Differential Peripheral Inflammatory Factors Associated with Cognitive Function in Patients with Heart Failure
Bibliographic record
Abstract
OBJECTIVE: Cognitive deficits are common in patients with heart failure (HF), and can negatively affect self-care, predict rehospitalizations, and increase mortality rates 5-fold. Inflammation can produce vascular pathology, reducing cerebral blood flow to brain regions necessary for optimal cognitive function. The purpose of the investigation was to identify a pattern of peripheral blood inflammation-related biomarkers associated with cognitive impairment in patients with HF. METHODS: Forty-five outpatients (median age = 67 years, SD = 9.9) were recruited from University of California, San Diego (UCSD) and Veterans Affairs San Diego Healthcare Systems (VASDHS), diagnosed with New York Heart Association Stages I-III HF. Participants were administered the Montreal Cognitive Assessment (MoCA) as a measure of global cognitive impairment, and blood was analyzed for plasma biomarkers, interferon-γ, tumor necrosis factor-α (TNFα), soluble intercellular adhesion molecule-1 (sICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), brain-derived neurotrophic factor (BDNF), interleukin-8 (IL-8), matrix metallopeptidase-9 (MMP-9), IL-6, C-reactive protein (CRP), and serum amyloid-A (SAA). RESULTS: Almost half the patients scored below the threshold on the MoCA, indicating at least mild cognitive impairment. A factor analysis produced three biomarker factors: vascular inflammatory factor-1: TNFα, sICAM1, sVCAM1; neuroinflammatory factor-2: BDNF, MMP-9, IL-8; peripheral inflammatory factor-3: IL-6, CRP, SAA. Only vascular inflammatory factor-1 was significantly associated with cognitive function (MoCA) (ΔR2 = 0.214, beta = -0.468, p = 0.008). CONCLUSIONS: In this cohort with HF, vascular inflammation appears related to poorer cognitive function. This could indicate targets for treatment to reduce cognitive deficits in HF. However, this is a preliminary study, and further research is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".