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This study shows that for variants initially classified as pathogenic that were later reclassified as benign, the misclassification would have been prevented had racially diverse populations been considered in the original studies of the variants. Although hypertrophic cardiomyopathy is best known as a fatal disease of young athletes, it causes considerable morbidity and mortality among patients of all ages and lifestyles. 1 , 2 The defining feature of hypertrophic cardiomyopathy is unexplained left ventricular hypertrophy, but its clinical presentation is variable; it can manifest as severe heart failure in some patients yet be asymptomatic in others. 3 In more than one third of patients, causal genetic lesions are identified, which enables clinicians to assess risk among the patient’s relatives 4 and, in rare circumstances, to tailor therapy for a patient who is found to have a tractable disorder, such . . .

We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability ( h 2  = 0.311) and shared environmental variance ( c 2  = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (var SES  = 0.002), daily air quality (var AQI  = 0.0004), and average temperature (var temp  = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities ( h 2  = 0.433, c 2  = 0.241) and average monthly cost ( h 2  = 0.290, c 2  = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application. Analysis of a health insurance dataset comprising more than 44 million individuals allows for the estimation of genetic and environmental contributions in 560 phenotypes by using twins and sibling pairs.

Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We perform a longitudinal analysis of COPD in the UK Biobank to derive and validate the Socioeconomic and Environmental Risk Score which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. The Socioeconomic and Environmental Risk Score is more predictive of COPD than smoking status and pack-years. Individuals in the highest decile of the risk score have a greater risk for incident COPD compared to the remaining population. Never smokers in the highest decile of exposure risk are more likely to develop COPD than previous and current smokers in the lowest decile. In general, the prediction accuracy of the Social and Environmental Risk Score is lower in non-European populations. While smoking status is often considered in screening COPD, our finding highlights the importance of other non-smoking environmental and socioeconomic variables. Many people who never smoke develop COPD. Here, the authors derive and validate the Socioeconomic and Environmental Risk Score (SERS) which captures cumulative exposure risks beyond tobacco smoking to predict and stratify risk of COPD.

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called “vibration of effects” (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

The COVID-19 pandemic has been a catalyst for the implementation of decentralized clinical trials (DCTs) enabled by digital health technologies (DHTs) in the field while curtailing in-person interactions and putting significant demands on health care resources. DHTs offer improvements in real-time data acquisition remotely while maintaining privacy and security. Here, we describe the implications of technologies, including edge computing, zero-trust environments, and federated computing in DCTs enabled by DHTs. Taken together, these technologies—in the setting of policy and regulation that enable their use while protecting the users—extend the scope and accelerate the pace of clinical research.

The editors announce both a series of articles focusing on AI and machine learning in health care and the 2024 launch of a new journal, NEJM AI , a forum for evidence, resource sharing, and discussion of the possibilities and limitations of medical AI.

Large Language Models and the Medical RecordInstead of facilitating communication and transparency, the insertion of LLM-generated text directly into the medical record risks diminishing the quality, efficiency, and humanity of health care.

Key PointsMedical Artificial Intelligence and Human ValuesAs large language models and other artificial intelligence models are used more in medicine, ethical dilemmas can arise depending on how the model was trained. A user must understand how human decisions and values can shape model outputs. Medical decision analysis offers lessons on measuring human values.A large language model will respond differently depending on the exact way a query is worded and how the model was directed by its makers and users. Caution is advised when considering the use of model output in decision making.

Genetic testing is essential for diagnosing and managing clinical conditions, particularly rare Mendelian diseases. Although efforts to identify rare phenotype-associated variants have focused on protein-truncating variants, interpreting missense variants remains challenging. Deep learning algorithms excel in various biomedical tasks 1 , 2 , yet distinguishing pathogenic from benign missense variants remains elusive 3 , 4 – 5 . Our investigation of AlphaMissense (AM) 5 , a deep learning tool for predicting the potential functional impact of missense variants and assessing gene essentiality, reveals limitations in identifying pathogenic missense variants over 45 rare diseases, including very early onset inflammatory bowel disease. For the expert-curated pathogenic variants identified in our cohort, AM’s precision was 32.9%, and recall was 57.6%. Notably, AM struggles to evaluate pathogenicity in intrinsically disordered regions (IDRs), resulting in unreliable gene-level essentiality scores for genes containing IDRs. This observation underscores ongoing challenges in clinical genetics, highlighting the need for continued refinement of computational methods in variant pathogenicity prediction.

Although many experts agree that we should reconsider the use of race in equations for estimated glomerular filtration rate and in medicine more generally, precisely how eGFR equations should change remains unclear.