Risk prediction model construction for post myocardial infarction heart failure by blood immune B cells

Area of Science:

• Cardiology
• Immunology
• Genomics

Background:

• Myocardial infarction (MI) is a leading cause of death, with post-MI heart failure (HF) significantly worsening patient prognosis.
• Predicting post-MI HF remains challenging due to a lack of reliable biomarkers.
• Current treatments for MI do not fully prevent the development of HF.

Purpose of the Study:

• To identify novel predictors of heart failure following myocardial infarction.
• To develop a diagnostic tool for assessing HF risk in MI patients.

Main Methods:

• Analysis of single-cell and bulk RNA sequencing data from peripheral blood of MI patients.
• Identification and validation of immune cell subtype marker genes.
• Development of a predictive model using a panel of 13 marker genes.

Main Results:

• A distinct subtype of immune-activated B cells was identified, differentiating patients who developed HF post-MI from those who did not.
• A 13-gene signature derived from B cell subtypes demonstrated predictive capability for post-MI HF risk.
• Specific genes like STING1, HSPB1, CCL5, ACTN1, and ITGB2 showed altered expression in post-MI HF patients.

Conclusions:

• Immune-activated B cell sub-clusters are significantly associated with the development of heart failure after myocardial infarction.
• The developed 13-gene prediction model offers a promising tool for early diagnosis and risk stratification of post-MI HF.
• Further research into the role of these B cell subtypes could lead to targeted therapeutic strategies.