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Background: We conducted exploratory analyses to identify distinct trajectories of estimated glomerular filtration rate (eGFR) and their relationship with hyperfiltration, subsequent rapid eGFR decline, and albuminuria in participants with youth-onset type 2 diabetes enrolled in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study. Methods: Annual serum creatinine, cystatin C, urine albumin, and creatinine measurements were obtained from 377 participants followed for ≥ 10 years. Albuminuria and eGFR were calculated. Hyperfiltration peak is the greatest eGFR inflection point during follow-up. Latent class modeling was applied to identify distinct eGFR trajectories. Results: At baseline, participants' mean age was 14 years, type 2 diabetes duration was 6 months, mean HbA1c was 6%, and mean eGFR was 120 ml/min/1.73 m2. Five eGFR trajectories associated with different rates of albuminuria were identified, including a "progressive increasing eGFR" group (10%), three "stable eGFR" groups with varying starting mean eGFR, and an "eGFR steady decline" group (1%). Participants who exhibited the greatest peak eGFR also had the highest levels of elevated albuminuria at year 10. This group membership was characterized by a greater proportion of female and Hispanic participants. Conclusions: Distinct eGFR trajectories that associate with albuminuria risk were identified, with the eGFR trajectory characterized by increasing eGFR over time associating with the highest level of albuminuria. These descriptive data support the current recommendations to estimate GFR annually in young persons with type 2 diabetes and provide insight into eGFR-related factors which may contribute to predictive risk strategies for kidney disease therapies in youth with type 2 diabetes. Trial registration: ClinicalTrials.gov Identifier: NCT00081328, date registered 2002. [ABSTRACT FROM AUTHOR]

Background: Established diabetes care ("diabetes home") and regular healthcare visits are important to achieve optimal health. Nothing is known about psychosocial factors that predict healthcare usage (HCU) in young adults with youth-onset type 2 diabetes, at risk for early complications. Objective: To identify psychosocial predictors of HCU in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Design: Longitudinal, measured at T1 (baseline) and T2 (1 year later). Logistic and linear regressions, adjusted for potential confounders, identified predictors of sub-optimal HCU (defined as no diabetes home, 0 visits for routine care, or ≥ 1 urgent care visit in prior 6 months). Participants: N = 366 TODAY2 participants with T1 and T2 data (381 consented). Mean age = 26.0 years, 67.8% female, 37.7% non-Hispanic Black, 35.8% Hispanic, 20.2% non-Hispanic white, 6.3% "other," mean HbA1c = 9.4%. Main Measures: HCU survey; reliable and valid measures of diabetes self-efficacy, depressive symptoms, anxiety symptoms, diabetes distress, beliefs about medicines, diabetes attitudes, material need insecurities, self-management support. Key Results: 25.4% had no diabetes home, 23.7% had 0 routine care visits, 46% had ≥ 1 urgent care visit (prior 6 months). Beliefs in the necessity of (adjusted odds ratio [OR] = 1.28; 95% confidence interval [CI] = 1.12, 1.46, p < 0.001), and concerns about (OR = 1.29;CI = 1.08,1.54, p = 0.004), diabetes medicines, and its negative psychosocial impacts (OR = 1.57;CI = 1.04, 2.38, p = 0.03), predicted higher odds of having a diabetes home at T2. Beliefs that medicines are harmful predicted lower odds of a diabetes home (OR = 0.56;CI = 0.37,0.85, p = 0.006). Necessity beliefs (OR = 1.2;CI = 1.06,1.36, p = 0.004), and self-management support (OR = 1.5;CI = 1.08,2.07, p = 0.01) predicted higher odds of having ≥ 1 diabetes care visit, harm beliefs predicted lower odds (OR = 0.6;CI = 0.41,0.88, p = 0.01). Conclusions: Sub-optimal healthcare usage, common in young adults with youth-onset type 2 diabetes, is predicted by beliefs about medicines, diabetes impact, and self-management support. We must address these factors to help this vulnerable group establish stable diabetes care. [ABSTRACT FROM AUTHOR]

In testing the significance of treatment effects in randomized clinical trials (RCTs), randomization-based inference is distinguished from population-based parametric and nonparametric inference, such as the t-test or permutation tests, taking into account three properties: preservation of type I error rate, relation of power to the randomization procedure, and flexibility in choosing the test statistic. In this paper, we revisit rationale of the properties and provide justification through simulations. We propose that the choice of randomization procedures and the analysis of RCTs can be facilitated by the application of randomization-based inference. [ABSTRACT FROM AUTHOR]