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A study published in the European Journal of Heart Failure examined the association between a diabetes-specific heart failure risk score (WATCH-DM) and the presence of subclinical cardiomyopathy in individuals with diabetes. The study included 150 adults with diabetes and found that those with high WATCH-DM scores had a significantly greater prevalence of diabetic cardiomyopathy (DbCM) compared to those with low scores. The study suggests that the WATCH-DM risk score may be a useful tool for identifying individuals with diabetes who are at high risk of developing heart failure. However, the study has limitations, such as a small sample size and the inability to establish causality. The study was supported by a research grant and the authors have disclosed potential conflicts of interest. [Extracted from the article]

Aims: To evaluate the contribution of baseline and longitudinal changes in cardiometabolic health (CMH) towards heart failure (HF) risk among adults with type 2 diabetes (T2D). Methods and results: Participants of the Look AHEAD trial with T2D and without prevalent HF were included. Adjusted Cox models were used to create a CMH score incorporating target levels of parameters weighted based on relative risk for HF. The associations of baseline and changes in the CMH score with risk of overall HF, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) were assessed using Cox models. Among the 5080 participants, 257 incident HF events occurred over 12.4 years of follow‐up. The CMH score included 2 points each for target levels of waist circumference, glomerular filtration rate, urine albumin‐to‐creatinine ratio, and 1 point each for blood pressure and glycated haemoglobin at target. High baseline CMH score (6–8) was significantly associated with lower overall HF risk (adjusted hazard ratio [HR], ref = low score (0–3): 0.31, 95% confidence interval [CI] 0.21–0.47) with similar associations observed for HFpEF and HFrEF. Improvement in CMH was significantly associated with lower risk of overall HF (adjusted HR per 1‐unit increase in score at 4 years: 0.80, 95% CI 0.70–0.91). In the ACCORD validation cohort, the baseline CMH score performed well for predicting HF risk with adequate discrimination (C‐index 0.70), calibration (chi‐square 5.53, p = 0.70), and risk stratification (adjusted HR [high (6–8) vs. low score (0–3)]: 0.35, 95% CI 0.26–0.46). In the Look AHEAD subgroup with available biomarker data, incorporating N‐terminal pro‐B‐type natriuretic peptide to the baseline CMH score improved model discrimination (C‐index 0.79) and risk stratification (adjusted HR [high (8–10) vs. low score (0–4)]: 0.18, 95% CI 0.09–0.35). Conclusions: Achieving target levels of more CMH parameters at baseline and sustained improvements were associated with lower HF risk in T2D. [ABSTRACT FROM AUTHOR]

Objective: This study examined the association between social determinants of health (SDOH) burden and overweight/obesity in a nationally representative sample of adults in the United States.Methods: Data for 161,795 adults aged ≥18 years from the 2013 to 2017 National Health Interview Survey were used. A total of 38 SDOH were aggregated to create a cumulative SDOH score, which was divided into quartiles (Q1-Q4) to denote levels of SDOH burden. Prevalence of overweight and obesity was examined across SDOH quartiles in the total population and by age, sex, and race/ethnicity. Multinomial logistic regression models were used to analyze the association between SDOH quartiles and overweight/obesity, adjusting for relevant covariates.Results: There was a graded increase in obesity prevalence with increasing SDOH burden. At nearly each quartile, overweight and obesity rates were higher for middle-aged and non-Hispanic Black adults compared with their counterparts; additional differences were observed by sex. In fully adjusted models, SDOH-Q4 was associated with 15%, 50%, and 70% higher relative prevalence of overweight, obesity class 1 and 2, and obesity class 3, respectively, relative to SDOH-Q1.Conclusions: Cumulative social disadvantage, denoted by higher SDOH burden, was associated with increased odds of obesity, independent of clinical and demographic factors. [ABSTRACT FROM AUTHOR]

Aims To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs). Methods and results Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning-[WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrell's concordance index (C-index) and calibration by the Greenwood-Nam-D'Agostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P =0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (<13) vs. high (=13)WATCH-DM(i) scores [0.71 (95% confidence interval 0.68-0.74) vs. 0.64 (95% confidence interval 0.61-0.66)]. When NP levels were combined with the WATCH-DM(i) score, HF risk discrimination improvement and NRI varied across the spectrum of risk with greater improvement observed at low/intermediate risk [WATCH-DM(i)<13] vs. high risk [WATCH-DM(i) =13] (C-index = 0.73 vs. 0.71; NRI = 0.45 vs. 0.17). Conclusion TheWATCH-DM risk score can accurately predict incident HF risk in community-based individuals with dysglycaemia. The addition of NP levels is associated with greater improvement in the HF risk prediction performance among individuals with low/intermediate risk than those with high risk. [ABSTRACT FROM AUTHOR]

Aims Methods and results Conclusion 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.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).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. [ABSTRACT FROM AUTHOR]

Aim: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.Methods and Results: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.Conclusions: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes. [ABSTRACT FROM AUTHOR]

Aims: The relationship between resting stroke volume (SV) and prognostic markers in heart failure with preserved ejection fraction (HFpEF) is not well established. We evaluated the association of SV index (SVI) at rest with exercise capacity and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) in stable patients with HFpEF. Methods and results: Participants enrolled in the Phosphodiesterase‐5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (RELAX) trial with available data on SVI by the Doppler method were included in this analysis (n = 185). A low‐flow state defined by resting SVI < 35 mL/m2 was present in 37% of study participants. Multivariable adjusted linear regression analysis suggested that higher resting heart rate, higher body weight, prevalent atrial fibrillation, and smaller left ventricular (LV) end‐diastolic dimension were each independently associated with lower SVI. Patients with low‐flow HFpEF had lower systolic blood pressure and smaller LV end‐diastolic dimension. In multivariable adjusted linear regression models, lower SVI was significantly associated with lower peak oxygen consumption (peak VO2) and higher NT‐proBNP levels at baseline, and greater decline in peak VO2 at 6 month follow‐up independent of other confounders. Resting LV ejection fraction was not associated with peak VO2 and NT‐proBNP levels. Conclusions: There is heterogeneity in the resting SVI distribution among patients with stable HFpEF, with more than one‐third of patients identified with the low‐flow HFpEF phenotype (SVI < 35 mL/m2). Lower SVI was independently associated with lower peak VO2, higher NT‐proBNP levels, and greater decline in peak VO2. These findings highlight the potential prognostic utility of SVI assessment in the management of patients with HFpEF. [ABSTRACT FROM AUTHOR]

Background Cardiac infiltration is an important cause of death in sarcoidosis. Transthoracic echocardiography ( TTE) has limited sensitivity for the detection of cardiac sarcoidosis ( CS). Late gadolinium enhancement ( LGE) cardiovascular magnetic resonance ( CMR) is used to diagnose CS but has limitations of cost and availability. We sought to determine whether TTE-derived global longitudinal strain ( GLS) may be used to identify individuals with CS, despite preserved left ventricular ejection fraction ( LVEF), and whether abnormal GLS is associated with major cardiovascular events ( MCE). Methods We studied 31 patients with biopsy-proven extra-cardiac sarcoidosis, LVEF>50% and LGE on CMR ( CS+ group), and 31 patients without LGE ( CS− group), matched by age, sex, and severity of lung disease. GLS was measured using vendor-independent speckle tracking software. Parameters of left and right ventricular systolic and diastolic function were also studied. Receiver-operating characteristic curves were used to identify GLS cutoff for CS detection, and Kaplan-Meier plots to determine the ability of GLS to predict MCE. Results LGE was associated with reduced GLS (−19.6±1.9% in CS− vs −14.7±2.4% in CS+, P<.01) and with reduced E/A ratio (1.1±0.3 vs 0.9±0.3, respectively, P =.01). No differences were noted in other TTE parameters. GLS magnitude inversely correlated with LGE burden ( r=−.59). GLS cutoff of −17% showed sensitivity and specificity 94% for detecting CS. Patients who experienced MCE had worse GLS than those who did not (−13.4±0.9% vs −17.7±0.4%, P=.0003). Conclusions CS is associated with significantly reduced GLS in the presence of preserved LVEF. GLS measurements may become part of the TTE study performed to screen for CS. [ABSTRACT FROM AUTHOR]

Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HF DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HF DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HF DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HF DM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P >0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P <0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P =0.96). TRS-HF DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HF DM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.