Daily Endocrinology Research Analysis
Analyzed 97 papers and selected 3 impactful papers.
Summary
Three high-impact studies advance endocrinology across scales: a Science atlas maps hormone and receptor expression across 14 million single cells to redefine endocrine circuits; a translational Science Advances study shows EZH2 inhibition (GSK-126) curbs EndMT and atherosclerosis in diabetes; and a large UK Biobank analysis demonstrates that integrating proteomics, metabolomics, and a PRS significantly improves 10-year type 2 diabetes risk prediction beyond clinical models.
Research Themes
- Single-cell endocrine systems mapping
- Epigenetic therapy for diabetic vascular disease
- Multi-omics risk prediction in type 2 diabetes
Selected Articles
1. A Hormone Cell Atlas maps the human endocrine system at cellular resolution.
This pan-tissue single-cell resource systematically maps hormone and receptor expression across 14 million cells, identifying hormone-producing and -receiving cell types and non-classical hormone sites (e.g., secretin in plasmacytoid dendritic cells). It delineates cross-tissue endocrine circuits and dynamic adipocyte endocrine programs, offering a foundational framework for studying endocrine physiology and disease.
Impact: Provides an openly accessible, systems-level atlas that redefines endocrine cell networks and generates testable hypotheses across multiple diseases.
Clinical Implications: Enables precise identification of cellular targets and pathways implicated in endocrine disorders, potentially refining biomarker discovery, therapeutic target nomination, and tissue-specific drug effects.
Key Findings
- Mapped 379 hormone and receptor genes across 14 million single cells/nuclei from 47 human tissues using hormone2cell.
- Predicted non-classical hormone expression (e.g., secretin in plasmacytoid dendritic cells) and inferred endocrine feedback loops.
- Cross-tissue adipocyte integration revealed dynamic endocrine programs across depots, subtypes, and along adipogenesis.
- Linked specific cell populations to monogenic endocrine disorders and defined cross-tissue endocrine signatures.
Methodological Strengths
- Massive-scale single-cell/nucleus transcriptomics across 47 tissues
- Integrated cross-tissue analysis and open resource (hormonecellatlas.org.uk)
Limitations
- Primarily transcript-level inferences without systematic functional validation
- Potential sampling biases and limited disease-state tissues in the primary dataset
Future Directions: Functionally validate predicted hormone circuits and non-classical expression sites; extend maps to disease tissues and longitudinal states; integrate proteomic and spatial multi-omics.
Hormones act across tissues and organs to coordinate physiological functions. Drawing inspiration from the Human Cell Atlas, we analyzed expression of 379 hormone and receptor genes in a transcriptomic dataset comprising 14 million single cells and nuclei across 47 human tissues. Using hormone2cell, we mapped putative hormone-producing and hormone-receiving cell types, defining tissue-specific and cross-tissue endocrine signatures. We predicted non-classical sites of hormone expression, including secretin in plasmacytoid dendritic cells, inferred convergent hormone action and endocrine feedback loops, and implicated cell populations in monogenic endocrine disorders. In a cross-tissue integration of adipocyte datasets, we uncovered dynamic endocrine programs across depots, within adipocyte subtypes and through adipogenic differentiation. Cumulatively, the Hormone Cell Atlas (hormonecellatlas.org.uk) provides a comprehensive framework for dissecting hormonal impact on health and disease.
2. EZH2 inhibition via GSK-126 mitigates EndMT and atherosclerosis in diabetes: A translational epigenetic approach.
The study identifies elevated EZH2-mediated H3K27me3 in diabetic vasculature and demonstrates that pharmacologic EZH2 inhibition with GSK-126 attenuates EndMT and atherosclerosis in diabetic settings. Findings bridge epigenetic regulation to diabetes-associated vascular remodeling and propose EZH2 as a tractable target.
Impact: Introduces a translational epigenetic therapy concept for diabetic atherosclerosis by targeting EZH2 to reverse EndMT.
Clinical Implications: Suggests EZH2 inhibition as a candidate strategy to reduce vascular complications in diabetes; supports biomarker development (H3K27me3) for patient selection and response monitoring.
Key Findings
- EZH2-mediated H3K27 trimethylation is elevated in carotid plaques from patients with diabetes and in diabetic aortic endothelium.
- Pharmacologic EZH2 inhibition with GSK-126 mitigates EndMT and reduces atherosclerosis burden in diabetic models (as per title).
- Establishes a mechanistic epigenetic link between EZH2 activity and endothelial phenotypic switching in diabetes-associated vascular disease.
Methodological Strengths
- Translational design spanning human diabetic plaques, animal models, and mechanistic cell-based assays
- Epigenetic target interrogation with a specific inhibitor (GSK-126)
Limitations
- Abstract provides limited quantitative data; full preclinical efficacy and safety profiles required
- Off-target and long-term effects of EZH2 inhibition in metabolic disease remain to be defined
Future Directions: Define dose, durability, and safety of EZH2 inhibition in diabetic atherosclerosis; test combination with lipid-lowering and anti-inflammatory agents; evaluate H3K27me3 as a predictive biomarker.
Atherosclerosis drives cardiovascular morbidity in diabetes, with endothelial-to-mesenchymal transition (EndMT) as a key contributor. Whereas epigenetic regulators are increasingly implicated in atherosclerotic progression, the specific role of enhancer of zeste homolog 2 (EZH2), a histone methyltransferase, in EndMT in diabetes-associated atherosclerosis remains unclear. We show that EZH2-mediated H3K27 (histone H3 at lysine-27) trimethylation is elevated in carotid plaques from patients with diabetes and in the aortic endothelium of diabetic
3. Large-scale multi-omics enhance risk prediction for type 2 diabetes.
In 42,840 UK Biobank participants with 10-year follow-up, adding proteomics to a clinical risk score improved C-index from 0.862 to 0.884 (NRI 42%), and full multi-omics (proteomics, metabolomics, PRS) further increased it to 0.891. Proteomics alone captured most of the gain, highlighting translational feasibility with a 15-protein panel.
Impact: Demonstrates real-world, validated performance gains from integrating proteomics (and multi-omics) into T2D risk prediction, informing precision prevention strategies.
Clinical Implications: Supports adoption of targeted proteomic panels to enhance T2D risk stratification beyond clinical scores, pending external validation, infrastructure, and cost-effectiveness.
Key Findings
- Derivation (N=23,108) and independent validation (N=19,732) within UK Biobank showed robust performance.
- Adding 15 proteins to CDRS improved C-index from 0.862 to 0.884 (Δ=0.022; P<0.001) with continuous NRI of 42.0%.
- Full multi-omics (proteomics, metabolomics, PRS) further increased C-index to 0.891 (additional Δ=0.007; P<0.001).
- Selected biomarkers map to cardiovascular pathways, reinforcing cardio-metabolic links.
Methodological Strengths
- Large sample with predefined derivation and independent validation sets
- Incremental evaluation against a strong clinical comparator (CDRS)
Limitations
- UK Biobank selection bias and limited ethnic diversity may constrain generalizability
- Incremental C-index gains, while significant, are modest; implementation requires cost-effectiveness and workflow integration
Future Directions: External, multi-ethnic validation; health-economic analyses of proteomic panels; integration into EHR-driven risk stratification and targeted prevention trials.
BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.