Obesity and its Relation With Diabetes and Hypertension: A Cross-Sectional Study Across 4 Geographical Regions
Bibliographic record
Abstract
BACKGROUND: The implications of rising obesity for cardiovascular health in middle-income countries has generated interest, in part because associations between obesity and cardiovascular health seem to vary across ethnic groups. OBJECTIVE: We assessed general and central obesity in Africa, East Asia, South America, and South Asia. We further investigated whether body mass index (BMI) and waist circumference differentially relate to cardiovascular health; and associations between obesity metrics and adverse cardiovascular health vary by region. METHODS: Using baseline anthropometric data collected between 2008 and 2012 from 7 cohorts in 9 countries, we estimated the proportion of participants with general and central obesity using BMI and waist circumference classifications, respectively, by study site. We used Poisson regression to examine the associations (prevalence ratios) of continuously measured BMI and waist circumference with prevalent diabetes and hypertension by sex. Pooled estimates across studies were computed by sex and age. RESULTS: This study analyzed data from 31,118 participants aged 20 to 79 years. General obesity was highest in South Asian cities and central obesity was highest in South America. The proportion classified with general obesity (range 11% to 50%) tended to be lower than the proportion classified as centrally obese (range 19% to 79%). Every standard deviation higher of BMI was associated with 1.65 and 1.60 times higher probability of diabetes and 1.42 and 1.28 times higher probability of hypertension, for men and women, respectively, aged 40 to 69 years. Every standard deviation higher of waist circumference was associated with 1.48 and 1.74 times higher probability of diabetes and 1.34 and 1.31 times higher probability of hypertension, for men and women, respectively, aged 40 to 69 years. Associations of obesity measures with diabetes were strongest in South Africa among men and in South America among women. Associations with hypertension were weakest in South Africa among both sexes. CONCLUSIONS: BMI and waist circumference were both reasonable predictors of prevalent diabetes and hypertension. Across diverse ethnicities and settings, BMI and waist circumference remain salient metrics of obesity that can identify those with increased cardiovascular risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".