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Published on: February 24, 2023
Screening of Secretory Proteins Linking Major Depressive Disorder with Heart Failure Based on Comprehensive
Chuanjing Zhang1, Yongfei Song1, Lichao Cen1
1Ningbo University Health Science Center, Ningbo 315040, China.
Insights
Major depressive disorder (MDD) increases heart failure (HF) risk. Researchers identified ISLR and SFRP4 as potential biomarkers for diagnosing MDD with HF, with animal studies confirming their link to reduced heart function.
Area of Science:
- Cardiovascular Medicine
- Psychiatry
- Genetics
Background:
- Major depressive disorder (MDD) is a significant risk factor for heart failure (HF) development.
- Understanding the underlying mechanisms linking MDD and HF is crucial for effective treatment and diagnosis.
- Identifying reliable biomarkers for MDD-associated HF is a key clinical challenge.
Purpose of the Study:
- To investigate the causal relationship between MDD and HF using genetic data.
- To identify key genes and molecular mechanisms involved in MDD-related HF pathogenesis.
- To discover and validate novel diagnostic biomarkers for MDD-associated HF.
Main Methods:
- Mendelian randomization (MR) analysis to assess causality between MDD and HF.
- Differential gene expression analysis (DEA) and Weighted Gene Co-expression Network Analysis (WGCNA) to identify key genes.
- Machine learning (ML) algorithms to screen and validate candidate biomarkers (ISLR/SFRP4).
- Animal models were used to validate biomarker associations with cardiac function (LVEF).
Main Results:
- MR analysis confirmed MDD as a risk factor for HF (OR = 1.129, p < 0.001).
- Bioinformatics analysis identified 78 MDD-related pathogenic genes for HF, primarily involved in immune and inflammatory pathways.
- Two hub genes, ISLR and SFRP4, were identified as potential diagnostic biomarkers, with a developed nomogram showing high predictive accuracy (AUC > 0.90).
- Animal studies demonstrated a negative correlation between ISLR/SFRP4 levels and left ventricular ejection fraction (LVEF).
Conclusions:
- MDD is genetically linked to an increased risk of HF.
- ISLR and SFRP4 show significant potential as diagnostic biomarkers for MDD-associated HF.
- Further clinical studies are warranted to confirm the diagnostic utility of ISLR and SFRP4 in HF patients with MDD.
Background:
Major depressive disorder (MDD) plays a crucial role in the occurrence of heart failure (HF). This investigation was undertaken to explore the possible mechanism of MDD's involvement in HF pathogenesis and identify candidate biomarkers for the diagnosis of MDD with HF.
Methods:
GWAS data for MDD and HF were collected, and Mendelian randomization (MR) analysis was performed to investigate the causal relationship between MDD and HF. Differential expression analysis (DEA) and WGCNA were used to detect HF key genes and MDD-associated secretory proteins. Protein-protein interaction (PPI), functional enrichment, and cMAP analysis were used to reveal potential mechanisms and drugs for MDD-related HF. Then, four machine learning (ML) algorithms (including GLM, RF, SVM, and XGB) were used to screen candidate biomarkers, construct diagnostic nomograms, and predict MDD-related HF. Furthermore, the MCPcounter algorithm was used to explore immune cell infiltration in HF, and MR analysis was performed to explore the causal effect of immunophenotypes on HF. Finally, the validation of the association of MDD with reduced left ventricular ejection fraction (LVEF) and the performance assessment of diagnostic biomarkers was accomplished based on animal models mimicking MDD.
Results:
The MR analysis showed that the MDD was linked to an increased risk of HF (OR = 1.129, p < 0.001). DEA combined with WGCNA and secretory protein gene set identified 315 HF key genes and 332 MDD-associated secretory proteins, respectively. Through PPI and MCODE analysis, 78 genes were pinpointed as MDD-related pathogenic genes for HF. The enrichment analysis revealed that these genes were predominantly enriched in immune and inflammatory regulation. Through four ML algorithms, two hub genes (ISLR/SFRP4) were identified as candidate HF biomarkers, and a nomogram was developed. ROC analysis showed that the AUC of the nomogram was higher than 0.90 in both the HF combined dataset and two external cohorts. In addition, an immune cell infiltration analysis revealed the immune dysregulation in HF, with ISLR/SFRP4 displaying notable associations with the infiltration of B cells, CD8 T cells, and fibroblasts. More importantly, animal experiments showed that the expression levels of ISLR (r = -0.653, p < 0.001) and SFRP4 (r = -0.476, p = 0.008) were significantly negatively correlated with LVEF.
Conclusions:
The MR analysis indicated that MDD is a risk factor for HF at the genetic level. Bioinformatics analysis and the ML results suggest that ISLR and SFRP4 have the potential to serve as diagnostic biomarkers for HF. Animal experiments showed a negative correlation between the serum levels of ISLR/SFRP4 and LVEF, emphasizing the need for additional clinical studies to elucidate their diagnostic value.
