Association of immune cell subsets with cardiac mechanics in the Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
BackgroundImmunomodulatory therapy may help prevent heart failure (HF). Data on immune cells and myocardial remodeling in older adults with cardiovascular risk factors are limited.MethodsIn the Multi-Ethnic Study of Atherosclerosis cohort, 869 adults had 19 peripheral immune cell subsets measured and underwent cardiac MRI during the baseline exam, of which 321 had assessment of left ventricular global circumferential strain (LV-GCS). We used linear regression with adjustment for demographics, cardiovascular risk factors, and cytomegalovirus serostatus to evaluate the cross-sectional association of immune cell subsets with left ventricular mass index (LVMI) and LV-GCS.ResultsThe average age of the cohort was 61.6 ± 10.0 years and 53% were women. Higher proportions of γ/δ T cells were associated with lower absolute (worse) LV-GCS (-0.105% [95% CI -0.164%, -0.046%] per 1 SD higher proportion of γ/δ T cells, P = 0.0006). This association remained significant after Bonferroni's correction. Higher proportions of classical monocytes were associated with worse absolute LV-GCS (-0.04% [95% CI -0.07%, 0.00%] per 1 SD higher proportion of classical monocytes, P = 0.04). This did not meet significance after Bonferroni's correction. There were no other significant associations with LV-GCS or LVMI.ConclusionPathways associated with γ/δ T cells may be potential targets for immunomodulatory therapy targeted at HF prevention in populations at risk.FundingContracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 and grant R01 HL98077 from the National Heart, Lung, and Blood Institute/NIH and grants KL2TR001424, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences/NIH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".