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What is Metabolic Syndrome? (5 STEPS TO REVERSE METABOLIC SYNDROME!)WHR and metabolic syndrome -
For each consenting participant, sociodemographic characteristics and cardiovascular risk factors were obtained. Blood pressure, anthropometrics and handgrip strength were also measured. Triglycerides, total cholesterol and high-density lipoprotein cholesterol were estimated by enzymatic colorimetric method in an automatic analyzer Hitachi , Boehringer Mannheim and LDL-c was calculated.
For detecting dysglycemia, the enzymatic hexokinase method was applied to determine glucose levels in each sample. Individuals with a low educational level were those without schooling, primary schooling, or unknown academic history.
We considered smokers all those who consumed a daily tobacco product in the last 12 months and included those who reported having quit smoking in the last year. Never drinking was defined as self-reported abstinence, former drinking was defined as having ceased alcohol consumption for 1 year or more, and current drinking was defined as consumption of alcohol in the past year.
Blood pressure was taken with no smoking, physical activity, or food consumption during the previous 30 min and after the participant sat for 5 min. Anthropometric measurements were taken following the standardized protocol of the PURE study. Weight was measured using a digital scale with the participant lightly clothed with no shoes.
Height was measured to the nearest millimeter using a tape measure with the participant standing without shoes. Waist and hip circumferences were measured unclothed using a tape measure. The WC was considered the smallest circumference between the costal margin and the iliac crest. The hip circumference was measured at the level of the greater trochanters.
Handgrip strength was measured was evaluated on the individual's non-dominant hand using a Jamar dynamometer Sammons Preston, Bolingbrook, IL, USA , according to a standardized protocol [ 9 ].
Standing, the participant held the dynamometer at the side of the body with the elbow flexed at degree angle and was asked to squeeze the device as hard as possible for 3 s. This was repeated twice with 30 s rest between each attempt. Physical activity PA was evaluated using the International Physical Activity Questionnaire IPAQ.
IPAQ which assesses physical activity undertaken across a comprehensive set of domains, including leisure-time physical activity, domestic and gardening activities, work-related physical activity, transport-related physical activity.
These thresholds take into account that the IPAQ queries PA in multiple domains of daily life, resulting in higher median MET-minutes estimates than would be that estimated from considering leisure-time participation alone.
One point was conferred for each alteration of the cluster of MetS as defined by IDF elevated triglycerides, low HDL-c, dysglycemia, or high blood pressure , generating a score of 0 to 4 for each participant, a high score was considered if 2 or more points were achieved.
WC was not included in the calculation of our metabolic score as it was also an outcome variable. Descriptive statistics were computed for variables of interests and included absolute and relative frequencies of categorical factors.
Testing for differences in categorical variables was accomplished using the Chi-square test. Moreover, we used unconditional multivariate logistic regression models to assess the associations between anthropometric variables and handgrip strength, and the MetS score.
These analyses were adjusted for potential confounders, such as age, socioeconomic status, income and education level. We re-coded the anthropometric variables and handgrip strength into sex-specific tertiles and compared the risk of a higher MetS score in each tertile with the lowest category of risk reference group.
All statistical analysis was carried out using the R software version 3. The mean age was The overall prevalence of MetS was MetS was more frequent in women, people older than 50 years; it was also more frequent in individuals living in urban areas, former drinkers, and smokers.
The prevalence of MetS was higher in participants with a lower level of education compared with those with a high school or college degree. The percentage of subjects with MetS was lower in tertile 1 of BMI There were no significant differences in the prevalence of MetS across tertiles of HGS tertile 3: However, the prevalence of MetS Figure 1 shows the sex-specific distribution of the MetS scores.
The association between anthropometric variables and the risk of a higher MetS score is shown in Table 2. A higher WC was associated with a risk of a higher MetS score, with women and men in the tertile 3 of WC mean Participants in tertile 3 of BMI mean In women, lower HGS was associated with a significantly higher MetS score T3 vs.
In men, there were no significant differences in MetS score across HGS tertiles. The overall prevalence of MetS in this cohort of Colombian adults was A lower prevalence was reported by Higuita-Guitierrez in Colombian adults of which Aging is associated with an increase in adipose tissue and a decreased muscle mass [ 17 ], body composition changes which predispose to the development of metabolic alterations.
The prevalence of MetS was higher in women Lower educational level was associated with a higher prevalence of MetS Educational level is an indicator of social inequity, lower levels reflecting not only less schooling, but also a higher risk of unhealthy life habits, and lower access to employment and physical activity participation.
Social factors associated with MetS prevalence, should be further examined. We found that lower muscle strength and higher central adiposity as defined by waist circumference, were independently associated with a higher MetS score, representing a greater number of alterations of the components of the MetS cluster.
Our cross-sectional analysis showed a stronger association between a higher MetS score and WC than BMI, confirming previous studies showing that in Latin-American and Chinese population, WC is a stronger predictor of major cardiovascular events such as myocardial infarction or stroke than BMI, particularly in men [ 8 , 21 ].
Similarly, in diabetic Chinese adults, high visceral fat measured by a visceral adiposity index and WC were associated with a higher prevalence of diabetic kidney disease and CVD compared to BMI [ 22 ].
These findings may be related to the higher inflammatory load associated with visceral adipose tissue accumulation, and inflammation is considered a key factor associated with insulin resistance, MetS and CVD [ 23 , 24 ]. The low-grade pro-inflammatory state characterized by high C-reactive protein levels is observed in adults and youth in our population with high visceral adiposity [ 25 , 26 ].
However, the accumulation of visceral fat is not the only contributing factor in the development of a pro-inflammatory state. The accumulation of cardiac fat is also associated with higher levels of pro-inflammatory cytokines such as IL-6, IL-1, TNF-α, and the expression of adipokine fatty acid-binding protein 4 FABP4 that are associated with the development of MetS and the extent of coronary artery disease [ 27 , 28 ].
Hence, overall fat measurement should not be underestimated. For example, in a cohort of 1, Italian children and adolescents However, BMI cannot discriminate between lean body mass and fat mass; hence, BMI is not necessarily an appropriate parameter of excessive adiposity.
Body fat distribution may be more valuable than overall adiposity in the prediction of metabolic alterations. This aligns with the concept of an obesity paradox whereby subjects with higher BMI levels were shown to have lower levels of cardiovascular events [ 30 ].
Obesity induced alterations in body composition include both an increase in adipose and in low-density lean tissue, without an increment in normal- lean density tissue, suggesting a fatty infiltration of muscular tissue [ 31 ].
Furthermore, studies in Colombian adults have demonstrated that individuals with a high BMI due to higher muscle mass have a lower risk of CVD than individuals with the same BMI due to elevated adipose mass [ 32 ]. This highlights that not only adipose tissue influences insulin action, other tissues such as muscle and hepatic tissue also affect this interaction.
Therefore, in our population, WC continues to be the most applicable, easy to perform anthropometric indicator of adiposity and predictor of metabolic alterations and CV risk.
Furthermore, rather than a specific weight value, the cardiometabolic dysfunction produced by the adipose tissue's inflammation and its involvement in the muscle tissue should be managed. Few studies have examined associations between strength, adiposity, and MetS or its components in adults in low and middle-income countries and considered its association with CVD and mortality [ 1 ].
The PURE study, a large international prospective cohort that included the present population, demonstrated an association between low HGS and CVD and all-cause mortality in the population as a whole [ 9 ]. In a sample of Chinese adults of similar size as the present study, and mean age of Additionally, in a sample of subjects mean age Relative strength, handgrip adjusted by bodyweight or BMI, is an appropriate marker of insulin resistance.
Several levels of evidence support the notion that muscle strength is protective, and more so than muscle mass [ 39 , 40 ]. Prospective studies have established that low muscle strength, typically characterized using handgrip dynamometry, is predictive of cardiometabolic risk and mortality, independent of aerobic fitness and physical activity [ 9 , 41 ].
Furthermore, intervention studies also consistently show benefits of strength training on components of MetS and other relevant markers of CVD risk, such as C-reactive protein [ 43 ]. This is particularly relevant in low and middle-income countries on the basis that in these regions 1 there are steeper increases in the burden of chronic disease in low and middle-income countries [ 45 ] 2 lower muscle strength is reported compared to high -income countries [ 9 ] and 3 the protective effect of muscle strength on cardiometabolic health may be accentuated in individuals with lower birth weight, an indicator or poorer early life nutrition and a more common phenotype in the lower socioeconomic status within middle-income countries [ 26 ].
Considering the association between MetS cluster metabolic alterations and CVD, our findings suggest that public health strategies should not only focus on adiposity but also identify and address lower muscular strength in our population [ 10 , 46 ].
Our study has the limitation of cross-sectional analyses, in that we demonstrated associations between adiposity, strength, and MetS in our population without establishing causality in these associations. We did not use body composition methods such as bioimpedance or dual-energy X-ray absorptiometry that estimate muscle and fat mass.
Therefore, quantifying relative muscle strength in an individual through the simple, quick and low-cost measurement of handgrip dynamometry in addition to the classic anthropometric measurements of adiposity i.
Having greater muscle strength could be a protective factor against the metabolic alterations that constitute this syndrome. Handgrip strength is also associated with frailty and other non-cardiometabolic related chronic physical and mental health outcomes [ 47 ], so from a clinical perspective it can also contribute to the wider a screening of patient health.
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The metabolic syndrome—a new worldwide definition. All authors revised the manuscript and approved the final manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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J Clin Epidemiol. Moosaie F, Fatemi Abhari SM, Deravi N, Karimi Behnagh A, Esteghamati S, Dehghani Firouzabadi F, et al. Comparison of body mass index, waist circumference and waist hip ratio in different diagnostic criteria for metabolic syndrom[J]. Chinese Journal of Public Health, , 25 3 : doi: PDF KB.
Comparison of body mass index, waist circumference and waist hip ratio in different diagnostic criteria for metabolic syndrom. Received Date: Publish Date: Methods Based on the population survey data in Xuzhou,MS was diagnosed according to the three definitions respectively.
BMI,WC,WHR and their coefficient correlation were computed and compared with SPSS Results The prevalence of MS were Average value of BMI was BMI and WC were component of MS and acriterion for judging obesity.
For Chinese people,obesity may be a component of MS. FullText HTML.
Website Dyndrome. Chinese Journal syndrmoe Public Gut health nutrients 辽ICP备号 Address: Editorial Office of Chinese Journal of Public Health, no. Supported by: Beijing Renhe Information Technology Co. All Title Author Keyword Abstract DOI Category Address Fund.WHR and metabolic syndrome -
The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. Bianchi C, Penno G, Daniele G, Russo E, Giovannitti MG, Del Prato S, et al. The metabolic syndrome is related to albuminuria in type 2 diabetes. Diabet Med.
Luk AO, Ma RC, So WY, Yang XL, Kong AP, Ozaki R, et al. The NCEP-ATPIII but not the IDF criteria for the metabolic syndrome identify Type 2 diabetic patients at increased risk of chronic kidney disease.
Paneni F, Gregori M, Tocci G, Palano F, Ciavarella GM, Pignatelli G, et al. Do diabetes, metabolic syndrome or their association equally affect biventricular function? A tissue Doppler study. Hypertens Res. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications.
Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. National Cholesterol Education Program NCEP Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Adult Treatment Panel III.
Third report of the national cholesterol education program NCEP expert panel on detection, evaluation, and treatment of high blood cholesterol in adults Adult Treatment Panel III final report.
PubMed Abstract Google Scholar. Bloomgarden ZT. American association of clinical endocrinologists AACE consensus conference on the insulin resistance syndrome: August , Washington, DC. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition.
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity.
Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, et al. Optimal cut-off values of BMI, waist circumference and waist:height ratio for defining obesity in Chinese adults.
Br J Nutr. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. Alshamiri MQ, Mohd AHF, Al-Qahtani SS, Alghalayini KA, Al-Qattan OM, El-Shaer F.
Waist-to-height ratio WHtR in predicting coronary artery disease compared to body mass index and waist circumference in a single center from Saudi Arabia. Cardiol Res Pract. Ke JF, Wang JW, Lu JX, Zhang ZH, Liu Y, Li LX. Waist-to-height ratio has a stronger association with cardiovascular risks than waist circumference, waist-hip ratio and body mass index in type 2 diabetes.
Diabetes Res Clin Pract. Wu L, Zhu W, Qiao Q, Huang L, Li Y, Chen L. Nutr Metab. Ma A, Fang K, Dong J, Dong Z. Prevalence and related factors of metabolic syndrome in Beijing, China Year Obes Facts.
Tian T, Zhang J, Zhu Q, Xie W, Wang Y, Dai Y. Predicting value of five anthropometric measures in metabolic syndrome among Jiangsu Province, China.
BMC Public Health. Suliga E, Ciesla E, Głuszek-Osuch M, Rogula T, Głuszek S, Kozieł D. The usefulness of anthropometric indices to identify the risk of metabolic syndrome. Sinaga M, Worku M, Yemane T, Tegene E, Wakayo T, Girma T, et al.
Optimal cut-off for obesity and markers of metabolic syndrome for Ethiopian adults. Nutr J. Li LX, Zhao CC, Ren Y, Tu YF, Lu JX, Wu X, et al. Prevalence and clinical characteristics of carotid atherosclerosis in newly diagnosed patients with ketosis-onset diabetes: a cross-sectional study.
Cardiovasc Diabetol. Zhang ZH, Ke JF, Lu JX, Liu Y, Wang AP, Li LX. Serum retinol-binding protein levels are associated with nonalcoholic fatty liver disease in chinese patients with type 2 diabetes mellitus: a Real-World Study.
Diabetes Metab J. Li LX, Dong XH, Li MF, Zhang R, Li TT, Shen J, et al. Serum uric acid levels are associated with hypertension and metabolic syndrome but not atherosclerosis in Chinese inpatients with type 2 diabetes.
J Hypertens. Wang J-W, Ke J-F, Zhang Z-H, Lu J-X, Li L-X. Albuminuria but not low eGFR is closely associated with atherosclerosis in patients with type 2 diabetes: an Observational Study.
Diabetol Metab Syndr. Li L, Yu H, Zhu J, Wu X, Liu F, Zhang F, et al. The combination of carotid and lower extremity ultrasonography increases the detection of atherosclerosis in type 2 diabetes patients. J Diabetes Complications. Després JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al.
Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity.
Int J Food Sci Nutr. Schneider HJ, Klotsche J, Silber S, Stalla GK, Wittchen HU. Measuring abdominal obesity: effects of height on distribution of cardiometabolic risk factors risk using waist circumference and waist-to-height ratio. Alves Junior CA, Mocellin MC, Gonçalves ECA, Silva DA, Trindade EB.
Anthropometric indicators as body fat discriminators in children and adolescents: a systematic review and meta-analysis. Adv Nutr. Nevill AM, Stewart AD, Olds T, Duncan MJ. A new waist-to-height ratio predicts abdominal adiposity in adults.
Res Sports Med. Ejtahed HS, Kelishadi R, Qorbani M, Motlagh ME, Hasani-Ranjbar S, Angoorani P, et al. Utility of waist circumference-to-height ratio as a screening tool for generalized and central obesity among Iranian children and adolescents: the CASPIAN-V Study. Pediatr Diabetes.
Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. Moosaie F, Fatemi Abhari SM, Deravi N, Karimi Behnagh A, Esteghamati S, Dehghani Firouzabadi F, et al.
Waist-to-height ratio is a more accurate tool for predicting hypertension than waist-to-hip circumference and BMI in patients with type 2 diabetes: a Prospective Study. Front Public Health. Cao L, Zhou J, Chen Y, Wu Y, Wang Y, Liu T, et al.
Effects of body mass index, waist circumference, waist-to-height ratio and their changes on risks of dyslipidemia among chinese adults: the Guizhou Population Health Cohort Study. Int J Environ Res Public Health. Yang S, Li M, Chen Y, Zhao X, Chen X, Wang H, et al.
Comparison of the correlates between body mass index, waist circumference, waist-to-height ratio, and chronic kidney disease in a rural chinese adult population. J Ren Nutr. Hukportie DN, Li FR, Zhou R, Zheng JZ, Wu XX, Wu XB. Anthropometric measures and incident diabetic nephropathy in participants with type 2 diabetes mellitus.
Front Endocrinol. Guo X, Ding Q, Liang M. Evaluation of eight anthropometric indices for identification of metabolic syndrome in adults with diabetes. Diabetes Metab Syndr Obes.
Savva SC, Lamnisos D, Kafatos AG. Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Yang YJ, Park HJ, Won KB, Chang HJ, Park GM, Kim YG, et al. Relationship between the optimal cut-off values of anthropometric indices for predicting metabolic syndrome and carotid intima-medial thickness in a Korean population.
Pan J, Wang M, Ye Z, Yu M, Shen Y, He Q, et al. Optimal cut-off levels of obesity indices by different definitions of metabolic syndrome in a southeast rural Chinese population.
J Diabetes Investig. Shao J, Yu L, Shen X, Li D, Wang K. 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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George Davey Smith, MD, DSc; Lavinia Paternoster, PhD; Caroline Relton, PhD. Connor A. Emdin, DPhil; Amit V. 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.
Save Preferences. Privacy Policy Terms of Use. This Issue. Views 28, Citations View Metrics. X Facebook More LinkedIn. Cite This Citation Emdin CA , Khera AV , Natarajan P, et al. Original Investigation. February 14, Emdin, DPhil 1,2 ; Amit V. Chinese Journal of Public Health 辽ICP备号 Address: Editorial Office of Chinese Journal of Public Health, no.
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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.
Department of Nutriology, the First Ans 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.
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