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Cardiometabolic Traits in Adult Twins: Heritability and BMI Impact with Age. Nutrients 2022; 15:nu15010164. [PMID: 36615821 PMCID: PMC9824881 DOI: 10.3390/nu15010164] [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: 11/22/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Background: The prevalence of obesity and cardiometabolic diseases continues to rise globally and obesity is a significant risk factor for cardiometabolic diseases. However, to our knowledge, evidence of the relative roles of genes and the environment underlying obesity and cardiometabolic disease traits and the correlations between them are still lacking, as is how they change with age. Method: Data were obtained from the Chinese National Twin Registry (CNTR). A total of 1421 twin pairs were included. Univariate structural equation models (SEMs) were performed to evaluate the heritability of BMI and cardiometabolic traits, which included blood hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Bivariate SEMs were used to assess the genetic/environmental correlations between them. The study population was divided into three groups for analysis: ≤50, 51−60, and >60 years old to assess the changes in heritability and genetic/environmental correlations with ageing. Results: Univariate SEMs showed a high heritability of BMI (72%) and cardiometabolic traits, which ranged from 30% (HbA1c) to 69% (HDL-C). With age increasing, the heritability of all phenotypes has different degrees of declining trends. Among these, BMI, SBP, and DBP presented significant monotonous declining trends. The bivariate SEMs indicated that BMI correlated with all cardiometabolic traits. The genetic correlations were estimated to range from 0.14 (BMI and LDL-C) to 0.39 (BMI and DBP), while the environmental correlations ranged from 0.13 (BMI and TC/LDL-C) to 0.31 (BMI and TG). The genetic contributions underlying the correlations between BMI and SBP and DBP, TC, TG, and HDL-C showed a progressive decrease as age groups increased. In contrast, environmental correlations displayed a significant increasing trend for HbA1c, SBP, and DBP. Conclusions: The findings suggest that genetic and environmental factors have essential effects on BMI and all cardiometabolic traits. However, as age groups increased, genetic influences presented varying degrees of decrement for BMI and most cardiometabolic traits, suggesting the increasing importance of environments. Genetic factors played a consistently larger role than environmental factors in the phenotypic correlations between BMI and cardiometabolic traits. Nevertheless, the relative magnitudes of genetic and environmental factors may change over time.
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Luo J, Hodge A, Hendryx M, Byles JE. Age of obesity onset, cumulative obesity exposure over early adulthood and risk of type 2 diabetes. Diabetologia 2020; 63:519-527. [PMID: 31858184 DOI: 10.1007/s00125-019-05058-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/31/2019] [Indexed: 02/08/2023]
Abstract
AIMS/HYPOTHESIS Obesity is a risk factor for type 2 diabetes, yet little is known about how timing and cumulative exposure of obesity are related to disease risk. The aim of this study was to examine the associations between BMI trajectories, age of onset of obesity and obese-years (a product of degree and duration of obesity) over early adulthood and subsequent risk of type 2 diabetes. METHODS Women aged 18-23 years at baseline (n = 11,192) enrolled in the Australian Longitudinal Study on Women's Health (ALSWH) in 1996 were followed up about every 3 years via surveys for up to 19 years. Self-reported weights were collected up to seven times. Incident type 2 diabetes was self-reported. A growth mixture model was used to identify distinct BMI trajectories over the early adult life course. Cox proportional hazards regression models were used to examine the associations between trajectories and risk of diabetes. RESULTS One hundred and sixty-two (1.5%) women were newly diagnosed with type 2 diabetes during a mean of 16 years of follow-up. Six distinct BMI trajectories were identified, varying by different initial BMI and different slopes of increase. Initial BMI was positively associated with risk of diabetes. We also observed that age at onset of obesity was negatively associated with risk of diabetes (HR 0.87 [95% CI 0.79, 0.96] per 1 year increment), and number of obese-years was positively associated with diabetes (p for trend <0.0001). CONCLUSIONS/INTERPRETATION Our data revealed the importance of timing of obesity, and cumulative exposure to obesity in the development of type 2 diabetes in young women, suggesting that preventing or delaying the onset of obesity and reducing cumulative exposure to obesity may substantially lower the risk of developing diabetes.
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Affiliation(s)
- Juhua Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, 1025 E 7th Street, Bloomington, IN, 47405, USA.
| | - Allison Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Michael Hendryx
- Department of Environmental and Occupational Health, School of Public Health, Indiana University, Bloomington, IN, USA
| | - Julie E Byles
- Research Centre for Generational Health and Ageing, University of Newcastle, Newcastle, NSW, Australia
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Breeze P, Squires H, Chilcott J, Stride C, Diggle P, Brunner E, Tabak A, Brennan A. A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study. J Public Health (Oxf) 2016; 38:679-687. [PMID: 28158533 PMCID: PMC6092879 DOI: 10.1093/pubmed/fdv160] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Novel epidemiology models are required to link correlated variables over time, especially haemoglobin A1c (HbA1c) and body mass index (BMI) for diabetes prevention policy analysis. This article develops an epidemiology model to correlate metabolic risk factor trajectories. Method BMI, fasting plasma glucose, 2-h glucose, HbA1c, systolic blood pressure, total cholesterol and high density lipoprotein (HDL) cholesterol were analysed over 16 years from 8150 participants of the Whitehall II prospective cohort study. Latent growth curve modelling was employed to simultaneously estimate trajectories for multiple metabolic risk factors allowing for variation between individuals. A simulation model compared simulated outcomes with the observed data. Results The model identified that the change in BMI was associated with changes in glycaemia, total cholesterol and systolic blood pressure. The statistical analysis quantified associations among the longitudinal risk factor trajectories. Growth in latent glycaemia was positively correlated with systolic blood pressure and negatively correlated with HDL cholesterol. The goodness-of-fit analysis indicates reasonable fit to the data. Conclusions This is the first statistical model that estimates trajectories of metabolic risk factors simultaneously for diabetes to predict joint correlated risk factor trajectories. This can inform comparisons of the effectiveness and cost-effectiveness of preventive interventions, which aim to modify metabolic risk factors.
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Affiliation(s)
- P. Breeze
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK
| | - H. Squires
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK
| | - J. Chilcott
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK
| | - C. Stride
- Institute of Work Psychology, University of Sheffield, Sheffield, UK
| | - P.J. Diggle
- Medical School, Lancaster University and Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| | - E. Brunner
- Epidemiology & Public Health, University College London, London, UK
| | - A. Tabak
- Epidemiology & Public Health, University College London, London, UK
- 1st Department of Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
| | - A. Brennan
- School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK
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Moschonis G, Karatzi K, Polychronopoulou MC, Manios Y. Waist circumference, trunk and visceral fat cutoff values for detecting hyperinsulinemia and insulin resistance in children: the Healthy Growth Study. Eur J Nutr 2015; 55:2331-4. [PMID: 26419584 DOI: 10.1007/s00394-015-1046-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 09/14/2015] [Indexed: 11/30/2022]
Affiliation(s)
- George Moschonis
- Department of Nutrition and Dietetics, Harokopio University of Athens, 70, El.Venizelou Ave, 17671, Kallithea, Athens, Greece.,EnviNHealth S.A., VasilissisSofias 22, Marousi, 15124, Athens, Greece
| | - Kalliopi Karatzi
- Department of Nutrition and Dietetics, Harokopio University of Athens, 70, El.Venizelou Ave, 17671, Kallithea, Athens, Greece
| | | | - Yannis Manios
- Department of Nutrition and Dietetics, Harokopio University of Athens, 70, El.Venizelou Ave, 17671, Kallithea, Athens, Greece.
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Heianza Y, Arase Y, Kodama S, Tsuji H, Tanaka S, Saito K, Hara S, Sone H. Trajectory of body mass index before the development of type 2 diabetes in Japanese men: Toranomon Hospital Health Management Center Study 15. J Diabetes Investig 2015; 6:289-94. [PMID: 25969713 PMCID: PMC4420560 DOI: 10.1111/jdi.12308] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Revised: 10/07/2014] [Accepted: 10/31/2014] [Indexed: 12/27/2022] Open
Abstract
Aims/Introduction We aimed to investigate the long-term trajectory of general adiposity assessed by the body mass index (BMI) before the onset of type 2 diabetes in Japanese individuals. Materials and Methods We retrospectively examined data on 1,553 Japanese men without diabetes. Mean BMI and incident cases of diabetes (diabetes indicated by fasting glucose concentrations ≥7.0 mmol/L, a self-reported history of clinician-diagnosed diabetes, or glycated hemoglobin ≥6.5% (≥48 mmol/mol) were assessed on an annual basis over a 10-year period after the baseline examination. Results Mean (standard deviation) BMI at the time of diagnosis was 24.4 kg/m2 (3.1 kg/m2) among cases of diabetes (n = 191). An increasingly high BMI was associated with the early stage of the disease development, such as an 8- to 10-year prediagnosis period; individuals who developed diabetes experienced a prolonged and stable elevated BMI of ≥24.4 kg/m2 over the 8 years before the diagnosis of diabetes. The mean BMI among the non-cases of diabetes did not exceed 23.2 kg/m2 throughout the period. Conclusions These results suggested that Japanese men who eventually developed diabetes during the 10-year observation period were not characterized as obese, but had stable high-normal BMIs before the onset of diabetes. Previous evidence showed that values for glycemic markers rapidly increased before the development of diabetes; however, the present study showed a slight gain in BMI in the earlier stage of the natural history of diabetes followed by a prolonged period of overweight.
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Affiliation(s)
- Yoriko Heianza
- Department of Internal Medicine, Niigata University Faculty of Medicine Niigata, Japan ; Health Management Center, Toranomon Hospital Minato-ku, Japan
| | - Yasuji Arase
- Health Management Center, Toranomon Hospital Minato-ku, Japan ; Okinaka Memorial Institute for Medical Research Tokyo, Japan
| | - Satoru Kodama
- Department of Internal Medicine, Niigata University Faculty of Medicine Niigata, Japan ; Health Management Center, Toranomon Hospital Minato-ku, Japan
| | - Hiroshi Tsuji
- Health Management Center, Toranomon Hospital Minato-ku, Japan ; Okinaka Memorial Institute for Medical Research Tokyo, Japan
| | - Shiro Tanaka
- Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University Kyoto, Japan
| | - Kazumi Saito
- Department of Internal Medicine, Niigata University Faculty of Medicine Niigata, Japan ; Health Management Center, Toranomon Hospital Minato-ku, Japan
| | - Shigeko Hara
- Health Management Center, Toranomon Hospital Minato-ku, Japan ; Okinaka Memorial Institute for Medical Research Tokyo, Japan
| | - Hirohito Sone
- Department of Internal Medicine, Niigata University Faculty of Medicine Niigata, Japan ; Health Management Center, Toranomon Hospital Minato-ku, Japan
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Kabat GC, Heo M, Van Horn LV, Kazlauskaite R, Getaneh A, Ard J, Vitolins MZ, Waring ME, Zaslavsky O, Smoller SW, Rohan TE. Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women. Ann Epidemiol 2014; 24:896-902. [PMID: 25453348 PMCID: PMC4654453 DOI: 10.1016/j.annepidem.2014.10.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 09/26/2014] [Accepted: 10/10/2014] [Indexed: 01/22/2023]
Abstract
PURPOSE Some studies suggest that anthropometric measures of abdominal obesity may be superior to body mass index (BMI) for the prediction of cardiometabolic risk factors; however, most studies have been cross-sectional. Our aim was to prospectively examine the association of change in BMI, waist-to-hip ratio (WHR), waist circumference (WC), and waist circumference-to-height ratio (WCHtR) with change in markers of cardiometabolic risk in a population of postmenopausal women. METHODS We used a subsample of participants in the Women's Health Initiative aged 50 to 79 years at entry with available fasting blood samples and anthropometric measurements obtained at multiple time points over 12.8 years of follow-up (n = 2672). The blood samples were used to measure blood glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides at baseline, and at years 1, 3, and 6. We conducted mixed-effects linear regression analyses to examine associations at baseline and longitudinal associations between change in anthropometric measures and change in cardiometabolic risk factors, adjusting for covariates. RESULTS In longitudinal analyses, change in BMI, WC, and WCHtR robustly predicted change in cardiometabolic risk, whereas change in WHR did not. The strongest associations were seen for change in triglycerides, glucose, and HDL-C (inverse association). CONCLUSION Increase in BMI, WC, and WCHtR strongly predicted increases in serum triglycerides and glucose, and reduced HDL-C. WC and WCHtR were superior to BMI in predicting serum glucose, HDL-C, and triglycerides. WCHtR was superior to WC only in predicting serum glucose. BMI, WC, and WCHtR were all superior to WHR.
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Affiliation(s)
- Geoffrey C. Kabat
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Moonseong Heo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Linda V. Van Horn
- Department of Preventive Medicine, Fineberg School of Medicine, Northwest University, 680 N Lake Shore Drive, Suite 1400, Chicago IL 60611, USA
| | - Rasa Kazlauskaite
- Department of Preventive Medicine, Rush University Medical Center, 1700 W. Van Buren St., Suite 470, Chicago, IL 60612, USA
| | - Asqual Getaneh
- MedStar Health Research Institute, MedStar Health, 6525 Belcrest Road, Suite 700, Hyattsville, MD 20782, USA
| | - Jamy Ard
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA
| | - Mara Z. Vitolins
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA
| | - Molly E. Waring
- Division of Epidemiology of Chronic Diseases and Vulnerable Populations, Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Avenue, North Worcester, MA 01655, USA
| | - Oleg Zaslavsky
- The Cheryl Spencer Institute for Nursing Research, University of Haifa, Main Building, Fl. 500, room 570, Haifa 31905, Israel
| | - Sylvia Wassertheil Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Thomas E. Rohan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
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