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.
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.
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.