@article {7549, title = {Predictors and outcomes of heart failure with mid-range ejection fraction.}, journal = {Eur J Heart Fail}, year = {2017}, month = {2017 Dec 11}, abstract = {

AIMS: While heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.

METHODS AND RESULTS: We pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49\%], HFpEF (EF >=50\%), and HFrEF (EF <=40\%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P~<= 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P~<= 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95\% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95\% CI 1.20-1.90, P~= 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95\% CI 1.41-1.73, P~= 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P~= 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P~= 0.78).

CONCLUSIONS: We found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

}, issn = {1879-0844}, doi = {10.1002/ejhf.1091}, author = {Bhambhani, Vijeta and Kizer, Jorge R and Lima, Jo{\~a}o A C and van der Harst, Pim and Bahrami, Hossein and Nayor, Matthew and de Filippi, Christopher R and Enserro, Danielle and Blaha, Michael J and Cushman, Mary and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Brouwers, Frank P and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Levy, Daniel and Herrington, David M and van Gilst, Wiek H and Bertoni, Alain G and Larson, Martin G and de Boer, Rudolf A and Gottdiener, John S and Shah, Sanjiv J and Ho, Jennifer E} } @article {7603, title = {Association of Cardiovascular Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction.}, journal = {JAMA Cardiol}, year = {2018}, month = {2018 Jan 10}, abstract = {

Importance: Nearly half of all patients with heart failure have preserved ejection fraction (HFpEF) as opposed to reduced ejection fraction (HFrEF), yet associations of biomarkers with future heart failure subtype are incompletely understood.

Objective: To evaluate the associations of 12 cardiovascular biomarkers with incident HFpEF vs HFrEF among adults from the general population.

Design, Setting, and Participants: This study included 4 longitudinal community-based cohorts: the Cardiovascular Health Study (1989-1990; 1992-1993 for supplemental African-American cohort), the Framingham Heart Study (1995-1998), the Multi-Ethnic Study of Atherosclerosis (2000-2002), and the Prevention of Renal and Vascular End-stage Disease study (1997-1998). Each cohort had prospective ascertainment of incident HFpEF and HFrEF. Data analysis was performed from June 25, 2015, to November 9, 2017.

Exposures: The following biomarkers were examined: N-terminal pro B-type natriuretic peptide or brain natriuretic peptide, high-sensitivity troponin T or I, C-reactive protein (CRP), urinary albumin to creatinine ratio (UACR), renin to aldosterone ratio, D-dimer, fibrinogen, soluble suppressor of tumorigenicity, galectin-3, cystatin C, plasminogen activator inhibitor 1, and interleukin 6.

Main Outcomes and Measures: Development of incident HFpEF and incident HFrEF.

Results: Among the 22 756 participants in these 4 cohorts (12 087 women and 10 669 men; mean [SD] age, 60 [13] years) in the study, during a median follow-up of 12 years, 633 participants developed incident HFpEF, and 841 developed HFrEF. In models adjusted for clinical risk factors of heart failure, 2 biomarkers were significantly associated with incident HFpEF: UACR (hazard ratio [HR], 1.33; 95\% CI, 1.20-1.48; P < .001) and natriuretic peptides (HR, 1.27; 95\% CI, 1.16-1.40; P < .001), with suggestive associations for high-sensitivity troponin (HR, 1.11; 95\% CI, 1.03-1.19; P = .008), plasminogen activator inhibitor 1 (HR, 1.22; 95\% CI, 1.03-1.45; P = .02), and fibrinogen (HR, 1.12; 95\% CI, 1.03-1.22; P = .01). By contrast, 6 biomarkers were associated with incident HFrEF: natriuretic peptides (HR, 1.54; 95\% CI, 1.41-1.68; P < .001), UACR (HR, 1.21; 95\% CI, 1.11-1.32; P < .001), high-sensitivity troponin (HR, 1.37; 95\% CI, 1.29-1.46; P < .001), cystatin C (HR, 1.19; 95\% CI, 1.11-1.27; P < .001), D-dimer (HR, 1.22; 95\% CI, 1.11-1.35; P < .001), and CRP (HR, 1.19; 95\% CI, 1.11-1.28; P < .001). When directly compared, natriuretic peptides, high-sensitivity troponin, and CRP were more strongly associated with HFrEF compared with HFpEF.

Conclusions and Relevance: Biomarkers of renal dysfunction, endothelial dysfunction, and inflammation were associated with incident HFrEF. By contrast, only natriuretic peptides and UACR were associated with HFpEF. These findings highlight the need for future studies focused on identifying novel biomarkers of the risk of HFpEF.

}, issn = {2380-6591}, doi = {10.1001/jamacardio.2017.4987}, author = {de Boer, Rudolf A and Nayor, Matthew and deFilippi, Christopher R and Enserro, Danielle and Bhambhani, Vijeta and Kizer, Jorge R and Blaha, Michael J and Brouwers, Frank P and Cushman, Mary and Lima, Jo{\~a}o A C and Bahrami, Hossein and van der Harst, Pim and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Shah, Sanjiv J and Levy, Daniel and Herrington, David M and Larson, Martin G and van Gilst, Wiek H and Gottdiener, John S and Bertoni, Alain G and Ho, Jennifer E} } @article {8488, title = {Sex-Specific Associations of Cardiovascular Risk Factors and Biomarkers With Incident Heart~Failure.}, journal = {J Am Coll Cardiol}, volume = {76}, year = {2020}, month = {2020 Sep 22}, pages = {1455-1465}, abstract = {

BACKGROUND: Whether cardiovascular (CV) disease risk factors and biomarkers associate differentially with heart failure (HF) risk in men and women is unclear.

OBJECTIVES: The purpose of this study was to evaluate sex-specific associations of CV risk factors and biomarkers with incident HF.

METHODS: The analysis was performed using data from 4 community-based cohorts with 12.5 years of follow-up. Participants (recruited between 1989 and 2002) were free of HF at baseline. Biomarker measurements included natriuretic peptides, cardiac troponins, plasminogen activator inhibitor-1, D-dimer, fibrinogen, C-reactive protein, sST2, galectin-3, cystatin-C, and urinary albumin-to-creatinine ratio.

RESULTS: Among 22,756 participants (mean age 60 {\textpm} 13 years, 53\% women), HF occurred in 2,095 participants (47\% women). Age, smoking, type 2 diabetes mellitus, hypertension, body mass index, atrial fibrillation, myocardial infarction, left ventricular hypertrophy, and left bundle branch block were strongly associated with HF in both sexes (p~<~0.001), and the combined clinical model had good discrimination in men (C-statistic~=~0.80) and in women (C-statistic~=~0.83). The majority of biomarkers were strongly and similarly associated with HF in both sexes. The clinical model improved modestly after adding natriuretic peptides in men (ΔC-statistic~=~0.006; likelihood ratio chi-square~=~146; p~<~0.001), and after adding cardiac troponins in women (ΔC-statistic~=~0.003; likelihood ratio chi-square~=~73; p~<~0.001).

CONCLUSIONS: CV risk factors are strongly and similarly associated with incident HF in both sexes, highlighting the similar importance of risk factor control in reducing HF risk in the community. There are subtle sex-related differences in the predictive value of individual biomarkers, but the overall improvement in HF risk estimation when included in~a~clinical HF risk prediction model is limited in both sexes.

}, issn = {1558-3597}, doi = {10.1016/j.jacc.2020.07.044}, author = {Suthahar, Navin and Lau, Emily S and Blaha, Michael J and Paniagua, Samantha M and Larson, Martin G and Psaty, Bruce M and Benjamin, Emelia J and Allison, Matthew A and Bartz, Traci M and Januzzi, James L and Levy, Daniel and Meems, Laura M G and Bakker, Stephan J L and Lima, Jo{\~a}o A C and Cushman, Mary and Lee, Douglas S and Wang, Thomas J and deFilippi, Christopher R and Herrington, David M and Nayor, Matthew and Vasan, Ramachandran S and Gardin, Julius M and Kizer, Jorge R and Bertoni, Alain G and Allen, Norrina B and Gansevoort, Ron T and Shah, Sanjiv J and Gottdiener, John S and Ho, Jennifer E and de Boer, Rudolf A} } @article {9382, title = {Effect of 2022 ACC/AHA/HFSA Criteria on Stages of Heart~Failure in a Pooled Community Cohort.}, journal = {J Am Coll Cardiol}, volume = {81}, year = {2023}, month = {2023 Jun 13}, pages = {2231-2242}, abstract = {

BACKGROUND: The 2022 American College of Cardiology (ACC)/American Heart Association (AHA)/Heart~Failure Society of America (HFSA) clinical practice guideline proposed an updated definition for heart failure (HF) stages.

OBJECTIVES: This study aimed to compare prevalence and prognosis of HF stages according to classification/definition originally described in 2013 and 2022 ACC/AHA/HFSA definitions.

METHODS: Study participants from 3 longitudinal cohorts (the MESA [Multi-Ethnic Study of Atherosclerosis], CHS [Cardiovascular Health Study], and the FHS [Framingham Heart Study]), were categorized into 4 HF stages according to the 2013 and 2022 criteria. Cox proportional hazards regression was used to assess predictors of progression to symptomatic HF and adverse clinical outcomes associated with each HF stage.

RESULTS: Among 11,618 study participants, according to the 2022 staging, 1,943 (16.7\%) were healthy, 4,348 (37.4\%) were in stage A (at risk), 5,019 (43.2\%) were in stage B (pre-HF), and 308 (2.7\%) were in stage C/D (symptomatic HF). Compared to the classification/definition originally described in 2013, the 2022 ACC/AHA/HFSA approach resulted in a higher proportion of individuals with stage B HF (increase from 15.9\% to 43.2\%); this shift disproportionately involved women as well as Hispanic and Black individuals. Despite the 2022 criteria designating a greater proportion of individuals as stage B, the relative risk of progression to symptomatic HF remained similar (HR: 10.61; 95\%~CI: 9.00-12.51; P~< 0.001).

CONCLUSIONS: New standards for HF staging resulted in a substantial shift of community-based individuals from stage A to stage B. Those with stage B HF in the new system were at high risk for progression to symptomatic HF.

}, keywords = {American Heart Association, Atherosclerosis, Cardiology, Female, Heart Failure, Humans, Longitudinal Studies, Prognosis, United States}, issn = {1558-3597}, doi = {10.1016/j.jacc.2023.04.007}, author = {Mohebi, Reza and Wang, Dongyu and Lau, Emily S and Parekh, Juhi K and Allen, Norrina and Psaty, Bruce M and Benjamin, Emelia J and Levy, Daniel and Wang, Thomas J and Shah, Sanjiv J and Gottdiener, John S and Januzzi, James L and Ho, Jennifer E} } @article {9745, title = {Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.}, journal = {Eur J Heart Fail}, year = {2024}, month = {2024 Sep 06}, abstract = {

AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.

METHODS AND RESULTS: Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high-risk DbCM phenotype was identified based on the incidence of HF on follow-up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community-based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup-3 (n = 324, 27\% of the cohort) had significantly higher 5-year HF incidence than other phenogroups (12.1\% vs. 4.6\% [phenogroup 2] vs. 3.1\% [phenogroup 1]) and was identified as the high-risk DbCM phenotype. The key echocardiographic predictors of high-risk DbCM phenotype were higher NT-proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16\% and 29\% of participants with DbCM, respectively. Participants with (vs. without) high-risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95\% confidence interval] 1.61 [1.18-2.19] in CHS and 1.34 [1.08-1.65] in the UT Southwestern EHR cohort).

CONCLUSION: Machine learning-based techniques may identify 16\% to 29\% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies.

}, issn = {1879-0844}, doi = {10.1002/ejhf.3443}, author = {Segar, Matthew W and Usman, Muhammad Shariq and Patel, Kershaw V and Khan, Muhammad Shahzeb and Butler, Javed and Manjunath, Lakshman and Lam, Carolyn S P and Verma, Subodh and Willett, DuWayne and Kao, David and Januzzi, James L and Pandey, Ambarish} }