Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors

Risk and Resilience Variants in the Retinoic Acid Metabolic and Developmental Pathways Associated with Risk of FASD Outcomes

Fetal Alcohol Spectrum Disorder (FASD) is a common neurodevelopmental disorder that affects an estimated 2-5% of North Americans. FASD is induced by prenatal alcohol exposure (PAE) during pregnancy and while there is a clear genetic contribution, few genetic factors are currently identified or understood. In this study, using a candidate gene approach, we performed a genetic variant analysis of retinoic acid (RA) metabolic and developmental signaling pathway genes on whole exome sequencing data of 23 FASD-diagnosed individuals. We found risk and resilience alleles in ADH and ALDH genes known to normally be involved in alcohol detoxification at the expense of RA production, causing RA deficiency, following PAE. Risk and resilience variants were also identified in RA-regulated developmental pathway genes, especially in SHH and WNT pathways. Notably, we also identified significant variants in the causative genes of rare neurodevelopmental disorders sharing comorbidities with FASD, including STRA6 (Matthew-Wood), SOX9 (Campomelic Dysplasia), FDG1 (Aarskog), and 22q11.2 deletion syndrome (TBX1). Although this is a small exploratory study, the findings support PAE-induced RA deficiency as a major etiology underlying FASD and suggest risk and resilience variants may be suitable biomarkers to determine the risk of FASD outcomes following PAE.

Does Serum Uric Acid to Creatinine Ratio Predict Mortality Risk in Patients With Heart Failure?

BACKGROUND

Previous studies have established a positive correlation between serum uric acid to creatinine (SUA/Cr) ratio and cardiovascular disease, but the relationship between SUA/Cr ratio and the prognosis of heart failure (HF) remains unknown. This study investigated the potential of SUA/Cr ratio as a prognostic predictor for patients with HF.

METHODS

This single-center prospective cohort study enrolled 2,122 patients with HF between March 2013 and June 2017. All patients were divided into 3 groups according to SUA/Cr ratio tertiles and were followed up with until December 31, 2022. The association between SUA/Cr ratio and the prognosis of HF was assessed using the Cox proportional hazards model.

RESULTS

The mean (SD) age and mean (SD) SUA/Cr ratio of the study cohort (66% male) were 59.3 (14.7) years and 4.71 (2.09), respectively. During a median follow-up period of 15 months (range, 11-26 months), 390 end-point events were observed. Prognosis analysis revealed that a high SUA/Cr ratio was associated with an increased mortality risk of HF (hazard ratio, 1.62 [95% CI, 1.26-2.09]; P < .001) compared with the SUA/Cr ratio in the lowest tertile. After adjusting for covariates, the hazard ratio for mortality risk of HF was 1.71 (95% CI, 1.23-2.37; P = .001). Subgroup analysis showed that mortality risk increased in direct proportion with the SUA/Cr ratio in female patients, patients with a history of hypertension and β-blocker use, and patients with UA levels below 428 μmol/L and creatinine levels less than 97 mg/dL. Stratification by age; by history of diabetes, hyperlipidemia, and smoking; and by level of fasting plasma glucose, however, had no obvious effect on the association between SUA/Cr ratio and HF prognosis. Patients with higher SUA/Cr ratios had reduced left ventricular ejection fraction and increased left ventricular end-diastolic diameter.

CONCLUSION

A high SUA/Cr ratio was an independent risk factor for the mortality risk of HF.

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

BACKGROUND

Despite the advancements in heart failure(HF) research, the early diagnosis of HF continues to be a challenging issue in clinical practice. This study aims to investigate the genes related to myocardial fibrosis and conduction block, with the goal of developing a diagnostic model for early treatment of HF in patients.

METHOD

The gene expression profiles of GSE57345, GSE16499, and GSE9128 were obtained from the Gene Expression Omnibus (GEO) database. After merging the expression profile data and adjusting for batch effects, differentially expressed genes (DEGs) associated with conduction block and myocardial fibrosis were identified. Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, and gene set enrichment analysis (GSEA) were utilized for functional enrichment analysis. A protein-protein interaction network (PPI) was constructed using a string database. Potential key genes were selected based on the bioinformatics information mentioned above. SVM and LASSO were employed to identify hub genes and construct the module associated with HF. The mRNA levels of TAC mice and external datasets (GSE141910 and GSE59867) are utilized for validating the diagnostic model. Additionally, the study explores the relationship between the diagnostic model and immune cell infiltration.

RESULTS

A total of 395 genes exhibiting differential expression were identified. Functional enrichment analysis revealed that these specific genes primarily participate in biological processes and pathways associated with the constituents of the extracellular matrix (ECM), immune system processes, and inflammatory responses. We identified a diagnostic model consisting of 16 hub genes, and its predictive performance was validated using external data sets and a transverse aortic coarctation (TAC) mouse model. In addition, we observed significant differences in mRNA expression of 7 genes in the TAC mouse model. Interestingly, our study also unveiled a correlation between these model genes and immune cell infiltration.

CONCLUSIONS

We identified sixteen key genes associated with myocardial fibrosis and conduction block, as well as diagnostic models for heart failure. Our findings have significant implications for the intensive management of individuals with potential genetic variants associated with heart failure, especially in the context of advancing cell-targeted therapy for myocardial fibrosis.

The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology

Novel genetic risk markers have helped us to advance the field of cardiovascular epidemiology and refine our current understanding and risk stratification paradigms. The discovery and analysis of variants can help us to tailor prognostication and management. However, populations underrepresented in cardiovascular epidemiology and cardiogenetics research may experience inequities in care if prediction tools are not applicable to them clinically. Therefore, the purpose of this article is to outline the barriers that underrepresented populations can face in participating in genetics research, to describe the current efforts to diversify cardiogenetics research, and to outline strategies that researchers in cardiovascular epidemiology can implement to include underrepresented populations. Mistrust, a lack of diverse research teams, the improper use of sensitive biodata, and the constraints of genetic analyses are all barriers for including diverse populations in genetics studies. The current work is beginning to address the paucity of ethnically diverse genetics research and has already begun to shed light on the potential benefits of including underrepresented and diverse populations. Reducing barriers for individuals, utilizing community-driven research processes, adopting novel recruitment strategies, and pushing for organizational support for diverse genetics research are key steps that clinicians and researchers can take to develop equitable risk stratification tools and improve patient care.

A novel polygenic risk score improves prognostic prediction of heart failure with preserved ejection fraction in the Chinese Han population

AIMS

Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF.

METHODS AND RESULTS

We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827-0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658-0.734, P = 4 × 10-11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605-0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145-0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4-11.9; P = 5.6 × 10-5) and 30-fold (HR = 29.8, 95% CI 14.0-63.5; P = 1.4 × 10-18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure.

CONCLUSION

The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.

Predictive roles of brain-derived neurotrophic factor Val66Met polymorphism on antidepressant efficacy of different forms of prefrontal brain stimulation monotherapy: A randomized, double-blind, sham-controlled study

BACKGROUND

Although repetitive transcranial magnetic stimulation (rTMS) and prolonged intermittent theta-burst stimulation (piTBS) can induce changes in synaptic plasticity, the influence of brain-derived neurotrophic factor (BDNF) genotypes on their antidepressant effects remain unknown. Hence, we investigated the BDNF polymorphism contribution to the antidepressant effect of different forms left-sided prefrontal stimulations in a randomized, sham-controlled study METHODS: Seventy-five patients with medication-resistant depression were randomly assigned into three monotherapy groups: piTBS, high-frequency(HF) rTMS, or sham. The acute treatment period was two weeks. 17-item Hamilton Depression Rating scale (HDRS-17) were applied at baseline, week-1, and week-2. The primary outcome was percentage changes of HDRS-17 (%HDRS-17 changes) analyzed by generalized estimating equation (GEE) model.

RESULTS

The GEE analysis revealed a significant interaction between group, time, and BDNF genotypes effects on %HDRS-17 changes over time.  In patients carrying Val homozygotes, piTBS and HF-rTMS both exhibited significantly greater %HDRS reduction than sham at week-2. In Met carriers, only piTBS showed better efficacy than sham at week-2 (piTBS vs. sham, -41.1% vs.-18.9%, p=0.004). Regarding the influence of different BDNF genotypes on antidepressant efficacy in each intervention, only HF-rTMS exhibited significantly different degrees of %HDRS-17 changes between Val homozygotes and Met carriers (-68.5% vs. -26.4%, p=0.012, respectively), but piTBS delivered the consistent efficacy regardless of the BDNF polymorphism.

CONCLUSIONS

This is the first study to confirm the different impacts of BDNF genotypes on the effect of different left-sided prefrontal brain stimulation. BDNF Val66Met polymorphism may play a role in the antidepressant response of piTBS and HF-rTMS. (Trial Registration Number UMIN-CTR:UMIN000020892: Registration date: Feb.4, 2016).