Development and Validation of a Risk-Score Model for Type 2 Diabetes: A Cohort Study of a Rural Adult Chinese Population

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

Objective

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

Methods

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

Results

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

Conclusion

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

Associations between eating speed and food temperature and type 2 diabetes mellitus: a cross-sectional study

OBJECTIVE

This study aimed to evaluate the relationship between eating speed and food temperature and type 2 diabetes mellitus (T2DM) in the Chinese population.

METHODS

A cross-sectional survey was conducted between December 2020 to March 2022 from the department of Endocrinology at the Shandong Provincial Hospital. All recruited participants were asked to complete structured questionnaires on their eating behaviors at the time of recruitment. Clinical demographic data such as gender, age, height, weight, familial history of T2DM, prevalence of T2DM and various eating behaviors were collected. Univariate and multivariate logistic regression analyses were used to analyze the associations between eating behaviors and T2DM.

RESULTS

A total of 1,040 Chinese adults were included in the study, including 344 people with T2DM and 696 people without T2DM. Multivariate logistic regression analysis of the general population showed that gender (OR = 2.255, 95% CI: 1.559-3.260, p < 0.001), age (OR = 1.091, 95% CI: 1.075-1.107, p < 0.001), BMI (OR = 1.238, 95% CI: 1.034-1.483, p = 0.020), familial history of T2DM (OR = 5.709, 95% CI: 3.963-8.224, p < 0.001), consumption of hot food (OR = 4.132, 95% CI: 2.899-5.888, p < 0.001), consumption of snacks (OR = 1.745, 95% CI: 1.222-2.492, p = 0.002), and eating speed (OR = 1.292, 95% CI:1.048-1.591, p = 0.016) were risk factors for T2DM.

CONCLUSION

In addition to traditional risk factors such as gender, age, BMI, familial history of T2DM, eating behaviors associated with Chinese culture, including consumption of hot food, consumption of snacks, and fast eating have shown to be probable risk factors for T2DM.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

Background

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

Methods

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test).

Results

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

Conclusions

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01622-5.

Bibliometric Analysis of the Utilisation of FINDRISC in Patients with Diabetes: 2005-2021

Research on risk factors for diabetes (DM) is growing. Identification of these risk factors aims to prevent DM as early as possible. This study intends to identify the utilization of the Finnish diabetes risk score (FINDRISC) and its development using bibliometric analysis. The keywords “FINDRISC AND Diabetes” were used to search for articles published in 2005-2021 in PubMed. A total of 249 articles were analyzed based on the number of publications per year, journals that publish the papers, number of publications by author and year of publication, number of publications by affiliation and year of publication, number of publications by country of origin of authors and year of publication, number of keywords, number of citations, types of articles, specific topics, and theme mapping. The data visualization was obtained from the Scopus database and the VOSviewer and Biblioshiny applications. Despite the increase in publications, the number of publications on FINDRISC in DM patients is still very few per year, with 92.8% being the primary study. Based on clusters of the country of origin, publications are still dominated by researchers from countries in the European region, and the researchers intensely relate to each other through citations. Research themes related to FINDRISC are not limited to DM risk factors. This study is the first study of a bibliometric analysis of the utilization of FINRISC in DM patients. The analysis results can be used to evaluate existing research gaps and identify future research opportunities.

Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation

BACKGROUND

When a predictor variable is measured in similar ways at the derivation and validation setting of a prognostic prediction model, yet both differ from the intended use of the model in practice (i.e., "predictor measurement heterogeneity"), performance of the model at implementation needs to be inferred. This study proposed an analysis to quantify the impact of anticipated predictor measurement heterogeneity.

METHODS

A simulation study was conducted to assess the impact of predictor measurement heterogeneity across validation and implementation setting in time-to-event outcome data. The use of the quantitative prediction error analysis was illustrated using an example of predicting the 6-year risk of developing type 2 diabetes with heterogeneity in measurement of the predictor body mass index.

RESULTS

In the simulation study, calibration-in-the-large of prediction models was poor and overall accuracy was reduced in all scenarios of predictor measurement heterogeneity. Model discrimination decreased with increasing random predictor measurement heterogeneity.

CONCLUSIONS

Heterogeneity of predictor measurements across settings of validation and implementation reduced predictive performance at implementation of prognostic models with a time-to-event outcome. When validating a prognostic model, the targeted clinical setting needs to be considered and analyses can be conducted to quantify the impact of anticipated predictor measurement heterogeneity on model performance at implementation.

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 DM 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 DM and pre-DM in Chinese adults.

METHODS

Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported DM, pre-DM, and other major chronic diseases were included. The outcome was defined as a newly detected DM or pre-DM 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 DM risk models were included for comparison.

RESULTS

The prevalence of newly-diagnosed DM and pre-DM 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 DM and pre-DM. 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 DM and pre-DM in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of DM and pre-DM.

Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population

AIMS

This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population.

METHODS

Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b'= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639-0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671-0.899) and 0.731 (95% CI 0.619-0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753-0.964) and 0.823 (95% CI 0.730-0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767-0.844) and 0.830 (95% CI 0.788-0.872), respectively, with seven as the cut-off point.

CONCLUSIONS

The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population.

Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.

Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

OBJECTIVES

This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).

DATA SOURCES

Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.

STUDY SELECTION

Articles published between December 2011 and October 2019 were considered.

DATA EXTRACTION

For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.

RESULTS

The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.

CONCLUSIONS

Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

The Finnish Diabetes Risk Score (FINDRISC), incident diabetes and low-grade inflammation

AIMS

The FINDRISC was created to predict the development of type 2 diabetes mellitus (T2DM). Since T2DM associates with inflammation we evaluated if the FINDRISC could predict either current or incident T2DM, and elevated high sensitivity C-reactive protein (hs-CRP).

METHODS

41,880 people (age 41.9 ± 9.7 years; 31% female) evaluated between 2008 and 2016 were included. First, the cross-sectional association between the FINDRISC with presence of either T2DM or hs-CRP ≥ 2.0 mg/L was tested. After a 5 ± 3 years follow-up we tested the score predictive value for incident T2DM and inflammation in respectively 10,559 individuals without diabetes and in a subset of 2,816 individuals having no elevated hs-CRP at baseline.

RESULTS

In the cross sectional analysis the FINDRISC was associated with both T2DM (OR 1.24, 95% CI: 1.23-1.26, P < 0.001) and inflammation (OR 1.10, 95% CI: 1.09-1.11, P < 0.001) per FINDRISC unit, as well as in longitudinal analyses (OR 1.17, 95% CI: 1.14-1.20, P < 0.001; and OR 1.04, 95% CI: 1.02-1.07, P < 0.001; respectively, per FINDRISC unit). The C-statistic for incident T2DM and inflammation was 0.79 (95% CI 0.77-0.82) and 0.55 (95% CI 0.53-0.58), respectively.

CONCLUSION

The FINDRISC shows good discrimination for incident T2DM but less for inflammation.

Association of age at menarche with hypertension in rural Chinese women

OBJECTIVE

The study aimed to explore the association of age at menarche with hypertension and whether adiposity and insulin resistance mediated the association in rural Chinese women.

METHODS

We conducted a cross-sectional study enrolling 7518 women (median age 56 years) from a rural Chinese area from 2013 to 2014. Adiposity was measured by BMI and waist circumference, and insulin resistance was measured by the homeostasis model assessment of insulin resistance (HOMA-IR) index. Odds ratios (ORs) and 95% confidence limits (Cls) for the association of age at menarche with hypertension were estimated by using multivariate logistic regression models. The contribution of adiposity and insulin resistance to the association was estimated by mediation analysis.

RESULTS

Among 7518 women, 3187 (42.39%) had hypertension. Age at menarche was inversely associated with hypertension (per additional year of menarche, OR = 0.965, 95% Cl: 0.935-0.995). BMI or waist circumference and HOMA-IR completely mediated the association of age at menarche with hypertension (for BMI and HOMA-IR: total indirect effect: OR = 0.970, 95% Cl: 0.962-0.978 and direct effect: OR = 0.994, 95% Cl: 0.963-1.026; for waist circumference and HOMA-IR: total indirect effect: OR = 0.981, 95% Cl: 0.973-0.988 and direct effect: OR = 0.983, 95% Cl: 0.952-1.014).

CONCLUSION

Early age at menarche was positively associated with hypertension. Adiposity and insulin resistance seemed to be two vital mediators of the association between age at menarche and hypertension in rural Chinese women.

Association of pulse pressure with all-cause and cause-specific mortality

Brachial pulse pressure (PP) was used as a measure of arterial stiffness, and we investigated whether PP was associated with all-cause and cause-specific mortality in a rural Chinese population. A total of 13,223 participants were enrolled in the Rural Chinese Cohort Study during 2007-2008 and followed up in 2013-2014. Data were collected by questionnaire interview, anthropometric, and laboratory measurements. A multivariate Cox proportional-hazard model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of PP (increased by 1 standard deviation) for all-cause and cause-specific mortality. Subgroup analyses were conducted by sex and age. During a mean follow-up of 5.96 years, the all-cause mortality was 78.61/10000 person-years. The association of PP with all-cause and other causes of mortality was significant, and the adjusted HRs (95% CIs) were 1.16 (1.06-1.28), and 1.18 (1.00-1.40), respectively. On subgroup analyses, PP was positively associated with all-cause and cardiovascular disease (CVD) in participants <65 years or males and positively associated with other causes of mortality in males. The risk of all-cause and other causes of mortality increased with increasing PP in a rural Chinese population. Higher PP may increase the risk of all-cause and CVD mortality for males and people <65 years as well as the risk of other causes of mortality for males in rural Chinese people.

Adiposity and insulin resistance as mediators between age at menarche and type 2 diabetes mellitus

OBJECTIVE

This study aimed to explore the association between age at menarche and type 2 diabetes mellitus (T2DM) and whether the association is mediated by adiposity and insulin resistance (IR) in rural Chinese women.

METHODS

This cross-sectional study analyzed data for 7,460 women (median age 56 y) from a rural Chinese area from 2013 to 2014. Data were collected by standardized interviews and anthropometric and laboratory measurements. Adiposity was measured by body mass index (BMI), and IR was measured by the homeostasis model assessment of IR (HOMA-IR) index. Multivariate logistic regression models were used to estimate odds ratios (ORs) and 95% confidence limits (CLs) for the association between age at menarche and T2DM. Mediation analysis was performed to explore the contribution of BMI and HOMA-IR to the association between age at menarche and T2DM.

RESULTS

Among 7,460 women, 840 (11.26%) had T2DM. After adjusting for potential confounding factors, the odds of T2DM with the latest age at menarche 18 years or older versus 13 years was reduced (OR = 0.65, 95% CL: 0.47, 0.91), and age at menarche was negatively associated with T2DM (per additional year of menarche, OR = 0.95, 95% CL: 0.91, 0.99). BMI and HOMA-IR completely mediated the association between age at menarche and T2DM (total indirect effect: OR = 0.973, 95% CL: 0.961, 0.986; direct effect: OR = 0.974, 95% CL: 0.930, 1.021).

CONCLUSIONS

Late menarche may be negatively associated with T2DM. The potential mechanism is adiposity and IR completely mediating the association between age at menarche and T2DM.

Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University

BACKGROUND

Risk scores were mainly proved to predict undiagnosed type 2 diabetes mellitus (UT2DM) in a non-invasive manner and to guide earlier clinical treatment. The objective of the present study was to assess the performance of the Finnish Diabetes Risk Score (FINDRISC) for detecting three outcomes: UT2DM, prediabetes, and the metabolic syndrome (MS).

METHODS

This was a prospective, cross-sectional study during which employees aged between 30 and 64, with no known diabetes and working within the faculties of the Lebanese University (LU) were conveniently recruited. Participants completed the FINDRISC questionnaire and their glucose levels were examined using both fasting blood glucose (FBG) and oral glucose tolerance tests (OGTT). Furthermore, they underwent lipid profile tests with anthropometry.

RESULTS

Of 713 subjects, 397 subjects (55.2% female; 44.8% male) completed the blood tests and thus were considered as the sample population. 7.6% had UT2DM, 22.9% prediabetes and 35.8% had MS, where men had higher prevalence than women for these 3 outcomes (P = 0.001, P = 0.003 and P = 0.001) respectively. The AUROC value with 95% Confidence Interval (CI) for detecting UT2DM was 0.795 (0.822 in men and 0.725 in women), 0.621(0.648 in men and 0.59 in women) for prediabetes and 0.710 (0.734 in men and 0.705 in women) for MS. The correspondent optimal cut-off point for UT2DM was 11.5 (sensitivity = 83.3% and specificity = 61.3%), 9.5 for prediabetes (sensitivity = 73.6% and specificity = 43.1%) and 10.5 (sensitivity = 69.7%; specificity = 56.5%) for MS.

CONCLUSION

The FINDRISC can be considered a simple, quick, inexpensive, and non-invasive instrument to use in a Lebanese community of working people who are unaware of their health status and who usually report being extremely busy because of their daily hectic work for the screening of UT2DM and MS. However, it poorly screens for prediabetes in this context.

Development and validation of prediction models for hypertension risks in rural Chinese populations

Background

Various hypertension predictive models have been developed worldwide; however, there is no existing predictive model for hypertension among Chinese rural populations.

Methods

This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively.

Results

During the follow-up period, 432 men and 604 women developed hypertension in the training set. Assessment for established models in men suggested men office-based model (M1) was better than others. C-index of M1 model in the testing set was 0.771 (95% confidence Interval (CI) = 0.750, 0.791), and calibration χ² = 6.3057 (P = 0.7090). In women, women office-based model (W1) and ANN were better than the other models assessed. The C-indexes for the W1 model and the ANN model in the testing set were 0.765 (95% CI = 0.746, 0.783) and 0.756 (95% CI = 0.737, 0.775) and the calibrations χ² were 6.7832 (P = 0.1478) and 4.7447 (P = 0.3145), respectively.

Conclusions

Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.

Validation of DIABSCORE in screening for Type 2 Diabetes and prediabetes in Tunisian population

AIMS

To perform a validation of DIABSCORE in a sample of Tunisian adults and find out the optimal cut-off point for screening of Type 2 diabetes (T2D) and prediabetes.

METHODS

225 adults 18-75 years and a subgroup of 138 adults (18-54 years), with undiagnosed T2D from the region of Cap-Bon, Tunisia were included in the present study. The DIABSCORE was calculated based on: age, waist/height ratio, family history of T2D and gestational diabetes. Receiver operating characteristics (ROC) curves and areas under curve (AUC) were obtained. The T2D and prediabetes prevalences odds ratios (OR) between patients exposed and not exposed to DIABSCORE≥90 and DIABSCORE≥80, respectively were calculated in both age ranges.

RESULTS

For screening of T2D the best value was DIABSCORE = 90 with a highest sensitivity (Se), negative predictive value (NPV) and lower negative likelihood ratio in participants aged 18-75 yr (Se = 97%; NPV = 97%) when compared to participants aged 18-54 yr (Se = 95%; NPV = 97%); for prediabetes, the best Se and NPV were for DIABSCORE = 80 in both age groups, but it showed a disbalanced sensitivity-specificity. The ROC curves for T2D showed a similar AUC in both age ranges (AUC = 0.62 and AUC = 0.61 respectively). The ROC curves for prediabetes showed a highest AUC in those aged 18-54 years than the older ones (AUC = 0.62 and AUC = 0.57, respectively). The prevalences OR of T2D for DIABSCORE≥90 was higher than for DIABSCORE≥80 in both age ranges. Nevertheless, the prevalences OR of prediabetes for DIABSCORE≥90 was half of the detected for DIABSCORE≥80 in both age ranges.

CONCLUSION

The DIABSCORE is a simple clinical tool and accurate method in screening for T2D and prediabetes in the adult Tunisian population.

A Mobile App for Identifying Individuals With Undiagnosed Diabetes and Prediabetes and for Promoting Behavior Change: 2-Year Prospective Study

BACKGROUND

To decrease the burden of diabetes in society, early screening of undiagnosed diabetes and prediabetes is needed. Integrating a diabetes risk score into a mobile app would provide a useful platform to enable people to self-assess their risk of diabetes with ease.

OBJECTIVE

The objectives of this study were to (1) assess the profile of Diabetes Risk Score mobile app users, (2) determine the optimal cutoff value of the Finnish Diabetes Risk Score to identify undiagnosed diabetes and prediabetes in the Chinese population, (3) estimate users' chance of developing diabetes within 2 years of using the app, and (4) investigate high-risk app users' lifestyle behavior changes after ascertaining their risk level from the app.

METHODS

We conducted this 2-phase study among adults via mobile app and online survey from August 2014 to December 2016. Phase 1 adopted a cross-sectional design, with a descriptive analysis of the app users' profile. We used a Cohen kappa score to show the agreement between the risk level (as shown in the app) and glycated hemoglobin test results. We used sensitivity, specificity, and area under the curve to determine the optimal cutoff value of the diabetes risk score in this population. Phase 2 was a prospective cohort study. We used a logistic regression model to estimate the chance of developing diabetes after using the app. Paired t tests compared high-risk app users' lifestyle changes.

RESULTS

A total of 13,289 people used the app in phase 1a. After data cleaning, we considered 4549 of these as valid data. Most users were male, and 1811 (39.81%) had tertiary education or above. Among them, 188 (10.4%) users agreed to attend the health assessment in phase 1b. We recommend the optimal value of the diabetes risk score for identifying persons with undiagnosed diabetes and prediabetes to be 9, with an area under the receiver operating characteristic curve of 0.67 (95% CI 0.60-0.74), sensitivity of 0.70 (95% CI 0.58-0.80), and specificity of 0.57 (95% CI 0.47-0.66). At the 2-year follow-up, people in the high-risk group had a higher chance of developing diabetes (odds ratio 4.59, P=.048) than the low-risk group. The high-risk app users improved their daily intake of vegetables (baseline: mean 0.76, SD 0.43; follow-up: mean 0.93, SD 0.26; t₈₁=-3.77, P<.001) and daily exercise (baseline: mean 0.40, SD 0.49; follow-up: mean 0.54, SD 0.50; t₈₁=-2.08, P=.04).

CONCLUSIONS

The Diabetes Risk Score app has been shown to be a feasible and reliable tool to identify persons with undiagnosed diabetes and prediabetes and to predict diabetes incidence in 2 years. The app can also encourage high-risk people to modify dietary habits and reduce sedentary lifestyle.

Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population

This research aims to assess application of different cardiovascular disease (CVD) mortality risk prediction models in Chinese rural population. Data was collected from a 6-year follow-up survey in rural area of Henan Province, China. 10338 participants aged 40 to 65 years were included. Baseline study was conducted between 2007 and 2008, and followed up from 2013 to 2014. Seven models: general Framingham risk score (general-FRS), simplified-FRS, Systematic Coronary Risk Evaluation for high (SCORE-high), SCORE-low, Chinese ischemic CVD (CN-ICVD), Pooled Cohort Risk Equation for white (PCE-white) and for African-American (PCE-AA) were assessed and recalibrated. The model performance was evaluated by C-statistics and modified Nam-D’Agostino test. 168 CVD deaths occurred during follow-up. All seven models showed moderate C-statics ranging from 0.727 to 0.744. Following recalibration, general-FRS, simplified-FRS, CN-ICVD, PCE-white and PCE-AA had improved C-statistics of 0.776, 0.795, 0.793, 0.779, and 0.776 for men and 0.756, 0.753, 0.755, 0.758 and 0.760 for women, respectively. Calibrations χ² of general-FRS, simplified-FRS, SCORE-high, CN-ICVD and PCE-AA model for men, and general-FRS, CN-ICVD and PCE-white model for women were statistically acceptable, indicating these models predicts CVD mortality risk more accurately than others and could be recommended in Chinese rural population.

Low levels of ApoA1 improve risk prediction of type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) has reported to be a major public health crisis in China.

OBJECTIVE

We examined the incidence of new T2DM over 4 years for association of clinical factors and lipids with development of T2DM in a community-based population.

METHODS

We included 923 Chinese subjects who participated in community-organized health checkout in both 2009 and 2013. Health history was collected; physical examination was performed; biochemistry, lipids, and glucose were measured. Of 923, 819 were confirmed without T2DM in 2009 and included in the analysis. Unadjusted and adjusted logistic regression models were used to estimate the effects of clinical factors and biomarkers on the risk of new T2DM.

RESULTS

Of 819 subjects without T2DM in 2009, 65 were identified as T2DM in 2013, 8% over 4 years. These 65 subjects, compared with those 754 without new T2DM, were older, more likely to be male and smokers. They had higher body mass index (BMI), fasting glucose, blood pressure and triglycerides, and lower levels of high-density lipoprotein-cholesterol and apolipoprotein A1 (ApoA1). Multivariate logistic regression identified larger BMI (odds ratio [OR] = 1.7; 95% confidence interval [CI], 1.22-2.39, P = .002), higher fasting glucose levels (OR = 4.2, 95% CI, 2.90-6.19, P < .001), and low levels of ApoA1 (OR = 0.51, 95% CI 0.33-0.76, P = .002) were independently associated with new T2DM. Furthermore, receiver operating characteristics curves for multivariate models for new T2DM showed that area under the curve improved from 0.87 to 0.89 when adding ApoA1 to the Framingham Diabetes Risk Scoring Model and from 0.85 to 0.89 when adding ApoA1 to a 4-variable (age, BMI, glucose, and triglycerides) Chinese model.

CONCLUSIONS

There is a high incidence of new T2DM at 8% over 4 years among Chinese. Larger BMI, higher glucose levels, and lower levels of ApoA1 are significantly and independently associated with new T2DM. Lower ApoA1 improves the risk prediction of new type 2 diabetes when it was added to the existing risk models.

Body mass index and waist circumference combined predicts obesity-related hypertension better than either alone in a rural Chinese population

Limited information is available on the association of obesity defined by both body mass index (BMI) and waist circumference (WC) with incident hypertension in rural China. A total of 9,174 participants ≥18 years old from rural areas in middle of China, free of hypertension, diabetes, myocardial infarction and stroke, were selected in this cohort study. Questionnaire interview and anthropometric and laboratory measurements were performed at baseline (2007–2008) and follow-up (2013–2014). During the 6 years of follow-up, hypertension developed in 733/3,620 men and 1,051/5,554 women. After controlling for age, education level, smoking, drinking, physical activity, and family history of hypertension, the relative risk of hypertension was lower for participants with high BMI but normal WC than those with both BMI and WC obesity for men 18–39 and 40–59 years old. Women 18–39 years old with normal BMI but high WC showed a 1.96-fold risk of hypertension, and being female with age 40–59 years and high BMI but normal WC was independently associated with hypertension incidence as compared with both normal BMI and WC. BMI is more associated with hypertension as compared with WC in both genders. High WC tends to add additional risk of hypertension in young women.