Clinical trial data validate the C-peptide estimate model in type 1 diabetes.
Ontology highlight
ABSTRACT:
SUBMITTER: Wentworth JM
PROVIDER: S-EPMC8159479 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Clinical trial data validate the C-peptide estimate model in type 1 diabetes.
Wentworth John M JM Bediaga Naiara G NG Gitelman Stephen E SE Evans-Molina Carmela C Gottlieb Peter A PA Colman Peter G PG Haller Michael J MJ Harrison Leonard C LC
Diabetologia 20200126 4
Similar Datasets
Project description:Autoimmune and allergic diseases occur when an individual mounts an inappropriate immune response to a self-antigen or an innocuous environmental antigen. This triggers a pathogenic T-cell response resulting in damage to specific tissues and organs. In type 1 diabetes (T1D), this manifests as destruction of the insulin-secreting β cells, resulting in a life-long dependency on recombinant insulin. Modulation of the pathogenic T-cell response with antigen-specific peptide immunotherapy offers the potential to restore the immune homeostasis and prevent further tissue destruction. Recent clinical advances with peptide therapy approaches in both T1D and other diseases are beginning to show encouraging results. New technologies targeting the peptides to specific cell types are also moving from pre-clinical development to the clinic. While many challenges remain in clinical development, not least selection of the optimal dose and dosing frequency, this is clearly becoming a very active field of drug development.
| S-EPMC5836708 | biostudies-literature
Project description:AimsThiazolidinediones are insulin sensitizers, and are associated with fluid retention and increased risk of heart failure (HF) in people with type 2 diabetes. We assessed fatal and non-fatal HF events and their outcome, and identified HF predictors in the RECORD (Rosiglitazone Evaluated for Cardiac Outcomes and Regulation of glycaemia in Diabetes) trial population.Methods and resultsIn a multicentre, open-label study, we randomized 4447 people with type 2 diabetes on metformin or sulfonylurea monotherapy with a mean HbA(1c) of 7.9% to add-on rosiglitazone (n = 2220) or to a combination of metformin and sulfonylurea (n = 2227) and followed them over 5.5 years on average. Heart failure hospitalizations and deaths were adjudicated by a Clinical Endpoint Committee using pre-specified criteria. Independent predictors of HF events were identified out of a group of 30 pre-specified clinical, demographic, and biological variables. In the rosiglitazone group, the risk of HF death or hospitalization was doubled: HR = 2.10 (95% CI, 1.35-3.27): the excess HF event rate was 2.6 (1.1-4.1) per 1000 person-years. An excess in HF deaths was observed (10 vs. two), including four HF deaths as first HF events. By contrast, there was no increase in cardiovascular mortality or hospitalization (HR = 0.99, 95% CI, 0.85-1.16) or in cardiovascular deaths (60 vs. 71). Independent predictors of HF were rosiglitazone assignment, age, urinary albumin : creatinine ratio, body mass index, and systolic blood pressure at baseline. A history of previous cardiovascular disease was not predictive of HF. Duration of HF hospitalization and rate of HF re-hospitalization were similar in the two groups.ConclusionThese findings confirm the increased risk of HF events in people treated with rosiglitazone and support the recommendation that this agent should not continue to be used in people developing symptomatic HF while using the medication. Close follow-up for the risk of HF should be offered to elderly people, people with markedly increased body mass index, people with microalbuminuria/proteinuria, and people with increased systolic blood pressure.
| S-EPMC2848325 | biostudies-literature
Project description:ObjectivesRecent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.MethodsWe selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.ResultsWe tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2-52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.ConclusionsThe K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our "multistage adjustment" model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.
| S-EPMC3766990 | biostudies-literature
Project description:Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.
| S-EPMC11330176 | biostudies-literature
Project description:Baseline array profiling data from 46 early breast cancer patients All patients were treated with neoadjuvant anthracycline/anthracycline-taxane regimens
2021-09-30 | GSE180295 | GEO
Project description:Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here ("PoDBAY," Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease ("PoD") curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.
| S-EPMC8568947 | biostudies-literature
Project description:Canagliflozin (CFZ) is a member of new class of glucose lowering agents, sodium-glucose co-transporter (SGLT) inhibitors, which got approval by food and drug administration. It has insulin independent action by blocking the transporter protein SGLT2 in the kidneys, resulting in urinary glucose excretion and reduction in blood glucose levels. In clinical trials, CFZ significantly decreased HbA1c level when administered either as monotherapy or as combined therapy with other anti-diabetic drugs. Intriguingly, it showed additional benefits like weight reduction and lowering of blood pressure. The commonly observed side effects were urinary and genital infections. It has exhibited favorable pharmacokinetic and pharmacodynamic profiles even in patients with renal and hepatic damage. Hence, this review purports to outline CFZ as a newer beneficial drug for type 2 diabetes mellitus.
| S-EPMC4058745 | biostudies-literature
Project description:BackgroundThe Association for Pathology Informatics (API) Extensible Mark-up Language (XML) TMA Data Exchange Specification (TMA DES) proposed in April 2003 provides a community-based, open source tool for sharing tissue microarray (TMA) data in a common format. Each tissue core within an array has separate data including digital images; therefore an organized, common approach to produce, navigate and publish such data facilitates viewing, sharing and merging TMA data from different laboratories. The AIDS and Cancer Specimen Resource (ACSR) is a HIV/AIDS tissue bank consortium sponsored by the National Cancer Institute (NCI) Division of Cancer Treatment and Diagnosis (DCTD). The ACSR offers HIV-related malignancies and uninfected control tissues in microarrays (TMA) accompanied by de-identified clinical data to approved researchers. Exporting our TMA data into the proposed API specified format offers an opportunity to evaluate the API specification in an applied setting and to explore its usefulness.ResultsA document type definition (DTD) that governs the allowed common data elements (CDE) in TMA DES export XML files was written, tested and evolved and is in routine use by the ACSR. This DTD defines TMA DES CDEs which are implemented in an external file that can be supplemented by internal DTD extensions for locally defined TMA data elements (LDE).ConclusionACSR implementation of the TMA DES demonstrated the utility of the specification and allowed application of a DTD to validate the language of the API specified XML elements and to identify possible enhancements within our TMA data management application. Improvements to the specification have additionally been suggested by our experience in importing other institution's exported TMA data. Enhancements to TMA DES to remove ambiguous situations and clarify the data should be considered. Better specified identifiers and hierarchical relationships will make automatic use of the data possible. Our tool can be used to reorder data and add identifiers; upgrading data for changes in the specification can be automatically accomplished. Using a DTD (optionally reflecting our proposed enhancements) can provide stronger validation of exported TMA data.
| S-EPMC1183103 | biostudies-literature
Project description:Array data from neoDDRD clinical trial
| PRJNA747706 | ENA
Project description:AimTo determine whether the low C-peptide levels (< 50 pmol/l) produced by the pancreas for decades after onset of Type 1 diabetes have clinical significance.MethodsWe evaluated fasting C-peptide levels, duration of disease and age of onset in a large cross-sectional series (n = 1272) of people with Type 1 diabetes. We then expanded the scope of the study to include the relationship between C-peptide and HbA1c control (n = 1273), as well as diabetic complications (n = 324) and presence of hypoglycaemia (n = 323). The full range of C-peptide levels was also compared with 1,5-Anhydroglucitol, a glucose responsive marker.ResultsC-peptide levels declined for decades after diagnosis, and the rate of decline was significantly related to age of onset (P < 0.0001), after adjusting for disease duration. C-peptide levels > 10 pmol/l were associated with protection from complications (e.g. nephropathy, neuropathy, foot ulcers and retinopathy; P = 0.03). Low C-peptide levels were associated with poor metabolic control measured by HbA1c (P < 0.0001). Severe hypoglycaemia was associated with the lowest C-peptide levels compared with mild (P = 0.049) or moderate (P = 0.04) hypoglycaemia. All levels of measurable C-peptide were responsive to acute fluctuations in blood glucose levels as assessed by 1,5-Anhydroglucitol (P < 0.0001).ConclusionsLow C-peptide levels have clinical significance and appear helpful in characterizing groups at-risk for faster C-peptide decline, complications, poorer metabolic control and severe hypoglycaemia. Low C-peptide levels may be a biomarker for characterizing at-risk patients with Type 1 diabetes.
| S-EPMC4578991 | biostudies-literature