Biobanking, consent, and commercialization in international genetics research: the Type 1 Diabetes Genetics Consortium

Background and Purpose This article describes several ethical, legal, and social issues typical of international genetics biobanking, as encountered in the Type 1 Diabetes Genetics Consortium (T1DGC)

Sample Size Requirements for Studies of Treatment Effects on Beta-Cell Function in Newly Diagnosed Type 1 Diabetes

Preservation of -cell function as measured by stimulated C-peptide has recently been accepted as a therapeutic target for subjects with newly diagnosed type 1 diabetes. In recently completed studies conducted by the Type 1 Diabetes Trial Network (TrialNet), repeated 2-hour Mixed Meal Tolerance Tests (MMTT) were obtained for up to 24 months from 156 subjects with up to 3 months duration of type 1 diabetes at the time of study enrollment. These data provide the information needed to more accurately determine the sample size needed for future studies of the effects of new agents on the 2-hour area under the curve (AUC) of the C-peptide values. The natural log(), log(+1) and square-root transformations of the AUC were assessed. In general, a transformation of the data is needed to better satisfy the normality assumptions for commonly used statistical tests. Statistical analysis of the raw and transformed data are provided to estimate the mean levels over time and the residual variation in untreated subjects that allow sample size calculations for future studies at either 12 or 24 months of follow-up and among children 8–12 years of age, adolescents (13–17 years) and adults (18+ years). The sample size needed to detect a given relative (percentage) difference with treatment versus control is greater at 24 months than at 12 months of follow-up, and differs among age categories. Owing to greater residual variation among those 13–17 years of age, a larger sample size is required for this age group. Methods are also described for assessment of sample size for mixtures of subjects among the age categories. Statistical expressions are presented for the presentation of analyses of log(+1) and transformed values in terms of the original units of measurement (pmol/ml). Analyses using different transformations are described for the TrialNet study of masked anti-CD20 (rituximab) versus masked placebo. These results provide the information needed to accurately evaluate the sample size for studies of new agents to preserve C-peptide levels in newly diagnosed type 1 diabetes

Failure to Preserve β-Cell Function With Mycophenolate Mofetil and Daclizumab Combined Therapy in Patients With New- Onset Type 1 Diabetes

OBJECTIVE This trial tested whether mycophenolate mofetil (MMF) alone or with daclizumab (DZB) could arrest the loss of insulin-producing β-cells in subjects with new-onset type 1 diabetes. RESEARCH DESIGN AND METHODS A multi-center, randomized, placebo-controlled, double-masked trial was initiated by Type 1 Diabetes TrialNet at 13 sites in North America and Europe. Subjects diagnosed with type 1 diabetes and with sufficient C-peptide within 3 months of diagnosis were randomized to either MMF alone, MMF plus DZB, or placebo, and then followed for 2 years. The primary outcome was the geometric mean area under the curve (AUC) C-peptide from the 2-h mixed meal tolerance test. RESULTS One hundred and twenty-six subjects were randomized and treated during the trial. The geometric mean C-peptide AUC at 2 years was unaffected by MMF alone or MMF plus DZB versus placebo. Adverse events were more frequent in the active therapy groups relative to the control group, but not significantly. CONCLUSIONS Neither MMF alone nor MMF in combination with DZB had an effect on the loss of C-peptide in subjects with new-onset type 1 diabetes. Higher doses or more targeted immunotherapies may be needed to affect the autoimmune process

The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants

Aims/hypothesis The incidence of type 1 diabetes is increasing at a rate of 3–5% per year. Genetics cannot fully account for this trend, suggesting an influence of environmental factors. The accelerator hypothesis proposes an effect of metabolic factors on type 1 diabetes risk. To test this in the TrialNet Pathway to Prevention (PTP) cohort, we analysed the influence of BMI, weight status and insulin resistance on progression from single to multiple islet autoantibodies (Aab) and progression from normoglycaemia to diabetes. Methods HOMA1-IR was used to estimate insulin resistance in Aab-positive PTP participants. Cox proportional hazards models were used to evaluate the effects of BMI, BMI percentile (BMI%), weight status and HOMA1-IR on the progression of autoimmunity or the development of diabetes. Results Data from 1,310 single and 1,897 multiple Aab-positive PTP participants were included. We found no significant relationships between BMI, BMI%, weight status or HOMA1-IR and the progression from one to multiple Aabs. Similarly, among all Aab-positive participants, no significant relationships were found between BMI, weight status or HOMA1-IR and progression to diabetes. Diabetes risk was modestly increased with increasing BMI% among the entire cohort, in obese participants 13–20 years of age and with increasing HOMA1-IR in adult Aab-positive participants. Conclusions/interpretation Analysis of the accelerator hypothesis in the TrialNet PTP cohort does not suggest a broad influence of metabolic variables on diabetes risk. Efforts to identify other potentially modifiable environmental factors should continue

Proinsulin Secretion Is a Persistent Feature of Type 1 Diabetes

OBJECTIVE: Abnormally elevated proinsulin secretion has been reported in type 2 and early type 1 diabetes when significant C-peptide is present. We questioned whether individuals with long-standing type 1 diabetes and low or absent C-peptide secretory capacity retained the ability to make proinsulin. RESEARCH DESIGN AND METHODS: C-peptide and proinsulin were measured in fasting and stimulated sera from 319 subjects with long-standing type 1 diabetes (≥3 years) and 12 control subjects without diabetes. We considered three categories of stimulated C-peptide: 1) C-peptide positive, with high stimulated values ≥0.2 nmol/L; 2) C-peptide positive, with low stimulated values ≥0.017 but <0.2 nmol/L; and 3) C-peptide <0.017 nmol/L. Longitudinal samples were analyzed from C-peptide-positive subjects with diabetes after 1, 2, and 4 years. RESULTS: Of individuals with long-standing type 1 diabetes, 95.9% had detectable serum proinsulin (>3.1 pmol/L), while 89.9% of participants with stimulated C-peptide values below the limit of detection (<0.017 nmol/L; n = 99) had measurable proinsulin. Proinsulin levels remained stable over 4 years of follow-up, while C-peptide decreased slowly during longitudinal analysis. Correlations between proinsulin with C-peptide and mixed-meal stimulation of proinsulin were found only in subjects with high stimulated C-peptide values (≥0.2 nmol/L). Specifically, increases in proinsulin with mixed-meal stimulation were present only in the group with high stimulated C-peptide values, with no increases observed among subjects with low or undetectable (<0.017 nmol/L) residual C-peptide. CONCLUSIONS: In individuals with long-duration type 1 diabetes, the ability to secrete proinsulin persists, even in those with undetectable serum C-peptide

Specific human leukocyte antigen DQ influence on expression of antiislet autoantibodies and progression to type 1 diabetes

Human leukocyte antigen (HLA) DQ haplotypes have the strongest genetic association with type 1 diabetes (T1DM) risk. OBJECTIVE: The objective of the study was to analyze whether HLA DQ alleles influence the development of antiislet autoantibodies, the progression to T1DM among autoantibody-positive relatives, or both. DESIGN: The Diabetes Prevention Trial-1 screened more than 90,000 nondiabetic relatives of patients for cytoplasmic islet-cell autoantibody (ICA) expression between 1994 and 2002. SETTING: The study was conducted in the general community. PARTICIPANTS: The Diabetes Prevention Trial-1 found 2817 ICA-positive relatives who were tested for biochemical autoantibodies (GAD65, ICA512, and insulin) and HLA-DQ haplotypes, and 2796 of them were followed up for progression to diabetes for up to 8 yr (median, 3.6 yr). MAIN OUTCOME MEASURE: Progression to T1DM was measured. RESULTS: High-risk DQ haplotypes and genotypes were associated with a higher percentage of relatives expressing multiple biochemical autoantibodies and higher T1DM risk (e.g., respectively, 59 and 36% at 5 yr for carriers of the DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201 genotype). The number of autoantibodies expressed significantly increased T1DM risk and across different DQ genotypes, autoantibody positivity directly correlated with diabetes risk. However, multivariate analyses indicated that the influence of most genotypes on T1DM risk was not independent from autoantibody expression, with the possible exception of DQA1*0102-DQB1*0602. Specific genotypic combinations conferred 5-yr diabetes risks significantly lower (e.g. 7%-DQA1*0201-DQB1*0201/DQA1*0501-DQB1*0201 and 14%-DQA1*0301-DQB1*0301/DQA1*0501-DQB1*0201) than when those haplotypes were found in other combinations. CONCLUSION: HLA DQ alleles determine autoantibody expression, which is correlated with diabetes progression. Among autoantibody-positive relatives, most HLA DQ genotypes did not further influence T1DM risk

Fall in C-peptide during first 2 years from diagnosis: Evidence of at least two distinct phases from composite type 1 diabetes trialnet data.

Interpretation of clinical trials to alter the decline in β-cell function after diagnosis of type 1 diabetes depends on a robust understanding of the natural history of disease. Combining data from the Type 1 Diabetes TrialNet studies, we describe the natural history of β-cell function from shortly after diagnosis through 2 years post study randomization, assess the degree of variability between patients, and investigate factors that may be related to C-peptide preservation or loss. We found that 93% of individuals have detectable C-peptide 2 years from diagnosis. In 11% of subjects, there was no significant fall from baseline by 2 years. There was a biphasic decline in C-peptide; the C-peptide slope was −0.0245 pmol/mL/month (95% CI −0.0271 to −0.0215) through the first 12 months and −0.0079 (−0.0113 to −0.0050) from 12 to 24 months (P \u3c 0.001). This pattern of fall in C-peptide over time has implications for understanding trial results in which effects of therapy are most pronounced early and raises the possibility that there are time-dependent differences in pathophysiology. The robust data on the C-peptide obtained under clinical trial conditions should be used in planning and interpretation of clinical trials

Screening for Type 1 Diabetes in the General Population:A Status Report and Perspective

Most screening programs to identify individuals at risk for type 1 diabetes have targeted relatives of people living with the disease to improve yield and feasibility. However, ∼90% of those who develop type 1 diabetes do not have a family history. Recent successes in disease-modifying therapies to impact the course of early-stage disease have ignited the consideration of the need for and feasibility of population screening to identify those at increased risk. Existing population screening programs rely on genetic or autoantibody screening, and these have yielded significant information about disease progression and approaches for timing for screening in clinical practice. At the March 2021 Type 1 Diabetes TrialNet Steering Committee meeting, a session was held in which ongoing efforts for screening in the general population were discussed. This report reviews the background of these efforts and the details of those programs. Additionally, we present hurdles that need to be addressed for successful implementation of population screening and provide initial recommendations for individuals with positive screens so that standardized guidelines for monitoring and follow-up can be established