Journal of Diabetes Science and Technology最新文献

{"title":"Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models.","authors":"Mandy M Shao, Agatha F Scheideman, David C Klonoff, Francisco Gude, Marcos Matabuena","doi":"10.1177/19322968261421954","DOIUrl":"10.1177/19322968261421954","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1054-1055"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12932125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Continuous Glucose Monitoring for Detecting and Understanding Early Dysglycemia in Cystic Fibrosis.","authors":"Holly Cooper, Christine L Chan","doi":"10.1177/19322968261422204","DOIUrl":"10.1177/19322968261422204","url":null,"abstract":"<p><p>Cystic fibrosis-related diabetes (CFRD) is a common extrapulmonary manifestation of cystic fibrosis (CF) affecting up to 50% of adults. Cystic fibrosis-related diabetes is associated with poorer lung function, lower nutrition, and increased mortality. Abnormal blood glucoses, especially post-prandial hyperglycemia, can precede a diagnosis of CFRD by many years, and even the prediabetic state has been associated with poorer health outcomes in people with CF (pwCF). With the advent of cystic fibrosis transconductance regulator (CFTR) modulator therapies, the clinical landscape of CF is changing. With increasing longevity, the prevalence of CFRD is anticipated to rise. Continuous glucose monitoring (CGM) technology has been applied in research and clinical settings for insights into CFRD pathophysiology, and its use for early dysglycemia detection in the CF population is increasing. However, guidance around management of these early glucose abnormalities is limited. This article aims to review and summarize the current literature on use of CGM in the prediabetic state in pwCF and to highlight ongoing areas of research need.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"684-694"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12906384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146197699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Gut Microbiome as a Predictor of Insulin Therapy Requirement in Gestational Diabetes.","authors":"Polina V Popova, Alexander A Loboda, Aleh Liaudanski, Stanislav I Sitkin, Anna D Anopova, Elena A Vasukova, Artem O Isakov, Alexandra S Tkachuk, Irina S Nemikina, Maria Akhmatova, Angelina I Eriskovskaya, Elena Y Vasilieva, Ilgiz V Galyautdinov, Alina Babenko, Soha Zgairy, Elad Rubin, Carmel Even, Sondra Turjeman, Tatiana M Pervunina, Anna A Kostareva, Aleksandra S Vatian, Viswanathan Mohan, Elena N Grineva, Omry Koren, Evgeny V Shlyakhto","doi":"10.1177/19322968261426025","DOIUrl":"10.1177/19322968261426025","url":null,"abstract":"<p><strong>Background: </strong>Gestational diabetes mellitus (GDM) often requires pharmacological intervention beyond lifestyle modification to achieve optimal glycemic control. This study aimed to develop machine learning models that integrate clinical and gut microbiome data to predict the need for insulin therapy (IT) in women with GDM.</p><p><strong>Methods: </strong>We characterized 205 pregnant women with GDM from the Genetic and Epigenetic Mechanisms of Developing Gestational Diabetes Mellitus and its Effects on the Fetus study, collecting clinical parameters, lifestyle questionnaires, self-monitored blood glucose records, and gut microbiome profiles based on 16S rRNA gene sequencing. Gradient-boosting models were trained to predict IT, basal insulin (BI), and prandial insulin (PI) requirements. Model discrimination was assessed using repeated stratified five-fold cross-validated area under the curve-receiver operating characteristic (AUC-ROC) (nested cross-validation). Feature importance and interpretability were evaluated with SHapley Additive exPlanations and permutation analyses. Differential microbial abundance was analyzed by ANCOM-BC2 (analysis of composition of microbiomes with bias correction, version 2), and metabolic pathways were predicted via PICRUSt2.</p><p><strong>Results: </strong>Women requiring insulin were older and had higher pre-pregnancy body mass index (BMI), fasting plasma glucose, 1-hour oral glucose tolerance test glucose, and glycated hemoglobin than diet-treated women (<i>P</i> < .05 for all). Adding microbiome data improved AUC-ROC for IT prediction from 0.63 (95% CI = 0.43, 0.83) to 0.70 (0.50, 0.89), BI from 0.77 (0.59, 0.95) to 0.82 (0.65, 0.99), and for PI from 0.69 (0.50, 0.88) to 0.70 (0.51, 0.89). Key influential features included glycemic markers, BMI, and microbial taxa, such as <i>Phascolarctobacterium faecium</i>, <i>Alistipes ihumii</i>, <i>Cloacibacillus evryensis</i>, <i>Ruthenibacterium lactatiformans</i>, and <i>Methanosphaera stadtmanae</i>, and the predicted microbial metabolic pathway PWY-5823.</p><p><strong>Conclusion: </strong>Our findings demonstrate that integrating gut microbiome characteristics with clinical data improves the prediction of insulin treatment needs in GDM, particularly for BI initiation.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"855-867"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12960267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147348473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using One-Shot Prompting of Non-Fine-Tuned Commercial Artificial Intelligence to Assess Nutrients from Photographs of Japanese Meals.","authors":"Yuexiang Ji, Kayo Waki, Toshimasa Yamauchi, Masaomi Nangaku, Kazuhiko Ohe","doi":"10.1177/19322968241309889","DOIUrl":"10.1177/19322968241309889","url":null,"abstract":"<p><strong>Background: </strong>Diet interventions often have poor adherence due to burdensome food logging. Approaches using photographs assessed by artificial intelligence (AI) may make food logging easier, if they are adequately accurate.</p><p><strong>Method: </strong>We used OpenAI's GPT-4o model with one-shot prompts and no fine-tuning to assess energy, fat, protein, carbohydrate, fiber, and salt through photographs of 22 meals, comparing assessments to weighed food records for each meal and to assessments of dieticians.</p><p><strong>Results: </strong>The model had poor performance overall. For fiber, though, the model achieved an intraclass correlation coefficient of 0.71 (0.67-0.74 95% CI), well above the dietician performance of 0.57.</p><p><strong>Conclusions: </strong>The simplest use of current AI via one-shot prompting and no fine-tuning accurately assesses fiber content in meals but is inaccurate for other nutritional parameters.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1015-1020"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Prospective Pilot Study Demonstrating Noninvasive Calibration-Free Glucose Measurement.","authors":"Martina Rothenbühler, Aritz Lizoain, Fabien Rebeaud, Adler Perotte, Marc Stoffel, J Hans DeVries","doi":"10.1177/19322968251313811","DOIUrl":"10.1177/19322968251313811","url":null,"abstract":"<p><strong>Background: </strong>Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy.</p><p><strong>Methods: </strong>A novel spectroscopy-based system for measuring glucose noninvasively was used in an exploratory, prospective, single-center clinical study (NCT06272136) to develop and test a machine learning-based computational model for continuous glucose monitoring without per-subject calibration. The study design blinded the development investigators to the validation analyses.</p><p><strong>Results: </strong>Twenty subjects were enrolled. Fifteen were used for the development set, and five in the validation set. All study participants were adults with insulin-treated diabetes and median glycated hemoglobin (HbA_1c) of 7.3% (interquartile range [IQR] = 6.7-7.7). The computational model resulted in a mean absolute relative difference (MARD) of 14.5% and 96.5% of the paired glucose data points in the A plus B zones of the Diabetes Technology Society (DTS) error grid. The correlation between the average model sensitivity by wavelength and the spectrum of glucose was 0.45 (<i>P</i> < .001).</p><p><strong>Conclusions: </strong>Our findings suggest that Raman spectroscopy coupled with advanced computational methods can enable continuous, noninvasive glucose measurement without per-subject invasive calibration.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"922-929"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Monogenic Diabetes Research and Clinical Care by Creating a Data Commons: The Precision Diabetes Consortium (PREDICT).","authors":"Michael E McCullough, Lisa R Letourneau-Freiberg, Rochelle N Naylor, Siri Atma W Greeley, David T Broome, Mustafa Tosur, Raymond J Kreienkamp, Erin Cobry, Neda Rasouli, Toni I Pollin, Miriam S Udler, Liana K Billings, Cyrus Desouza, Carmella Evans-Molina, Suzi Birz, Brian Furner, Michael Watkins, Kaitlyn Ott, Samuel L Volchenboum, Louis H Philipson","doi":"10.1177/19322968241310896","DOIUrl":"10.1177/19322968241310896","url":null,"abstract":"<p><p>Monogenic diabetes mellitus (MDM) is a group of relatively rare disorders caused by pathogenic variants in key genes that result in hyperglycemia. Lack of identified cases, along with absent data standards, and limited collaboration across institutions have hindered research progress. To address this, the UChicago Monogenic Diabetes Registry (UCMDMR) and UChicago Data for the Common Good (D4CG) created a national consortium of MDM research institutions called the PREcision DIabetes ConsorTium (PREDICT). Following the D4CG model, PREDICT has successfully established a multicenter MDM data commons. PREDICT has created a consensus data dictionary that will be utilized to address critical gaps in understanding of these rare types of diabetes. This approach may be useful for other rare conditions that would benefit from access to harmonized pooled data.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1034-1040"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11713946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142949717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Glucose Monitoring-Derived Glucose Metrics Over Time in Physically Active Adults Without Diabetes Using a Commercial Continuous Glucose Monitoring Application.","authors":"Kristina Skroce, Andrea Zignoli, Lauren V Turner, David J Lipman, Michael C Riddell, Howard C Zisser","doi":"10.1177/19322968261426376","DOIUrl":"10.1177/19322968261426376","url":null,"abstract":"<p><strong>Background: </strong>To describe changes in continuous glucose monitoring (CGM)-derived glucose metrics of healthy and physically active participants with mild dysglycemia at baseline (>5% time with glucose levels outside of 70-140 mg/dL) who wore a real-time CGM device (GSB, Glucose Sport Biosensor) integrated with a smartphone mobile application over an eight-week period (four GSB wear periods).</p><p><strong>Methods: </strong>Two hundred twenty-five participants (51 females and 174 males) aged 45.0 ± 10.1 years with body mass index 23.4 ± 3.9 kg/m² with suboptimal time in tight range (TITR) (ie, <95%) wore a GSB for approximately eight weeks. Linear mixed-effects models (LMEMs) were used to compare the cumulative time in different glycemic zones (% time below range [TBR, <70 mg/dL]; % TITR [70-140 mg/dL]; % time above range [TAR, >140 mg/dL]) with respect to each GSB wear time.</p><p><strong>Results: </strong>Linear-mixed effects model analysis returned significant effects of sensor on TITR and TBR across four wear periods (both <i>P</i> < .001), with inter-individual variability in baseline values and response slopes. Each day of sensor wear increased TITR by 0.59% (95% confidence interval [CI]: 0.50, 0.69, <i>P</i> < 0.001) and reduced TBR (-0.42 %, 95% CI: -0.50, -0.35, <i>P</i> <.001) and TAR (-0.17 %, 95% CI: -0.24, -0.10, <i>P</i> < .001), with small sensor-dependent differences in daily improvements.</p><p><strong>Conclusions: </strong>These findings indicate both cumulative and day-to-day gains in glucose control with repeated sensor use for individuals with a TITR <95%. Indeed, CGM use coincided with short-term improvements in glucose metrics. Future studies should directly measure lifestyle behaviors to determine which factors may contribute to improvements in glycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"650-658"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12956617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147344462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The DERMIS Study: Methodologies, Results, and Implications for the Future.","authors":"Irl B Hirsch, Dorrine Khakpour, Jeffrey Joseph, Michi M Shinohara, Ruikang K Wang, Ulrike Klueh, Donald Kreutzner, Jean-Pierre Riveline, Pauline Jacquemier, Lisa Maier, Michael T Longaker, Christopher G Parkin, Thomas Pieber, Andrea Kalus","doi":"10.1177/19322968241298005","DOIUrl":"10.1177/19322968241298005","url":null,"abstract":"<p><p>Ongoing innovation in diabetes technologies has led to the development of advanced tools such as automated insulin delivery (AID) systems that adjust insulin delivery in response to current and predicted glucose levels, residual insulin action, and other inputs (eg, meal and exercise announcements). However, infusion sets continue to be the \"Achilles heel\" of accurate and precise insulin delivery and continued device use. A recent study by Kalus et al (DERMIS Study) revealed higher vessel density and signals of inflammation by optical coherence tomography (OCT), in addition to increased inflammation, fat necrosis, fibrosis, and eosinophilic infiltration by histopathology. Although the study provided a comprehensive description of what was happening, the results raise important questions that require additional research. On February 29, 2024, the Leona M. and Harry B. Helmsley Charitable Trust sponsored a conference to begin addressing these issues. This article summarizes the DERMIS study findings and testing methodologies discussed at the conference and proposes the next steps for developing insulin infusion sets that reduce the variability in insulin delivery and extend wear.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"708-720"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Calculator for <i>HNF1A-</i> and <i>HNF4A-MODY</i> in Asian Indians.","authors":"Viswanathan Mohan, Ulagamadesan Venkatesan, Anandakumar Amutha, Ramasamy Aarthy, Venkatesan Radha, Arunkumar Pande, Ranjit Mohan Anjana, Ranjit Unnikrishnan","doi":"10.1177/19322968261429944","DOIUrl":"10.1177/19322968261429944","url":null,"abstract":"<p><strong>Aim: </strong>We aimed to develop a calculator to determine the probability of having <i>HNF1A-MODY</i> (hepatocyte nuclear factor 1 alpha-maturity-onset diabetes of the young) <i>or HNF4A</i> (hepatocyte nuclear factor 4 alpha)<i>-MODY</i> (the commonest forms of MODY) in Asian Indians using clinical and biochemical criteria.</p><p><strong>Methods: </strong>We extracted data on individuals with young-onset diabetes aged <30 years (<i>n</i> = 29 191) from electronic records. Genetically confirmed <i>HNF1A- and HNF4A-MODY</i> (<i>n</i> = 55) were selected along with 1000 individuals each of type 1 diabetes (T1D) and type 2 diabetes (T2D). These data sets were used to develop a classification model using logistic regression. The model's performance was evaluated using receiver operating characteristic (ROC) curves in an internal data set and validated in an external data set.</p><p><strong>Results: </strong>Eight predictive models were constructed, beginning with a basic model that included variables, such as age at diagnosis, body mass index (BMI), parental history, and glycated hemoglobin (HbA1c) (models 1 and 5). High-density lipoprotein (HDL) cholesterol was added in models 2 and 6, stimulated C-peptide in models 3 and 7, and all predictors were combined in models 4 and 8. Models 1 to 4, designed to distinguish MODY from T1D, achieved an ROC-area under the curve (AUC) value ranging from 0.884 to 0.957, while models 5 to 8, aimed at differentiating MODY from T2D, achieved an ROC-AUC value ranging from 0.914 to 0.936. All models demonstrated excellent performance in internal validation, with high five-fold cross-validation <i>c</i>-statistics. An online calculator using these models estimates MODY probability that is accessible at https://mdrf-t1d-calculator.shinyapps.io/MODY/.</p><p><strong>Conclusion: </strong>We developed an ethnicity-specific calculator to help identify individuals with possible <i>HNF1A-MODY or HNF4A-MODY</i> in Asian Indians. This user-friendly, web-based tool would be helpful to select candidates for genetic testing in this population.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"751-760"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147504105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Glucose Monitoring-Derived Glycemic Profiles in Adolescents With Obesity Without Diabetes.","authors":"Alaina P Vidmar, My H Vu, Jomanah A Bakhsh, David S Black","doi":"10.1177/19322968261427488","DOIUrl":"10.1177/19322968261427488","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) is increasingly used in individuals without diabetes. This study aimed to characterize CGM metrics in youth with obesity without diabetes and examined variation by sex, age, excess percent of the 95th percentile for body mass index (%BMI_p95), physical activity, and clinical biochemical fasting samples.</p><p><strong>Methods: </strong>The analytic sample comprised youth aged 12 to 18 years with obesity, defined as BMI > 95th percentile for age and sex, without diabetes or obesity-related complications. Participants were recruited for a 24-week behavioral weight loss intervention. Prior to intervention, participants wore an unmasked CGM for up to five days under free-living conditions. Glycemic metrics were calculated using the <i>iglu</i> R package and summarized as median (interquartile range [IQR]). Associations with sex, age, %BMI_p95, self-reported physical activity (PROMIS [Patient-Reported Outcomes Measurement Information System] Physical Activity questionnaire), and fasting samples were examined using Wilcoxon rank-sum tests, Spearman's correlations, and quantile regression.</p><p><strong>Results: </strong>Fifty-four youth (median age = 16 years [14-18]; BMI = 39.8 kg/m² [36.0-45.4]; 54% female; 85% Hispanic/Latino) wore CGM devices for up to five days. Median mean glucose was 114.8 mg/dL (107.0-128.3), estimated hemoglobin A1c (eHbA1c) was 5.6% (5.4-6.1), and glucose management indicator (GMI) 6.1 (5.9-6.4). Time above range 140 mg/dL was 10.1% (4.1-24.4). While no significant sex differences were observed for mean glucose, eHbA1c, or GMI, fasting plasma glucose was positively correlated with mean CGM glucose (Spearman's ρ = 0.36, <i>P</i> = .01), and baseline hemoglobin A1c (HbA1c) was positively associated with mean CGM glucose, with each +1% in HbA1c corresponding to +19 mg/dL higher mean CGM glucose (<i>P</i> = .01). Quantile regression revealed significant associations between %BMI_p95 and coefficient of variation when adjusting for sex, age, and PROMIS score (<i>P</i> = .01).</p><p><strong>Conclusion: </strong>Among youth with obesity without diabetes, CGM revealed modest BMI-related glycemic variability and no sex differences, providing normative CGM data for this population.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"639-649"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147608970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}