DIET QUALITY IS ASSOCIATED WITH FRAILTY IN THE PHYSICIAN’S HEALTH STUDY
Bibliographic record
Abstract
Increasing evidence suggests that healthier dietary patterns may be inversely associated with frailty, although whether this relationship holds across various diet indices is unknown. We investigated whether dietary patterns are associated with frailty among male physicians using 3 dietary assessments: the Alternate Healthy Eating Index (aHEI), Mediterranean diet (MED), and Dietary Approaches to Stop Hypertension (DASH). Our analysis included 12,240 men aged ≥60 years who provided data to determine frailty status and dietary habits. A cumulative deficit frailty index (FI) was calculated using 33 variables encompassing domains of comorbidity, functional status, mood, general health, social isolation, and change in weight. FI identified 36.6% of physicians as robust, 43.3% as pre-frail, and 20.1% as frail. Multinomial logistic regression models were adjusted for age, smoking status, aspirin and beta-carotene randomization, marital status, employment, and energy intake. Compared to the lowest quintiles of aHEI, MED, and DASH, those in the highest quintiles had a decreased odds of frailty [OR (95% CI) for aHEI: 0.53 (0.41, 0.67), MED: 0.42 (0.35, 0.51), and DASH: 0.45 (0.36, 0.56)] and pre-frailty [aHEI: 0.73 (0.62, 0.87), MED: 0.68 (0.59, 0.78), and DASH: 0.63 (0.54, 0.74)]. Restricted cubic splines showed a linear dose-response relationship of higher scores from each diet assessment with decreased odds of pre-frailty and frailty. Our findings confirm and inverse dose-response relationship of 3 different diet assessments with pre-frailty and frailty and suggest that healthier diet may be a potentially modifiable risk factor for frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".