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Background Heart failure (HF) is a prevalent and deadly disease, and preventive strategies focused on at-risk individuals are needed. Current HF prediction models have not examined HF subtypes. We sought to develop and validate risk prediction models for HF with preserved and reduced ejection fraction (HFpEF, HFrEF). Methods and Results Of 28,820 participants from four community-based cohorts, 982 developed incident HFpEF and 909 HFrEF during a median follow-up of 12 years. Three cohorts were combined and a 2:1 random split used for derivation and internal validation, with the fourth cohort as external validation. Models accounted for multiple competing risks (death, other HF subtype, unclassified HF). The HFpEF-specific model included age, sex, systolic blood pressure, body mass index, antihypertensive treatment, and prior myocardial infarction; it had good discrimination in derivation (c-statistic 0.80, 95% CI 0.78–0.82) and validation samples (internal 0.79, 95% CI 0.77–0.82; external 0.76, 95% CI 0.71–0.80). The HFrEF-specific model additionally included smoking, left ventricular hypertrophy (LVH), left bundle branch block (LBBB), and diabetes; it had good discrimination in derivation (c-statistic 0.82, 95% CI 0.80–0.84) and validation samples (internal 0.80, 95% CI 0.78–0.83; external 0.76, 95% CI 0.71–0.80). Age was more strongly associated with HFpEF, and male sex, LVH, LBBB, prior myocardial infarction, and smoking with HFrEF (P for each comparison ≤ 0.02). Conclusions We describe and validate risk prediction models for HF subtypes, and show good discrimination in a large sample. Some risk factors differed between HFpEF and HFrEF, supporting the notion of pathogenetic differences among HF subtypes.

AimsGrowth differentiation factor 15 (GDF15), ST2, high-sensitivity troponin T (hsTnT), and N-terminal pro brain natriuretic peptide (NT-proBNP) are biomarkers of distinct mechanisms that may contribute to the pathophysiology of heart failure (HF) [inflammation (GDF15); ventricular remodelling (ST2); myonecrosis (hsTnT); and wall stress (NT-proBNP)]. Methods and resultsWe compared circulating levels of GDF15, ST2, hsTnT, and NT-proBNP, as well as their combinations, in compensated patients with clinical HF with reduced ejection fraction (HFREF) (n ¼ 51), HF with preserved ejection fraction (HFPEF) (n¼ 50), and community-based controls (n ¼ 50). Compared with controls, patients with HFPEF and HFREF had higher median levels of GDF15 (540 pg/mL vs. 2529 and 2672 pg/mL, respectively), hsTnT (3.7 pg/mL vs. 23.7 and 35.6 pg/mL), and NT-proBNP (69 pg/mL vs. 942 and 2562 pg/mL), but not ST2 (27.6 ng/mL vs. 31.5 and 35.3 ng/mL), adjusting for clinical covariates. In receiver operating characteristic curve analyses, NT-proBNP distinguished HFREF from controls with an area under the curve (AUC) of 0.987 (P , 0.001); GDF15 distinguished HFPEF from controls with an AUC of 0.936 (P , 0.001); and the combination of NT-proBNP and GDF15 distinguished HFPEF from controls with an AUC of 0.956 (P , 0.001). NT-proBNP and hsTnT levels were higher in HFREF than in HFPEF (adjusted P , 0.04). The NT-proBNP:GDF15 ratio distinguished between HFPEF and HFREF with the largest AUC (0.709; P , 0.001). ConclusionsOur study provides comparative data on physiologically distinct circulating biomarkers in HFPEF, HFREF, and controls from the same community. These data suggest a prominent role for myocardial injury (hsTnT) with increased wall stress (NT-proBNP) in HFREF, and systemic inflammation (GDF15) in HFPEF.Heart failure with preserved ejection fraction † ST2 † Growth differentiation factor 15 † High-sensitivity troponin T † N-terminal pro brain natriuretic peptide † These authors contributed equally to the study.

Background Atrial fibrillation (AF) and heart failure (HF) frequently coexist and together confer an adverse prognosis. The association of AF with HF subtypes has not been well-described. We sought to examine differences in the temporal association of AF and HF with preserved versus reduced ejection fraction (HFpEF vs HFrEF). Methods and Results We studied Framingham Heart Study participants with new-onset AF and/or HF between 1980–2012. Among 1737 individuals with new AF, (mean-age 75±12, 48% women) more than one third (37%) had HF. Conversely among 1166 individuals with new HF (mean-age 79±11, 53% women), more than half (57%) had AF. Prevalent AF was more strongly associated with incident HFpEF (multivariable-adjusted hazard ratio [HR] 2.34, 95% confidence interval [CI] 1.48–3.70, no AF as referent) vs HFrEF (HR 1.32, 95%CI 0.83–2.10), with a trend toward difference between HF subtypes (P for difference 0.06). Prevalent HF was associated with incident AF (HR 2.18, 95%CI 1.26–3.76, no HF as referent). The presence of both AF and HF portended greater mortality risk compared with those without either condition, particularly among individuals with new HFrEF and prevalent AF (HR 2.72, 95%CI 2.12–3.48) compared with new HFpEF and prevalent AF (HR 1.83, 95%CI 1.41–2.37, P for difference 0.02). Conclusions AF occurs in more than half of individuals with HF, and HF in more than one third of individuals with AF. AF precedes and follows both HFpEF and HFrEF, with some differences in temporal association and prognosis. Future studies focused on underlying mechanisms of these dual conditions are warranted.

Aim Growth differentiation factor 15 (GDF15) is a cytokine highly expressed in states of inflammatory stress. We aimed to study the clinical correlates and prognostic significance of plasma GDF15 in heart failure with preserved ejection fraction (HFpEF) vs. reduced ejection fraction(HFrEF), compared with N‐terminal pro‐brain natriuretic peptide (NT‐proBNP), an indicator of haemodynamic wall stress. Methods Plasma GDF15 and NT‐proBNP were prospectively measured in 916 consecutive patients with HFrEF (EF <50%; n = 730) and HFpEF (EF ≥50%; n = 186), and measured again at 6 months in 488 patients. Patients were followed up for a composite outcome of death or first HF rehospitalization. Results Median GDF15baseline values were similarly elevated in HFpEF [2862 (1812 represent the 25th percentile and 4176 represent the 75th percentile) ng/L] and HFrEF [2517 (1555, 4030) ng/L] (P = 0.184), whereas NT‐proBNP was significantly lower in HFpEF than HFrEF (1119 ng/L vs. 2335 ng/L, P < 0.001). Independent correlates of GDF15baseline were age, systolic blood pressure, New York Heart Association (NYHA) class, diabetes, atrial fibrillation, sodium, haemoglobin, creatinine, diuretic therapy, high sensitivity troponin T (hsTnT) and NT‐proBNP (all P < 0.05). During a median follow‐up of 23 months, there were 379 events (307 HFrEF, 72 HFpEF). GDF15 remained a significant independent predictor for composite outcome even after adjusting for important clinical predictors including hsTnT and NT‐proBNP (adjusted hazard ratio 1.76 per 1 Ln U, 95% confidence interval 1.39–2.21; P < 0.001), regardless of HF group (Pinteraction = 0.275). GDF15baseline provided incremental prognostic value when added to clinical predictors, hsTnT and NT‐proBNP (area under receiver operating characteristic curve increased from 0.720 to 0.740, P < 0.019), with a net reclassification improvement of 0.183 (P = 0.004). Patients with ≥20% GDF156months increase had higher risk for composite outcome (adjusted hazard ratio 1.68, 95% confidence interval 1.15–2.45; P = 0.007) compared with those with GDF156months within ± 20% of baseline. Conclusions The similarly elevated levels and independent prognostic utility of GDF15 in HFrEF and HFpEF suggest that beyond haemodynamic stress (NT‐proBNP), inflammatory injury (GDF15) may play an important role in both HF syndromes.

AimsCurrent heart failure (HF) guidelines highlight the importance of iron deficiency (ID) in HF. Whether HF itself or age-related comorbidities contribute to ID is uncertain, and previous data were limited to Western populations. We aimed to study the prevalence, clinical correlates, functional significance and prognosis of ID in HF patients, compared with community-based controls in a multi-ethnic Southeast Asian population. Methods and resultsIron status was assessed in 751 HF patients (age 62.0 ± 12.2 years, 75.5% men, 64.7% Chinese, 23.9% Malay, 10.2% Indian) and 601 controls (age 56.9 ± 10.4 years, 49.8% men, 70.9% Chinese, 21.5% Malay, 7.2% Indian). ID, defined as ferritin <100 μg/L or ferritin 100-300 μg/L and transferrin saturation (Tsat) <20%, was present in 39.3% of controls and 61.4% of HF [odds ratio (OR) 3.5, 95% confidence interval (CI) 2.5-4.9, adjusting for clinical covariates]. Independent correlates of ID in HF were Indian ethnicity (OR 2.4 vs. Chinese, 95% CI 1.2-5.0), female gender (OR 2.8, 95% CI 1.7-4.8), larger body mass index (OR 1.05/unit increase, 95% CI 1.01-1.1) and decreased left ventricular ejection fraction (OR 1.03/unit decrease, 95% CI 1.01-1.04). In a subset of 48 HF patients undergoing cardiopulmonary exercise testing, Tsat correlated with peak oxygen consumption ( = 0.53, P < 0.01), independent of baseline characteristics. The HF patients with Tsat <20% as well as anaemia showed the poorest event-free survival after adjusting for clinical covariates.

Introduction: Heart failure (HF) is a major growing public health burden, and preventive strategies focused on at-risk individuals are needed. Recent initiatives have advocated for early prevention and aggressive treatment in ACC/AHA stage A/B HF, but prior HF risk prediction models remain poorly defined and validated. Moreover, risk factors for HF-specific subtypes have not yet been examined. Methods: We developed and validated separate HF risk prediction models for preserved and reduced ejection fraction (HFPEF, HFREF) in four community-based prospective cohorts (FHS, CHS, PREVEND, MESA). Fine-Gray proportional sub-distribution hazards models were used to account for competing risks (death, other HF subtype, and unclassified HF). FHS, CHS, and PREVEND samples were combined and a 2:1 random split was used for derivation and internal validation. MESA served for external validation. Results: There were 982 incident HFPEF and 909 HFREF events among 28,820 participants during follow-up (median 12 years). We created a HFPEF-specific model which included age, sex, systolic blood pressure, body mass index, hypertension treatment, and prior myocardial infarction; it had good discrimination in derivation (c-statistic 0.80, 95% CI 0.78-0.82) and validation samples (internal 0.79, 95% CI 0.77-0.82; external 0.76, 95% CI 0.71-0.80). The HFREF-specific model added smoking, left ventricular hypertrophy (LVH), left bundle branch block (LBBB), and diabetes; it had good discrimination in derivation (c-statistic 0.82, 95% CI 0.80-0.84) and validation samples (internal 0.80, 95% CI 0.78-0.83; external 0.76, 95% CI 0.71-0.80). Age had a greater effect on HFPEF risk, whereas male sex, LVH, LBBB, previous myocardial infarction, and smoking had greater effects on HFREF risk (P for comparison ≤ 0.02 for all). Conclusions: We describe and validate risk prediction models that are distinct for HF subtypes, and we demonstrate good discrimination in four community-based cohorts. Some risk factors differed in HFPEF vs HFREF, supporting distinct pathogenesis between HF subtypes. Studies are needed to examine the clinical utility of risk models, with the ultimate goal of targeted preventive strategies.

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