Association between the Dietary Inflammatory Index and fracture risk in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We explored correlations between the Dietary Inflammatory Index (DII) and fracture risk in older adults. METHODS: We systematically searched MEDLINE, PubMed, Science Direct, Scopus, and CNKI for all relevant epidemiological studies published through October 16, 2023. Because observational studies were included in the meta-analysis, we used a random-effects model to pool the study-specific effect sizes and 95% confidence intervals (CIs). We assessed study quality using the Newcastle-Ottawa scale. This meta-analysis was registered in PROSPERO. RESULTS: = 87.1%, indicating significant between-study heterogeneity; hence, the random-effects model was adopted to generate the combined effect size. We found that the DII was positively associated with fracture (relative risk: 1.188, 95% CI: 1.043-1.354). This result was further confirmed in leave-one-out sensitivity analysis. CONCLUSIONS: Our study provides evidence suggesting that diets high in pro-inflammatory components might increase the fracture risk among older people. Decreased consumption of pro-inflammatory foods and increased consumption of anti-inflammatory foods are suggested to prevent adverse fracture outcomes. More prospective studies involving both sexes are warranted to verify the results.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".