Daily Endocrinology Research Analysis
Three high-impact endocrinology studies stand out today. A multiomics integration in Diabetes identified causal biomarkers and markedly improved early diabetes risk prediction. A Lancet Diabetes & Endocrinology global analysis exposes major gaps in the diabetes care cascade. A reconstructed IPD meta-analysis quantifies hepatocellular carcinoma risk across MASLD fibrosis stages, informing surveillance strategies.
Summary
Three high-impact endocrinology studies stand out today. A multiomics integration in Diabetes identified causal biomarkers and markedly improved early diabetes risk prediction. A Lancet Diabetes & Endocrinology global analysis exposes major gaps in the diabetes care cascade. A reconstructed IPD meta-analysis quantifies hepatocellular carcinoma risk across MASLD fibrosis stages, informing surveillance strategies.
Research Themes
- Multiomics biomarker discovery and causal inference in diabetes
- Global gaps in the diabetes cascade of care
- Cancer risk stratification in MASLD by fibrosis stage
Selected Articles
1. Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes.
A large multiomics study linked 175 CpGs, 29 proteins, and 93 metabolites to diabetes, validated a subset externally, and used Mendelian randomization/mediation to identify 20 causal biomarkers and 190 cross-omics pathways. Prioritized biomarkers improved early diabetes prediction AUC by 27.5% over clinical features; COLEC11 was functionally validated in vitro, implicating lipid metabolism.
Impact: Integrative cross-omics with causal inference and functional validation advances biomarker discovery and prioritizes therapeutic targets while delivering immediate gains in risk prediction.
Clinical Implications: Prioritized biomarkers could underpin earlier identification of high-risk individuals and guide target selection for intervention trials; translation requires multi-ethnic validation and prospective testing.
Key Findings
- Identified 175 CpGs, 29 proteins, and 93 metabolites associated with diabetes; 43 CpGs and 25 metabolites validated in an independent cohort.
- Mendelian randomization and mediation analyses yielded 20 causal biomarkers and 190 cross-omics signaling pathways.
- Early prediction model using prioritized biomarkers improved AUC by 27.5% over a clinical-features-only model.
- COLEC11 prioritized as a therapeutic target and validated in vitro with effects on lipid metabolism.
Methodological Strengths
- Large cohort with external validation and cross-omics integration (epigenome, proteome, metabolome).
- Use of Mendelian randomization and mediation for causal inference plus in vitro functional validation.
Limitations
- Observational design limits causal claims beyond MR; potential residual confounding and cohort-specific biases.
- Functional validation performed for one prioritized protein (COLEC11); generalizability to diverse populations requires testing.
Future Directions: Prospective, multi-ethnic validation; head-to-head comparison with polygenic risk; interventional studies targeting prioritized pathways; open data/code to enhance reproducibility.
UNLABELLED: Diabetes holds significant social importance due to its high incidence rate and multitude of associated complications. The identification of diabetes biomarkers and the understanding of the intricate biological mechanisms underlying diabetes are crucial for the early diagnosis and treatment of diabetes. In this study, we conducted comprehensive omics profiling of CpGs, plasma proteins, and serum metabolites in an National Survey of Physical Traits (NSPT) cohort of 3,451 individuals, among whom 293 were patients with diabetes. Global association analysis identified 175 CpGs, 29 proteins, and 93 metabolites significantly linked to diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort comprising 532 individuals. Mendelian randomization and mediation analysis identified 20 causal biomarkers and 190 signaling pathways linking biomarkers from different layers. By integrating the cross-omics evidence, we provide a list of putative causal biomarkers of diabetes to serve as a valuable resource for the diabetes community. Cross-omics integration prioritized biomarkers for therapeutic targeting, highlighting COLEC11 as an example of a potential target and whose function was further validated in vitro. The early-prediction model using the prioritized biomarkers improved the area under the receiver operating characteristic curve by 27.5% compared with the baseline model, using clinical features alone. Our findings provide a comprehensive list of prioritized multiomics biomarkers and elucidate specific signaling pathways in diabetes, contributing significantly to the selection of therapeutic target and the understanding of diabetes pathophysiology.
2. Global, regional, and national cascades of diabetes care, 2000-23: a systematic review and modelling analysis using findings from the Global Burden of Disease Study.
Using GBD methods and a hierarchical Bayesian model across 204 countries (2000–2023), this study finds that only 55.8% of adults with diabetes are diagnosed and just 21.2% overall achieve optimal glycaemic control on treatment. Although diagnosis and treatment rates improved by 8.3 and 7.2 percentage points since 2000, optimal control among those treated improved by only 1.3 points, with marked regional disparities.
Impact: Establishes a comprehensive, policy-relevant benchmark for the global diabetes care cascade, guiding resource allocation and health-system interventions.
Clinical Implications: Prioritize scalable screening to reduce underdiagnosis and implement quality improvement for glycaemic management, focusing on regions with the largest deficits.
Key Findings
- In 2023, 55.8% of adults with diabetes were diagnosed; 91.4% of diagnosed were on treatment; 41.6% of treated achieved optimal glycaemic concentrations.
- Only 21.2% of all people with diabetes achieved optimal glycaemia on treatment globally.
- From 2000 to 2023, diagnosis and treatment rates increased by 8.3 and 7.2 percentage points; optimal control among treated improved by 1.3 points.
- Marked regional variation: highest diagnosis in high-income North America; best control among treated in southern Latin America.
Methodological Strengths
- Systematic synthesis of representative population surveys with hierarchical Bayesian meta-regression (DisMod-MR 2.1).
- Global coverage (204 countries/territories) with temporal trends from 2000 to 2023.
Limitations
- Treatment defined as medication use; lifestyle interventions not captured; glycaemic target definitions harmonized but may vary across surveys.
- Reliance on secondary data and modelling; residual biases in low-resource settings possible.
Future Directions: Incorporate CGM-derived metrics, capture non-pharmacologic treatment, and evaluate equity-focused interventions to close care gaps in LMICs.
BACKGROUND: Diabetes is a serious global health challenge, with a rising prevalence and substantial effect on disability and mortality worldwide. Despite medical advancements, gaps in the cascade of diabetes care-comprising diagnosis, treatment, and glycaemic management-persist, hindering effective management. We aimed to comprehensively assess the state of the diabetes cascade of care globally, identifying areas of strength and needs for improvement in diabetes management. METHODS: Using data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), this modelling analysis spanned the years 2000 to 2023 and covered 204 countries and territories. We systematically reviewed cross-sectional surveys that are representative of the general population and the published and grey literature to estimate the proportion of people with diabetes who are undiagnosed, diagnosed but untreated, receiving treatment with suboptimal glycaemic concentrations, and receiving treatment with optimal glycaemic concentrations. Treatment was defined as current use of insulin or other hypoglycaemic medication. We separately modelled these quantities by location, year, age, and sex using DisMod-MR 2.1, a hierarchical Bayesian meta-regression modelling tool, then scaled the estimates so that they sum to 100% of people living with diabetes in each stratum. Using GBD 2023 estimates of the number of people with diabetes, we calculated the diabetes cascade of care: proportion of people diagnosed among those with diabetes, proportion of people receiving treatment among those with diagnosed diabetes, and proportion of people with optimal glycaemic concentrations among those receiving treatment for diabetes across all strata. FINDINGS: In 2023, an estimated 55·8% (95% UI 49·3-62·3) of people with diabetes aged 15 years and older were diagnosed with diabetes globally. The proportion of people with diagnosed diabetes who were on treatment was 91·4% (88·0-94·2), and the proportion of people on diabetes treatment with optimal glycaemic concentrations was 41·6% (35·7-48·5). Among all people with diabetes, the proportion with optimal glycaemic concentrations on treatment was 21·2% (17·4-25·6) in 2023 globally. Substantial regional differences were observed, with the highest rates of diagnosis in high-income North America, the highest rates of treatment among those with diagnosed diabetes in high-income Asia Pacific, and the highest rates of optimal glycaemic concentrations among those receiving treatment for diabetes in southern Latin America. Between 2000 and 2023, globally, the proportion of people diagnosed with diabetes increased by 8·3 (6·6-10·0) percentage points, and the proportion of people receiving treatment among those diagnosed increased by 7·2 (5·7-8·8) percentage points. The proportion of people receiving treatment who had optimal glycaemic concentrations increased by 1·3 (0·8-1·8) percentage points. INTERPRETATION: Despite improvements over the past two decades, underdiagnosis and suboptimal glycaemic management of diabetes remain major challenges globally, particularly in low-income and middle-income countries. These findings highlight the urgent need for enhanced strategies and capacity building to improve the detection, treatment, and management of diabetes worldwide. Targeted interventions to bolster health-care systems' capacity to effectively diagnose and manage diabetes could lead to better health outcomes and reduce the burden of this growing disease. FUNDING: Bill & Melinda Gates Foundation.
3. Incidence of Hepatocellular Carcinoma in Metabolic Dysfunction-associated Steatotic Liver Disease: A Reconstructed Individual Patient Data Meta-analysis.
Reconstructing IPD from Kaplan–Meier curves across 26 studies (n≈4.0M), the authors estimate markedly higher HCC incidence in MASLD with advanced fibrosis versus without, and show 10-year cumulative incidence up to 8.8% (administrative datasets) or 48.5% (clinic-based). Advanced fibrosis confers ~11-fold higher risk, underscoring the need for fibrosis-stage–informed surveillance.
Impact: Provides time-to-event, fibrosis-stage–specific HCC incidence estimates in MASLD using reconstructed IPD, directly informing surveillance thresholds and risk communication.
Clinical Implications: Support fibrosis-stage–tailored HCC surveillance strategies in MASLD; emphasizes careful interpretation of clinic-based estimates due to selection bias.
Key Findings
- Across 26 studies (n=3,995,728), 10-year HCC incidence in MASLD with advanced fibrosis was 8.8% (administrative) vs 48.5% (clinic-based).
- Without advanced fibrosis, 10-year HCC incidence was 1.3% (administrative) vs 18.3% (clinic-based).
- Advanced fibrosis conferred ~11-fold higher HCC risk (HR 11.09 administrative; HR 10.50 clinic-based).
- Clinic-based estimates likely inflated by selection bias compared with administrative datasets.
Methodological Strengths
- Reconstructed individual participant data from published Kaplan–Meier curves enabling time-to-event meta-analysis.
- Large sample size and stratification by fibrosis stage and data source (administrative vs clinic-based).
Limitations
- Heterogeneity across studies and potential misclassification of fibrosis stage; reliance on published KM curves.
- Clinic-based cohorts subject to selection bias; unmeasured confounding cannot be excluded.
Future Directions: Prospective cohorts with standardized fibrosis staging and uniform surveillance to refine absolute risk; evaluate cost-effectiveness of stage-tailored screening.
BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the fastest rising etiology of hepatocellular carcinoma (HCC). The time-dependent incidence of HCC in people with MASLD has not been reported. We aimed to provide robust estimates for HCC incidence in MASLD. METHODS: Medline and Embase were searched from inception to November 2024. Individual participant data were reconstructed from published Kaplan-Meier curves, and a pooled analysis of cumulative HCC incidence from time-to-event data was performed using a random-effects model. RESULTS: We screened 4951 articles and included 26 studies (3,995,728 individuals). The 1-, 3-, 5-, and 10-year cumulative incidence of HCC in people with MASLD and known advanced fibrosis was 0.8%, 2.4%, 3.9%, and 8.8%, respectively, in administrative database studies, and 3.9%, 11.7%, 21.0% and 48.5%, respectively, in hospital/clinic-based studies. The 1-, 3-, 5-, and 10-year cumulative incidence of HCC in people with MASLD but without advanced fibrosis was 0.1%, 0.5%, 0.7%, and 1.3%, respectively, in administrative database studies, and 1.6%, 4.7%, 8.2%, and 18.3%, respectively, in hospital/clinic-based studies. Selection bias may contribute to the elevated risk in hospital/clinic-based studies. The risk of HCC in patients with advanced fibrosis was significantly higher compared with those without advanced fibrosis in both administrative database (hazard ratio [HR], 11.09; 95% confidence interval [CI], 2.68-45.89; P < .001) and hospital/clinic-based studies (HR, 10.50; 95% CI, 3.19-34.51; P < .001). CONCLUSION: This reconstructed individual participant data meta-analysis provides updated estimates for HCC incidence in people with MASLD. The incidence of HCC is elevated in people with MASLD and advanced fibrosis. These data may have implications for further research in HCC surveillance and future development of surveillance algorithms.