Association of a novel electrolyte index, SUSPPUP, based on the measurement of fasting serum and spot urinary sodium and potassium, with prediabetes and diabetes in Chinese population

Sang, Miaomiao; Liu, Yu; Wu, Tong; Zhou, Xiaoying; Wang, Duolao; Sun, Zilin; Qiu, Shanhu

doi:10.1016/j.cca.2022.04.1005

Cited by 1 publication

(1 citation statement)

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“…The present study was carried out in eight provinces in China from April 2020 to January 2021. A detailed description about the design of this study was published previously 17 . In brief, a total of 6,310 residents aged >18 years were invited to participate in this study, and the study protocol was approved by the ethics committee of Zhongda Hospital, Southeast University, Nanjing, China (2016ZDSYLL092‐P01).…”

Section: Methodsmentioning

confidence: 99%

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross‐sectional study

Xu,

Qiu,

et al. 2024

J of Diabetes Invest

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Aims/IntroductionMachine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.MethodsThis cross‐sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (n = 3,586) and the testing dataset (n = 896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30‐s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other.ResultsOf the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs >0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30‐s chair stand test to this approach did not increase the AUC further (P = 0.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (Pcomparison < 0.01), regardless of the addition of handgrip strength or the 30‐s chair stand test.ConclusionsThe ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.

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Section: Methodsmentioning

confidence: 99%