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Aims/hypothesis The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. Methods Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. Results A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. Conclusions/interpretation Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status. Graphical abstract

BackgroundPolygenic scores—which quantify inherited risk by integrating information from many common sites of DNA variation—may enable a tailored approach to clinical medicine. However, alongside considerable enthusiasm, we and others have highlighted a lack of standardized approaches for score disclosure. Here, we review the landscape of polygenic score reporting and describe a generalizable approach for development of a polygenic score disclosure tool for coronary artery disease.MethodsWe assembled a working group of clinicians, geneticists, data visualization specialists, and software developers. The group reviewed existing polygenic score reports and then designed a two-page mock report for coronary artery disease. We then conducted a qualitative user-experience study with this report using an interview guide focused on comprehension, experience, and attitudes. Interviews were transcribed and analyzed for themes identification to inform report revision.ResultsReview of nine existing polygenic score reports from commercial and academic groups demonstrated significant heterogeneity, reinforcing the need for additional efforts to study and standardize score disclosure. Using a newly developed mock score report, we conducted interviews with ten adult individuals (50% females, 70% without prior genetic testing experience, age range 20–70 years) recruited via an online platform. We identified three themes from interviews: (1) visual elements, such as color and simple graphics, enable participants to interpret, relate to, and contextualize their polygenic score, (2) word-based descriptions of risk and polygenic scores presented as percentiles were the best recognized and understood, (3) participants had varying levels of interest in understanding complex genomic information and therefore would benefit from additional resources that can adapt to their individual needs in real time. In response to user feedback, colors used for communicating risk were modified to minimize unintended color associations and odds ratios were removed. All 10 participants expressed interest in receiving a polygenic score report based on their personal genomic information.ConclusionsOur findings describe a generalizable approach to develop a polygenic score report understandable by potential patients. Although additional studies are needed across a wider spectrum of patient populations, these results are likely to inform ongoing efforts related to polygenic score disclosure within clinical practice.

OBJECTIVE To use islet autoantibody titers to improve the estimation of future type 1 diabetes risk in children. RESEARCH DESIGN AND METHODS Prospective cohort studies in Finland, Germany, Sweden, and the U.S. followed 24,662 children at increased genetic or familial risk to develop islet autoimmunity and diabetes. For 1,604 children with confirmed positivity, titers of autoantibodies against insulin (IAA), GAD antibodies (GADA), and insulinoma-associated antigen 2 (IA-2A) were harmonized for diabetes risk analyses. RESULTS Survival analysis from time of confirmed positivity revealed markedly different 5-year diabetes risks associated with IAA (n = 909), GADA (n = 1,076), and IA-2A (n = 714), when stratified by quartiles of titer, ranging from 19% (GADA 1st quartile) to 60% (IA-2A 4th quartile). The minimum titer associated with a maximum difference in 5-year risk differed for each autoantibody, corresponding to the 58.6th, 52.4th, and 10.2nd percentile of children specifically positive for each of IAA, GADA, and IA-2A, respectively. Using these autoantibody type-specific titer thresholds in the 1,481 children with all autoantibodies tested, the 5-year risk conferred by single (n = 954) and multiple (n = 527) autoantibodies could be stratified from 6 to 75% (P < 0.0001). The thresholds effectively identified children with a ≥50% 5-year risk when considering age-specific autoantibody screening (57–65% positive predictive value and 56–74% sensitivity for ages 1–5 years). Multivariable analysis confirmed the significance of associations between the three autoantibody titers and diabetes risk, informing a childhood risk surveillance strategy. CONCLUSIONS This study defined islet autoantibody type-specific titer thresholds that significantly improved type 1 diabetes risk stratification in children.

Myocardial interstitial fibrosis is a common thread in multiple cardiovascular diseases including heart failure, atrial fibrillation, conduction disease and sudden cardiac death. To investigate the biologic pathways that underlie interstitial fibrosis in the human heart, we developed a machine learning model to measure myocardial T1 time, a marker of myocardial interstitial fibrosis, in 42,654 UK Biobank participants. Greater T1 time was associated with impaired glucose metabolism, systemic inflammation, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation and conduction disease. In genome-wide association analysis, we identified 12 independent loci associated with native myocardial T1 time with evidence of high genetic correlation between the interventricular septum and left ventricle free wall (r2g = 0.82). The identified loci implicated genes involved in glucose homeostasis (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Transcriptome-wide association studies highlighted the role of expression of ADAMTSL1 and SLC2A12 in human cardiac tissue in modulating myocardial tissue characteristics and interstitial fibrosis. Harnessing machine learning to perform large-scale phenotyping of interstitial fibrosis in the human heart, our results yield novel insights into biologically relevant pathways for myocardial fibrosis and prioritize investigation of pathways for the development of anti-fibrotic therapies.

BackgroundThe left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined genetics of thoracic aortic diameter in a single plane. We sought to elucidate the genetic basis for the diameter of the LVOT, the aortic root, and the ascending aorta.MethodsWe used deep learning to analyze 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at six locations in the ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these polygenic scores and disease incidence.Results79 loci were significantly associated with at least one diameter. Of these, 35 were novel, and a majority were associated with one or two diameters. A polygenic score of aortic diameter approximately 13mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm in UK Biobank participants (n=427,016; HR=1.42 per standard deviation; CI=1.34-1.50, P=6.67×10−21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n=426,502; HR=1.08 per standard deviation; CI=1.03-1.12, P=5×10−6).ConclusionsWe detected distinct common genetic loci underpinning the diameters of the LVOT, the aortic root, and at several segments in the ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding the genetic contributions to the diameter of the proximal aorta may enable identification of individuals at risk for life-threatening aortic disease and facilitate prioritization of therapeutic targets.

As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous blood delivery to peripheral arteries. Distension during systole and recoil during diastole conserves ventricular energy and is enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and prematurely in vascular disease. To discover genetic determinants of aortic distensibility we trained a deep learning model to quantify aortic size throughout the cardiac cycle and calculate aortic distensibility and aortic strain in 42,342 participants in the UK Biobank with available cardiac magnetic resonance imaging. In up to 40,028 participants with genetic data, common variant analysis identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were specific to strain or distensibility and were not identified in a thoracic aortic diameter GWAS within the same samples. Loci associated with both aortic diameter and aortic strain or distensibility demonstrated a consistent, inverse directionality. Transcriptome-wide analyses, rare-variant burden tests, and analyses of gene expression in single nucleus RNA sequencing of human aorta were performed to prioritize genes at individual loci. Loci highlighted multiple genes involved in elastogenesis, matrix degradation, and extracellular polysaccharide generation. Characterization of the genetic determinants of aortic function may provide novel targets for medical intervention in aortic disease.

Background: Individuals of South Asian ancestry represent 23% of the global population, corresponding to 1.8 billion people, and have substantially higher risk of atherosclerotic cardiovascular disease compared with most other ethnicities. US practice guidelines now recognize South Asian ancestry as an important risk-enhancing factor. The magnitude of enhanced risk within the context of contemporary clinical care, the extent to which it is captured by existing risk estimators, and its potential mechanisms warrant additional study. Methods: Within the UK Biobank prospective cohort study, 8124 middle-aged participants of South Asian ancestry and 449 349 participants of European ancestry who were free of atherosclerotic cardiovascular disease at the time of enrollment were examined. The relationship of ancestry to risk of incident atherosclerotic cardiovascular disease—defined as myocardial infarction, coronary revascularization, or ischemic stroke—was assessed with Cox proportional hazards regression, along with examination of a broad range of clinical, anthropometric, and lifestyle mediators. Results: The mean age at study enrollment was 57 years, and 202 405 (44%) were male. Over a median follow-up of 11 years, 554 of 8124 (6.8%) individuals of South Asian ancestry experienced an atherosclerotic cardiovascular disease event compared with 19 756 of 449 349 (4.4%) individuals of European ancestry, corresponding to an adjusted hazard ratio of 2.03 (95% CI, 1.86–2.22; P 2-fold higher observed risk, the predicted 10-year risk of cardiovascular disease according to the American Heart Association/American College of Cardiology Pooled Cohort equations and QRISK3 equations was nearly identical for individuals of South Asian and European ancestry. Adjustment for a broad range of clinical, anthropometric, and lifestyle risk factors led to only modest attenuation of the observed hazard ratio to 1.45 (95% CI, 1.28–1.65, P Conclusions: Within a large prospective study, South Asian individuals had substantially higher risk of atherosclerotic cardiovascular disease compared with individuals of European ancestry, and this risk was not captured by the Pooled Cohort Equations.

While associations between the type and number of islet autoantibodies and progression to type 1 diabetes (T1D) have been reported, the effect of titer values is less well understood. We aim to quantify the ability of autoantibody titers at seroconversion to improve T1D onset prediction. Prospective cohorts in Finland, Germany, Sweden, and the US have followed 24662 children at increased genetic risk for development of islet autoantibodies and T1D. For the 1400 who seroconverted (523 developed T1D), the titers of insulin autoantibodies (IAA), glutamic acid decarboxylase autoantibodies (GADA), and insulinoma antigen-2 autoantibodies (IA2A) at time of initial and confirmed seroconversion, i.e., the respective first and consecutive second autoantibody-positive serum sample, were normalized (to log multiples of upper limit of normal) and analyzed. Prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross validation. Discriminative power for disease was estimated using the IPCW concordance index (c-index) with 95% confidence intervals estimated via bootstrap. Multivariate Cox proportional hazards models were used to quantify the impact of the autoantibody titers. A baseline model with covariates for data source, sex, HLA-DR/DQ genotype, and age at initial and confirmed seroconversion had a performance of 64 c-index; 95% CI 62-65. Significant improvement was observed after adding IAA, GADA, IA2A positivity indicators at initial and confirmed seroconversion (74; 72-75). Adding the corresponding autoantibody titers resulted in significant additional gains (76; 75-76). Adjusted hazard ratios (HR) from the Cox model showed that autoantibody titers at confirmed seroconversion were predictive of diabetes (HR 1.29; 95% CI 1.20-1.38 for IAA, 1.12; 1.07-1.18 for GADA, and 1.19; 1.15-1.25 for IA2A, all p Islet autoantibody titers at seroconversion improve T1D prediction. Disclosure K. Ng: Employee; Self; IBM. V. Anand: None. R. Veijola: None. M. Maziarz: None. K. Waugh: None. W. Hagopian: None. P. Achenbach: None. T1di study group: n/a. Funding JDRF (1-IND-2019-717-I-X)

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that is complex in its onset, pattern of spread, and disease progression. The heterogeneity of ALS makes it extremely challenging to determine if a disease modifying therapy is effectively slowing progression. While accurately modeling ALS progression is critical to developing therapeutics, current computational methods fail to capture the complexity of disease progression. We aimed to robustly characterize disease progression patterns in ALS.We obtained data from four clinical cohorts that cover more than 3,500 patients and include both observational and clinical trial studies. To determine whether there were common patterns of disease progression, we developed an approach based on a Mixture of Gaussian Processes (MoGP) to model longitudinal clinical data. Our approach automatically identifies clusters of patients who show similar disease progression patterns, modeling their average trajectory and the spread of the distribution in each cluster. Importantly, the method does not require any prior knowledge of the expected number of clusters.The MoGP approach revealed that ALS progression, as measured using the ALS functional rating scale (ALSFRS-R) or forced vital capacity, is often non-linear with periods of stable disease preceded or followed by rapid decline. Patterns of progression in ALSFRS-R were robust to sparse data. When at least one year of longitudinal data were available, MoGP predictions were significantly more accurate than linear models, which are commonly used in clinical trials. Progression patterns were consistent across different cohorts despite differences in the frequency of data collection and the lengths of follow-up periods. We further showed that clusters identified from one large, publicly available study population could be used to stratify unseen participants in other studies. We also showed that these progression trajectories correspond with survival outcomes.This work highlights the importance of modeling nonlinear disease progression for developing more advanced clinical trial endpoint analysis models. In ALS, sporadic, rapid decline (“functional cliffs”) and sigmoidal patterns in disease progression in untreated patients may obscure detection of therapeutic efficacy if linear models are used. We provide a pre-trained computational model of observed clinical patterns that can be used by others to analyze new ALS patient cohorts. We expect that the MoGP approach can also be applied to additional ALS outcome measures and to other progressive diseases. Our results provide a critical advance in characterizing the complex disease progression patterns of ALS.

The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such asNKX2-5, TBX3, WNT9B, andGATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function.

Objective To present clinicians at the point-of-care with real-world data on the effectiveness of various treatment options in a precision cohort of patients closely matched to the index patient. Materials and Methods We developed disease-specific, machine-learning, patient-similarity models for hypertension (HTN), type II diabetes mellitus (T2DM), and hyperlipidemia (HL) using data on approximately 2.5 million patients in a large medical group practice. For each identified decision point, an encounter during which the patient’s condition was not controlled, we compared the actual outcome of the treatment decision administered to that of the best-achieved outcome for similar patients in similar clinical situations. Results For the majority of decision points (66.8%, 59.0%, and 83.5% for HTN, T2DM, and HL, respectively), there were alternative treatment options administered to patients in the precision cohort that resulted in a significantly increased proportion of patients under control than the treatment option chosen for the index patient. The expected percentage of patients whose condition would have been controlled if the best-practice treatment option had been chosen would have been better than the actual percentage by: 36% (65.1% vs 48.0%, HTN), 68% (37.7% vs 22.5%, T2DM), and 138% (75.3% vs 31.7%, HL). Conclusion Clinical guidelines are primarily based on the results of randomized controlled trials, which apply to a homogeneous subject population. Providing the effectiveness of various treatment options used in a precision cohort of patients similar to the index patient can provide complementary information to tailor guideline recommendations for individual patients and potentially improve outcomes.

Importance Pathogenic DNA variants associated with familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome are widely recognized as clinically important and actionable when identified, leading some clinicians to recommend population-wide genomic screening. Objectives To assess the prevalence and clinical importance of pathogenic or likely pathogenic variants associated with each of 3 genomic conditions (familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome) within the context of contemporary clinical care. Design, Setting, and Participants This cohort study used gene-sequencing data from 49 738 participants in the UK Biobank who were recruited from 22 sites across the UK between March 21, 2006, and October 1, 2010. Inpatient hospital data date back to 1977; cancer registry data, to 1957; and death registry data, to 2006. Statistical analysis was performed from July 22, 2019, to November 15, 2019. Exposures Pathogenic or likely pathogenic DNA variants classified by a clinical laboratory geneticist. Main Outcomes and Measures Composite end point specific to each genomic condition based on atherosclerotic cardiovascular disease events for familial hypercholesterolemia, breast or ovarian cancer for hereditary breast and ovarian cancer syndrome, and colorectal or uterine cancer for Lynch syndrome. Results Among 49 738 participants (mean [SD] age, 57 [8] years; 27 144 female [55%]), 441 (0.9%) harbored a pathogenic or likely pathogenic variant associated with any of 3 genomic conditions, including 131 (0.3%) for familial hypercholesterolemia, 235 (0.5%) for hereditary breast and ovarian cancer syndrome, and 76 (0.2%) for Lynch syndrome. Presence of these variants was associated with increased risk of disease: for familial hypercholesterolemia, 28 of 131 carriers (21.4%) vs 4663 of 49 607 noncarriers (9.4%) developed atherosclerotic cardiovascular disease; for hereditary breast and ovarian cancer syndrome, 32 of 116 female carriers (27.6%) vs 2080 of 27 028 female noncarriers (7.7%) developed associated cancers; and for Lynch syndrome, 17 of 76 carriers (22.4%) vs 929 of 49 662 noncarriers (1.9%) developed colorectal or uterine cancer. The predicted probability of disease at age 75 years despite contemporary clinical care was 45.3% for carriers of familial hypercholesterolemia, 41.1% for hereditary breast and ovarian cancer syndrome, and 38.3% for Lynch syndrome. Across the 3 conditions, 39.7% (175 of 441) of the carriers reported a family history of disease vs 23.2% (34 517 of 148 772) of noncarriers. Conclusions and Relevance The findings suggest that approximately 1% of the middle-aged adult population in the UK Biobank harbored a pathogenic variant associated with any of 3 genomic conditions. These variants were associated with an increased risk of disease despite contemporary clinical care and were not reliably detected by family history.

Objective: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS CAD ) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS CAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed—16% for those in the lowest GPS CAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPS CAD —as assessed by change in the C-statistic from a baseline model including age and sex—among 5685 individuals with PCE risk estimates available. The increment for the GPS CAD (+0.045, P CAD and 10-year risk defined by the PCE ( r =0.03), and addition of GPS CAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS CAD , even among individuals within a given PCE clinical risk stratum. We replicated key findings—noting strikingly consistent results—in 325 003 participants of the UK Biobank. Conclusions: GPS CAD —a risk estimator available from birth—stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS CAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.

BackgroundGenetic variation can predispose to disease both through (i) monogenic risk variants in specific genes that disrupt a specific physiologic pathway and have a large effect on disease risk and (ii) polygenic risk that involves large numbers of variants of small effect that affect many different pathways. Few studies have explored the interaction between monogenic risk variants and polygenic risk.MethodsWe identified monogenic risk variants and calculated polygenic scores for three diseases, coronary artery disease, breast cancer, and colorectal cancer, in three study populations — case-control cohorts for coronary artery disease (UK Biobank; N=12,879) and breast cancer (Color Genomics; N=19,264), and an independent cohort of 49,738 additional UK Biobank participants.ResultsIn the coronary artery disease case-control cohort, increased risk for carriers of a monogenic variant ranged from 1.3-fold for those in the lowest polygenic score quintile to 12.6-fold for those in the highest. For breast cancer, increased risk ranged from 2.4 to 6.9-fold across polygenic score quintiles. Among the 49,738 UK Biobank participants who carried a monogenic risk variant, the probability of disease at age 75 years was strongly modified by polygenic risk. Across individuals in the lowest to highest percentiles of polygenic risk, the probability of disease ranged from 17% to 78% for coronary artery disease; 13% to 76% for breast cancer; and 11% to 80% for colon cancer.ConclusionsFor three important genomic conditions, polygenic risk powerfully modifies the risk conferred by monogenic risk variants.

BackgroundCross-sectional studies of various forms of dilated cardiomyopathy have noted a truncating mutation in the gene encoding titin (‘TTNtv’) in 7-30% of patients, but the clinical importance of identifying a TTNtv in an asymptomatic adult is largely unknown. In contrast to cross-sectional studies, prospective cohort studies allow for unbiased estimates of the disease risks associated with a genotype exposure.ObjectivesTo determine the prevalence of cardiac imaging abnormalities and risk of incident disease among middle-aged TTNtv carriers without known congestive heart failure.MethodsWe analyze exome sequencing data of 45,747 participants of the UK Biobank without known congestive heart failure to identify TTNtv carriers. Among 10,552 with cardiac magnetic resonance imaging (MRI), we determine the relationship between TTNtv carrier status and left ventricular ejection fraction. In this prospective cohort, we quantify the absolute and relative risks of incident disease in TTNtv carriers versus noncarriers.ResultsAmong 45,747 middle-aged participants without known congestive heart failure, 196 (0.43%) harbored a TTNtv. The average ejection fraction was 61% in TTNtv carriers versus 65% in noncarriers (P = 1.8 × 10−8), with a 9.3-fold increase (95% CI 3.9 – 22.2) in odds of subnormal ejection fraction (P = 5.7 × 10−5). Over a median follow-up of 6.9 years, a composite endpoint of incident dilated cardiomyopathy, congestive heart failure, or all-cause mortality was observed in 6.6% of TTNtv carriers versus 2.9% of non-carriers (adjusted hazard ratio 2.5; 95% CI 1.4 – 4.3; p = 1.1 × 10−3).ConclusionsApproximately 1 in 230 middle-aged adults without known congestive heart failure harbored a TTNtv. These carriers had a substantially increased relative risk—but modest absolute risk—of having a subnormal ejection fraction or manifesting clinical disease during prospective follow-up.Condensed AbstractCross-sectional studies of dilated cardiomyopathy have noted a truncating mutation in the gene encoding titin (‘TTNtv’) in up to 30% of patients—but the clinical importance of TTNtv in asymptomatic adults is largely unknown. Here, we observe a TTNtv in 0.43% of 45,747 middle-aged adults. Average ejection fraction was 61% in TTNtv carriers versus 65% in non-carriers (p

The development of multiple islet autoantibodies (IA) predicts progression to type 1 diabetes (T1D), and forms the basis of current staging of T1D. However, age at seroconversion, persistence of IA, and frequency of sampling influence the determination of multiple IA status. Individual IA may appear at different timepoints and fluctuate. This analysis aims to understand how differences in the stringency of multiple IA status definition impact risk estimates of progression to stage 3 T1D. We combined data from three birth-cohort studies (N=18,537): DAISY (U.S.), DiPiS (Sweden) and DIPP (Finland), defining four groups by IA positivity pattern and accounting for variability in persistence and timing in concurrent and sequential patterns of IAA, IA2A, and GADA. Subjects were placed exclusively in their most stringent IA group. Cumulative incidence of progression to T1D was estimated by survival analysis and log-rank test, which showed group differences (p Disclosure B. Liu: None. M. Ghalwash: None. V. Anand: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. M. Lundgren: None. H. Elding Larsson: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. R. Veijola: None. B.I. Frohnert: None. Funding JDRF

Progression to type 1 diabetes (T1D) is strongly predicted by multiple risk factors including genetic and non-genetic variables such as islet autoantibodies (AAb). Little is known, however, about the impact of interactions among the known risk factors on T1D risk prediction. Through consideration of interactions among risk factors, we aimed to evaluate time-to-diagnosis for T1D from seroconversion for AAb using a novel time-to-event model (RankSvx). RankSvx simultaneously predicts subject-level event times and risk scores as measured by Mean Absolute Error (MAE) and Concordance Index (CI), respectively. We control for subject-level characteristics (4 HLA groups, sex, and their interactions with AAb, age at seroconversion and visit-level AAb count) and use LASSO for feature selection. We also explored up to 12 months of AAb dynamics (i.e., appearance or disappearance of AAb in 3-month intervals) post seroconversion using an interaction model and compared it to a non-interaction (baseline model at seroconversion) and a traditional Cox model. We used a harmonized dataset of the DAISY, DiPiS, and DIPP studies with 1,880 AAb positive subjects of whom 512 (27%) progressed to T1D. First, we found the interaction model was superior to the baseline model without interactions (Wilcoxon Rank test p-value: 0.016). Second, incorporating AAb dynamics further improved T1D onset time prediction. When including post seroconversion AAb dynamics at 12 months, the interaction model performed 15% better than the baseline model (CI: 0.83 vs. 0.68, MAE: 2.5 years). Finally, the top identified interactions included: 1) young age at seroconversion interaction with IA-2A, and 2) HLA Group that includes at least one DR3-DQ2.5 haplotype interaction with ZnT8A or GADA. Our results are consistent with recent findings in the TEDDY dataset of patterns of AAb appearance and genetic association and age-related T1D incidence. Further investigations on seroconversion and post seroconversion endotypes are necessary. Disclosure B. Liu: None. V. Anand: None. M. Ghalwash: None. K. Ng: None. J.L. Dunne: None. R. Veijola: None. M. Rewers: None. M. Lundgren: None. Funding JDRF

Four birth-cohort studies in the U.S. (DAISY, DEWIT), Sweden (DiPiS) and Finland (DIPP) were initiated in 1990s to early 2000. General population newborns and young first-degree relatives (FDR) of T1D patients, preselected on increased HLA risk, were followed for islet autoantibodies (AAb)and T1D. We harmonized their common variables, including AAb to insulin, IA-2, GAD, or ZnT8, HLA-DR-DQ risk genotypes, socio-demographics, diet, family history, glucose/HbA1c and anthropometric measures. The combined cohort included 22,312 subjects with AAb measured in 256,243 visits (mean 11±10 per subject) at 3-12 month intervals. During follow-up, 2279 (10%) subjects developed at least one AAb, 914 (4%) multiple AAb and 603 (3%) T1D. The cumulative risk of T1D differed by the number of AAb at seroconversion (Figure). T1D developed in 26% (95% CI 24-32%) of 1AAb, 62% (55-68%) of 2AAb and 84% (75-88%) of >=3AAb positive subjects in the 15y after seroconversion. T1D risk varied by HLA genotype: 40% (35-48%) in DR3/4-DQ2/8, 22% (18-31%) in DR4/x-DQ8/x and 21% (18-35%) in DR3/x-DQ2/x of 1AAb subjects and, respectively, in 82% (68-88%), 61%(50-65%)and 87% (32-98%)of 2AAb subjects. Once ≥2 AAb positive, HLA genotype was less predictive of progression to T1D. This harmonized dataset offers an unparalleled opportunity to validate and improve upon reported risk estimates for T1D development via novel analytical methods. Disclosure V. Anand: None. B. Liu: None. M. Ghalwash: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. M. Lundgren: None. Å. Lernmark: None. J. Ilonen: None. R. Veijola: None. M. Rewers: None. Funding JDRF

Clinical type 1 diabetes (T1D) is preceded by asymptomatic islet-autoimmunity (IA) with presence of islet autoantibodies (AAb). However, both AAb profiles (clusters) during IA phase and progression rate to T1D are heterogeneous. It is unclear if AAb profiles affect the progression to T1D similarly in different countries. We analyzed data from 3 prospective birth cohorts, DAISY (U.S.), DiPiS (Sweden), and DIPP (Finland), to address this question. We developed a similarity algorithm that considers subject-level temporal IA profiles (based on 4 AAbs - IAA, GADA, IA2A, and ZnT8A positivity or negativity), age at which AAb developed, and imbalance in AAb positivity by weighting positive AAb matches higher than negative ones. Using this subject-level similarity measure, we performed hierarchical clustering of AAb positive subjects having at least 3 visits. We used a log-rank test to identify the number of clusters in 1125 DIPP subjects (329 T1D cases), 309 DAISY subjects (70 cases), and 226 DiPiS subjects (33 cases). Each dataset was individually clustered. We identified three clusters in each dataset associated with different rate of progression from seroconversion to T1D - slow progressors (SP), moderate progressors (MP) and fast progressors (FP). These were distributed as follows: DIPP (654 SP with 3% 10-year T1D risk, 356 MP with 60% risk, 115 FP with 96% risk); DAISY (215 SP with 6% 10-year risk, 92 MP with 62% risk, 2 FP with 100% risk); and DiPiS (160 SP with 3% 10-year risk, 62 MP with 58% risk, 4 FP with 100% risk). Finally, we tested the generalizability of the clusters by 1) learning a clustering from one data set “learned clusters,” 2) classifying subjects from another (independent) data set with the “learned clusters,” and 3) measuring how often the subjects were assigned to the same cluster (SP, MP, FP) as the “internal” clustering. The average accuracy of cluster transfer was high (mean = 87.9%, sd = 1.1%) suggesting that the IA profile predicts progression rate to T1D independently of the population. Disclosure M. Ghalwash: None. V. Anand: None. B. Liu: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. M. Lundgren: None. M. Rewers: None. R. Veijola: None. Funding JDRF

Background: Sudden cardiac death occurs in ∼220,000 U.S. adults annually, the majority of whom have no prior symptoms or cardiovascular diagnosis. Rare pathogenic DNA variants in any of 49 genes can pre-dispose to 4 important causes of sudden cardiac death: cardiomyopathy, coronary artery disease, inherited arrhythmia syndrome, and aortopathy or aortic dissection. Objectives: This study assessed the prevalence of rare pathogenic variants in sudden cardiac death cases versus controls, and the prevalence and clinical importance of such mutations in an asymptomatic adult population. Methods: The authors performed whole-exome sequencing in a case-control cohort of 600 adult-onset sudden cardiac death cases and 600 matched controls from 106,098 participants of 6 prospective cohort studies. Observed DNA sequence variants in any of 49 genes with known association to cardiovascular disease were classified as pathogenic or likely pathogenic by a clinical laboratory geneticist blinded to case status. In an independent population of 4,525 asymptomatic adult participants of a prospective cohort study, the authors performed whole-genome sequencing and determined the prevalence of pathogenic or likely pathogenic variants and prospective association with cardiovascular death. Results: Among the 1,200 sudden cardiac death cases and controls, the authors identified 5,178 genetic variants and classified 14 as pathogenic or likely pathogenic. These 14 variants were present in 15 individuals, all of whom had experienced sudden cardiac death—corresponding to a pathogenic variant prevalence of 2.5% in cases and 0% in controls (p < 0.0001). Among the 4,525 participants of the prospective cohort study, 41 (0.9%) carried a pathogenic or likely pathogenic variant and these individuals had 3.24-fold higher risk of cardiovascular death over a median follow-up of 14.3 years (p = 0.02). Conclusions: Gene sequencing identifies a pathogenic or likely pathogenic variant in a small but potentially important subset of adults experiencing sudden cardiac death; these variants are present in ∼1% of asymptomatic adults., National Heart, Lung, and Blood Institute (Grants HL-03783, HL-26490, HL-34595, HL-34594, HL-35464, HL-043851, HL-46959, HL-099355 and HL-080467), National Cancer Institute (Grants CA-167552, CA-186107, CA-49449CA-34944, CA-40360, CA-47988, CA-55075, CA-87969 and CA-97193)