@article {8976, title = {Diabetes Status Modifies the Association Between Different Measures of Obesity and Heart Failure Risk Among Older Adults: A Pooled Analysis of Community-Based NHLBI Cohorts.}, journal = {Circulation}, volume = {145}, year = {2022}, month = {2022 01 25}, pages = {268-278}, abstract = {

BACKGROUND: Obesity and diabetes are associated with a higher risk of heart failure (HF). The interrelationships between different measures of adiposity-overall obesity, central obesity, fat mass (FM)-and diabetes status for HF risk are not well-established.

METHODS: Participant-level data from the ARIC study (Atherosclerosis Risk in Communities; visit 5) and the CHS (Cardiovascular Health Study; visit 1) cohorts were obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center, harmonized, and pooled for the present analysis, excluding individuals with prevalent HF. FM was estimated in all participants using established anthropometric prediction equations additionally validated using the bioelectrical impedance-based FM in the ARIC subgroup. Incident HF events on follow-up were captured across both cohorts using similar adjudication methods. Multivariable-adjusted Fine-Gray models were created to evaluate the associations of body mass index (BMI), waist circumference (WC), and FM with risk of HF in the overall cohort as well as among those with versus without diabetes at baseline. The population attributable risk of overall obesity (BMI>=30 kg/m), abdominal obesity (WC>88 and 102 cm in women and men, respectively), and high FM (above sex-specific median) for incident HF was evaluated among participants with and without diabetes.

RESULTS: The study included 10 387 participants (52.9\% ARIC; 25.1\% diabetes; median age, 74 years). The correlation between predicted and bioelectrical impedance-based FM was high (=0.90; n=5038). During a 5-year follow-up, 447 participants developed HF (4.3\%). Higher levels of each adiposity measure were significantly associated with higher HF risk (hazard ratio [95\% CI] per 1 SD higher BMI=1.15 [1.05, 1.27], WC=1.22 [1.10, 1.36]; FM=1.13 [1.02, 1.25]). A significant interaction was noted between diabetes status and measures of BMI ( interaction=0.04) and WC ( interaction=0.004) for the risk of HF. In stratified analysis, higher measures of each adiposity parameter were significantly associated with higher HF risk in individuals with diabetes (hazard ratio [95\% CI] per 1 SD higher BMI=1.29 [1.14-1.47]; WC=1.48 [1.29-1.70]; FM=1.25 [1.09-1.43]) but not those without diabetes, including participants with prediabetes and euglycemia. The population attributable risk percentage of overall obesity, abdominal obesity, and high FM for incident HF was higher among participants with diabetes (12.8\%, 29.9\%, and 13.7\%, respectively) versus those without diabetes (<=1\% for each).

CONCLUSIONS: Higher BMI, WC, and FM are strongly associated with greater risk of HF among older adults, particularly among those with prevalent diabetes.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Female, Heart Failure, Humans, Male, National Heart, Lung, and Blood Institute (U.S.), Obesity, Risk Factors, United States}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.121.055830}, author = {Patel, Kershaw V and Segar, Matthew W and Lavie, Carl J and Kondamudi, Nitin and Neeland, Ian J and Almandoz, Jaime P and Martin, Corby K and Carbone, Salvatore and Butler, Javed and Powell-Wiley, Tiffany M and Pandey, Ambarish} } @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} }