Body mass index and all‐cause mortality in older adults: A scoping review of observational studies
Bibliographic record
Abstract
In older age, body composition changes as fat mass increases and redistributes. Therefore, the current body mass index (BMI) classification may not accurately reflect risk in older adults (65+). This study aimed to review the evidence on the association between BMI and all-cause mortality in older adults and specifically, the findings regarding overweight and obese BMI. A systematic search of the OVID MEDLINE and Embase databases was conducted between 2013 and September 2018. Observational studies examining the association between BMI and all-cause mortality within a community-dwelling population aged 65+ were included. Seventy-one articles were included. Studies operationalized BMI categorically (n = 60), continuously (n = 8) or as a numerical change/group transition (n = 7). Reduced risk of mortality was observed for the overweight BMI class compared with the normal BMI class (hazard ratios [HR] ranged 0.41-0.96) and for class 1 or 2 obesity in some studies. Among studies examining BMI change, increases in BMI demonstrated lower mortality risks compared with decreases in BMI (HR: 0.83-0.95). Overweight BMI classification or a higher BMI value may be protective with regard to all-cause mortality, relative to normal BMI, in older adults. These findings demonstrate the potential need for age-specific BMI cut-points in older adults.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".