Pediatric Diabetes最新文献

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.

Objective: The Diabetes Device Confidence Scale (DDCS) is a new scale designed to evaluate school nurse confidence with diabetes devices. We hypothesized that DDCS score would be associated with related constructs of school nurse diabetes knowledge, experience, and training.

Research design and methods: In a cross-sectional study, we co-administered the DDCS and Diabetes Knowledge Test 2 (DKT2) questionnaires to school nurses in Pennsylvania. We summarized DDCS scores (range 1-5) descriptively. We evaluated the relationship between DKT2 percent score and DDCS mean score with the Spearman correlation coefficient. Simple linear regression examined school nurse characteristics as predictors of DDCS score.

Results: A total of 271 completed surveys were received. Mean DDCS score was 3.16±0.94, indicating moderate confidence with devices overall. School nurses frequently reported low confidence in items representing specific skills, including suspending insulin delivery (40%), giving a manual bolus (42%), knowing when to calibrate a continuous glucose monitor (48%), changing an insulin pump site (54%), and setting a temporary basal rate (58%). Mean DKT2 score was 89.5±0.1%, which was weakly but not significantly correlated with DDCS score (r=0.12, p=0.06). Formal device training (p<0.001), assisting ≥5 students with diabetes devices in the past 5 years (p<0.01), and a student caseload between 1000-1500 students (p<0.001) were associated with higher mean DDCS score.

Conclusions: DDCS score is related to prior training and experience, providing evidence for the scale's convergent validity. The DDCS may be a useful tool for assessing school nurse readiness to use devices and identify areas to enhance knowledge and practical skills.

Background: Pediatric Type 2 diabetes (T2D) is highly heterogeneous. Previous reports on adult-onset diabetes demonstrated the existence of diabetes clusters. Therefore, we set out to identify unique diabetes subgroups with distinct characteristics among youth with T2D using commonly available demographic, clinical, and biochemical data.

Methods: We performed data-driven cluster analysis (K-prototypes clustering) to characterize diabetes subtypes in pediatrics using a dataset with 722 children and adolescents with autoantibody-negative T2D. The six variables included in our analysis were sex, race/ethnicity, age, BMI Z-score and hemoglobin A1c at the time of diagnosis, and non-HDL cholesterol within first year of diagnosis.

Results: We identified five distinct clusters of pediatric T2D, with different features, treatment regimens and risk of diabetes complications: Cluster 1 was characterized by higher A1c; Cluster 2, by higher non-HDL; Cluster 3, by lower age at diagnosis and lower A1c; Cluster 4, by lower BMI and higher A1c; and Cluster 5, by lower A1c and higher age. Youth in Cluster 1 had the highest rate of diabetic ketoacidosis (DKA) (p = 0.0001) and were most prescribed metformin (p = 0.06). Those in Cluster 2 were most prone to polycystic ovarian syndrome (p = 0.001). Younger individuals with lowest family history of diabetes were least frequently diagnosed with diabetic ketoacidosis (p = 0.001) and microalbuminuria (p = 0.06). Low-BMI individuals with higher A1c had the lowest prevalence of acanthosis nigricans (p = 0.0003) and hypertension (p = 0.03).

Conclusions: Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management.

Objective: Using continuous glucose monitoring (CGM), we examined patterns in glycemia during school hours for children with type 1 diabetes, exploring differences between school and non-school time.

Methods: We conducted a retrospective analysis of CGM metrics in children 7-12 years (n=217, diabetes duration 3.5±2.5 years, hemoglobin A1c 7.5±0.8%). Metrics were obtained for weekday school hours (8 AM to 3 PM) during four weeks in fall 2019. Two comparison settings included weekend (fall 2019) and weekday (spring 2020) data when children had transitioned to virtual school due to COVID-19. We used multilevel mixed models to examine factors associated with time in range (TIR) and compare glycemia between in-school, weekends, and virtual school.

Results: Though CGM metrics were clinically similar across settings, TIR was statistically higher, and time above range (TAR), mean glucose, and standard deviation (SD) lower, for weekends and virtual school (p<0.001). Hour and setting exhibited a significant interaction for several metrics (p<0.001). TIR in-school improved from a mean of 40.9% at the start of the school day to 58.0% later in school, with a corresponding decrease in TAR. TIR decreased on weekends (60.8 to 50.7%) and virtual school (62.2 to 47.8%) during the same interval. Mean glucose exhibited a similar pattern, though there was little change in SD. Younger age (p=0.006), lower hemoglobin A1c (p<0.001), and insulin pump use (p=0.02) were associated with higher TIR in-school.

Conclusion: Although TIR was higher for weekends and virtual school, glycemic metrics improve while in-school, possibly related to beneficial school day routines.

The incidence of pediatric diabetic ketoacidosis (DKA) increased during the peak of the COVID-19 pandemic. The objective of this study was to investigate whether rates of hyperosmolar therapy administration for suspected clinically apparent brain injury (CABI) complicating DKA also increased during this period as compared to the three years immediately preceding the pandemic and to compare the characteristics of patients with suspected CABI before the pandemic, patients with suspected CABI during the peak of the pandemic, and those with DKA but without suspected CABI during the pandemic. Patients aged ≤18 years presenting with DKA before (March 11, 2017-March 10, 2020) and during the peak of the pandemic (March 11, 2020-March 10, 2021) were identified through a rigorous search of two databases. Predefined criteria were used to diagnose suspected CABI. Biochemical, clinical, and sociodemographic data were collected from a comprehensive review of the electronic medical record. The proportion of patients with DKA who received hyperosmolar therapy was significantly higher (P = 0.014) during the pandemic compared to the prepandemic period; however, this was only significant among patients with newly diagnosed diabetes. Both groups with suspected CABI had more severe acidosis, lower Glasgow Coma Scale scores, and longer hospital admissions (P< 0.001 for all) than cases without suspected CABI. During the pandemic, the blood urea nitrogen concentration was significantly higher in patients with suspected CABI than those without suspected CABI, suggesting they were more severely dehydrated. The clinical, biochemical, and sociodemographic characteristics of patients with suspected CABI were indistinguishable before and during the pandemic. In conclusion, administration of hyperosmolar therapy for suspected CABI was more common during the peak of the COVID-19 pandemic, possibly a result of delayed presentation, highlighting the need for increased awareness and early recognition of the signs and symptoms of diabetes and DKA, especially during future surges of highly transmissible infections.

Introduction: The relationship of HbA1c versus the mean blood glucose (MBG) is an important guide for diabetes management but may differ between ethnic groups. In Africa, the patient's glucose information is limited or unavailable and the management is largely guided by HbA1c. We sought to determine if the reference data derived from the non-African populations led to an appropriate estimation of MBG from HbA1c for the East African patients.

Methods: We examined the relationship of HbA1c versus MBG obtained by the continuous glucose monitoring in a group of East African youth having type 1 diabetes in Kenya and Uganda (n = 54) compared with the data obtained from A1c-derived average glucose (ADAG) and glucose management indicator (GMI) studies. A self-identified White (European heritage) population of youth (n = 89) with type 1 diabetes, 3-18 years old, living in New Orleans, LA, USA metropolitan area (NOLA), was studied using CGM as an additional reference.

Results: The regression equation for the African cohort was MBG (mg/dL) = 32.0 + 16.73 × HbA1c (%), r = 0.55, p < 0.0001. In general, the use of the non-African references considerably overestimated MBG from HbA1c for the East African population. For example, an HbA1c = 9% (74.9 mmol/mol) corresponded to an MBG = 183 mg/dL (10.1 mmol/L) in the East African group, but 212 mg/dL (11.7 mmol/L) using ADAG, 237 mg/dL (13.1 mmol/L) using GMI and 249 mg/dL (13.8 mmol/L) using NOLA reference. The reported occurrence of serious hypoglycemia among the African patients in the year prior to the study was 21%. A reference table of HbA1c versus MBG from the East African patients was generated.

Conclusions: The use of non-African-derived reference data to estimate MBG from HbA1c generally led to the overestimation of MBG in the East African patients. This may put the East African and other African patients at higher risk for hypoglycemia when the management is primarily based on achieving target HbA1c in the absence of the corresponding glucose data.

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.