Non-HDL cholesterol level and depression among Canadian elderly—a cross-sectional analysis of the baseline data from the CLSA
Bibliographic record
Abstract
To explore whether non-high-density-lipoprotein cholesterol (non-HDL-c) is associated with depression, a total of 26 819 Canadians aged 45–85 from the Canadian Longitudinal Study on Aging (CLSA) were included in analysis. Non-HDL-c, the difference between total-c and HDL-c, was categorized into five levels, i.e., <2.6, 2.6 to <3.7, 3.7 to <4.8, 4.8 to 5.7, and ≥5.7 mmol/L. History of clinical depression was collected by questionnaire at an in-home interview, and current potential depression status was determined by CES-D10 (Center for Epidemiological Studies Depression Scale 10 questions version) score, i.e., ≥10 vs. <10. Logistic continuation ratio model for ordinal data was used to estimate the odds of being at or above a higher non-HDL-c category for depression status. Compared with those without clinical depression history and currently undepressed, the adjusted odds ratios (95% CI) were 1.09 (1.02, 1.17) for those without clinical depression history but currently depressed, 1.05 (0.98, 1.12) for those had clinical depression history but currently undepressed, and 1.21 (1.10, 1.32) for those had clinical depression history and currently depressed. The average of non-HDL-c for four depression groups were 3.64, 3.71, 3.69, and 3.82 mmol/L, respectively, and group 4 was statistically higher than others ( p < 0.001). In conclusion, people with both current depression and a history clinical depression are at an increased risk of having high level of non-HDL-c.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".