Machine learning for predicting diabetes risk in western China adults

Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia. Hyperglycemia can cause chronic damage to tissues over time [1]. Diabetes has become a major health problem worldwide with a significant increase in DM patients. According to the International Diabetes Federation (IDF), approximately 537 million adults worldwide had diabetes in 2021 (with a prevalence of 10.5%), and it is estimated that by 2045, approximately 783 million people worldwide are likely to have diabetes (with a prevalence of approximately 12.2%) [2, 3]. In China, the number of adults with diabetes ranked first in the world in 2021 (approximately 140.9 million patients, with a prevalence rate of approximately 13.0%) [3, 4]. According to a survey, because individuals with type-2 diabetes mellitus (T2DM) usually lack the relevant knowledge, or they are asymptomatic, some individuals with T2DM patients can not be detected in time (approximately 50% of individuals with T2DM are undiagnosed) [3, 5]. It is necessary to identify individuals with diabetes in the population in an efficient and accurate manner. Therefore that early preventive measures and treatment can be taken to avoid further escalation of T2DM.

Currently, the scientific community has shifted its focus to the use of powerful computational methods for early and accurate prediction of diabetes [6‐11]. Machine learning (ML) can iteratively learn nonlinear interactions from large amounts of data [12‐14]. At present, based on electronic medical records and hospitalization data, ML methods have been used in the diagnosis and prediction of diabetes, prediabetes, complications and disease progression [7, 8, 15‐17], as well as real-time blood glucose monitoring [18, 19], with some success. However, most of these models are created for the care of T2DM patients, and the sample size of training data is too small to reliably capture asymptomatic cases of early abnormal blood glucose, which are not suitable for mass screening of the population or public health planning [20, 21]. One study [22] reported that most models for diabetes prediction and risk assessment were rarely used because they relied on specific data. As physical examination data grows and ML rapidly develops, the use of physical examination data for disease risk assessment can provide better clinical guidance and facilitate large-scale screenings at an earlier stage [23]. However, at present, fewer scholars conduct diabetes screening based on health examination data [8, 24]. ML methods have not been applied to T2DM screening models and risk assessment in western China based on large-scale physical examination data.

We aimed to develop an ML model suitable for large-scale screening of T2DM among adults in western China. In this study, we established the model based on logistic regression (LR) and ML algorithms, including classification and regression tree (CART), light gradient boosting machine (LightGBM), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and TabNet model, and combined them with western China large-scale health examination data, which are characterized by wide coverage, large volume and strong representation. In addition, in order to improve the convenience and flexibility of the model, a diabetes risk score card was established based on logistic regression to assess the risk of the population. This study is the first T2DM screening model that systematically compares various algorithms on a multiethnic and large sample basis.

The increasing burden of diabetes has become a global challenge [3, 32]. Through mass screening, early identification and intervention of patients with diabetes can be achieved to delay or prevent the development of the disease [33, 34]. The most efficacious method for widespread screening of diabetes has yet to be identified. In this study, the T2DM risk prediction models were developed and validated on data from more than 4 million people. The data were obtained from the cross-sectional data of NPE, including more than 9 million people in 14 prefectures of Xinjiang, China, which can be considered representative of the overall population of Xinjiang. Following the evaluation of the model's performance, it was determined that the XGBoost model was the optimal model for predicting the risk of T2DM, with an AUC was is 0.9122.

In this study, we used questionnaires to obtain indicators of hypertension and cardiovascular diseases, genetic history and smoking and diet in the population, which not only captured the medical history of each patient, but also included demographic factors and laboratory test indicators. Univariate and multivariate logistic regression analyses showed that sex, age, ethnicity, EH, SS, HTN, CAD, PDM, WC, BMI, WBC, PLT, FBG, ECG, TC, TG, LDLC, and HDLC were important factors for diabetes. HTN, FGB, age, PDM, CAD, ethnicity, TG, WC, BMI, and TC were the most important predictors of diabetes. Except that the FGB was viewed as a recognized risk factor and predictor of T2DM, hypertension and CAD were the most important features of T2DM risk models, which presented with high predictive ability. Some studies have confirmed that hypertension, cardiovascular disease and diabetes are mutually promote and influence each other [35, 36]. Many pathophysiological mechanisms underlie the association between diabetes and cardiovascular disease. Among these mechanisms, several have been identified as potential contributors [36]. Including insulin resistance in the nitric-oxide pathway, the stimulatory effect of hyperinsulinemia on sympathetic drive, smooth muscle growth, and sodium-fluid retention, as well as the excitatory effect of hyperglycemia on the renin–angiotensin–aldosterone system, provide plausible explanations for the association between diabetes and cardiovascular disease. On the other hand, the functional changes occurring in the context of T2DM and hypertension significantly alter the hemodynamic stress on the heart and other organs. Some studies have also demonstrated the important role of ECG in the prediction of diabetes [37], and our study confirmed the association between abnormal ECG results and T2DM. Understanding these underlying mechanisms is crucial for developing targeted interventions to prevent and manage cardiovascular complications in individuals with diabetes.

Our study showed that age was also an important feature of diabetes prediction models. The FDRSMA is a classic and widely used diabetes risk scoring model [38]. The objective of FDRSM is to utilize six risk factors (including BMI, FBG, PDM, HDLC, blood pressure and TG) to evaluate the risk of T2DM among middle-aged individuals. T2DM is generally observed in adults and appears to be more prevalent among the elderly individuals. As people age, the glucose sensitivity of pancreatic cells decline and insulin secretion is impaired, leading to hyperglycemia and T2DM [39]. Several studies reported differences in the incidence of diabetes between ethnic groups [40‐42] and confirmed that ethnicity could be a predictors of diabetes [40, 43‐46]. In our study, we used Uyghur as a reference, with Han and Hui ethnic groups exhibiting a heightened susceptibility to diabetes. The kazakh, Mongolian and Tajik ethnic groups had a lower risk. Genetic and environmental differences (i.e., economic level, diet, lifestyle, climate) were taken into account. Family history of diabetes was also identified as an important risk factor for T2DM in our model, which is consistent with previous studies [47]. There is a significant genetic predisposition to T2DM, with a 2 to 30 fold increased risk for T2DM in those with a family history compared with those without a family history [48].

Many studies have demonstrated a connection between obesity and diabetes. Furthermore, our study discovered that augmented BMI and WC were correlated with a higher probability of having diabetes. The development of obesity gain can result in insulin resistance and diminished β-cell functionality in humans. According to the World Health Organization, the global increase in the prevalence of diabetes is believed to be related to chronic stress, being overweight [49], lacking of physical activity [50, 51], excessive consumption of alcohol [52, 53] and an unhealthy diet [54]. Our model also demonstrated that EH and SS were predictors of T2DM. In addition, we also found that people who smoked and those who had quit smoking had a higher risk of T2DM than those who did not smoke, and those who ate a vegetarian or meat-based diet had a higher probability of T2DM than those who ate a balanced meat-vegetarian diet.

We incorporated laboratory variables, including TC, TG and HDLC into the diabetes prediction model. Our findings indicated that TG was an independent risk factor for T2DM, while TC was not an independent risk factor for T2DM in our study. Consistent with other studies [55]. The feature importance ranking showed that TC, TG, LDLC and HDLC were all important features of the T2DM risk prediction model. Multiple studies have revealed that dyslipidemia and T2DM often coexist in individuals and share common pathological mechanisms, such as insulin resistance, metabolic disturbances, inflammation, and alterations in the gut microbiota [55, 56].

Currently, ML algorithms are increasingly used to predict diabetes and related diseases [11, 12, 18, 19, 30, 57‐59]. In this study, a diabetes screening model based on CART, LightGBM, RF, XGBoost TabNet and MLP models was constructed. The AUC (0.9122), PPV (0.2800), NPV (0.9829) and accuracy (0.8314) of the XGBoost prediction model showed good performance in the validation group. It appears that our model outperforms the majority of existing models, which may be because the model is built on the basis of multiple features and big data. Other studies also found that XGBoost was effective in predicting the risk of diabetes [8, 16].

The development of the diabetes risk assessment score card assists clinicians and individuals alike in conducting self-examinations, with the aim of increasing the rate of diabetes cascade screening and enhancing individual lifestyle management. Hence, utilizing large-scale physical examination information to achieve prompt risk notification and identification of diabetes is the most practicable course of action.

This study has several advantages. First, based on the NPE project, it not only has a wide coverage and a large amount of data, but also includes a number of major ethnic groups in China, which can enable better assessment of the characteristics of the population in Xinjiang, China; in addition the risk prediction model has a good generalization ability in Xinjiang, China. Second, the risk factors affecting diabetes were fully considered in this study. Laboratory examination, questionnaire survey and routine examination data were fully taken into account to obtain indicators such as hypertension and cardiovascular diseases, genetic history and exercise and diet in the population, and the influencing factors of diabetes were comprehensively analyzed. Third, the results of our model all showed satisfactory predictive effects (XGBoost: AUC = 0.9122). This study also has several limitations. First, it is not possible to establish causality using cross-sectional data derived from national health examinations, therefore, these results should be subject to further investigated in subsequent research. Second, the health examination data used in our study were highly heterogeneous and had a high rate of missing data, which affected the power of the model.

T2DM imposes an inexorable and significant burden on society, including intangible costs of lost productivity, premature death, and poor quality of life. Our model is based on large-scale health examination data in Xinjiang, China, which was used to construct a large-scale early diabetes risk screening model. Our model can be applied directly to the physical examination database, providing a highly efficient means for the identification of high-risk diabetes records over at a large range. This allows for the understanding of potential diabetes risk ratios at the public health level and the implementation of more effective diabetes prevention and control strategies. It is of great significance for the early control of diabetes to identify early risk warning sings and perform screening based on large-scale physical examination data.