Search

Objective: To compare treatment regimens and glycosylated hemoglobin (A1c) levels in Type 1 (T1D) and Type 2 diabetes (T2D) using diabetes registries from two countries—U.S. SEARCH for Diabetes in Youth (SEARCH) and Indian Registry of youth onset diabetes in India (YDR). Methods: The SEARCH and YDR data were harmonized to the structure and terminology in the Observational Medical Outcomes Partnership Common Data Model. Data used were from T1D and T2D youth diagnosed <20 years between 2006‐2012 for YDR, and 2006, 2008, and 2012 for SEARCH. We compared treatment regimens and A1c levels across the two registries. Results: There were 4003 T1D (SEARCH = 1899; YDR = 2104) and 611 T2D (SEARCH = 384; YDR = 227) youth. The mean A1c was higher in YDR compared to SEARCH (T1D:11.0% ± 2.9% vs 7.8% ± 1.7%, P <.001; T2D:9.9% ± 2.8% vs 7.2% ± 2.1%, P <.001). Among T1D youth in SEARCH, 65.1% were on a basal/bolus regimen, whereas in YDR, 52.8% were on once/twice daily insulin regimen. Pumps were used by 16.2% of SEARCH and 1.5% of YDR youth with T1D. Among T2D youth, in SEARCH and YDR, a majority were on metformin only (43.0% vs 30.0%), followed by insulin + any oral hypoglycemic agents (26.3% vs 13.7%) and insulin only (12.8% vs 18.9%), respectively. Conclusion: We found significant differences between SEARCH and YDR in treatment patterns in T1D and T2D. A1c levels were higher in YDR than SEARCH youth, for both T1D and T2D, irrespective of the regimens used. Efforts to achieve better glycemic control for youth are urgently needed to reduce the risk of long‐term complications. [ABSTRACT FROM AUTHOR]

Objective: Incidence of youth‐onset diabetes in India has not been well described. Comparison of incidence, across diabetes registries, has the potential to inform hypotheses for risk factors. We sought to compare the incidence of diabetes in the U.S.‐based registry of youth onset diabetes (SEARCH) to the Registry of Diabetes with Young Age at Onset (YDR—Chennai and New Delhi regions) in India. Methods: We harmonized data from both SEARCH and YDR to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Data were from youth registered with incident diabetes (2006‐2012). Denominators were from census and membership data. We calculated diabetes incidence by averaging the total cases across the entire follow‐up period and dividing this by the estimated census population corresponding to the source population for case ascertainment. Incidence was calculated for each of the registries and compared by type and within age and sex categories using a 2‐sided, skew‐corrected inverted score test. Results: Incidence of type 1 was higher in SEARCH (21.2 cases/100 000 [95% CI: 19.9, 22.5]) than YDR (4.9 cases/100 000 [95% CI: 4.3, 5.6]). Incidence of type 2 diabetes was also higher in SEARCH (5.9 cases/100 000 [95% CI: 5.3, 6.6] in SEARCH vs 0.5/cases/100 000 [95% CI: 0.3, 0.7] in YDR). The age distribution of incident type 1 diabetes cases was similar across registries, whereas type 2 diabetes incidence was higher at an earlier age in SEARCH. Sex differences existed in SEARCH only, with a higher rate of type 2 diabetes among females. Conclusion: The incidence of youth‐onset type 1 and 2 diabetes was significantly different between registries. Additional data are needed to elucidate whether the differences observed represent diagnostic delay, differences in genetic susceptibility, or differences in distribution of risk factors. [ABSTRACT FROM AUTHOR]

Background: Over the last decades, diabetes in youth has increased in both India and the United States, along with the burden of long‐term complications and healthcare costs. However, there are limited standardized population‐based data in contemporary youth cohorts for comparison of clinical and demographic characteristics of diabetes for both type 1 (T1D) and type 2 (T2D). Methods: In partnership, we harmonized demographic and clinical data from the SEARCH for Diabetes in Youth (SEARCH) registry in the United States and the Registry of People with Diabetes with Youth Age at Onset (YDR) in India to the structure and terminology of the Observational Medical Outcomes Partnership Common Data Model. Data were from youth with T1D and T2D, aged <20 years and newly diagnosed between 2006 and 2010. We compared key characteristics across registries using χ2 tests and t‐tests. Results: In total, there were 9650 youth with T1D and 2406 youth with T2D from 2006 to 2012. SEARCH youth were diagnosed at younger ages than YDR youth for T1D and T2D (10.0 vs 10.5 years, P <.001 and 14.7 vs 16.1 years, P <.001, respectively). For T2D, SEARCH had a higher proportion of females and significantly lower proportion of youth of high socioeconomic status compared to YDR. For T1D and T2D, SEARCH youth had higher BMI, lower blood pressure, and lower A1c compared to YDR youth. Conclusions: These data offer insights into the demographic and clinical characteristics of diabetes in youth across the two countries. Further research is needed to better understand why these differences exist. [ABSTRACT FROM AUTHOR]

Background: There is significant global variation in the prevalence of diabetic ketoacidosis (DKA) at diagnosis among youth with type 1 diabetes (T1D). However, data for youth with type 2 diabetes (T2D) are limited, even in developed countries. We compared the prevalence of DKA at diagnosis among individuals with T1D and T2D from the SEARCH for Diabetes in Youth (SEARCH) and the Registry of Youth Onset Diabetes in India (YDR) registries. Methods: We harmonized the SEARCH and YDR registries to the structure and terminology in the Observational Medical Outcome Partnership Common Data Model. Data used were from youth with T1D and T2D diagnosed before 20 years and newly diagnosed between 2006 and 2012 in YDR and 2009 and 2012 in SEARCH. Results: There were 5366 US youth (4078 with T1D, 1288 with T2D) and 2335 Indian youth (2108 with T1D, 227 with T2D). More than one third of T1D youth enrolled in SEARCH had DKA at diagnosis which was significantly higher than in YDR (35.3% vs 28.7%, P <.0001). The burden of DKA in youth with T1D was significantly higher among younger age groups; this relationship was similar across registries (P =.4). The prevalence of DKA among T2D in SEARCH and YDR were 5.5% and 6.6% respectively (P =.4). Conclusions: There is significant burden of DKA at diagnosis with T1D among youth from United States and India, especially among the younger age groups. The reasons for this high prevalence are largely unknown but are critical to developing interventions to prevent DKA at diagnosis. [ABSTRACT FROM AUTHOR]

Background: Although surveillance for diabetes in youth relies on provider‐assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown. Methods: Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider‐assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002‐2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance. Results: Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively. Conclusion: Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification. [ABSTRACT FROM AUTHOR]

Objective Dyslipidemia contributes to the increased risk of cardiovascular disease in persons with type 1 diabetes ( T1D). Weight control is commonly recommended as a treatment for dyslipidemia. However, the extent to which decreases in weight affect the lipid profile in youth with T1D is not known. Therefore, we tested the hypothesis that decreases in body mass index z-score ( BMIz) were associated with concomitant changes in the lipid profile in youth with T1D. Study Design We studied 1142 youth with incident T1D, who had at least two fasting lipid measurements over 2 yr (initial visit mean: age = 10.8 ± 3.9 yr, BMIz = 0.55 ± 0.97, T1D duration = 10.7 ± 7.6 months; 47.5% female, 77.9% non-Hispanic white) in the SEARCH for Diabetes in Youth Study. Longitudinal mixed models were used to examine the relationships between changes in BMIz and changes in total, low-density lipoprotein cholesterol ( LDL-C), high-density lipoprotein cholesterol ( HDL-C), non- HDL cholesterol, and log triglycerides (TG) adjusted for initial age, sex, race/ethnicity, clinical site, season of study visit, T1D duration, and glycated hemoglobin A1c ( HbA1c). Results We found that over 2 yr all lipid levels, except LDL-C, increased significantly (p < 0.05). Decreases in BMIz were associated with favorable changes in HDL-C and TG only and the magnitude of these changes depended on the initial BMIz value (interaction p < 0.05), so that greater improvements were seen in those with higher BMIz. Conclusions Our data suggest that weight loss may be an effective, but limited, therapeutic approach for dyslipidemia in youth with T1D. [ABSTRACT FROM AUTHOR]

The article discusses the use of automated algorithms in determining cases of diabetes in children and diabetes type classification based on demographic characteristics. Topics include how administrative and electronic health record data can be used in these algorithms, how age, race, and ethnicity impact the risk of diabetes, and how effective the algorithms were at identifying childhood diabetes of both types.