PROFILING OBESITY PHENOTYPES AND TRAJECTORIES IN OLDER ADULTS OF THE NUAGE COHORT: A CLUSTER ANALYSIS
Bibliographic record
Abstract
Background: Obesity in older adults results from several interacting factors. Consequently, interventions have shown mitigated effects. Objectives: We determined 1) the different subgroups of obese elderly based on clusters of associated comorbidities and 2) the trajectory of these clusters to assess their stability over 3 years and factors contributing to transitions. Methods: Obese men (n=193; BMI= 33.15 ± 2.69 kg/m2) and women (n=220; BMI= 33.71 ± 3.71 kg/m2) aged between 68 and 82 years were studied. Outcome variables were: body composition, strength, physical capacity, nutrition, psychological and physical health and social participation. Cluster analyses, stratified by sex, were used to identify obesity profiles at baseline and follow-up. Results: Three profiles were identified, based on general health (GH), psychological health (PH) and physical capacity (PC). Cluster 1: healthy obese (GH+, PH+, PC+); Custer 2: obese with low physical capacity (GH+/-, PH+/-, PC-); Cluster 3: unhealthy obese (GH-, PH-, PC-). After 3-years, 61% and 62% of men and women remained in their initial cluster, compared to 20.4% and 12.0% who transitioned toward a worse health Cluster and 18.3% and 14.2% who transitioned toward a more favorable cluster, partly explained by changes in physical health for men and physical and psychological health for women. Conclusion: The results of this study show that targeting physical capacity in men and physical and psychological health status in women could prevent further health decline in obese elderly. Further studies are needed to investigate the role of these clusters in the prediction of cardiometabolic complications and mortality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".