The determinants of the excess risk of type-2 diabetes amongst Indian Asians compared to Europeans

Background Indian Asians are at increased risk of type-2 diabetes (T2D), insulin resistance and related metabolic disturbances compared to Europeans. The contribution of known lifestyle and genetic risk factors to the excess risk of T2D in Indian Asians is not well understood. Methods and materials I investigated 16,774 Indian Asian and 7,301 European men and women participating in the in the London Life Sciences Population Study to determine the prevalence of T2D and related glycaemic disorders. I examined the contribution of adiposity, leisure time physical activity, major dietary macronutrients and known genetic susceptibility factors to the increased risk of T2D and related metabolic disturbances amongst Indian Asians compared to Europeans. Lastly I carried out a genome-wide association and replication study amongst 58,687 Indian Asian participants to identify novel genetic factors in this ethnic group. Results The prevalence of T2D is ~4-fold higher amongst Indian Asians than Europeans, and is not accounted for by differences in adiposity and leisure time physical activity. In dietary studies, intake of fibre is inversely related to risk of insulin resistance among Indian Asians. However, major dietary macronutrients do not account for differences in insulin resistance between Indian Asians and Europeans. In genetic studies I demonstrate association of 25 previously reported T2D genetic variants with T2D amongst Indian Asians. Of the 48 T2D genetic variants examined, risk allele frequencies were similar and effect sizes lower amongst Indian Asians compared to Europeans; therefore known T2D genetic variants do not account for the increased risk of T2D in this racial group. In the GWAS I discover 6 novel T2D genetic variants among Indian Asians (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A). Conclusions T2D has emerged as a major healthcare problem worldwide, with rates highest among individuals of Indian Asian descent. For the first time I discover six novel genetic susceptibility factors for T2D amongst Indian Asians. However, there is a ~4-fold higher risk of T2D among Indian Asians compared to Europeans, which remains largely unexplained.Open Acces

2-dimensional echocardiographic global longitudinal strain with Artificial Intelligence using open data from a UK-wide collaborative

Background Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake. Objectives The authors developed open machine-learning–based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative. Methods We trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages. Results The median GLS, averaged across the 11 individual experts, was −16.1 (IQR: −19.3 to −12.5). Using each case’s expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74. Conclusions Our open-source approach to calculating GLS agrees with experts’ consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online

Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative

Background: requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly

52 Genetic Loci Influencing Myocardial Mass.

BACKGROUND: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death. OBJECTIVES: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass. METHODS: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment. RESULTS: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo. CONCLUSIONS: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets

52 Genetic Loci Influencing Myocardial Mass

Background Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death. Objectives This meta-analysis sought to gain insights into the genetic determinants of myocardial mass. Methods We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment. Results We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p  and lt; 1 × 10?8. These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo. Conclusions Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets. © 2016 American College of Cardiology Foundatio