Introduction and Objective: With half the global population predicted to be obese by 2035, understanding the role of adiposity on obesity-associated comorbidities is critical. Adipose tissue (AT) inflammation negatively affects atherosclerosis, insulin resistance, and metabolic-associated fatty liver disease, but the mechanism behind interorgan signaling remains a mystery. Extracellular vesicles (EVs) deliver bioactive cargo to alter recipient cell function. With over 65% of circulating EV-miRNAs originating from adipocytes, we hypothesize that diet regulates adipocyte EV (AdEV) cargo trafficked to tissue-resident cells, dictating local inflammatory responses. Methods: We harvested AdEVs from the visceral AT of lean and obese C57BL/6J mice and lean and obese bariatric surgery patients who gave informed consent. Mouse AdEVs were administered in middle-aged Ldlr-/- mice, and human AdEVs were used for single-EV profiling, transcriptomics, and co-culturing. Results: Weight-matched middle-aged Ldlr-/- mice receiving obese AdEVs had increased en-face atherosclerosis (p<0.0001), insulin resistance (p<0.05), and hepatic steatosis (p<0.05), while lean AdEVs had no effect. The immune milieu in the aorta, liver, and spleen internalized AdEVs (p<0.05). Macrophages endocytosed human AdEVs into endosomal/lysosomal compartments with obese AdEVs colocalizing more with the latter (p<0.0001) and induced heightened pro-inflammatory responses, including IL12B(>250-fold; p<0.001) and IL1B(60-fold; p<0.01). AdEVs from lean and obese patients contained differential miRNA cargo (p<0.05) mirrored in AdEVs from lean and obese mice. Classical EV and adipocyte-specific biomarkers on the single-AdEV surface revealed more AdEVs in obese subjects’ plasma (p<0.05). Conclusion: In obesity, AdEVs accelerate atherosclerosis, insulin resistance, and hepatic steatosis through uptake, internalization, cargo delivery, and increased production of circulating AdEVs, suggesting AdEVs are a major mechanism. X.Y. Rima: None. D. Shantaram: None. J.Z. Liu: None. V.P. Wright: None. A. Amari: None. J. Doon-Ralls: None. T.K. Nguyen: None. J. Rottinghaus: None. J.M. Fernandes: None. D.S. Patel: None. A.D. Jalilvand: None. B.J. Needleman: Speaker's Bureau; Intuitive Surgical, Medtronic. S. Noria: None. S. Brethauer: None. K.A. Perry: None. E. Reategui: None. W. Hsueh: None. National Institutes of Health (5T32HL149637); Burroughs Wellcome Fund (1285320)
Introduction and Objective: Anxiety, depression, ADHD, and diabetes distress are all associated with adherence issues and risk for poor health outcomes in people with type 1 diabetes (T1D). However, there is limited research assessing youth at time of T1D diagnosis for psychological factors that increase risk for low treatment adherence. Our objective is to assess baseline history of anxiety, depression, and ADHD, as well as anxiety related to the onset of T1D diagnosis. Methods: An automatic psychology inpatient consult was added for all youth admitted with new onset of T1D at a large pediatric hospital. Psychologists assessed all patients admitted on weekdays using clinical interview with patients and parents. Parent interview assessed symptoms for infants and young children. Interventions included psychoeducation about the adjustment period, coping strategies to manage anxiety, and providing anticipatory guidance on how ADHD can affect diabetes management. Results: Psychologists screened 119 patients (range 9 months to 18 yrs; M = 9.05 years). Screening showed that 38 patients (32%) had a history of mental health concerns (25 with anxiety; 11 with depression; 21 with ADHD) and 64 patients (54%) endorsed anxiety related to diabetes diagnosis. Some patients had injection anxiety, general diabetes distress, worries about peer reactions to diabetes diagnosis, worries about managing diabetes at school, and worries about complications. Of note, 11 patients had mental health comorbidities such as both anxiety and depression. Conclusion: At T1D diagnosis, 32% of our pediatric cohort had a history of baseline anxiety, depression, and/or ADHD and 54% of patients had anxiety related to diabetes diagnosis. Since anxiety, depression, and ADHD can increase risk for hospitalizations and diabetes related complications, early identification of psychological concerns at T1D diagnosis can help promote earlier therapeutic interventions. K. Semenkovich: None. M.K. Kamboj: None. M.M. Perry: None. M. Abdelhadi: None. D.A. Buckingham: None. K. Hong: None. J.A. Indyk: None. H. Yardley: None.
Introduction and Objective: Studies have shown that continuous glucose monitoring (CGM) results in improvement in hemoglobin A1c (A1c), glycemic control, and diabetes distress. CGM is underutilized in patients with type 2 diabetes (T2D). This study evaluated patient reported outcomes in participants with T2D using real-time CGM following hospital discharge. Methods: This was a 12-week prospective cohort study in recently hospitalized patients with T2D. Inclusion criteria were age ≥18 years, A1c >8.0%, and basal insulin use >10 units/day. Exclusion criteria included type 1 diabetes, pregnancy, incarceration, and expected discharge to a skilled nursing facility. Outcomes included Diabetes Distress Scale (DDS) and perceived Benefits and Burdens of CGM (BenCGM and BurCGM). Results were stratified by consistent (data at all follow-up visits at week 2, 4, 8, and 12) and inconsistent (data at <4 visits) CGM use. Results: A total of 65 participants were included in analysis. Mean age was 53 ± 10 years, 55% were male, median A1c was 11.3 (IQR 10.0, 13.1) at baseline, 8.1 (IQR 6.8, 9.3) at 12 weeks, and 75% had consistent CGM use. Median total DDS score at baseline was 2.65 (IQR 1.82, 3.71) and 1.76 (IQR 1.10, 2.50) at 12 weeks (p<0.001). Change in DDS subscores (emotional burden, physician related distress, regimen distress, interpersonal distress) improved significantly (all p<0.01). Change in total DDS tended to be greater among consistent vs inconsistent users (-0.65 [IQR -1.8, -0.08] vs. -0.15 [IQR -0.93, 0.22], p=0.05). BenCGM was high (total mean score 4.1 [IQR 4.0, 4.6]) and BurCGM was low (2.1 [IQR 1.5, 2.1]) but did not differ by consistent CGM use. Conclusion: This study found improvements in A1c and diabetes distress following initiation of CGM; the latter may be dependent upon consistent CGM use. Perceived benefits and burdens were favorable. S. Folk: None. P. Duncan: None. E. Buschur: Research Support; Dexcom, Inc. T. Gatti: None. C. Harris: None. T. Sobol: None. K. Wyne: Research Support; Amryt Pharma, Corcept Therapeutics, Intercept Pharmaceuticals, Inc, TARGET PharmaSolutions, Inc, Ionis Pharmaceuticals. Consultant; NewAmsterdam Pharma. Research Support; Sanofi. Consultant; AstraZeneca. K.M. Dungan: Research Support; Abbott Diagnostics, Dexcom, Inc. Consultant; Dexcom, Inc. Other Relationship; UpToDate. Consultant; Oppenheimer & Co. Advisory Panel; Insulet Corporation. Research Support; Insulet Corporation. Board Member; Elsevier, Eli Lilly and Company. Speaker's Bureau; Medscape, Med Learning Group, Impact Education. The research was supported by Dexcom.
Introduction & Objective: Hospital discharge is a vulnerable period for persons with type 2 diabetes (T2D). We assessed the feasibility and efficacy of remote (RM) vs. manual (MM) monitoring using a continuous glucose monitor (CGM). Methods: In this prospective cohort study, persons with T2D, A1c >8%, and basal insulin use received a Dexcom G6 following discharge. This analysis includes 14-day CGM reports at 4 weeks after discharge for the first 100 participants. Key outcomes were stratified by either RM vs MM of CGM data and include % time in range (70-180 mg/dl, TIR). Results: Among the 100 participants, 52 were RM and 48 were MM. Barriers in the MM group included incompatible cell phone (48%), no smartphone (46%), or other (4.2%). The RM group was younger, more likely to be employed, and had higher numeracy compared to the MM group. CGM data were available at 4 weeks in 62% of RM vs 56% of MM, p=0.69, and time in use was >95% in both groups. Median glucose was 176 (IQR 143, 215) vs 203 (IQR 183, 248, p=0.02) mg/dL in the RM vs MM group. Median TIR was 53% (IQR 33, 82) vs 40% (IQR 20, 49, p=0.04) in the RM vs. MM group and % time <70 mg/dl was <1% for both groups. Conclusion: Early findings suggest that CGM with RM is associated with higher TIR compared to CGM with manual downloads following hospitalization. Overcoming technological barriers to RM may improve glycemic outcomes post-discharge. Disclosure S. Folk: None. T. Sobol: None. T. Gatti: None. C. Harris: None. E.R. Faulds: Advisory Panel; Dexcom, Inc. Research Support; Dexcom, Inc., Insulet Corporation. Other Relationship; A1Control. E. Buschur: Research Support; Dexcom, Inc. K. Wyne: None. P. Duncan: None. K.M. Dungan: Advisory Panel; Eli Lilly and Company, Dexcom, Inc. Research Support; Dexcom, Inc. Advisory Panel; Elsevier. Research Support; Abbott, ViaCyte, Inc., Sanofi, Omnipod. Advisory Panel; Omnipod. Other Relationship; Up-To-Date. Speaker's Bureau; Med Learning Group, Medscape, Cardiometabolic Health Congress. Consultant; Oppenheimer & Co. Speaker's Bureau; Integritas. Funding Dexcom
Background: The Columbus Free Clinic (CFC) is a student-run free clinic providing care to underserved residents in Central Ohio. Type 2 diabetes is the most common chronic condition diagnosed at CFC. Historical data shows CFC provides suboptimal diabetic preventative care, evidenced by non-compliance with American Diabetes Association (ADA) laboratory testing guidelines. Due to volunteer providers and students rotating weekly, a quality improvement (QI) project was developed to improve the quality of medical care for patients with diabetes by increasing volunteer adherence to a subset of ADA guidelines: documented hemoglobin A1c (HbA1c) within the past 6 months, micro-albumin/creatinine ratio (ACR) in the past 12 months, and lipid panel in the past 12 months. Methods: In March 2023, a chart review was conducted to obtain baseline data on ADA guideline measures. A QI intervention plan to improve adherence to ADA guidelines was developed and implemented April 2023. A dot phrase in CFC’s electronic health record system was programmed with questions about laboratory testing to prompt providers to ask patients to increase adherence. In December 2023, post-intervention data was collected for patients with type 2 diabetes who were seen at CFC pre and post-intervention. Results: Out of the 175 patients who were identified with type 2 diabetes, a subset of 71 patients visited CFC for treatment pre and post-intervention. Prior to implementation, 57.7% of patients had a recorded HbA1c in the past 6 months, 33.8% had an ACR recorded in the previous 12 months, and 67.6% had a lipid panel recorded in the past 12 months. Post intervention, these percentages increased to 66.2% (+8.5%, p=0.30), 84.5% (+16.9%, p=<0.05) and 69.0% (+35.2%, p=<0.001), respectively. Conclusion: Findings highlight that our intervention successfully increased adherence to ADA care guidelines in a student-run, free clinic setting. Disclosure M.C. Casola: None. T. Brar: None. G. Lee: None. M. Coyne: None. N.L.J. Purdy: None.
