Body mass index and waist circumference: Relationship to cardiometabolic risk factors in children - Busselton Health Study 2005-2007

2013 ◽  
Vol 49 (11) ◽  
pp. 955-962 ◽  
Author(s):  
Lana Bell ◽  
Joseph Hung ◽  
Matthew Knuiman ◽  
Mark Divitini ◽  
John Beilby ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Health Study

scholarly journals Body Mass Index, Waist Circumference, and the Clustering of Cardiometabolic Risk Factors in Early Childhood

2015 ◽  
Vol 30 (2) ◽  
pp. 160-170 ◽  
Author(s):  
Laura N. Anderson ◽  
Gerald Lebovic ◽  
Jill Hamilton ◽  
Anthony J. Hanley ◽  
Brian W. McCrindle ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Early Childhood ◽  
Waist Circumference ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors

scholarly journals Correlations of Anthropometric and Calculation-Modified Anthropometric Measurements with Cardiometabolic Risk Factors in Korean Adults: A National Survey-Based Study

2021 ◽  
Author(s):  
Kihong Hong
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Central Obesity ◽  
Cardiometabolic Risk ◽  
Anthropometric Measurements ◽  
Cardiometabolic Risk Factors ◽  
Male And Female

Abstract Background and Aims: There are studies that show NC measurements are associated with central obesity and upper body fat distribution and are strongly associated with metabolic markers. Recently, studies have been conducted regarding neck circumference (NC) as a novel index to screen for obesity and cardiometabolic risk factors. Here, we investigated various anthropometric measurements and their correlations with cardiometabolic risk factors, pulmonary function test (PFT) results and metabolic syndrome among the adult Korean population. Methods: This study was based on data acquired from the 8th edition (2019) of the Korea National Health and Nutrition Examination Survey (KNHANES), consisting of survey data on smoking and alcohol consumption, cardiometabolic risk factors, PFT results, metabolic syndrome profile and baseline characteristics such as age, gender and blood pressure. Continuous variables were analyzed by independent t-test, while categorical variables were analyzed using the Rao-Scott chi-square test Precision-recall (PR) plots were used to assess the diagnostic value of anthropometric measurements for cardiometabolic risk profiles such as insulin resistance, impaired glucose tolerance, dyslipidemia, hypertension, central obesity and PFT results. Area under the curve (AUC) was used to evaluate the predictive ability of the anthropometric measurements and calculation-modified anthropometric measurements (CMAMs) such as NC(neck circumference) devided by BMI(body mass index) and WC(waist circumference) and BMI devided by WC at different preset thresholds. Calculation was used to distinguish each anthropometric measurements aside from common features they may share. Results: A total of 3525study subjects aged over 40 years were included in the study the mean age for male and female were 56.8 and 58.5 respectively, percentage of male and female was 42% and 58% respectively. PR plots of anthropometric measurements and the metabolic syndrome profile indicated that in the presence of 3 metabolic syndrome criteria, waist circumference (WC) and body mass index (BMI) had the highest AUCs 0.62 and 0.587 respectively, in the presence of 4 metabolic syndrome criteria, waist circumference (WC) and body mass index (BMI) had the highest AUCs 0.342 and 0.317 respectively whereas in the presence of more than 5 metabolic syndrome criteria, NC and WC had the highest AUCs 0.09 and 0.083 respectively.Conclusion: This study showed that NC is correlated with metabolic syndrome. Additionally, each anthropometric measurement was uniquely correlated with specific cardiometabolic risk factor and differed according to sex. In such cases, applications in smartphones could be used to calculate for cardiometabolic risk assessments. Also it may be necessary to screen for metabolic syndrome in people whose NC is over 39.9cm for males and 34.5cm for females.

scholarly journals Cross-Sectional and Longitudinal Associations between Body Mass Index and Cardiometabolic Risk Factors in Adolescents in a Country of the African Region

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Tanica Lyngdoh ◽  
Bharathi Viswanathan ◽  
Edwin van Wijngaarden ◽  
Gary J. Myers ◽  
Pascal Bovet
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Body Mass Index ◽  
Young Adults ◽  
Body Mass ◽  
Weight Status ◽  
Cardiometabolic Risk ◽  
Linear Regression Analysis ◽  
Cardiometabolic Risk Factors ◽  
Cross Sectional

We assessed the association between several cardiometabolic risk factors (CRFs) (blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, uric acid, and glucose) in 390 young adults aged 19-20 years in Seychelles (Indian Ocean, Africa) and body mass index (BMI) measured either at the same time (cross-sectional analysis) or at the age of 12–15 years (longitudinal analysis). BMI tracked markedly between age of 12–15 and age of 19-20. BMI was strongly associated with all considered CRFs in both cross-sectional and longitudinal analyses, with some exceptions. Comparing overweight participants with those having a BMI below the age-specific median, the odds ratios for high blood pressure were 5.4/4.7 (male/female) cross-sectionally and 2.5/3.9 longitudinally (P<0.05). Significant associations were also found for most other CRFs, with some exceptions. In linear regression analysis including both BMI at age of 12–15 and BMI at age of 19-20, only BMI at age of 19-20 remained significantly associated with most CRFs. We conclude that CRFs are predicted strongly by either current or past BMI levels in adolescents and young adults in this population. The observation that only current BMI remained associated with CRFs when including past and current levels together suggests that weight control at a later age may be effective in reducing CRFs in overweight children irrespective of past weight status.

scholarly journals Serum Follicle-Stimulating Hormone Levels are Associated with Cardiometabolic Risk Factors in Post-Menopausal Korean Women

2020 ◽  
Vol 9 (4) ◽  
pp. 1161 ◽  
Author(s):  
Eun-Soo Jung ◽  
Eun-Kyung Choi ◽  
Byung-Hyun Park ◽  
Soo-Wan Chae
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Cardiometabolic Risk ◽  
Fasting Glucose ◽  
Follicle Stimulating Hormone ◽  
Cardiometabolic Risk Factors ◽  
Korean Women ◽  
Menopausal Women ◽  
Post Menopausal

Menopause compounds many cardiometabolic risk factors through endogenous estrogen withdrawal. This study aimed to find the association between serum follicle-stimulating hormone (FSH) levels and cardiometabolic risk factors in post-menopausal Korean women. A total of 608 post-menopausal women from eight randomized double-blind, placebo-controlled clinical trials on menopause during the year 2012–2019 were analyzed. Cardiometabolic risk factors such as body mass index, waist circumference, systolic blood pressure, fasting glucose, triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), and TG/HDL-C ratio were significantly improved as the FSH quartiles increased. Metabolic syndrome (MetS) and the number of components of MetS decreased as FSH quartiles increased. In regression analysis, FSH level was negatively associated with cardiometabolic risk factors including body mass index, body weight, waist circumference, fasting glucose and TG, while it was positively associated with HDL-C. The odds ratio of MetS in the first quartile of FSH was 2.682 compared with that in the fourth quartile of FSH in a logistic regression model. Serum FSH levels had a negative correlation with cardiometabolic risk factors in post-menopausal Korean women, suggesting that a low FSH can be a predictor for cardiovascular disease in post-menopausal women.

scholarly journals Performance of body mass index and percentage of body fat in predicting cardiometabolic risk factors in Thai adults

2018 ◽  
Vol Volume 11 ◽  
pp. 241-253 ◽  
Author(s):  
Somlak Vanavanan ◽  
Pornpen Srisawasdi ◽  
Mana Rochanawutanon ◽  
Nalinee Kumproa ◽  
Khanat Kruthkul ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Body Mass ◽  
Body Fat ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Percentage Of Body Fat
Sign in / Sign up

Export Citation Format

Share Document