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Background  The utility of bioelectrical impedance analysis (BIA) derived adiposity measures as compared to anthropometric measures for the assessment of adiposity-related health risk is not clear. We aimed to clarify the relationships of BIA and anthropometric derived adipose measures with blood pressure and hypertension, and to compare the discriminative ability of the respective measures for hypertension.    Methods  We used baseline data collected between 2015 and 2016 from the Indian Study on Health of Adults (ISHA), an ongoing population based cohort study in India ( N  = 5990; age 30–69 years). We examined and compared the associations and discriminative ability between anthropometric (body mass index, waist circumference, hip circumference, waist-hip ratio, waist-height ratio) and BIA (whole body and trunk fat percentage) derived adiposity measures with blood pressure components (systolic pressure, diastolic pressure, pulse pressure, mean arterial pressure, mid-blood pressure) and hypertension.    Results  Regardless of whether the adiposity measure was derived from BIA or anthropometry, all were strongly and positively associated with blood pressure and hypertension. For both men and women, the magnitude of association of BIA measures with blood pressure and hypertension were comparable to those of anthropometric measures. Further, the ability of BIA derived adiposity measures to distinguish between those with and without hypertension was similar to the discriminative ability of anthropometric measures.    Conclusions  As compared to simple anthropometric measures, BIA derived estimates of adiposity provide no apparent advantage in the assessment of blood pressure and hypertension. The observed similarities between adiposity measures suggest that simple anthropometrics may be sufficient to assess adiposity and adiposity-related risks.

bmc obesity

Comparative associations between anthropometric and bioelectric impedance analysis derived adiposity measures with blood pressure and hypertension in India: a cross-sectional analysis