scholarly journals Anthropometric indicators as predictors of high blood pressure among the Ao tribe of North-East India

2013 ◽  
Vol 4 (3) ◽  
pp. 14-22
Author(s):  
Temsutola Maken ◽  
Lalmunlien Robert Varte

Objective: Hypertension is related to increased body fat, which can be evaluated by anthropometric indicators among the Aos, a tribe of North-East India. Methods: Cross-sectional study with a sample of 1804 Ao adults (male= 890) (females= 914) aged 18 to 70 years. We considered the following anthropometric indicators: body mass index, waist circumference, waist-tohip ratio and waist-to-stature ratio. To identify predictors of high blood pressure, we adopted the analysis of receiver operating characteristic curves with a confidence interval of 95%. Result: For males, the area under curve with confidence intervals were BMI = 0.691 (0.67-0.712); waist circumference=0.757 (0.739-0.775); waist-to-hip ratio=0.692 (0.671-0.713); waist-to-stature ratio = 0.763 (0.745-0.781) and Conicity index = 0.734 (0.716-0.716). For females, the values were BMI = 0.754 (0.732-0.776); waist circumference = 0.762 (0.74-0.784); waist-to-hip ratio = 0.690 (0.668-0.784), waist-to-stature ratio=0.776 (0.753-0.799) and Conicity index=0.722 (0.701-0.743). Different cut off points of anthropometric indicators with better predictive power and their relevant sensitivities and specificities were identified. Conclusion: BMI does not show a very good area under the ROC curve. It seems that waist-to-stature ratio is the best predictor, followed by waist circumference and Conicity index among the males and results in high sensitivity and specificity to hypertension. We suggest the use of both waistto- stature ratio and waist circumference to predict hypertension among males. Among females, waist-to-stature ratio is the best predictor, followed by waist circumference and body mass index. DOI: http://dx.doi.org/10.3126/ajms.v4i3.6275 Asian Journal of Medical Sciences 4(2013) 15-22

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sooyoung Cho ◽  
Aesun Shin ◽  
Ji-Yeob Choi ◽  
Sang Min Park ◽  
Daehee Kang ◽  
...  

Abstract Background Obesity is well known as a risk factor for cardiovascular disease. We aimed to determine the performance of and the optimal cutoff values for obesity indices to discriminate the presence of metabolic abnormalities as a primary risk factor for cardiovascular diseases in a Health Examinees study (HEXA). Methods The current study analyzed 134,195 participants with complete anthropometric and laboratory information in a Health Examinees study, consisting of the Korean population aged 40 to 69 years. The presence of metabolic abnormality was defined as having at least one of the following: hypertension, hyperglycemia, or dyslipidemia. The area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were calculated for body mass index, waist to hip ratio, waist to height ratio, waist circumference, and conicity index. Results The AUC of metabolic abnormalities was the highest for waist-to-height ratio (AUC [95% CIs], 0.677 [0.672–0.683] among men; 0.691 [0.687–0.694] among women), and the lowest for the C index (0.616 [0.611–0.622] among men; 0.645 [0.641–0.649] among women) among both men and women. The optimal cutoff values were 24.3 kg/m2 for the body mass index, 0.887 for the waist-to-hip ratio, 0.499 for the waist-to-height ratio, 84.4 cm for waist circumference and 1.20 m3/2/kg1/2 for the conicity index among men, and 23.4 kg/m2 for the body mass index, 0.832 for the waist-to-hip ratio, 0.496 for the waist-to-height ratio, 77.0 cm for the waist circumference and 1.18 m3/2/kg1/2 for the conicity index among women. Conclusion The waist-to-height ratio is the best index to discriminate metabolic abnormalities among middle-aged Koreans. The optimal cutoff of obesity indices is lower than the international guidelines for obesity. It would be appropriate to use the indices for abdominal obesity rather than general obesity and to consider a lower level of body mass index and waist circumference than the current guidelines to determine obesity-related health problems in Koreans.


2017 ◽  
Vol 41 (2) ◽  
pp. 135-140 ◽  
Author(s):  
William Rodrigues Tebar ◽  
Raphael Mendes Ritti-Dias ◽  
Breno Quintella Farah ◽  
Edner Fernando Zanuto ◽  
Luiz Carlos Marques Vanderlei ◽  
...  

2014 ◽  
Vol 17 (2) ◽  
pp. 72-79 ◽  
Author(s):  
Fuling Ji ◽  
Feng Ning ◽  
Haiping Duan ◽  
Jaakko Kaprio ◽  
Dongfeng Zhang ◽  
...  

We evaluated the genetic and environmental contributions to metabolic cardiovascular risk factors and their mutual associations. Eight metabolic factors (body mass index, waist circumference, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, total serum cholesterol, serum triglycerides, and serum uric acid) were measured in 508 twin pairs aged 8–17 years from the Qingdao Twin Registry, China. Linear structural equation models were used to estimate the heritability of these traits, as well as the genetic and environmental correlations between them. Among boys, body mass index and uric acid showed consistently high heritability (0.49–0.81), whereas other traits showed moderate to high common environmental variance (0.37–0.73) in children (8–12 years) and adolescents (13–17 years) except total cholesterol. For girls, moderate to high heritability (0.39–0.75) were obtained for six metabolic traits in children, while only two traits showed high heritability and others mostly medium to large common environmental variance in adolescents. Genetic correlations between the traits were strong in both boys and girls in children (rg = 0.64–0.99 between body mass index and diastolic blood pressure; rg = 0.71–1.00 between body mass index and waist circumference), but decreased for adolescent girls (rg = 0.51 between body mass index and waist-to-hip ratio; rg = 0.55 between body mass index and uric acid; rg = 0.61 between body mass index and systolic blood pressure). The effect of genetic factors on most metabolic traits decreased from childhood to adolescence. Both common genetic and specific environmental factors influence the mutual associations among most of the metabolic traits.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenli Zhang ◽  
Kun He ◽  
Hao Zhao ◽  
Xueqi Hu ◽  
Chunyu Yin ◽  
...  

Abstract Background The relationship between obesity and prevalent high blood pressure in older adults has predominantly been estimated using categorical measures of body mass index (BMI) and waist circumference (WC), masking the shape of the dose-response relationship. We aimed to examine the precise relationship of BMI, WC with high blood pressure and to assess the appropriate level of BMI and WC for high blood pressure. Methods We examined data for 126,123 individuals in Xinzheng city aged ≥60 years from a population based study from January to December 2019. Logistic regression and restricted cubic spline models were applied to assess the relationship and the appropriate level of BMI and WC for high blood pressure. An additive interaction analysis was used to test synergistic effects between a higher BMI and WC for high blood pressure. Results The full-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of an increase of 1 kg/m2 in BMI and 1 cm in WC for high blood pressure were 1.084 (1.080–1.087) and 1.026(1.024–1.027), respectively. Multivariable adjusted restricted cubic spline analyses showed the nonlinear relationships of BMI and WC with high blood pressure in both men and women (all P < 0.001). The risk of high blood pressure increased steeply with increasing BMI from ≥25 kg/m2 and WC ≥ 88 cm or 86 cm for males and females, respectively. And we observed a significant additive interaction between a higher BMI and WC such that the prevalence of high blood pressure was significantly enhanced. Conclusion These findings suggest increased high blood pressure prevalence in the older adults with increased BMI and WC. BMI ≤ 25 kg/m2 and WC ≤ 88 cm or 86 cm for males and females may be the best suggestion with regard to primary prevention of high blood pressure in older adults.


2009 ◽  
Vol 40 (3) ◽  
pp. 208-215 ◽  
Author(s):  
Samuel Flores-Huerta ◽  
Miguel Klünder-Klünder ◽  
Lorenzo Reyes de la Cruz ◽  
José Ignacio Santos

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