. CONCLUSION Both GRS and LIS factors independently affect the incident diabetes, but their interaction effect showed insignificant association. Therefore, regardless of genetic susceptibility, more effort is needed to lower the risk for diabetes by improving lifestyle behaviors.
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Affiliation(s)
- Hye Ah Lee
- Clinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea.
| | - Hyesook Park
- Department of Preventive Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea; Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Bomi Park
- Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul, Republic of Korea
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Garfield V, Salzmann A, Burgess S, Chaturvedi N. A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach. Diabetes 2023; 72:175-183. [PMID: 36669000 PMCID: PMC7614590 DOI: 10.2337/db22-0110] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/24/2022] [Indexed: 01/21/2023]
Abstract
In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.
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Affiliation(s)
- Victoria Garfield
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
| | - Antoine Salzmann
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, MRC Biostatistics Unit, University of Cambridge, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
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Zhu Z, Wang K, Hao X, Chen L, Liu Z, Wang C. Causal Graph Among Serum Lipids and Glycemic Traits: A Mendelian Randomization Study. Diabetes 2022; 71:1818-1826. [PMID: 35622003 DOI: 10.2337/db21-0734] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 05/16/2022] [Indexed: 11/13/2022]
Abstract
We systematically investigated the bidirectional causality among HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TGs), fasting insulin (FI), and glycated hemoglobin A1c (HbA1c) based on genome-wide association summary statistics of Europeans (n = 1,320,016 for lipids, 151,013 for FI, and 344,182 for HbA1c). We applied multivariable Mendelian randomization (MR) to account for the correlation among different traits and constructed a causal graph with 13 significant causal effects after adjusting for multiple testing (P < 0.0025). Remarkably, we found that the effects of lipids on glycemic traits were through FI from TGs (β = 0.06 [95% CI 0.03, 0.08] in units of 1 SD for each trait) and HDL-C (β = -0.02 [-0.03, -0.01]). On the other hand, FI had a strong negative effect on HDL-C (β = -0.15 [-0.21, -0.09]) and positive effects on TGs (β = 0.22 [0.14, 0.31]) and HbA1c (β = 0.15 [0.12, 0.19]), while HbA1c could raise LDL-C (β = 0.06 [0.03, 0.08]) and TGs (β = 0.08 [0.06, 0.10]). These estimates derived from inverse-variance weighting were robust when using different MR methods. Our results suggest that elevated FI was a strong causal factor of high TGs and low HDL-C, which in turn would further increase FI. Therefore, early control of insulin resistance is critical to reduce the risk of type 2 diabetes, dyslipidemia, and cardiovascular complications.
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Affiliation(s)
- Ziwei Zhu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xingjie Hao
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Glycemic Control of Diabetes Mellitus Patients in Referral Hospitals of Amhara Region, Ethiopia: A Cross-Sectional Study. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6691819. [PMID: 33532494 PMCID: PMC7834781 DOI: 10.1155/2021/6691819] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/13/2021] [Indexed: 01/05/2023]
Abstract
Background Glycemic control is the level of glucose in diabetes patient. Evidence regarding glycemic control is scarce in resource-limited settings, and this study was conducted to generate information regarding the prevalence and predictors of glycemic control among diabetes mellitus patients attending their care from the referral hospitals of the Amhara region, Ethiopia. Methods A cross-sectional study design was implemented. A simple random sampling technique was used. Data were collected from March 2018 to January 2020. The data were collected using interviews, chart review, and blood samples. Hemoglobin A1c was measured using high-performance liquid chromatography. Data were entered into Epi-info software and analyzed by SPSS software. Descriptive statistics were used to estimate the prevalence of glycemic control; linear regression was used to identify the predictors of HbA1c. Results A total of 2554 diabetes patients were included giving for the response rate of 95.83%. The mean age of the study participants was 54.08 years [SD (standard deviation) ± 8.38 years]. The mean HbA1c of the study participants was 7.31% [SD ± 0.94%]. Glycemic control was poor in 55.32% [95% CI: 53.4%-57.25%] of diabetes patients. The glycemic control of diabetes patients was determined by BMI (β 0.1; [95% CI: 0.09-0.1]), type 2 diabetes (β -0.14; [95% CI: -0.11-0.16]), age (β 0.22; [95% CI: 0.02-0.024]), duration of the disease (β 0.04; [95% CI: 0.037-0.042]), the presence of hypertension (β 0.12; [95% CI:0.09-0.16]), regular physical exercise (β -0.06; [95% CI: -0.03-0.09]), medication adherence (β -0.16; [95% CI: -0.14-0.18]), and male (β 0.34; [95% CI: 0.31-.037]). Conclusion The glycemic control of diabetes patients was poor, and it needs the attention of decision-makers.
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Hsiung CN, Chang YC, Lin CW, Chang CW, Chou WC, Chu HW, Su MW, Wu PE, Shen CY. The Causal Relationship of Circulating Triglyceride and Glycated Hemoglobin: A Mendelian Randomization Study. J Clin Endocrinol Metab 2020; 105:5648095. [PMID: 31784746 DOI: 10.1210/clinem/dgz243] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/29/2019] [Indexed: 12/20/2022]
Abstract
CONTEXT The association between circulating triglyceride (TG) and glycated hemoglobin A1c (HbA1c), a biomarker for type 2 diabetes, has been widely addressed, but the causal direction of the relationship is still ambiguous. OBJECTIVE To confirm the causal relationship between TG and HbA1c by using bidirectional and 2-step Mendelian randomization (MR) approaches. METHODS We carried out a bidirectional MR approach using the summarized results from the public database to examine any potential causal effects between serum TG and HbA1c in 16 000 individuals of the Taiwan Biobank cohort. We used the MR estimate and the MR inverse variance-weighted method to reveal that relationship between TG and HbA1c. To further determine whether the DNA methylation at specific sequences mediate the causal pathway between TG and HbA1c, using the 2-step MR approach. RESULTS We identified that a single-unit increase in TG measured via log transformation of mg/dL data was associated with a significant increase of 10 units of HbA1c (95% CI = 1.05-18.95, P = 0.029). In contrast, the genetic determinants of HbA1c do not contribute to the amount of circulating TG (beta = 1.75, 95% CI = -11.50 to 14.90). Sensitivity analyses, included the weighted-median approach and MR-Egger regression, were performed to confirm no pleiotropic effect among these instrumental variables. Furthermore, we identified the genetic variant, rs1823200, is associated with both methylation of the CpG site adjacent to CADPS gene and HbA1c level. CONCLUSION Our study suggests that higher circulating TG can have an affect on genomic methylation status, ultimately causing elevated level of circulating HbA1c.
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Affiliation(s)
- Chia-Ni Hsiung
- Institute of Bioinformatics and Structure Biology, National Tsing Hua University, Hsinchu, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Yi-Cheng Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | | | | | - Wen-Cheng Chou
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hou-Wei Chu
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Ming-Wei Su
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Pei-Ei Wu
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- College of Public Health, China Medical University, Taichung, Taiwan
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