IMPACT OF HIGH BODY MASS INDEX ON FRAILTY AND MORTALITY IN MIDDLE-AGED AND OLDER ADULTS
Bibliographic record
Abstract
Abstract Obesity is associated with higher risk of metabolic diseases. How body mass index (BMI) relates to mortality across frailty levels is controversial. We investigated the association of high BMI with frailty, and their effects on mortality. We included 36,583 participants aged ≥50 years from the 1999-2006 National Health and Nutrition Examination Survey (NHANES) cohorts (7,372) and 29,211 participants aged ≥50 years from wave 1 (2004) of Survey of Health Ageing and Retirement in Europe (SHARE). BMI was categorized as: normal: 18.5-24.9 kg/m2, overweight: 25-29.9, obese I: 30-34.9 and obese II+III: >35. A frailty index (FI) was constructed excluding nutrition-related items using 36 items for NHANES and 68 items for SHARE. Mortality data were obtained until 2015. All analyses were adjusted for educational, marital, working and smoking status. In participant aged 50-65 years, higher BMI was associated with greater frailty. Being obese level II+III increased mortality risk in male participants aged 50-65 years with FI≤0.1 [NHANES (hazard ratio (HR) 2.10, 95%CI 1.17-3.79); SHARE (2.35,1.14-4.87)]. In males aged >65 years with FI>0.3, being overweight and obese (any level) decreased mortality risk. In females aged 50-65 years, higher BMI was not associated with mortality across all frailty levels. BMI and frailty were cross-sectionally associated. The subsequent mortality impact differed by age, sex, and frailty. Obesity was not associated with mortality in middle-aged females, regardless of the degree of frailty. In males, obesity was harmful in those who were fit in middle age and protective in moderately/severely frail older ones.
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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 | 0.001 | 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 | 0.001 |
| 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".