Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 9 appraisals
Current opinion in pediatrics
Artificial intelligence in pediatric endocrinology: clinical applications, governance, and future directions.
PURPOSE OF REVIEW: Artificial intelligence has transitioned from theoretical promise to practical implementation across medicine including pediatric endocrinology. This review examines artificial intelligence applications across diabetes care, obesity management, thyroid disorders, growth and puberty, and bone age assessment followed by discussion of limitations and future directions. RECENT FINDINGS: Artificial intelligence-driven tools ranging from machine learning algorithms to multimodal large language models enable personalized risk prediction, diagnostic support, and treatment optimization. Human-in-the-Loop frameworks enhance safety by integrating computational efficiency with clinician oversight. In diabetes, artificial intelligence supports glycemic forecasting, automated insulin delivery, complication screening, and digital twin modeling for therapeutic discovery. In obesity, thyroid disease, growth disorders, and bone age assessment, artificial intelligence enhances diagnostic accuracy, risk stratification, and clinical workflow efficiency. However, widespread implementation raises concerns regarding algorithmic bias, data privacy, transparency, regulatory oversight, and health equity. Addressing these limitations through explainable artificial intelligence, federated learning, and continuous performance monitoring is essential. SUMMARY: Artificial intelligence should be viewed as an augmentative tool that enhances clinical judgment in pediatric endocrinology. Preparing the next generation of pediatric endocrinologists will require intentional integration of artificial intelligence literacy into training programs, equipping clinicians to critically evaluate algorithms, interpret outputs responsibly, and participate in the ethical development of future artificial intelligence-enabled tools.
2 Aug 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Diabetes technology: an update.
Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. More than 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long-term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in glycated hemoglobin (HbA1c), time in range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data show that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin-requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.
19 June 2026
Read appraisal →European journal of pediatrics
Gut microbiota-modulating interventions in paediatric type 1 diabetes: a systematic review and meta-analysis
To evaluate the efficacy and safety of gut microbiota-modulating interventions in children and adolescents with type 1 diabetes. A systematic review and meta-analysis was conducted according to PRISMA 2020. PubMed/MEDLINE, Scopus, Web of Science, and CENTRAL were searched from inception to 28 February 2026 for randomised controlled trials of oral probiotics, prebiotics, or synbiotics in participants aged ≤ 19 years with type 1 diabetes. Primary outcomes were glycated haemoglobin, fasting blood glucose, total daily insulin dose, and C-peptide. Immune-inflammatory, microbiota-related, intestinal permeability, and safety outcomes were narratively synthesised. Twelve randomised controlled trials involving 808 paediatric participants were included. Interventions varied in formulation, dose, and duration. Pooled analyses showed significant reductions in glycated haemoglobin and fasting blood glucose, whereas no significant effects were found for total daily insulin dose or C-peptide. Immune-inflammatory, microbiota-related, and intestinal permeability outcomes suggested possible benefits, but reporting was inconsistent and quantitative pooling was not feasible. Overall, the evidence was limited by substantial clinical and methodological heterogeneity. Conclusion: Gut microbiota-modulating interventions may improve glycaemic control in children and adolescents with type 1 diabetes, particularly glycated haemoglobin and fasting blood glucose. However, current evidence is insufficient to support any specific formulation, dose, or duration, and larger well-designed paediatric trials are still needed. What is Known: • Gut microbiota-modulating interventions are biologically plausible adjunctive strategies in paediatric type 1 diabetes. • Earlier paediatric reviews included few randomised trials and reported inconsistent glycaemic findings. What is New: • This updated review included 12 randomised controlled trials involving 808 children and adolescents. • It adds a structured synthesis of immune-inflammatory, microbiota/barrier, metabolite-related and safety outcomes beyond glycaemic endpoints.
26 May 2026
Read appraisal →Postgraduate medical journal
Efficacy of tirzepatide in glycemic control and weight management in adults with type 2 diabetes: a systematic review and meta-analysis of real-world studies
A systematic review and meta-analysis of real-world studies on tirzepatide is essential to strengthen evidence of its effectiveness in improving glycated hemoglobin (HbA1c) and body weight in patients with type 2 diabetes. 13 real-world studies (N = 89 296; duration 3-18 months) with moderate to serious bias revealed a mean HbA1c reduction of 0.91% (95% confidence interval [CI]: -1.04 to -0.79), weight loss of 9.7 kg (95% CI: -14.05 to -5.35), and body mass index decrease of 2.09 kg/m2 (95% CI: -3.27 to -0.92). Overall, 64% of tirzepatide users reached HbA1c <7%. Furthermore, tirzepatide lowered HbA1c (mean difference [MD] -0.38%; 95% CI: -0.44 to -0.33) and body weight (MD -6.27 kg; 95% CI: -9.22 to -0.33) more than the control. 47%, 23%, 9%, and 4% of tirzepatide users lost ≥5%, ≥10%, ≥15%, and ≥ 20% of their baseline weight, respectively. Tirzepatide's effects on HbA1c and weight in observational studies support clinical trial findings.
22 May 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Sustained Action of Imapextide, A Glucagon-Like Peptide-1 Receptor Antagonist, in Healthy Volunteers
CONTEXT: After weight-loss surgery, some patients experience postbariatric hypoglycemia (PBH). Although PBH is customarily managed by dietary modifications with continuous glucose monitoring, advanced treatment options are needed. Imapextide is a sustained-action, selective, reversible glucagon-like peptide-1 (GLP-1) receptor antagonist designed for once-weekly administration for PBH treatment. OBJECTIVE: This work aimed to evaluate safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of single and multiple ascending doses (SAD/MAD) of imapextide in healthy volunteers (HVs). METHODS: This 3-part, phase 1, double-blind study enrolled healthy adults. In the SAD/MAD parts, randomly assigned participants (3:1) received subcutaneous imapextide (SAD, 10-200 mg; MAD, 10-30 mg once weekly, 4 doses) or placebo. The primary end point was safety/tolerability (treatment-emergent adverse events [TEAEs]). PK (SAD/MAD; time-to-maximal concentration [tmax] and half-life [t1/2]) and PD (MAD; GLP-1 and glycemic parameters during a mixed meal tolerance test [MMTT]) were assessed. Imapextide drug-drug interactions (DDIs) of OATP1B1/OATP1B3 inhibition and accelerated gastric emptying were evaluated with coproporphyrin-I, rosuvastatin, and acetaminophen. RESULTS: Overall, 69 HVs were randomly assigned (32 SAD, 23 MAD, 14 DDIs). Imapextide TEAE rates ranged from 33.3% to 50.0% (SAD), 33.3% to 100% (MAD), and 14.3% (DDIs) across cohorts. The most common TEAEs included injection site erythema (SAD, MAD) and injection site swelling (MAD). Imapextide exposure was dose proportional (median tmax 24-48 hours; mean t1/2 ∼90 hours). During the MMTT, imapextide increased GLP-1 concentrations at early time points, while effects on other glycemic parameters were variable. Minimal nonclinically relevant DDIs occurred with rosuvastatin. Imapextide slightly accelerated gastric emptying. CONCLUSION: Imapextide was generally well tolerated in HVs. PK findings supported weekly dosing. PD showed consistent effects on GLP-1 levels, supporting a potential role for imapextide in modulating PBH pathophysiology.
21 May 2026
Read appraisal →Journal of diabetes science and technology
Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring
BACKGROUND AND OBJECTIVE: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. METHODS: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. RESULTS: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. CONCLUSION: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.
9 May 2026
Read appraisal →FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Lysosomal Membrane Proteins: Key Regulators of Glucose Metabolism and Its Associated Diseases
Glucose homeostasis, which is critical for maintaining energy supply and health, involves glycogen metabolism, glycolysis, and gluconeogenesis. Lysosomal membrane proteins (LMPs) play core roles in regulating these processes. However, no focused systematic review of the topic has been reported. This review provides an in-depth analysis of the central roles of LMPs in glucose metabolism, focusing on the regulation of glucose homeostasis and their potential effects on metabolic diseases through the regulation of autophagy, signaling networks, specialized transporter functions, and other relevant mechanisms. In general, LMPs play core roles in lysosomal biosynthesis, and an in-depth study of their relationship with glucose metabolism could significantly highlight the important contribution of lysosomes in the development of related diseases.
3 May 2026
Read appraisal →BMJ open diabetes research & care
Sex differences in glycemic outcomes: a systematic review and meta-analysis of diabetes treatments
UNLABELLED: Sex differences in glycemic outcomes following insulin therapy remain underexplored despite biological and psychosocial factors that may influence individual responses. This systematic review examines sex-specific differences in glycemic control to guide personalized diabetes care and promote health equity. We searched PubMed, Scopus, Cochrane Library, and Google Scholar (August 2014-December 2025) for randomized and observational studies involving adults of both sexes on insulin. Twenty-four studies were included, with certainty of evidence assessed using GRADE. In type 1 diabetes, women showed no significant difference in achieving HbA1c <7% (RR 1.05, 95% CI 0.91 to 1.22; very low certainty) and toward higher time-in-range (SMD 0.78, -0.01 to 1.57; moderate certainty). In type 2 diabetes, men were more likely to achieve HbA1c targets (RR 0.86, 95% CI 0.72 to 1.03; low certainty), while women required higher weight-adjusted insulin doses (SMD 0.55, 0.23 to 0.86; very low certainty). Hypoglycemia risk showed opposing trends in inpatient (RR 0.78, 95% CI 0.33 to 1.83; very low certainty) versus outpatient settings (RR 1.08, 95% CI 0.61 to 1.89; low certainty) with substantial heterogeneity (I2>70%). These findings suggest that sex-related differences in glycemic outcomes vary by diabetes type and treatment context. Given the low certainty and heterogeneity of current evidence, results should be interpreted as hypothesis-generating. This review supports the consideration of biological sex within a broader, individualized diabetes management framework and highlights the need for future sex-stratified analyses with rigorous control of lifestyle and physiological factors. INTRODUCTION: Sex differences in glycemic outcomes following the same insulin therapy in diabetes remain underexplored. Numerous factors, encompassing both biological and psychosocial aspects, could potentially influence individual responses to insulin therapy. This systematic review examines sex-specific differences in glycemic control to guide personalized diabetes care and promote health equity. METHODS: PubMed, Scopus, Cochrane Library, and Google Scholar were searched (August 1, 2014-December 31, 2025) for randomized and observational studies. Eligible studies included adults of both sexes on insulin reporting sex-specific data for predefined outcomes. Data on glycated hemoglobin (HbA1c), blood glucose metrics, hypoglycemia, and insulin dose were extracted. Certainty of evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluations. RESULTS: Twenty-four studies were included. In type 1 diabetes, women showed no significant difference in achieving HbA1c <7% (risk ratio (RR) 1.05, 95% CI 0.91 to 1.22; very low certainty) and toward higher time-in-range (standardized mean difference (SMD) 0.78, -0.01 to 1.57; moderate certainty). In type 2 diabetes, men were more likely to achieve HbA1c targets (RR 0.86, 95% CI 0.72 to 1.03; low certainty), whereas women required higher weight-adjusted insulin doses (SMD 0.55, 0.23 to 0.86; very low certainty). Hypoglycemia risk showed opposing trends in inpatient (RR 0.78, 95% CI 0.33 to 1.83; very low certainty) versus outpatient settings (RR 1.08, 95% CI 0.61 to 1.89; low certainty), with substantial heterogeneity (I2 >70%). CONCLUSION: Sex-related differences in glycemic outcomes under insulin therapy were observed, with patterns varying by diabetes type, treatment context, and outcome. Given the low certainty and heterogeneity of the evidence, these findings should be interpreted as hypothesis-generating rather than directive for clinical practice. The results support consideration of biological sex as one component within a broader, individualized diabetes management framework. Future studies should prioritize sex-stratified analyses with rigorous control of polypharmacy, body composition, and lifestyle factors to determine whether sex-specific insulin strategies provide meaningful clinical benefit. PROSPERO REGISTRATION NUMBER: CRD420251144696.
25 Apr 2026
Read appraisal →Journal of diabetes investigation
A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.
BACKGROUND: Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics. METHODS: The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845). CONCLUSIONS: This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.
4 Apr 2026
Read appraisal →