Association of immune cell subsets with cardiac mechanics in the Multi-Ethnic Study of Atherosclerosis

Sinha, Arjun; Rivera, Adovich S.; Doyle, Margaret F.; Sitlani, Colleen M.; Fohner, Alison E.; Huber, Sally A.; Olson, Nels C.; Lima, João A.C.; Delaney, Joseph A.; Feinstein, Matthew J.; Shah, Sanjiv J.; Tracy, Russel; Psaty, Bruce M.

doi:10.1172/jci.insight.149193

Cited by 5 publications

(5 citation statements)

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“…In persons with a high burden of vascular and metabolic risk factors, chronic innate and adaptive immune activation work in concert to drive inflammasome activation and elaboration of inflammatory cytokines such as TNF‐a, IL‐1β, and IL‐6, exacerbating myocardial damage and progression to clinical sequelae 11–18 . Experimental models and cross‐sectional studies in humans have implicated certain immune cell populations such as classical monocytes (CD14++CD16−) and γδ T cells in promoting myocardial inflammation and other populations such as intermediate monocytes (CD14++CD16+) and natural killer (NK) cells in regulating myocardial inflammation 19–27 . Although the pro‐inflammatory cytokines associated with HF are classically involved with innate immunity, there is growing appreciation that dysregulated adaptive immunity can cause myocardial dysfunction 28–30 .…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14][15][16][17][18] Experimental models and cross-sectional studies in humans have implicated certain immune cell populations such as classical monocytes (CD14++CD16À) and γδ T cells in promoting myocardial inflammation and other populations such as intermediate monocytes (CD14++CD16+) and natural killer (NK) cells in regulating myocardial inflammation. [19][20][21][22][23][24][25][26][27] Although the pro-inflammatory cytokines associated with HF are classically involved with innate immunity, there is growing appreciation that dysregulated adaptive immunity can cause myocardial dysfunction. [28][29][30] Several animal models have demonstrated a causative role of CD4+ T helper (Th) 1 and Th17 cells in development of HF.…”

Section: Introductionmentioning

confidence: 99%

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Association of immune cell subsets with incident heart failure in two population‐based cohorts

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association of immune cell subsets with incident heart failure in two population‐based cohorts

et al. 2022

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“…These cytokines coordinate the immune response process and exert high cytotoxic activity against infected and transformed cells. One study has demonstrated that lower absolute (worse) left ventricular global circumferential strain was associated with higher proportions of γ/δ T cells ( 49 ). This is consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics and machine learning algorithms reveal the critical cellular senescence-associated genes and immune infiltration in heart failure due to ischemic cardiomyopathy

Ling

2023

Front. Immunol.

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Heart failure (HF) is the final stage of many cardiovascular illnesses and the leading cause of death worldwide. At the same time, ischemic cardiomyopathy has replaced valvular heart disease and hypertension as the primary causes of heart failure. Cellular senescence in heart failure is currently receiving more attention. In this paper, we investigated the correlation between the immunological properties of myocardial tissue and the pathological mechanisms of cellular senescence during ischemic cardiomyopathy leading to heart failure (ICM-HF) using bioinformatics and machine learning methodologies. Our goals were to clarify the pathogenic causes of heart failure and find new treatment options. First, after obtaining GSE5406 from the Gene Expression Omnibus (GEO) database and doing limma analysis, differential genes (DEGs) among the ICM-HF and control groups were identified. We intersected these differential genes with cellular senescence-associated genes (CSAG) via the CellAge database to obtain 39 cellular senescence-associated DEGs (CSA-DEGs). Then, a functional enrichment analysis was performed to elucidate the precise biological processes by which the hub genes control cellular senescence and immunological pathways. Then, the respective key genes were identified by Random Forest (RF) method, LASSO (Least Absolute Shrinkage and Selection Operator) algorithms, and Cytoscape’s MCODE plug-in. Three sets of key genes were taken to intersect to obtain three CSA-signature genes (including MYC, MAP2K1, and STAT3), and these three CSA-signature genes were validated in the test gene set (GSE57345), and Nomogram analysis was done. In addition, we assessed the relationship between these three CSA- signature genes and the immunological landscape of heart failure encompassing immunological infiltration expression profiles. This work implies that cellular senescence may have a crucial role in the pathogenesis of ICM-HF, which may be closely tied to its effect on the immune microenvironment. Exploring the molecular underpinnings of cellular senescence during ICM-HF is anticipated to yield significant advances in the disease’s diagnosis and therapy.

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“…Some studies have shown that specific peripheral immune cells, such as classical monocytes (CD14++CD16-), contribute to myocardial inflammation, whereas natural killer (NK) cells play a role in its regulation. 7 , 8 Furthermore, recent findings indicate a correlation between an increase in peripheral neutrophils and a decrease in CD4+ T cells with the progression of AHF. 9 – 11 Given these insights, we propose that peripheral immune cells could serve as significant predictors of 90-day readmission or mortality in patients with AHF, suggesting a nuanced approach to understanding and managing this condition.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients

Chen,

Yang,

Han

et al. 2024

Clin Appl Thromb Hemost

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Background Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period. Objective Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF. Methods In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of ‘with readmission/mortality’ and ‘without readmission/mortality’. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation. Results In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators—spanning peripheral immune cells and traditional clinical metrics—that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks. Conclusion Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.

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Association of immune cell subsets with cardiac mechanics in the Multi-Ethnic Study of Atherosclerosis

Cited by 5 publications

References 67 publications

Association of immune cell subsets with incident heart failure in two population‐based cohorts

Association of immune cell subsets with incident heart failure in two population‐based cohorts

Integrated bioinformatics and machine learning algorithms reveal the critical cellular senescence-associated genes and immune infiltration in heart failure due to ischemic cardiomyopathy

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients

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