Comparing cardiovascular risk factors in older persons with mild cognitive impairment and lifetime history of major depressive disorder
Bibliographic record
Abstract
OBJECTIVES: To compare the prevalence of select cardiovascular risk factors (CVRFs) in patients with mild cognitive impairment (MCI) versus lifetime history of major depression disorder (MDD) and a normal comparison group using baseline data from the Prevention of Alzheimer's Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation (PACt-MD) study. DESIGN: Baseline data from a multi-centered intervention study of older adults with MCI, history of MDD, or combined MCI and history of MDD (PACt-MD) were analyzed. SETTING: Community-based multi-centered study based in Toronto across 5 academic sites. PARTICIPANTS: Older adults with MCI, history of MDD, or combined MCI and history of MDD and healthy controls. MEASUREMENTS: We examined the baseline distribution of smoking, hypertension and diabetes in three groups of participants aged 60+ years in the PACt-MD cohort study: MCI (n = 278), MDD (n = 95), and healthy older controls (n = 81). Generalized linear models were fitted to study the effect of CVRFs on MCI and MDD as well as neuropsychological composite scores. RESULTS: A higher odds of hypertension among the MCI cohort compared to healthy controls (p < .05) was noted in unadjusted analysis. Statistical significance level was lost on adjusting for age, sex and education (p > .05). A history of hypertension was associated with lower performance in composite executive function (p < .05) and overall composite neuropsychological test score (p < .05) among a pooled cohort with MCI or MDD. CONCLUSIONS: This study reinforces the importance of treating modifiable CVRFs, specifically hypertension, as a means of mitigating cognitive decline in patients with at-risk cognitive conditions.
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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 | 0.000 |
| 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".