PMID- 38409633
OWN - NLM
STAT- MEDLINE
DCOM- 20240508
LR  - 20240508
IS  - 1463-1326 (Electronic)
IS  - 1462-8902 (Linking)
VI  - 26
IP  - 6
DP  - 2024 Jun
TI  - Clinically relevant stratification of patients with type 2 diabetes by using 
      continuous glucose monitoring data.
PG  - 2082-2091
LID - 10.1111/dom.15512 [doi]
AB  - AIM: The wealth of data generated by continuous glucose monitoring (CGM) provides 
      new opportunities for revealing heterogeneities in patients with type 2 diabetes 
      mellitus (T2DM). We aimed to develop a method using CGM data to discover T2DM 
      subtypes and investigate their relationship with clinical phenotypes and 
      microvascular complications. METHODS: The data from 3119 patients with T2DM who 
      wore blinded CGM at an academic medical centre was collected, and a glucose 
      symbolic pattern (GSP) metric was created that combined knowledge-based temporal 
      abstraction with numerical vectorization. The k-means clustering was applied to 
      GSP to obtain subgroups of patients with T2DM. Clinical characteristics and the 
      presence of diabetic retinopathy and albuminuria were compared among the 
      subgroups. The findings were validated in an independent population comprising 
      773 patients with T2DM. RESULTS: By using GSP, four subgroups were identified 
      with distinct features in CGM profiles and parameters. Moreover, the clustered 
      subgroups differed significantly in clinical phenotypes, including indices of 
      pancreatic beta-cell function and insulin resistance (all p < .001). After adjusting 
      for confounders, group C (the most insulin resistant) had a significantly higher 
      risk of albuminuria (odds ratio = 1.24, 95% confidence interval: 1.03-1.39) 
      relative to group D, which had the best glucose control. These findings were 
      confirmed in the validation set. CONCLUSION: Subtyping patients with T2DM using 
      CGM data may help identify high-risk patients for microvascular complications and 
      provide insights into the underlying pathophysiology. This method may help refine 
      clinically meaningful stratification of patients with T2DM and inform 
      personalized diabetes care.
CI  - (c) 2024 John Wiley & Sons Ltd.
FAU - Shao, Xiaopeng
AU  - Shao X
AD  - College of Information Science and Engineering, Northeastern University, 
      Shenyang, China.
FAU - Lu, Jingyi
AU  - Lu J
AD  - Department 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.
FAU - Tao, Rui
AU  - Tao R
AD  - College of Information Science and Engineering, Northeastern University, 
      Shenyang, China.
FAU - Wu, Liang
AU  - Wu L
AD  - Department 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.
FAU - Wang, Yaxin
AU  - Wang Y
AD  - Department 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.
FAU - Lu, Wei
AU  - Lu W
AD  - Department 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.
FAU - Li, Hongru
AU  - Li H
AD  - College of Information Science and Engineering, Northeastern University, 
      Shenyang, China.
FAU - Zhou, Jian
AU  - Zhou J
AUID- ORCID: 0000-0002-1534-2279
AD  - Department 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.
FAU - Yu, Xia
AU  - Yu X
AUID- ORCID: 0000-0001-8106-1208
AD  - College of Information Science and Engineering, Northeastern University, 
      Shenyang, China.
LA  - eng
GR  - 22XD1402300/Program of Shanghai Academic Research Leader/
GR  - 61973067/National Natural Science Foundation of China/
GR  - Shanghai Oriental Talent Program (Youth Project)/
GR  - 61903071/National Natural Science Foundation of China Youth Fund Project/
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20240226
PL  - England
TA  - Diabetes Obes Metab
JT  - Diabetes, obesity & metabolism
JID - 100883645
RN  - 0 (Blood Glucose)
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus, Type 2/blood/complications
MH  - Female
MH  - Male
MH  - Middle Aged
MH  - *Blood Glucose Self-Monitoring
MH  - *Blood Glucose/metabolism/analysis
MH  - *Albuminuria/blood
MH  - Aged
MH  - Diabetic Retinopathy/blood/etiology/diagnosis/epidemiology
MH  - Insulin Resistance
MH  - Diabetic Nephropathies/blood/diagnosis
MH  - Adult
MH  - Continuous Glucose Monitoring
OTO - NOTNLM
OT  - continuous glucose monitoring
OT  - feature representation
OT  - microvascular complications
OT  - subtype classification
OT  - type 2 diabetes
EDAT- 2024/02/27 06:44
MHDA- 2024/05/08 06:44
CRDT- 2024/02/27 00:19
PHST- 2024/01/30 00:00 [revised]
PHST- 2023/11/16 00:00 [received]
PHST- 2024/02/07 00:00 [accepted]
PHST- 2024/05/08 06:44 [medline]
PHST- 2024/02/27 06:44 [pubmed]
PHST- 2024/02/27 00:19 [entrez]
AID - 10.1111/dom.15512 [doi]
PST - ppublish
SO  - Diabetes Obes Metab. 2024 Jun;26(6):2082-2091. doi: 10.1111/dom.15512. Epub 2024 
      Feb 26.