Faculty Bibliography

OBJECTIVE:s rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these two conditions relate, and which subgroups of children are more likely to develop diabetes with and without obesity. METHODS:To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from healthcare claims data, from 2009-2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots. RESULTS:We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents. CONCLUSIONS:Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric type 2 diabetes. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these two populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

BACKGROUND:Studies demonstrate that children with type 1 diabetes may not be meeting exercise recommendations. This, coupled with the lack of data on the determinants of exercise promotion in youth, may indicate a need for additional focus on exercise guidelines and promotion in youth with type 1 diabetes. OBJECTIVE:The objective of this study is to understand provider perspectives regarding exercise promotion in children with type 1 diabetes. SUBJECTS AND METHODS/METHODS:An online survey regarding perspectives on exercise was emailed to Pediatric Endocrine Society members. RESULTS:Of the 84 respondents, 85.5% believe counseling regarding exercise recommendations is a priority. However, 87.8% did not identify Office of Disease Prevention and Health Promotion (ODPHP) guidelines correctly and 79.3% did not identify American Diabetes Association (ADA) guidelines correctly. Providers who exercised regularly (p = 0.009) and providers who identified ODPHP guidelines correctly (p = 0.004) were more likely to identify ADA guidelines correctly. Providers who identified ADA guidelines correctly were 4.21 times (OR 4.21; 95% CI 1.30-13.7) more likely to make good recommendations and those who discussed recommendations at diagnosis were 6.10 times (OR 6.10; 95% CI 1.76-21.2) more likely to make good recommendations. CONCLUSION/CONCLUSIONS:To our knowledge, this study is the first to investigate provider perspectives of exercise promotion in children with type 1 diabetes. We found provider recommendations were not consistent with ADA exercise guidelines and most providers were not fully aware of the recommendations. Future research should address increasing provider education regarding exercise guidelines and developing exercise promotion tools. This article is protected by copyright. All rights reserved.

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.

Background: Chronic illness in children has adverse effects on family members besides the patient and can impact the integrity and function of the family unit. Most previous studies have examined a single disease entity. However, there has been limited assessment comparing the effect of different illnesses on family function.
Method(s): Established patients treated in the pediatric ambulatory Nephrology or DM clinics were included in the study. Their parents were asked to complete the 2-page Pediatric Quality-of-Life Family Impact Module (PedsQL-FIM), version 2.0, a validated survey instrument. Clinical and laboratory data were retrieved from the electronic health record. Data were summarized as mean+/-SD. Disease group and child age were entered as predictors in linear regression analyses with FIM total and subscale scores as outcome variables. Comparisons between groups were assessed using paired t-tests.
Result(s): 96 patients (43 F: 53 M) were evaluated in the Nephrology Clinic and 55 (30 F: 25 M) in the DM Clinic. The mean age of the patients was 13.0+/-3.9 and 10.4+/- 6.3 yr, respectively. Within the KD sample, older age was significantly associated with lower scores on all FIM subscale scores. Gender was not a significant predicator for FIM scores in either disease group. Controlling for age, chronic illness group was a significant predictor of the FIM total and subscale scores. Parents of D patients endorsed significantly lower total FIM scores compared to the KD patients (D 58+/-16; KD 79+/-17 p <0.001) as well as on subscales of physical, emotional, social, and cognitive functioning, communication, worry, daily activities, family relationships, and reports of health-related quality of life (P<0.01).
Conclusion(s): Our findings confirm that chronic illness in childhood adversely affects a wide range of aspects of family function. The impact is greater in older children with KD and varies depending on the disease context. Families with children who have DM manifested greater disturbances than those with children who have isolated KD. Further study is warranted to assess the effects of the underlying renal disease and intensity of medical care and whether there are specific features can be used to identify vulnerable families

There is little data documenting cortisol levels in children with diabetic ketoacidosis (DKA), despite the fact that untreated adrenal insufficiency (AI) could worsen the outcome of DKA. In this cross-sectional study, we assessed serum cortisol levels in 28 children with DKA and new onset type 1 diabetes mellitus evaluated at our center over a 5-year period. Average duration of diabetes-related symptoms was positively associated with age ( P = .002), and significantly lower hemoglobin A1c levels were observed in the youngest children. The mean cortisol level was 40.9 microg/dL, with a range of 7.8 to 119 microg/dL. Cortisol levels were found to be inversely associated with serum pH ( P = .007). There was no difference in the clinical outcome of the 4 patients who had cortisol levels less than 18 microg/dL. Overall, we did not find clinical or laboratory evidence of diminished cortisol reserve; however, the possibility of AI must be kept in mind when treating children with DKA.

Haplotype-dependent allele-specific methylation (hap-ASM) can impact disease susceptibility, but maps of this phenomenon using stringent criteria in disease-relevant tissues remain sparse. Here we apply array-based and Methyl-Seq approaches to multiple human tissues and cell types, including brain, purified neurons and glia, T lymphocytes, and placenta, and identify 795 hap-ASM differentially methylated regions (DMRs) and 3,082 strong methylation quantitative trait loci (mQTLs), most not previously reported. More than half of these DMRs have cell type-restricted ASM, and among them are 188 hap-ASM DMRs and 933 mQTLs located near GWAS signals for immune and neurological disorders. Targeted bis-seq confirmed hap-ASM in 12/13 loci tested, including CCDC155, CD69, FRMD1, IRF1, KBTBD11, and S100A( *)-ILF2, associated with immune phenotypes, MYT1L, PTPRN2, CMTM8 and CELF2, associated with neurological disorders, NGFR and HLA-DRB6, associated with both immunological and brain disorders, and ZFP57, a trans-acting regulator of genomic imprinting. Polymorphic CTCF and transcription factor (TF) binding sites were over-represented among hap-ASM DMRs and mQTLs, and analysis of the human data, supplemented by cross-species comparisons to macaques, indicated that CTCF and TF binding likelihood predicts the strength and direction of the allelic methylation asymmetry. These results show that hap-ASM is highly tissue specific; an important trans-acting regulator of genomic imprinting is regulated by this phenomenon; and variation in CTCF and TF binding sites is an underlying mechanism, and maps of hap-ASM and mQTLs reveal regulatory sequences underlying supra- and sub-threshold GWAS peaks in immunological and neurological disorders.

OBJECTIVE: Monogenic diabetes (MD) is rare and can often be confused with type 1 diabetes (T1D) in a pediatric cohort. We sought to determine clinical criteria that could optimally identify candidates for genetic testing of two common forms of MD that alter therapy: glucokinase (GCK) and hepatocyte nuclear factor 1 alpha (HNF1alpha). RESEARCH DESIGN AND METHODS: We performed a retrospective chart review of 939 patients with a presumed diagnosis of T1D, 6 months-20 yr of age, and identified four clinical criteria that were unusual for T1D and could warrant further evaluation for MD: (i) negative pancreatic autoantibodies, (ii) evidence of prolonged endogenous insulin production, or (iii) strong family history of diabetes in multiple generations. One hundred and twenty-one patients were identified as having one or more of these high-risk clinical criteria and were offered screening for mutations in GCK and HNF1alpha; 58 consented for genetic testing. RESULTS: Of 58 patients with presumed T1D who underwent genetic testing, four were found to have GCK and one had HNF1alpha. No patients with only one high-risk feature were found to have MD. Of 10 patients who had two or more high risk criteria, five had MD (50%). CONCLUSION: A high frequency of MD from mutations in GCK/HNF1alpha may be identified among pediatric diabetic patients originally considered to have T1D by performing genetic testing on those patients with multiple clinical risk factors for MD.