Umbilical cord mesenchymal stem cells for COVID-19 acute respiratory distress syndrome: A double-blind, phase 1/2a, randomized controlled trial.

Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with high mortality. Mesenchymal stem cells are known to exert immunomodulatory and anti-inflammatory effects and could yield beneficial effects in COVID-19 ARDS. The objective of this study was to determine safety and explore efficacy of umbilical cord mesenchymal stem cell (UC-MSC) infusions in subjects with COVID-19 ARDS. A double-blind, phase 1/2a, randomized, controlled trial was performed. Randomization and stratification by ARDS severity was used to foster balance among groups. All subjects were analyzed under intention to treat design. Twenty-four subjects were randomized 1:1 to either UC-MSC treatment (n = 12) or the control group (n = 12). Subjects in the UC-MSC treatment group received two intravenous infusions (at day 0 and 3) of 100 ± 20 × 106 UC-MSCs; controls received two infusions of vehicle solution. Both groups received best standard of care. Primary endpoint was safety (adverse events [AEs]) within 6 hours; cardiac arrest or death within 24 hours postinfusion). Secondary endpoints included patient survival at 31 days after the first infusion and time to recovery. No difference was observed between groups in infusion-associated AEs. No serious adverse events (SAEs) were observed related to UC-MSC infusions. UC-MSC infusions in COVID-19 ARDS were found to be safe. Inflammatory cytokines were significantly decreased in UC-MSC-treated subjects at day 6. Treatment was associated with significantly improved patient survival (91% vs 42%, P = .015), SAE-free survival (P = .008), and time to recovery (P = .03). UC-MSC infusions are safe and could be beneficial in treating subjects with COVID-19 ARDS.

Slowed Metabolic Decline After 1 Year of Oral Insulin Treatment Among Individuals at High Risk for Type 1 Diabetes in the Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Oral Insulin Prevention Trials.

We assessed whether oral insulin slowed metabolic decline after 1 year of treatment in individuals at high risk for type 1 diabetes. Two oral insulin trials that did not show efficacy overall and had type 1 diabetes as the primary end point were analyzed: the Diabetes Prevention Trial-Type 1 (DPT-1) and the TrialNet oral insulin trials. Oral glucose tolerance tests at baseline and after 1 year of treatment were analyzed. Among those at high risk (with a Diabetes Prevention Trial-Type 1 Risk Score [DPTRS] ≥6.75), the area under the curve (AUC) C-peptide increased significantly from baseline to 1 year in each oral insulin group, whereas the AUC glucose increased significantly in each placebo group. At 1 year, the AUC C-peptide/AUC glucose (AUC Ratio) was significantly higher in the oral insulin group than in the placebo group in each trial (P < 0.05; P = 0.057 when adjusted for age in the TrialNet trial) and in both trials combined (P < 0.01 with or without adjustment for age). For a DPTRS <6.75, oral insulin groups did not differ from placebo groups in the AUC Ratio. The findings suggest that 1 year of treatment with oral insulin slows metabolic deterioration in individuals at high risk for type 1 diabetes. Moreover, the findings further suggest that metabolic end points can be useful adjuncts to the diagnostic end point in assessments of preventive treatments for the disorder.

Who Is Enrolling? The Path to Monitoring in Type 1 Diabetes TrialNet's Pathway to Prevention.

OBJECTIVE: 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.

Associations of HbA1c with the timing of C-peptide responses during the oral glucose tolerance test at the diagnosis of type 1 diabetes.

BACKGROUND: In new onset type 1 diabetes (T1D), overall C-peptide measures such as area under the curve (AUC) C-peptide and peak C-peptide are useful for estimating the extent of ß-cell dysfunction, and for assessing responses to intervention therapy. However, measures of the timing of C-peptide responsiveness could have additional value. OBJECTIVES: We assessed the contribution of the timing of C-peptide responsiveness during oral glucose tolerance tests (OGTTs) to hemoglobin A1c (HbA1c) variation at T1D diagnosis. METHODS: We analyzed data from 85 individuals <18 years with OGTTs and HbA1c measurements at diagnosis. Overall [AUC and peak C-peptide] and timing measures [30-0 minute C-peptide (early); 60 to 120 minute C-peptide sum-30 minutes (late); 120/30 C-peptide; time to peak C-peptide] were utilized. RESULTS: At diagnosis, the mean (±SD) age was 11.2 ± 3.3 years, body mass index (BMI)-z was 0.4 ± 1.1, 51.0% were male. The average HbA1c was 43.54 ± 8.46 mmol/mol (6.1 ± 0.8%). HbA1c correlated inversely with the AUC C-peptide (P < 0.001), peak C-peptide (P < 0.001), early and late C-peptide responses (P < 0.001 each), and 120/30 C-peptide (P < 0.001). Those with a peak C-peptide occurring at ≤60 minutes had higher HbA1c values than those with peaks later (P = 0.003). HbA1c variance was better explained with timing measures added to regression models (R2 = 11.6% with AUC C-peptide alone; R2 = 20.0% with 120/30 C-peptide added; R2 = 13.7% with peak C-peptide alone, R2 = 20.4% with timing of the peak added). Similar associations were seen between the 2-hour glucose and the C-peptide measures. CONCLUSIONS: These findings show that the addition of timing measures of C-peptide responsiveness better explains HbA1c variation at diagnosis than standard measures alone.

Type 1 Diabetes TrialNet: A Multifaceted Approach to Bringing Disease-Modifying Therapy to Clinical Use in Type 1 Diabetes.

What will it take to bring disease-modifying therapy to clinical use in type 1 diabetes? Coordinated efforts of investigators involved in discovery, translational, and clinical research operating in partnership with funders and industry and in sync with regulatory agencies are needed. This Perspective describes one such effort, Type 1 Diabetes TrialNet, a National Institutes of Health-funded and JDRF-supported international clinical trials network that emerged from the Diabetes Prevention Trial-Type 1 (DPT-1). Through longitudinal natural history studies, as well as trials before and after clinical onset of disease combined with mechanistic and ancillary investigations to enhance scientific understanding and translation to clinical use, TrialNet is working to bring disease-modifying therapies to individuals with type 1 diabetes. Moreover, TrialNet uses its expertise and experience in clinical studies to increase efficiencies in the conduct of trials and to reduce the burden of participation on individuals and families. Herein, we highlight key contributions made by TrialNet toward a revised understanding of the natural history of disease and approaches to alter disease course and outline the consortium's plans for the future.

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.

BACKGROUND/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 ß-cell impairment at diagnosis.

Use of Dried Capillary Blood Sampling for Islet Autoantibody Screening in Relatives: A Feasibility Study.

BACKGROUND: Islet autoantibody testing provides the basis for assessment of risk of progression to type 1 diabetes. We set out to determine the feasibility and acceptability of dried capillary blood spot-based screening to identify islet autoantibody-positive relatives potentially eligible for inclusion in prevention trials. MATERIALS AND METHODS: Dried blood spot (DBS) and venous samples were collected from 229 relatives participating in the TrialNet Pathway to Prevention Study. Both samples were tested for glutamic acid decarboxylase, islet antigen 2, and zinc transporter 8 autoantibodies, and venous samples were additionally tested for insulin autoantibodies and islet cell antibodies. We defined multiple autoantibody positive as two or more autoantibodies in venous serum and DBS screen positive if one or more autoantibodies were detected. Participant questionnaires compared the sample collection methods. RESULTS: Of 44 relatives who were multiple autoantibody positive in venous samples, 42 (95.5%) were DBS screen positive, and DBS accurately detected 145 of 147 autoantibody-negative relatives (98.6%). Capillary blood sampling was perceived as more painful than venous blood draw, but 60% of participants would prefer initial screening using home fingerstick with clinic visits only required if autoantibodies were found. CONCLUSIONS: Capillary blood sampling could facilitate screening for type 1 diabetes prevention studies.

The development and utility of a novel scale that quantifies the glycemic progression toward type 1 diabetes over 6 months.

OBJECTIVE: We developed a scale to serve as a potential end point for 6-month glycemic progression (PS6M) toward type 1 diabetes (T1D) in autoantibody-positive relatives of individuals with T1D. RESEARCH DESIGN AND METHODS: The PS6M was developed from Diabetes Prevention Trial-Type 1 (DPT-1) data and tested in the TrialNet Pathway to Prevention Study (PTP). It is the difference between 6-month glucose sum values (30-120 min oral glucose tolerance test values) and values predicted for nonprogressors. RESULTS: The PS6M predicted T1D in the PTP (P < 0.001). The area under the receiver operating chacteristic curve was greater (P < 0.001) for the PS6M than for the baseline-to-6-month difference. PS6M values were higher in those with two or more autoantibodies, 30-0 min C-peptide values <2.00 ng/mL, or DPT-1 Risk Scores >7.00 (P < 0.001 for all). CONCLUSIONS: The PS6M is an indicator of short-term glycemic progression to T1D that could be a useful tool for assessing preventive treatments and biomarkers.

A new approach for diagnosing type 1 diabetes in autoantibody-positive individuals based on prediction and natural history.

OBJECTIVE: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. RESEARCH DESIGN AND METHODS: Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. RESULTS: Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. CONCLUSIONS: An approach based on prediction and natural history appears to have utility for diagnosing T1D.

Use of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of the risk classification of type 1 diabetes.

OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants. RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). It was also compared between individuals with DPTRS values <7.00 or >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS. RESULTS The cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%). CONCLUSIONS DPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.

Acceleration of the loss of the first-phase insulin response during the progression to type 1 diabetes in diabetes prevention trial-type 1 participants.

We studied the change in the first-phase insulin response (FPIR) during the progression to type 1 diabetes (T1D). Seventy-four oral insulin trial progressors to T1D from the Diabetes Prevention Trial-Type 1 with at least one FPIR measurement after baseline and before diagnosis were studied. The FPIR was examined longitudinally in 26 progressors who had FPIR measurements during each of the 3 years before diagnosis. The association between the change from the baseline FPIR to the last FPIR and time to diagnosis was studied in the remainder (n = 48). The 74 progressors had lower baseline FPIR values than nonprogressors (n = 270), with adjustments made for age and BMI. In the longitudinal analysis of the 26 progressors, there was a greater decline in the FPIR from 1.5 to 0.5 years before diagnosis than from 2.5 to 1.5 years before diagnosis. This accelerated decline was also evident in a regression analysis of the 48 remaining progressors in whom the rate of decline became more marked with the approaching diagnosis. The patterns of decline were similar between the longitudinal and regression analyses. There is an acceleration of decline in the FPIR during the progression to T1D, which becomes especially marked between 1.5 and 0.5 years before diagnosis.

The prediction of type 1 diabetes by multiple autoantibody levels and their incorporation into an autoantibody risk score in relatives of type 1 diabetic patients.

OBJECTIVE: We assessed whether a risk score that incorporates levels of multiple islet autoantibodies could enhance the prediction of type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: TrialNet Natural History Study participants (n = 784) were tested for three autoantibodies (GADA, IA-2A, and mIAA) at their initial screening. Samples from those positive for at least one autoantibody were subsequently tested for ICA and ZnT8A. An autoantibody risk score (ABRS) was developed from a proportional hazards model that combined autoantibody levels from each autoantibody along with their designations of positivity and negativity. RESULTS: The ABRS was strongly predictive of T1D (hazard ratio [with 95% CI] 2.72 [2.23-3.31], P < 0.001). Receiver operating characteristic curve areas (with 95% CI) for the ABRS revealed good predictability (0.84 [0.78-0.90] at 2 years, 0.81 [0.74-0.89] at 3 years, P < 0.001 for both). The composite of levels from the five autoantibodies was predictive of T1D before and after an adjustment for the positivity or negativity of autoantibodies (P < 0.001). The findings were almost identical when ICA was excluded from the risk score model. The combination of the ABRS and the previously validated Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) predicted T1D more accurately (0.93 [0.88-0.98] at 2 years, 0.91 [0.83-0.99] at 3 years) than either the DPTRS or the ABRS alone (P ≤ 0.01 for all comparisons). CONCLUSIONS: These findings show the importance of considering autoantibody levels in assessing the risk of T1D. Moreover, levels of multiple autoantibodies can be incorporated into an ABRS that accurately predicts T1D.

The application of the diabetes prevention trial-type 1 risk score for identifying a preclinical state of type 1 diabetes.

OBJECTIVE: We assessed the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for identifying individuals who are highly likely to progress to type 1 diabetes (T1D) within 2 years. RESEARCH DESIGN AND METHODS: The DPTRS was previously developed from Diabetes Prevention Trial-Type 1 (DPT-1) data and was subsequently validated in the TrialNet Natural History Study (TNNHS). DPTRS components included C-peptide and glucose indexes from oral glucose tolerance testing, along with age and BMI. The cumulative incidence of T1D was determined after DPTRS thresholds were first exceeded and after the first occurrences of glucose abnormalities. RESULTS: The 2-year risks after the 9.00 DPTRS threshold was exceeded were 0.88 and 0.77 in DPT-1 (n = 90) and the TNNHS (n = 69), respectively. In DPT-1, the 2-year risks were much lower after dysglycemia first occurred (0.37; n = 306) and after a 2-h glucose value between 190 and 199 mg/dL was first reached (0.64; n = 59). Among those who developed T1D in DPT-1, the 9.00 threshold was exceeded 0.81 ± 0.53 years prior to the conventional diagnosis. Postchallenge C-peptide levels were substantially higher (P = 0.001 for 30 min; P < 0.001 for other time points) when the 9.00 threshold was first exceeded compared with the levels at diagnosis. CONCLUSIONS: A DPTRS threshold of 9.00 identifies individuals who are very highly likely to progress to the conventional diagnosis of T1D within 2 years and, thus, are essentially in a preclinical diabetic state. The 9.00 threshold is exceeded well before diagnosis, when stimulated C-peptide levels are substantially higher.

Validation of the Diabetes Prevention Trial-Type 1 Risk Score in the TrialNet Natural History Study.

OBJECTIVE: We assessed the accuracy of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), developed from the Diabetes Prevention Trial-Type 1 (DPT-1), in the TrialNet Natural History Study (TNNHS). RESEARCH DESIGN AND METHODS: Prediction accuracy of the DPTRS was assessed with receiver-operating characteristic curve areas. The type 1 diabetes cumulative incidence within the DPTRS intervals was compared between the TNNHS and DPT-1 cohorts. RESULTS: Receiver-operating characteristic curve areas for the DPTRS were substantial in the TNNHS (P < 0.001 at both 2 and 3 years). The type 1 diabetes cumulative incidence did not differ significantly between the TNNHS and DPT-1 cohorts within DPTRS intervals. In the TNNHS, 2-year and 3-year risks were low for DPTRS intervals <6.50 (<0.10 and <0.20, respectively). Thresholds ≥7.50 were indicative of high risk in both cohorts (2-year risks: 0.49 in the TNNHS and 0.51 in DPT-1). CONCLUSIONS: The DPTRS is an accurate and robust predictor of type 1 diabetes in autoantibody-positive populations.

A comparison of the baseline metabolic profiles between Diabetes Prevention Trial-Type 1 and TrialNet Natural History Study participants.

OBJECTIVE: We assessed whether differing autoantibody screening criteria for type 1 diabetes (T1D) prevention trials result in different baseline metabolic profiles of those who screen positive. METHODS: Diabetes Prevention Trial-Type 1 (DPT-1) participants were screened for islet cell autoantibodies, whereas TrialNet Natural History Study (TNNHS) participants were screened for biochemical autoantibodies. In both studies, those determined to be autoantibody positive underwent baseline oral glucose tolerance tests (OGTTs) in which glucose and C-peptide were measured. RESULTS: The percentage of those with an OGTT in the diabetic range was higher among the DPT-1 participants (10.0% of 956 vs. 6.4% of 645, p < 0.01). In a logistic regression analysis with adjustments for age and gender, the difference persisted (p < 0.01). Among those in the non-diabetic range (n = 860 for DPT-1 and n = 604 for the TNNHS), glucose levels were similar at all time points, except for higher fasting glucose levels in the TNNHS participants (p < 0.001). There was a higher percentage of impaired fasting glucose (IFG) in the TNNHS participants (10.9 vs. 6.7%, p < 0.01); however, with adjustments for age and gender, there was no longer a significant difference. There was no significant difference in the percentages with impaired glucose tolerance. C-peptide levels were much lower in the DPT-1 cohort at all OGTT time points (p < 0.001 for all). DISCUSSION: Differing criteria for autoantibody screening can result in marked differences in the baseline metabolic profiles of prospective participants of T1D prevention trials.

Glucose excursions between states of glycemia with progression to type 1 diabetes in the diabetes prevention trial-type 1 (DPT-1).

OBJECTIVE: We characterized fluctuations between states of glycemia in progressors to type 1 diabetes and studied whether those fluctuations are related to the early C-peptide response to oral glucose. RESEARCH DESIGN AND METHODS: Oral glucose tolerance tests (OGTTs) from differing states of glycemia were compared within individuals for glucose and C-peptide. Dysglycemic OGTTs (DYSOGTTs) were compared with normal OGTTs (NLOGTT), while transient diabetic OGTTs (TDOGTTs) were compared with subsequent nondiabetic OGTTs and with OGTTs performed at diagnosis. RESULTS: Of 135 progressors with four or more OGTTs, 30 (22%) went from NLOGTTs to DYSOGTTs at least twice. Area under the curve (AUC) glucose values from the second NLOGTT were higher (P < 0.001) than values from the first NLOGTT. Among 98 progressors whose DYSOGTTs and NLOGTTs were synchronized for the time before diagnosis, despite higher glucose levels (P < 0.01 at all time points) in the DYSOGTTs, 30- to 0-min C-peptide difference values changed little. Likewise, 30- to 0-min C-peptide difference values did not differ between TDOGTTs and subsequent (within 3 months) nondiabetic OGTTs in 55 progressors. In contrast, as glucose levels increased overall from the first to last OGTTs before diagnosis (P < 0.001 at every time point, n = 207), 30- to 0-min C-peptide difference values decreased (P < 0.001). CONCLUSIONS: Glucose levels fluctuate widely as they gradually increase overall with progression to type 1 diabetes. As glucose levels increase, the early C-peptide response declines. In contrast, glucose fluctuations are not related to the early C-peptide response. This suggests that changes in insulin sensitivity underlie the glucose fluctuations.

Trends of earlier and later responses of C-peptide to oral glucose challenges with progression to type 1 diabetes in diabetes prevention trial-type 1 participants.

OBJECTIVE We studied the C-peptide response to oral glucose with progression to type 1 diabetes in Diabetes Prevention Trial-Type 1 (DPT-1) participants. RESEARCH DESIGN AND METHODS Among 504 DPT-1 participants <15 years of age, longitudinal analyses were performed in 36 progressors and 80 nonprogressors. Progressors had oral glucose tolerance tests (OGTTs) at baseline and every 6 months from 2.0 to 0.5 years before diagnosis; nonprogressors had OGTTs over similar intervals before their last visit. Sixty-six progressors and 192 nonprogressors were also studied proximal to and at diagnosis. RESULTS The 30-0 min C-peptide difference from OGTTs performed 2.0 years before diagnosis in progressors was lower than the 30-0 min C-peptide difference from OGTTs performed 2.0 years before the last visit in nonprogressors (P < 0.01) and remained lower over time. The 90-60 min C-peptide difference was positive at every OGTT before diagnosis in progressors, whereas it was negative at every OGTT before the last visit in nonprogressors (P < 0.01 at 2.0 years). The percentage whose peak C-peptide occurred at 120 min was higher in progressors at 2.0 years (P < 0.05); this persisted over time (P < 0.001 at 0.5 years). However, the peak C-peptide levels were only significantly lower at 0.5 years in progressors (P < 0.01). The timing of the peak C-peptide predicted type 1 diabetes (P < 0.001); peak C-peptide levels were less predictive (P < 0.05). CONCLUSIONS A decreased early C-peptide response to oral glucose and an increased later response occur at least 2 years before the diagnosis of type 1 diabetes.