{"doi":"10.1210/clinem/dgae379","title":"Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics","abstract":"CONTEXT: Static measures of continuous glucose monitoring (CGM) data, such as time spent in specific glucose ranges (70-180 mg/dL or 70-140 mg/dL), do not fully capture the dynamic nature of blood glucose, particularly the subtle gradual deterioration of glycemic control over time in individuals with early-stage type 1 diabetes. OBJECTIVE: Develop a diabetes diagnostic tool based on 2 markers of CGM dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). METHODS: A total of 5754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute 2 individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between 8 glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden index was used to determine \"optimal\" cut-points for ER and S for health vs diabetes (case 1); type 1 vs type 2 (case 2); and low vs high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). RESULTS: Optimal cutoff points were determined for ER and S for each of the 3 cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI, 0.97-0.99) and AUC = 0.99 (95% CI, 0.99-1.00), respectively (cutoffs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.72 (95% CI, 0.58-0.86), and AUC = 0.66 (95% CI, 0.47-0.86), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CONCLUSION: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing to identify individuals at higher immunological risk of progressing to type 1 diabetes.","journal":"The Journal of Clinical Endocrinology & Metabolism","year":2024,"id":420453,"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":34,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9557,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1065251,"name":"Leon S. Farhy","orcid":null,"position":1,"is_corresponding":false},{"id":356158,"name":"Boris Kovatchev","orcid":"0000-0003-0495-3901","position":2,"is_corresponding":false},{"id":1064752,"name":"Eslam Montaser","orcid":"0000-0002-3138-1964","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:57:25.777459Z","pmid":"38820084","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":[]}