Objective Type 2 diabetes is an important risk factor for non-alcoholic fatty liver disease (NAFLD), but current guidelines provide conflicting recommendations on whether diabetic patients should be screened for NAFLD. We therefore studied the strategy of screening diabetic patients by FibroScan. Design Liver fat and fibrosis were assessed by controlled attenuation parameter (CAP) and liver stiffness measurements (LSM) by FibroScan at a diabetic centre for patients from primary care and hospital clinics. Probe-specific LSM cut-offs were used to detect advanced fibrosis. Results Of 1918 patients examined, 1799 (93.8%) had valid CAP and 1884 (98.2%) had reliable LSM (1770 with the M probe and 114 with the XL probe). The proportion of patients with increased CAP and LSM was 72.8% (95% CI 70.7% to 74.8%) and 17.7% (95% CI 16.0% to 19.5%), respectively. By multivariable analysis, female gender, higher body mass index, triglycerides, fasting plasma glucose and alanine aminotransferase (ALT) and non-insulin use were associated with increased CAP. Longer duration of diabetes, higher body mass index, increased ALT and spot urine albumin:creatinine ratio and lower high-density lipoprotein-cholesterol were associated with increased LSM. Ninety-four patients (80% had increased LSM) underwent liver biopsy: 56% had steatohepatitis and 50% had F3-4 disease. Conclusions Diabetic patients have a high prevalence of NAFLD and advanced fibrosis. Those with obesity and dyslipidaemia are at particularly high risk and may be the target for liver assessment. Our data support screening for NAFLD and/or advanced fibrosis in patients with type 2 diabetes.

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

Aims: Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results: We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to ‘black box’ DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the ‘black box’ DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions: Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.

Objective: Carotid artery intima-media thickness (cIMT) is a widely accepted marker of subclinical atherosclerosis. Twenty susceptibility loci for cIMT were previously identified and the identification of additional susceptibility loci furthers our knowledge on the genetic architecture underlying atherosclerosis. Approach and Results: We performed 3 genome-wide association studies in 45 185 participants from the UK Biobank study who underwent cIMT measurements and had data on minimum, mean, and maximum thickness. We replicated 15 known loci and identified 20 novel loci associated with cIMT at P 100 000 individuals identified 25 novel loci associated with cIMT providing insights into genes and tissue-specific regulatory mechanisms of proatherosclerotic processes. We found evidence for shared biological mechanisms with cardiovascular diseases.