Application of three statistical models for predicting the risk of diabetes

[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]

Objective

To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.

Methods

Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive β, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.

Results

A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined β values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals.

Conclusion

The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

OBJECTIVES

High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine.

DESIGN

A cross-sectional epidemiological study was employed.

SETTING

This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.

PARTICIPANTS

This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria.

OUTCOME MEASURES

We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics.

RESULTS

Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively.

CONCLUSION

A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

Diabetes risk prediction model based on community follow-up data using machine learning

Diabetes is a chronic metabolic disease characterized by hyperglycemia, the follow-up management of diabetes patients is mostly in the community, but the relationship between key lifestyle indicators in community follow-up and the risk of diabetes is unclear. In order to explore the association between key life characteristic indicators of community follow-up and the risk of diabetes, 252,176 follow-up records of people with diabetes patients from 2016 to 2023 were obtained from Haizhu District, Guangzhou. According to the follow-up data, the key life characteristic indicators that affect diabetes are determined, and the optimal feature subset is obtained through feature selection technology to accurately assess the risk of diabetes. A diabetes risk assessment model based on a random forest classifier was designed, which used optimal feature parameter selection and algorithm model comparison, with an accuracy of 91.24% and an AUC corresponding to the ROC curve of 97%. In order to improve the applicability of the model in clinical and real life, a diabetes risk score card was designed and tested using the original data, the accuracy was 95.15%, and the model reliability was high. The diabetes risk prediction model based on community follow-up big data mining can be used for large-scale risk screening and early warning by community doctors based on patient follow-up data, further promoting diabetes prevention and control strategies, and can also be used for wearable devices or intelligent biosensors for individual patient self examination, in order to improve lifestyle and reduce risk factor levels.

Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study

OBJECTIVES

To compare the prediction effects of six models based on machine learning theories, which can provide a methodological reference for predicting the risk of type 2 diabetes mellitus (T2DM).

SETTING AND PARTICIPANTS

This study was based on the monitoring data of chronic disease risk factors in Dongguan residents from 2016 to 2018. The multistage cluster random sampling method was adopted at each monitoring site, and 4157 people were finally selected. In the initial population, we excluded individuals with more than 20% missing data and eventually included 4106 subjects.

DESIGN

K nearest neighbour algorithm and synthetic minority oversampling technique were used to process the data. Single factor analysis was used for preliminary selection of variables. The 10-fold cross-validation was used to optimise the parameters of some models. The accuracy, precision, recall and area under receiver operating characteristic curve (AUC) were used to evaluate the prediction effect of models, and Delong test was used to analyse the differences of AUC values of each model.

RESULTS

After balancing data, the sample size increased to 8013, of which 4023 are patients with T2DM and 3990 in control group. The comparison results of the six models showed that back propagation neural network model has the best prediction effect with 93.7% accuracy, 94.6% accuracy, 92.8% recall and the AUC value of 0.977, followed by logistic model, support vector machine model, CART decision tree model and C4.5 decision tree model. Deep neural network has the worst prediction performance, with 84.5% accuracy, 86.1% precision, 82.9% recall and the AUC value of 0.845.

CONCLUSIONS

In this study, six types of risk prediction models for T2DM were constructed, and the predictive effects of these models were compared based on various indicators. The results showed that back propagation neural network based on the selected data set had the best prediction effect.

Prevalence and risk factors of diabetes mellitus and hypertension in North East Tunisia calling for efficient and effective actions

Diabetes and hypertension are a serious public health problem worldwide. In the last decades, prevalence of these two metabolic diseases has dramatically increased in the Middle East and North Africa region, especially in Tunisia. This study aimed to determine the prevalence of type 2 diabetes (T2D) and High Blood Pressure (HBP) in Zaghouan, a North-East region of Tunisia. To this end, an exploratory study with stratified random sampling of 420 participants has been carried out. Various data were collected. Blood samples and urine were drawn for biochemical assay. Then, all data were analyzed using the statistical R software. Results showed an alarming situation with an inter-regional difference in prevalence of obesity (50.0%, CI 95.0%), HBP (39.0%, CI 95.0%) and T2D (32.0%, CI 95.0%). This study allowed the discovery of 24, 17 and 2 new cases of T2D, HBP and T2D&HBP respectively. The association of some socio-economic factors and biochemical parameters with these chronic diseases has been highlighted. To conclude, the health situation in the governorate of Zaghouan requires urgent interventions to better manage the growing epidemic of non-communicable diseases (NCD) in the region. This study demonstrated the importance of engaging health policy makers in road mapping and implementing national NCD prevention programs.

MATHEMATICAL MODEL FOR PREDICTING FASTING BLOOD GLUCOSE LEVEL IN DIABETES MELLITUS PATIENTS

OBJECTIVE

The aim: To substantiate the use of data on patients' lifestyle, parameters of blood glucose, heart rate, blood pressure and bread units to build a mathematical model for predicting fasting blood glucose level in diabetes mellitus patients to improve existing measures for diabetes prevention.

PATIENTS AND METHODS

Materials and methods: An open database consisting of the studied parameters of 359 people was used in the research. The linear regression method was used to predict fasting blood glucose level in diabetes mellitus patients. The statistical software IBM SPSS Statistics Version 23 was chosen for calculations.

RESULTS

Results: To calculate the coefficients of the linear regression equation, stepwise elimination of parameters was chosen. The analysis of the coefficients of influence of independent variables on dependent showed that the greatest effect on the change in glucose level had value of consumed bread units. The model for women diagnosed with type 2 diabetes showed the highest accuracy.

CONCLUSION

Conclusions: Mathematical modeling made it clear that any malnutrition or health disorders can lead to a significant change in glucose levels. The obtained models consist of a number of parameters, some of which might depend on the presence of concomitant diseases. Further studies should focus on the optimal combination of various parameters taking into account methods of treating comorbidities.

Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset

BACKGROUND

Hyperglycemia is rising globally and its associated complications impose heavy health and economic burden on the countries. Developing effective survey-based screening tools for hyperglycemia using reliable surveillance data, such as the WHO STEPs surveys, would be of great importance in early detection and/or prevention of hyperglycemia, especially in low or middle-income regions.

METHODS

In this study, data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic factors associated with hyperglycemia. Furthermore, the ability of five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) in the prediction of hyperglycemia on STEPs dataset were compared via tenfold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve.

RESULTS

A total of 17,705 individuals were included in this study, of those 29.624% (n = 5245) had (undiagnosed) hyperglycemia. Multivariate logistic regression analysis showed that older age (for the elderly group: OR = 5.096; for the middle-aged group: OR = 2.784), high BMI status (morbidly obese: OR = 3.465; obese: OR = 1.992), having hypertension (OR = 1.647), consuming fish more than twice per week (OR = 1.496), and abdominal obesity (OR = 1.464) were the five most important risk factors for hyperglycemia. Furthermore, all the five hyperglycemia prediction models achieved AUC around 0.70, and logistic regression (specificity = 70.22%; sensitivity = 70.2%) and random forest (specificity = 70.75%; sensitivity = 69.78%) had the optimal performance.

CONCLUSIONS

This study shows that it is possible to develop survey-based screening tools for early detection of hyperglycemia using data from nationwide surveys, such as WHO STEPs surveys, and machine learning techniques, such as random forest and logistic regression, without using blood tests. Such screening tools can potentially improve hyperglycemia control, especially in low or middle-income countries.

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing

BACKGROUND

Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups.

METHODS

Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model.

RESULTS

Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses.

CONCLUSIONS

The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

A New Way of Investigating the Relationship Between Fasting Blood Sugar Level and Drinking Glucose Solution

The existing t-test of a correlation coefficient works under a determinate environment. In uncertainty, the existing t-test of a correlation coefficient is unable to investigate the significance of correlation. The study presents a modification of the existing t-test of a correlation coefficient using neutrosophic statistics. The test statistic is designed to investigate the significance of correlation when imprecise observations or uncertainties in the level of significance are presented. The test is applied to data obtained from patients with diabetes. From the data analysis, the proposed t-test of a correlation coefficient is found to be more effective than existing tests.