Daily Endocrinology Research Analysis
Analyzed 60 papers and selected 3 impactful papers.
Summary
Analyzed 60 papers and selected 3 impactful articles.
Selected Articles
1. Catecholamine-mediated release of miR-133a-3p from adipocytes regulates the onset of chronic primary pain.
Across human cohorts and rodent models, adipocyte-derived EV miR-133a-3p is reduced in chronic primary pain. Beta-adrenergic activation suppresses adipocyte miR-133a-3p, enabling upregulation of spinal pain genes; restoring miR-133a-3p in adipose tissue reverses mechanical hypersensitivity in both sexes.
Impact: This work uncovers a previously unrecognized adipose–spinal EV-miRNA axis that causally regulates pain, nominating miR-133a-3p as a cross-condition biomarker and therapeutic candidate.
Clinical Implications: miR-133a-3p could serve as a blood biomarker to stratify chronic primary pain and guide trials of EV/miRNA replacement or adrenergic-modulating strategies.
Key Findings
- Plasma miR-133a-3p is downregulated in humans with ≥1 chronic primary pain condition and in rodent primary pain models.
- White adipocyte beta-adrenergic activation decreases miR-133a-3p packaging in EVs that traffic to the spinal cord and repress pain-related genes (e.g., MAP3K3).
- Adipose-specific overexpression of miR-133a-3p reverses mechanical hypersensitivity in both male and female mice, indicating therapeutic potential.
Methodological Strengths
- Multi-species validation (human plasma, rat and mouse models) with convergent results
- Causal testing via adipose-specific overexpression and EV trafficking mechanism
Limitations
- Human cohort size and phenotypic heterogeneity are not detailed, limiting immediate generalizability
- Translational gap remains between preclinical efficacy and clinical delivery of miRNA/EV therapeutics
Future Directions: Develop scalable delivery systems for miR-133a-3p (e.g., engineered EVs), validate biomarker performance across CPPC subtypes, and initiate phase 1 safety studies.
Chronic primary pain conditions (CPPCs), such as fibromyalgia and vestibulodynia, affect over 100 million Americans, predominantly women, and pose a substantial healthcare challenge. CPPCs arise from genetic and environmental factors that enhance catecholamine tone, potentially through miRNA dysregulation following catecholamine activation of beta-adrenergic receptors. Here, we identified miR-133a-3p as a biomarker of CPPC status and investigated its functions using in vivo and in vitro approaches. Plasma levels of miR-133a-3p were consistently downregulated in humans with ≥1 CPPC and in rat and mouse models of primary pain. Our data suggest that miR-133a-3p is packaged in extracellular vesicles that are secreted by adipocytes and trafficked to the spinal cord. Activation of adrenergic receptors on white adipocytes resulted in downregulation of miR-133a-3p which negatively regulated pain-related genes in the spinal cord, such as MAP3K3, which is critical for sensory neuron activation. Adipose-specific overexpression of miR-133a-3p in a mouse model of primary pain reversed mechanical hypersensitivity in both sexes. These findings implicate miR-133a-3p dysregulation in primary pain across conditions and species and establish its role in multi-site mechanical hypersensitivity. Further, miR-133a-3p overexpression shows therapeutic potential for the millions of individuals with CPPCs.
2. Machine Learning Prediction of Liver Fibrosis in Patients with Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD).
In >3,600 biopsy-confirmed MASLD cases, transformer- and TabNet-based models using routine clinical variables outperformed FIB-4 and matched or exceeded LSM/AGILE3+ for advanced fibrosis, with small grey zones. An ordinal-aware MLP (CORAL) best captured multiclass staging performance.
Impact: Demonstrates externally validated, ordinal-aware AI that can triage MASLD fibrosis risk with routine data, potentially reducing reliance on elastography or biopsy.
Clinical Implications: These models could be embedded in EHRs to prioritize referrals for elastography or hepatology, minimize unnecessary biopsies, and standardize fibrosis risk stratification across centers.
Key Findings
- FTT achieved AUROC 0.860 for F≥3 (and 0.800 for F≥2), outperforming FIB-4 in binary tasks.
- For F≥3, FTT exceeded LSM (p=0.014) and was comparable to AGILE3+, with the lowest grey zone (8.2–8.4%).
- Ordinal MLP-CORAL provided the best multiclass staging (quadratic weighted kappa 0.616) with external geographic validation.
Methodological Strengths
- Large, biopsy-confirmed cohort with predefined external validation across centers
- Robust modeling (transformer/TabNet), ordinal-aware architecture, and benchmarking versus LSM and AGILE scores
Limitations
- Retrospective design with multiple imputation; prospective clinical impact and calibration not assessed
- Generalizability beyond participating centers and differing MASLD prevalence requires further testing
Future Directions: Prospective, multi-center impact studies integrating models into care pathways, real-time EHR deployment, and threshold optimization by local prevalence.
BACKGROUND: Accurate staging of liver fibrosis is crucial for risk stratification in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to develop and validate AI-based models capable of distinguishing fibrosis stages. METHODS: We developed and validated machine learning models to predict fibrosis stages in more than 3,600 biopsy-confirmed MASLD patients using 22 clinical features, liver stiffness measurement (LSM), and controlled attenuation parameter. Models included Feature Tokenizer Transformer (FTT), TabNet variants with distance-aware losses, ordinal MLP with the CORAL framework. Three centers were pre-specified for geographic external validation. In the remaining centers we used a stratified 75/25 split into a training pool and an internal random test set, tuned hyperparameters by stratified 10-fold cross-validation on the training pool, and trained one model per imputed dataset(M=5) with an internal 80/20 train-validation split for early stopping and operating-point selection. Performance was pooled across imputations using Rubin's Rules and reported for the internal random test set and the held-out centers. RESULTS: FTT and TabNet achieved the highest performance in binary tasks: area under the receiver-operating characteristic curve (AUROC)=0.860 and 0.855(F≥3vsF0-F2) and AUROC=0.800 and 0.788(F≥2vsF0-F1), significantly outperforming FIB-4. For F≥3, FTT had significantly higher AUROC than LSM(p=0.014) but only marginally higher than AGILE3+, and, together with TabNet, the highest accuracy and the lowest grey zone (8.2% and 8.4%, respectively). For multiclass staging, ordinal models performed best with MLP-CORAL achieving quadratic weighted kappa=0.616. CONCLUSIONS: Deep learning models with ordinal-aware architectures can accurately predict liver fibrosis stages using routinely available clinical data, offering a scalable alternative to biopsy without requiring specialized biomarkers.
3. Methimazole-Associated Neutropenia and Agranulocytosis in Pediatric Patients with Graves' Disease: A Chinese Cohort Study.
Among 432 pediatric Graves’ disease patients on methimazole, 24.1% developed neutropenia—most within 2–4 weeks—and 2.8% experienced severe agranulocytosis, all asymptomatic and detected by routine monitoring. TPOAb-negativity increased risk, whereas older age and higher baseline ANC were protective.
Impact: Defines time-critical monitoring windows and risk stratifiers for a potentially life-threatening adverse event in a vulnerable pediatric population, enabling actionable surveillance protocols.
Clinical Implications: Implement ANC checks every 1–2 weeks in the first month and monthly through 3 months after methimazole initiation; consider closer monitoring in TPOAb-negative, very young, or low baseline ANC patients and proactive caregiver education.
Key Findings
- Neutropenia occurred in 24.1% within 12 months; 59.6% within 2 weeks, 72.1% within 1 month, and 84.6% within 3 months of therapy.
- Severe neutropenia/agranulocytosis occurred in 2.8%, all asymptomatic and detected via routine early monitoring.
- Risk factors: TPOAb-negativity increased risk (OR 2.020); older age (OR 0.916) and higher baseline ANC (OR 0.775) were protective.
Methodological Strengths
- Relatively large pediatric cohort with scheduled follow-up timepoints
- Multivariable modeling to identify independent risk factors
Limitations
- Single-country cohort; external generalizability to other ethnic and healthcare settings needs validation
- Granularity on dosing, concomitant infections/medications, and genetic predisposition was limited in the abstract
Future Directions: Prospective multicenter validation with standardized ANC protocols, dose–response assessment, and integration into pediatric Graves’ disease care pathways.
BACKGROUND: Graves' disease (GD) is rare in children, and methimazole (MMI) is recommended as the first-line therapy. However, data on MMI-associated neutropenia and agranulocytosis in pediatric patients remain limited. In this study, we aimed to characterize the clinical features of these adverse events and to identify their associated risk factors. METHODS: A cohort study was conducted involving 432 pediatric patients with GD treated with MMI. Clinical and biochemical data were collected retrospectively and prospectively, with follow-up 0.5, 1, 2, 3, 4, 5, 6, 7-9, and 10-12 months after treatment initiation. Multivariable logistic regression analysis was performed to identify risk factors for MMI-associated neutropenia. RESULTS: During the 12-month follow-up period, 104 (24.1%) patients developed neutropenia, with 84.6% developing neutropenia within the first 3 months, 72.1% within 1 month, and 59.6% within 2 weeks. Among the affected patients, 83.7%, 13.5%, and 2.8% had mild, moderate, and severe neutropenia (agranulocytosis), respectively. All patients with moderate or severe neutropenia were asymptomatic and were identified through routine monitoring within the first month. Multivariable analysis revealed that a thyroid peroxidase antibody (TPOAb)-negative status (odds ratio [OR], 2.020; confidence interval [CI], 1.113-3.666) was associated with a higher prevalence of MMI-associated neutropenia, whereas older age (OR, 0.916; CI, 0.848-0.989) and higher baseline absolute neutrophil count (ANC; OR, 0.775; CI, 0.665-0.903) were protective factors. CONCLUSIONS: ANC monitoring is recommended every 1 to 2 weeks during the first month and monthly for the first 3 months after MMI initiation. Pediatric patients with GD younger than 3 years with TPOAb-negative status or a baseline ANC < 3 × 10