Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study)

A systematic review of multimorbidity clusters in heart failure: Effects of methodologies

BACKGROUND

Clustering algorithms can identify distinct heart failure (HF) subgroups. The choice of algorithms, modelling process, and input variables can impact clustering outcomes. Therefore, we reviewed analytical methods and variables used in studies that performed clustering in patients with HF.

METHODS

We systematically searched CINAHL, COCHRANE, EMBASE, OVID Medline, and Web of Science for eligible articles between inception and April 2023. We included primary studies that identified distinct HF multimorbid subgroups and were appraised for risk-of-bias and against methodological recommendations for cluster analysis. A narrative synthesis was performed.

RESULTS

Our analysis included 43 studies, mostly following a cohort design (n = 34, 79 %) and conducted primarily in Europe (n = 15, 35 %) and North America (n = 13, 30 %). Model-based (n = 22, 48 %), centre-based (n = 10, 22 %), and hierarchical class clustering (n = 9, 20 %) were the most frequently employed algorithms, identifying a range of 2-10 multimorbid clusters. Most studies used a combination of multi-modal parameters (i.e., socio-demographics, biochemistry, clinical characteristics, comorbidities and risk factors, cardiac imaging, and biomarkers) (n = 27, 63 %), followed by disease-based parameters (i.e., comorbidities and risk factors) (n = 11, 26 %) as input variables for clustering. Notably, variables used for clustering reflected cardiovascular and metabolic conditions. The phenogroups identified differed by input variables and algorithms used for clustering. We found substantial quality gaps in developing clustering models, variable selection, reporting of modelling processes, and model validation.

CONCLUSION

Cluster analysis results differed based on the clustering algorithms used and input variables. This review found substantial gaps in analysis quality and reporting. Implementing a methodological framework to develop, validate, and report clustering analysis can improve the clinical utility and reproducibility of clustering outcomes.

Proteomics in Acute Heart Transplant Rejection, On Behalf of the GRAfT Investigators

BACKGROUND

Proteomic phenotyping can provide insights into rejection pathophysiology, novel biomarkers, and therapeutic targets.

METHODS

Within the prospective, multicenter Genomic Research Alliance for Transplantation study, 181 proteins were evaluated from blood drawn at the time of endomyocardial biopsy; protein fold change, logistic regression, and pathway analyses were conducted, with protein discovery adjusted for a 5% false discovery rate.

RESULTS

Among 104 adult heart transplant patients (31% female sex, 53% Black race, median age 52 y), 74 had no rejection, 18 developed acute cellular rejection (ACR), and 12 developed antibody-mediated rejection (AMR). Differential expression was found in 2 proteins during ACR (inflammatory proteins CXCL10 and CD5) and 73 proteins during AMR. The most abundant AMR proteins were the heart failure biomarkers N-terminal pro-B-type natriuretic peptide and suppression of tumorigenicity 2. In univariate logistic regression, odds of identifying ACR on endomyocardial biopsy increased with doubling of CXCL10 (odds ratio [OR] 2.2 [95% confidence interval (CI), 1.3-3.6]) and CD5 (OR 4.7 [95% CI, 1.7-12.9]) concentrations, and odds of AMR increased with doubling of N-terminal pro-B-type natriuretic peptide (OR 13.0 [95% CI, 2.7-62.7) and suppression of tumorigenicity 2 (OR 4.8 [95% CI, 2.1-10.7]) concentrations. After multivariable analysis with clinical covariates, these proteins showed similar odds of ACR or AMR on biopsy. Pathway analysis identified T cell-receptor signaling and cell differentiation as key pathways in ACR and cardiovascular disease and cell turnover in AMR.

CONCLUSIONS

Proteomic analysis reveals unique biomarkers and biological pathway expression in ACR and AMR. Cardiac injury-associated biomarkers were more pronounced in AMR, whereas inflammatory biomarkers were more pronounced in ACR. Proteomic analysis may provide insights into rejection pathophysiology, detection, and therapy.

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk

BACKGROUND

We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk.

METHODS AND RESULTS

BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS.

CONCLUSIONS

Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

REVIEW PURPOSE

This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice.

FINDINGS

34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.