Defining Age-Appropriate BMI Cut-Points for Older Adults
Bibliographic record
Abstract
OBJECTIVES: In older age, body composition changes as fat mass increases and redistributes. Due to this, the current body mass index (BMI) classification proposed by the World Health Organization (WHO) may not accurately classify older adults (65+) by health risk. The objectives of thesis were to: 1) conduct a scoping review of the literature to investigate the association between BMI and mortality in older adults, 2) define age-specific BMI cut-offs for older adults with regards to health outcomes using data from the Canadian Longitudinal Study on Aging (CLSA), and 3) test the performance of the age-specific BMI thresholds in comparison to WHO thresholds. METHODS: The Ovid MEDLINE and EMBASE databases were searched for English language observational studies examining the association between BMI and all-cause mortality in older adults (objective 1). CART decision tree analysis was then used to define age-appropriate BMI cut-points in relation to health outcomes (i.e. cardiovascular (CV) conditions and frailty) (objective 2). Logistic regression models were utilized to determine the association between BMI and health outcomes, and area under the receiver operating characteristic curve (AUC) and sensitivity/specificity were used to test the performance of new BMI cut-offs (objective 3). RESULTS: The scoping review found that older adults classified as overweight had reduced mortality compared to normal BMI, thereby necessitating a need for revised cut-offs. In our analyses, age-specific cardiovascular- and frailty-BMI groups were created. Compared to the BMIFrailty- Risk groups, the BMI-CV-Risk groups demonstrated the most improvement in classification from the WHO groups. When evaluating the association between cut-points and outcomes, the model performance and specificity both improved for the new age-specific cut-points compared to the original WHO thresholds, suggesting improved classification with use of these revised groups. The results propose increased overweight thresholds (25.9-27.1) and lowered obese thresholds (28.7-30.9) for older adults. CONCLUSIONS: This novel analysis is the first attempt at revising the WHO-BMI thresholds for older Canadian adults. The age-specific BMI-CV-Risk groups offered improvements in classification of older adults from the WHO-BMI groups, and these findings suggest that a higher overweight but lowered obese thresholds may be best suited to older adults. Further work must be done to validate these thresholds in other populations and ethnicities, as well as in the context of other health outcomes.
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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 | 0.036 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".