Daily Endocrinology Research Analysis
Analyzed 144 papers and selected 3 impactful papers.
Summary
Analyzed 144 papers and selected 3 impactful articles.
Selected Articles
1. Metabolic state determines the brain and direct islet effects of liraglutide on enhanced insulin secretion.
Using human islets across metabolic states, liraglutide enhanced glucose-stimulated insulin secretion only in glucose-intolerant donors, not in normoglycaemic donors. GLP-1R mRNA expression declined with worsening glycaemia, and the authors propose a framework where tanycyte-mediated central effects dominate in health while direct islet effects emerge with dysglycaemia.
Impact: This study provides a mechanistic, metabolic-state–specific model for GLP-1RA action, informing patient stratification and potentially optimizing therapeutic decisions in type 2 diabetes.
Clinical Implications: Suggests that therapeutic response to liraglutide may depend on dysglycaemia and islet GLP-1R expression, supporting biomarker-informed selection and timing of GLP-1RA therapy.
Key Findings
- Liraglutide (25 nmol/L) enhanced GSIS in glucose-intolerant donors (n=7, p=0.021) but not in normoglycaemic donors (n=7).
- GLP-1R mRNA levels progressively decreased in islets with rising HbA1c in type 2 diabetes.
- Proposed dual mechanism: tanycyte-mediated central actions predominate in health, while direct islet effects emerge with glucose intolerance; insulin-independent mechanisms maintain efficacy across states.
Methodological Strengths
- Use of primary human islets across defined metabolic states with functional readouts (GSIS).
- Molecular correlation of GLP-1R mRNA levels with dysglycaemia to support mechanism.
Limitations
- Small sample sizes in human islet groups and incomplete reporting of total donor numbers.
- Mechanistic central (tanycyte) inferences are not linked to clinical outcomes in this study.
Future Directions: Prospective, biomarker-guided clinical trials testing GLP-1RA response by metabolic state (e.g., HbA1c strata, islet/serum GLP-1R surrogates) and mechanistic validation of central vs islet pathways in humans.
AIMS/HYPOTHESIS: Liraglutide, a glucagon-like peptide-1 receptor (GLP-1R) agonist for type 2 diabetes and obesity management, shows variable patient responses. We investigated the metabolic state-dependent mechanisms underlying this heterogeneity and how liraglutide's mode of action shifts across stages of metabolic dysfunction. METHODS: We employed human pancreatic islets from donors across metabolic states (normoglycaemic [HbA RESULTS: Liraglutide (25 nmol/l) enhanced glucose-stimulated insulin secretion specifically in donors with glucose intolerance (n=7, p=0.021), with no effect in normoglycaemic islets (n=7), despite preserved GLP-1 (7-36) responsiveness. In type 2 diabetes islets, GLP-1R mRNA levels progressively decreased with rising HbA CONCLUSIONS/INTERPRETATION: Liraglutide operates through complementary, metabolic state-dependent pathways: tanycyte-mediated brain actions predominate in healthy conditions, direct islet effects emerge during glucose intolerance and insulin-independent mechanisms maintain efficacy across metabolic states. This mechanistic framework enables potential patient stratification in type 2 diabetes therapy, suggesting that matching liraglutide's predominant mechanism to individual metabolic profiles could optimise treatment outcomes.
2. Machine Learning Algorithms to Accelerate Etiological Diagnosis of Congenital Disorders of Adrenal Steroidogenesis.
Using LC-MS/MS steroidomics and LightGBM with an interpretable decision-tree classifier, the authors achieved 97.1% accuracy in development and 98.9% in an independent validation cohort for classifying CDAS subtypes. SHAP analysis highlighted key discriminators (e.g., 11-deoxycortisol), and PCA/UMAP confirmed biologically coherent subtype clustering.
Impact: Delivers a validated, interpretable AI diagnostic that could shorten time-to-diagnosis and guide targeted management in pediatric endocrinology.
Clinical Implications: Supports integration of ML-assisted LC-MS/MS steroid panels into diagnostic pathways for rapid, accurate CDAS subtype classification and decision support.
Key Findings
- Development cohort (n=1027): accuracy 97.1%, sensitivity 99.5%, specificity 93.7%, macro-AUC 0.97.
- Independent validation cohort (n=507): accuracy 98.9%, sensitivity 93.6%, specificity 99.8%.
- SHAP identified 11-deoxycortisol, 17-hydroxyprogesterone, 21-deoxycortisol, and corticosterone as strongest discriminators; PCA/UMAP showed distinct, biologically coherent subtype clusters.
Methodological Strengths
- Large development cohort with genetic confirmation and independent external validation.
- Interpretable ML (SHAP) with dimensionality reduction (PCA/UMAP) supporting biological plausibility.
Limitations
- Model performance may vary with pre-analytical/analytical LC-MS/MS differences across laboratories.
- Retrospective datasets; prospective implementation and clinical impact not yet tested.
Future Directions: Prospective, multicenter implementation trials, health-economic evaluations, and integration with genetic testing/EHR decision support.
BACKGROUND: Early and accurate etiological diagnosis of congenital disorders of adrenal steroidogenesis (CDAS) is critical as timely targeted management can prevent life-threatening complications and improve long-term outcomes. OBJECTIVE: To develop and validate a machine learning-assisted decision tree model for classifying CDAS using plasma steroid hormone profiles quantified by liquid chromatography-mass spectrometry (LC-MS/MS). METHODS: A development cohort of 1027 participants (325 genetically confirmed CDAS patients representing eight subtypes/702 controls) was used for model construction. The Light Gradient Boosting Machine algorithm identified key discriminatory steroid hormones, which were integrated into an optimized decision-tree classifier. Internal performance was assessed through five-fold cross-validation. The performance of the model was further evaluated using a validation cohort comprising 507 independent LC-MS/MS steroid profiles. Additional analyses included Shapley additive explanations (SHAP), confusion matrix visualization, Principal Component Analysis (PCA), and Uniform Manifold Approximation and Projection (UMAP). RESULTS: In the development cohort, the model achieved a mean overall accuracy of 97.1%, sensitivity of 99.5%, and specificity of 93.7%, with a macro-AUC (area under curve) of 0.97. Subtype-level accuracy exceeded 98% for most major CDAS subtypes. In the validation cohort, overall accuracy was 98.9%, sensitivity 93.6%, specificity 99.8%. Feature importance analysis and SHAP identified 11-deoxycortisol, 17-hydroxyprogesterone, 21-deoxycortisol, and corticosterone as the strongest discriminators. PCA and UMAP revealed distinct clustering of CDAS subtypes, confirming the biological coherence of model predictions. CONCLUSION: Machine learning-assisted steroid profiling provides an accurate and highly interpretable diagnostic approach for CDAS, with potential for integration into pediatric endocrine diagnostics and decision-support systems.
3. Adipocyte Myoglobin Is a Determinant of Energy Expenditure and a Potential Target to Limit Obesity.
Adipose-specific myoglobin deletion reduced whole-body energy expenditure, impaired thermoregulation, and increased susceptibility to diet-induced obesity, with elevated circulating lipids and downregulated oxidative programs. Restoring myoglobin improved metabolism in vivo, and overexpressing it in primary human adipocytes enhanced thermogenesis.
Impact: Identifies adipocyte myoglobin as a modifiable determinant of thermogenic lipid metabolism and energy expenditure, opening a new avenue for obesity therapeutics.
Clinical Implications: While preclinical, the data support strategies to boost adipocyte myoglobin or its downstream pathways to increase energy expenditure as an adjunct to obesity treatment.
Key Findings
- Adipose tissue-specific myoglobin knockout reduced energy expenditure, impaired thermoregulation, and increased susceptibility to diet-induced obesity.
- ATMBKO mice showed elevated circulating triglycerides and fatty acids, with omics revealing downregulated oxidative phosphorylation and fatty acid metabolism.
- Myoglobin restoration improved metabolism in vivo; overexpression in primary human white adipocytes enhanced thermogenic activity.
Methodological Strengths
- Multimodal approach combining tissue-specific knockout, rescue, omics, and primary human adipocyte validation.
- Convergent phenotypes across mouse models and human cells strengthen translational relevance.
Limitations
- Preclinical study without human interventional data or long-term safety assessment.
- Specific mechanisms linking myoglobin to mitochondrial fatty acid oxidation require deeper molecular dissection.
Future Directions: Define druggable pathways downstream of adipocyte myoglobin, test pharmacologic/GENE-based augmentation in large animals, and evaluate metabolic benefits and safety in early-phase human studies.
Nutritional overflow and a positive energy balance are hallmarks of metabolic diseases including obesity and type 2 diabetes. Brown and beige adipocytes maintain systemic metabolic homeostasis by clearing and oxidizing energy-rich nutrients during thermogenic activation. Myoglobin (MB) is classically regarded as a muscle-associated oxygen-binding protein; however it is also expressed in brown and beige adipocytes, where it contributes to intracellular lipid handling and oxidative metabolism. Here, we report that loss of MB exclusively in adipose tissue (AT) lowers whole-body energy expenditure, impairs thermoregulation, and increases susceptibility to diet-induced obesity. AT-specific MB knockout (ATMBKO) mice exhibit elevated circulating triglycerides (TG) and fatty acids, indicating defective lipid clearance and utilization. Omics analyses reveal coordinated downregulation of oxidative phosphorylation, fatty acid metabolism, and myogenic programs. Conversely, restoration of MB in MB knockout (MBKO) mice improves metabolism in vivo. MB expression determines the capacity for mitochondrial fatty acid oxidation in brown adipocytes, whereas MB overexpression in primary human white adipocytes enhances thermogenic activity, confirming functional relevance of MB in human AT. Together, these findings establish MB as a key determinant of thermogenic lipid metabolism and energy expenditure in vivo and increasing adipocyte MB expression could increase energy expenditure and complement obesity treatment strategies.