Shearer, Joe

Introduction: Chronic kidney disease affects nearly half of heart failure (HF) patients, underscoring the importance of regular kidney function monitoring. However, longitudinal trajectories of kidney function in HF are not well characterized. Identifying clinical factors linked to long-term changes is critical for improving monitoring and informing early clinical decisions. Hypothesis: We hypothesized that longitudinal kidney function and its associations with clinical variables in HF patients differ from that of demographically similar comparators from the same community. Methods: We analyzed patients with validated HF from a community-based cohort and comparators individually matched to age and sex selected from participants in the Mayo Clinic Biobank cohort. Individuals with end-stage kidney disease, dialysis or kidney transplantation prior to enrollment were excluded. Serum creatinine-based estimated glomerular filtration rate (eGFR) from outpatient and inpatient visits was assessed from enrollment for up to 10 years of follow-up. The median of eGFR for 90-day intervals was used to estimate the follow-up eGFR (Figure 1) . We modeled percentage change from baseline and fit linear mixed-effect models with time, HF status, and their interaction to test whether the eGFR trajectories differed between the two groups. In addition, we examined the association between eGFR trajectories and clinical variables. Results: We studied 1182 HF patients and 825 comparators with at least three eGFR measurements. Median follow-up was 5 years (IQR: 3-10) for HF patients and 8 years (6-9) for comparators. In total, 16245 serum creatinine measurements from HF patients and 9858 from comparators were analyzed. Median age at enrollment was 76 years (67-84) for HF patients and 78 years (70-83) for comparators. A greater decline in percentage change in eGFR was observed in HF patients (-4.23%, 95% CI: -4.75 to -3.70% per year) than in comparators (-1.90%, 95% CI: -2.51 to -1.29% per year; P<0.001, Figure 2 ). Among HF patients, the decline in percentage change in eGFR was greater in the presence of diabetes, hyperlipidemia, or a higher comorbidity burden. Conclusions: In this community study, HF patients exhibited a greater percentage decline in eGFR compared to community comparators. Among HF patients, eGFR decline was more strongly associated with comorbidities—particularly metabolic risk factors—highlighting the relevance of the cardiovascular-kidney-metabolic syndrome in HF.

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.

Introduction: Coronary disease is the most common cause of heart failure (HF). HF is classified as ischemic (IHF) or non-ischemic (NIHF) based on this etiology. IHF and NIHF have distinct mechanisms not accounted for by ejection fraction (EF) and an improved understanding of the differences could drive the discovery of novel therapeutic targets. We therefore sought to identify proteomic signatures between IHF and NIHF. Methods: Proteomic profiling using the SomaScan®1.7K was performed using plasma samples from HF patients in a community cohort (Rochester Epidemiology Project, 2003-2012). IHF was defined by prior myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft, or obstructive disease in at least 1 vessel on coronary angiography. We compared the protein expression levels between IHF and NIHF using t-test and applied the Benjamini-Hochberg false-discovery rate (FDR) procedure for multiple comparisons. We explored pathway analysis for functional annotations using the biological process component of the Gene Ontology Resource (Panther 17v). Results: Among 1,388 patients, there were 518 IHF patients (age: 75±12years, 39.4% female, NYHA Class III/IV: 72.5%, EF:45±15%), and 870 NIHF patients (age: 75±14years, 53.6% female, NYHA Class III/IV: 66.9%, EF:49±17%). Among 7335 plasma proteins quantified, 63 unique proteins (FDR < 0.05) were differentially expressed between IHF and NIHF. Thirty-seven proteins including Troponin-I and T, and Myoglobin showed increased expression in IHF compared to NIHF and 26 including IGFBP-3 showed decreased expression. Pathophysiologic pathways including angiogenesis, coagulation, and lipid metabolism were represented in IHF, while inflammation and remodeling signaling pathways were dominant in relation to NIHF. Conclusions: High-throughput proteomic techniques are able to delineate IHF versus NIHF patients based on distinct biomarkers and pathophysiologic processes.

Introduction: Heart failure (HF) is a complex syndrome with high mortality often associated with impaired kidney function. Estimated glomerular filtration rate (eGFR) is central to kidney function assessment in HF. However, novel proteins related to kidney function (lipocalin 2 [LCN2], macrophage inhibitory cytokine 1 [MIC1], T cell immunoglobulin and mucin domain 1 [TIM1]) may improve risk stratification of HF patients, beyond eGFR. Their prognostic contribution remain to be fully explored. Hypothesis: Proteomic assessment of kidney function biomarkers improves risk stratification in HF. Methods: Clinical data and biospecimens were collected from a HF community cohort in Southeastern Minnesota from 2003-2012. Serum creatinine was measured in 1,388 patients and eGFR was calculated with the 2021 Chronic Kidney Disease (CKD) Epidemiology Collaboration equation. CKD was defined as an eGFR <60 mL/min/1.73m 2 . Plasma proteins were measured using the SomaLogic aptamer-based technology SomaScan®. In HF patients with and without CKD, Cox proportional hazard models were used to estimate the association between kidney function biomarkers and mortality, after adjustment for Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Results: At HF index, patients were on average 75±13 years of age and 48% female. Patients had a median eGFR of 57 mL/min/1.73m 2 and 64% had CKD. Adjusted geometric mean levels of LCN2, MIC1, and TIM1 levels were 20-30% higher in patients with CKD. Patients with CKD were at higher risk of dying (HR 1.6, 95% CI 1.4-1.8). Patients with above median levels of LCN2, TIM1, MIC1 were at higher risk of dying, irrespective of CKD (See Figure). Conclusions: In a HF community cohort, several novel kidney function biomarkers provided further risk stratification in patients with and without CKD.

Introduction: Over 6 million people in the U.S. have heart failure (HF), less than half are expected to survive beyond 5 years. The need to better stratify mortality risk in HF is recognized. Frailty is associated with mortality in HF but not routinely measured clinically. As frailty is linked to inflammation and malnutrition, we hypothesized that the Metabolic Vulnerability Index (MVX) a multimarker score of systemic inflammation (small HDL particles, GlycA) and malnutrition (leucine, valine, isoleucine, citrate), could serve as a biomarker of frailty to predict mortality risk. Methods: Clinical data and plasma were collected from 1,389 patients from a HF community cohort between 2003-2012. We measured frailty using the Rockwood Index as the proportion of deficits present out of 32 physical limitations and comorbidities. MVX was calculated from the nuclear magnetic resonance LipoProfile® test. Patients were categorized by frailty (0-0.15; 0.16-0.27; 0.28-0.78) and MVX (33.4-50, 50-60, 60-70, 70-85.8) cutpoints. Cox models estimated the association of frailty and MVX assignment with mortality, adjusted for Meta-Analysis Global Group in Chronic HF (MAGGIC) score, a validated clinical risk score for HF mortality. Results: Frailty and MVX scores were available in 985 patients (median age 77, IQR: 67-84; 48% women). Higher frailty was associated with higher MVX (p-trend < 0.001). The highest frailty and MVX groups experienced large increases in risk of death, after adjustment for MAGGIC score (HR=3.3, 95% CI=2.6-4.2) and (HR=2.7, 95% CI=2.1-3.5), respectively. When adjusted for one another and MAGGIC score, MVX and frailty associations with death were only minimally attenuated: frailty (HR=3.2, 95% CI=2.5-4.0) and MVX (HR=2.4, 95% CI=1.9-3.2) (Figure 1). Conclusion: In this community cohort of patients with HF, frailty and MVX are positively associated with one another. However, both indicators are independently associated with an increased risk of death and can contribute to risk stratification.

Introduction: Heart Failure (HF) is a syndrome of high comorbidity and mortality. The impact of comorbidities on life expectancy in HF is well studied, but guideline-recommended disease-centric risk scores such as the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) score may not fully account for the effect of comorbidities. The Charlson Comorbidity Index (Charlson) is a validated measure that quantifies the prognostic impact of comorbidities. Objective: To examine the incremental value of Charlson over MAGGIC in predicting death in HF. Methods: In a population-based HF cohort, we computed MAGGIC and Charlson scores using electronically linked medical records. Patients were divided based on ejection fraction (EF) into reduced [EF <50%, HFrEF] or preserved [EF ≥50%, HFpEF]. Cox regression was performed to assess the association between the risk scores and mortality. The incremental value of Charlson over MAGGIC was tested by calculating the area under the ROC [receiver operating characteristic] curves (AUCs) with 95% confidence interval (CI). Results: We studied 1,388 patients with HF validated using the Framingham criteria (mean age, 75±13 years, 48% female, mean EF, 49±17%). The median Charlson was 7.0 (interquartile range 5.0-9.0) and was higher in HFpEF than HFrEF (7.0 [5.0-9.0] vs. 6.0 [4.0-8.3] respectively. Over a median follow-up of 13.9 years (13.3-14.2), mortality was 17.4% (95% CI, 15.4 -19.3%) and 36.8% (95% CI, 34.2-39.3%) at 1, and 3 years respectively. In regression analysis, increasing MAGGIC and Charlson were independently associated with death (HR 1.09, 95% CI 1.08-1.11 and HR,1.22, 95% CI 1.20-1.24, both p <0.001) irrespective of EF. Discrimination ability was improved by adding Charlson over MAGGIC (Table), reaching statistical significance at 3 years. Conclusion: In this HF cohort, the Charlson index provided an added value over MAGGIC in predicting the risk of death at 3 years. These findings emphasize the importance of accounting for comorbidities in mid-term risk stratification in HF.