A Prediction Model for Prediabetes Risk in Middle-Aged and Elderly Populations: A Prospective Cohort Study in China

Wu, Jiahua; Zhou, Jian; Yin, Xiaoxing; Chen, Yixin; Lin, Xihua; Xu, Zhiye; Li, Hong

doi:10.1155/2021/2520806

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Furthermore, both training and validation groups showed high predictive performance, indicating good generalizability. In addition, it must be noted that the incidence of prediabetes in our study population was lower than in other similar studies (10.7% vs. 20.0–26.3%) 30 , 31 . A closer analysis of the relevant indicators revealed higher levels of age and BMI in their study population, as well as a higher proportion of family history of diabetes, smoking, and alcohol consumption.…”

Section: Discussioncontrasting

confidence: 74%

“…Categorizing them into groups will result in a loss of information and a reduced ability to detect real relationships 58 , 59 . In 2021, Jiahua Wu et al 31 developed a model to predict the risk of prediabetes in middle-aged and elderly populations in China based on WC, HbA1c, family history of diabetes, and FPG. The AUCs were 0.702.…”

Section: Discussionmentioning

confidence: 99%

“…Several predictive models for prediabetes based on Chinese cohorts have emerged recently. Because of their small sample size, failure to use Cox proportional hazards models that take into account the factors of follow-up time to build the model, and lack of evaluation of model accuracy and clinical value of use, the model's generalization is somewhat limited 30 , 31 . Our research aimed to use Cox proportional hazards models to build a nomogram based on the data in the Chinese medical examination reports.…”

Section: Introductionmentioning

confidence: 99%

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A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Hu,

Han,

Liu

et al. 2023

Sci Rep

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Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290–0.7392) for the training cohort and 0.7336 (95% CI 0.7285–0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

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Section: Discussioncontrasting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Hu,

Han,

Liu

et al. 2023

Sci Rep

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“…Consistent with other studies, the FPG base was found to be the leading determinator for an increased FPG end . In 2021, We et al found that FPG was the most important predictor for prediabetes in a 3.35-year follow-up period among 551 Chinese subjects, aged from 40-70 years old [26]. However, that study used multiple logistic regression and provided a hazard ratio (HR: 2.284; 95% confidence interval: 1.556, 3.352; p < 0.001).…”

Section: Discussionmentioning

confidence: 98%

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up

Liu,

Chang,

Chen

et al. 2024

Diagnostics

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The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

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“…For example, hypertension [5], high total cholesterol [6], depression [7], glucocorticoids [8], chronic obstructive pulmonary disease (COPD) [9], osteoarthritis [10], and increased age have all been found to be associated with greater risk of developing T2D. Because risk factors of diabetes are well-known, many studies have applied machine learning (ML) models to clinical data to predict diabetes [11][12][13][14][15][16]. However, there are no Canadian studies developing prescriptive ML models to predict prediabetes.…”

Section: Introductionmentioning

confidence: 99%

Identifying Prediabetes in Canadian Populations Using Machine Learning

Lu,

Sheth,

Zhou

et al. 2024

Preprint

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Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among at-risk individuals. This study aims to pinpoint the most effective machine learning (ML) model for prediabetes prediction and to elucidate the key biological variables critical for distinguishing individuals with prediabetes. Utilizing data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), our analysis included 6,414 participants identified as either nondiabetic or prediabetic. A rigorous selection process led to the identification of ten variables for the study, informed by literature review, data completeness, and the evaluation of collinearity. Our comparative analysis of seven ML models revealed that the Deep Neural Network (DNN), enhanced with early stop regularization, outshined others by achieving a recall rate of 60%. This model's performance underscores its potential in effectively identifying prediabetic individuals, showcasing the strategic integration of ML in healthcare. While the model reflects a significant advancement in prediabetes prediction, it also opens avenues for further research to refine prediction accuracy, possibly by integrating novel biological markers or exploring alternative modeling techniques. The results of our work represent a pivotal step forward in the early detection of prediabetes, contributing significantly to preventive healthcare measures and the broader fight against the global epidemic of Type 2 diabetes.

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A Prediction Model for Prediabetes Risk in Middle-Aged and Elderly Populations: A Prospective Cohort Study in China

Cited by 6 publications

References 28 publications

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up

Identifying Prediabetes in Canadian Populations Using Machine Learning

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