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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 −8 . Seven novel loci ( ZNF385D , AD AMTS9 , EDNRA , HAND2 , MYOCD , ITCH/EDEM2/MMP24 , and MRTFA ) were identified in all 3 phenotypes. An additional new locus ( LOXL1 ) was identified in the meta-analysis of the 3 phenotypes. Sex interaction analysis revealed sex differences in 7 loci including a novel locus ( SYNE3 ) in males. Meta-analysis of UK Biobank data with a previous meta-analysis led to identification of three novel loci ( APOB, FIP1L1, and LOXL4 ). Transcriptome-wide association analyses implicated additional genes ARHGAP42 , NDRG4 , and KANK2 . Gene set analysis showed an enrichment in extracellular organization and the PDGF (platelet-derived growth factor) signaling pathway. We found positive genetic correlations of cIMT with coronary artery disease r g =0.21 ( P =1.4×10 -7 ), peripheral artery disease r g =0.45 ( P =5.3×10 -5 ), and systolic blood pressure r g =0.30 ( P =4.0×10 -18 ). A negative genetic correlation between average of maximum cIMT and high-density lipoprotein was found r g =−0.12 ( P =7.0×10 -4 ). Conclusions: Genome-wide association meta-analyses in >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.

Background Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. Methods and Results We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR Conclusion Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level.

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models that tend to be more robust regarding deviations from the target population.

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.

Background: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization.Methods and results: 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables.While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001).Conclusions: Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance. (C) 2021 The Authors. Published by Elsevier B.V.

The complexity and volume of data associated with population-based cohorts means that generating health-related outcomes can be challenging. Using one such cohort, the UK Biobank-a major open access resource-we present a protocol to efficiently integrate the main dataset and record-level data files, to harmonize and process the data using an R package named "ukbpheno". We describe how to use the package to generate binary phenotypes in a standardized and machine-actionable manner. For complete details on the use and execution of this protocol, please refer to Yeung et al. (2022).

Purpose of Review As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. Recent Findings and Summary There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. Graphical Abstract AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of diabetic kidney disease is not well understood. We aimed to identify patterns of estimated GFR (eGFR) trajectory and to determine the clinical and genetic factors and their associations of these different patterns with all-cause mortality in patients with type 2 diabetes. Among 6330 patients with baseline eGFR >60 ml/min per 1.73 m2 in the Hong Kong Diabetes Register, a total of 456 patients (7.2%) developed Stage 5 chronic kidney disease or ESRD over a median follow-up of 13 years (incidence rate 5.6 per 1000 person-years). Joint latent class modeling was used to identify different patterns of eGFR trajectory. Four distinct and non-linear trajectories of eGFR were identified: slow decline (84.3% of patients), curvilinear decline (6.5%), progressive decline (6.1%) and accelerated decline (3.1%). Microalbuminuria and retinopathy were associated with accelerated eGFR decline, which was itself associated with all-cause mortality (odds ratio [OR] 6.9; 95% confidence interval [CI]: 5.6–8.4 for comparison with slow eGFR decline). Of 68 candidate genetic loci evaluated, the inclusion of five loci (rs11803049, rs911119, rs1933182, rs11123170, and rs889472) improved the prediction of eGFR trajectories (net reclassification improvement 0.232; 95% CI: 0.057-–0.406). Our study highlights substantial heterogeneity in the patterns of eGFR decline among patients with diabetic kidney disease, and identifies associated clinical and genetic factors that may help to identify those who are more likely to experience an accelerated decline in kidney function.

Nonalbuminuric diabetic kidney disease (DKD) has become the prevailing DKD phenotype. We compared the risks of adverse outcomes among patients with this phenotype compared with other DKD phenotypes.Multicenter prospective cohort study.19,025 Chinese adults with type 2 diabetes enrolled in the Hong Kong Diabetes Biobank.DKD phenotypes defined by baseline estimated glomerular filtration rate (eGFR) and albuminuria: no DKD (no decreased eGFR or albuminuria), albuminuria without decreased eGFR, decreased eGFR without albuminuria, and albuminuria with decreased eGFR.All-cause mortality, cardiovascular disease (CVD) events, hospitalization for heart failure (HF), and chronic kidney disease (CKD) progression (incident kidney failure or sustained eGFR reduction ≥40%).Multivariable Cox proportional or cause-specific hazards models to estimate the relative risks of death, CVD, hospitalization for HF, and CKD progression. Multiple imputation was used for missing covariates.Mean participant age was 61.1 years, 58.3% were male, and mean diabetes duration was 11.1 years. During 54,260 person-years of follow-up, 438 deaths, 1,076 CVD events, 298 hospitalizations for HF, and 1,161 episodes of CKD progression occurred. Compared with the no-DKD subgroup, the subgroup with decreased eGFR without albuminuria had higher risks of all-cause mortality (hazard ratio [HR], 1.59 [95% CI, 1.04-2.44]), hospitalization for HF (HR, 3.08 [95% CI, 1.82-5.21]), and CKD progression (HR, 2.37 [95% CI, 1.63-3.43]), but the risk of CVD was not significantly greater (HR, 1.14 [95% CI, 0.88-1.48]). The risks of death, CVD, hospitalization for HF, and CKD progression were higher in the setting of albuminuria with or without decreased eGFR. A sensitivity analysis that excluded participants with baseline eGFR 30 mL/min/1.73 mPotential misclassification because of drug use.Nonalbuminuric DKD was associated with higher risks of hospitalization for HF and of CKD progression than no DKD, regardless of baseline eGFR.

Objective: Lipoprotein(a) (Lp[a]) is associated with coronary artery disease (CAD) but also to LDL (low-density lipoprotein) cholesterol. The genetic architecture of Lp(a) remains incompletely understood, as well as its independence of LDL cholesterol in its association to CAD. We investigated the genetic determinants of Lp(a) concentrations in a large prospective multiethnic cohort. We tested the association for potential causality between genetically determined higher Lp(a) concentrations and CAD using a multivariable Mendelian randomization strategy. Approach and Results: We studied 371 212 participants of the UK Biobank with available Lp(a) and genome-wide genetic data. Genome-wide association analyses confirmed 2 known and identified 37 novel loci ( P −8 ) associated with Lp(a). Testing these loci as instrumental variables in an independent cohort with 60 801 cases and 123 504 controls, each SD genetically elevated Lp(a) conferred a 1.30 ([95% CI, 1.20–1.41] P =5.53×10 − 11 ) higher odds of CAD. Importantly, this association was independent of LDL cholesterol. Genetic fine-mapping in the LPA gene region identified 15 potential causal variants. This included a rare missense variant (rs41267813[A]) associated with lower Lp(a) concentration. We observed a strong interaction between rs41267813 and rs10455872 on Lp(a) concentrations, indicating a protective effect of rs41267813(A). Conclusions: This study supports an LDL cholesterol–independent causal link between Lp(a) and CAD. A rare missense variant in the LPA gene locus appears to be protective in people with the Lp(a) increasing variant of rs10455872. In the search for therapeutic targets of Lp(a), future work should focus on understanding the functional consequences of this missense variant.

This chapter discusses the current status and challenges in applying machine learning in three closely connected fields, namely genomics, proteomics, and drug discovery. Usage of machine learning methods are described and compared in the context of respective fields through selected literature. The current performance of implemented machine learning methods is described in comparison to traditional statistical methods. Finally, this chapter discusses potential future perspectives for implementation of machine learning in genomics, proteomics, and drug discovery.

Background and aim Deep Learning has revolutionised image analysis and its implementation in cardiology is rapidly advancing. DL offers the possibility to abstract highly-complex patterns from any image in order to optimise classification and prediction tasks. At the same time, positron emission tomography (PET) represents the reference technique for quantitative evaluation of myocardial perfusion and is a powerful tool for the diagnosis of myocardial ischemia and infarction. Although pathological perfusion patterns in PET are easily recognised by expert clinicians, it is unclear whether PET images harbour complex patterns inherent to traditional cardiovascular risk traits (factors) recognisable in an individual-patient basis. Hence, we aimed to deploy Deep Learning in order to predict cardiovascular risk traits from individual quantitative PET myocardial perfusion images. Methods Data form 1180 patients evaluated through quantitative N13-ammonia PET-imaging for suspected myocardial ischemia was analysed. We implemented transfer learning with fine-tuning of individual concatenated instances of an ImageNet pre-trained convolutional neural network (ResNet-50). From each PET scan, the 3 standard quantitative polar maps (rest/stress/perfusion reserve) were used as network input. A 5-fold cross validation policy was applied to training and validation for hyperparameters optimisation. Deep Learning modelling was deployed to independently predict: sex, smoking, arterial hypertension, dyslipidemia and type 2 diabetes mellitus as cardiovascular risk traits. Final model performance was evaluated through AUC and accuracy (with associated standard deviations [SD]) on a hold-out test set of 104 scans. Deep Learning-based heat maps were generated to identify regions of interest in PET imaging related to each of the predicted risk trait Results Deep Learning was able to strongly predict sex (0.87±0.04, 78%±4) and diabetes mellitus (0.75±0.13,71%±8), while its performance was only discrete for smoking (0.63±0.11, 85%±2), hypertension (0.61±0.06,58%±3) and dyslipidemia (0.59±0.05,57%±3), albeit all statistically significant (p Conclusion Deep Learning is able to significantly predict cardiovascular risk traits from individual quantitative PET myocardial perfusion images. This suggests the existence of complex high-dimensional and localised features within cardiac imaging that relate to cardiovascular risk factors at the individual level, which definitely warrants further research. Funding Acknowledgement Type of funding source: None

Background & Aims Increasing evidence suggests that non-alcoholic fatty liver disease (NAFLD) may be an independent risk factor for chronic kidney disease (CKD). Given the high prevalence of NAFLD among patients with diabetes who are also at risk of CKD, we aimed to investigate the association between NAFLD and albuminuria, a marker commonly found in diabetic nephropathy. Methods This study included a cohort of Chinese patients with type 2 diabetes from the Hong Kong Diabetes Registry recruited between March 2013 and May 2014. Liver stiffness measurement (LSM), with probe-specific cut-offs, was used to detect advanced liver fibrosis. While controlled attenuation parameter (CAP) was used to assess liver steatosis using transient elastography. Results A total of 1,763 Chinese patients with type 2 diabetes were recruited in this analysis. The mean (standard deviation) age and duration of diabetes were 60.7 (11.5) years and 10.8 (8.5) years, respectively. The prevalence of albuminuria was higher in diabetic patients with liver steatosis and those with advanced fibrosis (no NAFLD vs. liver steatosis vs. advanced fibrosis: 41.4% vs. 46.2% vs. 64.2%, p p = 0.039) in patients with eGFR ≥60 ml/min/1.73 m 2 . The odds of albuminuria increased with greater severity of liver fibrosis in a dose dependent manner, with the highest odds observed in patients with LSM scores ≥11.5 kPa assessed by M probe or ≥11.0 kPa assessed by XL probe (adjusted OR 1.53; 95% CI 1.07–2.20; p = 0.021). Conclusions Advanced liver fibrosis, but not steatosis, is independently associated with albuminuria in Chinese patients with type 2 diabetes. Attention should be paid to liver fibrosis in patients with obesity and type 2 diabetes complicated with albuminuria. Lay summary In this study, we assessed the link between non-alcoholic fatty liver disease (NAFLD) and albuminuria in a cohort of 1,763 Chinese patients with type 2 diabetes. This study shows that advanced liver fibrosis, a severe form of NAFLD, was independently associated with increased risk of albuminuria. The risk of albuminuria increased with greater severity of liver fibrosis.

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.