Background: To compare the cardiovascular event risk in type 2 diabetes patients newly receiving dapagliflozin vs. empagliflozin.Methods: We conducted a retrospective cohort study by analyzing a multi-institutional electronic medical records database (Chang Gung Research Database) in Taiwan and included adult type 2 diabetes patients who were newly receiving sodium–glucose co-transporter 2 (SGLT2) inhibitors from 2016 to 2017. The primary outcome was a composite of cardiovascular death, myocardial infarction, ischemic stroke and heart failure. We followed up patients from initiation of SGLT2 inhibitors until the occurrence of cardiovascular events before December 31, 2018. We performed multivariable Cox proportional hazard modeling, adjusting for patients’ age, sex, laboratory data, co-morbidities, and concomitant medications.Results: We identified 12,681 new SGLT2 inhibitor users with a mean age of 58.9 (SD 11.8) years, of whom 43.9% were female and 45.8% were new dapagliflozin users. A total of 10,442 person-years of dapagliflozin use and 12,096 person-years of empagliflozin use were included. Compared to empagliflozin users, new users of dapagliflozin were found to have similar risks for primary composite outcome (adjusted HR: 0.91; 95% CI 0.73–1.14), cardiovascular death (adjusted HR: 0.54; 95% CI 0.14–2.12), myocardial infarction (adjusted HR: 0.77, 95% CI 0.49–1.19) and ischemic stroke (adjusted HR: 1.15; 95% CI 0.80–1.65), but a lower risk of heart failure (adjusted HR: 0.68; 95% CI 0.49–0.95).Conclusion: The risk of cardiovascular events was similar between dapagliflozin and empagliflozin new users, but dapagliflozin may have a better outcome in the reduction of heart failure in type 2 diabetes patients. Future prospective studies are required to confirm the findings.

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual’s quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Background and Objectives: The relationship between three-dimensional (3D) scanning-derived body surface measurements and biomarkers in patients with coronary artery disease (CAD) were assessed. Methods and Methods: The recruitment of 98 patients with CAD confirmed by cardiac catheterization and 98 non-CAD patients were performed between March 2016 and December 2017. A health questionnaire on basic information, life style variables, and past medical and family history was completed. 3D body surface measurements and biomarkers were obtained. Differences between the two groups were assessed and multivariable analysis performed. Results: It was found that chest width (odds ratio [OR] 0.761, 95% confidence interval [CI] = 0.586–0.987, p = 0.0399), right arm length (OR 0.743, 95% CI = 0.632–0.875, p = 0.0004), waist circumference (OR 1.119, 95% CI = 1.035–1.21, p = 0.0048), leptin (OR 1.443, 95% CI = 1.184–1.76, p = 0.0003), adiponectin (OR 0.978, 95% CI = 0.963–0.994, p = 0.006), and interleukin 6 (OR 1.181, 95% CI = 1.021–1.366, p = 0.0254) were significantly associated with CAD. The combination of biomarker scores and body measurement scores had the greatest area under the curve and best association with CAD (area under the curve of 0.8049 and 95% CI = 0.7440–0.8657). Conclusions: Our study suggests that 3D derived body surface measurements in combination with leptin, adiponectin, and interleukin 6 levels may direct us to those at risk of CAD, allowing a non-invasive approach to identifying high-risk patients.