Evidence map›Paper›PMID 41889264›Full record

ArticleDiabetes, obesity & metabolism2026

Glycemic Variability Bridges Time in Range and Time in Tight Range: A Unified Equation for Both Type 1 and Type 2 Diabetes Based on Large-Scale Continuous Glucose Monitoring Data.

Yuan Yao, Zhigang Hu, Yifei Mo, Mingsong Han, Li Cao, Jinghao Cai, Xiaojing Ma, Jingyi Lu, Xiaobing Wu, Jian Zhou

Abstract read
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Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yuan YaoDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Zhigang HuShenzhen Sibionics Technology co., LTD, Shenzhen, China.
Yifei MoShenzhen Sibionics Technology co., LTD, Shenzhen, China.
Mingsong HanShenzhen Sibionics Technology co., LTD, Shenzhen, China.
Li CaoDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Jinghao CaiDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Xiaojing MaDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Jingyi LuDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Xiaobing WuDepartment of Cardio-Cerebrovascular and Diabetes Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, China.
Jian ZhouDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.ORCID 0000-0002-1534-2279

Funding

Noncommunicable Chronic Diseases‑National Science and Technology Major Project 2024ZD0532000Noncommunicable Chronic Diseases‑National Science and Technology Major Project 2024ZD0532001Sanming Project of Medicine in Shenzhen SZSM202311019Shanghai Leading Talent Program of Eastern Talent Plan LJ2024121Shenzhen Medical Research Fund C2406002
6 · The paper itself

Abstract

aimsThis study aimed to establish a regression model for the relationship between time in range (TIR) and time in tight range (TITR) in individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) based on real-world continuous glucose monitoring (CGM) data. MATERIALS AND

methodsA cross-sectional analysis was conducted on over 200 000 CGM users with diabetes. Participants self-reported basic demographic and clinical details via in-app fields. Exponential regression models were constructed to examine the TIR-TITR association for individuals with T1D and T2D, respectively. After controlling for coefficient of variation (CV), the model was extended to provide more precise glycemic targets for clinical use. Model performance was evaluated using the coefficient of determination (R

resultsThe TIR-TITR relationship exhibited a nonlinear relationship. Exponential models (TITR

conclusionsThis study established the exponential model for TIR-TITR relationship in individuals with T1D and T2D, using a real-world CGM dataset. The model may provide new insights into the setting of individualized treatment goals.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Glycemic ControlAdultBlood Glucose Self-MonitoringContinuous Glucose MonitoringCross-Sectional StudiesFemaleHumansHypoglycemic AgentsMaleMiddle AgedTime FactorsBlood GlucoseHypoglycemic Agentscoefficient of variationdiabetesreal‐time continuous glucose monitoringtime in rangetime in tight range

Identifiers

PMID41889264
PMCPMC13146142

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.