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Browsing by Author "Rafkin, Lisa E."
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Item The influence of body mass index and age on C-peptide at the diagnosis of type 1 diabetes in children who participated in the diabetes prevention trial-type 1(Wiley, 2018-05) Sosenko, Jay M.; Geyer, Susan; Skyler, Jay S.; Rafkin, Lisa E.; Ismail, Heba M.; Libman, Ingrid M.; Liu, Yuk-Fun; DiMeglio, Linda A.; Evans-Molina, Carmella; Palmer, Jerry P.; Medicine, School of MedicineBACKGROUND/OBJECTIVE: The extent of influence of BMI and age on C-peptide at the diagnosis of type 1 diabetes (T1D) is unknown. We thus studied the impact of body mass index Z-scores (BMIZ) and age on C-peptide measures at and soon after the diagnosis of T1D. METHODS: Data from Diabetes Prevention Trial-Type 1 (DPT-1) participants <18.0 years at diagnosis was analyzed. Analyses examined associations of C-peptide measures with BMIZ and age in 2 cohorts: oral glucose tolerance tests (OGTTs) at diagnosis (n = 99) and mixed meal tolerance tests (MMTTs) <6 months after diagnosis (n = 80). Multivariable linear regression was utilized. RESULTS: Fasting and area under the curve (AUC) C-peptide from OGTTs (n = 99) at diagnosis and MMTTs (n = 80) after diagnosis were positively associated with BMIZ and age (P < .001 for all). Associations persisted when BMIZ and age were included as independent variables in regression models (P < .001 for all). BMIZ and age explained 31%-47% of the variance of C-peptide measures. In an example, 2 individuals with identical AUC C-peptide values had an approximate 5-fold difference in values after adjustments for BMIZ and age. The association between fasting glucose and C-peptide decreased markedly when fasting C-peptide values were adjusted (r = 0.30, P < .01 to r = 0.07, n.s.). CONCLUSIONS: C-peptide measures are strongly and independently related to BMIZ and age at and soon after the diagnosis of T1D. Adjustments for BMIZ and age cause substantial changes in C-peptide values, and impact the association between glycemia and C-peptide. Such adjustments can improve assessments of β-cell impairment at diagnosis.Item Who Is Enrolling? The Path to Monitoring in Type 1 Diabetes TrialNet’s Pathway to Prevention(American Diabetes Association, 2019-12) Sims, Emily K.; Geyer, Susan; Bennett Johnson, Suzanne; Libman, Ingrid; Jacobsen, Laura M.; Boulware, David; Rafkin, Lisa E.; Matheson, Della; Atkinson, Mark A.; Rodriguez, Henry; Spall, Maria; Elding Larsson, Helena; Wherrett, Diane K.; Greenbaum, Carla J.; Krischer, Jeffrey; DiMeglio, Linda A.; Pediatrics, School of MedicineObjective: To better understand potential facilitators of individual engagement in type 1 diabetes natural history and prevention studies through analysis of enrollment data in the TrialNet Pathway to Prevention (PTP) study. Research design and methods: We used multivariable logistic regression models to examine continued engagement of eligible participants at two time points: 1) the return visit after screening to confirm an initial autoantibody-positive (Ab+) test result and 2) the initial oral glucose tolerance test (OGTT) for enrollment into the monitoring protocol. Results: Of 5,387 subjects who screened positive for a single autoantibody (Ab), 4,204 (78%) returned for confirmatory Ab testing. Younger age was associated with increased odds of returning for Ab confirmation (age <12 years vs. >18 years: odds ratio [OR] 2.12, P < 0.0001). Racial and ethnic minorities were less likely to return for confirmation, particularly nonwhite non-Hispanic (OR 0.50, P < 0.0001) and Hispanic (OR 0.69, P = 0.0001) relative to non-Hispanic white subjects. Of 8,234 subjects, 5,442 (66%) were identified as eligible to be enrolled in PTP OGTT monitoring. Here, younger age and identification as multiple Ab+ were associated with increased odds of returning for OGTT monitoring (age <12 years vs. >18 years: OR 1.43, P < 0.0001; multiple Ab+: OR 1.36, P < 0.0001). Parents were less likely to enroll into monitoring than other relatives (OR 0.78, P = 0.004). Site-specific factors, including site volume and U.S. site versus international site, were also associated with differences in rates of return for Ab+ confirmation and enrollment into monitoring. Conclusions: These data confirm clear differences between successfully enrolled populations and those lost to follow-up, which can serve to identify strategies to increase ongoing participation.