669-P: Factor Analysis of the Diabetes-Specific Risk-Taking Inventory

We aimed to compare diagnosis characteristics, diabetes management, and comorbidities to identify differences between childhood and adult onset T1D. This analysis was performed in the T1D Exchange registry across the following age at diagnosis groups: <10, 10-17, 18-24, 25-39, and ≥40 years. Diagnosis characteristics were compared using a regression model adjusting for possible confounders. Current characteristics came from the participant’s most recent clinic exam and were restricted to those with current age ≥25 years and T1D duration ≥1 year. For each outcome, two models were fit, one including adjustment for current age and one including adjustment for T1D duration. Age at diagnosis was associated with an outcome if an association was present and the direction of effect was consistent across models. DKA at diagnosis was more common at childhood onset. The use of oral agents preceding diagnosis was higher in participants diagnosed as adults, likely due to initial misdiagnosis as type 2 (Table). Current pump use and celiac disease were more frequent in participants diagnosed at younger ages; however, HbA1c, CGM use, insulin requirements, and other comorbidities were not significantly different across age at diagnosis groups. A better understanding of differences in persons diagnosed with T1D as children vs. adults is needed to understand prognosis and subtype-specific therapeutic approaches. Disclosure A.L. Peters: Advisory Panel; Self; Abbott, Bigfoot Biomedical. Research Support; Self; Dexcom, Inc.. Advisory Panel; Self; Eli Lilly and Company, Insulin Algorithms, JDRF, Lexicon Pharmaceuticals, Inc., Livongo Health. Research Support; Self; MannKind Corporation. Other Relationship; Self; Medscape. Advisory Panel; Self; Merck & Co., Inc.. Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc., Omada Health, Inc., Optum Rx, Inc., Sanofi. Research Support; Self; T1D Exchange. Advisory Panel; Self; The Endocrine Society. Research Support; Self; The Leona M. and Harry B. Helmsley Charitable Trust. Advisory Panel; Spouse/Partner; Johnson & Johnson Diabetes Institute, LLC. I.B. Hirsch: Research Support; Self; Medtronic MiniMed, Inc.. Consultant; Self; Abbott, Bigfoot Biomedical, Roche Diabetes Care Health and Digital Solutions, ADOCIA. L.M. Laffel: Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Sanofi US, MannKind Corporation, Roche Diagnostics Corporation, Dexcom, Inc., Insulet Corporation, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Johnson & Johnson Diabetes Institute, LLC. D.J. DeSalvo: Consultant; Self; Dexcom, Inc.. Speaker's Bureau; Self; Insulet Corporation. S. Lyons: None. F. Vendrame: Consultant; Self; Novo Nordisk Inc. G. Aleppo: Research Support; Self; AstraZeneca, Novo Nordisk Inc.. Consultant; Self; Dexcom, Inc.. Advisory Panel; Self; Novo Nordisk Inc. L.D. Mastrandrea: Research Support; Self; Novo Nordisk Inc., AstraZeneca, Sanofi-Aventis. Advisory Panel; Self; Pediatric Diabetes Consortium. Other Relationship; Self; American Academy of Pediatrics. Advisory Panel; Self; JAEB Center For Health Research. R.E. Pratley: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis, Takeda Development Center Americas, Inc. M.R. Rickels: Consultant; Self; Hua Medicine, Xeris Pharmaceuticals, Inc..

The objective of the study was to evaluate the prevalence of cardiovascular, renal, and hepatic biomarkers that fall outside guideline-based thresholds in adults with type 1 diabetes (T1D), and to identify demographic and clinical factors associated with these biomarkers. This observational study linked data on 2,923 adults with T1D from the T1D Exchange Registry and NorstellaLinQ's laboratory data (2022-2023). Prevalence of biomarkers outside guideline-recommended thresholds was calculated. Generalized estimating equations (GEE) were used to model associations with age, sex, race/ethnicity, insurance, T1D duration, body mass index (BMI), and hemoglobin A1c (HbA1c), while accounting for within-subject correlation due to repeated measures. Elevated low-density lipoprotein (LDL) cholesterol (≥70 mg/dL, above the recommended goal for individuals with diabetes at high risk for atherosclerotic cardiovascular disease) was common (80.2%), as were overweight (33.8%) and obesity (30.3%). Additional findings included high total cholesterol (≥200 mg/dL, 22.6%), low high-density lipoprotein (HDL) cholesterol (<40 mg/dL males, <50 mg/dL females, 13.4%), elevated urinary albumin-to-creatinine ratio (≥30 mg/g, 13.0%), reduced estimated glomerular filtration rate (<60 mL/min/1.73 m2, 6.2%), and elevated fibrosis-4 index (FIB-4) (≥1.3, 14.4%). In adjusted GEE models, higher HbA1c was significantly associated with increased odds of abnormal cardiovascular and renal biomarkers across most measures. Male sex and BMI were linked to lower HDL cholesterol, while older age was strongly associated with elevated FIB-4. Obesity and elevated LDL cholesterol are highly prevalent in adults with T1D, indicating substantial cardiometabolic risk. The lack of outcome trials in this population underscores the need for studies to evaluate strategies to improve weight and lipid management and thereby reduce cardiovascular and renal risk in adults with T1D.

BackgroundPeople often avoid and try to stay ignorant of the negative aspects of meat production and consumption. When asked, people justify their meat consumption by emphasizing the necessity of meat for health, its desirable taste, naturalness, and normality. Relevant measures were developed to measure how much individuals rely on meat-eating justifications. Thus, the current work aims to translate and adapt the 4Ns scale by Piazza et al. (Appetite 91:114–128, 2015) and the Meat-eating Justifications Scale (MEJ) by Rothgerber (Psychol Men Masc 14(4):363–375, 2013) to Turkish to enable theoretical and applied studies of meat-eating in Turkish.MethodsThe relevant measures are translated and adapted to Turkish by expanding the item pool. The data of the translated measures and other relevant variables is collected between May 22 and December 31, 2022 (N = 520). The sample is divided into two parts: the first subsample is used to conduct Exploratory Factor Analysis (EFA), and the other is used to conduct Confirmatory Factor Analysis (CFA). Finally, a correlation matrix is created with the finalized versions of the meat-eating justification measures and other variables of interest.ResultsEFA for the 4Ns revealed a four-factor solution aligning with the original theoretical framework. Since the original measure accepted a single-factor solution, the current work improved on the original by reworking the low-loading items and introducing culture-specific items. After decisions about certain items, CFA is conducted, which indicated good fit (χ2(98) = 193.4, p < .05, CFI = .96, RMSEA = .061, [CI = .048, .074]), internal consistency reliability (Cronbach’s α = .95, McDonald’s ω_total = .95) and test–retest reliability (.92). EFA for MEJ indicated an eight-factor solution, where a subscale was discarded. CFA showed good fit (χ2(224) = 435.9, p < .05, CFI = .945, RMSEA = .06, [CI = .052, .069]), internal consistency reliability (Cronbach’s α = .89, McDonald’s ω_total = .91) and test–retest reliability (.89).ConclusionCross-cultural adaptation and validation of the meat-eating justification scales enable a better theoretical understanding of the psychology of meat-eating. This is demonstrated by replicating the strong associations between meat-eating, its justifications, social dominance orientation, speciesism, and masculinity in a different cultural context. This understanding is essential for prevention and intervention efforts to reduce meat consumption.

Constructing a professional competence scale for foodservice research & development employees from an industry viewpoint

Young adults (YA) with type 1 diabetes (T1D) are vulnerable to short and long-term complications as they incorporate disease self-management into the transition to adulthood. Relationships between race-ethnicity and socioeconomic status (SES) have not been examined in this population, despite poorer outcomes in minority and low SES subgroups. We administered a survey on demographics, clinical factors, self-management, and social determinants of health to non-Hispanic black (NHB), Hispanic, and non-Hispanic white (NHW) YA from 6 centers in the T1D Exchange. We compared survey responses and diabetes outcomes among the racial-ethnic groups using Fisher’s exact test or Wilcoxon rank-sum test, as appropriate. Participants (97 NHB, 103 Hispanic, 100 NHW) had a mean age of 21±2 years and T1D duration of 11±5 years. Low SES was more common in NHB and Hispanic vs. NHW YA (Medicaid 57% and 76% vs. 22%; high school education or less 66% and 70% vs. 24%, respectively; p&amp;lt;0.001). NHB and Hispanic YA had lower use of continuous glucose monitors (28% and 37%) and insulin pumps (18% and 39%) vs. NHW (71% and 72%) (p&amp;lt;0.001). Non-Hispanic black, but not Hispanic, YA had higher food insecurity, poorer self-management, greater diabetes-related distress, and more adverse childhood experiences than NHW (all p&amp;lt;0.001). NHB also had higher A1c levels (NHB 10.5%, Hispanic 8.6%, NHW 8.1%) and higher diabetes-specific health care utilization, with 46% of NHB having ≥1 hospital or ER admission in the past year vs. 17% and 12% in Hispanic and NHW (p&amp;lt;0.001 for NHB vs. NHW). Non-Hispanic black and Hispanic YA with T1D face similar economic disadvantages, yet NHB YA exhibit poorer glycemic control and higher acute healthcare utilization than Hispanic YA. NHB differed from Hispanic YA on self-management, psychosocial factors, and adverse childhood experiences. Factors contributing to these disparities (e.g., healthcare system interactions, cultural differences) should be explored to design effective interventions. Disclosure S. Agarwal: None. L. Kanapka: None. J. Raymond: Other Relationship; Self; Insulet Corporation. A.F. Walker: None. A. Gerard Gonzalez: None. D.F. Kruger: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Insulet Corporation. Consultant; Self; CeQur Corporation, Hygieia. Speaker's Bureau; Self; AstraZeneca, Dexcom, Inc., Eli Lilly and Company. Stock/Shareholder; Self; Dexcom, Inc. Other Relationship; Self; Novo Nordisk Inc. M.J. Redondo: None. M.R. Rickels: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. A. Butler: None. V. Miller: None. R.L. Gal: None. S.M. Willi: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Roche Diagnostic USA. Research Support; Self; Jaeb Center for Health Research, National Institute of Allergy and Infectious Diseases, Sanofi-Aventis. Other Relationship; Self; National Institute of Diabetes and Digestive and Kidney Diseases. J.A. Long: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust; National Institute of Diabetes and Digestive and Kidney Diseases

Aims/hypothesisWe aimed to compare diabetic retinopathy outcomes in people with type 1 diabetes following introduction of continuous subcutaneous insulin infusion (CSII) therapy with outcomes in people receiving continuing therapy with multiple daily insulin injections (MDI).MethodsThis is a retrospective cohort study using the Scottish Care Information – Diabetes database for retinal screening outcomes and HbA1c changes in 204 adults commenced on CSII therapy between 2013 and 2016, and 211 adults eligible for CSII during the same period but who continued on MDI therapy. Diabetic retinopathy progression (time to minimum one-grade worsening in diabetic retinopathy from baseline grading) was plotted for CSII and MDI cohorts using Kaplan–Meier curves, and outcomes were compared using multivariate Cox regression analysis adjusting for age, sex, baseline HbA1c, blood pressure, cholesterol, smoking status and socioeconomic quintile. Impact of baseline HbA1c and change in HbA1c on diabetic retinopathy progression was assessed within CSII and MDI cohorts.ResultsCSII participants were significantly younger, were from less socially deprived areas, and had lower HbA1c and higher diastolic BP at baseline. There was a larger reduction in HbA1c at 1 year in those on CSII vs MDI (−6 mmol/mol [−0.6%] vs −2 mmol/mol [−0.2%], p < 0.01). Diabetic retinopathy progression occurred in a smaller proportion of adults following commencement of CSII vs continued MDI therapy over mean 2.3 year follow-up (26.5% vs 18.6%, p = 0.0097). High baseline HbA1c (75 mmol/mol [9%]) was associated with diabetic retinopathy progression in the MDI group (p = 0.0049) but not the CSII group (p = 0.93). Change in HbA1c at follow-up, irrespective of baseline glycaemic status, did not significantly affect diabetic retinopathy progression in either group.Conclusions/interpretationCSII was associated with reduced diabetic retinopathy progression compared with continued MDI therapy, and may be protective against diabetic retinopathy progression for those with high baseline HbA1c. Progression of diabetic retinopathy over 3 years was not associated with a change in HbA1c.Graphical abstract

ObjectiveThe weight bias internalization scale (WBIS) is widely used in several languages. However, there is no Japanese version of the WBIS. The purpose of this study is to develop a Japanese version of the WBIS and to verify its reliability and validity. We translated the original version of the WBIS and had approval from the scale developer. Adults who perceived themselves to be obese completed online baseline survey (N = 285) and two-week follow-up survey (N = 100). We used the Japanese WBIS and scales for obesity-related quality of life, self-esteem, self-efficacy, and depression. We calculated Cronbach’s alpha and intra-class correlation coefficient (ICC) to assess reliability of the WBIS and conducted confirmatory factor analysis (CFA) and correlation analysis to assess its validity.ResultsCronbach’s alpha was 0.91 at baseline and 0.92 at follow-up, and the ICC was 0.87. CFA showed that a one-factor model demonstrated an acceptable fit (χ2 (44) = 158.6, CFI = 0.93, RMSEA = 0.096, SRMR = 0.051), similar to the original version. As we had hypothesized, overall, the Japanese version of the WBIS was significantly correlated with obesity-related quality of life, self-esteem, self-efficacy, and depression. These results confirmed its adequate reliability and validity.

&lt;b&gt;Objective:&lt;/b&gt; We hypothesized that there is heterogeneity in long-term patterns of glycemic control with respect to cardiovascular disease (CVD) development in type 1 diabetes and that risk factors for CVD differ by glycemic control pattern. Thus, we estimated associations between data-derived latent HbA1c trajectories and 30-year CVD risk in the Pittsburgh Epidemiology of Diabetes Complications (EDC) study of childhood-onset (&lt;17 years old) type 1 diabetes.&lt;b&gt; &lt;/b&gt; &lt;p&gt;&lt;b&gt;Research Design and Methods: &lt;/b&gt;Participants (n=536 with ≥2 HbA1c measurements [median 6] and CVD-free at baseline; mean age 27 and diabetes duration 18 years) were followed from 1986-88 to 2016-18 to ascertain CVD incidence (CVD death, myocardial infarction, stroke, coronary revascularization or blockage ≥50%, ischemic ECG, or angina). Latent HbA1c trajectories and their association with time-to-CVD incidence were simultaneously assessed using Joint Latent Class Mixed Models.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; Two HbA1c trajectories with respect to differential CVD risk were identified: Low (HbA1c ~8% [64 mmol/mol] and improving over follow-up, 76% of cohort) and High (HbA1c ~10% [86 mmol/mol] and stable, 24%). Overall, 30-year CVD incidence was 47.4% (n=253); MACE incidence 31.0% (n=176). High HbA1c was associated with 3-fold increased CVD risk versus Low HbA1c. Both groups had similar age and diabetes duration. Non-HDLc and estimated glomerular filtration rate were associated with CVD risk only in Low HbA1c; albumin excretion rate was associated with CVD risk only in High HbA1c.&lt;b&gt; &lt;/b&gt;&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions: &lt;/b&gt;These risk factor differences suggest that pathways to CVD may differ by glycemic control, potentially resulting in important implications for prognosis in type 1 diabetes.&lt;/p&gt;

Background: Insulin pump use is known to improve glycemic control in patients with type 1 diabetes (T1D), however, uptake of insulin pump varies among people in U.S. This study aims to examine the distribution of patient demographics and frequency of clinical outcomes across people using insulin pump. Methods: The analysis included 21,821 T1D patients in the T1D Exchange Quality Improvement (T1DX-QI) database for whom electronic medical record data was available in the T1DX-QI collaborative. Patients over 2 years with at least one completed clinic encounter between January 2014 - February 2020 were included for analysis. Results: In this population, 58% (N=12,729) of T1D patients were insulin pump users, whereas 42% (N=9,092) were non-users. HbA1c levels in the insulin pump group were lower compared to non- users (Mean [SD], %: 8.5 [2.6] vs. 8.9[2.1] [p Conclusions: This study highlights inequities in uptake of insulin pump therapy, while confirming previous findings demonstrating the use of insulin pump in combination with CGMs to be critical in effective management of blood glucose levels in one of the largest multi-center observational studies in the U.S. Disclosure N. Noor: Research Support; Self; Dexcom, Inc. M. A. Clements: Consultant; Self; Eli Lilly and Company, Employee; Self; Glooko, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc. G. T. Alonso: None. S. Rompicherla: None. N. Noor: Research Support; Self; Dexcom, Inc. S. Lyons: None. J. M. Lee: Advisory Panel; Self; GoodRx. M. Gallagher: None. J. A. Indyk: None. O. Ebekozien: None. M. Greenfield: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

The association of morning serum cortisol with glucose metabolism and diabetes: The Jackson Heart Study

Diabetic peripheral neuropathy (DPN) is a major cause of disability, mortality and poor quality of life in patients with T1D, with prior reported prevalence rates of up to 35%. The contemporary prevalence of DPN in T1D patients was evaluated in T1D Exchange Registry centers throughout the United States. The Michigan Neuropathy Screening Instrument (MNSI), a validated 15-item self-administered questionnaire, was used to assess DPN in adults ≥18 years with ≥ 5 years of T1D duration. A score of ≥4 was used to define DPN. Diabetes-related characteristics and laboratory data were obtained through the most recent clinic update. Chi-square and t-tests were used to compare demographic and diabetes-related characteristics between those with and without DPN. Linear regression was used to determine the effect of DPN on HbA1c, adjusted for possible confounders. In preliminary analyses of 5,058 participants across 62 sites (mean age 39±18 years, T1D duration 22±14 years, 56% female, 88% non-hispanic white, mean HbA1c 8.1±1.6%), the prevalence of DPN was 10%. Those with DPN were older (52±17 vs. 37±18 years), more likely to be female (61% vs. 55%), had longer T1D duration (32±16 vs. 21±13 years), lower annual household income (37% vs. 59% earning ≥$75K), and lower education level (55% vs. 69% with college degree) than those without DPN (all p&amp;lt;0.001). They also had higher systolic blood pressure (126±17 vs. 123±14 mmHg), triglycerides (117±89 vs. 95±62 mg/dL), tobacco use (9% vs. 4%) and prevalence of established CVD (26% vs. 6%), despite higher use of CVD-modifying agents such as statins (64% vs. 31%) and ACE-inhibitors/ARBs (45% vs. 23%) (all p&amp;lt;0.001). Participants with DPN had higher HbA1c (8.4±1.7% vs. 8.1±1.6%), even after adjusting for multiple confounders (p &amp;lt;0.01). The prevalence of DPN in this national T1D cohort is lower than prior published reports, reflecting current clinical care practices, and highlighting other non-glycemic risk factors for DPN including CVD risk factors and socioeconomic status. Disclosure K.R. Mizokami-Stout: None. C.T. Boyle: None. V.N. Shah: None. G. Aleppo: Research Support; Self; AstraZeneca, Novo Nordisk Inc.. Consultant; Self; Dexcom, Inc.. Advisory Panel; Self; Novo Nordisk Inc. J.B. McGill: Research Support; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Aegerion Pharmaceuticals. Consultant; Self; Bayer AG, Dexcom, Inc., Intarcia Therapeutics, Inc.. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., MannKind Corporation. Research Support; Self; Novartis Pharmaceuticals Corporation. Consultant; Self; Novo Nordisk A/S. R. Pratley: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc., Eli Lilly and Company, Merck &amp; Co., Inc., Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis, Takeda Development Center Americas, Inc.. E. Toschi: None. L. Ang: None. R. Pop-Busui: Research Support; Self; AstraZeneca.

Objective: To describe the state of type 1 diabetes (T1D) in minority adults in the South Bronx, and their experience with continuous glucose monitoring (CGM). Introduction: In a recent analysis of data from the Type 1 Diabetes Exchange Registry, one notable finding was the difference in metabolic control and use of diabetes technology in patients of different socioeconomic status and racial/ethnic backgrounds. With limited data available on Hispanic and Black patients, we sought to examine the use of and experience with continuous glucose monitoring (CGM) in our hospital system, which primarily serves a low-income, minority population in the South Bronx. Methods: 68 adults with T1D who attended the Endocrinology clinic at our hospital from 2017 to 2019 were identified. Patients were contacted by telephone to complete a questionnaire regarding CGM use and satisfaction. A retrospective chart review was conducted to obtain additional demographic and clinical information. Results: Out of 68 patients with T1D in the hospital database who were contacted, 47 patients completed the questionnaire. The age range was 23 to 63 years. 42.6% were male. 59.6% were Hispanic, 19.1% Black/African American (AA), 4.3% Caucasian, and 17% not specified. 87.2% had public insurance. Overall, 48.9% of patients were actively using CGM, 19.1% had discontinued use of CGM, and 31.9% had never used CGM. In Hispanic patients using CGM, mean HbA1C was 8.2% compared to 10.1% in Hispanic non-users. In Black/AA patients using CGM, mean HbA1C was 9.2% compared to 9.9% in Black/AA non-users. Hospitalizations for acute diabetes complications were lower in CGM users (4.3%) compared to non-CGM users (16.7%). Among active CGM users, 74% rated their satisfaction as “extremely satisfied” or “very satisfied.” Perceived benefits included the prevention of hypoglycemia and awareness of inappropriate food intake. Discussion: Our study population, mainly comprised of Hispanic and Black T1D adults, showed a higher CGM utilization rate than previously reported. After stratification by socioeconomic status, CGM utilization was reported to be as low as 16% in Hispanic and 10% in Black patients with household income <$50,000/year in the T1D Exchange Registry. By comparison, 49% of our studied population possessing similar demographics was actively using CGM. This study demonstrated that CGM acceptance was high in this largely minority, low-income population in the South Bronx, and was associated with lower A1C levels, high degree of patient satisfaction and reduction in diabetes-related hospitalizations. However, glycemic control remained suboptimal overall despite CGM access. Additional strategies to optimize the utility of CGM are needed to improve clinical outcomes such as HbA1C levels in minority T1D patients.

The Official Journal of ATTD Advanced Technologies &amp; Treatments for Diabetes Conference <i>Berlin, Germany—February 20–23, 2019</i>

Some ethnic minorities with type 1 diabetes (T1D) have worse glycemic control (higher glycated hemoglobin (HbA1c)) and increased risk for vascular complications. There is limited evidence on the impact of...

Technology use has been shown to improve diabetes control, but minority youths tend to have low rates of technology use and exhibit suboptimal glycemic control. We examined the impact of continuous glucose monitors (CGM) and continuous subcutaneous insulin infusion (CSII) on glycemic control in a racial-ethnic minority cohort of children and adolescents with type 1 diabetes (T1D). A cross-sectional study was conducted among 140 pediatric T1D patients seen at a multidisciplinary clinic. From January to November 2022, data on demographics and glycated hemoglobin (HbA1c) levels were collected. Patients were categorized as technology (CGM, CSII, or both) or non-technology users (finger stick meter (FS) and multiple daily injections (MDI)). The majority identified as Hispanic (79 %) and had public health insurance (71 %). Sixty-nine percent used technology. Compared with non-technology users, technology users had significantly lower mean HbA1c levels (9.60 vs. 8.40 %, respectively) (p=0.0024), though no group (CGM+CSII, CGM+MDI, FS+CSII, and FS+MDI) achieved a mean HbA1c level of <7.0 %. Regarding minority status, no significant differences in mean HbA1c levels existed between Hispanics and Blacks in the CGM+MDI and FS+CSII groups (p=0.2232 and p=0.9224, respectively). However, there was a significant difference in mean HbA1c levels between Hispanic and Black non-technology users (9.19 vs. 11.26 %, respectively) (p=0.0385). Technology users demonstrated better glycemic control than non-technology users. Further research is needed to investigate factors affecting glycemic control in minority youths with T1D.