Examining the Link Between Cardiovascular Risk Factors and Neuropsychiatric Symptoms in Mild Cognitive Impairment and Major Depressive Disorder in Remission
Bibliographic record
Abstract
BACKGROUND: Cardiovascular risk factors (CVRFs) have been linked to both depression and cognitive decline but their role in neuropsychiatric symptoms (NPS) has yet to be clarified. OBJECTIVE: Understanding the role of CVRFs in the etiology of NPS for prospective treatments and preventive strategies to minimize these symptoms. METHODS: We examined the distribution of NPS using the Neuropsychiatric Inventory (NPI) scores in three cohorts from the Prevention of Alzheimer's Dementia with Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression (PACt-MD) study: older patients with a lifetime history of major depressive disorder (MDD) in remission, patients with mild cognitive impairment (MCI), and patients with combined MCI and MDD. We also examined the link between individual NPS and CVRFs, Framingham risk score, and Hachinski ischemic score in a combined sample. RESULTS: Analyses were based on a sample of 140 subjects, 70 with MCI, 38 with MCI plus MDD, and 32 with MDD. There was no effect of age, gender, education, cognition, or CVRFs on the presence (NPI >1) or absence (NPI = 0) of NPS. Depression was the most prevalent affective NPS domain followed by night-time behaviors and appetite changes across all three diagnostic groups. Agitation and aggression correlated negatively while anxiety, disinhibition, night-time behaviors, and irritability correlated positively with CVRFs (all p-values <0.05). Other NPS domains showed no significant association with CVRFs. CONCLUSION: CVRFs are significantly associated with individual NPI sub-scores but not with total NPI scores, suggesting that different pathologies may contribute to different NPS domains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".