Role of Macrophage Phagocytosis as Predictive Marker of the Prevalence of Coronary Heart Disease and Acute Coronary Syndromes
Bibliographic record
Abstract
Background: Atherosclerotic cardiovascular disease (ASCVD) pathogenesis is closely associated with macrophages. This study sought to explore the role of phagocytosis by monocyte-derived macrophages (MDMs) in the blood in the context of coronary heart disease (CHD) and acute coronary syndromes (ACSs). Methods: This study employed a matched case-control design. Individuals with suspected CHD were recruited and allocated to a control cohort or a CHD cohort, with the latter further stratified into stable angina pectoris and ACS subgroups according to clinical diagnoses. Clinical data were collected, MDMs were isolated, and macrophage phagocytic activity was evaluated using fluorescent-labeled latex microspheres. Results: Macrophage phagocytic rates were significantly reduced in the CHD group relative to the control group, with further decreases observed in the ACS subgroup. Multivariable linear regression revealed that age, low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), and fibrinogen were independently and negatively correlated with macrophage phagocytic rates. Multivariable analyses suggested that diminished macrophage phagocytic rates were linked to an elevated risk of both CHD and ACS. Receiver operating characteristic (ROC) curve analysis identified the optimal cutoff values of macrophage phagocytic rates for predicting CHD and ACS as 62.6% and 63.4%, respectively, with the area under the curves (AUCs) measured at 0.679 and 0.669. Conclusions: Macrophage phagocytic activity is reduced in CHD patients, particularly in those with ACS. Diminished macrophage phagocytic function is linked to CHD and ACS. Macrophage phagocytosis could act as a protective biomarker in CHD and ACS, providing new insights into the pathophysiology of ASCVD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".