Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nation-wide diabetes survey

The temporal dynamics of the gut mycobiome and its association with cardiometabolic health in a nationwide cohort of 12,641 Chinese adults

The dynamics of the gut mycobiome and its association with cardiometabolic health remain largely unexplored. Here, we employ internal transcribed spacer (ITS) sequencing to capture the gut mycobiome composition and dynamics within a nationwide human cohort of 12,641 Chinese participants, including 1,946 participants with repeated measurements across three years. We find that the gut mycobiome is associated with cardiometabolic diseases and related biomarkers in both cross-sectional and dynamic analyses. Fungal alpha diversity indices and 19 mycobiome genera are the major contributors to the mycobiome-cardiometabolic disease link. Particularly, Saccharomyces emerges as an effect modifier of traditional risk factors in promoting type 2 diabetes risk. Further integration of multi-omics data reveals key metabolites such as γ-linolenic acid and L-valine linking the gut mycobiome to type 2 diabetes. This study advances our understanding of the potential roles of the gut mycobiome in cardiometabolic health.

External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care

AIMS/INTRODUCTION

Two Hong Kong Chinese non-laboratory-based prediabetes/diabetes mellitus (pre-DM/DM) risk models were developed using logistic regression (LR) and machine learning, respectively. We aimed to evaluate the models' validity in case finding of pre-DM/DM in a Chinese primary care (PC) population. We also evaluated the validity of a risk-scoring algorithm derived from the LR model.

MATERIALS AND METHODS

This was a cross-sectional external validation study on Chinese adults, without a prior DM diagnosis, who were recruited from public/private PC clinics in Hong Kong. A total of 1,237 participants completed a questionnaire on the models' predictors. Of that, 919 underwent blood glucose testing. The primary outcome was the models' and the algorithm's sensitivity in finding pre-DM/DM cases. The secondary outcomes were the models' and the algorithm's specificity, positive/negative predictive values, discrimination and calibration.

RESULTS

The models' sensitivity were 0.70 (machine learning) and 0.72 (LR). Both showed good external discrimination (area under the receiver operating characteristic curve: machine learning 0.744, LR 0.739). The risks estimated by the models were lower than the observed incidence, indicating poor calibration. Both models were more effective among participants with lower pretest probabilities; that is, age 18-44 years. The algorithm's sensitivity was 0.77 at the cut-off score of ≥16 out of 41.

CONCLUSION

This study showed the validity of the models and the algorithm for finding pre-DM/DM cases in a Chinese PC population in Hong Kong. They can facilitate more cost-effective identification of high-risk individuals for blood testing to diagnose pre-DM/DM in PC. Further studies should recalibrate the models for more precise risk estimation in PC populations.

Risk factors and prediction score for new-onset diabetes mellitus after liver transplantation

AIM

New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks.

METHODS

We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database.

RESULTS

A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724-0.846) in the experimental population and 0.782 (95% CI: 0.708-0.856) in the validation population.

CONCLUSIONS

Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.

Sex-specific association of circulating Isthmin-1 with isolated post-challenge hyperglycemia

Introduction

To explore the distribution of Isthmin-1 (ISM1) level and its association with isolated post-challenge hyperglycemia (IPH).

Methods

A total of 522 participants without a history of diabetes were invited to attend a standard 75g 2-h oral glucose tolerance test (OGTT), and 71 subjects were further invited for a 3-h oral minimal model test. Insulin sensitivity and β-cell function were evaluated using both HOMA and estimated from OGTT. Circulating ISM1 levels were determined by a commercially available ELISA kit.

Results

A total of 76 (14.6%) participants were diagnosed as IPH, accounting for 61.3% of the newly diagnosed diabetes. ISM1 levels were significantly higher in men than in women (1.74 ng/mL versus 0.88 ng/mL). The inverse correlation between ISM1 and β-cell function and IPH was only significant in men. After multivariate adjustment, per unit increment in ISM1 was associated with 0.68-fold (95% CI: 0.49-0.90) reduced odds ratio (OR) of IPH in men. Compared to men with the lowest ISM1 levels, the adjusted OR of IPH with the highest ISM1 levels decreased by 73% (95% CI: 0.11-0.61). Moreover, incorporation of ISM1 into the New Chinese Diabetes Risk Score (NCDRS) model yielded a substantial improvement in net reclassification improvement of 58% (95% CI: 27%-89%) and integrated discrimination improvement of 6.4% (95% CI: 2.7%-10.2%) for IPH.

Conclusions

ISM1 was significantly and independently associated with IPH, and serves as a feasible biomarker for the early identification of men with high risk of IPH.

Predictors of and barriers to follow-up uptake: analysis of factors and perceived barriers among high-risk individuals with diabetes after screening in China

OBJECTIVE

The objective of this study was to identify variables that predict adherence to follow-up visits among people who are positive for diabetes during screening and to investigate barriers to follow-up.

STUDY DESIGN

A retrospective cohort study linking individual-level registry data was performed.

METHODS

First, we compared the characteristics of attenders and non-attenders. Second, we investigated perceived barriers using a questionnaire in a random sample of people who failed to attend the follow-up visit.

RESULTS

A total of 27,806 (16.4%) patients attended the follow-up visits. Multiple logistic regression analysis revealed that individuals aged ≥75 years were more likely to attend follow-up than were those aged 35-45 years (odds ratio [OR]: 1.97 [95% confidence interval {CI}: 1.82-2.15]), male (OR: 1.15 [95% CI: 1.12-1.18]), obese (OR: 1.36 [95% CI: 1.29-1.43]), had positive family history of diabetes (OR: 1.37 [95% CI: 1.30-1.45]), hypertension (OR: 1.05 [95% CI: 1.01-1.09]), high glucose levels (OR: 1.10 [95% CI: 1.09-1.11]), and high diabetes risk scores (OR: 1.02 [95% CI: 1.02-1.03]) facilitated follow-up. However, overweight (OR: 0.95 [95% CI: 0.92-0.99]) and central obesity (OR: 0.86 [95% CI: 0.83-0.90]) predicted no follow-up. Among nonattenders, diabetes beliefs, time restrictions and distance from home to hospitals were the top three barriers hindering follow-up visits.

CONCLUSIONS

Specific individual-level characteristics predicted adherence to follow-up visits, and some personal and sociocultural barriers hindered follow-up visits.

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

AIMS/INTRODUCTION

Machine 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.

METHODS

This 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.

RESULTS

Of 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.

CONCLUSIONS

The ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.

Nonlinear relationship between untraditional lipid parameters and the risk of prediabetes: a large retrospective study based on Chinese adults

BACKGROUND

Abnormal lipid metabolism poses a risk for prediabetes. However, research on lipid parameters used to predict the risk of prediabetes is scarce, and the significance of traditional and untraditional lipid parameters remains unexplored in prediabetes. This study aimed to comprehensively evaluate the association between 12 lipid parameters and prediabetes and their diagnostic value.

METHODS

This cross-sectional study included data from 100,309 Chinese adults with normal baseline blood glucose levels. New onset of prediabetes was the outcome of concern. Untraditional lipid parameters were derived from traditional lipid parameters. Multivariate logistic regression and smooth curve fitting were used to examine the nonlinear relationship between lipid parameters and prediabetes. A two-piecewise linear regression model was used to identify the critical points of lipid parameters influencing the risk of prediabetes. The areas under the receiver operating characteristic curve estimated the predictive value of the lipid parameters.

RESULTS

A total of 12,352 participants (12.31%) were newly diagnosed with prediabetes. Following adjustments for confounding covariables, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol were negatively correlated with prediabetes risk. Conversely, total cholesterol, triglyceride (TG), lipoprotein combine index (LCI), atherogenic index of plasma (AIP), non-HDL-C, atherogenic coefficient, Castelli's index-I, remnant cholesterol (RC), and RC/HDL-C ratio displayed positive correlations. In younger adults, females, individuals with a family history of diabetes, and non-obese individuals, LCI, TG, and AIP exhibited higher predictive values for the onset of prediabetes compared to other lipid profiles.

CONCLUSION

Nonlinear associations were observed between untraditional lipid parameters and the risk of prediabetes. The predictive value of untraditional lipid parameters for prediabetes surpassed that of traditional lipid parameters, with LCI emerging as the most effective predictor for prediabetes.

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

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.

Machine learning for predicting diabetes risk in western China adults

OBJECTIVE

Diabetes mellitus is a global epidemic disease. Long-time exposure of patients to hyperglycemia can lead to various type of chronic tissue damage. Early diagnosis of and screening for diabetes are crucial to population health.

METHODS

We collected the national physical examination data in Xinjiang, China, in 2020 (a total of more than 4 million people). Three types of physical examination indices were analyzed: questionnaire, routine physical examination and laboratory values. Integrated learning, deep learning and logistic regression methods were used to establish a risk model for type-2 diabetes mellitus. In addition, 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.

RESULTS

An XGBoost-based risk prediction model outperformed the other five risk assessment algorithms. The AUC of the model was 0.9122. Based on the feature importance ranking map, we found that hypertension, fasting blood glucose, age, coronary heart disease, ethnicity, parental diabetes mellitus, triglycerides, waist circumference, total cholesterol, and body mass index were the most important features of the risk prediction model for type-2 diabetes.

CONCLUSIONS

This study established a diabetes risk assessment model based on multiple ethnicities, a large sample and many indices, and classified the diabetes risk of the population, thus providing a new forecast tool for the screening of patients and providing information on diabetes prevention for healthy populations.

Data collaboration analysis in predicting diabetes from a small amount of health checkup data

Recent studies showed that machine learning models such as gradient-boosting decision tree (GBDT) can predict diabetes with high accuracy from big data. In this study, we asked whether highly accurate prediction of diabetes is possible even from small data by expanding the amount of data through data collaboration (DC) analysis, a modern framework for integrating and analyzing data accumulated at multiple institutions while ensuring confidentiality. To this end, we focused on data from two institutions: health checkup data of 1502 citizens accumulated in Tsukuba City and health history data of 1399 patients collected at the University of Tsukuba Hospital. When using only the health checkup data, the ROC-AUC and Recall for logistic regression (LR) were 0.858 ± 0.014 and 0.970 ± 0.019, respectively, while those for GBDT were 0.856 ± 0.014 and 0.983 ± 0.016, respectively. When using also the health history data through DC analysis, these values for LR improved to 0.875 ± 0.013 and 0.993 ± 0.009, respectively, while those for GBDT deteriorated because of the low compatibility with a method used for confidential data sharing (although DC analysis brought improvements). Even in a situation where health checkup data of only 324 citizens are available, the ROC-AUC and Recall for LR were 0.767 ± 0.025 and 0.867 ± 0.04, respectively, thanks to DC analysis, indicating an 11% and 12% improvement. Thus, we concluded that the answer to the above question was "Yes" for LR but "No" for GBDT for the data set tested in this study.

Association of Spousal Diabetes Status and Ideal Cardiovascular Health Metrics With Risk of Incident Diabetes Among Chinese Adults

Importance

Spouses share common socioeconomic, environmental, and lifestyle factors, and multiple studies have found that spousal diabetes status was associated with diabetes prevalence. But the association of spousal diabetes status and ideal cardiovascular health metrics (ICVHMs) assessed by the American Heart Association's Life's Essential 8 measures with incident diabetes has not been comprehensively characterized, especially in large-scale cohort studies.

Objective

To explore the association of spousal diabetes status and cardiovascular health metrics with risk of incident diabetes in Chinese adults.

Design, Setting, and Participants

This cohort study included individuals in the China Cardiovascular Disease and Cancer Cohort without diabetes who underwent baseline and follow-up glucose measurements and had spouses with baseline glucose measurements. The data were collected in January 2011 to December 2012 and March 2014 to December 2016. The spousal study had a mean (SD) follow-up of 3.6 (0.9) years (median [IQR], 3.2 [2.9-4.5] years). Statistical analysis was performed from July to November 2022.

Exposure

Spousal diabetes status was diagnosed according to the 2010 American Diabetes Association (ADA) criteria. All participants provided detailed clinical, sociodemographic, and lifestyle information included in cardiovascular health metrics.

Main Outcomes and Measures

Incident diabetes, diagnosed according to 2010 ADA criteria.

Results

Overall, 34 821 individuals were included, with a mean (SD) age of 56.4 (8.3) years and 16 699 (48.0%) male participants. Spousal diabetes diagnosis was associated with an increased risk of incident diabetes (hazard ratio [HR], 1.15; 95% CI, 1.03-1.30). Furthermore, participants whose spouses had uncontrolled glycated hemoglobin (HbA1c) had a higher risk of diabetes (HR, 1.20; 95% CI, 1.04-1.39) but the risk of diabetes in participants whose spouses had controlled HbA1c did not increase significantly (HR, 1.10; 95% CI, 0.92-1.30). Moreover, this association varied with composite cardiovascular health status. Diabetes risk in individuals who had poor cardiovascular health status (<4 ICVHMs) was associated with spousal diabetes status (3 ICVHMs: HR, 1.50; 95% CI, 1.15-1.97), while diabetes risk in individuals who had intermediate to ideal cardiovascular health status (≥4 ICVHMs) was not associated with it (4 ICVHMs: HR, 1.01; 95% CI, 0.69-1.50).

Conclusions and Relevance

In this study, spousal diabetes diagnosis with uncontrolled HbA1c level was associated with increased risk of incident diabetes, but strict management of spousal HbA1c level and improving ICVHM profiles may attenuate the association of spousal diabetes status with diabetes risk. These findings suggest the potential benefit of couple-based lifestyle or pharmaceutical interventions for diabetes.

Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study

The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.

Predicting the Risk of Type 2 Diabetes Mellitus with the New Chinese Diabetes Risk Score in a Cohort Study

Objectives: The New Chinese Diabetes Risk Score (NCDRS) is a noninvasive tool to assess the risk of type 2 diabetes mellitus (T2DM) in the Chinese population. Our study aimed to evaluate the performance of the NCDRS in predicting T2DM risk with a large cohort. Methods: The NCDRS was calculated, and participants were categorized into groups by optimal cutoff or quartiles. Hazard ratios (HRs) and 95% confidential intervals (CIs) in Cox proportional hazards models were used to estimate the association between the baseline NCDRS and the risk of T2DM. The performance of the NCDRS was assessed by the area under the curve (AUC). Results: The T2DM risk was significantly increased in participants with NCDRS ≥25 (HR = 2.12, 95% CI 1.88-2.39) compared with NCDRS <25 after adjusting for potential confounders. T2DM risk also showed a significant increasing trend from the lowest to the highest quartile of NCDRS. The AUC was 0.777 (95% CI 0.640-0.786) with a cutoff of 25.50. Conclusion: The NCDRS had a significant positive association with T2DM risk, and the NCDRS is valid for T2DM screening in China.

ModAsian FINDRISC as a Screening Tool for People with Undiagnosed Type 2 Diabetes Mellitus in Vietnam: A Community-Based Cross-Sectional Study

Purpose

Our study aims to evaluate the risk of developing type 2 diabetes mellitus in the next 10 years using ModAsian FINDRISC and additionally explore associated factors among the Vietnam population.

Participants and Methods

A cross-sectional study was conducted on 2258 participants aged 25 years old or above in Thua Thien Hue Province, Vietnam. The sample size is calculated based on the estimated sensitivity, and participants were randomly selected from different geographical and socio-economic areas. All participants were thoroughly medically examined, taking blood lipid profile and fasting blood glucose, taking blood pressure, anthropometric indexes, 12-lead electrocardiogram, and behavioral factors were investigated using the Vietnamese version of the WHO STEPS toolkit. The risk of developing T2DM was made based on the ModAsian FINDRISC.

Results

The incidence of developing type 2 diabetes mellitus among the study population was 4.21%. The group with a high or very high risk of developing type 2 diabetes mellitus in the next 10 years accounted for 2.52%. Body mass index (AUC = 0.840, 95% CI: 0.792-0.888), waist circumference (AUC = 0.824, 95% CI: 0.777-0.871), family history of diabetes mellitus (AUC = 0.751, 95% CI = 0.668-0.833), and history of antihypertensive medication use regularly (AUC = 0.708, 95% CI: 0.632-0.784) are the most associated factors of the ModAsian FINDRISC. Residential location (OR = 5.62, 95% CI: 1.91-16.54) and occupational status (OR = 0.35, 95% CI: 0.20-0.62) were significant factors associated with a high and very high risk of developing type 2 diabetes mellitus in the next 10 year.

Conclusion

Screening for the risk of type 2 diabetes mellitus and implementing intervention programs focusing on controlling weight, waist circumference, and blood pressure are essential for reducing type 2 diabetes mellitus incidence and burden in Vietnam.

Cost-effectiveness of two screening strategies based on Chinese diabetes risk score for pre-diabetes in China

Objective

Studies have shown that screening for pre-diabetes mellitus (pre-DM) is essential to prevent type 2 diabetes mellitus (T2DM). This study evaluates the cost-effectiveness of two screening strategies that apply the Chinese Diabetes Risk Score (CDRS) to screen for pre-DM in China.

Methods

A Markov microsimulation model was conducted from a social perspective, and the input parameters were obtained from published literature or publicly available data. Two screening strategies for pre-DM based on CDRS were built and compared with the control group to determine the cost-effective strategy. The screening strategy of the control group was screening for pre-DM by fasting plasma glucose (FPG) test in adults undergoing annual health examination and no screening in adults without an annual health examination (status quo). Two screening strategies were strategy 1: screening for pre-DM using CDRS in all adults (including with or without an annual health examination); and strategy 2: supplemental self-screening for pre-DM using CDRS in adults without an annual health examination, based on the status quo. We focus on the cumulative prevalence of T2DM and the incremental cost-effectiveness ratio which signifies the cost per case of T2DM prevented. We also evaluated the cost-effectiveness from the health system perspective. One-way and probabilistic sensitivity analyses were conducted to verify the robustness of the results.

Results

The costs a case of T2DM prevented for strategy 1 compared with the control group and strategy 2 were $299.67 (95% CI 298.88, 300.46) and $385.89 (95% CI 381.58, 390.20), respectively. In addition, compared with the control group, the cost of strategy 2 to prevent a case of T2DM was $272.23 (95% CI 271.50, 272.96).

Conclusions

Screening for pre-DM using CDRS in all adults was the most cost-effective health policy. We suggest that medical institutions replace FPG with CDRS for pre-DM screening; at the same time, self-screening for pre-DM using CDRS is widely promoted among adults without an annual health examination. There were still some disputes about how CDRS is included in the health examination projects, so strategy 2 should be considered as an alternative screening strategy. Findings provide a reference for the application of the CDRS in pre-DM screening and contribute to T2DM prevention.

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.

Use of the new guidelines on an earlier age threshold of 35 years for diabetes screening can identify an additional 6.3 million undiagnosed individuals with diabetes and 72.3 million individuals with prediabetes among Chinese adults: An analysis of a nationally representative survey

BACKGROUND

Young-onset diabetes has been increasingly prevalent in China and most of the young patients with diabetes remain undiagnosed. Recently, the American Diabetes Association (ADA) updated their screening criteria and turned down the age threshold of diabetes screening from 45 years to 35 years, which highlighted the importance of identifying young individuals with diabetes. Herein, we aimed to evaluate the clinical relevance of updated ADA screening recommendations in Chinese adults and the metabolic features and risk factor profiles of these newly diagnosed individuals.

STUDY DESIGN AND METHODS

Using a complex, multistage, probability sampling design, we analyzed data from a nationally representative sample of 98,658 Chinese adults in 2010. Participants without previously diagnosed diabetes were included into the present study. We calculated the proportion of individuals with diabetes eligible for screening and the number needed to screen (NNS) to identify one patient with diabetes by age groups.

RESULTS

Setting an earlier age threshold of diabetes screening can identify additional 6.3 million patients with diabetes and 72.3 million individuals with prediabetes, and the proportion of identified individuals increased more in rural, underdeveloped, and central areas. The NNS in Chinese adults dropped significantly from 28 in 30-34 age group to 15 in 35-45 years of age and remained low afterwards. The undiagnosed patients with diabetes who met the new screening age threshold of ADA recommendation were characterized by younger age, lower blood pressure and blood lipids, but higher proportion of overweight and higher level of insulin resistance, and tended to have an unhealthy diet habit, including low intake of fruits and vegetables and high intake of sugar-sweetened beverages, compared to those aged over 45 years.

CONCLUSIONS

The new age threshold of 35 years for diabetes screening would reduce the proportion of undiagnosed diabetes with high cost-effectiveness, given the NNS for a positive test result was much lower in 35-45 age group comparing to the lower age group in Chinese adults.

Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care

INTRODUCTION

More than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults.

METHODS

Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison.

RESULTS

The prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models.

CONCLUSIONS

Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.

Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper

INTRODUCTION

Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care.

METHODS AND ANALYSIS

A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated.

ETHICS AND DISSEMINATION

Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study

BACKGROUND

Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for diabetes screening in community and primary care settings.

METHODS

8425 participants were involved from a population-based study in Hubei, China since 2011. The dataset was split into a development set and a testing set. Seven different ML algorithms were compared to generate predictive models. Non-laboratory features were employed in the ML model for community settings, and laboratory test features were further introduced in the ML+lab models for primary care. The area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (auPR), and the average detection costs per participant of these models were compared with their counterparts based on the New China Diabetes Risk Score (NCDRS) currently recommended for diabetes screening.

RESULTS

The AUC and auPR of the ML model were 0·697and 0·303 in the testing set, seemingly outperforming those of NCDRS by 10·99% and 64·67%, respectively. The average detection cost of the ML model was 12·81% lower than that of NCDRS with the same sensitivity (0·72). Moreover, the average detection cost of the ML+FPG model is the lowest among the ML+lab models and less than that of the ML model and NCDRS+FPG model.

CONCLUSION

The ML model and the ML+FPG model achieved higher predictive accuracy and lower detection costs than their counterpart based on NCDRS. Thus, the ML-augmented algorithm is potential to be employed for diabetes screening in community and primary care settings.

Development and validation of a nomogram for assessing risk of isolated high 2-hour plasma glucose

A tool was constructed to assess need of an oral glucose tolerance test (OGTT) in patients whose fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are normal. Data was collected from the longitudinal REACTION study conducted from June to November 2011 (14,686 subjects, aged ≥ 40 y). In people without a prior history of diabetes, isolated high 2-hour plasma glucose was defined as 2-hour plasma glucose ≥ 11.1 mmol/L, FPG < 7.0 mmol/L, and HbA1c < 6.5%. A predictive nomogram for high 2-hour plasma glucose was developed via stepwise logistic regression. Discrimination and calibration of the nomogram were evaluated by the area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow test; performance was externally validated in Northeast China. Parameters in the model included gender, age, drinking status, marriage status, history of hypertension and hyperlipidemia, waist-to-hip ratio, FPG, and HbA1c. All variables were noninvasive, except FPG and HbA1c. The AUC of the nomogram for isolated high 2-hour plasma glucose was 0.759 (0.727-0.791) in the development dataset. The AUCs of the internal and externally validation datasets were 0.781 (0.712-0.833) and 0.803 (0.778-0.829), respectively. Application of the nomogram during the validation study showed good calibration, and the decision curve analysis indicated that the nomogram was clinically useful. This practical nomogram model may be a reliable screening tool to detect isolated high 2-hour plasma glucose for individualized assessment in patients with normal FPG and HbA1c. It should simplify clinical practice, and help clinicians in decision-making.

New risk score model for identifying individuals at risk for diabetes in southwest China

The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.

Cohort study evaluation of New Chinese Diabetes Risk Score: A new non-invasive indicator for predicting metabolic syndrome

OBJECTIVES

To investigate the association of the baseline New Chinese Diabetes Risk Score (NCDRS) with metabolic syndrome (MetS) risk and to evaluate the power of the baseline NCDRS to predict MetS based on the rural Chinese cohort study.

METHODS

Study participants were classified by baseline quartiles of NCDRS by gender. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for risk of MetS according to different diagnostic criteria. The receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) were used to evaluate the power of the baseline NCDRS for predicting MetS according to different diagnostic criteria.

RESULTS

We included 7,133 participants, and 1,651 MetS cases were identified after 6 years follow-up. After adjusting for multivariable confounding factors and with NCDRS quartile 1 as the reference, with quartile 4, the risk of MetS was increased for all participants, men and women: ORs (95% CIs) 4.03 (3.23-5.02), 3.59 (2.56-5.05) and 5.71 (4.23-7.70), respectively. Similar results were found on sensitivity analysis. The baseline NCDRS was a good predictor of MetS for all participants, men and women with MetS defined according to the diagnostic criteria of the Chinese Joint Committee on the Development of Guidelines for the Prevention and Treatment of Dyslipidemia in Adults (JCDCG).

CONCLUSIONS

Our study, based on the cohort study, found that the baseline NCDRS was positively associated with risk of MetS. Furthermore, our study might provide suggestions for developing a useful and inexpensive tool for predicting MetS in the Chinese population.

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen's Behavioral Model

Undiagnosed diabetes is a threat to public health. This study aims to identify potential variables related to undiagnosed diabetes using Andersen’s behavioral model. Baseline data including blood test data from the China Health and Retirement Longitudinal Study (CHARLS) were adopted. First, we constructed health service related variables based on Andersen model. Second, univariate analysis and multiple logistic regression were used to analyze the relations of variables to undiagnosed diabetes. The strength of relationships was presented by odds ratios (ORs) and 95% confidence intervals (CIs). Finally, the prediction of multiple logistic regression model was assessed using the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). According to diagnosis standards, 1234 respondents had diabetes, among which 560 were undiagnosed and 674 were previously diagnosed. Further analysis showed that the following variables were significantly associated with undiagnosed diabetes: age as the predisposing factor; medical insurance, residential places and geographical regions as enabling factors; having other chronic diseases and self-perceived health status as need factors. Moreover, the prediction of regression model was assessed well in the form of ROC and AUC. Andersen model provided a theoretical framework for detecting variables of health service utilization, which may not only explain the undiagnosed reasons but also provide clues for policy-makers to balance health services among diverse social groups in China.

Risk Prediction Model of Gestational Diabetes Mellitus in a Chinese Population Based on a Risk Scoring System

INTRODUCTION

Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system.

METHODS

This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively.

RESULTS

In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916).

CONCLUSION

The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.

A Scoring System for Outpatient Orthopedist to Preliminarily Distinguish Spinal Metastasis from Spinal Tuberculosis: A Retrospective Analysis of 141 Patients

Objective

Spinal tuberculosis (TB) misdiagnosed of spinal metastasis was not rarely reported, especially in outpatients department. This study was aimed to establish an outpatient scoring system to preliminarily distinguish spinal metastasis from spinal TB.

Methods

We retrospectively reviewed consecutive 141 patients with a pathological diagnosis of spinal metastasis (82 cases) or spinal TB (59 cases) in our hospital from January 2017 to June 2018. The following clinical characteristics which can be obtained by outpatient orthopedist were recorded and analyzed: age, gender, malignant tumor history, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and imaging features including distribution characteristics of vertebral lesions, subligamentous spread, paravertebral or psoas abscess, involved vertebral element, intervertebral disc, and sequestra formation. The prevalence of clinical characteristics in spinal metastasis was evaluated, and the scoring system was established using logistic regression analysis. The performance of the scoring system was also prospectively validated.

Results

The outpatient scoring system was based on five clinical characteristics confirmed as significant predictors of spinal metastasis, namely, malignant tumor history, subligamentous spread, posterior element lesions, preserved discs, and no sequestra formation. Spinal metastasis showed a significant higher score than spinal TB (8.17 points vs. 1.97 points, t = 18.621, P < 0.001), and the optimal cut-off value for the scoring system was 5 points. The sensitivity and specificity of the scoring system for predicting spinal metastasis were 97.85% and 88.33%, respectively, in the validation set.

Conclusion

Spinal lesions with the score of 5 to 10 would be considered a diagnosis of spinal metastasis, while the score of 0 to 4 may be spinal TB. Because the scoring system is mainly based on the clinical characteristics that can be obtained by an outpatient orthopedist, it is suitable to be used as a diagnostic tool in the outpatient department.

Differential and prognostic value of cardiovascular magnetic resonance derived scoring algorithm in cardiac tumors

OBJECTIVES

To establish a scoring algorithm based on cardiovascular magnetic resonance (CMR) parameters for differentiating between benign and malignant cardiac tumors and for predicting outcome.

METHODS

Patients referred for CMR for suspected cardiac tumors were prospectively enrolled. Tumors were categorized as benign or malignant based on pathology, imaging, and clinical information. The CMR protocol included cine, T1-weighted, T2-weighted, first-pass perfusion, and late gadolinium enhancement (LGE) sequences. Variables independently associated with malignancy in the multivariable logistic analysis were used to construct the scoring algorithm, and receiver operating characteristic analyses were used to assess the ability to discriminate malignant from benign tumors. The ability of the score to predict outcome (all-cause mortality) was also assessed by Kaplan-Meier survival analysis.

RESULTS

Among the 105 enrolled patients, 74 had benign and 31 had malignant tumors. In multivariable analysis, the independent predictors of malignant tumors were invasiveness (odds ratio, OR = 11.4, 2 points), irregular border (OR = 5.8, 1 point), and heterogenous LGE (OR 10.6, 2 points). The area under curves (AUC) of the scoring algorithm was 0.912 (cut-off score of 5) and showed significantly higher AUCs than individual variables (all P < 0.05) in differentiating benign and malignant tumors. After median follow-up of 18.2 months, mortality was significantly higher in patients with a score of 5 than in patients with score ≤ 4.

CONCLUSIONS

The scoring algorithm based on CMR-detected invasiveness, irregularity of border, and heterogenous LGE is an effective method for differentiating malignant from benign cardiac tumors and for predicting outcome.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study

Background:

Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups.

Objective(s):

We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands.

Methods:

A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one.

Conclusions:

Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.

Development and validation of screening scores of non-alcoholic fatty liver disease in middle-aged and elderly Chinese

AIM

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease and also closely related to cardiometabolic disease. Its prevalence was estimated at over one-fourth in the general population in China. We aimed to develop effective score tools for detecting NAFLD.

METHODS

A total of 17,212 participants aged 45-70 years old were surveyed in Shanghai between 2013 and 2014, and 13,293 participants were included in this analysis. All participants were randomly classified into the exploratory group or the validation group. Candidate categorical variables were selected using a logistic regression model. The score points were generated according to the β-coefficients.

RESULTS

We developed the Shanghai Nicheng NAFLD Score I (SHNC NAFLD Score I), which included body mass index and waist circumference with an area under the receiver-operating characteristic curve (AUC) of 0.802 (95% CI 0.792-0.811) in the exploratory group and 0.802 (95% CI 0.793-0.812) in the validation group. We further developed the SHNC NAFLD Score II by adding fasting plasma glucose, triglyceride, and alanine aminotransferase/aspartate aminotransferase ratio to the SHNC NAFLD Score I, achieving an AUC of 0.852 (95% CI 0.843-0.861) in the exploratory group and 0.843 (95% CI 0.834-0.852) in the validation group. The two score tools also performed well in subjects with normal alanine aminotransferase (ALT) levels.

CONCLUSIONS

Based on anthropometric and clinical categorical variables, our two scores are effective tools for detecting NAFLD in both this southern Chinese population and their subpopulation with normal ALT levels.

Evaluation of the value of diabetes risk scores in screening for undiagnosed diabetes and prediabetes: a community-based study in southwestern China

OBJECTIVES

To evaluate the performance and cost-effectiveness of existing diabetes risk scores (DRSs) to screen for undiagnosed diabetes mellitus (UDM) and prediabetes (PD) in a community-based southwestern Chinese population.

METHODS

Participants in TIDE-Chengdu survey with requisite data and without known diabetes were included. Five Chinese-derived DRSs and six non-Chinese-derived DRSs were included for evaluation. Their performance in detecting UDM and UMD or PD (UDM/PD) was assessed using the C-statistic. The cost-effectiveness of the optimal DRS was compared with that of capillary fasting blood glucose (CFBG).

RESULTS

Of the 1,692 TIDE-Chengdu survey participants included, 177 (10.5%) had UDM and 339 (20.0%) had PD. The rural participants (N = 737) were more likely to have UDM (13.4% vs. 8.2%) and PD (24.8% vs. 16.3%) than their urban counterparts (N = 955) (P < 0.0001). In the full population, the included DRSs all showed good discrimination in detecting UDM (C-statistic: 0.699 to 0.762) and UDM/PD (C-statistic: 0.717 to 0.769), but the New Chinese DRS (NCDRS) performed best for both UDM and UDM/PD. The DRSs evaluated all showed better performance in urban participants than rural participants for both UDM (C-statistic: 0.718 to 0.795 vs. 0.642 to 0.720) and UDM/PD (C-statistic: 0.729 to 0.793 vs. 0.682 to 0.726) (all P < 0.05). The mean cost per UDM/PD case identified was lower with NCDRS at score 25 (¥503.3($71.9)) and 27 (¥490.5 ($70.1)) than CFBG at 5.0, 5.1, 5.2, or 5.3 mmol/L (¥631.7 ($90.2), ¥611.8 ($87.4), ¥579.2 ($82.7) and ¥551.9 ($78.8)), whereas the mean costs per UDM case identified was higher with NCDRS at score 25 (¥1379.3 ($197.0)) and 27 (¥1315.1 ($187.9)) than CFBG at 5.3, 5.4, or 5.5 mmol/L (¥1301.7 ($186.0), ¥1247.7 ($178.2) and ¥1173.3 ($167.6)).

CONCLUSION

The NCDRS represents a valid and cost-effective tool for use in southwestern China to identify high-risk patients with UDM or PD who need a diagnostic test.

Nomogram to predict risk for early ischemic stroke by non-invasive method

Stroke is the acute onset of neurological deficits and is associated with high morbidity, mortality, and disease burden. In the present study, we aimed to develop a scientific, nomogram for non-invasive predicting risk for early ischemic stroke, in order to improve stroke prevention efforts among high-risk groups. Data were obtained from a total of 2151 patients with early ischemic stroke from October 2017 to September 2018 and from 1527 healthy controls. Risk factors were examined using logistic regression analyses. Nomogram and receiver operating characteristic (ROC) curves were drawn, cutoff values were established. Significant risk factors for early ischemic stroke included age, sex, blood pressure, history of diabetes, history of genetic, history of coronary heart disease, history of smoking. A nomogram predicting ischemic stroke for all patients had an internally validated concordance index of 0.911. The area under the ROC curve for the logistic regression model was 0.782 (95% confidence interval [CI]: 0.766-0.799, P < .001), with a cutoff value of 2.5. The nomogram developed in this study can be used as a primary non-invasive prevention tool for early ischemic stroke and is expected to provide data support for the revision of current guidelines.

Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China

PURPOSE

To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.

METHODS

A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.

RESULTS

In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.

CONCLUSION

Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Clinical Characteristics, Associated Factors, and Predicting COVID-19 Mortality Risk: A Retrospective Study in Wuhan, China

INTRODUCTION

COVID-19 has become a serious global pandemic. This study investigates the clinical characteristics and the risk factors for COVID-19 mortality and establishes a novel scoring system to predict mortality risk in patients with COVID-19.

METHODS

A cohort of 1,663 hospitalized patients with COVID-19 in Wuhan, China, of whom 212 died and 1,252 recovered, were included in this study. Demographic, clinical, and laboratory data on admission were collected from electronic medical records between January 14, 2020 and February 28, 2020. Clinical outcomes were collected until March 26, 2020. Multivariable logistic regression was used to explore the association between potential risk factors and COVID-19 mortality. The receiver operating characteristic curve was used to predict COVID-19 mortality risk. All analyses were conducted in April 2020.

RESULTS

Multivariable regression showed that increased odds of COVID-19 mortality was associated with older age (OR=2.15, 95% CI=1.35, 3.43), male sex (OR=1.97, 95% CI=1.29, 2.99), history of diabetes (OR=2.34, 95% CI=1.45, 3.76), lymphopenia (OR=1.59, 95% CI=1.03, 2.46), and increased procalcitonin (OR=3.91, 95% CI=2.22, 6.91, per SD increase) on admission. Spline regression analysis indicated that the correlation between procalcitonin levels and COVID-19 mortality was nonlinear (p=0.0004 for nonlinearity). The area under the receiver operating curve of the COVID-19 mortality risk was 0.765 (95% CI=0.725, 0.805).

CONCLUSIONS

The independent risk factors for COVID-19 mortality included older age, male sex, history of diabetes, lymphopenia, and increased procalcitonin, which could help clinicians to identify patients with poor prognosis at an earlier stage. The COVID-19 mortality risk score model may assist clinicians in reducing COVID-19-related mortality by implementing better strategies for more effective use of limited medical resources.

Performance and costs of multiple screening strategies for type 2 diabetes: two population-based studies in Shanghai, China

INTRODUCTION

To compare the performance and the costs of various assumed screening strategies for type 2 diabetes mellitus (T2DM) among Chinese adults, and identify an optimal one for the population.

RESEARCH DESIGN AND METHODS

Two multistage-sampling surveys were conducted in Shanghai, China, in 2009 and 2017. All participants were interviewed, had anthropometry, measured fasting plasma glucose (FPG), hemoglobin A1c (A1c) and/or postprandial glucose. The 1999 WHO diagnostic criteria was used to identify undiagnosed T2DM. A previously developed Chinese risk assessment system and a specific risk assessment system developed in this study were applied to calculate diabetes risk score (DRS) 1 and 2. Optimal screening strategies were selected based on the sensitivity, Youden index and the costs using the 2009 survey data as the training set and the 2017 survey data as the validation set. A twofold cross-validation was also performed.

RESULTS

Of numerous assumed strategies, FPG ≥5.6 mmol/L alone performed well (Youden index of 71.8%) and cost least (US$18.4 for each case detected), followed by the strategy of DRS2 ≥8 combining with FPG ≥5.6 mmol/L (Youden index of 71.7% and US$20.2 per case detected) and the strategy of DRS1 ≥17 combining with FPG ≥5.6 mmol/L (Youden index of 72.0% and US$21.6 per case detected). However, FPG alone resulted in more subjects requiring oral glucose tolerance test (OGTT) than did combining with DRS. The strategy of FPG ≥5.6 mmol/L combining with A1c ≥4.7% achieved a Youden index of 72.1%, but had a cost as high as US$48.8 for each case identified. Twofold cross-validation also supported the use of FPG alone, but with an optimal cut-off of 6.1 mmol/L.

CONCLUSIONS

Our results support the use of FPG alone in T2DM screening in Chinese adults. DRS may be used combining with FPG in populations with available electronic health records to reduce the number of OGTT and save costs of screening.

Serum alanine transaminase levels predict type 2 diabetes risk among a middle-aged and elderly Chinese population

INTRODUCTION AND AIM

It is indicated that high levels of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are associated with increased incident type 2 diabetes risk. However, whether serum ALT levels could improve the discrimination of type 2 diabetes remains unclear.

METHODS

The data was derived from the Dongfeng-Tongji cohort study, which was established in 2008 and followed until October 2013. A total of 17,173 participants free of type 2 diabetes at baseline were included and 1159 participants developed diabetes after 4.51 (0.61) years of follow-up. Cox proportional hazard regression model was used to calculate the hazard ratios (HRs) for the association between ALT and AST levels with incident diabetes risk. Receiver-operating characteristic (ROC) curves analysis was used to evaluate the predictive accuracy of models incorporating traditional risk factors with and without ALT.

RESULTS

Compared with the lowest quartile of ALT and AST levels, the highest quartile had a significantly higher risk of developing type 2 diabetes (HR: 2.17 [95% CI: 1.78-2.65] and 1.29 [1.08-1.54], respectively) after adjustment for potential confounders. The addition of ALT levels into the traditional risk factors did not improve the predictive ability of type 2 diabetes, with AUC increase from 0.772 to 0.774; P=0.86.

CONCLUSIONS

Although elevated ALT or AST levels increased incident type 2diabetes risk, addition of ALT levels into the prediction model did not improve the discrimination of type 2 diabetes.

Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

With the development of data mining, machine learning offers opportunities to improve discrimination by analyzing complex interactions among massive variables. To test the ability of machine learning algorithms for predicting risk of type 2 diabetes mellitus (T2DM) in a rural Chinese population, we focus on a total of 36,652 eligible participants from the Henan Rural Cohort Study. Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM). The model performance was measured in an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value and area under precision recall curve. The importance of variables was identified based on each classifier and the shapley additive explanations approach. Using all available variables, all models for predicting risk of T2DM demonstrated strong predictive performance, with AUCs ranging between 0.811 and 0.872 using laboratory data and from 0.767 to 0.817 without laboratory data. Among them, the GBM model performed best (AUC: 0.872 with laboratory data and 0.817 without laboratory data). Performance of models plateaued when introduced 30 variables to each model except CART model. Among the top-10 variables across all methods were sweet flavor, urine glucose, age, heart rate, creatinine, waist circumference, uric acid, pulse pressure, insulin, and hypertension. New important risk factors (urinary indicators, sweet flavor) were not found in previous risk prediction methods, but determined by machine learning in our study. Through the results, machine learning methods showed competence in predicting risk of T2DM, leading to greater insights on disease risk factors with no priori assumption of causality.

Three-component non-invasive risk score for undiagnosed diabetes in Chinese people: Development, validation and longitudinal evaluation

AIMS/INTRODUCTION

To develop a new non-invasive risk score for undiagnosed diabetes in Chinese people, and to evaluate the incident diabetes risk in those with high-risk scores, but no diabetes on initial testing.

MATERIALS AND METHODS

A total of 2,609 participants with no known diabetes (aged 25-74 years) who underwent oral glucose tolerance tests in Hong Kong (HK) were investigated for independent risk factors of diabetes to develop a categorization point scoring system, the Non-invasive Diabetes Score (NDS). This NDS was validated in a cross-sectional study of 2,746 participants in Shaanxi, China. HK participants tested to not have diabetes at baseline were assessed for subsequent incident diabetes rates.

RESULTS

In the HK cohort, hypertension, age and body mass index were the key independent risk factors selected to develop the NDS, with ≥28 out of 50 NDS points considered as high risk. The area under the receiver operating characteristic curve for undiagnosed diabetes was 0.818 and 0.720 for the HK and Shaanxi cohort, respectively. The negative predictive value was 97.4% (HK) and 95.8% (Shaanxi); the number needed to screen to identify one case of diabetes was five (HK) and 11 (Shaanxi), respectively. Among those that tested non-diabetes at baseline, individuals with NDS ≥28 had a threefold risk of incident diabetes during the subsequent 20.9 years, compared with those with NDS <28 (P < 0.001), with a steeper rise in incident diabetes observed in those with NDS at higher tertiles.

CONCLUSIONS

This new three-component risk score is a user-friendly tool for diabetes screening, and might inform the subsequent testing interval for high-risk non-diabetes individuals.

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.

A Novel Risk Score for Type 2 Diabetes Containing Sleep Duration: A 7-Year Prospective Cohort Study among Chinese Participants

BACKGROUND

Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration.

METHODS

We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration.

RESULTS

After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C-index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit.

CONCLUSIONS

The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50.

The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P < 0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

Prediction of 5-year risk of diabetes mellitus in relatively low risk middle-aged and elderly adults

AIMS

To determine the potential risk factors and construct the predictive model of diabetic risk among a relatively low risk middle-aged and elderly Chinese population.

METHODS

Information of participants was collected in the Dongfeng-Tongji cohort study, a perspective cohort study of Chinese occupational population. The main outcome was incident type 2 diabetes (T2DM). Based on the conventional risk factors of diabetes, we defined low risk participants without underlying diseases such as coronary heart disease, stroke, cancer, dyslipidemia, hypertension, metabolic syndrome, obesity and family history of diabetes. Totally, 4833 participants from the Dongfeng-Tongji cohort study were enrolled, and of them, 171 had an incident diagnosis of T2DM during 4.6 years of follow-up period. A Cox proportional hazards model was used to estimate effects of risk factors. The restricted cubic spline regression and the Youden index were used to explore the optimal cutoffs of risk factors, and the C index was used to assess the discrimination power of prediction models.

RESULTS

There were significant linear relationships between BMI/TG level/fasting glucose level and incident diabetic risk among low risk participants. In the restricted cubic spline regression, when fasting glucose level was above 5.4 mmol/L, TG above 1.06 mmol/L and BMI above 22 kg/m², the HRs (95% CIs) of diabetes were above 1.0. The detailed HRs (95% CI) were 1.29 (1.01, 1.64), 2.57 (1.00, 6.58), and 1.49 (1.00, 2.22), respectively. The optimal cutoff determined by the Yonden index was 1.1 mmol/L for TG, 24 kg/m² for BMI and 5.89 mmol/L for fasting plasma glucose, respectively. The C index was 0.75 (95% CI: 0.7-0.81) when age, sex, smoke status, physical activity, BMI (< 24 kg/m² and ≥ 24 kg/m²), TG (< 1.1 mmol/L and ≥ 1.1 mmol/L), and FPG (< 5.89 mmol/L and ≥ 5.89 mmol/L) were introduced into the diabetes predictive model.

CONCLUSIONS

Fasting plasma glucose level, BMI, and triglyceride level were still dominated factors to predict 5-year diabetic risk among the relatively low risk participants. The cutoff values for fasting plasma glucose, TG, and BMI set as 5.89 mmol/L, 1.1 mmol/L, and 24 kg/m², respectively, had the best predictive discrimination of diabetes.

Use of capillary glucose combined with other non-laboratory examinations to screen for diabetes and prediabetes

AIM

To evaluate the value and feasibility of capillary glucose assessment, combined with other non-laboratory measures, in screening for diabetes and prediabetes in the community.

METHODS

In this cross-sectional study, we assessed fasting capillary glucose, fasting plasma glucose, and both capillary glucose and plasma glucose values after 2-h oral glucose tolerance tests in a total of 3736 samples. We determined the optimal threshold of capillary glucose using receiver-operating characteristic curve analysis. The effect of screening methods using capillary glucose combined with other variables, such as age, BMI and waist circumference, was assessed according to area under the receiver-operating characteristic curve.

RESULTS

There was a strong positive correlation between capillary glucose and venous plasma glucose. The area under the curve for the model using fasting capillary glucose to screen for impaired fasting glucose was 0.722, while that for the model using capillary glucose after a 2-h oral glucose tolerance test to screen for impaired glucose tolerance was 0.916. The area under the curve for the model using fasting capillary glucose to screen for diabetes was 0.835, while that for the model using 2-h oral glucose tolerance test capillary glucose was 0.912. The area under the curve for the model using fasting capillary glucose + 2-h oral glucose tolerance test capillary glucose to screen for diabetes was 0.945. The discriminatory capability of models using capillary glucose was somewhat improved by adding non-laboratory variables.

CONCLUSIONS

Capillary glucose could be an alternative for screening for diabetes and prediabetes, especially in low-resource areas.

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes

Background

This study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems.

Methods

We evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems.

Results

Existing score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems.

Conclusion

Our proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.

Mortality pattern trends and disparities among Chinese from 2004 to 2016

BACKGROUND

With the changes in environmental, medical technique, population structure and national health projects, human mortality rates have undergone great changes all over the world. According to "World Health Statistics 2016: Monitoring Health for the SDGs (Sustainable Development Goals)", we can draw a globally vision about life expectancy and cause of death; also, significant inequality still persists within and among countries. This study was designed to research into the trend of mortality pattern in China, evaluate the disparities of age-specific and disease-specific mortality rates between male and female, and provides a scientific basis for further prevention strategies and policies design.

METHODS

Data from the Chinese Disease Surveillance Points system were used to calculate crude and age-adjusted death rates, annual percent changes (APC) for men and women during 2004 to 2016. Age-standardized mortality rates (ASMR) were performed through the direct method with the World Health Organization's World Standard Population. APC, according to log linear model, was adopted to describe the mortality rate trend. The χ2 test was used to compare differences between age-specific and cause-specific mortality rates of men and women. Data analysis and figures were completed by R software.

RESULTS

The mortality rates of men and women have decreased significantly (P < 0.05) during 2004-2016, and the APC were1.98 and 2.45%, respectively. In 2016, the crude mortality rate (CMR) and ASMR in all causes of death were 658.50 and 490.28 per 100,000 per year, respectively. The 5 leading causes of death were malignant neoplasm, cerebrovascular disease, heart disease, COPD, and accidental injury. The mortality rates of men were higher than that of women in all age groups.

CONCLUSIONS

There are severe health gaps and disparities between male and female, and the chronic non-communicable diseases continue to be a serious health threat to Chinese residents.

Colorectal Cancer Screening Modalities in Chinese Population: Practice and Lessons in Pudong New Area of Shanghai, China

Background: Parallel test of risk stratification and two-sample qualitative fecal immunochemical tests (FITs) are used to screen colorectal cancer (CRC) in Shanghai, China. This study was designed to identify an optimal initial screening modality based on available data. Methods: A total of 538,278 eligible residents participated in the program during the period of January 2013 to June 2017. Incident CRC was collected through program reporting system and by record linkage with the Shanghai Cancer Registry up to December 2017. Logistic regression model was applied to identify significant factors to calculate risk score for CRC. Cutoff points of risk score were determined based on Youden index and defined specificity. Sensitivity, specificity, and positive predictive values (PPVs) were computed to evaluate validity of assumed screening modalities. Results: A total of 446 CRC were screen-detected, and 777 interval or missed cases were identified through record linkage. The risk score system had an optimal cutoff point of 19 and performed better in detecting CRC and predicting long-term CRC risk than did the risk stratification. When using a cutoff point of 24, parallel test of risk score, and FIT were expected to avoid 56 interval CRCs with minimal decrease in PPV and increase in colonoscopy. However, the observed detection rates were much lower than those expected due to low compliance to colonoscopy. Conclusions: Risk score is superior to risk stratification used in the program, particularly when combined with FIT. Compliance to colonoscopy should be improved to guarantee the effectiveness of CRC screening in the population.

Development and validation of a non-invasive assessment tool for screening prevalent undiagnosed diabetes in middle-aged and elderly Chinese

To develop a non-invasive assessment tool and compare it to other assessment tools among middle-aged and elderly Shanghainese, 15,309 individuals, who were 45-70 years old, not previously diagnosed with diabetes, and from a cross-sectional survey conducted between April 2013 and August 2014 in Shanghai, were selected into this study. The participants were randomly assigned to either the exploratory group or the validation group. Undiagnosed diabetes was defined according to the American Diabetes Association diagnostic criteria, and score points were generated according to the logistic regression coefficients. Age, family history of diabetes, hypertension, overweight/obesity, and central obesity all contributed to the constructed model, the Shanghai Nicheng diabetes screening score, with the area under the receiver-operating characteristic curve (AUC) being 0.654 (95% CI 0.637-0.670) in the exploratory group and 0.669 (95% CI 0.653-0.686) in the validation group. The score value of 6 was the optimal cut-point with the largest Youden's index. When applied to the validation group, our model had a similar discriminative ability to the New Chinese Diabetes Risk Score (AUC: 0.669 vs. 0.662, p = 0.187), and performed better than other screening scores for Chinese. However, our model was inferior to fasting plasma glucose, 2-hour plasma glucose, and glycosylated hemoglobin in detecting prevalent undiagnosed diabetes (AUC: 0.669 (0.653-0.686) vs. 0.881 (0.868-0.894), 0.934 (0.923-0.944), and 0.834 (0.819-0.848), all p < 0.001). Although non-invasive models, based on demographic and clinical information, are advisable in resource-scarce developing areas, regular blood glucose screening is still necessary among those aged 45 or older.

Different Contributions of Dyslipidemia and Obesity to the Natural History of Type 2 Diabetes: 3-Year Cohort Study in China

AIM

It is known that different stages of type 2 diabetes represent distinct pathophysiological changes, but how the spectrum of risk factors varies at different stages is not yet clarified. Hence, the aim of this study was to compare the effect of different metabolic variables on the natural history of type 2 diabetes.

METHODS

A total of 5,213 nondiabetic (normal glucose tolerance (NGT) and prediabetes) Chinese older than 40 years participated this prospective cohort study, and 4,577 completed the 3-year follow-up. Glycemic status was determined by standard oral glucose tolerance test both at enrollment and follow-up visit. Predictors for conversion in glycemic status were studied in a corresponding subcohort using the multiple logistic regression analysis.

RESULTS

The incidence of prediabetes and diabetes of the cohort was 93.6 and 42.2 per 1,000 person-years, respectively. After a 3-year follow-up, 33.1% of prediabetes patients regressed to NGT. The predictive weight of body mass index (BMI), serum triglyceride, total cholesterol, and systolic blood pressure in different paths of conversions among diabetes, prediabetes, and NGT differed. Specifically, BMI was the strongest predictor for regression from prediabetes to NGT, while triglyceride was most prominent for onset of diabetes. One SD increase in serum triglyceride was associated with a 1.29- (95% CI 1.10-1.52; P = 0.002) or 1.12- (95% CI 1.01-1.27; P = 0.039) fold higher risk of diabetes for individuals with NGT or prediabetes, respectively.

CONCLUSION

Risk factors for different stages of diabetes differed, suggesting personalized preventive strategies for individuals with different basal glycemic statuses.

[Development and validation of a screening score for prediabetes and undiagnosed diabetes.]

OBJECTIVE

To develop and validate an easy-to-use risk score to detect prediabetes and undiagnosed diabetes in Mexican population.

MATERIALS AND METHODS

Using information from the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán's cohort study of 10 234 adults, risk factors were identified and included in multiple logistic regression models stratified by sex. The beta coefficients of the final model were multiplied by 10, thus obtaining the weights of each variable in the score.

RESULTS

The proposed score correctly classifies 55.4% of women with undiagnosed diabetes and 57.2% of women with prediabetes or diabetes. While for men it correctly classifies them at 68.6% and 69.9%, respectively.

CONCLUSIONS

We present the design and validation of a risk score stratified by sex, to determine if an adult could have prediabetes or diabetes, in which case laboratory studies should be performed to confirm or not the diagnosis.