The relationship between hypertension and obesity across different ethnicities
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Hypertension and obesity are major contributors to cardiovascular disease, and the relationship between these conditions is known to vary among ethnicities. However, this relationship has not previously been examined in aboriginal populations. The present investigation aimed to identify and compare this relationship among white (n = 3566), aboriginal (n = 850), East Asian (n = 446), and South Asian (n = 222) individuals from the province of British Columbia, Canada. METHODS: Blood pressure, BMI, and waist circumference were directly measured along with self-reported antihypertensive medication usage. Relative risk ratios were calculated to evaluate the risk of hypertension among individuals of varying BMI and waist circumference measurements. The relative risks of hypertension were compared both within and between four ethnic groups. RESULTS: Greater relative risks for hypertension were observed among individuals with increased BMI or increased waist circumference among all four ethnic groups. Aboriginal individuals appear to experience the greatest increases in relative risk for hypertension with increased BMI or waist circumference compared to other ethnic groups. The differences in the risk of developing hypertension between aboriginal and white populations appear to be largely associated with differences in body composition (i.e., BMI or waist circumference). East Asian and South Asian populations experience greater relative risk for hypertension than white populations at the same level of BMI or waist circumference. CONCLUSION: Hypertension prevention and treatment strategies among aboriginal, East Asian, and South Asian populations should target reducing fat mass and abdominal fat.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".