Daily Endocrinology Research Analysis
Analyzed 28 papers and selected 3 impactful papers.
Summary
A phase 2 randomized trial shows an oral small-molecule GLP-1 receptor agonist (HRS-7535) reduces albuminuria in diabetic kidney disease on top of contemporary therapy. A large single-center informatics study integrating clinical and social determinants predicts post-discharge diabetes-related ED visits with strong discrimination and temporal stability. Regional MODY sequencing in Türkiye yields actionable diagnoses that changed therapy, including insulin discontinuation in KATP-channel MODY.
Research Themes
- Diabetes therapeutics and renal protection
- Precision endocrinology and genomics
- Clinical informatics integrating social determinants of health
Selected Articles
1. Efficacy and safety of HRS-7535, an oral small-molecule GLP-1 receptor agonist, in patients with diabetic kidney disease (SOLID-DKD): a randomised, double-blind, placebo-controlled, phase 2 trial.
In 281 randomized participants with diabetic kidney disease on contemporary therapy, the oral small-molecule GLP-1RA HRS-7535 reduced albuminuria dose-dependently over 16 weeks, with the 90 mg dose achieving a placebo-corrected UACR reduction of 32% (ITT) and 38% (on-treatment). Glycated hemoglobin decreased by 1.09% and body weight by 2.95% versus placebo; safety was mainly mild-to-moderate gastrointestinal events.
Impact: This trial demonstrates clinically meaningful albuminuria reduction and metabolic benefits with an oral small-molecule GLP-1RA on top of SGLT2 inhibitors/finerenone, addressing adherence and access barriers of injectables while targeting renal risk.
Clinical Implications: If confirmed in phase 3 outcomes trials, an oral small-molecule GLP-1RA could be added to SGLT2 inhibitors and finerenone to further reduce renal risk in diabetic kidney disease. For now, clinicians should await hard-outcome data and consider trial enrollment.
Key Findings
- At week 16, placebo-corrected UACR change: −32% (95% CI −43 to −18) for 90 mg (ITT) and −38% (−49 to −24) on-treatment
- 30 mg dose showed smaller reductions: −14% (−29 to 4) ITT and −19% (−34 to −2) on-treatment
- HbA1c decreased by −1.09% (−1.32 to −0.86) and body weight by −2.95% (−3.94 to −1.95) versus placebo with 90 mg
- Safety profile consistent with GLP-1RA class; mainly mild-to-moderate GI events during dose escalation; no deaths
Methodological Strengths
- Multicentre randomized double-blind placebo-controlled design with ITT and efficacy estimands reported
- Conducted on top of contemporary background therapy (SGLT2 inhibitors and finerenone in many participants)
Limitations
- Short duration (16 weeks) using a surrogate endpoint (UACR) rather than hard renal outcomes
- Geographic limitation to China and industry funding may affect generalizability
Future Directions: Proceed to phase 3 renal outcome trials, assess long-term safety in CKD, compare with peptide GLP-1RAs, and evaluate adherence and patient-reported outcomes.
BACKGROUND: The efficacy of non-peptide small-molecule glucagon-like peptide-1 (GLP-1) receptor agonists in diabetic kidney disease remains uncertain, particularly as add-on therapy to contemporary high-intensity treatments. We assessed HRS-7535, a novel oral small-molecule GLP-1 receptor agonist, in this population. METHODS: SOLID-DKD was a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial conducted at 72 clinical sites in China. Adults with diabetic kidney disease (urinary albumin-to-creatinine ratio [UACR] 300-<3000 mg/g; estimated glomerular filtration rate [eGFR] ≥30 mL/min per 1.73 m FINDINGS: Between June 21, 2024, and March 30, 2025, 281 participants were randomised; 280 received at least one dose (30 mg: n = 93; 90 mg: n = 94; placebo: n = 93). Baseline median UACR was 763 mg/g; 181 (64.6%) participants were receiving SGLT2 inhibitors and 68 (24.3%) were receiving finerenone. At week 16, the placebo-corrected reduction in UACR with 90 mg was -32% (95% CI -43 to -18; treatment-policy estimand [intention-to-treat]) and -38% (-49 to -24; efficacy estimand [on-treatment analysis]); with 30 mg, -14% (-29 to 4) and -19% (-34 to -2), respectively. Compared with placebo, the mean difference in glycated haemoglobin with HRS-7535 90 mg was -1.09% (-1.32 to -0.86) and in body weight was -2.95% (-3.94 to -1.95). Adverse events were primarily mild-to-moderate gastrointestinal events during dose escalation. Serious adverse events occurred in 5 (5.4%), 4 (4.3%), and 2 (2.2%) participants in the 30 mg, 90 mg, and placebo groups. Discontinuation due to adverse events occurred in 3 (3.2%), 1 (1.1%), and 0 participants, respectively. No deaths occurred. INTERPRETATION: HRS-7535 dose-dependently reduced albuminuria in patients with diabetic kidney disease receiving intensive contemporary therapy, with a short-term safety profile consistent with the GLP-1 receptor agonist class, supporting its evaluation in phase 3 renal outcome trials. FUNDING: Jiangsu Hengrui Pharmaceuticals.
2. Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.
Using 178,227 encounters for development and validation, an XGBoost model integrating clinical and SDoH features predicted diabetes-related ED visits within 3 months post-discharge with AUC 0.846 and preserved performance on temporal validation (AUC 0.842). Targeting the top 20% of predicted risk captured 64.1% of ED visits, with acceptable calibration and consistent discrimination across racial groups.
Impact: This work operationalizes SDoH-informed prediction for diabetes care transitions with large-scale temporal validation and a live pilot, enabling targeted post-discharge case management.
Clinical Implications: Health systems can prioritize high-risk patients for outreach (e.g., education, medication reconciliation, social support linkage) to potentially reduce ED utilization; external validation and prospective impact evaluation are needed before broad deployment.
Key Findings
- XGBoost achieved AUC 0.846 (temporal validation AUC 0.842), with precision 0.420, sensitivity 0.296, specificity 0.972
- Top 20% risk stratum captured 64.1% of post-discharge diabetes-related ED visits
- Key predictors spanned clinical variables and SDoH (e.g., prior ED use, insulin prescriptions, age, area-level indices, home safety issues, work disability)
- Model performance was consistent across racial subgroups and showed acceptable calibration
Methodological Strengths
- Very large cohort with temporal validation and thorough calibration assessment
- Integration of both area-level and individual-level SDoH with model interpretability options (decision trees) and pilot implementation
Limitations
- Single-center retrospective design limits external generalizability; unmeasured confounding possible
- Sensitivity was modest (0.296), raising risk of missed events without careful threshold selection
Future Directions: Conduct multi-site external validation, prospective impact analyses with randomized or stepped-wedge designs, continuous model updating, and fairness audits with mitigation strategies.
OBJECTIVE: Patients hospitalized for diabetes-related conditions face elevated risks of emergency department (ED) visits post-discharge, driven by both clinical factors and social determinants of health (SDoH). This study aimed to develop and validate predictive models integrating clinical and SDoH data to identify high-risk patients for post-hospitalization diabetes case management. METHODS: We conducted a retrospective cohort study using electronic health record data from the University of Alabama at Birmingham Medical Center, including 162,063 inpatient encounters (January 2020-June 2024) for training and testing and 16,164 encounters (January-May 2025) for temporal validation. Patients were identified by diabetes-related ICD-10 codes or HbA1c ≥ 6.5 %, reflecting the scope of the institution's diabetes case management program. Predictors included demographics, diabetes-related comorbidities, surgical procedures, laboratory values, medications, and both area-level and individual-level SDoH. Logistic regression, decision trees, and XGBoost models were developed to predict diabetes-related ED visits within 3 months post-hospitalization. Hyperparameters for decision tree and XGBoost models were tuned via 10-fold cross-validation, and calibration was assessed using Brier scores and calibration plots. RESULTS: Among 162,063 hospitalizations, 6.2 % resulted in a diabetes-related ED visit. XGBoost achieved the best performance (area under the curve [AUC] 0.846, precision 0.420, sensitivity 0.296, specificity 0.972), maintained on temporal validation (AUC 0.842). Key predictors included past ED visit frequency, insulin prescriptions, age, and area-level SDoH indices. Individual-level SDoH factors, including home safety issues and work disability, also contributed to prediction. Targeting the top 20 % of predicted risk captured 64.1 % of all ED visits. Model discrimination was consistent across racial subgroups (AUC range: 0.843-0.851). Calibration was clinically acceptable across datasets. CONCLUSIONS: Integration of clinical and SDoH data achieved effective prediction of post-hospitalization ED visits. XGBoost provided excellent discrimination with temporal stability. Decision trees offered greater interpretability. A pilot implementation delivering daily risk-stratified patient lists to the diabetes case manager is underway, demonstrating a practical pathway from model development to clinical decision support.
3. Genetic spectrum and treatment implications of maturity-onset diabetes of the young in the eastern Black Sea region of Türkiye: a combined adult and pediatric cohort of 296 patients with identification of rare and novel variants.
In a retrospective cohort of 296 suspected MODY cases, targeted NGS identified pathogenic/likely pathogenic variants in 14.5%, predominantly GCK (72.1%), including a recurrent regional founder frameshift. Rare subtypes (PDX1, NEUROD1, CEL, INS, ABCC8, KCNJ11) were observed. Genetic diagnoses led to therapy changes in 14 patients, including insulin discontinuation in KATP-channel and INS-MODY.
Impact: This study demonstrates real-world diagnostic yield and direct therapeutic consequences of MODY testing, identifies a regional founder variant, and supports broader implementation of precision endocrinology.
Clinical Implications: In regions with similar phenotypes, routine MODY testing should be integrated into endocrinology workflows. GCK-MODY often requires no pharmacotherapy; KATP-channel MODY can be transitioned to sulfonylureas; cascade testing and counseling are indicated.
Key Findings
- Pathogenic/likely pathogenic variants detected in 14.5% (43/296), rising to 26.0% including VUS
- GCK-MODY comprised 72.1% of P/LP calls; a recurrent c.1256del p.(Phe419SerfsTer12) founder variant identified in nine families
- Rare MODY subtypes detected: PDX1, NEUROD1, CEL, INS, ABCC8, KCNJ11
- Genetic diagnosis changed therapy in 14 patients, including insulin discontinuation in three KATP-channel MODY and one INS-MODY
Methodological Strengths
- Consecutive regional cohort spanning adults and pediatrics with standardized ACMG/AMP variant classification
- Direct linkage of genotype to clinical treatment changes in medical records
Limitations
- Retrospective single-region design with potential selection bias
- Targeted panel limited to 14 genes; limited longitudinal outcome data after therapy changes
Future Directions: Evaluate cost-effectiveness, expand panels (including CNVs), perform cascade testing at scale, and prospectively track outcomes after genotype-guided therapy changes.
BACKGROUND: Maturity-onset diabetes of the young (MODY) is a clinically and genetically heterogeneous group of monogenic diabetes subtypes that is frequently misdiagnosed as type 1 or type 2 diabetes, and accurate genetic diagnosis enables a precision-medicine treatment approach. Regional Turkish data are limited, and previous Turkish series have been pediatric only. We characterized the genetic spectrum and therapeutic consequences of next-generation sequencing (NGS)-based MODY testing in a combined adult-and-pediatric cohort from the eastern Black Sea region of Türkiye. METHODS AND RESULTS: We retrospectively analyzed 296 consecutive patients with clinically suspected MODY referred between January 2022 and June 2025. A targeted NGS panel covering 14 MODY genes was applied, variants were classified per 2015 ACMG/AMP criteria, and treatment changes attributable to the genetic diagnosis were extracted from medical records. Pathogenic or likely pathogenic (P/LP) variants were identified in 43 of 296 patients (14.5%); diagnostic yield rose to 26.0% with variants of uncertain significance included. GCK-MODY accounted for 72.1% of P/LP findings, with a recurrent frameshift c.1256del p.(Phe419SerfsTer12) across nine apparently unrelated families consistent with a regional founder allele. Rare subtypes included MODY4 (PDX1), MODY6 (NEUROD1), MODY8 (CEL), MODY10 (INS), MODY12 (ABCC8) and MODY13 (KCNJ11). The genetic diagnosis directly modified pharmacological therapy in 14 patients, including insulin discontinuation in three KATP-channel MODY and one INS-MODY case. CONCLUSIONS: NGS-based MODY testing yields actionable findings in approximately one in seven clinically selected patients in this region and supports inclusion of MODY testing in routine endocrinology practice.