Pediatric Diabetes最新文献

Background/objective: Growth and obesity have been associated with increased risk of islet autoimmunity (IA) and progression to type 1 diabetes. We aimed to estimate the effect of energy-yielding macronutrient intake on the development of IA through BMI.

Research design and methods: Genetically at-risk children (n = 5,084) in Finland, Germany, Sweden, and the USA, who were autoantibody negative at 2 years of age, were followed to the age of 8 years, with anthropometric measurements and 3-day food records collected biannually. Of these, 495 (9.7%) children developed IA. Mediation analysis for time-varying covariates (BMI z-score) and exposure (energy intake) was conducted. Cox proportional hazard method was used in sensitivity analysis.

Results: We found an indirect effect of total energy intake (estimates: indirect effect 0.13 [0.05, 0.21]) and energy from protein (estimates: indirect effect 0.06 [0.02, 0.11]), fat (estimates: indirect effect 0.03 [0.01, 0.05]), and carbohydrates (estimates: indirect effect 0.02 [0.00, 0.04]) (kcal/day) on the development of IA. A direct effect was found for protein, expressed both as kcal/day (estimates: direct effect 1.09 [0.35, 1.56]) and energy percentage (estimates: direct effect 72.8 [3.0, 98.0]) and the development of GAD autoantibodies (GADA). In the sensitivity analysis, energy from protein (kcal/day) was associated with increased risk for GADA, hazard ratio 1.24 (95% CI: 1.09, 1.53), p = 0.042.

Conclusions: This study confirms that higher total energy intake is associated with higher BMI, which leads to higher risk of the development of IA. A diet with larger proportion of energy from protein has a direct effect on the development of GADA.

The prevalence of overweight and obesity in youth with type 1 diabetes mellitus (T1D) now exceeds that of youth without T1D. Comorbid T1D and excess adiposity are associated with multiple serious negative health outcomes. Unfortunately, youth with T1D are often excluded from and/or not referred to standard behavioral lifestyle interventions. This is often attributed to the complexities of managing T1D and an effort not to overburden persons who have T1D. Furthermore, standard behavioral weight management intervention recommendations can be perceived as contradicting T1D disease management (e.g., removing sugar-sweetened beverages from diet, energy balance with exercise, and caloric restriction). A weight management intervention specifically designed for youth with T1D is needed to provide treatment to youth with comorbid T1D and overweight/obesity. The current study interviewed adolescents with T1D and overweight/obesity (n = 12), their caregivers (n = 12), and pediatric endocrinologists (n = 9) to understand (a) whether they would be interested in a weight management intervention adapted for youth with T1D and (b) specific adaptations they would want and need. Five central themes emerged following applied thematic analysis: (1) program content, (2) programmatic messaging, (3) program structure, (4) social support, and (5) eating disorder risk. Results provide detailed recommendations for the adaptation of a behavioral weight management intervention for youth with T1D.

Background: Adolescents with type 1 diabetes (T1D) are particularly vulnerable to poor psychosocial outcomes-high rates of diabetes distress and poor quality of life are common among this cohort. Previous work in the general population demonstrated positive associations between quality of life and increases in moderate-to-vigorous physical activity (MVPA), as well as decreased sedentary behavior. While survey-based assessments of young adults with T1D observed similar trends, these studies were limited by their use of subjective assessments of MVPA and sedentary behavior. The use of direct activity monitoring is needed to establish the association between psychosocial outcomes and MVPA and sedentary behavior among adolescents with T1D.

Objective: To explore the association between objectively measured MVPA and sedentary behavior on psychosocial outcomes among adolescents with T1D.

Subjects and methods: The current study is a secondary analysis of baseline data collected for a pilot trial of sleep-promoting intervention for adolescents with T1D. Participants (n = 29, with a mean age of 15.9 ± 1.3 years) completed baseline surveys and wore an actigraph for a week following the baseline visit. We examined minutes per week of MVPA and proportion of awake time spent sedentary in relation to adolescents' diabetes distress, depressive symptoms, and diabetes-related quality of life.

Results: Participants engaged in a mean of 19.6 ± 22.4 minutes of MVPA per day and spent 68.6 ± 9.9% of their awake time sedentary. MVPA was associated with lower diabetes distress in unadjusted (-3.6; 95% CI: -6.4 to -0.8) and adjusted (-2.6; 95% CI: -5.0--0.3) analyses. Sedentary time was associated with higher diabetes distress in adjusted (6.3; 95% CI: 1.3-11.2) but not unadjusted (6.0; 95% CI: -5.6-12.6) analyses. In secondary analyses, we did not observe significant associations between quality of life or depressive symptoms with either MVPA or sedentary behavior.

Discussion: Our findings extend previous survey-based work demonstrating an association between decreased diabetes distress with greater weekly MVPA and lower sedentary time. The current study highlights the multifaceted benefits of physical activity in this population and provides preliminary evidence for developing interventions to reduce sedentary time as an alternative method to improve psychosocial outcomes in this at-risk population.

Background: There is a paucity of data on the risk factors for the hyperosmolar hyperglycemic state (HHS) compared with diabetic ketoacidosis (DKA) in pediatric type 2 diabetes (T2D).

Methods: We used the national Kids' Inpatient Database to identify pediatric admissions for DKA and HHS among those with T2D in the years 2006, 2009, 2012, and 2019. Admissions were identified using ICD codes. Those aged <9yo were excluded. We used descriptive statistics to summarize baseline characteristics and Chi-squared test and logistic regression to evaluate factors associated with admission for HHS compared with DKA in unadjusted and adjusted models.

Results: We found 8,961 admissions for hyperglycemic emergencies in youth with T2D, of which 6% were due to HHS and 94% were for DKA. These admissions occurred mostly in youth 17-20 years old (64%) who were non-White (Black 31%, Hispanic 20%), with public insurance (49%) and from the lowest income quartile (42%). In adjusted models, there were increased odds for HHS compared to DKA in males (OR 1.77, 95% CI 1.42-2.21) and those of Black race compared to those of White race (OR 1.81, 95% CI 1.34-2.44). Admissions for HHS had 11.3-fold higher odds for major or extreme severity of illness and 5.0-fold higher odds for mortality.

Conclusion: While DKA represents the most admissions for hyperglycemic emergencies among pediatric T2D, those admitted for HHS had higher severity of illness and mortality. Male gender and Black race were associated with HHS admission compared to DKA. Additional studies are needed to understand the drivers of these risk factors.

Background: Adolescents and young adults with type 1 diabetes have high HbA1c levels and often struggle with self-management behaviors and attention to diabetes care. Hybrid closed-loop systems (HCL) like the t:slim X2 with Control-IQ technology (Control-IQ) can help improve glycemic control. The purpose of this study is to assess adolescents' situational awareness of their glucose control and engagement with the Control-IQ system to determine significant factors in daily glycemic control.

Methods: Adolescents (15-25 years) using Control-IQ participated in a 2-week prospective study, gathering detailed information about Control-IQ system engagements (boluses, alerts, and so on) and asking the participants' age and gender about their awareness of glucose levels 2-3 times/day without checking. Mixed models assessed which behaviors and awareness items correlated with time in range (TIR, 70-180 mg/dl, 3.9-10.0 mmol/L).

Results: Eighteen adolescents/young adults (mean age 18 ± 1.86 years and 86% White non-Hispanic) completed the study. Situational awareness of glucose levels did not correlate with time since the last glucose check (p = 0.8). In multivariable modeling, lower TIR was predicted on days when adolescents underestimated their glucose levels (r = -0.22), received more CGM alerts (r = -0.31), and had more pump engagements (r = -0.27). A higher TIR was predicted when adolescents responded to CGM alerts (r = 0.20) and entered carbohydrates into the bolus calculator (r = 0.49).

Conclusion: Situational awareness is an independent predictor of TIR and may provide insight into patterns of attention and focus that could positively influence glycemic outcomes in adolescents. Proactive engagements predict better TIR, whereas reactive engagement predicted lower TIR. Future interventions could be designed to train users to develop awareness and expertise in effective diabetes self-management.

Objectives: Evaluate whether increased diabetes screening in youth is associated with lower HbA1c at T2D diagnosis and improved HbA1c outcomes in youth.

Research design and methods: Diabetes screening rates from 2009 to 2018 were calculated. Electronic medical records identified obese youth ages 8-18 with first HbA1c ≥6.5% from 2009 to 2018; chart review confirmed incident T2D. Demographics, BMI and HbA1c values, and use of glucometer and diabetes medications were collected.

Results: 142 youth had T2D. Median age was 14 years (range 8-18); 58% were female. 46% were identified on first HbA1c testing. 69 (49%) had 1st HbA1c 6.5%-6.9%, 43 (30%) 7.0%-7.9%, and 30 (21%) ≥8%. Follow-up from 1st to last HbA1c was median 2.6 years (range 0-10). 121 youth had follow-up testing ≥1 year after diagnosis; of these, 87 (72%) had persistent T2D-range HbA1c or were taking diabetes medications. 85% of youth with 1st HbA1c ≥7% had persistent T2D versus 52% of those with 1st HbA1c <7% (p < 0.001). Poorly controlled diabetes at last test was present in 19% of youth with baseline HbA1c 6.5%-6.9%, 30% with 7.0%-7.9%, and 63% with ≥8% (p < 0.001). 47 (68%) with HbA1c <7% were prescribed a glucometer; 9% of youth prescribed a meter and 41% of youth not prescribed a meter had poorly controlled diabetes at last test (p = 0.009).

Conclusions: Youth with HbA1c <7% at diagnosis were less likely to have poorly controlled diabetes at follow-up. Prescription of glucometers for youth with HbA1c in this range was associated with improved HbA1c outcomes and deserves further study including components of glucometer teaching.