期刊
PLOS ONE
卷 11, 期 5, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0154894
关键词
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资金
- South African Medical Research Council
- National Health Laboratory Services
- University of the Witwatersrand Iris Ellen Hodges Cardiovascular Research Trust
- National Research Foundation (NRF) of South Africa
- Wellcome Trust (UK)
- DST-NRF Professional Development Programme
Developing countries are disproportionately affected by hypertension, with Black women being at greater risk, possibly due to differences in body fat distribution. The objectives of this study were: (1) To examine how different measures of body composition are associated with blood pressure (BP) and incident hypertension; (2) to determine the association between baseline or change in body composition, and hypertension; and (3) to determine which body composition measure best predicts hypertension in Black South African women. The sample comprised 478 non-hypertensive women, aged 29-53 years. Body fat and BP were assessed at baseline and 8.3 years later. Body composition was assessed using dual-energy X-ray absorptiometry (DXA) (n = 273) and anthropometry. Hypertension was diagnosed based on a systolic/diastolic BP >= 140/90 mmHg, or medication use at follow-up. All body composition measures increased (p<0.0001) between baseline and follow-up. SBP and DBP increased by >= 20%, resulting in a 57.1% cumulative incidence of hypertension. Both DXA- and anthropometric-derived measures of body composition were significantly associated with BP, explaining 3-5% of the variance. Baseline BP was the most important predictor of hypertension (adjusted OR: 98-123%). Measures of central adiposity were associated with greater odds (50-65%) of hypertension than total adiposity (44-45%). Only change in anthropometric-derived central fat mass predicted hypertension (adjusted OR: 32-40%). This study highlights that body composition is not a major determinant of hypertension in the sample of black African women. DXA measures of body composition do not add to hypertension prediction beyond anthropometry, which is especially relevant for African populations globally, taking into account the severely resource limited setting found in these communities.
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