Characterization of depression subtypes and depression chronicity in middle aged and older adults: An Analysis of the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Depression has heterogeneous symptom presentations and long-term courses, but little effort has been made to categorize depressive symptoms more finely among middle-aged and older adults, despite the increased tendency for chronic course of depression in older adults compared to younger adults. Adverse childhood experiences (ACE) and allostatic load (AL) are known to be associated with depression, but there is no comprehensive research linking these stressors to depression subtypes and its chronicity. The objectives of this research are: 1) to identify symptom-based depression subtypes at baseline among participants in the Canadian Longitudinal Study on Aging; 2) to assess their relationships with profiles of stress-related biological markers and early life adversities; and 3) to assess depression chronicity, its relationships with baseline depression subtypes, and its prognostic risk factors at three-year follow up. Participants with a baseline score of 10 or more on the Center for Epidemiological Studies Depression-10 item scale (CESD-10) were included in the analyses, and chronic depression was defined as a CESD-10 score of 10 more at both time points. Latent profile analyses were applied to baseline data on depressive symptoms, AL biomarkers and ACE, within the cross-sectional (n=3966) and longitudinal (n=3473) samples. In the cross-sectional study, multinominal logistic regression was used to determine the relationships between depression subtypes, stressors and other covariates. In the longitudinal study, chronic depression was regressed based on baseline variables using logistic regression. We identified four distinct depression subtypes, named positive affect, melancholic, typical and atypical, as well as three profiles of ACEs (low, moderate, physical abuse) and three profiles of AL (average, high-cardiovascular, low-cardiovascular). Depression subtypes had unique significant associations with stressor profiles. The strongest associations were observed for the atypical subtype (versus positive affect subtype) including a significantly lower relative risk (RRR 0.73, 95% CI: 0.57-0.93) for physical abuse-ACE, a higher risk for low-cardiovascular AL (RRR 1.31, 95%CI: 1.02-1.68), and a lower risk of high-cardiovascular AL (RRR 0.64, 95% CI: 0.49, 0.85), compared with the positive affect subtype. The prevalence of chronic depression was (46.6%), and was significantly associated with increased age group, total annual household income category, and chronic conditions score; decreased perceived social standing score; and current smoker status. Depression heterogeneity was identified, regarding symptom-based subtypes, their relationships with stress-related biological markers and early life adversities, and their relative risks for chronicity at three-year follow-up. Additionally, we characterized the prevalence of depression chronicity, relative to baseline factors including depression subtypes, which fills a gap in the literature regarding binary courses of depression subtypes within middle-aged and older adults. We found that prognostic factors for chronic depression are consistent with commonly identified depression incidence risk factors, and that stress profiles had distinct relationships with chronicity. Important factors for chronicity include baseline depressive symptom profiles, as well as ACE profile exposures and some AL profiles. Depression subtypes had distinct associations with stress-related biological markers and early life adversity profiles, as well as distinct risks for a more chronic course. Moreover, stressor exposures may not only have an impact on the profile of depressive symptoms experienced, but may also be significantly associated with depression chronicity. Such findings have implications for personalized clinical depression management strategies, earlier identification of depression, and potential primary and secondary intervention strategies
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".