Co-occurrence of cardiometabolic diseases and frailty in older Chinese adults in the Beijing Longitudinal Study of Ageing
Bibliographic record
Abstract
BACKGROUND: all cardiometabolic disorders become more common with age. Frailty and increased vulnerability to adverse outcomes are also common with aging. Even so, how commonly elderly people who are affected by cardiometabolic disorders are also frail remains unclear. OBJECTIVES: (i) to evaluate the prevalence of cardiometabolic disorders in relation to frailty. (ii) To estimate to which extent cardiometabolic diseases, when compared with frailty, affects mortality. METHODS: this is a secondary analysis of the Beijing Longitudinal Study of Ageing, a population-based representative cohort study (n = 3,257) assembled in 1992 and followed to 2007. The baseline frailty index (FI) considered 35 potential health deficits. People with an FI >0.22 were considered frail. The relationships between frailty and cardiometabolic disorders and mortality outcomes were evaluated using the Cox proportional hazard model, adjusted for baseline age, sex and education. RESULTS: the mean FI was 0.11 in men (SD = 0.10) and 0.14 (SD = 0.11) in women. On average, the FI increased with each cardiometabolic disorder (e.g. in men, mean ± SD = 0.16 ± 0.11 with hypertension, 0.23 ± 0.14 with stroke). As the number of disorders increased, so did the mean FI, and the proportion with the FI >0.22. For each condition, people with the FI >0.22 had a higher mortality, even after adjusting for sex, age and education. CONCLUSION: cardiometabolic disorders do not occur in isolation and commonly increase not just together, but in the presence of other health deficits. Healthcare providers who work with older adults with such problems need to develop methods to adapt their treatments to the needs of frail 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 | 0.001 | 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 | 0.000 | 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".