Associations of Dietary Intake with the Intestinal Microbiota and Short-Chain Fatty Acids Among Young Adults with Type 1 Diabetes and Overweight or Obesity.

BACKGROUND: Diet, a key component of type 1 diabetes (T1D) management, modulates the intestinal microbiota and its metabolically active byproducts-including SCFA-through fermentation of dietary carbohydrates such as fiber. However, the diet-microbiome relationship remains largely unexplored in longstanding T1D. OBJECTIVES: We evaluated whether increased carbohydrate intake, including fiber, is associated with increased SCFA-producing gut microbes, SCFA, and intestinal microbial diversity among young adults with longstanding T1D and overweight or obesity. METHODS: Young adult men and women with T1D for ≥1 y, aged 19-30 y, and BMI of 27.0-39.9 kg/m2 at baseline provided stool samples at baseline and 3, 6, and 9 mo of a randomized dietary weight loss trial. Diet was assessed by 1-2 24-h recalls. The abundance of SCFA-producing microbes was measured using 16S rRNA gene sequencing. GC-MS measured fecal SCFA (acetate, butyrate, propionate, and total) concentrations. Adjusted and Bonferroni-corrected generalized estimating equations modeled associations of dietary fiber (total, soluble, and pectins) and carbohydrate (available carbohydrate, and fructose) with microbiome-related outcomes. Primary analyses were restricted to data collected before COVID-19 interruptions. RESULTS: Fiber (total and soluble) and carbohydrates (available and fructose) were positively associated with total SCFA and acetate concentrations (n = 40 participants, 52 visits). Each 10 g/d of total and soluble fiber intake was associated with an additional 8.8 µmol/g (95% CI: 4.5, 12.8 µmol/g; P = 0.006) and 24.0 µmol/g (95% CI: 12.9, 35.1 µmol/g; P = 0.003) of fecal acetate, respectively. Available carbohydrate intake was positively associated with SCFA producers Roseburia and Ruminococcus gnavus. All diet variables except pectin were inversely associated with normalized abundance of Bacteroides and Alistipes. Fructose was inversely associated with Akkermansia abundance. CONCLUSIONS: In young adults with longstanding T1D, fiber and carbohydrate intake were associated positively with fecal SCFA but had variable associations with SCFA-producing gut microbes. Controlled feeding studies should determine whether gut microbes and SCFA can be directly manipulated in T1D.

Weight management in young adults with type 1 diabetes: The advancing care for type 1 diabetes and obesity network sequential multiple assignment randomized trial pilot results.

AIMS: Co-management of weight and glycaemia is critical yet challenging in type 1 diabetes (T1D). We evaluated the effect of a hypocaloric low carbohydrate, hypocaloric moderate low fat, and Mediterranean diet without calorie restriction on weight and glycaemia in young adults with T1D and overweight or obesity. MATERIALS AND METHODS: We implemented a 9-month Sequential, Multiple Assignment, Randomized Trial pilot among adults aged 19-30 years with T1D for ≥1 year and body mass index 27-39.9 kg/m2 . Re-randomization occurred at 3 and 6 months if the assigned diet was not acceptable or not effective. We report results from the initial 3-month diet period and re-randomization statistics before shutdowns due to COVID-19 for primary [weight, haemoglobin A1c (HbA1c), percentage of time below range <70 mg/dl] and secondary outcomes [body fat percentage, percentage of time in range (70-180 mg/dl), and percentage of time below range <54 mg/dl]. Models adjusted for design, demographic and clinical covariates tested changes in outcomes and diet differences. RESULTS: Adjusted weight and HbA1c (n = 38) changed by -2.7 kg (95% CI -3.8, -1.5, P < .0001) and -0.91 percentage points (95% CI -1.5, -0.30, P = .005), respectively, while adjusted body fat percentage remained stable, on average (P = .21). Hypoglycaemia indices remained unchanged following adjustment (n = 28, P > .05). Variability in all outcomes, including weight change, was considerable (57.9% were re-randomized primarily due to loss of <2% body weight). No outcomes varied by diet. CONCLUSIONS: Three months of a diet, irrespective of macronutrient distribution or caloric restriction, resulted in weight loss while improving or maintaining HbA1c levels without increasing hypoglycaemia in adults with T1D.

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.

Associations of disordered eating with the intestinal microbiota and short-chain fatty acids among young adults with type 1 diabetes.

BACKGROUND AND AIMS: Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D. METHODS AND RESULTS: We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey-Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin "At least sometimes" at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a -0.34 (95% CI -0.56, -0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was -0.94 (95% CI -1.5, -0.42), p = 0.02 lower when insulin restriction was reported "At least sometimes" compared to "Rarely or Never". CONCLUSION: DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D.

Mindfulness, disordered eating, and impulsivity in relation to glycemia among adolescents with type 1 diabetes and suboptimal glycemia from the Flexible Lifestyles Empowering Change (FLEX) intervention trial.

OBJECTIVE: To assess the relationship between mindfulness and glycemia among adolescents with type 1 diabetes (T1D) with suboptimal glycemia, and evaluate the potential mediation by ingestive behaviors, including disordered eating, and impulsivity. RESEARCH DESIGN AND METHODS: We used linear mixed models for hemoglobin A1c (HbA1c) and linear regression for continuous glucose monitoring (CGM) to study the relationship of mindfulness [Child and Adolescent Mindfulness Measure (CAMM)] and glycemia in adolescents with T1D from the 18-month Flexible Lifestyles Empowering Change (FLEX) trial. We tested for mediation of the mindfulness-glycemia relationship by ingestive behaviors, including disordered eating (Diabetes Eating Problem Survey-Revised), restrained eating, and emotional eating (Dutch Eating Behavior Questionnaire); and impulsivity (total, attentional, and motor, Barrett Impulsiveness Scale). RESULTS: At baseline, participants (n = 152) had a mean age of 14.9 ± 1.1 years and HbA1c of 9.4 ± 1.2% [79 ± 13 mmol/mol]. The majority of adolescents were non-Hispanic white (83.6%), 50.7% were female, and 73.0% used insulin pumps. From adjusted mixed models, a 5-point increase in mindfulness scores was associated with a -0.19% (95%CI -0.29, -0.08, p = 0.0006) reduction in HbA1c. We did not find statistically significant associations between mindfulness and CGM metrics. Mediation of the relationship between mindfulness and HbA1c by ingestive behaviors and impulsivity was not found to be statistically significant. CONCLUSIONS: Among adolescents with T1D and suboptimal glycemia, increased mindfulness was associated with lower HbA1c levels. Future studies may consider mindfulness-based interventions as a component of treatment for improving glycemia among adolescents with T1D, though more data are needed to assess feasibility and efficacy.

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.

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.

Diabetic ketoacidosis at diagnosis among youth with type 1 and type 2 diabetes: Results from SEARCH (United States) and YDR (India) registries.

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.

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.

Metabolic effects of high-intensity interval training and essential amino acids.

High-intensity interval training (HIIT) promotes positive cardiometabolic and body composition changes. Essential amino acids (EAA) may support changes associated with HIIT, but evaluation of potential synergistic effects is lacking. The purpose of this study was to compare independent and combined effects of HIIT and EAA on total body composition and metabolism in men and women considered overweight/obese; an exploratory aim was to evaluate the modulatory effects of sex. Sixty-six healthy adults (50% female; Age: 36.7 ± 6.0 years; BMI: 32.0 ± 4.2 kg/m2) completed 8 weeks of: (1) HIIT, 2 days/weeks; (2) EAA supplementation, 3.6 g twice daily; (3) HIIT + EAA; or (4) control. Body composition, resting metabolic rate (RMR), substrate metabolism (respiratory exchange ratio; RER), and cardiorespiratory fitness were measured at baseline, 4 weeks, and 8 weeks; cardiometabolic blood markers were measured at baseline and 8 weeks. Differences between groups were assessed by linear mixed models covaried for baseline values, followed by 95% confidence intervals (CI) on adjusted mean change scores. There were no significant changes in body composition (p > 0.05) for any group. Changes in RER, but not RMR, occurred with HIIT (mean change; [95% CI]: - 0.04; [- 0.07, - 0.02]) and EAA (- 0.03; [- 0.06, - 0.01]) after 8 weeks. Cardiorespiratory fitness increased following 8 weeks of HIIT (+ 5.1 ml/kg/min [3.3,6.8]) and HIIT + EAA (+ 4.1 ml/kg/min [1.0,6.4]). Changes with HIIT + EAA were not significantly different from HIIT. There were no changes in cardiometabolic markers (p > 0.05) and no sex interaction (p > 0.05). HIIT is efficacious for promoting positive changes in cardiorespiratory fitness and resting substrate metabolism in adults considered overweight/obese. Addition of EAA did not significantly enhance HIIT-induced adaptations. ClinicalTrials.gov ID#NCT04080102.

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.

Impact of Hurricane Matthew on Diabetes Self-Management and Outcomes.

BACKGROUND: Individuals with diabetes require extensive self-management. Little is known about how Hurricane Matthew (Matthew) or Hurricane Florence (Florence) impacted diabetes self-management and outcomes in Robeson County, North Carolina. METHODS: Mixed methods were used to assess the impact of hurricanes on diabetes self-management and outcomes. Individuals with diabetes were recruited for focus groups to understand the perceived impact on diabetes self-management. Health care providers were recruited for parallel key informant interviews. Mean hemoglobin A1c (HbA1c) and frequency of diabetic ketoacidosis (DKA) from hospital data six months before and after Matthew were compared using Student t-tests. RESULTS: A demographic breakdown of 34.25% white, 21.70% Black or African American, and 21.38% American Indian or Alaska Native was observed from focus groups. Qualitative results highlight a limited access to a balanced diet and medications. No significant differences were found between mean HbA1c values before and after Matthew (before Matthew: mean HbA1c 8.34 ± 1.87%; after Matthew: mean HbA1c 8.31 ± 1.93 %; P = .366). The period prevalence (PP) of DKA was higher after Matthew than before (before Matthew: 39 cases out of 4,025 visits, PP = .010; after Matthew: 87 cases out of 3,779 visits, PP = .023; P <.0001). LIMITATIONS: Limitations include non-random sampling and limited sample sizes. Also, the cross-sectional panel approach did not follow the same individuals over time. CONCLUSIONS: The period prevalence of DKA was higher in the six-month time period following Matthew compared to before the hurricane. Future interventions may improve outcomes via increased access to foods and medications recommended for those with diabetes.

Temporal trends in diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes between 2006 and 2016: results from 13 countries in three continents.

AIMS/HYPOTHESIS: The aim of this work was to evaluate geographical variability and trends in the prevalence of diabetic ketoacidosis (DKA), between 2006 and 2016, at the diagnosis of childhood-onset type 1 diabetes in 13 countries over three continents. METHODS: An international retrospective study on DKA at diagnosis of diabetes was conducted. Data on age, sex, date of diabetes diagnosis, ethnic minority status and presence of DKA at diabetes onset were obtained from Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA and the UK (Wales). Mean prevalence was estimated for the entire period, both overall and by country, adjusted for sex and age group. Temporal trends in annual prevalence of DKA were estimated using logistic regression analysis for each country, before and after adjustment for sex, age group and ethnic minority status. RESULTS: During the study period, new-onset type 1 diabetes was diagnosed in 59,000 children (median age [interquartile range], 9.0 years [5.5-11.7]; male sex, 52.9%). The overall adjusted DKA prevalence was 29.9%, with the lowest prevalence in Sweden and Denmark and the highest in Luxembourg and Italy. The adjusted DKA prevalence significantly increased over time in Australia, Germany and the USA while it decreased in Italy. Preschool children, adolescents and children from ethnic minority groups were at highest risk of DKA at diabetes diagnosis in most countries. A significantly higher risk was also found for females in Denmark, Germany and Slovenia. CONCLUSIONS/INTERPRETATION: DKA prevalence at type 1 diabetes diagnosis varied considerably across countries, albeit it was generally high and showed a slight increase between 2006 and 2016. Increased awareness of symptoms to prevent delay in diagnosis is warranted, especially in preschool children, adolescents and children from ethnic minority groups.

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.

Dietary intake on days with and without hypoglycemia in youth with type 1 diabetes: The Flexible Lifestyle Empowering Change trial.

OBJECTIVE: To address a common perception that hypoglycemia is associated with increased dietary intake, we examined calorie and carbohydrate consumption on days with and without hypoglycemia among adolescents with type 1 diabetes (T1D). METHODS: Days (N = 274) with 24-hour dietary recalls and continuous glucose monitoring were available for 122 adolescents with T1D in the Flexible Lifestyle Empowering Change trial (age 13-16 years, diabetes duration >1 year, hemoglobin A1c 8%-13%). Days with no hypoglycemia, clinical hypoglycemia (54-69 mg/dL) or clinically serious hypoglycemia (<54 mg/dL) were further split into night (12-5:59 am) and day (6 am-11:59 pm). Mixed models tested whether intake of calories or carbohydrates was greater on days with than without hypoglycemia. RESULTS: Fifty-nine percent, 23% and 18% of days had no hypoglycemia, clinical hypoglycemia and clinically serious hypoglycemia, respectively. Intake of calories and carbohydrates was not statistically significantly different on days with clinical hypoglycemia (57.2 kcal [95% CI -126.7, 241.5]; 12.6 g carbohydrate [95% CI -12.7, 38.0]) or clinically serious hypoglycemia (-74.0 kcal [95% CI -285.9, 137.9]; (-7.8 g carbohydrate [95% CI -36.8, 21.1]), compared to days without hypoglycemia. Differences by day and night were not statistically significant. CONCLUSIONS: Among adolescents with T1D, daily intake of calories and carbohydrates did not differ on days with and without hypoglycemia. It is possible that hypoglycemic episodes caused by undereating relative to insulin dosing, followed by overeating, leading to a net neutral difference. Given the post-hoc nature of these analyses, larger studies should be designed to prospectively test the hypoglycemia-diet relationship.

The accuracy of provider diagnosed diabetes type in youth compared to an etiologic criteria in the SEARCH for Diabetes in Youth Study.

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.

Dietary strategies to manage diabetes and glycemic control in youth and young adults with youth-onset type 1 and type 2 diabetes: The SEARCH for diabetes in youth study.

AIMS: Examine associations of dietary strategies used to manage diabetes over time with hemoglobin A1c in youth-onset type 1 or type 2 diabetes. METHODS: The SEARCH for Diabetes in Youth observational study assessed dietary strategies used by 1814 participants with diabetes (n = 1558 type 1, n = 256 type 2) at two to three research visits over 5.5 years (range 1.7-12.2). Participants reported often, sometimes, or never using 10 different dietary strategies, and use over time was categorized into five mutually exclusive groups: often using across visits; started using at later visits; sometimes using across visits; stopped using at later visits; or never using across visits. General multivariable linear models evaluated most recent A1c by use category for each strategy. RESULTS: In type 1 diabetes, A1c was lower among those who starting tracking calories (-0.4%, Tukey P < .05), often counted carbs (-0.8%, Tukey P < .001), or sometimes chose low glycemic index foods (-0.5%, Tukey P = .02) vs those with less use, while participants who never drank more milk had the lowest A1c (-0.5%, Tukey P = .04). In type 2 diabetes, A1c was lower among those who often limited high fat foods (-2.0%, Tukey P = .02) or started counting carbohydrates (-1.7%, Tukey P = .07) than those who did so less. CONCLUSIONS: For several dietary strategies, more frequent use over time was related to lower A1c in youth-onset type 1 and type 2 diabetes, suggesting these strategies can likely support diabetes management for this population. Investigation into factors predicting receipt of advice for specific strategies and corresponding impact on intake might be considered.

Dysglycemia among youth with type 1 diabetes and suboptimal glycemic control in the Flexible Lifestyle Empowering Change trial.

OBJECTIVE: To examine the prevalence and correlates of non-severe hypoglycemia among adolescents with type 1 diabetes and suboptimal glycemic control, an understudied topic in this group. METHODS: Seven days of blinded continuous glucose monitor data were analyzed in 233 adolescents at baseline of the Flexible Lifestyle Empowering Change trial (13-16 years, type 1 diabetes duration >1 year, and hemoglobin A1c [HbA1c] 8-13% [64-119 mmol]). Incidence of clinical hypoglycemia (54-69 mg/dL) and clinically serious hypoglycemia (<54 mg/dL) was defined as number of episodes ≥15 minutes. Logistic regression modeling was used to determine the correlates of long duration of hypoglycemia, categorized by median split among those who experienced hypoglycemia. RESULTS: The sample was 76.1% non-Hispanic white, 49.8% female, age = 14.9 ± 1.1 years, diabetes duration = 6.4 ± 3.7 years, and HbA1c = 9.6 ± 1.2% (81 ± 13 mmol/mol). Over 7 days, 79.4% of youth experienced ≥1 hypoglycemic episodes of <70 mg/dL, and 55.4% of youth experienced ≥1 hypoglycemic episodes of <54 mg/dL. Among all adolescents, the median duration of clinical hypoglycemia and clinically serious hypoglycemia was 21.9 (range 0-250.2) and 4.3 (range 0-209.7) minutes/day, respectively. Long duration of clinical hypoglycemia (range 1.8-17.4% time overall) and clinically serious hypoglycemia (range 1.2-14.6% time overall) was associated with older age and decreasing HbA1c. Long duration of clinically serious hypoglycemia also was associated with insulin pump use. CONCLUSIONS: Almost 80% of adolescents with elevated HbA1c had an episode of clinical hypoglycemia, and >50% had clinically serious hypoglycemia in a week. Increased education alongside access to emerging diabetes technologies may help to prevent hypoglycemia while improving glycemic control.

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

BACKGROUND/OBJECTIVE: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes." METHODS: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). RESULTS: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05). CONCLUSIONS: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.