Deep-Learning Prediction of Cardiovascular Outcomes from Routine Retinal Images in Individuals with Type 2 Diabetes

BackgroundPrior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.MethodsWe included 6,127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry. The cohort was divided into training (70%), validation (10%) and testing (20%) cohorts. Clinical 10-year CVD risk was calculated using the pooled cohort equation (PCE) risk score. A polygenic risk score (PRS) for coronary heart disease was also obtained. Retinal images were analysed using an EfficientNet-B2 network to predict 10-year CVD risk. The primary outcome was time to first major adverse CV event (MACE) including CV death, myocardial infarction or stroke.Results1,241 individuals were included in the test cohort (mean PCE 10-year CVD risk 35%). There was a strong correlation between retinal predicted CVD risk and the PCE risk score (r=0.66) but not the polygenic risk score (r=0.05). There were 288 MACE events. Higher retina-predicted risk was significantly associated with increased 10-year risk of MACE (HR 1.05 per 1% increase; 95% CI 1.04-1.06, p<0.001) and remained so after adjustment for the PCE and polygenic risk score (HR 1.03; 95% CI 1.02-1.04, p<0.001). The retinal risk score had similar performance to the PCE (both AUC 0.697) and when combined with the PCE and polygenic risk score had significantly improved performance compared to the PCE alone (AUC 0.728). An increase in retinal-predicted risk within 3 years was associated with subsequent increased MACE likelihood.ConclusionsA deep-learning AI model could accurately predict MACE from routine retinal screening photographs with a comparable performance to traditional clinical risk assessment in a diabetic cohort. Combining the AI-derived retinal risk prediction with a coronary heart disease polygenic risk score improved risk prediction. AI retinal assessment might allow a one-stop CVD risk assessment at routine retinal screening

Artificial targeting of autophagy components to mitochondria reveals both conventional and unconventional mitophagy pathways

Macroautophagy/autophagy enables lysosomal degradation of a diverse array of intracellular material. This process is essential for normal cellular function and its dysregulation is implicated in many diseases. Given this, there is much interest in understanding autophagic mechanisms of action in order to determine how it can be best targeted therapeutically. In mitophagy, the selective degradation of mitochondria via autophagy, mitochondria first need to be primed with signals that allow the recruitment of the core autophagy machinery to drive the local formation of an autophagosome around the target mitochondrion. To determine how the recruitment of different core autophagy components can drive mitophagy, we took advantage of the mito-QC mitophagy assay (an outer mitochondrial membrane-localized tandem mCherry-GFP tag). By tagging autophagy proteins with an anti-mCherry (or anti-GFP) nanobody, we could recruit them to mitochondria and simultaneously monitor levels of mitophagy. We found that targeting ULK1, ATG16L1 and the different Atg8-family proteins was sufficient to induce mitophagy. Mitochondrial recruitment of ULK1 and the Atg8-family proteins induced a conventional mitophagy pathway, requiring RB1CC1/FIP200, PIK3C3/VPS34 activity and ATG5. Surprisingly, the mitophagy pathway upon recruitment of ATG16L1 proceeded independently of ATG5, although it still required RB1CC1 and PIK3C3/VPS34 activity. In this latter pathway, mitochondria were alternatively delivered to lysosomes via uptake into early endosomes