Alcohol intake, hypertension development and mortality in black South Africans
Bibliographic record
Abstract
BACKGROUND: Excessive alcohol intake is a risk factor for cardiovascular disease (CVD) and predicts cardiovascular and all-cause mortality. We determined which alcohol marker (self-reported alcohol intake, gamma-glutamyltransferase (GGT) or percentage carbohydrate deficient transferrin (%CDT)) relates best with mortality and predicts hypertension development over five years in black South Africans. DESIGN: This was a longitudinal study as part of the PURE (Prospective Urban and Rural Epidemiology) study in the North West Province, South Africa. METHOD: We included 2010 participants and followed 1471 participants. Over five years, 230 deaths occurred, of which 66 were cardiovascular-related. At enrolment, participants completed questionnaires on alcohol intake (yes, for former and current use; no, for alcohol never used). We measured blood pressure, collected blood samples and measured GGT and %CDT. RESULTS: When comparing hazard ratios (HRs) of self-report, GGT and %CDT, we found that only GGT predicted cardiovascular (HR = 2.76 (1.49-5.12)) and all-cause mortality (HR = 2.47 (1.75-3.47)) and hypertension development ((HR = 1.31 (1.06-1.62)). Participants self-reporting yes for alcohol intake had a 30% increased risk of developing hypertension (HR = 1.30 (1.07-1.60)) but not an increased risk for mortality. When adding both GGT and self-report in the prediction model for hypertension, only self-reporting of alcohol was significant (HR = 1.24 (1.01-1.53)). The alcohol marker, %CDT, did not show any significant association with mortality or hypertension development. CONCLUSION: GGT independently predicted cardiovascular and all-cause mortality, as well as hypertension development in black South Africans. Despite non-specificity to excessive alcohol consumption, GGT may be a useful general marker for hypertension development and mortality, also due to its significant association with self-reported alcohol intake.
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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 | 0.001 | 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".