High-sensitivity C-reactive protein and cardiovascular events in a multi-ethnic cohort: the HELIUS study
Bibliographic record
Abstract
AIM: Elevated high-sensitivity C-reactive protein (hsCRP) concentrations are associated with an increased atherosclerotic cardiovascular risk, but this has not been extensively investigated in multi-ethnic populations, including South Asians, who are characterized by an increased cardiometabolic risk. METHODS: In this general population study we included 14,663 participants from the HEalthy Life In an Urban Setting (HELIUS) study (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin). Mortality and cardiovascular event data were collected from nationwide registries. Differences in hsCRP levels and the association between hsCRP and cardiovascular events across the ethnic groups were assessed. RESULTS: The mean age was 44±13 years, 55% were female. Median hsCRP at baseline was 1.0 (0.5-2.1) mg/L in the European, 1.3 (0.6-2.7) mg/L in the African, and 1.6 (0.7-3.3) mg/L in the South Asian participants (p <0.001). A total number of 568 cardiovascular events were recorded over a median follow-up time of 8.8 (7.8-9.7) years. hsCRP was associated with cardiovascular events in the European and African participants (adjusted HR per 1 mg/L increase in hsCRP: 1.08 [95% CI 1.00-1.16]; and 1.09 [95% CI 1.03-1.16], respectively), but not in the South Asian participants, despite having the highest event rate. CONCLUSIONS: hsCRP was associated with incident cardiovascular events in individuals of European and African descent but not in South Asian individuals, despite higher hsCRP values. These findings suggest the need to find alternative inflammatory biomarkers to allow for a more reliable assessment of the increased cardiometabolic risk in South Asian individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".