Diabetes最新文献

{"title":"2117-P: Using a Simple Meal Bolus Strategy with Adolescents on Automated Insulin Delivery Systems","authors":"ALEXANDRA SAWYER, AMY B. STEIN, LINDSEY TOWERS, SAMANTHA LANGE, CARI BERGET, GREGORY FORLENZA","doi":"10.2337/db26-2117-p","DOIUrl":"https://doi.org/10.2337/db26-2117-p","url":null,"abstract":"Introduction and Objective: Our randomized crossover trial in adolescents/young adults with T1D on automated insulin delivery (AID) showed a simple bolus approach (simple; entering 30/60/90g) achieved non-inferior time-in-range (TIR) vs carbohydrate counting (CC). We describe bolus behaviors and compare self-reported burden and related outcomes. Methods: Participants (age 14-26) using AID completed 4 weeks of simple and 4 weeks of CC. Surveys were collected at baseline and after each study period. Results were summarized as means; higher scores indicated worse outcomes (frequency, burden, worry, difficulty, or negative impact). Survey items were compared using a Friedman test. Results: Thirty-one participants (17.4 years; 51.6% female; T1D duration 8.3 years; baseline TIR 63.8%) delivered 4.6 user-initiated boluses/day at baseline, 4.5 during simple, and 5.1 during CC. Baseline bolus habits included educated guessing (90.3%), entering similar values each meal (6.5%), or using labels/resources to CC (3.23%). After the trial, 40.6% preferred simple, 31.3% CC, and 28.1% baseline habits. Meal bolusing felt significantly less burdensome during simple (baseline 2.0, simple 1.6, CC 2.4; p = 0.0007). Need for compensatory behaviors (extra eating or correction insulin) did not differ. Conclusion: A simple meal bolus strategy can reduce perceived burden for some adolescents and young adults on AID without an impact on glycemic outcomes, supporting its use as a pragmatic alternative to CC. Disclosure A. Sawyer: None. A.B. Stein: None. L. Towers: None. S. Lange: None. C. Berget: Consultant; Current; Insulet Corporation. Consultant; Ended; Medtronic. Advisory Panel; Current; Tandem Diabetes Care, Inc. Consultant; Current; Tandem Diabetes Care, Inc. Advisory Panel; Current; Sequel. G. Forlenza: Other - Research Support, Speaker, Ad Board Member; Current; Medtronic. Other - Research Support, Speaker, Consultant; Current; Dexcom, Inc. Research Support; Current; Abbott. Other - Research Support, Speaker, Consultant; Current; Insulet Corporation, Tandem Diabetes Care, Inc. Research Support; Current; MannKind Corporation. Other - Research Support, Consultant; Current; Sequel. Funding NIDDK (T32: 2T32DK063687)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"157 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2095-P: Maternal Sociodemographic, Prepregnancy, and Pregnancy Factors Associated with Type 2 Diabetes in Adolescents and Young Adults: The PAPAYA Study","authors":"SAHER DAREDIA, SARA AGHAEE, JULIA ACKER, JENNIFER Y. LIU, CINDY J. HUANG, ANDREW KARTER, AI KUBO, JULIANNA DEARDORFF","doi":"10.2337/db26-2095-p","DOIUrl":"https://doi.org/10.2337/db26-2095-p","url":null,"abstract":"Introduction and Objective: Youth type 2 diabetes (T2D) rates have risen globally. Preconception and pregnancy are sensitive periods for later T2D risk, yet contributing exposures remain unclear. We examined associations between maternal and pregnancy factors and early-onset T2D in the Predictors of Adverse Cardiometabolic Problems in Adolescents and Young Adults (PAPAYA) study at Kaiser Permanente Northern California (KPNC). Methods: This retrospective cohort included 303,037 individuals born at KPNC in 2003-2011. Maternal (education, age, parity, Medicaid status, pre-pregnancy BMI and diabetes) and pregnancy (gestational diabetes, preeclampsia, preterm birth, delivery mode) factors were obtained from electronic health records and birth certificates. Early-onset T2D (ages 10-21; N=453) was identified via the KPNC Diabetes Registry. Cox proportional hazards models estimated hazard ratios adjusting for confounders. Results: The cohort was 48.8% female and racially diverse (37.7% White, 25.5% Hispanic, 19.6% Asian/Pacific Islander, 7.0% Black, 0.3% Native American, 9.9% Other). Associations were significant in all models except preterm birth and delivery mode, with strongest effect estimates for pre-pregnancy BMI and diabetes (see Table). Conclusion: Several maternal sociodemographic, pre-pregnancy, and pregnancy factors were associated with early-onset T2D, providing targets for early-life interventions. Disclosure S. Daredia: None. S. Aghaee: None. J. Acker: None. J.Y. Liu: None. C.J. Huang: None. A. Karter: Research Support; Current; Dexcom, Inc. A. Kubo: None. J. Deardorff: None. Funding National Heart Lung and Blood Institute (1R01HL177516-01); National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK139116-01A1)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"20 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2059-P: Provider Data Quality in Medicare Advantage vs. Fee-for-Service among Adults Newly Diagnosed with Type 2 Diabetes","authors":"MANDANA REZAEIAHARI, JOSEPH HENSKE, BRADLEY C. MARTIN, KAYO FUJIMOTO, MOIZ BHAI, MARK L. WILLIAMS","doi":"10.2337/db26-2059-p","DOIUrl":"https://doi.org/10.2337/db26-2059-p","url":null,"abstract":"Introduction and Objective: Incomplete provider data in Medicare Advantage (MA) claims can reduce provider attribution accuracy, which is important for network analyses and population health efforts. This study examines the extent and patterns of missing provider information in MA claims compared with Medicare Fee-for-Service (FFS) among beneficiaries with diabetes. Methods: Using the Arkansas All-Payer Claims Database (APCD), we identified beneficiaries newly diagnosed with type 2 diabetes and extracted all medical claims from two months before through one year after diagnosis for MA and FFS populations (2015-2019). Claims were classified using Restructured BETOS classification system, which group HCPCS codes into clinically meaningful categories. Results: For MA beneficiaries (n=8,334), we identified 116,256 outpatient E/M claims, of which 3,394 (2.9%) had missing NPIs. Using provider name and ZIP code available in the APCD provider file, 634 (19%) could be matched to identify an NPI; most others lacked provider and organization names. After NPI assignment, 915 claims lacked specialty codes, and 88% were resolved using taxonomy and ZIP linkage. For the Medicare FFS cohort (n=17,425), we identified 66,011 outpatient E/M claims and 272,068 carrier E/M claims. Attending NPIs and performing physician NPIs were available for 100% of outpatient claims and carrier claims, respectively. Unlike MA, 100% of FFS claims had individual-level NPIs, while MA claims were 83% individual and 17% organization NPIs. Conclusion: Though the vast majority of MA claims have NPIs or provider information to identify NPIs, these claims are not as complete as FFS claims. These data quality issues affect provider attribution accuracy for diabetes care and highlight the need for improved provider data reporting standards within MA plans. Disclosure M. Rezaeiahari: None. J. Henske: None. B.C. Martin: Other - I receive royalties from Trestle Tree LLC for the commercialization of an opioid risk prediction too which is unrelated to this investigation.; Current; Trestle Tree LLC. K. Fujimoto: None. M. Bhai: None. M.L. Williams: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases of the National Institute of Health [K25DK136966], Access to the Arkansas All-Payer Claims Database for this study was provided by the support from the Arkansas Biosciences Institute/Arkansas Insurance Department/Arkansas Healthcare Transparency Initiative Collaboration.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"21 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2090-P: High Day-to-Day Glycemic Variability in Youth-Onset Type 2 Diabetes","authors":"NICOLE RANGOS, ILA N. KACKER, MADILYN M. GAYDOS, SAMSON L. CANTOR, NATHAN GRANT SALA, NATALIE A. MACHERET, LILIAN MABUNDO, NOEMI MALANDRINO, DORIS E. ESTRADA, STEPHANIE CHUNG","doi":"10.2337/db26-2090-p","DOIUrl":"https://doi.org/10.2337/db26-2090-p","url":null,"abstract":"Introduction and Objective: Free-living glycemic variability (GV), measured by continuous glucose monitors (CGM), is an independent risk factor for cardiovascular disease (CVD) in adults with diabetes, but GV data in youth-onset type 2 diabetes (Y-T2D) is scarce. To identify CVD risk factors in Y-T2D, we characterized GV in Y-T2D. Methods: This was a pooled cohort secondary analysis in Y-T2D enrolled in clinical trials between 2021-2025. GV within a day was measured by glucose standard deviation (SD) and GV between days was measured with mean of daily differences (MODD) using Dexcom CGM over 7-10 days. The relationship with contemporaneous HbA1c was assessed with Spearman’s correlation and linear regression, adjusted for age and BMI. Results: In 52 Y-T2D participants (age 17.0±4.4y, 33% male; 81% Black; 14% Hispanic/Latino; BMI 38.7±10.1 kg/m2, mean±SD), 10 were on basal insulin and 26 were on a glucagon-like peptide-1 receptor agonist. Median HbA1c was 6.3% (5.5, 7.6%) (25th, 75th percentile). Average CGM wear time was 8.7±1.6 days. SD and MODD were elevated at 27 (18, 41 mg/dL) and 25 (16, 37 mg/dL), respectfully. GV correlated with HbA1c and a 1% increase in HbA1c increased GV by ~3 mg/dL (Figure 1). Only insulin therapy was associated with higher HbA1c (10.8 vs. 6%, P<0.01), SD (46 vs. 24 mg/dl, P<0.01), and MODD (46 vs. 22 mg/dl, P<0.01). Conclusion: GV was high in Y-T2D, implying increased risk for CVD beyond HbA1c. Future studies examining CVD risk in Y-T2D should consider both GV and HbA1c. Disclosure N. Rangos: None. I.N. Kacker: None. M.M. Gaydos: None. S.L. Cantor: None. N. Sala: None. N.A. Macheret: None. L. Mabundo: None. N. Malandrino: None. D.E. Estrada: None. S. Chung: None. Funding NIDDK, NIH Z99 DK 999999","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"137 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2058-P: Integrating Sleep Health into Cardiometabolic Care","authors":"NAZLI PARAST, JAMES KIM, AKSHAY JAIN, W. JEROME ALONSO","doi":"10.2337/db26-2058-p","DOIUrl":"https://doi.org/10.2337/db26-2058-p","url":null,"abstract":"Introduction and Objective: Background: Insomnia is highly prevalent and contributes to cardiometabolic disease through adverse effects on glucose metabolism, insulin resistance, sympathetic activation, cortisol elevation, and inflammation. These mechanisms are increasingly recognized as clinically relevant to diabetes risk and glycemic instability. Despite this, sleep disturbances remain underassessed in diabetes and obesity care. Methods: A two-phase study was conducted. Phase 1 assessed sleep concerns in patients with diabetes and/or obesity attending specialist endocrine/metabolic clinics. Sixty-nine participants completed structured assessments evaluating sleep satisfaction, insomnia symptoms, and functional impact. Phase 2 focused on knowledge translation to primary care networks, including dissemination of findings and provision of educational materials to support insomnia screening and management. Results: Only 46% of patients reported prior sleep assessment by their primary care provider. Twenty-three percent preferred discussing sleep concerns with their provider, and 9% required and agreed to pharmacologic treatment. Approximately 70% stated that being asked about sleep increased their understanding of its importance for metabolic and glycemic health. Most participants were satisfied or very satisfied with sleep assessment in the specialist setting. Conclusion: Insomnia is prevalent, underassessed, and clinically meaningful in patients with diabetes and obesity. The physiologic effects of insomnia—including increased insulin resistance and impaired glucose metabolism—underscore its importance as a modifiable component of cardiometabolic care. Feasibility data from specialty clinics and positive patient engagement support integrating structured sleep assessment into routine practice. Knowledge translation to primary care providers may enhance early identification and management of insomnia, with potential benefits for glycemic outcomes and overall cardiometabolic risk reduction. Disclosure N. Parast: None. J. Kim: Advisory Panel; Current; Abbott Diabetes. Speaker's Bureau; Current; Abbott Diabetes. Advisory Panel; Current; AbbVie Inc. Speaker's Bureau; Current; AbbVie Inc. Research Support; Current; AbbVie Inc. Speaker's Bureau; Current; Bayer AG. Advisory Panel; Current; Bayer AG. Research Support; Current; Bayer AG. Advisory Panel; Current; Boehringer Ingelheim International GmbH. Speaker's Bureau; Current; Boehringer Ingelheim International GmbH. Advisory Panel; Current; Eisai Inc. Speaker's Bureau; Current; Eisai Inc. Research Support; Current; Eisai Inc. Advisory Panel; Current; Eli Lilly and Company, embecta. Speaker's Bureau; Current; embecta. Advisory Panel; Current; GlaxoSmithKline plc. Speaker's Bureau; Current; GlaxoSmithKline plc. Advisory Panel; Current; Lundbeck. Speaker's Bureau; Current; Lundbeck. Research Support; Current; Idorsia Pharmaceuticals Ltd. Advisory Panel; Current; Novo Nordisk Canada Inc. Speaker's Bureau; C","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"254 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2055-P: Characterizing Diabetes Technology Access Interruptions in an Ambulatory Endocrinology Practice","authors":"THOMAS KNOERL, MERCEDES FALCIGLIA, ANDREW WELCH","doi":"10.2337/db26-2055-p","DOIUrl":"https://doi.org/10.2337/db26-2055-p","url":null,"abstract":"Introduction and Objective: Prior studies show that interruptions in access to prescribed diabetes technology supplies (continuous glucose monitors [CGM] and insulin pumps) are associated with adverse clinical outcomes. We aimed to determine the frequency, duration, and causes of interruptions in prescribed diabetes technology supplies experienced by adults with diabetes in an ambulatory endocrinology practice. Methods: An online questionnaire was distributed to adults with diabetes prior to their appointments between November 2024 to December 2025. Patients were asked “How many times have you not had access to your prescribed CGM (and/or insulin pump) in the past 3 months?” and “How many days on average did these interruptions in access last?”. The total days of diabetes technology access interruptions were calculated by multiplying the number of interruptions by the interruption duration. Results: There were 357 responses with mean age of 50±18 years, 58% female, 87% White, 71% publicly insured, 54% type 1 diabetes, and mean A1c of 7.2±1.3%. Of 340 CGM users, 43% reported any CGM access interruption over the prior 3 months, with a median (IQR) of 5 (2-16) days and mean of 13±18 days of CGM interruption, and 17% reporting ≥30 days of interruption. Of 166 pump users, 14% reported any pump access interruption over the prior 3 months, with a median of 3 (2-23) days and mean of 17±27 days of pump interruption, and 26% reporting ≥30 days of interruption. The most common reasons for CGM interruptions were early supply failure and supply company delay reported by 55% and 16%, respectively. Conclusion: Interruptions in access to prescribed diabetes technology supplies were common with a significant subset experiencing extended interruptions. Early supply failure and vendor delays were the main causes for CGM interruptions, indicating areas for future intervention. Additional real-world studies of diabetes technology performance are needed to elucidate barriers to sustained access and ultimately reduce supply interruptions. Disclosure T. Knoerl: None. M. Falciglia: None. A. Welch: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"32 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2075-P: Characterizing Total, Regional, and Ectopic Adipose Tissue Fat Content and Diabetes Risk Factors among Youth, Using Lancet Commission Obesity Definitions","authors":"MACY L. SHEPARD, ARMANDO PENA","doi":"10.2337/db26-2075-p","DOIUrl":"https://doi.org/10.2337/db26-2075-p","url":null,"abstract":"Introduction and Objective: The field has yet to characterize cardiometabolic disease risk profiles in youth using new obesity definitions set by The Lancet Diabetes & Endocrinology Commission. We compared total, regional, and ectopic adipose tissue fat content & diabetes risk factors across obesity phenotypes in youth with BMI≥95th%. Methods: We used baseline data from the Preventing Diabetes in Latino Youth study that enrolled 117 Latino youth (12-16 y) with obesity (BMI≥95th%). Guided by the Commission’s definitions, we defined Clinical Obesity (CO) as excess body fat (boys>36%, girls>42%) measured by DXA with ≥3 metabolic risk factors; Preclinical Obesity (PO) as excess body fat with 0-2 risk factors; & No Obesity (NO) as having no excess body fat. Metabolic risk factors included glucose intolerance (fasting glucose 100-125 mg/dL, 2hr glucose 140-200 mg/dL, or HbA1c 5.7-6.4%), TRG≥110 mg/dL, HDL<40 mg/dL, & SBP or DBP>90th%. We compared total fat mass (FM) & lean mass (LM) using DXA; regional adipose tissue (subcutaneous abdominal [SAAT], visceral [VAT]) & ectopic fat depots (hepatic [HFF] & pancreatic fat fraction [PFF]) using MRI; insulin dynamics (HOMA-IR, Matsuda Index, insulin AUC, oral disposition index [oDI]); glucose regulation (1hr glucose, glucose AUC); and serum adiponectin [AD]. One-way ANOVA was analyzed (α = .05; Bonferroni post-hoc corrections). Results: The prevalence of CO, PO, and NO was 40% (N=47), 41% (N=48), & 19% NO (N=22), respectively. CO & PO showed significantly higher BMI%, waist circumference, FM, BF%, and SAAT (all p<0.05) but not VAT, HFF, or PFF compared to NO. CO demonstrated higher HOMA-IR and insulin AUC compared to PO & NO. CO had lower AD and oDI and higher LM, 1hr glucose, and fasting insulin versus NO. Conclusion: CO is distinguished from PO & NO by higher insulin resistance, beta cell dysfunction, and LM. CO and PO were distinguished by total and regional adiposity. New obesity definitions identify youth with greater cardiometabolic disease risk using sub-clinical markers. Disclosure M.L. Shepard: None. A. Pena: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"34 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2063-P: Implementation of HbA1c Point-of-Care Testing in Primary Care Settings","authors":"RASHA KHATIB, JOHN BRILL, MARTA CLENDENIN, KENNETH KUPFER, QUAN ZOU, NICOLE GLOWACKI, RAMONA DONOVAN, KYLE FORTNER, NASEER AHMED","doi":"10.2337/db26-2063-p","DOIUrl":"https://doi.org/10.2337/db26-2063-p","url":null,"abstract":"Introduction and Objective: Point-of-care testing (POCT) for hemoglobin A1c (HbA1c) can improve testing frequency and glycemic control. This study evaluated test frequency, change in HbA1c, and diabetes therapy changes among patients with poorly controlled diabetes after introducing HbA1c POCT in primary care settings. Methods: HbA1c POCT was introduced at 6 primary care clinics. Patients with Type 2 diabetes with ≥1 HbA1c test value ≥8.0% within 13 months and no medical conditions affecting red blood cell turnover were eligible for POCT testing during routine clinic visits. The Test Cohort consisted of patients with ≥1 POCT test during the study period. The Control Cohort was selected from sites not participating in the study using propensity score matching to the Test Cohort. Outcomes were compared 13 months pre- and post-implementation of POCT and to the Control Cohort. Logistic regression was used to evaluate the association between test frequency and HbA1c reduction. The time from HbA1c tests to changes in diabetes therapy was calculated. Results: The Test Cohort consisted of N=943 subjects and N=3,003 in the Control Cohort. The number of HbA1c tests per subject in the Test Cohort increased from 2.38 to 2.65 post-implementation of POCT (p=0.002), vs. no change in the Control Cohort (2.11 and 2.16 respectively). There was no significant difference in mean HbA1c reduction between the two Cohorts. In both cohorts, more frequent testing was associated with clinically significant HbA1c reduction (≥0.4%). HbA1c tests >120 days apart were associated with increased HbA1c in both cohorts. In the Test Cohort, 34.8% of intervals between HbA1c tests were >120 days vs. 47.9% in the Control Cohort (p<0.0001). The median time to therapy change was within 1 day of POCT, vs. 7 days for a laboratory HbA1c test. Use of POCT was associated with a higher rate of therapy changes (hazard ratio=1.71, 95% confidence interval=1.53, 1.91). Conclusion: POCT improves HbA1c testing frequency and diabetes therapy changes in primary care settings. More frequent HbA1c testing is associated with lower HbA1c levels. Disclosure R. Khatib: None. J. Brill: None. M. Clendenin: Employee; Current; Abbott. K. Kupfer: Employee; Current; Abbott. Q. Zou: Employee; Current; Abbott Diagnostics. N. Glowacki: None. R. Donovan: None. K. Fortner: Employee; Current; Abbott Diagnostics. N. Ahmed: Employee; Ended; Abbott Diagnostics. Employee; Current; Janssen Research & Development, LLC.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"9 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2061-P: Glycemic Outcomes and Patient Satisfaction with Virtual Insulin Pump Training in Type 1 Diabetes: Karaz Academy Data across Five Regions of Saudi Arabia","authors":"MOHAMMED E. AL-SOFIANI, MAHA ALSUFIANI, YAZEED K. ALMUTAIRI, SAHAR K. ALHARTHI, LAMA ALHUSSAIN, ALWALEED ALASKAR, AMIRAH L. ALSAKHRI, FAHAD B. ALBEDAIWI, RABIA K. ALDURAIBI, HAILA S. ALSHELOWI, SULIMAN ALBALWI, ERFAN ALLABABIDI, SAUD ALBALAWI","doi":"10.2337/db26-2061-p","DOIUrl":"https://doi.org/10.2337/db26-2061-p","url":null,"abstract":"Introduction and Objective: Access to automated insulin delivery (AID) systems in remote areas is limited by shortage of trained specialists. We evaluated glucose levels, insulin delivery patterns, user behaviors, and training-related patient satisfaction over 6 months among PWT1D who attended Karaz virtual training program, the first national, virtually delivered, structured insulin pump training program designed to onboard patients onto AID therapy across Saudi Arabia (SA) Methods: We retrospectively analyzed pump/CGM data from the first 98 Karaz app users from five regions of SA who completed the Karaz virtual pump training in 2025, initiated AID therapy, and had at least 3 months of available pump/CGM data. Data were assessed at week 1 and months 1, 3, and 6. Logistic regression was used to identify early CGM predictors of achieving the “double target” of TIR>70% and TBR<4% by month 3. Patient satisfaction was assessed using a post-training survey Results: Participants had a mean age of 19 years and 56% were female. From week 1 to month 6, CGM metrics improved as follows: TIR: 62 to 75%; TAR>250: 12 to 5%; TAR>180: 23 to 18% (all p<0.05); and GRI: 45 to 27 (p>0.05). AutoMode utilization increased from 15% at week 1 to 97% at month 6 (p<0.05). Having TIR>70%, GRI<20, and GRI 21-40 at month 1 were significant predictors of achieving the double target by month 3 after adjustment for age, sex, and pump setting (ORs [95% CI]: 10.15 [3.13-32.91]; 56.01 [5.96-526.16]; and 41.87 [7.45-235.33]; respectively). Overall, 86.6% of participants reported being extremely satisfied with the duration of the training; and 87.5% stated they would recommend the program to others Conclusion: Initiation of AID therapy through a national virtual insulin pump training program was associated with sustained improvements in CGM outcomes and high autoMode use and patient satisfaction. Our findings support virtual pump training as an effective strategy to expand equitable access to AID therapy in regions with limited availability of pump specialists Disclosure M.E. Al-Sofiani: Speaker's Bureau; Ended; Medtronic, Dexcom, Inc. Research Support; Current; Dexcom, Inc. Research Support; Ended; Medtronic. Speaker's Bureau; Ended; Insulet Corporation, Abbott Diabetes, Sanofi. M. Alsufiani: None. Y.K. Almutairi: None. S.K. Alharthi: None. L. Alhussain: None. A. Alaskar: None. A.L. Alsakhri: None. F.B. Albedaiwi: None. R.K. Alduraibi: None. H.S. Alshelowi: None. S. Albalwi: None. E. Allababidi: None. S. Albalawi: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"21 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2052-P: Mapping Urban Diabetes: Identifying Priority Areas for Prevention within the Cities for Better Health Initiative","authors":"STEFANO MASSARINI, MARIA BIANCHI, GIUSEPPE CATANOSO, PAOLO BULGHERONI, ANNA FERRULLI, OLGA EUGENIA DISOTEO, MICHELE CARRUBA, FEDERICO SERRA, ANDREA LENZI, LIVIO LUZI","doi":"10.2337/db26-2052-p","DOIUrl":"https://doi.org/10.2337/db26-2052-p","url":null,"abstract":"Introduction and Objective: Cities for Better Health is an international public-private partnership promoting urban health through preventive strategies. Urban environments influence lifestyle behaviors and obesogenic exposures, contributing to the rising burden of type 2 diabetes mellitus (DM). Como joined the initiative to examine local determinants of DM. This study assessed DM prevalence in the Como area and its ecological associations with demographic, socioeconomic, and clinical factors. Methods: We conducted an ecological analysis using ATS Insubria data from 7 districts and 132 municipalities. Diabetes prevalence was calculated, and linear regression assessed associations with cardiovascular diseases, nephropathy, aging, and structural dependency, university education, mortality, unemployment, and altitude. Results: Mean DM prevalence across districts was 7.61% (range 7.34-8.22%). At the district level, prevalence showed a very strong positive association with cardiovascular diseases (r=0.9774, p<0.001), while correlations with other variables were not significant. Across 132 municipalities, DM prevalence was positively associated with cardiovascular diseases, aging, and structural dependency, mortality, and altitude (all p<0.05), and inversely associated with university education (p<0.001). No significant associations were found with nephropathy or unemployment, indicating that demographic, socioeconomic, and territorial factors, rather than renal comorbidities or employment status, may better explain variations in DM prevalence at the local level. Conclusion: Urban DM prevalence is associated with cardiometabolic burden, population aging, socioeconomic vulnerability, and territorial characteristics. The observed association with altitude suggests a role for geographical and environmental factors in DM risk. These findings support integrated, place-based prevention strategies aligned with the Cities for Better Health framework to promote health equity. Disclosure S. Massarini: None. M. Bianchi: None. G. Catanoso: None. P. Bulgheroni: None. A. Ferrulli: None. O. Disoteo: None. M. Carruba: None. F. Serra: None. A. Lenzi: None. L. Luzi: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"1 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148286858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}