Fat from dairy foods and ‘meat’ consumed within recommended levels is associated with favourable serum cholesterol levels in institutionalised older adults
Bibliographic record
Abstract
Abstract CVD is common in older adults. Consumption of ‘meat’ (beef, pork, lamb, game, poultry, seafood, eggs) and dairy foods (milk, cheese, yoghurt) is encouraged in older adults as these foods provide protein and nutrients such as essential fatty acids, Ca, Fe, Zn and vitamins A, D and B12 required for healthy ageing. However, these foods also contain saturated fats considered detrimental to cardiovascular health. To determine the effect of their consumption on CVD risk we assessed associations between fat intake from ‘meat’ and dairy foods and serum cholesterol levels in 226 aged-care residents (mean age 85·5 years, 70 % female). Dietary intake was determined over 2 d using visual estimation of plate waste. Fat content of foods was determined using nutrition analysis software (Xyris, Australia). Fasting serum total cholesterol (TC), LDL-cholesterol and HDL-cholesterol were measured, and the TC:HDL-cholesterol ratio calculated. Associations were determined using random-effect models adjusted for CVD risk factors using STATA/IC 13.0. Total fat and saturated fat from ‘meat’ and dairy foods were associated with higher serum HDL-cholesterol levels, and dairy fat intake and number of servings were associated with a lower TC:HDL-cholesterol ratio. Every 10 g higher intake of fat and saturated fat from dairy products, and each additional serving was associated with a −0·375 (95 % CI −0·574, −0·175; P = 0·0002), a −0·525 (95 % CI −0·834, −0·213; P = 0·001) and a −0·245 (95 % CI −0·458, −0·033; P = 0·024) lower TC:HDL-cholesterol ratio, respectively. Provision of dairy foods and ‘meat’ in recommended amounts to institutionalised older adults potentially improves intakes of key nutrients with limited detriment to cardiovascular health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".