PMID- 38476673
OWN - NLM
STAT- MEDLINE
DCOM- 20240314
LR  - 20240314
IS  - 1664-2392 (Print)
IS  - 1664-2392 (Electronic)
IS  - 1664-2392 (Linking)
VI  - 15
DP  - 2024
TI  - Predicting the risk of subclinical atherosclerosis based on interpretable machine 
      models in a Chinese T2DM population.
PG  - 1332982
LID - 10.3389/fendo.2024.1332982 [doi]
LID - 1332982
AB  - BACKGROUND: Cardiovascular disease (CVD) has emerged as a global public health 
      concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early 
      indicator of CVD, is critical for improving cardiovascular outcomes. This study 
      aimed to construct interpretable machine learning models for predicting SCAS risk 
      in type 2 diabetes mellitus (T2DM) patients. METHODS: This study included 3084 
      T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, 
      China, from January 2018 to December 2022. The least absolute shrinkage and 
      selection operator combined with random forest-recursive feature elimination were 
      used to screen for characteristic variables. Linear discriminant analysis, 
      logistic regression, Naive Bayes, random forest, support vector machine, and 
      extreme gradient boosting were employed in constructing risk prediction models 
      for SCAS in T2DM patients. The area under the receiver operating characteristic 
      curve (AUC) was employed to assess the predictive capacity of the model through 
      10-fold cross-validation. Additionally, the SHapley Additive exPlanations were 
      utilized to interpret the best-performing model. RESULTS: The percentage of SCAS 
      was 38.46% (n=1186) in the study population. Fourteen variables, including age, 
      white blood cell count, and basophil count, were identified as independent risk 
      factors for SCAS. Nine predictors, including age, albumin, and total protein, 
      were screened for the construction of risk prediction models. After validation, 
      the random forest model exhibited the best clinical predictive value in the 
      training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated 
      good predictive value in the internal validation set [AUC: 0.715 (95% CI: 
      0.688-0.742)]. The model interpretation revealed that age, albumin, total 
      protein, total cholesterol, and serum creatinine were the top five variables 
      contributing to the prediction model. CONCLUSION: The construction of SCAS risk 
      models based on the Chinese T2DM population contributes to its early prevention 
      and intervention, which would reduce the incidence of adverse cardiovascular 
      prognostic events.
CI  - Copyright (c) 2024 Tusongtuoheti, Shu, Huang and Mao.
FAU - Tusongtuoheti, Ximisinuer
AU  - Tusongtuoheti X
AD  - Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 
      Ningbo University, Ningbo, China.
AD  - Health Science Center, Ningbo University, Ningbo, China.
FAU - Shu, Yimeng
AU  - Shu Y
AD  - Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 
      Ningbo University, Ningbo, China.
AD  - Health Science Center, Ningbo University, Ningbo, China.
FAU - Huang, Guoqing
AU  - Huang G
AD  - Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 
      Ningbo University, Ningbo, China.
AD  - Health Science Center, Ningbo University, Ningbo, China.
FAU - Mao, Yushan
AU  - Mao Y
AD  - Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 
      Ningbo University, Ningbo, China.
LA  - eng
PT  - Journal Article
DEP - 20240227
PL  - Switzerland
TA  - Front Endocrinol (Lausanne)
JT  - Frontiers in endocrinology
JID - 101555782
RN  - 0 (Albumins)
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus, Type 2
MH  - Bayes Theorem
MH  - *Atherosclerosis
MH  - Risk Factors
MH  - Albumins
MH  - *Cardiovascular Diseases
MH  - China
PMC - PMC10929018
OTO - NOTNLM
OT  - independent risk factors
OT  - interpretable machine learning
OT  - prediction model
OT  - subclinical atherosclerosis
OT  - type 2 diabetes mellitus
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2024/03/13 06:46
MHDA- 2024/03/14 06:46
PMCR- 2024/01/01
CRDT- 2024/03/13 03:56
PHST- 2023/11/04 00:00 [received]
PHST- 2024/02/07 00:00 [accepted]
PHST- 2024/03/14 06:46 [medline]
PHST- 2024/03/13 06:46 [pubmed]
PHST- 2024/03/13 03:56 [entrez]
PHST- 2024/01/01 00:00 [pmc-release]
AID - 10.3389/fendo.2024.1332982 [doi]
PST - epublish
SO  - Front Endocrinol (Lausanne). 2024 Feb 27;15:1332982. doi: 
      10.3389/fendo.2024.1332982. eCollection 2024.