Evaluating lifestyle and health‐related characteristics of older adults with co‐occurring depressive symptoms and cardiometabolic abnormalities
Bibliographic record
Abstract
OBJECTIVE: Comorbid depression and cardiometabolic abnormalities might represent an important subgroup of depression. The aim of the present study was to evaluate lifestyle and health-related characteristics of individuals with both depressive symptoms and cardiometabolic abnormalities. METHODS: Data were from the English Longitudinal Study of Ageing. The sample was comprised of 5365 adults aged 50-80 years. High depressive symptoms were based on the eight-item Center for Epidemiologic Studies - Depression scale. Cardiometabolic abnormalities were defined as having ≥3 cardiometabolic risk factors (hypertension, impaired glycemic control, systemic inflammation, low high-density lipoprotein cholesterol, hypertriglyceridemia, and central obesity). Four groups were created based on Center for Epidemiologic Studies - Depression scores and cardiometabolic abnormalities: those with (i) comorbid depressive symptoms and cardiometabolic abnormalities (DCM); (ii) depressive symptoms only (DnoCM); (iii) cardiometabolic abnormalities only; and (iv) neither depressive symptoms nor cardiometabolic abnormalities. Lifestyle and health-related characteristics of the four groups were compared using chi-square tests. A modified Poisson regression analysis was performed to compare the DCM and the DnoCM groups with respect to lifestyle and health-related characteristics. RESULTS: Those in the DCM group were significantly less physically active (p = 0.003), had poorer self-rated health (p < 0.001), had lower income (p = 0.001), and were more likely to be retired (p < 0.001) than those in the DnoCM group. The pattern of results remained after controlling for other lifestyle and health-related factors. CONCLUSION: These results provide support for a cardiometabolic subgroup of depression that is associated with physical inactivity, poorer self-rated health, lower income, and retirement. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".