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Hemoglobin A1c (HbA1c) monitoring over time is thought to facilitate care and support diabetes management goals, but only half of youth and young adults with diabetes report ≥3 HbA1c tests/year despite poor glycemic control. We explored disparities in HbA1c testing over time and association with HbA1c levels and microvascular complications (retinopathy, neuropathy, or nephropathy) in the SEARCH for Diabetes in Youth study. Individuals diagnosed with diabetes at In conclusion, we report that mainly healthcare-related factors are associated with disparities in HbA1c testing frequency over time in youth-onset type 1 and type 2 diabetes, and these disparities are associated with HbA1c levels in type 1 diabetes. Disclosure K. A. Sauder: None. R. Dagostino: Consultant; Self; AstraZeneca, Biogen, Daiichi Sankyo. D. Dabelea: None. J. M. Stafford: None. S. R. Ehrlich: None. A. D. Liese: None. S. M. Marcovina: None. A. K. Mottl: Advisory Panel; Self; Bayer U. S. C. Pihoker: None. S. Saydah: None. A. S. Shah: None. Funding National Institutes of Health; Centers for Disease Control and Prevention

The objective was to assess changes in diabetes autoantibodies (DA) in children and adolescents with diabetes and explore whether observed changes were associated with participants’ characteristics, clinical parameters and diabetes complications. Participants with diabetes had DA (GAD, IA2 and ZnT8) measured at baseline (within 10.3± 7.1 months of diagnosis) and at 1, 2 and/or ≥ 5 years after baseline. At the > 5 year follow-up, the following measures were obtained: HbA1c, insulin dose, c-peptide, frequency of diabetes ketoacidosis (DKA) and severe hypoglycemia in the past 6 months, presence of microalbuminuria, hypertension, arterial stiffness, retinopathy and neuropathy. In those with positive baseline DAs, we examined the association between the change in the number of positive DA and change in each DA status with participants’ characteristics and clinical parameters. At baseline, 3,183 participants had positive DAs. At a mean diabetes duration of 7.3±2.3 years, 1,749 participants had longitudinal DA data, with 83.4% remaining positive (35.0% had 1 DA, 48.4% had 2 or more DAs). There was less DA persistence associated with longer diabetes duration (p=0.003 for those with baseline 1 DA and p Conclusions: DA status remained relatively stable at 7 years after diabetes diagnosis in those with baseline positive DA. Younger age at diagnosis and longer duration were associated with less DA persistence. Measuring DAs after initial presentation may aid in diabetes classification but not in prediction of clinical course. Disclosure L. Merjaneh: None. D. Dabelea: None. C. Pihoker: None. L. M. Dolan: None. C. Suerken: None. R. Dagostino: Consultant; Self; AstraZeneca, Biogen, Daiichi Sankyo. G. Imperatore: None. S. Saydah: None. A. J. Roberts: None. S. M. Marcovina: None. E. J. Mayer-davis: None. Funding Centers for Disease Control and Prevention (00097, DP-05-069, DP-10-001); National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108173, 1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139); Kaiser Permanente Southern California (U18DP006133, U48/CCU919219, U01DP000246, U18DP002714); University of Colorado Denver (U18DP006139, U48/CCU819241-3, U01DP000247, U18DP000247-06A1); Cincinnati’s Children’s Hospital Medical Center (U18DP006134, U48/CCU519239, U01DP000248, 1U18DP002709); University of North Carolina at Chapel Hill (U18DP006138, U48/CCU419249, U01DP000254, U18DP002708); Seattle Children’s Hospital (U18DP006136, U58/CCU019235-4, U01DP000244, U18DP002710-01); Wake Forest University School of Medicine (U18DP006131, U48/CCU919219, U01DP000250, 200-2010-35171)

Poor cognition has been observed in children and adolescents with type 1 (T1D) and type 2 (T2D) diabetes. Differences in cognition between young adults with youth-onset T1D and T2D, however, have not been explored, nor is the effect of shared etiologic processes, e.g., poor glycemic control, on cognition fully understood. Using data from SEARCH for Diabetes in Youth, a multicenter, observational cohort study, we compared cognition between young adults with youth-onset T1D and T2D, testing whether glycemic control modified the relationship between diabetes type and cognition. Cognition was assessed via the NIH Toolbox Cognition Battery and age-corrected composite Fluid Cognition scores used. Glycemic control was estimated from repeated measures of HbA1c. Linear regression modeling was used to test the interaction effect of diabetes type and glycemic control on Fluid Cognition score while controlling for sex, race/ethnicity, waist-height ratio, duration of diabetes, depression, alcohol and drug use, any hypoglycemic episode in the past year, parental highest level of education, household income, and clinic site. The T2D group (n = 254) had significantly lower Fluid Cognition scores, compared to the T1D group (n = 953) (mean [SD] 84.7 [17.3] vs. 95.4 [16.8], p Disclosure A. Shapiro: None. D. Dabelea: None. J.M. Stafford: None. R. Dagostino: Consultant; Self; Amgen, AstraZeneca, Celgene, Daiichi Sankyo. C. Pihoker: None. A.D. Liese: None. A.S. Shah: None. A. Bellatorre: None. J.M. Lawrence: None. L. Henkin: None. G. Wilkening: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108173-01)

Medical nutrition therapy (MNT) is integral to diabetes (DM) management, but little is known about MNT use in youth and young adults. The SEARCH for Diabetes in Youth study assessed MNT use among 1476 participants with type 1 DM (T1D) and 245 with type 2 DM (T2D) who had up to 3 research visits over 5.2 years (range 1.7-11.5). Participants reported often, sometimes, or never using each of 10 MNT strategies, which we categorized with 5 mutually exclusive groups: often using across visits; start using at later visits; sometimes using across visits; stop using at later visits; or never using at every visit. We used general linear models to examine A1c levels at the most recent visit (mean 11.0 [range 6.3-17.0] years after DM diagnosis) for each MNT strategy, adjusting for sociodemographic and DM-related variables. Most participants with T1D reported often counting carbs (n=1080, 75%), while half (n=107, 47%) of those with T2D reported often limiting sweets. Table presents significant associations of A1c levels with MNT use by DM type. In T1D, mean A1c differed significantly across MNT use of 7 strategies, but only for 2 strategies in T2DM. For both T1D and T2D, A1c did not differ by use of dietary exchanges, drinking more milk, or eating more fruits/vegetables. Our data suggest that often using most MNT strategies is related to relatively better glycemic control in T1D, but this applies to fewer MNT strategies for T2DM. Disclosure K.A. Sauder: None. J.M. Stafford: None. N.S. The: None. E.J. Mayer-Davis: None. J. Thomas: None. J.M. Lawrence: None. G. Kim: None. K.R. Siegel: None. E.T. Jensen: None. A.S. Shah: None. R. Dagostino: Consultant; Self; Amgen, AstraZeneca, Celgene, Daiichi Sankyo. D. Dabelea: None. Funding National Institutes of Health

Individuals with diabetes are at high risk for heart failure with preserved ejection fraction due to diabetic cardiomyopathy (DCM), defined as abnormal myocardial structure and function in the absence of overt coronary artery disease. DCM leads to myocardial fibrosis and remodeling with one of the first signs being left ventricular (LV) diastolic dysfunction. The objective was to compare the prevalence of diastolic dysfunction among young adults with type 1 diabetes (T1D) vs. type 2 diabetes (T2D) with diabetes diagnosed in childhood. Participants had T1D or T2D and were enrolled in the SEARCH for Diabetes in Youth Study. Cardiovascular risk factors and diastolic function via echocardiography were measured at an in-person visit, after an average disease duration of 10.9 years (both groups). Unadjusted means were compared between those with T1D and T2D, p Disclosure A.S. Shah: None. D. Dabelea: None. L.M. Dolan: None. S. Isom: None. R. Dagostino: Consultant; Self; Amgen, AstraZeneca, Celgene, Daiichi Sankyo. L.E. Wagenknecht: None. G. Imperatore: None. S. Saydah: None. A.D. Liese: None. J.M. Lawrence: None. C. Pihoker: None. E.M. Urbina: None. Funding Centers for Disease Control and Prevention (1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139); National Institutes of Health (1UC4DK108173)

Limited information exists regarding the evolution of dyslipidemia in youth with type 2 diabetes (T2D). Understanding dyslipidemia risk factors may influence treatment. We studied 212 youth with T2D not on lipid lowering therapy (68% female, 43% non-Hispanic black, mean age 14.9±2.7y at baseline, mean follow-up 7.0±2.0y). Dyslipidemia was defined as HDL-C100mg/dL, or triglycerides (TG)>150mg/dL. Progression was defined as normal lipids at baseline, dyslipidemia at follow-up; regression as dyslipidemia at baseline, normal at follow-up; stable abnormal as dyslipidemia at both; stable normal as normal at both. Associations of A1c and abdominal adiposity (waist to height ratio, WHtR) over time (area under the curve, AUC) with progression and stable abnormal were examined and relative risks estimated. HDL-C progressed, regressed, was stable normal and stable abnormal in 12%, 11%, 62%, and 14%, respectively. Corresponding LDL-C values were 16%, 13%, 43% and 29%; TG were 17%, 11%, 56% and 16%. Each 1% increase in A1c AUC was associated with 13% higher risk of TG>150mg/dL and 22% higher risk of LDL-C>100mg/dL, after adjusting for WHtR (Table). There were no significant associations with WHtR. In youth with T2D, 33% and 45% had progression or stable dyslipidemia of TG and LDL-C, respectively. Higher A1c over time is associated with worse TG and LDL-C, highlighting the importance of glycemic control. Disclosure R.P. Brady: None. A.S. Shah: None. E.T. Jensen: None. J.M. Stafford: None. R. Dagostino: Consultant; Self; Amgen, AstraZeneca, Celgene, Daiichi Sankyo. L.M. Dolan: None. L.M. Knight: None. G. Imperatore: None. C.B. Turley: None. A.D. Liese: None. E.M. Urbina: None. J.M. Lawrence: None. C. Pihoker: None. S.M. Marcovina: None. D. Dabelea: None. Funding Centers for Disease Control and Prevention (1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139); National Institutes of Health (1UC4DK108173)

Accurate classification of diabetes type guides correct treatment. We recently showed a type 1 diabetes (T1D) genetic risk score (GRS-1), combined with clinical features and biomarkers discriminates between T1D and type 2 (T2D) and MODY in European adults. We aimed to test the ability of an improved 67 SNP T1D score (GRS-2) to discriminate T1D in an ethnically diverse U.S. pediatric population. We included 1818 SEARCH Study participants who had a C-peptide at a median (IQR) of 8 (6-9) years post diagnosis. T1D was defined based on severe insulin deficiency (fasting C-peptide The T1D GRS-2 was discriminative of T1D across all three ethnic groups AUC (95% CI) for GRS-1 vs. GRS-2 was 0.88 (0.84, 0.93) vs. 0.88 (0.83, 0.92) in white, 0.82 (0.77, 0.87) vs. 0.87 (0.83, 0.92) in blacks, and 0.87 (0.82, 0.92) vs. 0.9 (0.86, 0.95) in Hispanics). Combined risk scores were most accurate at classifying T1D. AUC for different scores were: 0.93 (0.91, 0.95) for type assigned by provider at diagnosis, 0.95 (0.93, 0.96) for age and BMI, 0.95 (0.94, 0.97) for GAD/IA-2/ZNT8 autoantibodies, and 0.99 (0.99, 1.00) for a combined approach using autoantibodies, GRS-2 and clinical features. These results were similar across all race/ethnic groups. A new T1D GRS-2 is discriminative of T1D in a racial/ethnically diverse pediatric population. Using GRS2 in a combined model of clinical features, autoantibodies and genetics offers near perfect classification of T1D and could be used to accurately classify diabetes type. Disclosure R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. S.A. Sharp: None. C. Pihoker: None. L.A. Ferrat: None. G. Imperatore: None. S. Saydah: None. A.H. Williams: None. L.E. Wagenknecht: None. J.M. Lawrence: None. M.N. Weedon: None. R. Dagostino: Consultant; Self; Acelity, Amgen Inc. W. Hagopian: Research Support; Self; Novo Nordisk A/S. J. Divers: None. D. Dabelea: None. Funding National Institutes of Health (UC4DK108173)

Urinary (U) neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) are markers of renal tubular injury. Whether NGAL and KIM-1 are elevated in early diabetic kidney disease (DKD) is not known. We performed a pilot study to compare the U concentrations (C) of NGAL and KIM-1 in YA with T1D or T2D from the SEARCH registry and healthy controls (n 45 in each group). The YA with diabetes were selected based on age at dx > 10 years and duration >8 years and were randomly selected across the quartiles of eGFR with similar age, gender and ethnicity. T1D (age 21 ± 2 years; duration 9.6 ± 1.0 years; HbA1c 9.3 ± 2.1% eGFR 126 ± 19 mL/min/1.73m2, UACR 11 ± 16 µg/mg), T2D (age 24 ± 3 years, duration 9.8 ± 1.2 years, HbA1c 8.5 ± 3.0%, eGFR 128 ± 21 mL/min/1.73m2, UACR 38 ± 71 µg/mg), controls (age 23 ± 3 years). In each group 50% were female, 41% white, 43% AA, and 16% Hisp. NGAL and KIM-1 were significantly elevated in T1D and T2D compared with controls (p < 0.001, p< 0.007). Although there was a trend for a greater UC of NGAL in T2D than T1D and a greater UC of KIM-1 in T1D than T2D, these differences were not statistically significant. Elevated UC of NGAL and KIM-1 are present in YA with T1D and T2D suggesting the presence of tubular damage early in the course of diabetes. Further investigation will determine if these markers can be used to assess the natural history of tubular injury or monitor therapeutic intervention in DKD. Disclosure N.M. Sheanon: None. A.K. Mottl: None. R. Dagostino: Consultant; Self; Teva Pharmaceutical Industries Ltd., Acelity, RedHill Biopharma, Edwards Lifesciences. C. Suerken: None. M. Afkarian: None. D. Dabelea: None. G. Imperatore: None. S.M. Marcovina: None. D.J. Pettitt: None. S. Saydah: None. L.M. Dolan: None.

INTRODUCTION: Although recognition of insulin sensitivity as a risk factor for cardiovascular disease is growing, a deeper understanding of its role is impeded by the cost and complexity of currently available measures. This report evaluates previously described alternative indices of insulin sensitivity with the goal of identifying a reliable, but logistically simpler, alternative. METHODS: Data from 1460 participants in the Insulin Resistance Atherosclerosis Study (IRAS) were used to assess the proportion of the relationship between a recognized measure of insulin sensitivity (Bergman’s S I ) and cardiovascular risk factors that is contained in each of nine alternative measures. RESULTS: A number of the alternative indices contained a substantial proportion of the information available in Bergman’s S I . The Galvin’s index and the homeostasis model were most promising. However, there remained a significant amount of the information in Bergman’s S I that was not contained in any of the alternative indices. DISCUSSION: There are simpler alternative indices of insulin sensitivity for use in epidemiological studies, but each alternative is associated with some loss of information. It may be possible that this loss can be overcome with an increased sample size; however, using the alternative indices may also confound the assessment of insulin sensitivity with other underlying factors (i.e., hyperinsulinemia). The alternative indices are not recommended for the clinical assessment of insulin sensitivity for an individual patient or subject.