Daily Endocrinology Research Analysis

Identifying individuals at high risk of type 1 diabetes (T1D) is crucial as disease-delaying medications are available. Here we report a microRNA (miRNA)-based dynamic (responsive to the environment) risk score developed using multicenter, multiethnic and multicountry ('multicontext') cohorts for T1D risk stratification. Discovery (wet and dry lab) analysis identified 50 miRNAs associated with functional β cell loss, which is a hallmark of T1D. These miRNAs measured across n = 2,204 individuals from four contexts (4C: Australia, Denmark, Hong Kong SAR People's Republic of China, India) led to a four-context, miRNA-based dynamic risk score (DRS) that effectively stratified individuals with and without T1D. Generative artificial intelligence was used to create an enhanced four-context, miRNA-based DRS, which offered good predictive power (area under the curve = 0.84) for T1D stratification in a separate multicontext validation dataset (n = 662), and accurately predicted future exogenous insulin requirement at 1 hour of islet transplantation. In a clinical trial assessing the imatinib drug therapy, baseline miRNA signature, rather than clinical characteristics, distinguished drug responders from nonresponders at 1 year. This study harnessed machine learning/generative artificial intelligence approaches, identifying and validating a miRNA-based DRS for T1D discrimination and treatment efficacy prediction.

BACKGROUND: Obesity is a known cancer risk factor, yet its classification by body mass index fails to capture organ dysfunction. METHODS: In 459,342 UK Biobank participants enrolled between 2006 and 2010, we applied the Lancet Diabetes and Endocrinology Commission's definitions of preclinical (excess adiposity without organ dysfunction) and clinical obesity (with organ dysfunction) to prospectively assess associations with 28 cancers. Multivariable Cox regression estimated hazard ratios and 95% confidence intervals for each obesity classification and cancer type. FINDINGS: During 11.6 years of follow-up, 47,060 incident cancer cases were identified. Preclinical obesity was positively associated with 11 cancer types across multiple organ systems, including cancers of the digestive (esophageal adenocarcinoma, gastric cardia, liver, biliary tract, pancreas, colorectum), reproductive (endometrium, postmenopausal breast, fatal prostate), urinary (kidney), and endocrine (thyroid) systems. Clinical obesity was positively associated with 12 cancers, showing stronger relations particularly for metabolically driven malignancies such as hepatocellular carcinoma and endometrial, colorectal, and pancreatic cancers. It was also positively associated with lung cancer. Conversely, preclinical and clinical obesity were inversely associated with non-fatal prostate cancer, suggesting a distinct underlying mechanism. We estimate that in the UK Biobank cohort, preclinical obesity accounted for 5.5% (4.7, 6.3) and clinical obesity for 4.3% (3.6, 4.9) of obesity-related cancer. INTERPRETATION: The link between preclinical obesity and increased cancer risk suggests that obesity-related carcinogenesis begins before clinically detectable abnormalities, highlighting the need for early risk identification. Stronger associations with clinical obesity, particularly in metabolically driven cancers, reinforce the role of organ dysfunction in exacerbating carcinogenesis, emphasizing medical monitoring and intervention. FUNDING: Funding for IIG_FULL_2021_027 was obtained from World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme. This study was supported by the French National Cancer Institute (l'Institut National du Cancer, INCA_16824) and the German Research Foundation (BA 5459/2-1).

Osteoporosis is a common metabolic bone disease with aging, characterized by low bone mineral density (BMD) and higher fragility fracture risk. Although current pharmacological interventions provide therapeutic benefits, long-term use is limited by side effects and comorbidities. In this study, we employed driver signaling network identification (DSNI) and drug functional networks (DFN) to identify repurposable drugs from the Library of Integrated Network-Based Cellular Signatures. We constructed osteoporosis driver signaling networks (ODSN) using multi-omics data and developed DFN based on drug similarity. By integrating ODSN and DFN with drug-induced transcriptional responses, we screened 10,158 compounds and identified several drugs with strong targeting effects on ODSN. Mendelian randomization assessed potential causal links between cis-eQTLs of drug targets and BMD using genome-wide association study data. Our findings indicate four drugs, including Ruxolitinib, Alfacalcidol, and Doxercalciferol, may exert anti-osteoporosis effects. Notably, Acebutolol, a β-blocker for hypertension, has not previously been implicated in osteoporosis therapy. For validation, zebrafish osteoporosis models were established using Dexamethasone-induced bone loss, followed by treatment with Acebutolol hydrochloride and Alfacalcidol. Both compounds demonstrated significant protective effects against osteoporosis-related bone deterioration. Furthermore, a population-based data set, utilizing propensity score matching and analyzed via a generalized linear model, revealed that individuals taking β-blocker drugs exhibited significantly higher BMD than users of other cardiovascular medications. In summary, this study integrates multi-omics approaches, experimental validation, and real-world population data to propose acebutolol as a novel candidate for osteoporosis treatment. These findings warrant further mechanistic studies and clinical trials to evaluate its efficacy in osteoporosis management.