Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

MOTIVATION: statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. RESULTS: We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. AVAILABILITY AND IMPLEMENTATION: REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

Self-Reported Food Security in Adolescents with Type 1 Diabetes: Association with Hemoglobin A1c and Mental Health Symptoms Independent of Household Food Security.

BACKGROUND: Typically, child exposure to food insecurity is assessed by caregiver reports of household food security. Child report has the potential for greater accuracy because it pertains only to the child whose experiences may differ from caregiver reports. OBJECTIVE: We assessed if adolescent-reported food insecurity was associated with levels of hemoglobin A1c (HbA1c), acute diabetes-related complications, depressive symptoms, and disordered eating behaviors in adolescents with type 1 diabetes, independently from household food security. METHODS: In a cross-sectional analysis of the multicenter SEARCH for Diabetes in Youth Cohort Study (phase 4, 2016-2019) including 601 adolescents aged 10-17 y with type 1 diabetes and their caregivers, household food security, and adolescent-reported food security were assessed using the 18-item Household Food Security Survey Module and the 6-item Child Food Security Assessment questionnaire. Age-stratified (10-13 and 14-17) regression models were performed to estimate independent associations, adjusting for sociodemographics, clinical factors, and household food security. RESULTS: Food insecurity was reported by 13.1% (n = 79) of adolescents and 15.6% (n = 94) of caregivers. Among adolescent-caregiver dyads, 82.5% (n = 496) of reports were concordant and 17.5% (n = 105) discordant, Cohen's κ= 0.3. Adolescent-reported food insecurity was not independently associated with HbA1c, diabetic ketoacidosis, and severe hypoglycemia, including in age-stratified analyses. Adolescent-reported food insecurity was independently associated with elevated odds of depressive symptoms [odds ratio (OR): 3.6; 95% confidence interval (CI): 1.3, 10.3] and disordered eating behaviors (OR: 2.5, 95% CI: 1.4, 4.6) compared with adolescents reporting food security; these associations remained in both age groups for disordered eating behaviors and in the older group for depressive symptoms. CONCLUSIONS: Adolescents with type 1 diabetes may experience food insecurity differently than caregivers. Adolescent-reported food insecurity was independently associated with depressive symptoms and disordered eating behaviors and thus may be an important attribute to assess in addition to household food security in adolescents with type 1 diabetes.

Properties of the Household Food Security Survey Module Scale in Young Adults with Diabetes.

BACKGROUND: The Household Food Security Survey Module (HFSSM) was not tailored to people with chronic diseases or young adults (YAs). OBJECTIVES: We aim to evaluate whether the 18-item HFSSM meets assumptions underlying the scale among YAs with diabetes. METHODS: Data from 1887 YAs with youth-onset type 1 diabetes or type 2 diabetes were used from the SEARCH for Diabetes in Youth Study, 2016-2019, and on 925 who returned for the SEARCH Food Security Cohort Study, 2018-2021, all of whom had completed the HFSSM. Guttman scaling properties (affirmation of preceding less severe items) and Rasch model properties (probability to answer an item based on difficulty level) were assessed. RESULTS: Items 3 (balanced meals) and 6 (eating less than one should) were affirmed more frequently than expected (nonmonotonic response pattern). At 1.2%-3.5%, item nonresponse was rare among type 1 diabetes but higher among type 2 diabetes (range: 3.1%-10.6%). Items 9 (not eating the whole day) and 3 did not meet the Guttman scaling properties. Rasch modeling revealed that item 3 had the smallest difficulty parameter. INFIT indices suggested that some responses to item 3 did not match the pattern in the rest of the sample. Classifying household food insecurity (HFI) based on items 1 and 2 compared with other 2-item combinations, including item 3, revealed a substantial undercount of HFI ranging from 5% to 8% points. CONCLUSIONS: Use of the HFSSM among YAs with diabetes could potentially result in biased HFI reporting and affect estimates of HFI prevalence in this population.

Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study.

BACKGROUND: Incidence is one of the most important epidemiologic indices in surveillance. However, determining incidence is complex and requires time-consuming cohort studies or registries with date of diagnosis. Estimating incidence from prevalence using mathematical relationships may facilitate surveillance efforts. The aim of this study was to examine whether a partial differential equation (PDE) can be used to estimate diabetes incidence from prevalence in youth. METHODS: We used age-, sex-, and race/ethnicity-specific estimates of prevalence in 2001 and 2009 as reported in the SEARCH for Diabetes in Youth study. Using these data, a PDE was applied to estimate the average incidence rates of type 1 and type 2 diabetes for the period between 2001 and 2009. Estimates were compared to annual incidence rates observed in SEARCH. Precision of the estimates was evaluated using 95% bootstrap confidence intervals. RESULTS: Despite the long period between prevalence measures, the estimated average incidence rates mirror the average of the observed annual incidence rates. Absolute values of the age-standardized sex- and type-specific mean relative errors are below 8%. CONCLUSIONS: Incidence of diabetes can be accurately estimated from prevalence. Since only cross-sectional prevalence data is required, employing this methodology in future studies may result in considerable cost savings.

Household Food Insecurity and Cognition in Youth and Young Adults with Youth-Onset Diabetes.

Objective: We evaluated the association of household food insecurity (FI) with cognition in youth and young adults with type 1 diabetes (T1D) or type 2 diabetes (T2D). Design: In this cross-sectional study, age-adjusted scores for composite Fluid Cognition, and sub-domain scores for Receptive Language and Inhibitory Control and Attention, were modeled stratified by diabetes-type using linear regression, with FI in the past year as the predictor, controlling for covariates. Tests for processing speed, inhibitory control/attention, working memory, episodic memory, and cognitive flexibility were administered to measure composite Fluid Cognition score. The NIHT-CB Picture Vocabulary Test was used to assess Crystallized Cognition score and rapid identification of congruent versus noncongruent items were used to assess Inhibitory Control and Attention score. Setting: The SEARCH for Diabetes in Youth study, representative of 5 U.S. states. Participants: Included 1574 youth and young adults with T1D or T2D, mean age of 21 years, mean diabetes duration of 11 years, 51% non-Hispanic white, and 47% had higher HbA1c levels (>9% HbA1c). Results: Approximately 18% of the 1,240 participants with T1D and 31% of the 334 with T2D experienced FI. The food-insecure group with T1D had a lower composite Fluid Cognition score (ß= -2.5, 95% confidence interval (CI)= -4.8, -0.1) and a lower Crystallized Cognition score (ß= -3.4, CI= -5.6, -1.3) than food-secure peers. Findings were attenuated to non-significance after adjustment for demographics. Among T2D participants, no associations were observed. In participants with T1D effect modification by glycemic levels were found in the association between FI and composite Fluid Cognition score but adjustment for socioeconomic characteristics attenuated the interaction (p=0.0531). Conclusions: Food-insecure youth and young adults with T1D or T2D did not have different cognition compared to those who were food-secure after adjustment for confounders. Longitudinal research is needed to further understand relations amongst these factors.

Diabetes complications and cognitive function in young adults with youth-onset type 1 or type 2 diabetes: the SEARCH for Diabetes in Youth Study.

Aims/hypotheses: People with type 1 (T1D) or type 2 diabetes (T2D) who also have diabetes complications can have pronounced cognitive deficits. It remains unknown, however, whether and how multiple diabetes complications co-occur with cognitive dysfunction, particularly in youth-onset diabetes. Methods: Using data from the SEARCH for Diabetes in Youth study cohort, a prospective longitudinal cohort, we examined clustering of complications and their underlying clinical factors with performance on cognitive tests in young adults with youth-onset T1D or T2D. Cognition was assessed via the NIH Toolbox Cognition Battery. The main cognitive variables were age-corrected scores for composite fluid cognition and associated cognitive subdomains. Diabetes complications included retinopathy, microalbuminuria, and peripheral neuropathy (PN). Lipids, systolic blood pressure (SBP), hemoglobin A1c, and other clinical factors were included in the analyses. Clustering was applied separately to each group (T1D=646; T2D=165). A three-cluster(C) solution was identified for each diabetes type. Mean values and frequencies of all factors were compared between resulting clusters. Results: The average age-corrected score for composite fluid cognition differed significantly across clusters for each group (p<0.001). People with T1D and the lowest average fluid cognition scores had the highest frequency of self-reporting at least one episode of hypoglycemia in the year preceding cognitive testing and the highest prevalence of PN. Persons with T2D and the lowest average fluid cognition scores had the highest SBP, the highest central systolic and diastolic blood pressures, and highest prevalence of PN. Conclusions/interpretations: These findings highlight shared (PN) and unique factors (hypoglycemia in T1D; SBP in T2D) that could be targeted to potentially mitigate cognitive issues in young people with youth-onset diabetes.

Preconception Counseling in Women With Diabetes: The SEARCH for Diabetes in Youth Study.

Preconception counseling is recommended for all women with diabetes starting at puberty to convey the importance of optimal diabetes management for maternal and fetal outcomes. This study included 622 female participants from the SEARCH for Diabetes in Youth study with a mean age of 22.2 years (range 14-35 years). Only 53.7% reported ever receiving preconception counseling, which was significantly lower among women seeing pediatric providers than those seeing adult or all-age providers. Older age and history of prior pregnancy were associated with increased odds of reporting having received preconception counseling. Identification of barriers to delivering preconception counseling to young females with diabetes and strategies to overcome them are needed to reduce the risk for pregnancy complications and adverse offspring health outcomes.

Health Care Transition to Adult Care in Type 1 Diabetes: Associations With Student and Employment Status-The SEARCH for Diabetes in Youth Study.

Successful transition from a pediatric to adult diabetes care provider is associated with reduced ambulatory diabetes care visits and increased acute complications. This study aimed to determine whether the degree of independence in diabetes care and the rate of acute complications after transition to adult diabetes care were associated with individuals' student or employment status. Nonstudents were found to be less likely than students to be independent with diabetes care, and employed nonstudents were at lower risk of diabetic ketoacidosis than unemployed nonstudents. Additional support may be needed for young adults who are not students or are unemployed to improve independence and reduce the risk for acute complications.

A longitudinal assessment of diabetes autoantibodies in the SEARCH for diabetes in youth study.

To assess changes in diabetes autoantibodies (DAs) over time in children and young adults with diabetes and determine whether observed changes were associated with demographic characteristics, clinical parameters and diabetes complications. Participants had DAs measured at baseline (10.3 ± 7.1 months after diabetes diagnosis) and at 12, 24 months and ≥5 years after the baseline measurement. At the ≥5-year follow-up, the presence of diabetes complications was assessed. We examined the associations between change in number of positive DAs and changes in individual DA status with the participants' characteristics and clinical parameters over time. Out of 4179 participants, 62% had longitudinal DA data and 51% had complications and longitudinal DA data. In participants with ≥1 baseline positive DA (n = 1699), 83.4% remained positive after 7.3 ± 2.3 years duration of diabetes. Decrease in number of positive DAs was associated with longer diabetes duration (p = 0.003 for 1 baseline positive DA; p < 0.001 for 2 baseline positive DAs) and younger age at diagnosis (p < 0.001 for 2 baseline positive DAs). No associations were found between change in number of positive DAs in participants with ≥1 baseline positive DA (n = 1391) and HbA1c, insulin dose, acute, or chronic complications after 7.7 ± 1.9 years duration of diabetes. DA status likely remains stable in the first 7 years after diabetes diagnosis. Younger age at diabetes diagnosis and longer duration were associated with less persistence of DAs. Measuring DAs after initial presentation may aid in diabetes classification but not likely in predicting the clinical course.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.

BACKGROUND: Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. METHODS: A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. RESULTS: Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. CONCLUSIONS: Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Changes to care delivery at nine international pediatric diabetes clinics in response to the COVID-19 global pandemic.

BACKGROUND: Pediatric diabetes clinics around the world rapidly adapted care in response to COVID-19. We explored provider perceptions of care delivery adaptations and challenges for providers and patients across nine international pediatric diabetes clinics. METHODS: Providers in a quality improvement collaborative completed a questionnaire about clinic adaptations, including roles, care delivery methods, and provider and patient concerns and challenges. We employed a rapid analysis to identify main themes. RESULTS: Providers described adaptations within multiple domains of care delivery, including provider roles and workload, clinical encounter and team meeting format, care delivery platforms, self-management technology education, and patient-provider data sharing. Providers reported concerns about potential negative impacts on patients from COVID-19 and the clinical adaptations it required, including fears related to telemedicine efficacy, blood glucose and insulin pump/pen data sharing, and delayed care-seeking. Particular concern was expressed about already vulnerable patients. Simultaneously, providers reported 'silver linings' of adaptations that they perceived as having potential to inform care and self-management recommendations going forward, including time-saving clinic processes, telemedicine, lifestyle changes compelled by COVID-19, and improvements to family and clinic staff literacy around data sharing. CONCLUSIONS: Providers across diverse clinical settings reported care delivery adaptations in response to COVID-19-particularly telemedicine processes-created challenges and opportunities to improve care quality and patient health. To develop quality care during COVID-19, providers emphasized the importance of generating evidence about which in-person or telemedicine processes were most beneficial for specific care scenarios, and incorporating the unique care needs of the most vulnerable patients.

Clinical profile at diagnosis with youth-onset type 1 and type 2 diabetes in two pediatric diabetes registries: SEARCH (United States) and YDR (India).

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.

Treatment regimens and glycosylated hemoglobin levels in youth with Type 1 and Type 2 diabetes: Data from SEARCH (United States) and YDR (India) registries.

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.

Glycemic control is associated with dyslipidemia over time in youth with type 2 diabetes: The SEARCH for diabetes in youth study.

BACKGROUND: Dyslipidemia has been documented in youth with type 2 diabetes. There is a paucity of studies examining dyslipidemia over time in youth with type 2 diabetes and associated risk factors. OBJECTIVE: To evaluate lipids at baseline and follow-up and associated risk factors in youth with type 2 diabetes. METHODS: We studied 212 youth with type 2 diabetes at baseline and after an average of 7 years of follow-up in the SEARCH for Diabetes in Youth Study. Abnormal lipids were defined as high-density lipoprotein cholesterol (HDL-C) < 35, low-density lipoprotein cholesterol (LDL-C) > 100, or triglycerides >150 (all mg/dl). We evaluated participants for progression to abnormal lipids (normal lipids at baseline and abnormal at follow-up), regression (abnormal lipids at baseline and normal at follow-up), stable normal, and stable abnormal lipids over time for HDL-C, LDL-C, and triglycerides. Associations between hemoglobin A1c (HbA1c) and adiposity over time (area under the curve [AUC]) with progression and stable abnormal lipids were evaluated. RESULTS: HDL-C progressed, regressed, was stable normal, and stable abnormal in 12.3%, 11.3%, 62.3%, and 14.2% of participants, respectively. Corresponding LDL-C percentages were 15.6%, 12.7%, 42.9%, and 28.8% and triglycerides were 17.5%, 10.8%, 55.7%, and 16.0%. Each 1% increase in HbA1c AUC was associated with a 13% higher risk of progression and stable abnormal triglycerides and a 20% higher risk of progression and stable abnormal LDL-C. Higher adiposity AUC was marginally (p = 0.049) associated with abnormal HDL-C. CONCLUSIONS: Progression and stable abnormal LDL-C and triglycerides occur in youth with type 2 diabetes and are associated with higher HbA1c.

The relationship between traffic-related air pollution exposures and allostatic load score among youth with type 1 diabetes in the SEARCH cohort.

OBJECTIVE: We investigated the effects of chronic exposures to particulate and traffic-related air pollution on allostatic load (AL) score, a marker of cumulative biological risk, among youth with type 1 diabetes. RESEARCH DESIGN AND METHODS: Participants were drawn from five clinical sites of the SEARCH for Diabetes in Youth (SEARCH) study (n = 2338). Baseline questionnaires, anthropometric measures, and a fasting blood test were taken at a clinic visit between 2001 and 2005. AL was operationalized using 10 biomarkers reflecting cardiovascular, metabolic, and inflammatory risk. Annual residential exposures to PM2.5 and proximity to heavily-trafficked major roadways were estimated for each participant. Poisson regression models adjusted for sociodemographic and lifestyle factors were conducted for each exposure. RESULTS: No significant associations were observed between exposures to PM2.5 or proximity to traffic and AL score, however analyses were suggestive of effect modification by race for residential distance to heavily-trafficked major roadways (p = 0.02). In stratified analyses, residing <100, 100-<200 and 200-<400 m compared to 400 m or more from heavily-trafficked major roadways was associated with 11%, 26% and 14% increases in AL score, respectively (95% CIs: -4, 29; 9, 45; -1, 30) for non-white participants compared to 6%, -2%, and -2% changes (95% CIs: -2, 15; -10, 7; -8, 6) for white participants. CONCLUSIONS: Among this population of youth with type 1 diabetes, we did not observe consistent relationships between chronic exposures to particulate and traffic-related air pollution and changes in AL score, however associations for traffic-related pollution exposures may differ by race/ethnicity and warrant further examination.

Screening for Mental Health Comorbidities in a Pediatric Diabetes Clinic Setting.

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes an effort to improve mental health screening of patients at a pediatric diabetes clinic in Seattle, WA.

Trends in Prevalence of Type 1 and Type 2 Diabetes in Children and Adolescents in the US, 2001-2017.

Importance: Changes in the prevalence of youth-onset diabetes have previously been observed. Objective: To estimate changes in prevalence of type 1 and type 2 diabetes in youths in the US from 2001 to 2017. Design, Setting, and Participants: In this cross-sectional observational study, individuals younger than 20 years with physician-diagnosed diabetes were enumerated from 6 areas in the US (4 geographic areas, 1 health plan, and select American Indian reservations) for 2001, 2009, and 2017. Exposures: Calendar year. Main Outcomes and Measures: Estimated prevalence of physician-diagnosed type 1 and type 2 diabetes overall and by race and ethnicity, age, and sex. Results: Among youths 19 years or younger, 4958 of 3.35 million had type 1 diabetes in 2001, 6672 of 3.46 million had type 1 diabetes in 2009, and 7759 of 3.61 million had type 1 diabetes in 2017; among those aged 10 to 19 years, 588 of 1.73 million had type 2 diabetes in 2001, 814 of 1.85 million had type 2 diabetes in 2009, and 1230 of 1.85 million had type 2 diabetes in 2017. The estimated type 1 diabetes prevalence per 1000 youths for those 19 years or younger increased significantly from 1.48 (95% CI, 1.44-1.52) in 2001 to 1.93 (95% CI, 1.88-1.98) in 2009 to 2.15 (95% CI, 2.10-2.20) in 2017, an absolute increase of 0.67 per 1000 youths (95%, CI, 0.64-0.70) and a 45.1% (95% CI, 40.0%-50.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic White (0.93 per 1000 youths [95% CI, 0.88-0.98]) and non-Hispanic Black (0.89 per 1000 youths [95% CI, 0.88-0.98]) youths. The estimated type 2 diabetes prevalence per 1000 youths aged 10 to 19 years increased significantly from 0.34 (95% CI, 0.31-0.37) in 2001 to 0.46 (95% CI, 0.43-0.49) in 2009 to 0.67 (95% CI, 0.63-0.70) in 2017, an absolute increase of 0.32 per 1000 youths (95% CI, 0.30-0.35) and a 95.3% (95% CI, 77.0%-115.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic Black (0.85 per 1000 youths [95% CI, 0.74-0.97]) and Hispanic (0.57 per 1000 youths [95% CI, 0.51-0.64]) youths. Conclusions and Relevance: In 6 areas of the US from 2001 to 2017, the estimated prevalence of diabetes among children and adolescents increased for both type 1 and type 2 diabetes.

Mental health comorbidities in adolescents and young adults with type 2 diabetes.

BACKGROUND: Screening for mental health comorbidities is recommended in adolescents and young adults (AYAs) with diabetes. There is a paucity of data on mental health comorbidities in AYAs with type 2 diabetes (T2D). OBJECTIVE: To assess rates of depression, suicidal ideation, and diabetes distress (DD) in AYAs with T2D overall and by sociodemographic and clinical factors. METHODS: AYAs with T2D ages 13-21 years seen in a pediatric diabetes clinic between March 2018 and June 2019 completed the Patient Health Questionnaire-9 (PHQ-9) for depression screening and the Problem Areas in Diabetes - teen version (PAID-T) survey to assess DD. Chi-square tests were used to assess whether rates of depression and DD were associated with participant characteristics. RESULTS: The sample consisted of 64 AYAs with T2D (58% female, mean age 15.8 ± 2.0 years, mean HbA1c 8.3% ± 2.6%, mean BMI z-score 2.2 ± 0.6, 59% on insulin). Overall, 31% of participants had high depression and/or DD. Twenty-two percent of participants reported high depressive symptoms and 9% endorsed suicidal ideation on the PHQ-9. There were no differences in rates of depression by sociodemographic factors. Twenty-three percent of participants reported high DD. Rates of DD were higher among those on insulin (p = 0.014) and on public health insurance (p = 0.014). CONCLUSIONS: Almost 1 in 3 AYAs with T2D endorsed depression and/or DD. Our findings support the importance of mental health screening in AYAs with T2D, as well as the need for strategies to address psychological comorbidities in this population.

Incidence Trends of Type 1 and Type 2 Diabetes among Youths, 2002-2012.

BACKGROUND: Diagnoses of type 1 and type 2 diabetes in youths present a substantial clinical and public health burden. The prevalence of these diseases increased in the 2001-2009 period, but data on recent incidence trends are lacking. METHODS: We ascertained cases of type 1 and type 2 diabetes mellitus at five study centers in the United States. Denominators (4.9 million youths annually) were obtained from the U.S. Census or health-plan member counts. After the calculation of annual incidence rates for the 2002-2012 period, we analyzed trends using generalized autoregressive moving-average models with 2-year moving averages. RESULTS: A total of 11,245 youths with type 1 diabetes (0 to 19 years of age) and 2846 with type 2 diabetes (10 to 19 years of age) were identified. Overall unadjusted estimated incidence rates of type 1 diabetes increased by 1.4% annually (from 19.5 cases per 100,000 youths per year in 2002-2003 to 21.7 cases per 100,000 youths per year in 2011-2012, P=0.03). In adjusted pairwise comparisons, the annual rate of increase was greater among Hispanics than among non-Hispanic whites (4.2% vs. 1.2%, P<0.001). Overall unadjusted incidence rates of type 2 diabetes increased by 7.1% annually (from 9.0 cases per 100,000 youths per year in 2002-2003 to 12.5 cases per 100,000 youths per year in 2011-2012, P<0.001 for trend across race or ethnic group, sex, and age subgroups). Adjusted pairwise comparisons showed that the relative annual increase in the incidence of type 2 diabetes among non-Hispanic whites (0.6%) was lower than that among non-Hispanic blacks, Asians or Pacific Islanders, and Native Americans (P<0.05 for all comparisons) and that the annual rate of increase among Hispanics differed significantly from that among Native Americans (3.1% vs. 8.9%, P=0.01). After adjustment for age, sex, and race or ethnic group, the relative annual increase in the incidence of type 1 diabetes was 1.8% (P<0.001) and that of type 2 diabetes was 4.8% (P<0.001). CONCLUSIONS: The incidences of both type 1 and type 2 diabetes among youths increased significantly in the 2002-2012 period, particularly among youths of minority racial and ethnic groups. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and the Centers for Disease Control and Prevention.).

Trends in Incidence of Type 1 and Type 2 Diabetes Among Youths - Selected Counties and Indian Reservations, United States, 2002-2015.

Diabetes is one of the most common chronic diseases among persons aged <20 years (1). Onset of diabetes in childhood and adolescence is associated with numerous complications, including diabetic kidney disease, retinopathy, and peripheral neuropathy, and has a substantial impact on public health resources (2,3). From 2002 to 2012, type 1 and type 2 diabetes incidence increased 1.4% and 7.1%, respectively, among U.S. youths (4). To assess recent trends in incidence of diabetes in youths (defined for this report as persons aged <20 years), researchers analyzed 2002-2015 data from the SEARCH for Diabetes in Youth Study (SEARCH), a U.S. population-based registry study with clinical sites located in five states. The incidence of both type 1 and type 2 diabetes in U.S. youths continued to rise at constant rates throughout this period. Among all youths, the incidence of type 1 diabetes increased from 19.5 per 100,000 in 2002-2003 to 22.3 in 2014-2015 (annual percent change [APC] = 1.9%). Among persons aged 10-19 years, type 2 diabetes incidence increased from 9.0 per 100,000 in 2002-2003 to 13.8 in 2014-2015 (APC = 4.8%). For both type 1 and type 2 diabetes, the rates of increase were generally higher among racial/ethnic minority populations than those among whites. These findings highlight the need for continued surveillance for diabetes among youths to monitor overall and group-specific trends, identify factors driving these trends, and inform health care planning.