A prediction model for type 2 diabetes risk among Chinese people

The proper application of logistic regression model in complex survey data: a systematic review

BACKGROUND

Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when applying logistic regression to complex survey data, which includes complex sampling designs, specific methodological issues are often overlooked.

METHODS

The systematic review extensively searched the PubMed and ScienceDirect databases from January 2015 to December 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focusing primarily on the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). 810 articles met the inclusion criteria and were included in the analysis. When discussing logistic regression, the review considered multiple methodological problems such as the model adequacy assessment, handling dependence of observations, utilization of complex survey design, dealing with missing values, outliers, and more.

RESULTS

Among the selected articles, the DHS database was used the most (96%), with MICS accounting for only 3%, and both DHS and MICS accounting for 1%. Of these, it was found that only 19.7% of the studies employed multilevel mixed-effects logistic regression to account for data dependencies. Model validation techniques were not reported in 94.8% of the studies with limited uses of the bootstrap, jackknife, and other resampling methods. Moreover, sample weights, PSUs, and strata variables were used together in 40.4% of the articles, and 41.7% of the studies did not use any of these variables, which could have produced biased results. Goodness-of-fit assessments were not mentioned in 75.3% of the articles, and the Hosmer-Lemeshow and likelihood ratio test were the most common among those reported. Furthermore, 95.8% of studies did not mention outliers, and only 41.0% of studies corrected for missing information, while only 2.7% applied imputation techniques.

CONCLUSIONS

This systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey design, and proper validation techniques, along with neglecting outliers, missing data, and goodness-of-fit assessments, all of which point to the need for clearer methodological standards and more thorough reporting to improve the reliability of results. Future research should focus on consistently following these standards to ensure stronger and more dependable findings.

The Association Between the Triglyceride to High-Density Lipoprotein Cholesterol Ratio and the Incidence of Type 2 Diabetes Mellitus in the Japanese Population

Abstract Aims: To explore whether the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) was independently associated with the risk of incident type 2 diabetes mellitus (T2DM) in a large Japanese cohort. Methods: A secondary analysis was performed using open-access data from a retrospective cohort study. A total of 12,716 eligible participants who had standard medical examinations at the Murakami Memorial Hospital were included in this study. New-onset T2DM was the main outcome during follow-up. The risk of T2DM based on the TG/HDL-C ratio was evaluated using Cox regression analysis and Kaplan-Meier analysis. Subgroup analysis was performed to understand further the significance of the TG/HDL-C ratio in particular populations. To assess the potential of the TG/HDL-C ratio for predicting T2DM, a receiver operating characteristic (ROC) analysis was performed. Results: One hundred fifty new-onset T2DM cases were observed during a median follow-up of 5.39 years. The incidence of T2DM increased with a rise in the TG/HDL-C ratio based on the Kaplan-Meier curves (P < 0.0001). After controlling for potential confounding variables, the TG/HDL-C ratio was positively related to incidence of T2DM (hazard ratio = 1.08, 95% confidence interval: 1.01-1.15, P = 0.0239). In subgroup analysis, those with a body mass index of ≥18.5 and <24 kg/m2 showed a significantly positive relationship. The area under the ROC curve for the TG/HDL-C ratio as a T2DM predictor was 0.684. The optimal TG/HDL-C ratio cutoff value for T2DM was 1.609, with a sensitivity of 54.7% and a specificity of 73.6%. Conclusion: The authors' results showed a significant relationship between the TG/HDL-C ratio and the incidence of T2DM in the Japanese population.

Enhanced prediction of abnormal glucose tolerance using an extended non-invasive risk score incorporating routine renal biochemistry

INTRODUCTION

Type 2 diabetes is preventable in subjects with impaired glucose tolerance based on 2-hour plasma glucose (2hPG) during 75 g oral glucose tolerance test (OGTT). We incorporated routine biochemistry to improve the performance of a non-invasive diabetes risk score to identify individuals with abnormal glucose tolerance (AGT) defined by 2hPG≥7.8 mmol/L during OGTT.

RESEARCH DESIGN AND METHODS

We used baseline data of 1938 individuals from the community-based "Better Health for Better Hong Kong - Hong Kong Family Diabetes Study (BHBHK-HKFDS) Cohort" recruited in 1998-2003. We incorporated routine biochemistry in a validated non-invasive diabetes risk score, and evaluated its performance using area under receiver operating characteristics (AUROC) with internal and external validation.

RESULTS

The AUROC of the original non-invasive risk score to predict AGT was 0.698 (95% CI, 0.662 to 0.733). Following additional inclusion of fasting plasma glucose, serum potassium, creatinine, and urea, the AUROC increased to 0.778 (95% CI, 0.744 to 0.809, p<0.001). Net reclassification improved by 31.9% (p<0.001) overall, by 30.8% among people with AGT and 1.1% among people without AGT. The extended model showed good calibration (χ2=11.315, p=0.1845) and performance on external validation using an independent data set (AUROC=0.722, 95% CI, 0.680 to 0.764).

CONCLUSIONS

The extended risk score incorporating clinical and routine biochemistry can be integrated into an electronic health records system to select high-risk subjects for evaluation of AGT using OGTT for prevention of diabetes.

Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data

BACKGROUND

An 8-year prediction of the Framingham Diabetes Risk Model (FDRM) was proposed, but the predictor has a gap with current clinical standards. Therefore, we evaluated the validity of the original FDRM in Korean population data, developed a modified FDRM by redefining the predictors based on current knowledge, and evaluated the internal and external validity.

METHODS

Using data from a community-based cohort in Korea (n = 5,409), we calculated the probability of diabetes through FDRM, and developed a modified FDRM based on modified definitions of hypertension (HTN) and diabetes. We also added clinical features related to diabetes to the predictive model. Model performance was evaluated and compared by area under the curve (AUC).

RESULTS

During the 8-year follow-up, the cumulative incidence of diabetes was 8.5%. The modified FDRM consisted of age, obesity, HTN, hypo-high-density lipoprotein cholesterol, elevated triglyceride, fasting glucose, and hemoglobin A1c. The expanded clinical model added γ-glutamyl transpeptidase to the modified FDRM. The FDRM showed an estimated AUC of 0.71, and the model's performance improved to an AUC of 0.82 after applying the redefined predictor. Adding clinical features (AUC = 0.83) to the modified FDRM further improved in discrimination, but this was not maintained in the validation data set. External validation was evaluated on population-based cohort data and both modified models performed well, with AUC above 0.82.

CONCLUSION

The performance of FDRM in the Korean population was found to be acceptable for predicting diabetes, but it was improved when corrected with redefined predictors. The validity of the modified model needs to be further evaluated.

An early prediction model for type 2 diabetes mellitus based on genetic variants and nongenetic risk factors in a Han Chinese cohort

Aims

We aimed to construct a prediction model of type 2 diabetes mellitus (T2DM) in a Han Chinese cohort using a genetic risk score (GRS) and a nongenetic risk score (NGRS).

Methods

A total of 297 Han Chinese subjects who were free from type 2 diabetes mellitus were selected from the Tianjin Medical University Chronic Disease Cohort for a prospective cohort study. Clinical characteristics were collected at baseline and subsequently tracked for a duration of 9 years. Genome-wide association studies (GWASs) were performed for T2DM-related phenotypes. The GRS was constructed using 13 T2DM-related quantitative trait single nucleotide polymorphisms (SNPs) loci derived from GWASs, and NGRS was calculated from 4 biochemical indicators of independent risk that screened by multifactorial Cox regressions.

Results

We found that HOMA-IR, uric acid, and low HDL were independent risk factors for T2DM (HR >1; P<0.05), and the NGRS model was created using these three nongenetic risk factors, with an area under the ROC curve (AUC) of 0.678; high fasting glucose (FPG >5 mmol/L) was a key risk factor for T2DM (HR = 7.174, P< 0.001), and its addition to the NGRS model caused a significant improvement in AUC (from 0.678 to 0.764). By adding 13 SNPs associated with T2DM to the GRS prediction model, the AUC increased to 0.892. The final combined prediction model was created by taking the arithmetic sum of the two models, which had an AUC of 0.908, a sensitivity of 0.845, and a specificity of 0.839.

Conclusions

We constructed a comprehensive prediction model for type 2 diabetes out of a Han Chinese cohort. Along with independent risk factors, GRS is a crucial element to predicting the risk of type 2 diabetes mellitus.

Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts

AIMS

To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c.

METHODS

We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720).

RESULTS

The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012).

CONCLUSIONS

Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

Development and validation of risk prediction models for new-onset type 2 diabetes in adults with impaired fasting glucose

AIMS

To develop and validate sex-specific risk prediction models based on easily obtainable clinical data for predicting 5-year risk of type 2 diabetes (T2D) among individuals with impaired fasting glucose (IFG), and generate practical tools for public use.

METHODS

The data used for model training and internal validation came from a large prospective cohort (N = 18,384). Two independent cohorts were used for external validation. A two-step approach was applied to screen variables. Coefficient-based models were constructed by multivariate Cox regression analyses, and score-based models were subsequently generated. The predictive power was evaluated by the area under the curve (AUC).

RESULTS

During a median follow-up of 7.55 years, 5697 new-onset T2D cases were identified. Predictor variables included age, body mass index, waist circumference, diastolic blood pressure, triglycerides, fasting plasma glucose, and fatty liver. The proposed models outperformed five existing models. In internal validation, the AUCs of the coefficient-based models were 0.741 (95% CI 0.723-0.760) for men and 0.762 (95% CI 0.720-0.802) for women. External validation yielded comparable prediction performance. We finally constructed a risk scoring system and a web calculator.

CONCLUSIONS

The risk prediction models and derived tools had well-validated performance to predict the 5-year risk of T2D in IFG adults.

Association of four lipid-derived indicators with the risk of developing type 2 diabetes: a Chinese population-based cohort study

BACKGROUND

Studies have reported that lipid-derived indicators are associated with type 2 diabetes (T2D) in various populations; however, it is unclear which lipid-derived indicators could effectively predict T2D risk. Therefore, this study aimed to explore the association between four lipid-derived indicators and T2D risk.

METHODS

This was a post-hoc analysis from a large cohort that included data from 114,700 Chinese individuals aged 20 years and older from 11 cities and 32 sites. The association between four lipid-derived indicators and T2D risk was determined using Kaplan-Meier (KM) survival curves, Cox regression, and restricted cubic spline analyses. This study used receiver operating characteristic (ROC) curves for assessing the ability of four lipid-derived indicators to accurately predict the development of T2D during follow-up.

RESULTS

This study included a total of 114,700 participants, with a mean age of 44.15. These individuals were followed up for 3.1 years, of which 2668 participants developed T2D. ROC curve analysis showed that TyG was the most robust predictor of 3-year [aera under the ROC (AUC) = 0.77, 95% CI: 0.768, 0.772] and 5-year T2D risk (AUC = 0.763, 95% CI: 0.760, 0.765). In addition, sensitivity analysis showed an association between TyG and an increased incidence of T2D.

CONCLUSIONS

The results suggest that TyG was a superior for predicting the risk of developing T2D in the general Chinese population.

External validation of non-invasive diabetes score in a 15-year prospective study

BACKGROUND

A novel scoring system called Non-invasive Diabetes Score (NDS) was developed. The model showed prominent discrimination and calibration in the original study population. However, before a new model could be adopted in clinical practice and acquire widespread use, it is necessary to confirm that it also performs well in external validations in different settings of people. The aim of this study was to investigate whether the novel user-friendly score predicting diabetes mellitus (DM) could have satisfying performance in predicting DM in Southwest China in a 15-year prospective cohort study.

METHODS

This prospective cohort study was carried out based on a general Chinese population of 711 individuals from 1992 to 2007. We excluded 24 of them at baseline because they were diabetics. The end point was DM, and the risk was calculated using the model formula.

RESULTS

During a follow-up of 15 years, 74 (10.77%) patients reached the end point. Evaluation of this model in our cohort, with Harrell's C-index of 0.662 (95% CI: 0.600-0.723) for the whole cohort and 0.695 (95% CI: 0.635-0.756) in sensitivity analysis, indicated the possibly helpful discrimination. The calibration capability in our cohort was useful that the observed incidence of diabetes mellitus was near the predicted.

CONCLUSIONS

Our external validation suggested NDS had possibly helpful discrimination and satisfying calibration for predicting DM during 15-year follow-up.

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.

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.

Temporal sequence of blood lipids and insulin resistance in perimenopausal women: the study of women's health across the nation

INTRODUCTION

To explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women.

RESEARCH DESIGN AND METHODS

The longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women's Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR).

RESULTS

Three latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, -0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (-0.091, P < 0.001) and HOMA-IR→RCT (-0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (-0.028, p=0.113) were not. The sensitivity analyses showed consistent results.

CONCLUSIONS

These findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.

Screening for diabetes and impaired glucose metabolism in Qatar: Models' development and validation

AIM

To establish two scoring models for identifying individuals at risk of developing Impaired Glucose Metabolism (IGM) or Type two Diabetes Mellitus (T2DM) in Qatari.

MATERIALS AND METHODS

A sample of 2000 individuals, from Qatar BioBank, was evaluated to determine features predictive of T2DM and IGM. Another sample of 1000 participants was obtained for external validation of the models. Several scoring models screening for T2DM were evaluated and compared to the model proposed by this study.

RESULTS

Age, gender, waist-to-hip-ratio, history of hypertension and hyperlipidemia, and levels of educational were statistically associated with the risk of T2DM and constituted the Qatar diabetes mellitus risk score (QDMRISK). Along with, the 6 aforementioned variables, the IGM model showed that BMI was statistically significant. The QDMRISK performed well with area under the curve (AUC) 0.870 and .815 in the development and external validation cohorts, respectively. The QDMRISK showed overall better accuracy and calibration compared to other evaluated scores. The IGM model showed good accuracy and calibration, with AUCs .796 and .774 in the development and external validation cohorts, respectively.

CONCLUSIONS

This study developed Qatari-specific diabetes and IGM risk scores to identify high risk individuals and can guide the development of a nationwide primary prevention program.

Triglyceride to High-Density Lipoprotein Cholesterol (TG/HDL-C) Ratio, a Simple but Effective Indicator in Predicting Type 2 Diabetes Mellitus in Older Adults

BACKGROUND

A simple and readily available biomarker can provide an effective approach for the surveillance of type 2 diabetes mellitus (T2DM) in the elderly. In this research, we aim to evaluate the role of triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio as an indicator for new-onset T2DM in an elderly Chinese population aged over 75 years.

METHODS

This longitudinal retrospective cohort study was conducted using a free database from a health check screening project in China. Participants with baseline TG and HDL measurements were enrolled, and the data of T2DM development were collected. The cumulative incident T2DM rates in different quintile groups of TG/HDL-C ratio (Q1 to Q5) were calculated and plotted. The independent effect of baseline TG/HDL-C ratio on T2DM risk during the follow-up period was tested by the Cox proportional hazard model. Subgroup analysis was also conducted to clarify the role of TG/HDL-C ratio in specific populations.

RESULTS

A total of 231 individuals developed T2DM among 2,571 subjects aged over 75 years during follow-up. Regardless of adjustment for potential confounding variables, elevated TG/HDL-C ratio independently indicated a higher risk of incident T2DM [hazard ratio (HR) = 1.29; 95% confidence interval (CI), 1.14-1.47; P < 0.01. As compared with the lowest quintile (Q1), elevated TG/HDL-C ratio quintiles (Q2 to Q5) were associated with larger HR estimates of incident T2DM [HR (95% CI), 1.35 (0.85-2.17), 1.31 (0.83-2.06), 1.85 (1.20-2.85), and 2.10 (1.38-3.20), respectively]. In addition, a non-linear correlation was found between TG/HDL-C ratio and the risk of T2DM, and the slope of the curve decreased after the cutoff point of 2.54. Subgroup analysis revealed a stronger positive correlation among male individuals and those with body mass index <24 kg/m².

CONCLUSIONS

Increased TG/HDL-C ratio indicates a greater risk of new-onset T2DM regardless of confounding variables. TG/HDL-C ratio is a simple but effective indicator in predicting T2DM in older adults. More future investigations are warranted to further promote the clinical application of TG/HDL-C ratio.

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.

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.

Prediction model for the onset risk of impaired fasting glucose: a 10-year longitudinal retrospective cohort health check-up study

BACKGROUND

Impaired fasting glucose (IFG) is a prediabetic condition. Considering that the clinical symptoms of IFG are inconspicuous, these tend to be easily ignored by individuals, leading to conversion to diabetes mellitus (DM). In this study, we established a prediction model for the onset risk of IFG in the Chongqing health check-up population to provide a reference for prevention in a health check-up cohort.

METHODS

We conducted a retrospective longitudinal cohort study in Chongqing, China from January 2009 to December 2019. The qualified subjects were more than 20 years old and had more than two health check-ups. After following the inclusion and exclusion criteria, the cohort population was randomly divided into a training set and a test set at a ratio of 7:3. We first selected the predictor variables through the univariate generalized estimation equation (GEE), and then the training set was used to establish the IFG risk model based on multivariate GEE. Finally, the sensitivity, specificity, and receiver operating characteristic curves were used to verify the performance of the model.

RESULTS

A total of 4,926 subjects were included in this study, with an average of 3.87 check-up records, including 2,634 males and 2,292 females. There were 442 IFG cases during the follow-up period, including 286 men and 156 women. The incidence density was 26.88/1000 person-years for men and 18.53/1000 person-years for women (P<0.001). The predictor variables of our prediction model include male (relative risk (RR) =1.422, 95 % confidence interval (CI): 0.923-2.193, P=0.3849), age (RR=1.030, 95 %CI: 1.016-1.044, P<0.0001), waist circumference (RR=1.005, 95 %CI: 0.999-1.012, P=0.0975), systolic blood pressure (RR=1.004, 95 %CI: 0.993-1.016, P=0.4712), diastolic blood pressure (RR=1.023, 95 %CI: 1.005-1.041, P=0.0106), obesity (RR=1.797, 95 %CI: 1.126-2.867, P=0.0140), triglycerides (RR=1.107, 95 %CI: 0.943-1.299, P=0.2127), high-density lipoprotein cholesterol (RR=0.992, 95 %CI: 0.476-2.063, P=0.9818), low-density lipoprotein cholesterol (RR=1.793, 95 %CI: 1.085-2.963, P=0.0228), blood urea (RR=1.142, 95 %CI: 1.022-1.276, P=0.0192), serum uric acid (RR=1.004, 95 %CI: 1.002-1.005, P=0.0003), total cholesterol (RR=0.674, 95 %CI: 0.403-1.128, P=0.1331), and serum creatinine levels (RR=0.960, 95 %CI: 0.945-0.976, P<0.0001). The area under the receiver operating characteristic curve (AUC) in the training set was 0.740 (95 %CI: 0.712-0.768), and the AUC in the test set was 0.751 (95 %CI: 0.714-0.817).

CONCLUSIONS

The prediction model for the onset risk of IFG had good predictive ability in the health check-up cohort.

Triglyceride to high-density lipoprotein cholesterol ratio variability and incident diabetes: A 7-year prospective study in a Chinese population

AIMS/INTRODUCTION

The correlation between triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio variability and incident diabetes has not been fully elucidated. We aimed to characterize the relationship between TG/HDL-C ratio variability and new-onset diabetes in Chinese adults.

MATERIALS AND METHODS

A total of 45,911 patients with three TG and HDL measurements between 2006 and 2011 were enrolled. Average real variability (ARV) were used to evaluate variability, and participants were grouped according to tertiles of TG/HDL-ARV.

RESULTS

There were 3,724 cases of incident diabetes mellitus during the observation period (6.24 ± 1.2 years). The 7-year cumulative incidences of diabetes mellitus in tertiles 1, 2 and 3 were 6.13%, 8.09% and 11.77%, respectively. New-onset diabetes increased with the tertiles of TG/HDL-ARV. This association was further confirmed after adjustment for mean TG/HDL-C ratio, TG/HDL-C ratio change slope, fasting plasma glucose variability (ARV) and other traditional risk factors for diabetes, the hazard ratio value for incident diabetes was 1.38 (1.25-1.50) for the highest tertile, and risk of diabetes increases by 4% with a one standard deviation increase in TG/HDL-C ratio variability. Restricted cubic splines showed a dose-response relationship between TG/HDL-C ratio variability and incident diabetes. Similar results were obtained in various subgroup and sensitivity analyses.

CONCLUSIONS

High TG/HDL-C variability was associated with a higher risk of diabetes in Chinese adults, independent of the direction of TG/HDL-C variability.

Epidemiology of chronic kidney disease of undetermined aetiology in Taiwanese farmers: a cross-sectional study from Changhua Community-based Integrated Screening programme

OBJECTIVES

Chronic kidney disease of undetermined or non-traditional aetiology (CKDu or CKDnT) has been reported in Mesoamerica among farmers under heat stress. Epidemiological evidence was lacking in Asian countries with similar climatic conditions. The objective of this study was to investigate the prevalence of CKDu and possible risk factors.

METHODS

We used the data from the Changhua Community-based Integrated Screening programme from 2005 to 2014, which is the annual screening for chronic diseases in Taiwan's largest rice-farming county since 2005. Our study population included farmers and non-farmers aged 15-60 years. CKDu was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² at age under 60 years without hypertension, diabetes, proteinuria, haematuria or using Chinese herbal medicine. We estimated the adjusted prevalence OR (POR) of CKDu by farmers, age, sex, education, urbanisation, smoking, body mass index, hyperuricaemia, hyperlipidaemia, heart disease and chronic liver disease.

RESULTS

5555 farmers and 35 761 non-farmers were included in this study. CKDu accounted for 48.9% of all CKD cases. The prevalence of CKDu was 2.3% in the farmers and 0.9% in the non-farmers. The crude POR of CKDu in farmers compared with non-farmers was 2.73 (2.13-3.50), and the adjusted POR was 1.45 (1.10-1.90). Dehydration (blood urea nitrogen-to-creatinine ratio >20) was found in 22% of the farmers and 14% of the non-farmers.

CONCLUSIONS

Farmers in subtropical Asian countries are at increased risk of CKDu. Governments should take the CKDu epidemics seriously and provide farmers with occupational health education programmes on thermal hazards.

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

Blood lipids-related dietary patterns derived from reduced rank regression are associated with incident type 2 diabetes

BACKGROUND & AIMS

Blood lipids play a critical role in the pathogenesis of type 2 diabetes, and they are closely related to dietary factors. However, the associations between blood lipids-related dietary patterns and risk of type 2 diabetes are controversial and not fully clear. In this study, we aimed to derive dietary patterns that explained variation in blood lipids and to investigate their associations with incident type 2 diabetes.

METHODS

The analysis was based on 39,000 women and 25,777 men participating in the Lifelines cohort study (aged 18-65 years, mean 43.2 years for women and 43.5 years for men). Dietary intake was measured using a 110-item semi-quantitative food frequency questionnaire. Reduced rank regression was used to derive dietary patterns with blood lipids (HDL-cholesterol, LDL-cholesterol, triglycerides, total cholesterol, and total cholesterol:HDL-cholesterol ratio) as response variables for women and men separately. The first dietary pattern identified for each sex was selected because they explained the largest variance in blood lipids. The associations between the identified dietary patterns and incident type 2 diabetes were subsequently investigated using multivariate logistic regression models. All analyses were performed separately for women and men.

RESULTS

During an average follow-up of 43 months, 479 new cases (incidence 0.74%) of type 2 diabetes were identified. Using reduced rank regression, we identified two sex-specific blood lipids-associated dietary patterns characterized by high intake of sugary beverages, added sugar, and low intake of vegetables, fruits, tea, and nuts/seeds. These two sex-specific dietary patterns were similar in food groups but differed in factor loadings. High dietary pattern scores were associated with increased risk of type 2 diabetes after adjustment for age, total energy intake, body mass index, waist-hip ratio, and blood pressure (ORs for the fifth quintile [Q5] using the first quintile [Q1] as reference, 1.87 [95% CI 1.23, 2.83] for women [P-trend < 0.001], and 1.72 [95% CI 1.11, 2.66] for men [P-trend = 0.018]). The associations were attenuated but remained significant after further adjustment for lifestyle and socio-economic factors.

CONCLUSIONS

Dietary patterns associated with adverse blood lipids are associated with incidence of type 2 diabetes. The present study provides new insights in optimizing blood lipids for the prevention of type 2 diabetes through dietary approaches.

Risk assessment of type 2 diabetes in northern China based on the logistic regression model

BACKGROUND:

Type 2 diabetes mellitus (T2DM) is a complex disease with high incidence and serious harm associated with polygenic determination. This study aimed to develop a predictive model so as to assess the risk of T2DM and apply it to health care and disease prevention in northern China.

OBJECTIVE:

Based on genotyping results, a risk warning model for type 2 diabetes was established.

METHODS:

Blood samples of 1042 patients with T2DM in northern China were collected. Multiplex polymerase chain reaction and high-throughput sequencing (NGS) techniques were used to design the amplification-based targeted sequencing panel to sequence the 21 T2DM susceptibility genes.

RESULT:

The related key gene KQT-like subfamily member 1 played an important role in the T2DM risk model, and single-nucleotide polymorphism rs2237892 was highly significant, with a P value of 1.2 × 10-5.

CONCLUSIONS:

Susceptibility genes in different populations were examined, and a model was developed to assess the risk-based genetic analysis. The performance of the model reached 92.8%.

Diabetes after pregnancy: a study protocol for the derivation and validation of a risk prediction model for 5-year risk of diabetes following pregnancy

BACKGROUND

Pregnancy offers a unique opportunity to identify women at higher future risk of type 2 diabetes mellitus (DM). In pregnancy, a woman has greater engagement with the healthcare system, and certain conditions are more apt to manifest, such as gestational DM (GDM) that are important markers for future DM risk. This study protocol describes the development and validation of a risk prediction model (RPM) for estimating a woman's 5-year risk of developing type 2 DM after pregnancy.

METHODS

Data will be obtained from existing Ontario population-based administrative datasets. The derivation cohort will consist of all women who gave birth in Ontario, Canada between April 2006 and March 2014. Pre-specified predictors will include socio-demographic factors (age at delivery, ethnicity), maternal clinical factors (e.g., body mass index), pregnancy-related events (gestational DM, hypertensive disorders of pregnancy), and newborn factors (birthweight percentile). Incident type 2 DM will be identified by linkage to the Ontario Diabetes Database. Weibull accelerated failure time models will be developed to predict 5-year risk of type 2 DM. Measures of predictive accuracy (Nagelkerke's R2), discrimination (C-statistics), and calibration plots will be generated. Internal validation will be conducted using a bootstrapping approach in 500 samples with replacement, and an optimism-corrected C-statistic will be calculated. External validation of the RPM will be conducted by applying the model in a large population-based pregnancy cohort in Alberta, and estimating the above measures of model performance. The model will be re-calibrated by adjusting baseline hazards and coefficients where appropriate.

DISCUSSION

The derived RPM may help identify women at high risk of developing DM in a 5-year period after pregnancy, thus facilitate lifestyle changes for women at higher risk, as well as more frequent screening for type 2 DM after pregnancy.

A method to validate scoring systems based on logistic regression models to predict binary outcomes via a mobile application for Android with an example of a real case

BACKGROUND AND OBJECTIVES

To use a points system based on a logistic regression model to predict a binary event in a given population, the validation of this system is necessary. The most correct way to do this is to calculate discrimination and calibration using bootstrapping. Discrimination can be addressed through the area under the receiver operating characteristic curve (AUC) and calibration through the representation of the smoothed calibration plot (most recommended method). As this is not a simple task, we developed a methodology to construct a mobile application in Android to perform this task.

METHODS

The construction of the application is based on source code written in language supported by Android. It is designed to use a database of subjects to be analyzed and to be able to apply statistical methods widely used in the scientific literature to validate a points system (bootstrap, AUC, logistic regression models and smooth curves). As an example our methodology was applied on simulated points system data (doi: 10.1111/ijcp.12851) to predict mortality on admission to intensive care units (Google Play: ICU mortality). The results were compared with those obtained applying the same methods in the R statistical package.

RESULTS

No differences were found between the results obtained in the mobile application and those from the R statistical package, an expected result when applying the same mathematical techniques.

CONCLUSIONS

Our methodology may be applied to other point systems for predicting binary events, as well as to other types of predictive models.

Association of TG/HDLC ratio trajectory and risk of type 2 diabetes: A retrospective cohort study in China

BACKGROUND

The association of ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-C ratio) change trajectory with risk of type 2 diabetes mellitus (T2DM) remains unknown. The aim of this study was to evaluate the association between risk of T2DM and TG/HDL-C ratio change trajectory.

METHODS

A total of 18 444 participants aged 18-80 years old were included in this cohort study. Linear regression and quadratic regression models were used to determine the TG/HDL-C ratio change trajectory. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between TG/HDL-C ratio change trajectory and probability of T2DM.

RESULTS

T2DM developed in 714 participants during a median follow-up of 5.74 years (92 076.23 person-years of follow-up). After adjusting for baseline potential confounders, odds of T2DM were greater for participants with the increasing, U-shape, bell-shape, and other shape change vs decreasing change (adjusted OR [aOR] 2.01, 95% CI 1.42-2.81; 1.56, 1.15-2.13; 1.60, 1.17-2.20; and 1.49, 1.13-2.00, respectively). The results were robust in the sensitivity analyses on excluding baseline participants with T2DM. Moreover, the associations remained significant with male sex, age <60 years and body mass index <24 kg/m2 .

CONCLUSIONS

This retrospective study revealed increased probability of T2DM with increasing, U-shape, bell-shape, and other-shape TG/HDL-C ratio change trajectories, especially with male sex, age <60 years and body mass index <24 kg/m2 .

Development of a clinical risk score for incident diabetes: A 10-year prospective cohort study

Aims/Introduction

We developed a self‐assessable Korean Diabetes Risk score using the data of the Korean Genome and Epidemiology Study.

Materials and Methods

A total of 8,740 participants without diabetes at baseline were followed up biannually over a period of 10 years. We included variables that were significantly different between participants who developed diabetes mellitus and those who did not in the development cohort at baseline. We assigned a maximum score of 100 to the selected variable in each gender group. Next, the 10‐year probability of incident diabetes was calculated and validated in the validation cohort. Finally, we compared the predictive power of Korean Diabetes Risk score with models including fasting plasma glucose or glycated hemoglobin and other cohort models of Atherosclerosis Risk in Communities and Korea National Health and Nutrition Examination Survey.

Results

During a median follow‐up period of 9.7 years, 22.7% of the participants progressed to diabetes. The Korean Diabetes Risk score included age, living location (urban or rural area), waist circumference, hypertension, family history of diabetes and smoking history. The developed risk score yielded acceptable discrimination for incident diabetes (area under the curve 0.657) and the predictive power was improved when the model included fasting plasma glucose (area under the curve 0.690) or glycated hemoglobin (area under the curve 0.746). In addition, our model predicted incident diabetes more accurately than previous Western or Korean models.

Conclusions

This newly developed self‐assessable diabetes risk score is easily applicable to predict the future risk of diabetes even without the necessity for laboratory tests. This score is useful for the Korean diabetes prevention program, because high‐risk individuals can be easily screened.

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.

A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Can We Reduce Mortality of COVID-19 if We do Better in Glucose Control?

The SARS-CoV-2 has infected more than 3 million people and caused more than 240,000 death globally. Among the COVID-19 patients, the prevalence of people with other chronic diseases, such as diabetes, high blood pressure, and coronary heart disease is much higher than others. More strikingly, the survival rate of diabetic patients is also much lower than in non-diabetic patients. In addition to the general damage of high glucose to cells and tissues, a recent discovery that high glucose activates interferon regulatory factor 15 promotes influenza virus -induced cytokine storm. This discovery may shed light on the high incidence of diabetes in COVID-19. Several diabetes prevention strategies together with recent significant data-driven diabetes prediction approaches, which may help COVID-19 treatments, have been proposed.

•

The mortality rate of COVID-19 patients is about 6% worldwide.

•

Out of the ease patients, one third have diabetes, which has significant impact on the disease progress and mortality.

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If we can prevent and even cure diabetes, the mortality rate will be significantly reduced.

•

We summarize the potential reasons that high glucose increases the damage of human cells and immune system together with the infection of SARS-CoV-2 virus, and give suggestions for prevention and prediction of diabetes in the AI era.

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.

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.

Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women

BACKGROUND

Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

METHODS

Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

RESULTS

The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P=0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P=0.032).

CONCLUSION

A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

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.

High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults

OBJECTIVE

Comprehensive assessment of serum lipidomic aberrations before type 2 diabetes mellitus (T2DM) onset has remained lacking in Han Chinese. We evaluated changes in lipid coregulation antecedent to T2DM and identified novel lipid predictors for T2DM in individuals with normal glucose regulation (NGR).

RESEARCH DESIGN AND METHODS

In the discovery study, we tested 667 baseline serum lipids in subjects with incident diabetes and propensity score-matched control subjects (n = 200) from a prospective cohort comprising 3,821 Chinese adults with NGR. In the validation study, we tested 250 lipids in subjects with incident diabetes and matched control subjects (n = 724) from a pooled validation cohort of 14,651 individuals with NGR covering five geographical regions across China. Differential correlation network analyses revealed perturbed lipid coregulation antecedent to diabetes. The predictive value of a serum lipid panel independent of serum triglycerides and 2-h postload glucose was also evaluated.

RESULTS

At the level of false-discovery rate <0.05, 38 lipids, including triacylglycerols (TAGs), lyso-phosphatidylinositols, phosphatidylcholines, polyunsaturated fatty acid (PUFA)-plasmalogen phosphatidylethanolamines (PUFA-PEps), and cholesteryl esters, were significantly associated with T2DM risk in the discovery and validation cohorts. A preliminary study found most of the lipid predictors were also significantly associated with the risk of prediabetes. Differential correlation network analysis revealed that perturbations in intraclass (i.e., non-PUFA-TAG and PUFA-TAGs) and interclass (i.e., TAGs and PUFA-PEps) lipid coregulation preexisted before diabetes onset. Our lipid panel further improved prediction of incident diabetes over conventional clinical indices.

CONCLUSIONS

These findings revealed novel changes in lipid coregulation existing before diabetes onset and expanded the current panel of serum lipid predictors for T2DM in normoglycemic Chinese individuals.

Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents

Aims

To develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population.

Methods

A 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction.

Results

The 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m², 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively.

Conclusion

Our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.

Electronic supplementary material

The online version of this article (10.1007/s13167-019-00181-2) contains supplementary material, which is available to authorized users.

A Risk Score System for Myopia Symptom Warning

Myopia is the leading cause of visual impairments worldwide. Some studies revealed that visual experience in early life affected the final myopia, indicating that environmental factors play an impellent role in the development of myopia. However, risk factors of myopia are still not identified among adolescents in China. A total of 4104 cases of myopia symptom and 3306 emmetropia controls were selected from students in primary and middle schools in Wuhan in 2008. We identified the risk factors associated with myopia symptom by multivariate logistic regression in this cross-sectional study and constructed a risk score system for myopia symptom. The value of the area under the receiver operating characteristic curve (ROC) was 0.735. Furthermore, we followed up 93 students aged 7-9 years for one year and calculated the total points using the score system. We found no significant difference between the final myopia symptom and the results predicted by the total points by pair chi-square test (P>0.05). The score system had a modest ability to estimate the risk factors of myopia symptom. Using this score system, we could identify the students who are at risk of myopia symptom in the future according to their behaviors and environmental factors, and take measures to slow the progress of myopia symptom.

Nomograms for incident risk of post-partum type 2 diabetes in Chinese women with prior gestational diabetes mellitus

OBJECTIVE

Counselling patients with gestational diabetes mellitus (GDM) on their individual risk of post-partum type 2 diabetes (T2D) is challenging. This study aimed to develop nomograms for predicting incident risk of post-partum T2D in women with GDM diagnosed by WHO 1998 criteria.

METHODS

We performed a retrospective cohort study in 1263 Chinese women with GDM, of whom 83 were diagnosed as T2D at 2.3 years post-partum. Multivariate Cox proportional hazards models were used to investigate the independent predictors for post-partum T2D. The results of multivariate analyses were used to formulate nomograms for predicting incident risk of post-partum T2D. The predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUROC).

RESULTS

On multivariate analysis, independent predictors of post-partum T2DM in women with GDM included family history of diabetes [hazard ratio (HR) and its 95% confidential interval (95% CI): 2.06 (95% CI: 1.32-3.22)], history of pregnancy-induced hypertension [3.11 (95% CI: 1.86-5.21)], pre-pregnancy BMI [1.00, 1.90 (95% CI: 1.14-3.16), and 3.67 (95% CI: 2.03-6.63) for BMI <24, 24-28, and ≥28 kg/m² ], and 2-hour glucose at 26-30 gestational weeks [1.00, 2.84 (95% CI: 1.42-5.69), and 9.42 (95% CI: 4.46-19.90) for 2-hour glucose at 7.8 ~ <8.5, 8.5 ~ <11.1, and ≥11.1 mmol/L). The overall AUROC of nomogram was 82.8% (95% CI: 78.1%-87.5%), with AUROCs of 85.9% (95% CI: 79.7%-92.1%) and 83.2% (95% CI: 77.9%-88.6%) for post-partum 2-year and 3-year risk of T2D, respectively.

CONCLUSIONS

This easy-to-use nomogram, with non-invasive clinical characteristics, can accurately predict the risk of post-partum T2D in women with GDM. It may facilitate risk communication between patients and clinicians.

[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.

Clusters of fatty acids in the serum triacylglyceride fraction associate with the disorders of type 2 diabetes

Our aim was to examine longitudinal associations of triacylglyceride fatty acid (TGFA) composition with insulin sensitivity (IS) and β-cell function. Adults at risk for T2D (n = 477) had glucose and insulin measured from a glucose challenge at three time points over 6 years. The outcome variables Matsuda insulin sensitivity index, homeostatic model of assessment 2-percent sensitivity (HOMA2-%S), Insulinogenic Index over HOMA-IR (IGI/IR), and Insulin Secretion-Sensitivity Index-2 were computed from the glucose challenge. Gas chromatography quantified TGFA composition from the baseline. We used adjusted generalized estimating equation (GEE) models and partial least squares (PLS) regression for the analysis. In adjusted GEE models, four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7 as mol%) had strong negative associations with IS, whereas others (e.g., 18:1n-7, 18:1n-9, 20:2n-6, and 20:5n-3) had strong positive associations. Few associations were seen for β-cell function, except for 16:0, 18:1n-7, and 20:2n-6. PLS analysis indicated four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7) that clustered together and strongly related with lower IS. These four TGFAs also correlated highly (r > 0.4) with clinically measured triacylglyceride. We found that higher proportions of a cluster of four TGFAs strongly related with lower IS as well as hypertriglyceridemia, suggesting that only a few FAs within the TGFA composition may primarily explain lipids' role in glucose dysregulation.

Comparing the Consistency and Performance of Various Coronary Heart Disease Prediction Models for Primary Prevention Using a National Representative Cohort in Taiwan

BACKGROUND

Predicting future coronary artery disease (CAD) risk by model-based approaches can facilitate identification of high-risk individuals for prevention and management. Therefore, we compared the consistency and performance of various CAD models for primary prevention using 1 external validation dataset from a national representative cohort in Taiwan.

METHODS AND RESULTS

The 10 CAD prediction models were assessed in a validation cohort of 3559 participants (≥35 years old, 53.5% women) from a Taiwanese national representative cohort that was followed up for a median 9.70 (interquartile range, 9.63-9.74) years; 63 cases were documented as developing CAD events. The overall κ value was 0.51 for all 10 models, with a higher value for women than for men (0.53 for women, 0.40 for men). In addition, the areas under the receiver operating characteristics curves ranged from 0.804 (95% confidence interval, 0.758-0.851) to 0.847 (95% confidence interval, 0.805-0.889). All non-significant chi-square values indicated good calibration ability.

CONCLUSIONS

Our study demonstrated these 10 CAD prediction models for primary prevention were feasible and validated for use in Taiwanese subjects. Further studies of screening and management are warranted.

Development of a High Coverage Pseudotargeted Lipidomics Method Based on Ultra-High Performance Liquid Chromatography-Mass Spectrometry

Lipid coverage is crucial in comprehensive lipidomics studies challenged by high diversity in lipid structures and wide dynamic range in lipid levels. Current state-of-the-art lipidomics technologies are mostly based on mass spectrometry (MS), including direct-infusion MS, chromatography-MS, and matrix-assisted laser desorption ionization (MALDI) imaging MS, each with its pros and cons. Due to the need or favorability for measurement of isomers and isobars, chromatography-MS is preferable for lipid profiling. The ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based nontargeted lipidomics approach and UHPLC-tandem MS (UHPLC-MS/MS)-based targeted approach are two representative methodological platforms for chromatography-MS. In the present study, we developed a high coverage pseudotargeted lipidomics method combining the advantages of nontargeted and targeted lipidomics approaches. The high coverage of lipids was achieved by integration of the detected lipids derived from nontargeted UHPLC-HRMS lipidomics analysis of multiple matrices (e.g., plasma, cell, and tissue) and the predicted lipids speculated on the basis of the structure and chromatographic retention behavior of the known lipids. A total of 3377 targeted lipid ion pairs with over 7000 lipid molecular structures were defined. The pseudotargeted lipidomics method was well validated with satisfactory analytical characteristics in terms of linearity, precision, reproducibility, and recovery for lipidomics profiling. Importantly, it showed better repeatability and higher coverage of lipids than the nontargeted lipidomics method. The applicability of the developed pseudotargeted lipidomics method was testified in defining differential lipids related to diabetes. We believe that comprehensive lipidomics studies will benefit from the developed high coverage pseudotargeted lipidomics approach.

Unexplained reciprocal regulation of diabetes and lipoproteins

PURPOSE OF REVIEW

Type 2 diabetes is associated with a characteristic dyslipidemia that may exacerbate cardiovascular risk. The causes of, and the effects of new antihyperglycemia medications on, this dyslipidemia, are under investigation. In an unexpected reciprocal manner, lowering LDL-cholesterol with statins slightly increases the risk of diabetes. Here we review the latest findings.

RECENT FINDINGS

The inverse relationship between LDL-cholesterol and diabetes has now been confirmed by multiple lines of evidence. This includes clinical trials, genetic instruments using aggregate single nucleotide polymorphisms, as well as at least eight individual genes - HMGCR, NPC1L1, HNF4A, GCKR, APOE, PCKS9, TM6SF2, and PNPLA3 - support this inverse association. Genetic and pharmacologic evidence suggest that HDL-cholesterol may also be inversely associated with diabetes risk. Regarding the effects of diabetes on lipoproteins, new evidence suggests that insulin resistance but not diabetes per se may explain impaired secretion and clearance of VLDL-triglycerides. Weight loss, bariatric surgery, and incretin-based therapies all lower triglycerides, whereas SGLT2 inhibitors may slightly increase HDL-cholesterol and LDL-cholesterol.

SUMMARY

Diabetes and lipoproteins are highly interregulated. Further research is expected to uncover new mechanisms governing the metabolism of glucose, fat, and cholesterol. This topic has important implications for treating type 2 diabetes and cardiovascular disease.

The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans

BACKGROUND

Screening for risk of type 2 diabetes mellitus (T2DM) is an important public health issue. Previous studies report that fasting plasma glucose (FPG) and triglyceride (TG)-related indices, such as lipid accumulation product (LAP) and the product of fasting glucose and triglyceride (TyG index), are associated with incident T2DM. We aimed to evaluate whether FPG or TG-related indices can improve the predictive ability of a diabetes risk model for middle-aged Koreans.

METHODS

7708 Koreans aged 40-69 years without diabetes at baseline were eligible from the Korean Genome and Epidemiology Study. The overall cumulative incidence of T2DM was 21.1% (766 cases) in men and 19.6% (797 cases) in women. Therefore, the overall cumulative incidence of T2DM was 20.3% (1563 cases). Multiple logistic regression analysis was conducted to compare the odds ratios (ORs) for incident T2DM for each index. The area under the receiver operating characteristic curve (AROC), continuous net reclassification improvement (cNRI), and integrated discrimination improvement (IDI) were calculated when each measure was added to the basic risk model for diabetes.

RESULTS

All the TG-related indices and FPG were more strongly associated with incident T2DM than WC in our study population. The adjusted ORs for the highest quartiles of WC, TG, FPG, LAP, and TyG index compared to the lowest, were 1.64 (95% CI, 1.13-2.38), 2.03 (1.59-2.61), 3.85 (2.99-4.97), 2.47 (1.82-3.34), and 2.79 (2.16-3.60) in men, and 1.17 (0.83-1.65), 2.42 (1.90-3.08), 2.15 (1.71-2.71), 2.44 (1.82-3.26), and 2.85 (2.22-3.66) in women, respectively. The addition of TG-related parameters or FPG, but not WC, to the basic risk model for T2DM (including age, body mass index, family history of diabetes, hypertension, current smoking, current drinking, and regular exercise) significantly increased cNRI, IDI, and AROC in both sexes.

CONCLUSIONS

Adding either TyG index or FPG into the basic risk model for T2DM increases its prediction and reclassification ability. Compared to FPG, TyG index was a more robust T2DM predictor in the stratified sex and fasting glucose level. Therefore, TyG index should be considered as a screening tool for identification of people at high risk for T2DM in practice.

A Point System for Predicting 10-Year Risk of Developing Type 2 Diabetes Mellitus in Japanese Men: Aichi Workers' Cohort Study

Background

Relatively little evidence exists for type 2 diabetes mellitus (T2DM) prediction models from long-term follow-up studies in East Asians. This study aims to develop a point-based prediction model for 10-year risk of developing T2DM in middle-aged Japanese men.

Methods

We followed 3,540 male participants of Aichi Workers’ Cohort Study, who were aged 35–64 years and were free of diabetes in 2002, until March 31, 2015. Baseline age, body mass index (BMI), smoking status, alcohol consumption, regular exercise, medication for dyslipidemia, diabetes family history, and blood levels of triglycerides (TG), high density lipoprotein cholesterol (HDLC) and fasting blood glucose (FBG) were examined using Cox proportional hazard model. Variables significantly associated with T2DM in univariable models were simultaneously entered in a multivariable model for determination of the final model using backward variable selection. Performance of an existing T2DM model when applied to the current dataset was compared to that obtained in the present study’s model.

Results

During the median follow-up of 12.2 years, 342 incident T2DM cases were documented. The prediction system using points assigned to age, BMI, smoking status, diabetes family history, and TG and FBG showed reasonable discrimination (c-index: 0.77) and goodness-of-fit (Hosmer-Lemeshow test, P = 0.22). The present model outperformed the previous one in the present subjects.

Conclusion

The point system, once validated in the other populations, could be applied to middle-aged Japanese male workers to identify those at high risk of developing T2DM. In addition, further investigation is also required to examine whether the use of this system will reduce incidence.

Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese

AIMS

The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population.

METHODS

This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score.

RESULTS

The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models.

CONCLUSIONS

The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.

Inflammation in the Prediction of Type 2 Diabetes and Hypertension in Healthy Adults

BACKGROUND

While inflammation is associated with obesity and insulin resistance, their inter-relationships in the development of type 2 diabetes or hypertension are not clear.

AIM OF THE STUDY

To evaluate inflammatory markers in prediction of type 2 diabetes and hypertension.

METHODS

The study population of this retrospective cohort study consisted of individuals who participated in a comprehensive health screening program with measurement of white blood cell count and C-reactive protein from 2002-2010 (N = 96,606) in nondiabetic and normotensive Koreans. Median follow up time were 3.7 years for incident type 2 diabetes and 3.3 years for hypertension. Multivariate Cox proportional hazards models were performed to assess risk for type 2 diabetes or hypertension by white blood cell or C-reactive protein quartiles with adjustment of various possible confounding factors including insulin resistance.

RESULTS

During the follow-up period, 1448 (1.5%) developed type 2 diabetes and 10,405 (10.8%) developed hypertension. Among men, comparison of adjusted hazard ratios (HR) for incident type 2 diabetes in the highest versus lowest white blood cell or C-reactive protein quartiles were 1.48 [95% confidence interval (CI), 1.20-1.83] and 1.30 (95% CI, 1.07-1.57), respectively. Among women, white blood cell but not C-reactive protein was significantly associated with type 2 diabetes [HR 1.79 (95% CI 1.24-2.57)]. White blood cell and C-reactive protein quartiles were also modestly associated with incident hypertension in both sexes.

CONCLUSIONS

Although white blood cell and C-reactive protein are associated with adiposity and insulin resistance, these inflammatory markers also independently predict type 2 diabetes and/or hypertension.