Daily Endocrinology Research Analysis
Today's top endocrinology-related papers span reproductive surgery, diabetes diagnostics, and rare metabolic bone disorders. A PROSPERO-registered meta-analysis quantifies intrauterine adhesion risks and supports gel barriers for primary prevention; a machine-learning model accurately predicts stimulated C-peptide in type 1 diabetes; and a 15-year Indian registry delineates the landscape, genetics, and management of rare metabolic bone diseases.
Summary
Today's top endocrinology-related papers span reproductive surgery, diabetes diagnostics, and rare metabolic bone disorders. A PROSPERO-registered meta-analysis quantifies intrauterine adhesion risks and supports gel barriers for primary prevention; a machine-learning model accurately predicts stimulated C-peptide in type 1 diabetes; and a 15-year Indian registry delineates the landscape, genetics, and management of rare metabolic bone diseases.
Research Themes
- Adhesion prevention and outcomes in reproductive surgery
- Noninvasive estimation of beta-cell function in type 1 diabetes
- Epidemiology and genetics of rare metabolic bone diseases
Selected Articles
1. The epidemiology, clinical burden, and prevention of intrauterine adhesions (IUAs) related to surgically induced endometrial trauma: a systematic literature review and selective meta-analyses.
Across 249 studies, new-onset intrauterine adhesion risk was 16–28% after adhesiogenic hysteroscopic procedures and 17% after early pregnancy loss management. Intrauterine gel barriers significantly reduced primary adhesion formation (RR ~0.29–0.45), whereas recurrence after adhesiolysis remained high (35–43%) and was not clearly reduced by balloons or IUDs; gel barriers showed lower but heterogeneous recurrence rates. Adverse obstetrical outcomes were increased, with signals of mitigation by adjuvants.
Impact: This PROSPERO-registered, PRISMA-compliant review provides quantitative, procedure-specific IUA risks and comparative effectiveness of adjuvants, informing prevention strategies and patient counseling in reproductive surgery.
Clinical Implications: Adopt intrauterine gel barriers for primary prevention after adhesiogenic procedures; counsel patients on high recurrence after adhesiolysis and limited benefit of balloons/IUDs; anticipate and monitor obstetrical risks; prioritize standardized protocols and RCTs.
Key Findings
- New-onset IUA risk: 17% after early pregnancy loss products removal; 16% after hysteroscopic myomectomy; 28% after septum metroplasty.
- Primary prevention with intrauterine gel barriers reduced IUA risk (RR 0.45, 0.38, and 0.29 across procedures; I2 = 0%).
- Recurrence after adhesiolysis was high (35–43%); intrauterine balloons and IUDs did not clearly lower recurrence; gel barriers showed 28% recurrence with high heterogeneity.
- IUA associated with increased obstetrical complications including preterm delivery, placenta accreta spectrum, placenta previa, hemorrhage, and hysterectomy; adjuvants may mitigate some risks.
Methodological Strengths
- PROSPERO-registered, PRISMA-compliant systematic review with NIH risk-of-bias assessment
- Selective meta-analyses with reporting of proportions, 95% CIs, and heterogeneity (I2)
Limitations
- High heterogeneity across studies and variable evidence quality
- Limited randomized data and potential differences in definitions and surgical techniques
Future Directions: Conduct adequately powered RCTs comparing adjuvants for primary and secondary prevention; standardize IUA definitions and outcome reporting; evaluate long-term obstetrical outcomes.
2. Predicting stimulated C-peptide in type 1 diabetes using machine learning: a web-based tool from the T1D exchange registry.
Using five routine variables from 319 T1D participants, a random forest model predicted MMTT-stimulated C-peptide categories with AUC 0.97 in an internal test set. Importantly, nearly one in five individuals with undetectable non-fasting C-peptide had measurable stimulated levels, underscoring the value of predictive triage.
Impact: Provides a practical, accessible alternative to MMTT for estimating residual beta-cell function, with a deployed web tool to facilitate clinical adoption and trial screening.
Clinical Implications: Enable noninvasive triage to identify patients with meaningful residual beta-cell function for treatment optimization and trial eligibility without performing MMTT.
Key Findings
- Random forest model using age at diagnosis, diabetes duration, HbA1c, non-fasting glucose, and non-fasting C-peptide achieved AUC 0.97 (test set), sensitivity 88%, specificity 94%.
- Cross-validation performance was strong (AUC 0.94; sensitivity 0.84; specificity 0.92).
- 17.7% of individuals with undetectable non-fasting C-peptide had measurable stimulated C-peptide after MMTT.
Methodological Strengths
- Recursive feature elimination with 10-fold cross-validation and independent test set evaluation
- Comparison across multiple ML algorithms with deployment as an accessible web tool
Limitations
- Single-registry dataset with modest sample size and no external validation
- Potential selection bias and variability in assay/clinical data capture
Future Directions: External validation across diverse cohorts and assay platforms; calibration for longitudinal prediction; clinical utility studies comparing model-guided decisions vs standard care.
3. Clinical Characteristics and Management of Rare Metabolic Bone Diseases: An Audit of the Rare Metabolic Bone Disease Registry of India.
Over 15 years, an Indian registry captured 218 patients across 29 rare metabolic bone diseases, with demineralization disorders predominating. Fractures affected 57.7% and skeletal deformities 31.1%; pathogenic variants in SOST, TGFβ1, SLC34A3, ALPL, and VCP validated diagnoses. Management strategies ranged from antiresorptives and anabolic agents to surgery.
Impact: Provides a comprehensive national snapshot of rare metabolic bone diseases with genetic confirmation in key subtypes, informing diagnostic pathways and therapeutic strategies.
Clinical Implications: Supports early genetic testing and structured care pathways for rare metabolic bone diseases; highlights fracture and deformity burden to guide surveillance and therapy selection.
Key Findings
- Among 218 patients, 29 rare metabolic bone diseases were identified: 50.4% demineralization, 32.5% bone matrix/cartilage formation disorders, 13.7% sclerotic, 2.7% unclassified.
- Most common entities: rickets/osteomalacia (27.1%), osteogenesis imperfecta (23.4%), fibrous dysplasia/McCune-Albright syndrome (18.8%).
- Fractures occurred in 57.7% (24.5% multiple) and skeletal deformities in 31.1%. Pathogenic variants identified in SOST, TGFβ1, SLC34A3, ALPL, and VCP.
- Management included teriparatide, bisphosphonates (zoledronate/alendronate), denosumab, calcium/active vitamin D, rhGH, and total parathyroidectomy in select cases.
Methodological Strengths
- 15-year disease-specific registry focusing exclusively on rare metabolic bone conditions
- Genetic confirmation of diagnoses in a subset, enabling genotype-phenotype correlations
Limitations
- Potential referral and selection bias; incomplete genetic testing across all cases
- Heterogeneous management and limited standardized outcome measures
Future Directions: Expand genetic testing coverage, implement standardized outcomes, and develop multicenter prospective cohorts to assess treatment effectiveness.