{"doi":"10.2337/db25-0310","title":"Novel Approach for Assessing Outcomes of Type 1 Diabetes Prevention Trials Over a Fixed Time Interval","abstract":"We evaluated whether a binary metabolic end point for change (Δ) from baseline to 1-year postrandomization could be useful in type 1 diabetes (T1D) prevention trials. Using 2-h oral glucose tolerance testing data from the stage 1 participants in the recent abatacept prevention trial and similar participants in the observational TrialNet Pathway to Prevention (PTP) study, we assessed Δmetabolic measures, plotted glucose and C-peptide response curves, and categorized vectors for Δ from baseline to 1 year as metabolic treatment failure versus success. Analyses were validated using the teplizumab prevention study. PTP participants with Δglucose >0 and ΔC-peptide <0 from baseline to 1 year were at substantially higher risk for stage 3 T1D than those with Δglucose <0 and ΔC-peptide >0 (P < 0.0001). Based on this, we compared placebo versus treatment groups in both trials for failure (Δglucose >0 with ΔC-peptide <0) versus success (Δglucose <0 with ΔC-peptide >0) after 1 year. Using this end point, a favorable metabolic impact of abatacept was found after 12 months of treatment. An analytic approach using a binary metabolic end point of failure versus success at a fixed time interval appears to detect treatment effects at least as well as standard primary end points with shorter follow-up. ARTICLE HIGHLIGHTS: Challenges in time to event type 1 diabetes (T1D) prevention trial design can yield negative results even for treatments that may actually improve disease pathology. We evaluated whether a binary metabolic end point for 12-month change from baseline to 1 year postrandomization could be useful in T1D prevention trials. This approach detected treatment effects at least as well as standard primary end points with shorter follow-up. Fixed interval metabolic end points should be used in combination with traditional T1D end points to better understand treatment effects of preventive agents.","journal":"Diabetes","year":2025,"id":546457,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9387,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":374833,"name":"William E. Russell","orcid":"0000-0001-7609-0434","position":1,"is_corresponding":false},{"id":328459,"name":"David Cuthbertson","orcid":"0000-0002-3785-9834","position":2,"is_corresponding":false},{"id":432889,"name":"Jay S. Skyler","orcid":"0000-0003-1136-8110","position":3,"is_corresponding":false},{"id":506257,"name":"Laura M. Jacobsen","orcid":"0000-0002-5144-7836","position":4,"is_corresponding":false},{"id":577042,"name":"Heba M. Ismail","orcid":"0000-0003-0102-0030","position":5,"is_corresponding":false},{"id":7390,"name":"María J. Redondo","orcid":"0000-0001-5871-4645","position":6,"is_corresponding":false},{"id":804490,"name":"Brandon M. Nathan","orcid":"0000-0001-6405-3994","position":7,"is_corresponding":false},{"id":506254,"name":"Alice L. J. Carr","orcid":"0000-0003-0704-8843","position":8,"is_corresponding":false},{"id":239874,"name":"Peter Taylor","orcid":"0000-0002-3436-422X","position":9,"is_corresponding":false},{"id":68127,"name":"Colin Dayan","orcid":"0000-0002-6557-3462","position":10,"is_corresponding":false},{"id":425771,"name":"Alfonso Galderisi","orcid":"0000-0001-8885-3056","position":11,"is_corresponding":false},{"id":230352,"name":"Kevan C. Herold","orcid":"0000-0003-1534-6613","position":12,"is_corresponding":false},{"id":512512,"name":"Jay M. Sosenko","orcid":"0000-0002-7204-8367","position":13,"is_corresponding":false},{"id":500635,"name":"Emily K. Sims","orcid":"0000-0002-4393-954X","position":0,"is_corresponding":true}],"reference_count":15,"raw_metadata":null,"created_at":"2026-07-19T02:53:32.285899Z","pmid":"40877213","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}