WHR and genetic predispositions -
Department of Clinical Sciences, Lund University, Malmö, Sweden. Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK. Leicester National Institute for Health Research NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK.
South Karelia Central Hospital, Lappeenranta, Finland. Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, Helsinki, Finland. Department of Medicine, Atherosclerosis Research Unit, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands. Center of Medical Systems Biology, Leiden University Medical Center, Leiden, The Netherlands. Institut für Klinische Chemie und Laboratoriumsmedizin, Universität Greifswald, Greifswald, Germany.
Department of Physiatrics, Lapland Central Hospital, Rovaniemi, Finland. School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Western Australia, Australia. School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia, Australia.
Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland. Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, Tampere, Finland. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
The Department of Clinical Physiology, Turku University Hospital, Turku, Finland. Department of Medicine, University of Leipzig, Leipzig, Germany. Leipziger Interdisziplin? r Forschungskomplex zu molekularen Ursachen umwelt- und lebensstilassoziierter Erkrankungen LIFE Study Centre, University of Leipzig, Leipzig, Germany.
Coordination Centre for Clinical Trials, University of Leipzig, Leipzig, Germany. Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland. INSERM Centre de Recherche en Epidémiologie et Santé des Populations CESP U, Villejuif, France.
University Paris Sud 11, Unité Mixte de Recherche en Santé UMRS , Villejuif, France. Department of Social Medicine, University of Bristol, Bristol, UK.
The London School of Hygiene and Tropical Medicine, London, UK. South Asia Network for Chronic Disease, New Delhi, India. Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK.
Faculty of Health Science, University of Southern Denmark, Odense, Denmark. Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany. Regensburg University Medical Center, Innere Medizin II, Regensburg, Germany. Department of Social Services and Health Care, Jakobstad, Finland.
Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark. Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark.
NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK. Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hôpitaux de Paris, Paris, France. Cardiovascular Genetics Research Unit, Université Henri Poincaré-Nancy 1, Nancy, France.
Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany. Institute of Human Genetics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Institute of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark. Faculty of Health Science, University of Aarhus, Aarhus, Denmark. Department of Medicine III, Prevention and Care of Diabetes, University of Dresden, Dresden, Germany.
Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland. Research Program of Molecular Medicine, University of Helsinki, Helsinki, Finland. Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland.
South Ostrobothnia Central Hospital, Seinajoki, Finland. Collaborative Health Studies Coordinating Center, Seattle, Washington, USA. Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois CHUV University Hospital, Lausanne, Switzerland.
Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas, USA. Division of Research, Kaiser Permanente Northern California, Oakland, California, USA. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
Department of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Unit for Child and Adolescent Mental Health, Helsinki, Finland.
Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands. Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. Department of Psychiatry, University Medical Centre Groningen, Groningen, The Netherlands. Department of Neurology, General Central Hospital, Bolzano, Italy.
Department of Neurology, University of Lübeck, Lübeck, Germany. Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington, USA. Group Health Research Institute, Group Health, Seattle, Washington, USA. Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland, USA.
Department of Medical Sciences, Uppsala University, Molecular Medicine, Uppsala, Sweden. Institut für Community Medicine, Greifswald, Germany. Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois CHUV University Hospital, Lausanne, Switzerland.
Division of Biostatistics, Washington University School of Medicine, St. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
Carolina Center for Genome Sciences, School of Public Health, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA. Department of Medical Genetics, University of Helsinki, Helsinki, Finland.
Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland, USA. Division of Community Health Sciences, St George's, University of London, London, UK.
Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. Ludwig-Maximilians-Universität, Institute of Medical Informatics, Biometry and Epidemiology, Munich, Germany.
Faculty of Medicine, University of Iceland, Reykjavík, Iceland. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, Cambridge, UK.
Division of Intramural Research, National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, Massachusetts, USA. You can also search for this author in PubMed Google Scholar. Writing group: I. lead , C. lead , M. Mohlke, L. Steinthorsdottir, G. Waist phenotype working group: T.
lead , R. Steinthorsdottir, K. Stefansson, G. Data cleaning and analysis: S. lead , E. lead , J. Sex-specific analyses: S. Monda, K. lead , V. eQTL and expression analyses: S. Secondary analyses: S. Mangino, M. Mohlke, D. Study-specific analyses: G. Hayward, N. Johnson, J. Kaakinen, K.
Kapur, S. Ketkar, J. Kraft, A. Kettunen, C. Lamina, R. Lecoeur, H. Mangino, B. Monda, A. Polasek, I. Prokopenko, N. Silander, E. Stark, S. Steinthorsdottir, D. Vitart, L. Study-specific genotyping: D.
Dei, M. Hansen, A. Hayward, A. Kovacs, A. Kettunen, P. Kraft, R. Mangino, W. Pedersen, L. Vatin, P. Study-specific phenotyping: H. Dörr, C. Hengstenberg, A. Hofman, F. Jørgensen, P. Kuusisto, K. Kvaloy, R. Koskinen, V. Kähönen, P. Kovacs, O. Pedersen, C.
Pichler, K. Polasek, A. Salomaa, J. Saramies, P. Silander, N. Sinisalo, T. Study-specific management: G. Hansen, C. Hengstenberg, K. Hamsten, T. Jørgensen, M. Kaprio, M.
Kähönen, M. Marre, T. Mohlke, P. Salomaa, P. Stefansson, D. Steering committee: G. Haritunians, J. chair , D. Mohlke, K. Correspondence to Iris M Heid , Mark I McCarthy , Caroline S Fox , Karen L Mohlke or Cecilia M Lindgren.
and spouse own stock in Incyte Ltd and GlaxoSmithKline. is a member of the Scientific Advisory Board, Correlagen, Inc. is employed by Amgen. and G. are employed by deCODE Genetics. On behalf of the MAGIC Meta-Analyses of Glucose and Insulin-related traits Consortium investigators.
Reprints and permissions. Heid, I. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42 , — Download citation. Received : 06 May Accepted : 15 September Published : 10 October Issue Date : November Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.
European Journal of Human Genetics Reviews in Endocrine and Metabolic Disorders Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature.
nature nature genetics articles article. Subjects Genetic predisposition to disease Genome-wide association studies Physical examination Sexual dimorphism.
This article has been updated. Abstract Waist-hip ratio WHR is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity.
Access through your institution. Buy or subscribe. Change institution. The measurement of weight and WC was performed annually All participants were genotyped using the Illumina Infinium Global Screening Array version 1. After the QC, whereby a total of 13, markers were removed, , markers were imputed for each race by MiniMac V3 29 using the Genomes reference panel We constructed effect size—weighted genetic risk scores for BMI and WHR adjBMI.
The final genetic risk scores included independent single nucleotide polymorphisms SNPs for BMI and for WHR adjBMI Supplementary Tables 5 and 6. The scores were normally distributed among all ancestries Supplementary Fig.
We aligned each SNP based on the trait-increasing allele. The effect size of each trait-increasing allele was multiplied by the number of risk alleles carried by an individual, and the genetic risk score was calculated as the sum of the weighted alleles carried by the individual. The scores explained 1.
However, our findings showed that the performance of the score was not improved. The outcome measures were weight loss and WC reduction during the 1-year weight loss, as well as change in weight and WC from year 1 to year 2 and year 1 to year 4.
Data are reported as mean ± SD. Linear regression models were used to test genetic associations, using R version 4. All analyses were adjusted for age, self-reported sex, height, and baseline values of the outcome traits. Because the majority of participants in this study were of White ancestry and the genetic variants being studied were originally identified in primarily White European populations, a separate analysis was conducted for this group.
The analyses of combined ancestries were further adjusted for the first four genetic principal components PC to account for differences in genetic ancestry Supplementary Fig. We also examined the outcomes separately in the two intervention arms and in the subset of participants who regained weight from years 1 to 2 and years 1 to 4.
In the analyses of changes in WC, we adjusted for the year 1 value of WC, as well as corresponding changes in body weight. Before the analyses, we confirmed that the outcome traits followed a normal distribution, by visual inspection of the residuals from each model. The code is available upon request to the authors.
The Look AHEAD data are available in the National Institute of Diabetes and Digestive and Kidney Diseases repository.
The genetic data are not available, because of limitations in consent. On average, the ILI group lost From year 1 to year 2, ILI participants regained, on average, 2.
From year 1 to year 4, the weight for the ILI participants changed by an average of 5. Intergroup P values between ILI and DSE are derived from two-sample t test if the data followed a normal distribution, determined by examining histograms.
For nonnormal distributed traits weight loss from baseline to year 1 and relative weight loss , the Wilcoxon test was used.
For ancestries, the χ 2 test was applied due to the categorical variables. GRS, genetic risk score. We first examined whether there were differences in the effect of the BMI and WHR adjBMI genetic risk scores on weight and WC loss and regain between the ILI and DSE groups by testing for the significance of the interaction term between the genetic risk score and study group Supplementary Table 4.
We found no significant interaction between the BMI or WHR adjBMI genetic risk score and the study group in any of the analyses, so we combined the ILI and DSE groups in all analyses and adjusted for the study group as a covariate.
The BMI and WHR adjBMI genetic scores were not associated with weight loss during the 1-year intervention Supplementary Table 1. The BMI score was also not associated with the loss in WC Supplementary Table 2. The BMI and WHR adjBMI genetic scores were not associated with the change in body weight from year 1 to 2 or years 1 to 4 Table 2.
The BMI score was also not associated with the change in WC from year 1 to 2 or years 1 to 4 Table 3. No significant interactions were found, indicating that the genetic effects on weight loss or weight regain were not dependent on sex or age.
To test genetic associations specifically with weight regain, we conducted additional analysis of the subset of participants who regained weight.
The BMI and WHR adjBMI genetic scores were not associated with weight regain from year 1 to 2 or years 1 to 4 in the White ancestry group Supplementary Table 3. The BMI score was also not associated with WC regain from year 1 to 2 or years 1 to 4 Supplementary Table 3.
Hence, genetic predisposition to higher BMI was not associated with either weight loss or weight regain. Our sample size was limited for identifying SNPs associated with weight loss and weight regain.
In the present analyses of 1, participants from the Look AHEAD trial, individuals with a genetic predisposition to a higher WHR adjBMI experienced a smaller reduction in abdominal obesity weight loss during the first year of a weight loss intervention and a larger regain in WC over the following 3 years.
Genetic predisposition to higher BMI was not associated with either weight loss or weight regain. Weight loss and maintenance are controlled by a complex interplay of biological and behavioral mechanisms, and genetic diversity in these mechanisms can affect the effectiveness of weight loss and maintenance efforts Despite this, there is a lack of research on how genetic variation affects weight regain.
Furthermore, to our knowledge, there have been no studies that have examined the relationship between genetic factors and changes in abdominal obesity after weight loss interventions. One study found that a higher WHR adjBMI genetic risk score was associated with less weight loss 34 , but no influence was found for a BMI genetic risk score.
Another study found that a higher WHR adjBMI genetic risk score was associated with a smaller loss of abdominal obesity during the first year of weight loss interventions In the present study, we replicate these associations with a more comprehensive genetic risk score and, additionally, we show that a higher WHR adjBMI genetic risk score also was associated with a higher regain of abdominal fat in the years that follow weight loss.
Furthermore, our findings indicate that the detrimental effect of the WHR adjBMI genetic risk score on WC during the weight loss and weight maintenance periods leads to a compounded effect on abdominal adiposity. Previous research on the association between individual BMI risk variants and weight loss or regain has yielded mixed results, with some studies finding an association and others reporting a weak or no association 25 , 35 , In the present study, we included variants in the BMI score and did not observe an association between them and weight loss during a lifestyle intervention.
Additionally, this study is one of the first to examine whether BMI-associated variants predict weight gain after weight loss. We did not find any associations between BMI-associated variants and change in weight after initial weight loss. Furthermore, we found that the BMI-increasing genetic risk score was not associated with the change in abdominal obesity during the 1-year weight loss intervention or after.
Overall, our results suggest that obesity risk variants identified in cross-sectional studies may not influence longitudinal changes in body weight during weight loss interventions. This finding suggests that biological mechanisms regulating weight change during such interventions may differ from those that determine body weight in a stable state.
Consequently, there is a need for GWAS to identify genetic variants specifically associated with weight loss and weight regain to enable the design of appropriate polygenic scores and elucidate the underlying biology. After weight loss, a coordinated decrease in energy expenditure and an increase in appetite contribute to weight regain.
Previous research has identified many other potential mechanisms for weight regain, such as decreased resting metabolic rate and lowered leptin levels 37 , Adding a genetic association, or the lack thereof, to the broader context of weight regain can contribute to our understanding of the mechanisms behind the regain of WC and guide future research.
The distinct effects of the BMI and WHR adjBMI genetic risk scores likely reflect their distinct biological effects: genetic variants associated with BMI primarily influence central nervous system—related pathways, whereas WHR adjBMI variants have been implicated in adipose tissue biology and insulin resistance 39 , and this seems to be an important factor for abdominal fat mass change during weight loss The role of these variants associated with WHR adjBMI requires further investigation to determine whether they overlap with the mechanisms previously associated with weight regain, such as leptin or resting metabolic rate, or if they are independent of them.
This study adds new insights into the function of the variants associated both with overall adiposity and body fat distribution, an important perspective for understanding the significance of this research.
We found no interaction between the genetic risk scores and intervention arms on changes in weight and WC after weight loss. This implies that the results may not be specific to lifestyle intervention and might also apply to other types of weight loss interventions, such as pharmaceutical treatment or obesity surgery.
More research is needed to determine if these results hold for different intervention modalities. Currently, genetic variations for BMI and WHR do not appear to influence weight loss or regain after obesity surgery 41 , 42 , although there may be potential in combining clinical markers with genetic risk scores to improve the predictability of weight loss response after surgery Abdominal obesity is a major risk factor for cardiometabolic disease and type 2 diabetes 8 , Previous studies in the Look AHEAD trial have revealed that individuals who experienced the least favorable change in WC during weight loss had an increased risk of cardiovascular morbidity and mortality, regardless of the amount of weight loss The negative impact of the WHR adjBMI genetic risk score on abdominal obesity during weight loss and weight maintenance may undermine the benefits of the weight loss intervention The present study has several strengths, including the use of data from the large and well-documented Look AHEAD study, which resulted in significant weight loss and reduction in WC during the first year of the intervention, with annual follow-up of the participants.
The use of a polygenic approach with hundreds of SNPs improved our ability to detect associations compared with previous studies.
However, the study also has limitations. Thus, the results may not be applicable to younger or nondiabetic populations. The weight change in older participants in Look AHEAD may have been influenced by aging and reduced lean mass Furthermore, some participants continued to lose weight after the 1-year intervention.
However, we conducted a sensitivity analysis of those participants who regained weight from years 1 to 2 or years 1 to 4 to ensure the robustness of our findings. Our study was limited to analyzing the associations between genetic variants and obesity in combining races and ethnicities and in individuals of White ancestry, due to lack of sufficient sample sizes for other races and Hispanic ethnicity.
Despite this, some of the associations in the White ancestry did not reach statistical significance, which may have been due to the reduced sample size.
Additionally, the genetic variants used in the BMI and WHR adjBMI genetic risk scores were primarily identified in populations of European ancestry, which may not be optimal for studying diverse ancestries.
More research is needed to investigate potential differences in the genetic effects on general and abdominal obesity across different ethnicities. The GIANT Consortium is a major international collaboration of more than scientists that seeks to identify genetic sites that affect human body size and shape, including height and measures of obesity.
Identifying genetic variants associated with obesity is central to developing targeted interventions that can reduce the risk of chronic illnesses, such as hypertension, type 2 diabetes, and heart disease, to which obesity contributes in significant ways.
Genome-wide association studies previously identified 49 loci where the related genetic variants are located, that predispose individuals to a higher WHR.
Lower values of WHR are associated with lower incidence of these diseases. In this study, with a specific focus on coding variation, the team found 24 coding loci—15 common and nine rare—in individuals that are predisposed to higher WHR.
Further analysis revealed pathways and gene sets that influenced not only metabolism but also the regulation of body fat tissue, bone growth, and adiponectin, a hormone that controls glucose levels and breaks down fat.
The team also performed functional studies across other organisms and identified two genes that were associated with a significant increase in triglyceride levels and body fat across species. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, the researchers observed a significant increase in the total body triglyceride levels for two genes DNAH10 and PLXND1.
This could lead to a better understanding of how obesity causes downstream diseases such as type 2 diabetes and cardiovascular disease.
The identification of obesity-associated genes has been a genstic area of Goji Berry Snacks WHR and genetic predispositions the last two decades, altering predisposirions understanding of obesity. Specifically, predkspositions researchers present the first association of body-fat distribution, egnetic by predspositions ratio Predisopsitions adjusted for body mass index, WHR and genetic predispositions low-frequency and WHR and genetic predispositions genetic variants. The new study from the Genetic Investigation of Anthropometric Traits GIANT consortium identifies multiple genetic variants associated with how the body regulates and distributes body-fat tissue. The GIANT Consortium is a major international collaboration of more than scientists that seeks to identify genetic sites that affect human body size and shape, including height and measures of obesity. Identifying genetic variants associated with obesity is central to developing targeted interventions that can reduce the risk of chronic illnesses, such as hypertension, type 2 diabetes, and heart disease, to which obesity contributes in significant ways. Genetic variants, which are assigned at birth predisposihions largely randomly assorted in a population, Cultivates a harmonious mood be used as instrumental variables to estimate the predisppsitions association of predispostiions exposure eg, genetkc ratio WHR and genetic predispositions adjusted for body mass index [BMI] with an outcome of interest eg, coronary heart disease. This approach rests on 3 assumptions. First, the genetic variants must be associated with the exposure assumption 1. Second, the genetic variants must not be associated with confounders assumption 2. Third, the genetic variants must influence risk of the outcome through the exposure and not through other pathways assumption 3.
Vollkommen
Bemerkenswert, das sehr lustige Stück
ich beglückwünsche, welche nötige Wörter..., der glänzende Gedanke
die Maßgebliche Antwort, neugierig...
Nach meiner Meinung sind Sie nicht recht. Schreiben Sie mir in PM, wir werden besprechen.