Vitamin D serum concentration is prospectively associated with depressive symptoms in the EpiFloripa Aging Cohort Study: a structural equation modeling approach
Bibliographic record
Abstract
OBJECTIVE: To evaluate the direct, indirect, and total prospective effects of serum concentrations of 25-hydroxycholecalciferol (25[OH]D) on depressive symptoms in older adults. METHODS: Data from the second (2013-2015) and third (2017-2019) waves of the EpiFloripa Aging Cohort Study (= 60 years) were analyzed. Depressive symptoms were assessed with the 15-item Geriatric Depression Scale. 25(OH)D levels were measured using the microparticle chemiluminescence method. A directed acyclic graph was constructed to identify the minimum set of adjustments. Structural equation modeling analysis was used to determine the effects of 25(OH)D on depressive symptoms. RESULTS: Data from 574 older adults (63.1% female) were analyzed. In the follow-up (n=390), 16.2% of them presented severe depressive symptoms (= 6 points). Structural equation modeling analysis revealed that 25(OH)D had a small direct negative effect (ß = -0.11, p < 0.05) and an overall negative effect (ß = -0.13; p < 0.05) on depressive symptoms in wave 3 (increased 25[OH]D led to decreased depressive symptoms). No direct or indirect effect on depressive symptoms was found in wave 2. CONCLUSION: Our findings indicate a prospective association between 25(OH)D and depressive symptoms, suggesting a long-term effect in older adults from southern Brazil.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".