Association of Immune Cell Subsets with Incident Heart Failure in Two Population-Based Cohorts
Bibliographic record
Abstract
AIMS: Circulating inflammatory markers are associated with incident heart failure (HF), but prospective data on associations of immune cell subsets with incident HF are lacking. We determined the associations of immune cell subsets with incident HF as well as HF subtypes [with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF)]. METHODS AND RESULTS: Peripheral blood immune cell subsets were measured in adults from the Multi-Ethnic Study of Atherosclerosis (MESA) and Cardiovascular Health Study (CHS). Cox proportional hazard models adjusted for demographics, HF risk factors, and cytomegalovirus serostatus were used to evaluate the association of the immune cell subsets with incident HF. The average age of the MESA cohort at the time of immune cell measurements was 63.0 ± 10.4 years with 51% women, and in the CHS cohort, it was 79.6 ± 4.4 years with 62% women. In the meta-analysis of CHS and MESA, a higher proportion of CD4+ T helper (Th) 1 cells (per one standard deviation) was associated with a lower risk of incident HF [hazard ratio (HR) 0.91, (95% CI 0.83-0.99), P = 0.03]. Specifically, higher proportion of CD4+ Th1 cells was significantly associated with a lower risk of HFrEF [HR 0.73, (95% CI 0.62-0.85), <0.001] after correction for multiple testing. No association was observed with HFpEF. No other cell subsets were associated with incident HF. CONCLUSIONS: We observed that higher proportions of CD4+ Th1 cells were associated with a lower risk of incident HFrEF in two distinct population-based cohorts, with similar effect sizes in both cohorts demonstrating replicability. Although unexpected, the consistency of this finding across cohorts merits further investigation.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".