A novel monocyte-based biomarker of cardiovascular risk: comparison with traditional cardiovascular risk calculators
Bibliographic record
Abstract
TEXT ABSTRACT Background Traditional cardiovascular risk calculators estimate population-level risk but perform less reliably at the individual level and do not capture biological processes that underlie early atherogenesis. Monocyte activation and platelet–monocyte interactions contribute to early atherogenesis, yet their potential as biomarkers of silent disease in asymptomatic adults has not been defined. The aim of this study was to investigate their utility in prediction of early atherosclerosis in asymptomatic subjects, in comparison to traditional cardiovascular risk calculators. Methods Asymptomatic adults in a discovery cohort (n=39) underwent flow cytometric profiling of monocyte subsets and monocyte–platelet aggregates (MPA), together with carotid ultrasonography to assess carotid intima–media thickness (cIMT) and plaque. From these data, we derived a composite biomarker, the Monocyte Atherosclerotic Risk Score (MARS). Cardiovascular risk was calculated using QRISK3. An independent validation cohort of clinically healthy subjects (n=151) attending the Physical Examination Centre at Drum Tower Hospital, Nanjing, China, underwent identical biomarker and imaging assessments, with cardiovascular risk calculated using China-PAR. Results In the discovery cohort, MARS showed a strong association with cIMT (r²=0.87, P<0.0001), substantially outperforming QRISK3 (r²=0.30, P=0.003). MARS predicted high-risk cIMT (AUC 0.93, P=0.0001) and carotid plaque (AUC 0.94, P=0.0022), whereas QRISK3 did not. In the validation cohort, MARS again discriminated individuals with carotid plaque (AUC 0.81, P<0.0001), while China-PAR showed no significant predictive ability. Conclusions The MARS biomarker generated from flow cytometric profiling detects silent atherosclerosis with higher accuracy than QRISK3 or China-PAR in two independent and ethnically different asymptomatic populations. These findings support further evaluation of MARS as a scalable blood-based tool for identifying individuals who may benefit from targeted imaging and preventive strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".