The association between dietary acid load with cardiometabolic risk factors and inflammatory markers amongst elderly men: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Existing epidemiological data on dietary acid load and cardiovascular disease (CVD) are controversial. There is no literature evaluating the association between dietary acid load (DAL) with cardiometabolic risk factors and inflammatory markers in elderly. OBJECTIVE: To evaluate the association between DAL and cardiometabolic risk factors amongst Iranian elders. METHOD: A cross-sectional study was completed using 357 Iranian elders above >60 years of age. Anthropometric, clinical, and biochemical measurements were performed. Dietary intake was assessed using a validated and reliable food frequency questionnaire. DAL was estimated using the Potential Renal Acid Load (PRAL) score, Net Endogenous Acid Production (NEAP) and the Net Endogenous Acid Excretion (NAE) score. Metabolic syndrome (MetS) was defined according to the ATP-III criteria. Multivariable-adjusted odds ratios (ORs) of CVD risk factors were estimated using logistic regression. RESULTS: After adjustment for confounders, a higher PRAL score was associated with higher odds of hypertriglyceridemia (OR: 2.28, 95% CI: 1.15, 4.50). We also observed that the NEAP score was positively associated with MetS (OR: 17.2, 95% CI: 2.34, 127). Finally, there was a positive association between NAE and lipid accumulation product (LAP) (OR: 1.81, 95% CI: 1.04, 3.17) and hypertriglyceridemia (OR: 2.46 95% CI: 1.22, 4.95). CONCLUSION: Men with higher DAL scores had a higher risk of MetS, hypertriglyceridemia and LAP. Our findings suggest that further prospective studies are required to appraise DAL-CVD risk factors in populations with varying dietary patterns.
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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 | 0.001 |
| 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 | 0.001 | 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".