Abstract 4146369: Association of Midlife Systemic Inflammatory Markers and Interval Change with Cognition: Insights from the HeartSCORE Study
Bibliographic record
Abstract
Introduction: Systemic inflammation may be associated with risk for neuroinflammation and cognitive decline. We investigated the association of midlife inflammatory markers and their 1-year change with Montreal Cognitive Evaluation (MoCA) score in a longitudinal cohort. Methods: In the Heart Strategies Concentrating on Risk Evaluation (HeartSCORE) study, baseline serum levels of high-sensitivity c-reactive protein (hs-CRP|), interleukin-6 (IL-6), CD40L, and intercellular adhesion molecule-1 (ICAM-1) were measured, along with their one-year changes (delta). Using linear regression, association of inflammatory biomarkers and interval change with MoCA scores taken 10-12 years later, were examined in models adjusted for traditional risk factors (age, sex, race, smoking, smoking, triglycerides, high density lipoprotein and total cholesterol, systolic and diastolic blood pressure, hypertension, diabetes, metabolic syndrome, antihypertensive use, statin use, and aspirin use). Model 2 added the one-year change in each biomarker. Results: Among 673 participants (mean age [SD]: 59 [6.8] years), 63.9% were women and 31.6% were self-reported Black. While univariate modelling showed an inverse association of IL-6 with MoCA; this was not significant when adjusted for covariates. Baseline ICAM levels had a significant inverse association with MoCA scores [β: -0.47 (-0.93 --0.02) p<0.05] in fully adjusted models (Table). One year change in these markers did not show any statistically significant association with MoCA scores. Conclusion: Midlife ICAM, known to initiate neuro and systemic inflammatory responses, may be an early risk marker for cognitive impairment. Future studies can examine the mechanisms of ICAM and its role in inflammatory cascade and neurocognitive decline.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".