Deep-Learning for Epicardial Adipose Tissue Assessment with Computed Tomography: Implications for Cardiovascular Risk Prediction

Background: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented./ Objectives: This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care./ Methods: The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value./ Results: External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI:1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post–cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01). Conclusions: Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification

The effect of CETP inhibitors on new-onset diabetes: a systematic review and meta-analysis

BACKGROUND: Despite the increasing prevalence of type 2 diabetes mellitus (T2DM), limited pharmacologic options are available for prevention. Cholesteryl ester transfer protein inhibitors (CETPis) have been studied primarily as a therapy to reduce cardiovascular disease, but have also been shown to reduce new-onset diabetes. As new trial data have become available, this meta-analysis examines the effect of CETP inhibitors on new-onset diabetes and related glycaemic measures. METHODS AND RESULTS: We searched MEDLINE, EMBASE, and Cochrane databases (all articles until 4 March, 2021) for randomised controlled trials (RCT) ≥1-year duration, with at least 500 participants, comparing CETPi to placebo, and that reported data on new-onset diabetes or related glycaemic measures [haemoglobin A1C (HbA1C), fasting plasma glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)]. A fixed effects meta-analysis model was applied to all eligible studies to quantify the effect of CETPi therapy on new-onset diabetes. Four RCTs (n = 75 102) were eligible for quantitative analysis of the effect of CETPi on new-onset diabetes. CETPis were found to significantly decrease the risk of new-onset diabetes by 16% (RR: 0.84; 95% CI: 0.78, 0.91; P < 0.001), with low between-trial heterogeneity (I2 = 4.1%). Glycaemic measures were also significantly improved or trended towards improvement in those with and without diabetes across most trials. CONCLUSION: Although RCTs have shown mixed results regarding the impact of CETPi on cardiovascular disease, they have shown a consistent reduction in the risk of new-onset diabetes with CETPi therapy. Future trials of CETPis and potentially other HDL-raising agents should therefore specify new-onset diabetes and reversal of existing T2DM as secondary endpoints