Jonathan J. H. Bray

Aims To conduct a meta‐analysis of randomized controlled trials (RCTs) to assess the effect of sodium‐glucose cotransporter‐2 (SGLT2) inhibitors on inflammatory biomarkers. Methods Medline, Embase and the Cochrane Library were searched for RCTs investigating the effect of SGLT2 inhibitors on inflammatory biomarkers, adipokine profiles and insulin sensitivity. Results Thirty‐eight RCTs were included (14 967 participants, 63.3% male, mean age 62 ± 8.6 years) with a median (interquartile range) follow‐up of 16 (12–24) weeks. Meta‐analysis showed that SGLT2 inhibitors significantly improved adiponectin, interleukin‐6, tumour necrosis factor receptor‐1 (vs. placebo alone: standardized mean difference [SMD] 0.34 [95% confidence interval {CI} 0.23, 0.45], mean difference [MD] −0.85 pg/mL [95% CI −1.32, −0.38], SMD −0.13 [95% CI −0.20, −0.06], respectively), leptin and homeostatic model assessment of insulin resistance index (vs. control: SMD −0.20 [95% CI −0.33, −0.07], MD −0.83 [95% CI −1.32, −0.33], respectively). There were no significant changes in C‐reactive protein (CRP), tumour necrosis factor‐α, plasminogen activator inhibitor‐1, fibroblast growth factor‐21 or monocyte chemoattractant protein‐1. Conclusions Our analysis shows that SGLT2 inhibitors likely improve adipokine biomarkers and insulin sensitivity, but there is little evidence that SGLT2 inhibitors improve other inflammatory biomarkers including CRP.

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.