OBJECTIVES 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.

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Objective: To determine whether glycemia early in the course of type 1 diabetes changed between 2002 and 2016 across discrete youth cohorts. Research Design and Methods: Data from 3,956 youth in the SEARCH for Diabetes in Youth study were used to test whether glycemia worsened between 2002 and 2016 among successive cohorts of youth with type 1 diabetes examined within 30 months of diagnosis. We used linear regression for hemoglobin A1c (HbA1c) and logistic regression for poor glycemic control (HbA1c>9%) stratified by diabetes duration (<6 months, 6-12 months, and 12-30 months) at the time of measurement. Models included survey weights and were adjusted for potential demographic confounders. Results: Fully adjusted models revealed that mean HbA1c and frequency of poor glycemic control remained stable over time across successive youth cohorts with type 1 diabetes. Overall mean unadjusted HbA1c was 7.9% ± 1.5 (63 mmol/mol ± 16.4) in 2002 and 7.8% ± 2.4 (62 mmol/mol ± 26.2) in 2016, p=0.6 for trend. Estimates from weighted linear regression models found no overall change in HbA1c with index year, but a small linear reduction in HbA1c with each index year in the <6 months diabetes duration stratum, which was no longer statistically significant after adjusting for age, sex, race/ethnicity, and clinical site. No time trends were found for either of the other two duration strata (6-12 and 12-30 months). Although interaction effects with race/ethnicity and gender were not detected, the proportion of youth with poor glycemic control was higher among non-white (mean 28.9%, range 24.9-33.7%) than among white youth (mean 12.5%, range 8.8-17.8%) across all years. Conclusions: HbA1c levels remained stable but higher than recommended across discrete cohorts of SEARCH youth with type 1 diabetes duration ≤ 30 months, particularly among non-white youth. Future studies should elucidate predictors of elevated glycemia among youth early in the course of type 1 diabetes. Disclosure D. Igudesman: None. F. Malik: None. E. J. Mayer-davis: None. B. A. Reboussin: None. K. J. Souris: None. C. Pihoker: None. L. M. Dolan: None. S. Saydah: None. D. Dabelea: None. N. Clouet-foraison: None. S. M. Marcovina: None. Funding National Institute for Health Research (1UC4DK108173-01); Centers for Disease Control and Prevention (RFPDP15-002); Kaiser Permanente Southern California (U18DP006133, U48/CCU919219, U01DP000246, U18DP002714); University of Colorado Denver (U18DP006139, U48/CCU819241-3, U01DP000247, U18DP000247-06A1); Cincinnati Children’s Hospital Medical Center (U18DP006134, U48/CCU519239, U01DP000248, 1U18DP002709); University of North Carolina at Chapel Hill (U18DP006138, U48/CCU419249, U01DP000254, U18DP002708); Seattle Children’s Hospital (U18DP006136, U58/CCU019235-4, U01DP000244, U18DP002710-01); Wake Forest University School of Medicine (U18DP006131, U48/CCU919219, U01DP000250, 200-2010-35171); South Carolina Clinical and Translational Research Institute at the Medical University of South Carolina (UL1TR001450); Seattle Children’s Hospital and the University of Washington (UL1TR00423); University of Colorado Pediatric Clinical and Translational Research Center (UL1TR000154); Barbara Davis Center for Diabetes at the University of Colorado Denver (P30-DK-57516); University of Cincinnati (UL1TR001425); Ohio Department of Health

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The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703). In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal and urinary). The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions [Control, Western Diet (WD) vs. MBD]. The secondary endpoints were enteroendocrine hormones, hunger/satiety, and food intake. Here we show that, compared to the WD, the MBD leads to an additional 116 ± 56 kcals (P < 0.0001) lost in feces daily and thus, lower metabolizable energy for the host (89.5 ± 0.73%; range 84.2-96.1% on the MBD vs. 95.4 ± 0.21%; range 94.1-97.0% on the WD; P < 0.0001) without changes in energy expenditure, hunger/satiety or food intake (P > 0.05). Microbial 16S rRNA gene copy number (a surrogate of biomass) increases (P < 0.0001), beta-diversity changes (whole genome shotgun sequencing; P = 0.02), and fermentation products increase (P < 0.01) on an MBD as compared to a WD along with significant changes in the host enteroendocrine system (P < 0.0001). The substantial interindividual variability in metabolizable energy on the MBD is explained in part by fecal SCFAs and biomass. Our results reveal the complex host-diet-microbiome interplay that modulates energy balance.

Background The impact of weight management in persons with type 1 diabetes (T1D) from childhood into adulthood has not been well described. The purpose of the study was to explore qualitative themes presented by young adults with T1D with respect to the dual management of weight and T1D. Methods We analyzed focus group data from 17 young adults with T1D (65% female, age 21.7 ± 2.1 years, HbA1c 8.1% ± 1.5) via inductive qualitative analysis methods. Major themes were compared to themes presented by youth with T1D ages 13‐16 years in previously published study in order to categorize thematic progression from early adolescence through adulthood. Results Themes from young adults with T1D, when compared to those from youth were categorized as: (a) persistent and unchanged themes, (b) evolving themes, and (c) newly reported themes. Hypoglycemia and a sense of futility around exercise was an unchanged theme. Importance of insulin usage and a healthy relationship with T1D evolved to gather greater conviction. Newly reported themes are unique to integration of adulthood into T1D, such as family planning and managing T1D with work obligations. Young adults also reported negative experiences with providers in their younger years and desire for more supportive provider relationships. Conclusions Issues identified by youth regarding the dual management of T1D and weight rarely resolve, but rather, persist or evolve to integrate other aspects of young adulthood. Individualized and age‐appropriate clinical support and practice guidelines are warranted to facilitate the dual management of weight and T1D in persons with T1D.

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