Age and sex-specific associations of anthropometric measures of adiposity with blood pressure and hypertension in India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: A determinant of blood pressure is adiposity; however, there are uncertainties surrounding whether general or central adiposity is the more important determinant of blood pressure. Further, inconsistent results exist for the relationships of anthropometric measures with blood pressure and hypertension, and whether these relationships differ substantially by age and sex is unclear. We aimed to elucidate the associations of anthropometric measures of general and central adiposity with blood pressure and hypertension, and determine the effect of age and sex on these relationships. METHODS: We used cross-sectional data from the Centre for Global Health Research health check-up survey conducted during 2006-2007 of the general population in India (n = 7 601; age 18-59 years). We examined the associations of anthropometric measures (body mass index, waist circumference, hip circumference, waist-hip ratio, waist-height ratio) with blood pressure components (systolic pressure, diastolic pressure, pulse pressure, mean arterial pressure, mid-blood pressure) and hypertension within four (18-29 years, 30-39 years, 40-49 years, 50-59 years) age groups, by sex. We adjusted all analyses for education and location, with further adjustments, variously, for either a measure of central (waist circumference) or general (body mass index) adiposity. RESULTS: greater body mass index or 10 cm wider waist circumference was associated with a 5 and 4 mmHg higher systolic blood pressure, respectively. When considered separately, each anthropometric measure was strongly and positively associated with most blood pressure components in all age groups, and for both sexes. However, with few exceptions, when considered jointly (body mass index adjusted for waist circumference), the associations of body mass index with blood pressure components and hypertension were greatly diminished for both sexes, and particularly in the ≥30 years age groups. By contrast, further adjustment of waist circumference for body mass index did not materially alter the associations of waist circumference with blood pressure components and hypertension. CONCLUSIONS: Our findings indicate that central adiposity, as assessed with anthropometric measures, may be a more important determinant of blood pressure and hypertension than general adiposity for adults in India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".