The evaluation of depression prevalence and its association with obesity phenotypes in a community-dwelling aged population
Bibliographic record
Abstract
BACKGROUND: Depression is one of the most debilitating mental disorders and a risk factor for many other chronic diseases that are commonly seen in the geriatric population. It has been claimed in previous studies that depression can be associated with obesity in this age group, but there is no common consensus between their results. AIM: This study aims to evaluate the association between depression metabolic syndrome and obesity phenotypes in community-dwelling older adults living in the East of Iran. METHOD AND MATERIALS: As a part of the Birjand Longitudinal Aging Study, this retrospective cross-sectional study was conducted on participants older than 60. They were categorized based on their body mass index and components of metabolic syndrome into four phenotypes: metabolic non-healthy obese (MNHO), metabolic healthy obese (MHO), metabolic healthy non-obese (MHNO), and metabolic non-healthy non-obese (MNHNO). The relative risk ratio (RRR) of the obesity phenotypes, the severity of depressive symptoms, and the 95% confidence intervals (95% CI) were evaluated by univariate and multinomial logistic regression. RESULTS: Of 1344 eligible participants, 268 (19.94%) had depression. Moderate, moderate-severe, and severe depression were observed in 179 (13.32%), 67 (4.99%), and 22 (1.64%) participants, respectively. Our findings showed a non-significant increase in the RRR of mild depressive symptoms in MNHO (RRR:1.22, 95% CI 0.56-2.66) and severe symptoms in MNHNO (RRR:1.20, 95% CI 0.02-63.17) females. However, in male participants, the RRR of moderate-severe depressive symptoms only increased non-significantly for the MNHO category (RRR:1.34, 95% CI 0.45-3.98). CONCLUSION: We did not observe a meaningful association between depressive symptoms and obesity phenotypes. Also, other than malnutrition or its risk, various severities of depressive symptoms correlate with different sociodemographic and medical risk factors among male and female senior citizens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".