C-reactive protein and cardiovascular risk in the general population
Bibliographic record
Abstract
BACKGROUND AND AIMS: High-sensitivity C-reactive protein (hsCRP) is a marker of inflammation and predicts cardiovascular (CV) risk in individuals without known atherosclerotic CV disease (ASCVD). More information about its clinical relevance will help evaluate the general utility of hsCRP as a routine clinical biomarker to identify patients at residual risk. METHODS: In this population-based study, hsCRP was measured in 448 653 UK Biobank participants without known ASCVD. The association of hsCRP with major adverse cardiovascular events (MACE), CV death and all-cause death was assessed using Cox proportional hazards models. RESULTS: The cohort had a median age of 57 years, 55.4% were female, and median hsCRP levels were 1.32 mg/L. A repeat hsCRP measurement in 15 967 participants after 4.4 years showed long-term stability. In covariate-adjusted models individuals with hsCRP levels >3 mg/L had a 34% higher risk of MACE, a 61% and 54% increased risk of CV death and all-cause death compared to those with hsCRP <1 mg/L. Subjects with hsCRP levels ≥2 mg/L vs <2 mg/L had a 22% increased risk of MACE, and a 37% and 34% higher risk of CV death and all-cause death. The association of hsCRP with all endpoints was consistent across subgroups. Predictive performance of hsCRP ranked above conventional risk factors. Integration of hsCRP improved SCORE2 and provided a total net reclassification improvement of 14.1% for prediction of MACE. CONCLUSIONS: These data confirm hsCRP as a clinically relevant predictor of CV events in individuals without known ASCVD and support its assessment in primary prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".