Trajectories of body mass index among Canadian seniors and associated mortality risk
Bibliographic record
Abstract
BACKGROUND: This study aims to characterize the heterogeneity in BMI trajectories and evaluate how different BMI trajectories predict mortality risk in Canadian seniors. METHODS: Data came from the Canadian National Population Health Survey (NPHS, 1994-2011) and 1480 individuals aged 65-79 years with at least four BMI records were included in this study. Group-based trajectory model was used to identify distinct subgroups of longitudinal trajectories of BMI measured over 19 years for men and women. Cox proportional hazards models were used to examine the association between BMI trajectories and mortality risks. RESULTS: Distinct trajectory patterns were found for men and women: 'Normal Weight-Down'(N-D), 'Overweight-Normal weight' (OV-N), 'Obese I-Down' (OB I-D), and 'Obese II- Down' (OB II-D) for women; and 'Normal Weight-Down' (N-D), 'Overweight-Normal weight' (OV-N), 'Overweight-Stable' (OV-S), and 'Obese-Stable' (OB-S) for men. Comparing with OV-N, men in the OV-S group had the lowest mortality risk followed by the N-D (HR = 1.66) and OB-S (HR = 1.98) groups, after adjusting for covariates. Compared with OV-N, women in the OB II-D group with three or more chronic health conditions had higher mortality risk (HR = 1.61); however, women in OB II-D had lower risk (HR = 0.56) if they had less than three conditions. CONCLUSION: The course of BMI over time in Canadian seniors appears to follow one of four different patterns depending on gender. The findings suggest that men who were overweight at age 65 and lost weight over time had the lowest mortality risk. Interestingly, obese women with decreasing BMI have different mortality risks, depending on their chronic health conditions. The findings provide new insights concerning the associations between BMI and mortality risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".