{"doi":"10.1089/dia.2023.0064","title":"Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning","abstract":"Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m 2 with zero ( n = 21), one ( n = 18), and ≥2 ( n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00–06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly ( P = 0.04), overnight CGM incremental AUC ( P = 0.005), and T180 for 75 min post-SLMM CGM traces ( P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.","journal":"Diabetes Technology & Therapeutics","year":2023,"id":335339,"datarank":0.7409719422676677,"base_score":3.044522437723423,"endowment":3.044522437723423,"self_citation_contribution":0.4566783656585135,"citation_network_contribution":0.2842935766091543,"self_endowment_contribution":0.4566783656585135,"citer_contribution":0.2842935766091543,"corpus_percentile":null,"corpus_rank":null,"citation_count":20,"citer_count":15,"citers_with_citation_signal":10,"citers_with_endowment":10,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9564,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT02663661"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":356160,"name":"Marc D. Breton","orcid":"0000-0001-7645-2693","position":1,"is_corresponding":false},{"id":356162,"name":"Sue A. Brown","orcid":"0000-0002-8658-6270","position":2,"is_corresponding":false},{"id":426461,"name":"Mark D. DeBoer","orcid":"0000-0003-1462-591X","position":3,"is_corresponding":false},{"id":356158,"name":"Boris Kovatchev","orcid":"0000-0003-0495-3901","position":4,"is_corresponding":false},{"id":1065251,"name":"Leon S. Farhy","orcid":null,"position":5,"is_corresponding":false},{"id":1064752,"name":"Eslam Montaser","orcid":"0000-0002-3138-1964","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T01:09:58.338267Z","pmid":"37184602","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":[]}