P4‐534: COUPLED BIVARIATE CHANGES IN C‐REACTIVE PROTEIN AND COGNITION IN LATE LIFE
Bibliographic record
Abstract
Systemic inflammatory markers, such as C-reactive protein (CRP) have been associated with risk and progression of cognitive impairment. However, most of the work in this area has examined cross-sectional associations or has used single assessments of inflammatory markers as predictors of cognitive change. Repeated assessments of CRP are needed to explore potentially reciprocal associations between inflammation and cognition as these variables fluctuate over time. Data were from 6073 US adults over age 50 (Mage = 66.88 years, 60.05% female) from the Health and Retirement Study who provided blood specimens beginning in 2006. Bivariate growth modeling, an extension of longitudinal structural equation modeling, was used to simultaneously estimate trajectories of CRP and cognition over an eight-year follow-up. Two separate models were estimated. The first model included all available participants and estimated changes in CRP and cognition as a function of time in years since the initial assessment. The second model examined a subset of 445 participants who developed dementia after the baseline assessment with time structured as time to diagnosis. Age, sex, education, chronic conditions, and dementia status were included as covariates on the intercept and slope parameters in both models. In the overall sample, cognition and CRP declined with advancing age. Male participants and those with higher education had lower initial levels of CRP, but greater increases over time. A significant negative correlation (r = −.09, p = .017) was observed between intercepts for CRP and cognitive function, but not between slopes (r = −.07, p = .612). Cognitive intercepts were marginally associated with CRP slopes (r = .11, p = .085), indicating that higher cognitive functioning at baseline predicted less decline or more stable CRP levels across the study. In the dementia subsample, no significant associations were observed in levels or rates of change in CRP and cognition. CRP may predict level of cognitive ability, though these variables do not appear to change concurrently over time. Cognition and CRP were not associated in the subsample with incident dementia, suggesting that CRP may be a better predictor of normative cognitive aging.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".