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What is Metabolic Syndrome? (5 STEPS TO REVERSE METABOLIC SYNDROME!)WHR and metabolic syndrome -
Study of relationship between BMI, WHR and prevalence rate of metabolic syndrome. Received Date: Publish Date: Objective To explore the relationship between body mass index BMI , waist to hip ratio WHR and the prevalence rate of hypertension, hyperglycemia and disorder in lipo-metabolism amongmental lobourers and to provide reference for further prevention and control of chronic disease.
Methods 4 subjects above 35 years old were selected by cluster sampling. The comparison of prevalence rate of the metabolic abnormalities in different abonormal BMI, WHR groups. Results The rate of the BMI abnormal and WHR abnormal were Their relative risk were 3. Conclusion Obesity, expecially in obesity of abnormal type, may increase the risk of the metabolic abnormalities.
Prevention and intervention of obesity are in urgent need among senior mental lobourers. FullText HTML. References 4. 中华流行病学杂志, , 23 1 : 中华糖尿病杂志, , 12 3 : 中华内科杂志, , 39 4 : 杨琼芬, 李显文, 黄梅芳, 等. 中国公共卫生, , 6 : Relative Articles. Supplements 0. Cited By. Proportional views. 通讯作者: 陈斌, bchen63 com 1.
沈阳化工大学材料科学与工程学院 沈阳 本站搜索 百度学术搜索 万方数据库搜索 CNKI搜索. Get Citation. Genetic variants, which are assigned at birth and largely randomly assorted in a population, can be used as instrumental variables to estimate the causal association of an exposure eg, waist-to-hip ratio [WHR] 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.
Mendelian randomization can be extended to estimate the association of exposure with outcome that is mediated by a given a mediator eg, triglycerides. A polygenic score of 48 single-nucleotide polymorphisms was used as an instrument to estimate the causal association of waist-to-hip ratio WHR adjusted for body mass index BMI with cardiometabolic quantitative traits, type 2 diabetes, and coronary heart disease; sources of data for analysis included the UK Biobank and publicly available genome-wide association studies.
Results are standardized to a 1-SD increase in waist-to-hip ratio WHR adjusted for body mass index BMI due to polygenic risk score. For systolic blood pressure, a 1-SD genetic increase in WHR adjusted for BMI is associated with a 2. For anthropometric traits, estimates from Genetic Investigation of Anthropometric Traits GIANT derived using inverse variance—weighted fixed-effects meta-analysis 14 , 15 were pooled with data from the UK Biobank derived instrumental variables regression adjusting for age, sex, 10 principal components of ancestry, and array type using inverse variance—weighted fixed-effects meta-analysis.
For lipids, glycemic, and renal function traits, estimates were derived from genome-wide association studies Global Lipids Genetics, 16 Meta-analyses of Glucose and Insulin-Related Traits, 17 and Chronic Kidney Genetics Consortia, 12 respectively.
For blood pressure, estimates were derived from UK Biobank. Two-hour glucose refers to measured blood glucose levels 2 hours after consumption of dissolved glucose.
Size of data markers is inversely proportional to variance of estimate. eGFR indicates estimated glomerular filtration rate; HbA 1c , hemoglobin A 1c ; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; WHR, waist-to-hip ratio.
Results are standardized to a 1-SD increase in waist-to-hip ratio adjusted for body mass index due to polygenic risk score. All estimates were derived in UK Biobank using instrumental variables regression adjusting for age, sex, and 10 principal components of ancestry.
COPD indicates chronic obstructive pulmonary disease; OR, odds ratio; SNP, single-nucleotide polymorphism.
eTable 1. Forty-Eight Single Nucleotide Polymorphisms Used as Instrumental Variables in the Primary Analysis. eTable 2. Eight Single Nucleotide Polymorphisms Used as an Instrument for Increased Waist-To-Hip Ratio Adjusted for Body Mass Index in Women but not in Men.
eTable 5. Association of WHRadjBMI Polygenic Risk Score With Potential Confounders in UK Biobank. eTable 6. Association of Observational WHRadjBMI With Potential Confounders in UK Biobank.
eTable 7. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes and Coronary Heart Disease, Overall and by Quintile of WHRadjBMI.
eTable 8. P-Value for Association of 48 Variants With WHRadjBMI and Unadjusted WHR in Sex Combined and Sex Specific Analysis. eFigure 1. Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Cardiometabolic Traits Using Three Instruments.
eFigure 2. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes Using Three Instruments.
eFigure 3. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Coronary Heart Disease Using Three Instruments. eFigure 4. Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Cardiometabolic Traits Using Three Instruments, After Additional Adjustment for Body Mass Index.
eFigure 5. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes Using Three Instruments With Additional Adjustment for Body Mass Index.
eFigure 6. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Coronary Heart Disease Using Three Instruments With Additional Adjustment for Body Mass Index. eFigure 7. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes and Coronary Heart Disease Using Weighted Median Regression.
eFigure 8. eFigure 9. eFigure Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Coronary Heart Disease, Before and After Adjustment for the Mediating Association of Triglycerides, Using the Primary 48 SNP Polygenic Risk Score. Association of WHRadjBMI With Asthma, With Estimates of the Association of Variants With Asthma Derived from the GABRIEL Collaboration.
Emdin CA , Khera AV , Natarajan P, et al. Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease. Question Is genetic evidence consistent with a causal relationship among waist-to-hip ratio adjusted for body mass index a measure of abdominal adiposity , type 2 diabetes, and coronary heart disease?
Findings In this mendelian randomization study, a polygenic risk score for increased waist-to-hip ratio adjusted for body mass index was significantly associated with adverse cardiometabolic traits and higher risks for both type 2 diabetes and coronary heart disease.
Meaning These results provide evidence supportive of a causal association between abdominal adiposity and the development of type 2 diabetes and coronary heart disease. Importance In observational studies, abdominal adiposity has been associated with type 2 diabetes and coronary heart disease CHD.
Whether these associations represent causal relationships remains uncertain. Objective To test the association of a polygenic risk score for waist-to-hip ratio WHR adjusted for body mass index BMI , a measure of abdominal adiposity, with type 2 diabetes and CHD through the potential intermediates of blood lipids, blood pressure, and glycemic phenotypes.
Design, Setting, and Participants A polygenic risk score for WHR adjusted for BMI, a measure of genetic predisposition to abdominal adiposity, was constructed with 48 single-nucleotide polymorphisms.
The association of this score with cardiometabolic traits, type 2 diabetes, and CHD was tested in a mendelian randomization analysis that combined case-control and cross-sectional data sets. Exposures Genetic predisposition to increased WHR adjusted for BMI.
A 1-SD genetic increase in WHR adjusted for BMI was also associated with a higher risk of type 2 diabetes odds ratio, 1. Conclusions and Relevance A genetic predisposition to higher waist-to-hip ratio adjusted for body mass index was associated with increased risk of type 2 diabetes and coronary heart disease.
These results provide evidence supportive of a causal association between abdominal adiposity and these outcomes. Obesity, typically defined on the basis of body mass index BMI , is a leading cause of type 2 diabetes and coronary heart disease CHD in the population. In observational studies, abdominal adiposity has been associated with cardiometabolic disease 6 , 7 ; however, whether this association is causal remains unclear.
For example, unmeasured lifestyle factors 8 might confound observational studies that link WHR adjusted for BMI with type 2 diabetes and CHD. Furthermore, reverse causality could similarly lead to a statistically robust but noncausal relationship.
For example, individuals with subclinical CHD might develop abdominal adiposity because of an inability to exercise. Quiz Ref ID Mendelian randomization is a human genetics tool that leverages the random assortment of genetic variants at time of conception to facilitate causal inference.
In this study, a mendelian randomization approach was used to determine whether a genetic predisposition to increased WHR adjusted for BMI is associated with cardiometabolic quantitative traits, type 2 diabetes, and CHD. Observational epidemiology studies test association of an exposure eg, WHR adjusted for BMI with an outcome eg, CHD.
However, unobserved confounders may affect both exposure and outcome, thus biasing the observed association Figure 1 ; eMethods A in the Supplement. Because genetic variants are both randomly assorted in a population and assigned at conception, they are largely unassociated with confounders and can be used as instrumental variables to estimate the causal association of an exposure WHR adjusted for BMI with an outcome.
This mendelian randomization approach has 3 assumptions. Second, genetic variants must not be associated with confounders assumption 2 in Figure 1. Third, genetic variants must not be associated with outcome independently of the exposure assumption 3 in Figure 1.
The second and third assumptions are collectively known as independence from pleiotropy. Mendelian randomization can be extended to conduct a mediation analysis, estimating the proportion of an observed association of an exposure WHR adjusted for BMI with an outcome CHD that occurs through a given mediator Figure 1.
A mendelian randomization study using publicly available, summary-level data from large-scale genome-wide association studies GWASs both cross-sectional and case-control data sets as well as individual-level data from the UK Biobank a cross-sectional data set was conducted Figure 2.
A recent large-scale GWAS from the Genome-Wide Investigation of Anthropometric Traits GIANT Consortium identified 48 single-nucleotide polymorphisms SNPs , or genetic variants, associated with WHR adjusted for BMI eTable 1 in the Supplement.
Summary-level data from 6 GWAS consortia were used GWASs conducted from to eTable 3; eMethods B in the Supplement. The results from 5 additional GWAS conducted from to examining blood lipids, glycemic traits, renal function, type 2 diabetes, and CHD, and predominantly including individuals of European descent, were also assessed.
Informed consent was obtained from all participants of contributing studies. Contributing studies received ethical approval from their respective institutional review boards. Analysis of the UK Biobank data was approved by the Partners Health Care institutional review board protocol P Informed consent was obtained from all participants by the UK Biobank.
WHR adjusted for BMI was derived in the UK Biobank through inverse normal transformation of WHR after adjustment for age, sex, and BMI as in the GIANT Consortium Type 2 diabetes and CHD were both ascertained at baseline by self-report, followed by a verbal interview with a trained nurse to confirm the diagnosis eTable 4 in the Supplement.
Type 2 diabetes was defined as report of type 2 diabetes, report of type 2 diabetes unspecified, or current use of insulin medication. CHD was defined as report of previous myocardial infarction or diagnosis of angina or hospitalization for myocardial infarction International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes II In addition to the primary outcomes of type 2 diabetes and CHD, a phenome-wide association study an analysis of the association of a genetic variant or polygenic risk score with a broad range of diseases, outcomes, or both for 35 additional diseases, including endocrine, renal, urologic, gastrointestinal, neurologic, musculoskeletal, respiratory, and cancer disorders, was conducted in the UK Biobank to attempt to identify whether the polygenic risk score for WHR adjusted for BMI is associated with any additional disorders eTable 4 in the Supplement.
For analyses of both summary-level data and UK Biobank data, a weighted polygenic risk score was derived based on the magnitude of association of each SNP with WHR adjusted for BMI in the previously published GIANT analysis.
For the summary-level data, this approach is equivalent to an inverse-variance—weighted fixed-effects meta-analysis of the association of each SNP with the trait or outcome of interest eg, CHD , divided by the association of each SNP with WHR adjusted for BMI.
To validate that the polygenic risk score for WHR adjusted for BMI was a strong instrument for WHR adjusted for BMI assumption 1 in Figure 1 , an F statistic for the instrument was calculated in the UK Biobank. An F statistic is a measure of the significance of an instrument the polygenic risk score for prediction of the exposure WHR adjusted for BMI , controlling for additional covariates age, sex, 10 principal components of ancestry, and a dummy variable for the array type used in genotyping.
An F statistic greater than 10 is evidence of a strong instrument. For individual-level data from the UK Biobank, logistic regression was used to determine association of a polygenic risk score for WHR adjusted for BMI and dichotomous outcomes type 2 diabetes, CHD, and 35 additional diseases eMethods C in the Supplement.
All UK Biobank analyses included adjustment for age, sex, 10 principal components of ancestry, and a dummy variable for the array type used in genotyping. The inclusion of principal components of ancestry as covariates is commonly implemented to correct for population stratification according to ancestral background.
To test assumption 2 independence of polygenic risk score for WHR adjusted for BMI from potential confounders Figure 1 , the relationship of the polygenic risk score to smoking, alcohol use, physical activity, vegetable consumption, red meat consumption, and breastfeeding status as a child was determined among individuals in the UK Biobank.
Test for trend was performed across quartiles of the polygenic risk score for WHR adjusted for BMI using logistic regression, with each potential confounder as the outcome. For comparison, individuals in the UK Biobank were stratified into quartiles by observational WHR adjusted for BMI and test for trend performed using logistic regression.
Five additional sensitivity analyses were conducted to test the robustness of the results eMethods D in the Supplement. Three additional polygenic risk scores were used, including one that included variants not significantly associated with BMI, a second that included variants significantly associated with gene expression in adipose tissue, and a third that included variants significantly associated with increased WHR adjusted for BMI in women but not in men.
The association of genetic variants with BMI was adjusted for, and median regression was used eMethods D in the Supplement. Absolute increases associated with WHR adjusted for BMI for type 2 diabetes and CHD were calculated using the United States population incidence of type 2 diabetes and CHD eMethods E in the Supplement.
Tests for nonlinear associations of a genetic predisposition to increased WHR adjusted for BMI with type 2 diabetes and CHD were performed using nonlinear instrumental variable estimation eMethods F in the Supplement. Among continuous traits, the polygenic risk score for WHR adjusted for BMI was most strongly associated with plasma triglyceride levels.
The extent to which the polygenic risk score association with CHD was mediated by plasma triglycerides was tested using mediation analysis, conducted post hoc after triglyceride level was identified as the cardiometabolic trait most strongly associated with WHR adjusted for BMI.
An estimate of the genetic association of triglyceride level on CHD risk, previously derived by Do et al 26 odds ratio [OR], 1. To derive the remaining proportion of CHD risk unaccounted for by an increase in triglyceride levels, the magnitude of association of the change in triglyceride level with CHD was subtracted from the estimate of the genetic association of WHR adjusted for BMI with CHD estimated using logistic regression.
Analyses were performed using R version 3. The characteristics of UK Biobank participants are reported in the Table. The mean age was To test assumption 2 independence of polygenic risk score for WHR adjusted for BMI from potential confounders, Figure 1 , the relationship of the polygenic risk score to smoking, alcohol use, physical activity, vegetable consumption, red meat consumption, and breastfeeding status as a child was determined among individuals in the UK Biobank.
In each case, no significant relationship was noted eTable 5 in the Supplement. For comparison, a similar analysis that categorized individuals according to observed WHR adjusted for BMI instead of genetic predisposition to WHR adjusted for BMI was conducted eTable 6 in the Supplement.
In this observational epidemiology analysis, WHR adjusted for BMI was associated with each potential confounder. A 1-SD increase in WHR adjusted for BMI due to the polygenic risk score was associated with higher total cholesterol level 5. A 1-SD increase in WHR adjusted for BMI due to the polygenic risk score was associated with higher log-transformed fasting insulin levels 0.
A 1-SD increase in WHR adjusted for BMI due to the polygenic risk score was associated with a higher risk of type 2 diabetes OR, 1. A 1-SD increase in WHR adjusted for BMI due to the polygenic risk score was also associated with higher risk of CHD OR, 1. Five sensitivity analyses eMethods D, eFigures in the Supplement of the genetic association of WHR adjusted for BMI with cardiometabolic traits, type 2 diabetes, and CHD were conducted to examine if results were influenced by pleiotropy ie, a violation of assumptions 2 or 3 in Figure 1.
Four of the 5 sensitivity analyses were consistent with the results not being influenced by pleiotropy eFigures in the Supplement. In the fifth sensitivity analysis, 8 SNPs associated with increased WHR adjusted for BMI in women but not men were combined in an additive risk score.
If increased WHR adjusted for BMI causes CHD rather than results being due to pleiotropy , then a risk score that increases WHR adjusted for BMI in women but not in men should increase risk of CHD in women but not in men. Using the polygenic risk score of 48 SNPs associated with WHR adjusted for BMI, a phenome-wide association study of 35 additional diseases in the UK Biobank was conducted Figure 5.
In mediation analysis, the association of polygenic risk score for WHR adjusted for BMI with CHD was attenuated from an OR of 1. Mendelian randomization analyses tested if human genetic evidence supported a causal relationship of WHR adjusted for BMI a measure of abdominal adiposity with type 2 diabetes and CHD.
Quiz Ref ID Genetic predisposition to higher WHR adjusted for BMI was associated with increased levels of quantitative risk factors lipids, insulin, glucose, and systolic blood pressure as well as a higher risk for type 2 diabetes OR, 1.
These results permit several conclusions. First, these findings lend human genetic support to previous observations associating abdominal adiposity with cardiometabolic disease.
Indeed, in this study, observational WHR adjusted for BMI was strongly associated with potential confounders, illustrating a limitation of observational epidemiology. Here, these prior findings were extended by testing a polygenic risk score that appeared independent of measured confounders eTable 5 in the Supplement.
Elevated levels of triglyceride-rich lipoproteins, a risk factor for CHD with genetic and experimental evidence for causality, 26 , 27 appeared to mediate a substantial proportion of the increased risk for CHD. Second, these results suggest that body fat distribution, beyond simple measurement of BMI, could explain part of the variation in risk of type 2 diabetes and CHD noted across individuals and subpopulations.
For example, increased abdominal adiposity at a given BMI has been proposed as an explanation for the excess risk of CHD observed in South Asians.
Third, WHR adjusted for BMI might prove useful as a biomarker for the development of therapies to prevent type 2 diabetes and CHD. Although a substantial focus of drug development has been toward therapeutics to reduce overall adiposity, 30 there has been little effort toward the development of therapies that modify body fat distribution to reduce abdominal adiposity.
Ongoing research to understand the mechanistic links between the numerous genetic loci that influence WHR adjusted for BMI may lead to novel therapeutic strategies to reduce abdominal adiposity and reduce the risk of type 2 diabetes and CHD.
The mendelian randomization approach used in this study rests on 2 major principles Figure 1. First, it requires a strong link between the genetic variants used as an instrument and the exposure WHR adjusted for BMI, assumption 1 in Figure 1.
The SNP polygenic risk score explained 1. Although it is not possible to directly test whether pleiotropy is present in any mendelian randomization study, 32 a number of steps were taken in this study to reduce the risk of pleiotropy, including use of 3 different genetic instruments, use of weighted median regression, and use of an instrument associated with higher WHR adjusted for BMI in women but not men.
Results from 4 of 5 of these sensitivity analyses were consistent with the primary results. Tests for interaction using sex-specific instruments for CHD and diabetes were directionally consistent with expectation but did not demonstrate significant heterogeneity of effect by sex. This analysis required individual-level data available only in UK Biobank participants and may have been underpowered to detect a difference.
Future research that explores such sex-specific instruments in larger data sets may prove more conclusive. Quiz Ref ID This study has several limitations. First, although a number of approaches were used in an attempt to rule out pleiotropy, it is possible that these results represent a shared genetic basis between WHR adjusted for BMI and CHD rather than a causal relationship.
Second, prevalent events largely derived from a verbal interview with a nurse were used for the phenome-wide association study of 35 different disorders. Although these events are likely to be of greater specificity and sensitivity than coded mortality data, they have not been independently validated.
Third, the phenome-wide association study may have been underpowered to detect an association of genetic WHR adjusted for BMI with outcomes other than type 2 diabetes and CHD. Fourth, this analysis was restricted to individuals of European ancestry; the association of genetic WHR adjusted for BMI with type 2 diabetes and CHD may differ by ethnicity or genetic ancestry.
A genetic predisposition to higher WHR adjusted for BMI was associated with increased risk of type 2 diabetes and CHD. Corresponding Author: Sekar Kathiresan, MD, Center for Genomic Medicine, Massachusetts General Hospital, Cambridge St, CPZN 5.
Author Contributions: Dr Emdin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Metabolism boosting foods for a flat stomach Syndrrome. Chinese Journal of Public Health 辽ICP备号 Address: Editorial Office of Chinese Journal of Sndrome Health, no. Supported by: Beijing Renhe Information Technology Co. All Title Author Keyword Abstract DOI Category Address Fund. PDF Cite Share facebook twitter google linkedin All Title Author Keyword Abstract DOI Category Address Fund.
Ane of Nutriology, Hydration importance First Affiliated Hospital, Anhui Medical University HefeiChina. Tables 3. Website Copyright. Chinese Journal of Public Health 辽ICP备号 Address: Editorial Office of Chinese Journal of Public Health, no. WHR and metabolic syndrome -
Corresponding Author: Sekar Kathiresan, MD, Center for Genomic Medicine, Massachusetts General Hospital, Cambridge St, CPZN 5. Author Contributions: Dr Emdin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Drs Emdin and Khera contributed equally. Critical revision of the manuscript for important intellectual content: Emdin, Khera, Klarin, Zekavat, Hsiao, Kathiresan.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Khera reported receiving personal fees from Merck and Amarin Pharmaceuticals.
Dr Kathiresan reported receiving grants from Bayer Healthcare, Amarin, and Regeneron; serving on scientific advisory boards for Catabasis, Regeneron Genetics Center, Merck, Celera, and Genomics PLC; receiving personal fees from Novartis, Sanofi, AstraZeneca, Alnylam, Eli Lilly, Lerink Partners, Noble Insights, Bayer, and Ionis; receiving consulting fees from Regeneron, Merck, Quest Diagnostics, Novartis, Amgen, Genentech, Corvidia, Genomics PLC, Ionis Pharmaceuticals, and Eli Lilly; and holding equity in Catabasis and San Therapeutics.
No other authors reported disclosures. LaDue Memorial Fellowship in Cardiology. Dr Klarin is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health NIH under award T32 HL Dr Kathiresan is supported by the Ofer and Shelly Nemirovsky Research Scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, and R01HL from the NIH.
Additional Information: This project was conducted using the UK Biobank resource project ID full text icon Full Text. Download PDF Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References. Figure 1. Assumptions of a Mendelian Randomization Analysis.
View Large Download. Figure 3. Association of SNP Polygenic Risk Score for WHR Adjusted for BMI With Cardiometabolic Quantitative Traits.
a Units reported in column 1. b Calculated as weight in kilograms divided by height in meters squared. Figure 4. Association of SNP Polygenic Risk Score for WHR Adjusted for BMI With Type 2 Diabetes and Coronary Heart Disease. Figure 5. Phenome-Wide Association Study Testing if SNP Polygenic Risk Score for WHR Adjusted for BMI Is Associated With a Range of Disease Phenotypes.
Characteristics of UK Biobank Participants. eMethods eTable 1. Forty-Eight Single Nucleotide Polymorphisms Used as Instrumental Variables in the Primary Analysis eTable 2.
Eight Single Nucleotide Polymorphisms Used as an Instrument for Increased Waist-To-Hip Ratio Adjusted for Body Mass Index in Women but not in Men eTable 3. Summary of Included Genome-Wide Association Studies eTable 4.
Definitions of Diseases Ascertained at Baseline in UK Biobank eTable 5. Association of WHRadjBMI Polygenic Risk Score With Potential Confounders in UK Biobank eTable 6.
Association of Observational WHRadjBMI With Potential Confounders in UK Biobank eTable 7. Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes and Coronary Heart Disease, Overall and by Quintile of WHRadjBMI eTable 8.
P-Value for Association of 48 Variants With WHRadjBMI and Unadjusted WHR in Sex Combined and Sex Specific Analysis eFigure 1. Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Cardiometabolic Traits Using Three Instruments eFigure 2.
Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes Using Three Instruments eFigure 3.
Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Coronary Heart Disease Using Three Instruments eFigure 4. Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Cardiometabolic Traits Using Three Instruments, After Additional Adjustment for Body Mass Index eFigure 5.
Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes Using Three Instruments With Additional Adjustment for Body Mass Index eFigure 6.
Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Coronary Heart Disease Using Three Instruments With Additional Adjustment for Body Mass Index eFigure 7.
Association of Genetically-Elevated Waist-to-Hip Ratio Adjusted for Body Mass Index One Standard Deviation Increase With Type 2 Diabetes and Coronary Heart Disease Using Weighted Median Regression eFigure 8.
Association of Genetic Waist-to-Hip Ratio Adjusted for Body Mass Index With Coronary Heart Disease, Before and After Adjustment for the Mediating Association of Triglycerides, Using the Primary 48 SNP Polygenic Risk Score eFigure Association of WHRadjBMI With Asthma, With Estimates of the Association of Variants With Asthma Derived from the GABRIEL Collaboration eReferences.
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Khera, MD; Sekar Kathiresan, MD. JAMA Genomics Website. See More About Genetics and Genomics Diabetes Diabetes and Endocrinology Cardiology Ischemic Heart Disease Obesity. Select Your Interests Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below.
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Original Investigation. February 14, Emdin, DPhil 1,2 ; Amit V. Khera, MD 1,2 ; Pradeep Natarajan, MD 1,2 ; et al Derek Klarin, MD 1,2 ; Seyedeh M. Zekavat, BSc 1,2 ; Allan J. Hsiao, MPhil 3 ; Sekar Kathiresan, MD 1,2.
Author Affiliations Article Information 1 Center for Genomic Medicine and Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston.
visual abstract icon Visual Abstract. Key Points Question Is genetic evidence consistent with a causal relationship among waist-to-hip ratio adjusted for body mass index a measure of abdominal adiposity , type 2 diabetes, and coronary heart disease? Study Design and Instruments. Data Sources and Study Participants.
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Volume 22 Issue 12 Jun. Turn off MathJax Article Contents. JIANG Jianhua, XIAO Yongkang, HU Chuanlai,. Study of relationship between BMI, WHR and prevalence rate of metabolic syndrome[J]. Chinese Journal of Public Health, , 22 12 : doi: PDF KB. Study of relationship between BMI, WHR and prevalence rate of metabolic syndrome.
Received Date: Publish Date: Objective To explore the relationship between body mass index BMI , waist to hip ratio WHR and the prevalence rate of hypertension, hyperglycemia and disorder in lipo-metabolism amongmental lobourers and to provide reference for further prevention and control of chronic disease.
Methods 4 subjects above 35 years old were selected by cluster sampling. The comparison of prevalence rate of the metabolic abnormalities in different abonormal BMI, WHR groups. Results The rate of the BMI abnormal and WHR abnormal were Their relative risk were 3.
Conclusion Obesity, expecially in obesity of abnormal type, may increase the risk of the metabolic abnormalities.
Prevention and intervention of obesity are in urgent need among senior mental lobourers. FullText HTML. References 4. 中华流行病学杂志, , 23 1 :
Diet and exercise for body transformation Diabetology volume 20Syndroem number: 68 Cite this article. Metrics details. Adiposity is a WHR and metabolic syndrome metabbolic of Metabolism boosting foods for a flat stomach metabolic syndrome MetSmetaholic muscle strength metaboli also been identified as a risk factor for MetS and for cardiovascular disease. We describe the prevalence of MetS and evaluate the relationship between muscle strength, anthropometric measures of adiposity, and associations with the cluster of the components of MetS, in a middle-income country. MetS was defined by the International Diabetes Federation criteria.
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