Vegetarian diets and cardiovascular risk factors in black members of the Adventist Health Study-2
Bibliographic record
Abstract
OBJECTIVE: To compare cardiovascular risk factors between vegetarians and non-vegetarians in black individuals living in the USA. DESIGN: A cross-sectional analysis of a sub-set of 592 black women and men enrolled in the Adventist Health Study-2 (AHS-2) cohort of Seventh-day Adventists. SETTING: Members of the AHS-2 cohort, who lived in all states of the USA and provinces of Canada. SUBJECTS: Black/African-American members of two sub-studies of AHS-2 where blood and physiological measurements were obtained. RESULTS: Of these women and men, 25% were either vegan or lacto-ovo-vegetarians (labelled 'vegetarian/vegans'), 13% were pesco-vegetarian and 62% were non-vegetarian. Compared with non-vegetarians, the vegetarian/vegans had odds ratios for hypertension, diabetes, high blood total cholesterol and high blood LDL-cholesterol of 0·56 (95% CI 0·36, 0·87), 0·48 (95% CI 0·24, 0·98), 0·42 (95% CI 0·27, 0·65) and 0·54 (95% CI 0·33, 0·89), respectively, when adjusted for age, gender, education, physical activity and sub-study. Corresponding odds ratios for obesity in vegetarian/vegans and pesco-vegetarians, compared with non-vegetarians, were 0·43 (95% CI 0·28, 0·67) and 0·47 (95% CI 0·27, 0·81), respectively; and for abdominal obesity 0·54 (95% CI 0·36, 0·82) and 0·50 (95% CI 0·29, 0·84), respectively. Results for pesco-vegetarians did not differ significantly from those of non-vegetarians for other variables. Further adjustment for BMI suggested that BMI acts as an intermediary variable between diet and both hypertension and diabetes. CONCLUSIONS: As with non-blacks, these results suggest that there are sizeable advantages to a vegetarian diet in black individuals also, although a cross-sectional analysis cannot conclusively establish cause.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".