Relationship between modified homeostasis model assessment/correlative serum factors and diabetic retinopathy among type 2 diabetics with insulin therapy in Guangzhou, China

AIM:To explore the related risk factors for diabetic retinopathy(DR)in type 2 diabetes with insulin therapy.METHODS:We studied the relationships among blood glucose,serum C-peptide,plasma insulin,beta-cell function and the development of DR.Beta-cell function was assessed by a modified homeostasis model assessment(modified HOMA)which was gained by using C-peptide to replace insulin in the homeostasis model assessment(HOMA)of beta-cell function.We also studied the relationships between modified HOMA index and serum C-peptide response to 100 g tasteless steamed bread to determine the accuracy of modified HOMA.RESULTS:Our study group consisted of 170 type 2diabetic inpatients with DR(age:58.35±13.87y,mean±SD)and 205 type 2 diabetic inpatients with no DR(NDR)(age:65.52±11.59y).DR patients had higher age,longer diabetic duration,higher hypertension grade,higher postprandial plasma glucose,higher fluctuation level of plasma glucose,lower body mass index(BMI),lower postprandial serum insulin and C-peptide,lower fluctuation level of serum insulin and C-peptide(P【0.05).In our logistic regression model,duration of diabetes,hypertension grade,fasting plasma insulin and glycosylated hemoglobin(HbA1C)were significantly associated with the presence of DR after adjustment for confounding factors(P【0.05).CONCLUSION:Our results suggested although modified HOMA showed significant correlation to the occurrence of DR on Spearman’s rank-correlationanalysis,logistic regression showed no significant association between these two variables after adjustment for relevant confounding factors(such as age,sex,duration of diabetes,BMI,hypertension grade,HbA1C,plasma insulin).Duration of diabetes,hypertension grade,fasting plasma insulin and HbA1C were independently associated with the development of DR in Chinese type 2 diabetics.

Insulin Sensitivity and Insulin Secretion Estimated by Homeostatic Model Assessment (HOMA) in Gestational Diabetes Mellitus

Background: Progressive insulin resistance (IR) is an important pathophysiologic mechanism of gestational diabetes mellitus (GDM). Homeostatic model assessment (HOMA) is commonly used as a parameter of the severity of insulin resistance. Aims: To determine indices of insulin resistance (IR) and β-cell function in gestational diabetes mellitus (GDM). Methods: This cross sectional study was conducted from March 2017 to September 2018 at Department of Endocrinology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. The study was performed with 41 GDM and equal number of pregnant women with normal glucose tolerance (NGT) diagnosed on basis of WHO criterion-2013 during 24 - 40 weeks of gestation. Serum glucose was measured by glucose oxidase method and fasting serum insulin was measured by chemiluminescent immunoassay. Equations of homeostatic model assessment (HOMA) were used to calculate insulin indices like-insulin resistance (HOMA-IR), β-cell function (HOMA-B) and insulin sensitivity (HOMA-%S). Data were analyzed and compared by statistical tests. Results: A total of eighty-two (82) subjects [41 women with GDM (age: 28.29 ± 3.79 years, BMI: 27.16 ± 4.13 kg/m2) and 41 women with NGT (age: 26.22 ± 5.13 years, BMI: 25.27 ± 3.01 kg/m2)] were included in this study. It was observed that GDM women were significantly older (p = 0.041) and had significantly higher BMI (p = 0.020) than pregnant women with NGT. The GDM group had significantly higher IR as indicated by higher fasting insulin value [GDM vs. NGT;10.19 (7.71 - 13.34) vs. 6.88 (5.88 - 8.47) μIU/ml, median (IQR);p = 0.001] and HOMA-IR [GDM vs. NGT;2.31 (1.73 - 3.15) vs. 1.42 (1.15 - 1.76), median (IQR);p < 0.001], poor β-cell secretory capacity [GDM vs. NGT;HOMA-B: 112.63 (83.52 - 143.93) vs. 128.60 (108.77 - 157.58), median (IQR);p = 0.04] and low insulin sensitivity [GDM vs. NGT;HOMA-%S: 43.29 (31.77 - 57.98) vs. 70.42 (56.86 - 86.59), median (IQR);p < 0.001]. Conclusions: GDM is associated with both insulin resistance and inadequate insulin secretion.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Towards Automated Assessment of Learning Management Systems in Higher Education Institutions in Zambia

Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.

Evaluation of meteorological predictions by the WRF model at Barrow, Alaska and Summit, Greenland in the Arctic in April 2019

Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.

Human Health Risk Assessment Model of Organic Pollution in Groundwater:Shijiazhuang Industrial Zone

In this study, a risk-based management model is developed and applied to an industrial zone. The models proposed by the United States Environmental Protection Agency and Han Bing have been improved by adding a residual ratio of volatile organic compounds （VOC） after boiling and deleting the related parameters in half-life. Using this improved model, an integrated process was used to assess human health risk level in the study area. Compared with water quality analysis, the results highlight the importance of applying an integrated approach for decision making on risk levels and water protection. The results of this study demonstrated that： （1） Compared with these permissible level standards in China （GB 3838-2002） and National Primary Drinking Water Regulations of the United States, the residents＇ daily life had not been affected by the groundwater in this area （except for relative bad water quality of HB3-4 and HB3-6）; （2） The typical detected organic contaminants of all groundwater samples were chloroform, carbon tetrachloride, trichloroethylene and tetrachloroethene, and the pollution sources were mainly industrial sources by preliminary investigations; （3） As for groundwater, the non-carcinogenic risk values of all samples do not exceed the permissible level of 1.0 and the carcinogenic risk values are relatively lower than the permissible level of 1.00E-06 to 1.00E-04; （4） Drinking water pathway of trichloroethylene and tetrachloroethylene mainly contribute to increasing the health risk of residents＇ in study areas; （5） In terms of non-carcinogenic risk and carcinogenic risk, the health risk order for drinking water pathway and dermal contact pathway was： drinking water pathway 〉 dermal contact pathway.

The Agricultural Assessment of Climatic Resource and Modeling in Jianghan Plain-Lake District

The feature of the climatic resource and the agricultural assessment in Jianghan plain lake district are discussed. In order to exploit the climatic resource, we have recommended some three dimensional agriculture development models in this region according to the types of land use, such as paddy field ecological zone, dry land ecological zone, outskirts ecological zone of the city, waterbody ecological zone, woodland ecological zone.

New Three-Dimensional Assessment Model and Optimization of Acoustic Positioning System

This paper addresses the problem of assessing and optimizing the acoustic positioning system for underwater target localization with range measurement.We present a new three-dimensional assessment model to evaluate the optimal geometric beacon formation whether meets user requirements.For mathematical tractability,it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance,which is distance-dependent.Then,the relationship between DOP parameters and positioning accuracy can be derived by adopting dilution of precision(DOP)parameters in the assessment model.In addition,the optimal geometric beacon formation yielding the best performance can be achieved via minimizing the values of geometric dilution of precision(GDOP)in the case where the target position is known and fixed.Next,in order to ensure that the estimated positioning accuracy on the region of interest satisfies the precision required by the user,geometric positioning accuracy(GPA),horizontal positioning accuracy(HPA)and vertical positioning accuracy(VPA)are utilized to assess the optimal geometric beacon formation.Simulation examples are designed to illustrate the exactness of the conclusion.Unlike other work that only uses GDOP to optimize the formation and cannot assess the performance of the specified size,this new three-dimensional assessment model can evaluate the optimal geometric beacon formation for each dimension of any point in three-dimensional space,which can provide guidance to optimize the performance of each specified dimension.

Spatial distribution prediction and benefits assessment of green manure in the Pinggu District,Beijing,based on the CLUE-S model

Green manure use in China has declined rapidly since the 1980 s with the extensive use of chemical fertilizers.The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure.Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting.However,little information is available regarding on where,at what rates,and in which ways（i.e.,intercropping green manure in orchards or rotating green manure in cropland） to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale.This paper presents the conversion of land use and its effects at small region extent（CLUE-S） model,which is specifically developed for the simulation of land use changes originally,to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing,China.Four types of land use for planting or not planting green manure were classified and the future land use dynamics（mainly croplands and orchards） were considered in the prediction.Two scenarios were used to predict the spatial distribution of green manure based on data from 2011：The promotion of green manure planting in orchards（scenario 1） and the promotion of simultaneous green manure planting in orchards and croplands（scenario 2）.The predictions were generally accurate based on the receiver operating characteristic（ROC） and Kappa indices,which validated the effectiveness of the CLUE-S model in the prediction.In addition,the spatial distribution of the green manure was acquired,which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan,the western and southern regions of Wangxinzhuang,the middle region of Shandongzhuang,the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1.Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang,and croplands in most regions of Daxingzhuang,southern Pinggu,northern Xiagezhuang and most of Mafang.The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators.The economic and ecological gains of scenarios 1 and 2 were 175691 900 and143000 300 CNY,respectively,which indicated that the first scenario was more beneficial for promoting the same area of green manure.These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

A multi-model assessment of climate change damage in China and the world

Quantifying climate damage is essential to informing rational climate policies,but only a few studies have systematically compared the climate damage estimates made by different models,especially for China.In this study,we used three widely applied integrated assessment models-FUND,RICE,and PAGE-to estimate the damage under coupled shared socioeconomic pathways and representative concentration pathways(RCPs).Results show that the costs of climate damage constitute approximately 1.5%and 0.7%of China's GDP and global GDP per 1℃ temperature rise on average,respectively.Mitigation can reduce climate risk by lowering the average estimate and worst-case effects of climate damage.Compared with business-as-usual emissions(RCP8.5),the 2℃ target will reduce the average estimate of climate damage for China and the world by 93%and 87%,respectively,and by 80%and 84%,respectively,in the worst-case situation.Sectorial analysis of climate damage highlights the inconsistency of sector scope and significant parameter uncertainties in damage modules,requiring further improvement to integrate subfield research advances,particularly for damage related to rising sea levels and cooling energy demand.

Smoke Hazard Assessment Model Based on Fire Dynamics Simulator and FED Model

Smoke is the main cause of fire death. In order to minimize the potential danger of smoke hazard, a rational VR based fire training simulator should fully consider all aspects of smoke hazard. In the simulator, the visualization of data based on FDS (Fire Dynamics Simulator) and FED fire dynamic data and volume rendering is further optimized, which can be effectively and quickly applied to virtual fire protection. In addition, a comprehensive smoke hazard assessment model based on FED and FED is established to assess the IHD value of different paths, which represents the safety of different paths, and can be used for evacuation or rescue in virtual training. Taking the case of campus fire drill as an experiment, the research shows the accuracy and effectiveness of smoke assessment based on FDS and FED model. The road force with the highest safety can be selected through the comprehensive model. So the assessment model is proved to be valuable.

Establishment and assessment of rodent models of medication-related osteonecrosis of the jaw(MRONJ)

Medication-related osteonecrosis of the jaw(MRONJ)is primarily associated with administering antiresorptive or antiangiogenic drugs.Despite significant research on MRONJ,its pathogenesis and effective treatments are still not fully understood.Animal models can be used to simulate the pathophysiological features of MRONJ,serving as standardized in vivo experimental platforms to explore the pathogenesis and therapies of MRONJ.Rodent models exhibit excellent effectiveness and high reproducibility in mimicking human MRONJ,but classical methods cannot achieve a complete replica of the pathogenesis of MRONJ.Modified rodent models have been reported with improvements for better mimicking of MRONJ onset in clinic.This review summarizes representative classical and modified rodent models of MRONJ created through various combinations of systemic drug induction and local stimulation and discusses their effectiveness and efficiency.Currently,there is a lack of a unified assessment system for MRONJ models,which hinders a standard definition of MRONJ-like lesions in rodents.Therefore,this review comprehensively summarizes assessment systems based on published peer-review articles,including new approaches in gross observation,histological assessments,radiographic assessments,and serological assessments.This review can serve as a reference for model establishment and evaluation in future preclinical studies on MRONJ.

A stock assessment for Illex argentinus in Southwest Atlantic using an environmentally dependent surplus production model

The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role in regulating the population dynamics of the I. argentinus population. This study develops an environmentally dependent surplus production（EDSP） model to evaluate the stock abundance of I. argentines during the period of 2000 to 2010. The environmental factors（favorable spawning habitat areas with sea surface temperature of 16–18°C） were assumed to be closely associated with carrying capacity（K） in the EDSP model. Deviance Information Criterion（DIC） values suggest that the estimated EDSP model with environmental factors fits the data better than a Schaefer surplus model without environmental factors under uniform and normal scenarios.The EDSP model estimated a maximum sustainable yield（MSY） from 351 600 t to 685 100 t and a biomass from 1 322 400 t to1 803 000 t. The fishing mortality coefficient of I. argentinus from 2000 to 2010 was smaller than the values of F（0.1） and F（MSY）. Furthermore, the time series biomass plot of I. argentinus from 2000 to 2010 shows that the biomass of I.argentinus and this fishery were in a good state and not presently experiencing overfishing. This study suggests that the environmental conditions of the habitat should be considered within squid stock assessment and management.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

Analysis of a Conceptual Model and Assessment of the e-Procurement System in Bangladesh

The public procurement system in Bangladesh has been running by traditional manual process under the flagship of Public Procurement Act (PPA 2006) and Public Procurement Rules (PPR 2008). Public procurement agencies have been facing challenges in this manual tendering system. To overcome this problem and to bring reality to the “Digital Bangladesh” slogan, the Government of Bangladesh introduced the e-Procurement system under the e-GP (Electronic Government Procurement) guideline 2011. After the inception of e-procurement, there is no e-procurement assessment model to improve the e-GP system. The purpose of this research is to develop a conceptual framework and to design an e-procurement assessment model. With this view, we have considered one of the biggest entity Roads and Highways Department (RHD) of the Government of Bangladesh, for field study. Mixed methods along with FGD (Focus Group Discussion), KII (Key Informant Interview), and survey questionnaires are used to collect data from RHD, and then Statistical Package for Social Science (SPSS) software is used for regression analysis and hypothesis testing to develop the e-Procurement assessment model. The novel contribution of the study lies in the test of the hypothesis that focuses on developing the conceptual model of the e-procurement assessment system in Bangladesh. Findings of the study are essential for all Procurement Entity (PE) and suppliers i.e. contractors of RHD who are engaged in the construction of the infrastructure project development project.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Establishment of ocean dumping area capacity assessment model

Dumping area capacity is mainly affected by the hydrodynamic process （tidal sediment, storm surge and wave, etc.） as well as the size and depth of dumping area. Based on three-dimensional ocean circulation model known as FVCOM （Finite Volume Coast and Ocean Model） and the stochastic dynamic statistical analysis model, taking advantage of dumping ground topography evolution and dumping quantity, the author aims to discuss the influence of hydrodynamic processes and dumping activity so as to built a new model of ocean dumping area capacity. With the data of depth and dumped amount in the dumping area, the changes of bottom topographic which caused by tidal current under the natural condition based on the FVCOM hydrodynamic and sediment module, the author strive to analyze the statistical relation of the changes for dumping amount, tidal current and bottom topographic. Through real data to fit revision coefficient values, which will be regarded as topographic changes reference value affected by wave and storm surges. Thus taking this evaluation as the long-term changes in the dumping capacity. In the premise of setting up the threshold of bottom topographic changes, the dumping area capacity is calculated. Take Yangtze Estuary No. 1 dumping area as an example, As the water depth reduces by 0.5 m annually, the dumping area capacity is about 6.7 million m3/a, the model results are in reasonable agreement with the actual amount. Then the model is validated in Luoyuan Bay dumping area, Shengsishangchuan Mountain dumping area, Dongding dumping area, Dongshan dumping area, and Wenzhou Port dumoin~ area. it is turns out the results are similar to that of the actual observations.

Recycling Strategy and Recyclability Assessment Model Based on the Artificial Neural Network

Now, a rapidly growing concern for the environmental protection and resource utilization has stimulated many new activities in the in dustrialized world for coping with urgent environmental problems created by the steadily increasing consumption of industrial products. Increasingly stringent r egulations and widely expressed public concern for the environment highlight the importance of disposing solid waste generated from industrial and consumable pr oducts. How to efficiently recycle and tackle this problem has been a very impo rtant issue over the world. Designing products for recyclability is driven by environmental and economic goals. To obtain good recyclability, two measures can be adopted. One is better recycling strategy and technology; the other is design for recycling (DFR). The recycling strategies of products generally inclu de: reuse, service, remanufacturing, recycling of production scraps during the p roduct usage, recycle (separation first) and disposal. Recyclability assessment is a very important content in DFR. This paper first discusses the content of D FR and strategies and types related to products recyclability, and points out th at easy or difficult recyclability depends on the design phase. Then method and procedure of recyclability assessment based on ANN is explored in detail. The pr ocess consists of selection of the ANN input and output parameters, control of t he sample quality and construction and training of the neural network. At la st, the case study shows this method is simple and operative.

Development and validation of a risk assessment model for prediabetes in China national diabetes survey

BACKGROUND Prediabetes risk assessment models derived from large sample sizes are scarce.AIM To establish a robust assessment model for prediabetes and to validate the model in different populations.METHODS The China National Diabetes and Metabolic Disorders Study(CNDMDS)collected information from 47325 participants aged at least 20 years across China from 2007 to 2008.The Thyroid Disorders,Iodine Status and Diabetes Epidemiological Survey(TIDE)study collected data from 66108 participants aged at least 18 years across China from 2015 to 2017.A logistic model with stepwise selection was performed to identify significant risk factors for prediabetes and was internally validated by bootstrapping in the CNDMDS.External validations were performed in diverse populations,including populations of Hispanic(Mexican American,other Hispanic)and non-Hispanic(White,Black and Asian)participants in the National Health and Nutrition Examination Survey(NHANES)in the United States and 66108 participants in the TIDE study in China.C statistics and calibration plots were adopted to evaluate the model’s discrimination and calibration performance.RESULTS A set of easily measured indicators(age,education,family history of diabetes,waist circumference,body mass index,and systolic blood pressure)were selected as significant risk factors.A risk assessment model was established for prediabetes with a C statistic of 0.6998(95%CI:0.6933 to 0.7063)and a calibration slope of 1.0002.When externally validated in the NHANES and TIDE studies,the model showed increased C statistics in Mexican American,other Hispanic,Non-Hispanic Black,Asian and Chinese populations but a slightly decreased C statistic in non-Hispanic White individuals.Applying the risk assessment model to the TIDE population,we obtained a C statistic of 0.7308(95%CI:0.7260 to 0.7357)and a calibration slope of 1.1137.A risk score was derived to assess prediabetes.Individuals with scores≥7 points were at high risk of prediabetes,with a sensitivity of 60.19%and specificity of 67.59%.CONCLUSION An easy-to-use assessment model for prediabetes was established and was internally and externally validated in different populations.The model had a satisfactory performance and could screen individuals with a high risk of prediabetes.