Association of Obesity, Metabolic Syndrome, and Diabetes With Urinary Incontinence and Chronic Kidney Disease: Analysis of the National Health and Nutrition Examination Survey, 2003-2020.

PURPOSE: Diabetes and obesity, components of the metabolic syndrome (MetS), are risk factors for urinary incontinence (UI) and chronic kidney disease (CKD). We interrogated US population-based data to explore independent, sex-specific associations between nondiabetic MetS, with and without obesity, and UI and/or CKD. MATERIALS AND METHODS: We analyzed data from 8586 males and 8420 females ≥20 years from the National Health and Nutrition Examination Survey. Multivariable logistic regression models were used to examine associations of UI or CKD with diabetes and 4 nondiabetic obesity/metabolic phenotypes: non-MetS/nonobese, MetS/nonobese, non-MetS/obese, and MetS/obese. Multinominal logistic regression models were used to assess associations of co-occurring UI/CKD with obesity/metabolic phenotypes. RESULTS: Male MetS/obese participants had increased odds of any UI (1.25; 95% CI 1.00-1.57) and urgency UI (1.36; 1.03-1.80), compared with non-MetS/nonobese participants. Female MetS/obese participants had increased odds of any UI (2.16; 95% CI 1.76-2.66), stress UI (1.51; 1.21-1.87), and mixed UI (1.66; 1.31-2.11) compared with non-MetS/nonobese participants. The odds of co-occurring UI/CKD were increased relative to either condition alone in persons with diabetes, and in males with MetS/obese phenotypes and females with MetS phenotypes as compared to same sex participants with neither obesity nor MetS. CONCLUSIONS: We found novel associations between MetS/obese and urgency UI in males without diabetes, and between SUI and both MetS and obesity in females without diabetes. Odds estimates for UI/CKD were increased by existing obesity or MetS as compared to those for UI or CKD alone. Improved understanding of modifiable factors associated with UI will inform prevention and treatment opportunities.

Blood glucose, blood pressure, and cholesterol testing among adults with diabetes before and during the COVID-19 pandemic, USA, 2019 vs 2021.

INTRODUCTION: Regular blood glucose/A1c, blood pressure (BP), and cholesterol (ABC) testing is important for diabetes management. It is unknown whether pandemic-related disruptions in medical care were negatively associated with ABC testing among US adults with diagnosed diabetes. RESEARCH DESIGN AND METHODS: A cross-sectional analysis was conducted among adults ≥18 years with diagnosed diabetes who participated in the 2019 or 2021 National Health Interview Survey (n=3355 and n=3127, respectively). Adults with diabetes self-reported sociodemographic and diabetes-related characteristics, ABC testing in the past year, and delays or not getting medical care due to the pandemic (2021 only). Descriptive statistics were used to determine differences in ABC testing in 2019 vs 2021. Logistic regression models were used to assess the association between delays or not getting medical care due to the pandemic and ABC testing, adjusting for sociodemographic characteristics, diabetes duration, and diabetes medication use. RESULTS: Overall, the prevalence of having a blood glucose/A1c or a BP test in the past year was high (>90%) but it was significantly lower in 2021 compared with 2019 (A1c: 94.2% vs 96.8%, p<0.001; BP: 96.8% vs 98.4%, p=0.002, respectively). Cholesterol testing remained stable (93.0% in 2021 vs 94.5% in 2019, p=0.053). In logistic regression analysis, after full adjustment, adults who reported delaying or not getting medical care when needed due to the pandemic were 50% less likely to get an ABC test in the past year compared with those who promptly received medical care (A1c: adjusted OR (aOR)=0.44, 95% CI 0.29-0.68; BP: aOR=0.48, 95% CI 0.27-0.85; cholesterol: aOR=0.48, 95% CI 0.31-0.75). CONCLUSIONS: Disruptions in medical care during the pandemic were associated with a decrease in ABC testing. Future research is needed to assess whether blood glucose/A1c and BP testing returns to prepandemic levels and if reductions in these tests result in excess diabetes-related complications.

Prevalence, Progression, and Modifiable Risk Factors for Diabetic Retinopathy in Youth and Young Adults With Youth-Onset Type 1 and Type 2 Diabetes: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To determine the prevalence, progression, and modifiable risk factors associated with the development of diabetic retinopathy (DR) in a population-based cohort of youth-onset diabetes. RESEARCH DESIGN AND METHODS: We conducted a multicenter, population-based prospective cohort study (2002-2019) of youth and young adults with youth-onset type 1 diabetes (n = 2,519) and type 2 diabetes (n = 447). Modifiable factors included baseline and change from baseline to follow-up in BMI z score, waist/height ratio, systolic and diastolic blood pressure z score, and A1C. DR included evidence of mild or moderate nonproliferative DR or proliferative retinopathy. Prevalence estimates were standardized to estimate the burden of DR, and inverse probability weighting for censoring was applied for estimating risk factors for DR at two points of follow-up. RESULTS: DR in youth-onset type 1 and type 2 diabetes is highly prevalent, with 52% of those with type 1 diabetes and 56% of those with type 2 diabetes demonstrating retinal changes at follow-up (mean [SD] 12.5 [2.2] years from diagnosis). Higher baseline A1C, increase in A1C across follow-up, and increase in diastolic and systolic blood pressure were associated with the observation of DR at follow-up for both diabetes types. Increase in A1C across follow-up was associated with retinopathy progression. BMI z score and waist/height ratio were inconsistently associated, with both positive and inverse associations noted. CONCLUSIONS: Extrapolated to all youth-onset diabetes in the U.S., we estimate 110,051 cases of DR developing within ∼12 years postdiagnosis. Tight glucose and blood pressure management may offer the opportunity to mitigate development and progression of DR in youth-onset diabetes.

Trends in incidence of youth-onset type 1 and type 2 diabetes in the USA, 2002-18: results from the population-based SEARCH for Diabetes in Youth study.

BACKGROUND: The incidence of diabetes is increasing in children and young people. We aimed to describe the incidence of type 1 and type 2 diabetes in children and young people aged younger than 20 years over a 17-year period. METHODS: The SEARCH for Diabetes in Youth study identified children and young people aged 0-19 years with a physician diagnosis of type 1 or type 2 diabetes at five centres in the USA between 2002 and 2018. Eligible participants included non-military and non-institutionalised individuals who resided in one of the study areas at the time of diagnosis. The number of children and young people at risk of diabetes was obtained from the census or health plan member counts. Generalised autoregressive moving average models were used to examine trends, and data are presented as incidence of type 1 diabetes per 100 000 children and young people younger than 20 years and incidence of type 2 diabetes per 100 000 children and young people aged between 10 years and younger than 20 years across categories of age, sex, race or ethnicity, geographical region, and month or season of diagnosis. FINDINGS: We identified 18 169 children and young people aged 0-19 years with type 1 diabetes in 85 million person-years and 5293 children and young people aged 10-19 years with type 2 diabetes in 44 million person-years. In 2017-18, the annual incidence of type 1 diabetes was 22·2 per 100 000 and that of type 2 diabetes was 17·9 per 100 000. The model for trend captured both a linear effect and a moving-average effect, with a significant increasing (annual) linear effect for both type 1 diabetes (2·02% [95% CI 1·54-2·49]) and type 2 diabetes (5·31% [4·46-6·17]). Children and young people from racial and ethnic minority groups such as non-Hispanic Black and Hispanic children and young people had greater increases in incidence for both types of diabetes. Peak age at diagnosis was 10 years (95% CI 8-11) for type 1 diabetes and 16 years (16-17) for type 2 diabetes. Season was significant for type 1 diabetes (p=0·0062) and type 2 diabetes (p=0·0006), with a January peak in diagnoses of type 1 diabetes and an August peak in diagnoses of type 2 diabetes. INTERPRETATION: The increasing incidence of type 1 and type 2 diabetes in children and young people in the USA will result in an expanding population of young adults at risk of developing early complications of diabetes whose health-care needs will exceed those of their peers. Findings regarding age and season of diagnosis will inform focused prevention efforts. FUNDING: US Centers for Disease Control and Prevention and US National Institutes of Health.

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.

Racial Disparities in Severe Maternal Morbidity in an Integrated Health Care System, Southern California, 2008-2017.

OBJECTIVE: The study's objectives were to examine rates of severe maternal morbidity (SMM) over a 10-year period and assess racial/ethnic disparities in SMM among insured women in a large, integrated health care system in Southern California. METHODS: We included Kaiser Permanente Southern California (KPSC) health plan members who gave birth at ≥20 weeks' gestation in a KPSC-owned hospital during 2008-2017. An SMM case was defined as presence of one or more indicators of an SMM event during a birth hospitalization, identified using maternal electronic health records. Crude SMM rates/10,000 births were calculated by year and maternal race/ethnicity. Modified Poisson regression models were used to assess the association between race/ethnicity and SMM adjusted for other maternal demographics, pregnancy characteristics, and preexisting conditions. RESULTS: We identified 5,915 SMM cases among 335,310 births. Crude SMM rates increased from 94.7 per 10,000 in 2008 to 192.6 in 2015 and 249.5 in 2017. Non-Hispanic Black (adjusted risk ratio [aRR] 1.52; 95% confidence interval [CI] 1.37-1.69), Asian/Pacific Islander (aRR 1.29, 95% CI 1.18-1.41), and Hispanic (aRR 1.18, 95% CI 1.10-1.27) women had greater likelihood of SMM than non-Hispanic White women. After further adjusting for preexisting health conditions, differences in SMM by race/ethnicity remained. CONCLUSIONS: SMM rates increased during 2008-2017 and women of racial and ethnic minority groups, particularly non-Hispanic Black women, were more likely to experience an SMM event than non-Hispanic White women. Multilevel approaches to understanding structural and social factors that may be associated with racial and ethnic disparities in SMM are needed to develop and test effective interventions to reduce SMM.

Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2060: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To project the prevalence and number of youths with diabetes and trends in racial and ethnic disparities in the U.S. through 2060. RESEARCH DESIGN AND METHODS: Based on a mathematical model and data from the SEARCH for Diabetes in Youth study for calendar years 2002-2017, we projected the future prevalence of type 1 and type 2 diabetes among youth aged <20 years while considering different scenarios of future trends in incidence. RESULTS: The number of youths with diabetes will increase from 213,000 (95% CI 209,000; 218,000) (type 1 diabetes 185,000, type 2 diabetes 28,000) in 2017 to 239,000 (95% CI 209,000; 282,000) (type 1 diabetes 191,000, type 2 diabetes 48,000) in 2060 if the incidence remains constant as observed in 2017. Corresponding relative increases were 3% (95% CI -9%; 21%) for type 1 diabetes and 69% (95% CI 43%; 109%) for type 2 diabetes. Assuming that increasing trends in incidence observed between 2002 and 2017 continue, the projected number of youths with diabetes will be 526,000 (95% CI 335,000; 893,000) (type 1 diabetes 306,000, type 2 diabetes 220,000). Corresponding relative increases would be 65% (95% CI 12%; 158%) for type 1 diabetes and 673% (95% CI 362%; 1,341%) for type 2 diabetes. In both scenarios, substantial widening of racial and ethnic disparities in type 2 diabetes prevalence are expected, with the highest prevalence among non-Hispanic Black youth. CONCLUSIONS: The number of youths with diabetes in the U.S. is likely to substantially increase in future decades, which emphasizes the need for prevention to attenuate this trend.

Household Food Insecurity and Fear of Hypoglycemia in Adolescents and Young Adults With Diabetes and Parents of Youth With Diabetes.

OBJECTIVE: To evaluate the relation between household food insecurity (HFI) and fear of hypoglycemia among young adults with type 1 and type 2 diabetes and adolescents with type 1 diabetes and their parents. RESEARCH DESIGN AND METHODS: We analyzed cross-sectional data of 1,676 young adults with youth-onset diabetes (84% type 1, 16% type 2) and 568 adolescents (<18 years old; mean age 15.1 years) with type 1 diabetes from the SEARCH for Diabetes in Youth study. Adult participants and parents of adolescent participants completed the U.S. Household Food Security Survey Module. Adults, adolescents, and parents of adolescents completed the Hypoglycemia Fear Survey, where answers range from 1 to 4. The outcomes were mean score for fear of hypoglycemia and the behavior and worry subscale scores. Linear regression models identified associations between HFI and fear of hypoglycemia scores. RESULTS: Adults with type 1 diabetes experiencing HFI had higher fear of hypoglycemia scores (0.22 units higher for behavior, 0.55 units for worry, 0.40 units for total; all P < 0.0001) than those without HFI. No differences by HFI status were found for adolescents with type 1 diabetes. Parents of adolescents reporting HFI had a 0.18 unit higher worry score than those not reporting HFI (P < 0.05). Adults with type 2 diabetes experiencing HFI had higher fear of hypoglycemia scores (0.19 units higher for behavior, 0.35 units for worry, 0.28 units for total; all P < 0.05) than those in food secure households. CONCLUSIONS: Screening for HFI and fear of hypoglycemia among people with diabetes can help providers tailor diabetes education for those who have HFI and therefore fear hypoglycemia.

Prevalence and Predictors of Household Food Insecurity and Supplemental Nutrition Assistance Program Use in Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To assess the prevalence of household food insecurity (HFI) and Supplemental Nutrition Assistance Program (SNAP) participation among youth and young adults (YYA) with diabetes overall and by type, and sociodemographic characteristics. RESEARCH DESIGN AND METHODS: The study included participants with youth-onset type 1 diabetes and type 2 diabetes from the SEARCH for Diabetes in Youth study. HFI was assessed using the 18-item U.S. Household Food Security Survey Module (HFSSM) administered from 2016 to 2019; three or more affirmations on the HFSSM were considered indicative of HFI. Participants were asked about SNAP participation. We used χ2 tests to assess whether the prevalence of HFI and SNAP participation differed by diabetes type. Multivariable logistic regression models were used to examine differences in HFI by participant characteristics. RESULTS: Of 2,561 respondents (age range, 10-35 years; 79.6% ≤25 years), 2,177 had type 1 diabetes (mean age, 21.0 years; 71.8% non-Hispanic White, 11.8% non-Hispanic Black, 13.3% Hispanic, and 3.1% other) and 384 had type 2 diabetes (mean age, 24.7 years; 18.8% non-Hispanic White, 45.8% non-Hispanic Black, 23.7% Hispanic, and 18.7% other). The overall prevalence of HFI was 19.7% (95% CI 18.1, 21.2). HFI was more prevalent in type 2 diabetes than type 1 diabetes (30.7% vs. 17.7%; P < 0.01). In multivariable regression models, YYA receiving Medicaid or Medicare or without insurance, whose parents had lower levels of education, and with lower household income had greater odds of experiencing HFI. SNAP participation was 14.1% (95% CI 12.7, 15.5), with greater participation among those with type 2 diabetes compared with those with type 1 diabetes (34.8% vs. 10.7%; P < 0.001). CONCLUSIONS: Almost one in three YYA with type 2 diabetes and more than one in six with type 1 diabetes reported HFI in the past year-a significantly higher prevalence than in the general U.S. population.

Food insecurity, diet quality, and suboptimal diabetes management among US adults with diabetes.

INTRODUCTION: A healthy diet is recommended to support diabetes management, including HbA1c, blood pressure, and cholesterol (ABC) control, but food insecurity is a barrier to consuming a healthy diet. We determined the prevalence of food insecurity and diet quality among US adults with diabetes and the associations with ABC management. RESEARCH DESIGN AND METHODS: Cross-sectional analyses were conducted among 2075 adults ≥20 years with diagnosed diabetes who participated in the 2013-2018 National Health and Nutrition Examination Surveys. Food insecurity was assessed using a standard questionnaire and diet quality was assessed using quartiles of the 2015 Healthy Eating Index. Adjusted ORs (aOR, 95% CI) were calculated from logistic regression models to determine the association between household food insecurity/diet quality and the ABCs while controlling for sociodemographic characteristics, healthcare utilization, smoking, medication for diabetes, blood pressure, or cholesterol, and body mass index. RESULTS: Overall, 17.6% of adults had food insecurity/low diet quality; 14.2% had food insecurity/high diet quality; 33.1% had food security/low diet quality; and 35.2% had food security/high diet quality. Compared with adults with food security/high diet quality, those with food insecurity/low diet quality were significantly more likely to have HbA1c ≥7.0% (aOR=1.85, 95% CI 1.23 to 2.80) and HbA1c ≥8.0% (aOR=1.79, 95% CI 1.04 to 3.08); food insecurity/high diet quality was significantly associated with elevated HbA1c; and food security/low diet quality with elevated A1c. CONCLUSIONS: Food insecurity, regardless of diet quality, was significantly associated with elevated A1c. For people with food insecurity, providing resources to reduce food insecurity could strengthen the overall approach to optimal diabetes management.

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.

Trends in Glycemia between 2002 and 2016 among Incident Youth Cohorts Early in the Course of Type 1 Diabetes: The SEARCH for Diabetes in Youth Study.

Objective: Hyperglycemia early in the course of type 1 diabetes (T1D) may increase the risk of cardiometabolic complications later in life. We tested the hypothesis that there were temporal trends in population-level glycemia and insulin pump use near T1D diagnosis among incident youth cohorts diagnosed between 2002 and 2016. Methods: Weighted and adjusted regression models were applied to data from the SEARCH for Diabetes in Youth study to analyze trends in hemoglobin A1c (HbA1c), suboptimal glycemia (HbA1c > 9% or not), and insulin pump use among youth with T1D within 30 months of diagnosis. We tested the interaction of year with race and ethnicity, sex, and insulin regimen to assess potential disparities. Results: Among the 3,956 youth with T1D, there was a small, clinically insignificant reduction in HbA1c between 2002 (7.9% ± 1.5) and 2016 (7.8% ± 2.4) (fully adjusted change by year (-0.013% [95% CI -0.026, -0.0008], p = 0.04). The proportion of youth with suboptimal glycemia increased with each year, but the adjusted odds did not change. Insulin pump use increased more than fivefold. Although interaction effects of time with race and ethnicity, sex, and insulin regimen were not detected, in 2016, suboptimal glycemia was 4.3 and 1.8 times more prevalent among Black and Hispanic than among non-Hispanic White youth, respectively. Conclusions: There was not a clinically significant population-level improvement in glycemia across incident youth cohorts early in the course of T1D, despite severalfold increases in insulin pump use. Comprehensive clinical interventions to improve glycemia early in the T1D course and address disparities are urgently needed.

Household food insecurity is associated with diabetic ketoacidosis but not severe hypoglycemia or glycemic control in youth and young adults with youth-onset type 2 diabetes.

OBJECTIVE: To examine the association between household food insecurity (HFI), glycemic control, severe hypoglycemia and diabetic ketoacidosis (DKA) among youth and young adults (YYA) with youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS: This cross-sectional study included 395 YYA with type 2 diabetes from the SEARCH for Diabetes in Youth Study (2015-2019). HFI was reported by young adult participants or parents of minor participants via the US Household Food Security Survey Module. Glycemic control was assessed by HbA1c and analyzed as a continuous and categorical variable (optimal: <7.0%, suboptimal: ≥7.0%-9.0%, poor: >9.0%). Acute complications included self-reported severe hypoglycemia or DKA in the last 12 months. Adjusted logistic and linear regression were used for binary and continuous outcomes, respectively. RESULTS: Approximately 31% reported HFI in the past 12 months. Mean HbA1c among those with HFI was 9.2% compared to 9.5% without HFI. Of those with HFI, 56% had an HbA1c >9.0% compared to 55% without HFI. Adjusted models showed no associations between HFI and glycemic control. Of those with HFI, 14.4% reported experiencing DKA and 4.7% reported severe hypoglycemia. YYA with HFI had 3.08 times (95% CI: 1.18-8.06) the odds of experiencing DKA as those without HFI. There was no association between HFI and severe hypoglycemia. CONCLUSIONS: HFI was associated with markedly increased odds of DKA but not with glycemic control or severe hypoglycemia. Future research among YYA with type 2 diabetes should evaluate longitudinally whether alleviating HFI reduces DKA.

Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth.

OBJECTIVE: Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S. RESEARCH DESIGN AND METHODS: We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other). RESULTS: T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency. CONCLUSIONS: Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.

Trends in Glycemic Control Among Youth and Young Adults With Diabetes: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. RESEARCH DESIGN AND METHODS: The study included 6,369 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods of 2002-2007, 2008-2013, and 2014-2019, diabetes durations of 1-4, 5-9, and ≥10 years, and age groups of 1-9, 10-14, 15-19, 20-24, and ≥25 years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A1c (HbA1c) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and diabetes duration, overall and stratified for each diabetes duration and age group. RESULTS: Adjusted mean HbA1c for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8 ± 0.04%. YYA with type 1 diabetes in the 10-14-, 15-19-, and 20-24-year-old age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income, and treatment regimen predicted differences in glycemic control in participants with type 1 diabetes from the 2014-2019 cohort. Adjusted mean HbA1c was 8.6 ± 0.12% for 2014-2019 YYA with type 2 diabetes. Participants aged ≥25 years with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in participants with type 2 diabetes. CONCLUSIONS: Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.

The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study.

OBJECTIVE: To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario in which non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. RESEARCH DESIGN AND METHODS: Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). In the White versus non-White subgroups, the propensity score models estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use. An analysis based on policy evaluation techniques in reinforcement learning estimated the effect of each treatment regimen on mean hemoglobin A1c (HbA1c) and the prevalence of diabetes complications for non-White YYA. RESULTS: The study included 978 YYA. The sample was 47.5% female and 77.5% non-Hispanic White, with a mean age of 12.8 ± 2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference of 0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95% CI -0.45, -0.21) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining ∼35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (P < 0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations. CONCLUSIONS: Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part of but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race and ethnicity-based health inequity.

Inequalities in Glycemic Control in Youth with Type 1 Diabetes Over Time: Intersectionality Between Socioeconomic Position and Race and Ethnicity.

BACKGROUND: Racial/ethnic health inequities have been well-documented among youth and young adults with type 1 diabetes (T1D), yet little is known about how socioeconomic position (SEP) intersects with the risk marker of race/ethnicity to predict inequities in longitudinal glycemic control. PURPOSE: To identify patterns of SEP, race/ethnicity, and clinical characteristics that differentiate hemoglobin A1c (HbA1c) trajectories among youth and young adults after T1D diagnosis. METHODS: The SEARCH for Diabetes in Youth cohort includes youth with diabetes diagnosed from 2002 to 2006 and 2008 who were followed through 2015. We analyzed data from 1,313 youth and young adults with T1D with ≥3 HbA1c measures. Classification tree analysis identified patterns of baseline demographic, SEP, and clinical characteristic that best predicted HbA1c trajectories over an average of 8.3 years using group-based trajectory modeling. RESULTS: Two HbA1c trajectories were identified: Trajectory 1 (77%) with lower baseline HbA1c and mild increases (from mean 7.4% to 8.4%) and Trajectory 2 (23%) with higher baseline HbA1c and major increases (from 8.5% to 11.2%). Race/ethnicity intersected with different SEP characteristics among non-Hispanic white (NHW) than in non-whites. Public health insurance predicted high-risk Trajectory 2 membership in non-whites, whereas parental education, household structure, diagnosis age and glucose checking frequency predicted membership for NHW youth and young adults. Two characteristics, race/ethnicity and parental education alone identified 80% of the Trajectory 2 members. CONCLUSIONS: Race/ethnicity intersects with multiple SEP and clinical characteristics among youth and young adults with T1D, which is associated with particularly high risk of poor long-term glycemic control.

Demographic Correlates of Short-Term Mortality Among Youth and Young Adults With Youth-Onset Diabetes Diagnosed From 2002 to 2015: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To examine short-term mortality and cause of death among youth and young adults (YYAs) with youth-onset diabetes. RESEARCH DESIGN AND METHODS: We included 19,717 YYAs newly diagnosed with diabetes before 20 years of age from 1 January 2002 to 31 December 2015 enrolled in the SEARCH for Diabetes in Youth Study. Of these, 14,721 had type 1; 4,141 type 2; and 551 secondary and 304 other/unknown diabetes type. Cases were linked with the National Death Index through 31 December 2017. We calculated standardized mortality ratios (SMRs) and 95% CIs based on age, sex, and race/ethnicity for state and county population areas and examined underlying causes of death. RESULTS: During 170,148 person-years (PY) (median follow-up 8.5 years), 283 individuals died: 133 with type 1 (103.0/100,000 PY), 55 with type 2 (161.5/100,000 PY), 87 with secondary (1,952/100,000 PY), and 8 with other/unknown diabetes type (312.3/100,000 PY). SMRs (95% CI) for the first three groups were 1.5 (1.2-1.8), 2.3 (1.7-3.0), and 28.0 (22.4-34.6), respectively. Diabetes was the underlying cause of death for 42.1%, 9.1%, and 4.6% of deaths, respectively. The SMR was greater for type 2 than for type 1 diabetes (P < 0.001). SMRs were significantly higher for individuals with type 1 diabetes who were <20 years of age, non-Hispanic White and Hispanic, and female and for individuals with type 2 diabetes who were <25 years of age, from all race/ethnic minority groups, and from both sexes. CONCLUSIONS: Excess mortality was observed among YYAs for each type of diabetes with differences in risk associated with diabetes type, age, race/ethnicity, and sex. The root causes of excess mortality among YYAs with diabetes merit further study.

Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration.

OBJECTIVE: Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS: We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS: Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, P = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, P = 0.006). CONCLUSIONS: By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.

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.