Daily Endocrinology Research Analysis

The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective cohort. To date, approximately 28,000 participants have enrolled, with more than 13,000 completing their initial visit. The project is aimed at identifying novel molecular signatures with diagnostic, prognostic and therapeutic value, and at developing artificial intelligence (AI)-based predictive models for disease onset and progression. The HPP includes longitudinal profiling encompassing medical history, lifestyle and nutrition, anthropometrics, blood tests, continuous glucose and sleep monitoring, imaging and multi-omics data, including genetics, transcriptomics, microbiome (gut, vaginal and oral), metabolomics and immune profiling. Analysis of these data highlights the variation of phenotypes with age and ethnicity and unravels molecular signatures of disease by comparison with matched healthy controls. Leveraging extensive dietary and lifestyle data, we identify associations between lifestyle factors and health outcomes. Finally, we present a multi-modal foundation AI model, trained using self-supervised learning on diet and continuous-glucose-monitoring data, that outperforms existing methods in predicting disease onset. This framework can be extended to integrate other modalities and act as a personalized digital twin. In summary, we present a deeply phenotyped cohort that serves as a platform for advancing biomarker discovery, enabling the development of multi-modal AI models and personalized medicine approaches.

Pancreatic alpha cells modulate beta cell function in a paracrine manner through the release of glucagon. However, the detailed molecular architecture underlying alpha-to-beta cell regulation remains poorly characterized. Here, we show that the glucagon-like peptide-1 receptor (GLP1R) is enriched as nanodomains on beta cell membranes that contact alpha cells, in keeping with increased single-molecule transcript expression. At low glucose, beta cells next to alpha cells directly sense micromolar glucagon release by pre-internalizing GLP1R. Pre-internalized GLP1R is associated with earlier beta cell Ca

The availability of genomic sequencing has revealed that variants in genes that cause rare monogenic disorders are relatively common, which raises the question of variant pathogenicity. Autosomal-dominant hypocalcemia type 1 (ADH1) is a rare genetic form of hypoparathyroidism caused by gain-of-function (GoF) variants in the calcium-sensing receptor (CaSR) encoded by CASR. We examined the prevalence, penetrance, and expressivity of GoF CASR variants in the UK Biobank (UKB; n = 433,793), All of Us (AOU; n = 229,987), and Mass General Brigham Biobank (n = 39,081). Individuals with previously reported ADH1-associated variants indeed showed ADH1 symptoms, including hypocalcemia (60% in the UKB and 78% in AOU). However, less than half had an ADH1-relevant diagnosis code (17% in the UKB and 44% in AOU), suggesting that individuals with ADH1 are present in these biobanks but may be underdiagnosed. We then developed a scoring algorithm and identified nine low-frequency ADH1-associated variants, which were further validated using genetic sequencing of individuals with nonsurgical hypoparathyroidism (n = 169) and an in vitro functional assay. These nine variants have an intermediate effect and frequency relative to previously reported ADH1-associated variants, completing an allelic series with respect to serum calcium, and alone are responsible for a symptom burden roughly equivalent to all previously reported ADH1-associated variants. Our work indicates that hypocalcemia due to GoF in CASR with ADH1-associated symptoms is underdiagnosed, provides a deeper understanding of the genotype-phenotype relationship of CASR variants, and illustrates that variants in genes underlying rare disorders may cause a much greater symptom burden than currently appreciated.