Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

Development and Validation of Prediction Models for the 5-year Risk of Type 2 Diabetes in a Japanese Population: Japan Public Health Center-based Prospective (JPHC) Diabetes Study

BACKGROUND

This study aimed to develop models to predict the 5-year incidence of type 2 diabetes mellitus (T2DM) in a Japanese population and validate them externally in an independent Japanese population.

METHODS

Data from 10,986 participants (aged 46-75 years) in the development cohort of the Japan Public Health Center-based Prospective Diabetes Study and 11,345 participants (aged 46-75 years) in the validation cohort of the Japan Epidemiology Collaboration on Occupational Health Study were used to develop and validate the risk scores in logistic regression models.

RESULTS

We considered non-invasive (sex, body mass index, family history of diabetes mellitus, and diastolic blood pressure) and invasive (glycated hemoglobin [HbA1c] and fasting plasma glucose [FPG]) predictors to predict the 5-year probability of incident diabetes. The area under the receiver operating characteristic curve was 0.643 for the non-invasive risk model, 0.786 for the invasive risk model with HbA1c but not FPG, and 0.845 for the invasive risk model with HbA1c and FPG. The optimism for the performance of all models was small by internal validation. In the internal-external cross-validation, these models tended to show similar discriminative ability across different areas. The discriminative ability of each model was confirmed using external validation datasets. The invasive risk model with only HbA1c was well-calibrated in the validation cohort.

CONCLUSION

Our invasive risk models are expected to discriminate between high- and low-risk individuals with T2DM in a Japanese population.

Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

INTRODUCTION

Insulin resistance and defects in pancreatic beta cells are the two major pathophysiologic abnormalities that underlie type 2 diabetes. In addition, visceral fat area (VFA) is reported to be a stronger predictor for diabetes than body mass index (BMI). Here, we tested whether the performance of diabetes prediction models could be improved by adding HOMA-IR and HOMA-β and replacing BMI with VFA.

RESEARCH DESIGN AND METHODS

We developed five prediction models using data from a cohort study (5578 individuals, of whom 94.7% were male, and 943 had incident diabetes). We conducted a baseline model (model 1) including age, sex, BMI, smoking, dyslipidemia, hypertension, and HbA1c. Subsequently, we developed another four models: model 2, predictors in model 1 plus fasting plasma glucose (FPG); model 3, predictors in model 1 plus HOMA-IR and HOMA-β; model 4, predictors in model 1 plus FPG, HOMA-IR, and HOMA-β; model 5, replaced BMI with VFA in model 2. We assessed model discrimination and calibration for the first 10 years of follow-up.

RESULTS

The addition of FPG to model 1 obviously increased the value of the area under the receiver operating characteristic curve from 0.79 (95% CI 0.78, 0.81) to 0.84 (0.83, 0.85). Compared with model 1, model 2 also significantly improved the risk reclassification and discrimination, with a continuous net reclassification improvement index of 0.61 (0.56, 0.70) and an integrated discrimination improvement index of 0.09 (0.08, 0.10). Adding HOMA-IR and HOMA-β (models 3 and 4) or replacing BMI with VFA (model 5) did not further materially improve the performance.

CONCLUSIONS

This cohort study, primarily composed of male workers, suggests that a model with BMI, FPG, and HbA1c effectively identifies those at high diabetes risk. However, adding HOMA-IR, HOMA-β, or replacing BMI with VFA does not significantly improve the model. Further studies are needed to confirm our findings.

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.

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.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

Background

Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

Aims

This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

Methods

A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

Results

The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

Conclusion

This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

Development of Various Diabetes Prediction Models Using Machine Learning Techniques

BACKGROUND

There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.

METHODS

Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.

RESULTS

The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.

CONCLUSION

We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

Osteocalcin and Risks of Incident Diabetes and Diabetic Kidney Disease: A 4.6-Year Prospective Cohort Study

OBJECTIVE

We aimed to examine the relationship between osteocalcin (OC) and the risk of incident diabetes and the risk of incident diabetic kidney disease (DKD).

RESEARCH DESIGN AND METHODS

We followed 5,396 participants without diabetes (nondiabetes subcohort) and 1,174 participants with diabetes and normal kidney function (diabetes subcohort) at baseline. Logistic regression and modified Poisson regression models were used to estimate the relative risk (RR) of baseline OC levels with incident diabetes and DKD.

RESULTS

During a mean 4.6-year follow-up period, 296 cases of incident diabetes and 184 cases of incident DKD were identified. In the nondiabetes subcohort, higher OC levels were linearly associated with a decreased risk of diabetes (RR for 1-unit increase of loge-transformed OC 0.51 [95% CI 0.35-0.76]; RR for highest vs. lowest quartile 0.65 [95% CI 0.44-0.95]; P for trend < 0.05). In the diabetes subcohort, OC levels were linearly inversely associated with incident DKD (RR for 1-unit increase of loge-transformed OC 0.49 [95% CI 0.33-0.74]; RR for highest vs. lowest quartile 0.56 [95% CI 0.38-0.83]; P for trend < 0.05), even independent of baseline estimated glomerular filtration rate and urinary albumin-to-creatinine ratio. No significant interactions between OC and various subgroups on incident diabetes or DKD were observed.

CONCLUSIONS

Lower OC levels were associated with an increased risk of incident diabetes and DKD.

The association between the reduction of body weight and new-onset type 2 diabetes remission in middle-aged Japanese men: Population-based Panasonic cohort study 8

AIM

This study aimed to investigate the association between change in body weight (BW) and type 2 diabetes remission in Japanese men with new-onset type 2 diabetes.

METHODS

This study enrolled 1,903 patients with new-onset type 2 diabetes between 2008 and 2013 from a medical health checkup program conducted by the Panasonic Corporation, Osaka, Japan. The baseline was defined as the year of new-onset diabetes. We assessed the type 2 diabetes remission five years after baseline and the association between the change in BW and type 2 diabetes remission using logistic regression analyses. To evaluate the predictive performance of the change in BW, we employed the receiver operating characteristic curves and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

The BW loss was associated with type 2 diabetes remission in the participants with a BMI ≥25 kg/m² but not in the participants with a BMI <25 kg/m². The odds ratios were 1.96 (95% CI: 1.19-3.29) and 3.72 (95% CI: 2.14-6.59) in the participants with a loss of 5-9.9% and loss of ≥10% for five years, respectively, in the participants with a BMI ≥25 kg/m² (reference; stable group [0.9% gain to 0.9% loss]). The AUC and cut-off values for the rate of change in BW for type 2 diabetes remission were 0.59 and 5.0%.

DISCUSSION

Body weight loss of ≥5% effectively achieved diabetes remission in Japanese men with a BMI ≥25 kg/m² and new-onset type 2 diabetes.

Fasting plasma glucose level in the range of 90-99 mg/dL and the risk of the onset of type 2 diabetes: Population-based Panasonic cohort study 2

Aim/Introduction

As the association between a fasting glucose concentration of 90–99 mg/dL and the onset of type 2 diabetes is still controversial, we aimed to assess it in 37,148 Japanese individuals with a normal plasma glucose concentration.

Materials and Methods

This long‐term retrospective cohort study included individuals having a medical checkup at Panasonic Corporation from 2008 to 2018. In total, 1,028 participants developed type 2 diabetes.

Results

Cox regression analyses revealed that the risk for the onset of diabetes increased by 9.0% per 1 mg/dL increase in fasting plasma glucose concentration in subjects with the concentration ranging from 90 to 99 mg/dL. Compared with individuals with a fasting glucose concentration of ≤89 mg/dL, the adjusted hazard ratios for developing diabetes were 1.53 (95% CI; 1.22–1.91), 1.76 (95% CI; 1.41–2.18), 1.89 (95% CI; 1.52–2.35), 3.17 (95% CI; 2.61–3.84), and 3.41 (95% CI; 2.79–4.15) at fasting plasma glucose concentrations of 90–91, 92–93, 94–95, 96–97, and 98–99 mg/dL, respectively. In populations with obesity, the adjusted hazards ratios for developing diabetes were 1.56 (95% CI; 1.15–2.09), 1.82 (95% CI; 1.37–2.40), 2.05 (95% CI; 1.55–2.69), 3.53 (95% CI; 2.79–4.46), and 3.28 (95% CI; 2.53–4.22) at fasting plasma glucose concentrations of 90–91, 92–93, 94–95, 96–97, and 98–99 mg/dL, respectively.

Conclusions

This study demonstrates that the risk of type 2 diabetes among subjects having a fasting plasma glucose concentration of 90–99 mg/dL, is progressively higher with an increasing level of fasting plasma glucose concentration in a Japanese people.

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.

Oral glucose tolerance test does not affect degree of hemoglobin glycation as measured by routine assay

BACKGROUND

Hemoglobin A1c (HbA1c) is an accurate index of fluctuation in glycemia over the 2-3 months prior to quantitative assessment. During this time, hemoglobin (Hb) slowly glycates until it shows the properties of advanced glycation end-products. Glycation kinetics is intensified by prolonged glucose exposure. In subjects undergoing oral glucose tolerance testing (OGTT), immediately after ingestion, glucose is ostensibly transported by the glucose transporter 1 (GLUT1) to erythrocyte corpuscular hemoglobin. The earliest significant measurable level of hemoglobin glycation associated with this transportation is still not clear.

SUBJECTS AND METHODS

We attempted to explore the early impact of short-term glucose load on HbA1c levels, because it is now known that transmembrane GLUT1-mediated glucose transport occurs immediately. A total of 88 participants (46 patients and 42 clinically healthy controls) underwent fasting plasma glucose quantitation during an OGTT. HbA1c, revealed by a monoclonal anti-glycation epitope antibody and adiponectin, was quantitated before (T0) and 2 hours (T120) after 80 g glucose ingestion.

RESULTS

Wilcoxon test revealed that the HbA1c values did not significantly vary (P=0.15) during the OGTT, whereas glucose concentration varied strongly between T0 and T120.

DISCUSSION

It is well known that quantitative estimation of HbA1c is informative for clinical care, independently of glucose level. The molecular mechanisms and dynamics by which glucose enters/exits red blood cells are incompletely known and may differ between individuals. We here show, for the first time, that HbA1c levels do not significantly increase during OGTT, supporting the view that non-enzymatic glycation of hemoglobin occurs slowly and that glycation during the 2 hours of an OGTT is insignificant.

Systematic review with meta-analysis of the epidemiological evidence relating smoking to type 2 diabetes

BACKGROUND

Evidence relating tobacco smoking to type 2 diabetes has accumulated rapidly in the last few years, rendering earlier reviews considerably incomplete.

AIM

To review and meta-analyse evidence from prospective studies of the relationship between smoking and the onset of type 2 diabetes.

METHODS

Prospective studies were selected if the population was free of type 2 diabetes at baseline and evidence was available relating smoking to onset of the disease. Papers were identified from previous reviews, searches on Medline and Embase and reference lists. Data were extracted on a range of study characteristics and relative risks (RRs) were extracted comparing current, ever or former smokers with never smokers, and current smokers with non-current smokers, as well as by amount currently smoked and duration of quitting. Fixed- and random-effects estimates summarized RRs for each index of smoking overall and by various subdivisions of the data: Sex; continent; publication year; method of diagnosis; nature of the baseline population (inclusion/exclusion of pre-diabetes); number of adjustment factors; cohort size; number of type 2 diabetes cases; age; length of follow-up; definition of smoking; and whether or not various factors were adjusted for. Tests of heterogeneity and publication bias were also conducted.

RESULTS

The literature searches identified 157 relevant publications providing results from 145 studies. Fifty-three studies were conducted in Asia and 53 in Europe, with 32 in North America, and seven elsewhere. Twenty-four were in males, 10 in females and the rest in both sexes. Fifteen diagnosed type 2 diabetes from self-report by the individuals, 79 on medical records, and 51 on both. Studies varied widely in size of the cohort, number of cases, length of follow-up, and age. Overall, random-effects estimates of the RR were 1.33 [95% confidence interval (CI): 1.28-1.38] for current vs never smoking, 1.28 (95%CI: 1.24-1.32) for current vs non-smoking, 1.13 (95%CI: 1.11-1.16) for former vs never smoking, and 1.25 (95%CI: 1.21-1.28) for ever vs never smoking based on, respectively, 99, 156, 100 and 100 individual risk estimates. Risk estimates were generally elevated in each subdivision of the data by the various factors considered (exceptions being where numbers of estimates in the subsets were very low), though there was significant (P < 0.05) evidence of variation by level for some factors. Dose-response analysis showed a clear trend of increasing risk with increasing amount smoked by current smokers and of decreasing risk with increasing time quit. There was limited evidence of publication bias.

CONCLUSION

The analyses confirmed earlier reports of a modest dose-related association of current smoking and a weaker dose-related association of former smoking with type 2 diabetes risk.

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

Background

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

Methods

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

Results

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

Conclusion

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

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

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

Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia

BACKGROUND

Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts.

OBJECTIVES

To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia.

SEARCH METHODS

We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining.

SELECTION CRITERIA

We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only.

DATA COLLECTION AND ANALYSIS

One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework.

MAIN RESULTS

We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018.

AUTHORS' CONCLUSIONS

Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.

Development and Validation of the Korean Diabetes Risk Score: A 10-Year National Cohort Study

BACKGROUND

A diabetes risk score in Korean adults was developed and validated.

METHODS

This study used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) of 359,349 people without diabetes at baseline to derive an equation for predicting the risk of developing diabetes, using Cox proportional hazards regression models. External validation was conducted using data from the Korean Genome and Epidemiology Study. Calibration and discrimination analyses were performed separately for men and women in the development and validation datasets.

RESULTS

During a median follow-up of 10.8 years, 37,678 cases (event rate=10.4 per 1,000 person-years) of diabetes were identified in the development cohort. The risk score included age, family history of diabetes, alcohol intake (only in men), smoking status, physical activity, use of antihypertensive therapy, use of statin therapy, body mass index, systolic blood pressure, total cholesterol, fasting glucose, and γ glutamyl transferase (only in women). The C-statistics for the models for risk at 10 years were 0.71 (95% confidence interval [CI], 0.70 to 0.73) for the men and 0.76 (95% CI, 0.75 to 0.78) for the women in the development dataset. In the validation dataset, the C-statistics were 0.63 (95% CI, 0.53 to 0.73) for men and 0.66 (95% CI, 0.55 to 0.76) for women.

CONCLUSION

The Korean Diabetes Risk Score may identify people at high risk of developing diabetes and may be an effective tool for delaying or preventing the onset of condition as risk management strategies involving modifiable risk factors can be recommended to those identified as at high risk.

Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study

AIMS/INTRODUCTION

We previously developed a 3-year diabetes risk score in the working population. The objective of the present study was to develop and validate flexible risk models that can predict the risk of diabetes for any arbitrary time-point during 7 years.

MATERIALS AND METHODS

The participants were 46,198 Japanese employees aged 30-59 years, without diabetes at baseline and with a maximum follow-up period of 8 years. Incident diabetes was defined according to the American Diabetes Association criteria. With routine health checkup data (age, sex, abdominal obesity, body mass index, smoking status, hypertension status, dyslipidemia, glycated hemoglobin and fasting plasma glucose), we developed non-invasive and invasive risk models based on the Cox proportional hazards regression model among a random two-thirds of the participants, and used another one-third for validation.

RESULTS

The range of the area under the receiver operating characteristic curve increased from 0.73 (95% confidence interval 0.72-0.74) for the non-invasive prediction model to 0.89 (95% confidence interval 0.89-0.90) for the invasive prediction model containing dyslipidemia, glycated hemoglobin and fasting plasma glucose. The invasive models showed improved integrated discrimination and reclassification performance, as compared with the non-invasive model. Calibration appeared good between the predicted and observed risks. These models performed well in the validation cohort.

CONCLUSIONS

The present non-invasive and invasive models for the prediction of diabetes risk up to 7 years showed fair and excellent performance, respectively. The invasive models can be used to identify high-risk individuals, who would benefit greatly from lifestyle modification for the prevention or delay of diabetes.

Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study

AIMS

To assess the predictive ability of a genetic risk score for the incidence of Type 2 diabetes in a general Japanese population.

METHODS

This prospective case-control study, nested within a Japan Public Health Centre-based prospective study, included 466 participants with incident Type 2 diabetes over a 5-year period (cases) and 1361 control participants, as well as 1463 participants with existing diabetes and 1463 control participants. Eleven susceptibility single nucleotide polymorphisms, identified through genome-wide association studies and replicated in Japanese populations, were analysed.

RESULTS

Most single nucleotide polymorphism loci showed directionally consistent associations with diabetes. From the combined samples, one single nucleotide polymorphism (rs2206734 at CDKAL1) reached a genome-wide significance level (odds ratio 1.28, 95% CI 1.18-1.40; P = 1.8 × 10^-8 ). Three single nucleotide polymorphisms (rs2206734 in CDKAL1, rs2383208 in CDKN2A/B, and rs2237892 in KCNQ1) were nominally significantly associated with incident diabetes. Compared with the lowest quintile of the total number of risk alleles, the highest quintile had a higher odds of incident diabetes (odds ratio 2.34, 95% CI 1.59-3.46) after adjusting for conventional risk factors such as age, sex and BMI. The addition to the conventional risk factor-based model of a genetic risk score using the 11 single nucleotide polymorphisms significantly improved predictive performance; the c-statistic increased by 0.021, net reclassification improved by 6.2%, and integrated discrimination improved by 0.003.

CONCLUSIONS

Our prospective findings suggest that the addition of a genetic risk score may provide modest but significant incremental predictive performance beyond that of the conventional risk factor-based model without biochemical markers.

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.

Prediction of Type 2 Diabetes by Hemoglobin A1c in Two Community-Based Cohorts

OBJECTIVE

Hemoglobin A_1c (HbA_1c) can be used to assess type 2 diabetes (T2D) risk. We asked whether HbA_1c was associated with T2D risk in four scenarios of clinical information availability: 1) HbA_1c alone, 2) fasting laboratory tests, 3) clinic data, and 4) fasting laboratory tests and clinic data.

RESEARCH DESIGN AND METHODS

We studied a prospective cohort of white (N = 11,244) and black (N = 2,294) middle-aged participants without diabetes in the Framingham Heart Study and Atherosclerosis Risk in Communities study. Association of HbA_1c with incident T2D (defined by medication use or fasting glucose [FG] ≥126 mg/dL) was evaluated in regression models adjusted for 1) age and sex (demographics); 2) demographics, FG, HDL, and triglycerides; 3) demographics, BMI, blood pressure, and T2D family history; or 4) all preceding covariates. We combined results from cohort and race analyses by random-effects meta-analyses. Subsidiary analyses tested the association of HbA_1c with developing T2D within 8 years or only after 8 years.

RESULTS

Over 20 years, 3,315 individuals developed T2D. With adjustment for demographics, the odds of T2D increased fourfold for each percentage-unit increase in HbA_1c. The odds ratio (OR) was 4.00 (95% CI 3.14, 5.10) for blacks and 4.73 (3.10, 7.21) for whites, resulting in a combined OR of 4.50 (3.35, 6.03). After adjustment for fasting laboratory tests and clinic data, the combined OR was 2.68 (2.15, 3.34) over 20 years, 5.79 (2.51, 13.36) within 8 years, and 2.23 (1.94, 2.57) after 8 years.

CONCLUSIONS

HbA_1c predicts T2D in different common scenarios and is useful for identifying individuals with elevated T2D risk in both the short- and long-term.

A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study

Objective

To develop a new non-invasive risk score for predicting incident diabetes in a rural Chinese population.

Methods

Data from the Handan Eye Study conducted from 2006–2013 were utilized as part of this analysis. The present study utilized data generated from 4132 participants who were ≥30 years of age. A non-invasive risk model was derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression. The model was subsequently validated using data from individuals from the final third of the sample cohort. In addition, a simple point system for incident diabetes was generated according to the procedures described in the Framingham Study. Incident diabetes was defined as follows: (1) fasting plasma glucose (FPG) ≥ 7.0 mmol/L; or (2) hemoglobin A1c (HbA1c) ≥ 6.5%; or (3) self-reported diagnosis of diabetes or use of anti-diabetic medications during the follow-up period.

Results

The simple non-invasive risk score included age (8 points), Body mass index (BMI) (3 points), waist circumference (WC) (7 points), and family history of diabetes (9 points). The score ranged from 0 to 27 and the area under the receiver operating curve (AUC) of the score was 0.686 in the validation sample. At the optimal cutoff value (which was 9), the sensitivity and specificity were 74.32% and 58.82%, respectively.

Conclusions

Using information based upon age, BMI, WC, and family history of diabetes, we developed a simple new non-invasive risk score for predicting diabetes onset in a rural Chinese population, using information from individuals aged 30 years of age and older. The new risk score proved to be more optimal in the prediction of incident diabetes than most of the existing risk scores developed in Western and Asian countries. This score system will aid in the identification of individuals who are at risk of developing incident diabetes in rural China.

Evaluation of the modified FINDRISC to identify individuals at high risk for diabetes among middle-aged white and black ARIC study participants

OBJECTIVE

To evaluate a modified Finnish Diabetes Risk Score (FINDRISC) for predicting the risk of incident diabetes among white and black middle-aged participants from the Atherosclerosis Risk in Communities (ARIC) study.

RESEARCH DESIGN AND METHODS

We assessed 9754 ARIC cohort participants who were free of diabetes at baseline. Logistic regression and receiver operator characteristic (ROC) curves were used to evaluate a modified FINDRISC for predicting incident diabetes after 9 years of follow-up, overall and by race/gender group. The modified FINDRISC used comprised age, body mass index, waist circumference, blood pressure medication and family history.

RESULTS

The mean FINDRISC (range, 2 [lowest risk] to 17 [highest risk]) for black women was higher (9.9 ± 3.6) than that for black men (7.6 ± 3.9), white women (8.0 ± 3.6) and white men (7.6 ± 3.5). The incidence of diabetes increased generally across deciles of FINDRISC for all 4 race/gender groups. ROC curve statistics for the FINDRISC showed the highest area under the curve for white women (0.77) and the lowest for black men (0.70).

CONCLUSIONS

We used a modified FINDRISC to predict the 9-year risk of incident diabetes in a biracial US population. The modified risk score can be useful for early screening of incident diabetes in biracial populations, which may be helpful for early interventions to delay or prevent diabetes.

Predictive models for conversion of prediabetes to diabetes

AIM

To clarify the natural course of prediabetes and develop predictive models for conversion to diabetes.

METHODS

A retrospective longitudinal study of 2105 adults with prediabetes was carried out with a mean observation period of 4.7years. Models were developed using multivariate logistic regression analysis and verified by 10-fold cross-validation. The relationship between [final BMI minus baseline BMI] (δBMI) and incident diabetes was analyzed post hoc by comparing the diabetes conversion rate for low (< -0.31kg/m²) and high δBMI (≥ -0.31kg/m²) subjects after matching the two groups for the covariates.

RESULTS

Diabetes developed in 252 (2.5%/year), and positive family history, male sex, higher systolic blood pressure, plasma glucose (fasting and 1h- and 2h-values during 75g OGTT), hemoglobin A1c (HbA1c) and alanine aminotransferase were significant, independent predictors for the conversion. By using a risk score (RS) that took account of all these variables, incident diabetes was predicted with an area under the ROC curve (95% CI) of 0.80 (0.70-0.87) and a specificity of prediction of 61.8% at 80% sensitivity. On division of the participants into high- (n=248), intermediate- (n=336) and low-risk (n=1521) populations, the conversion rates were 40.1%, 18.5% and 5.9%, respectively. The conversion rate was lower in subjects with low than high δBMI (9.2% vs 14.4%, p=0.003).

CONCLUSIONS

Prediabetes conversion to diabetes could be predicted with accuracy, and weight reduction during the observation was associated with lowered conversion rate.

Smoking and the risk of type 2 diabetes in Japan: A systematic review and meta-analysis

Cigarette smoking is the leading avoidable cause of disease burden. Observational studies have suggested an association between smoking and risk of type 2 diabetes mellitus (T2DM). We conducted a meta-analysis of prospective observational studies to investigate the association of smoking status, smoking intensity, and smoking cessation with the risk of T2DM in Japan, where the prevalence of smoking has been decreasing but remains high. We systematically searched MEDLINE and the Ichushi database to December 2015 and identified 22 eligible articles, representing 343,573 subjects and 16,383 patients with T2DM. We estimated pooled relative risks (RRs) using a random-effects model and conducted subgroup analyses by participant and study characteristics. Compared with nonsmoking, the pooled RR of T2DM was 1.38 (95% confidence interval [CI], 1.28–1.49) for current smoking (19 studies) and 1.19 (95% CI, 1.09–1.31) for former smoking (15 studies). These associations persisted in all subgroup and sensitivity analyses. We found a linear dose-response relationship between cigarette consumption and T2DM risk; the risk of T2DM increased by 16% for each increment of 10 cigarettes smoked per day. The risk of T2DM remained high among those who quit during the preceding 5 years but decreased steadily with increasing duration of cessation, reaching a risk level comparable to that of never smokers after 10 years of smoking cessation. We estimated that 18.8% of T2DM cases in men and 5.4% of T2DM cases in women were attributable to smoking. The present findings suggest that cigarette smoking is associated with an increased risk of T2DM, so tobacco control programs to reduce smoking could have a substantial effect to decrease the burden of T2DM in Japan.

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This meta-analysis examined smoking and diabetes risk among Japanese.

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Current and former smokers showed a higher risk of diabetes than non-smokers.

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Diabetes risk linearly increased with higher consumption of cigarettes.

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Diabetes risk steadily decreased after smoking cessation.

The utility of diabetes risk score items as predictors of incident type 2 diabetes in Asian populations: An evidence-based review

BACKGROUND

The prevalence of type 2 diabetes mellitus is rising, with many Asian countries featured in the top 10 countries with the highest numbers of persons with diabetes. Reliable diabetes risk scores enable the identification of individuals at risk of developing diabetes for early intervention.

OBJECTIVES

This article aims to identify common risk factors in the risk scores with the highest discrimination; factors with the most influence on the risk score in Asian populations, and to propose a set of factors translatable to the multi-ethnic Singapore population.

METHODS

A systematic search of PubMed and EMBASE databases was conducted to identify studies published before August 2016 that developed risk prediction models for incident diabetes.

RESULTS

12 studies were identified. Risk scores that included laboratory measurements had better discrimination. Coefficient analysis showed fasting glucose and HbA1c having the greatest impact on the risk score.

CONCLUSION

A proposed Asian risk score would include: family history of diabetes, age, gender, smoking status, body mass index, waist circumference, hypertension, fasting plasma glucose, HbA1c, HDL-cholesterol and triglycerides. Future research is required on the influence of ethnicity in Singapore. The risk score may potentially be used to stratify individuals for enrolment into diabetes prevention programmes.

Utility of nonblood-based risk assessment for predicting type 2 diabetes mellitus: A meta-analysis

OBJECTIVE

Nonblood-based risk assessment for type 2 diabetes mellitus (T2DM) that depends on data based on a questionnaire and anthropometry is expected to avoid unnecessary diagnostic testing and overdiagnosis due to blood testing. This meta-analysis aims to assess the predictive ability of nonblood-based risk assessment for future incident T2DM.

METHODS

Electronic literature search was conducted using EMBASE and MEDLINE (from January 1, 1997 to October 1, 2014). Included studies had to use at least 3 predictors for T2DM risk assessment and allow reproduction of 2×2 contingency table data (i.e., true positive, true negative, false positive, false negative) to be pooled with a bivariate random-effects model and hierarchical summary receiver-operating characteristic model. Considering the importance of excluding individuals with a low likelihood of T2DM from diagnostic blood testing, we especially focused on specificity and LR-.

RESULTS

Eighteen eligible studies consisting of 184,011 participants and 7038 cases were identified. The pooled estimates (95% confidence interval) were as follows: sensitivity=0.73 (0.66-0.79), specificity=0.66 (0.59-0.73), LR+=2.13 (1.81-2.50), and LR-=0.41 (0.34-0.50).

CONCLUSIONS

Nonblood-based assessment of risk of T2DM could produce acceptable results although the feasibility of such a screener needs to be determined in future studies.

Development of new diabetes risk scores on the basis of the current definition of diabetes in Japanese subjects [Rapid Communication]

To develop diabetes risk score (RS) based on the current definition of diabetes, we retrospectively analyzed consecutive 4,159 health examinees who were non-diabetic at baseline. Diabetes, diagnosed by fasting plasma glucose (FPG) ≥7.0 mmol/L, 2hPG ≥11.1 mmol/L and/or HbA1c ≥6.5% (48 mmol/mol), developed in 279 of them during the mean period of 4.9 years. A full RS (RS_Full), a RS without 2hPG (RS_-2hPG) and a non-invasive RS (RS_NI) were created on the basis of multivariate Cox proportional model by weighted grading based on hazard ratio in half the persons assigned. The RSs were verified in the remaining half of the participants. Positive family history (FH), male sex, smoking and higher age, systolic blood pressure (SBP), FPG, 2hPG and HbA1c were independent predictors for RS_Full. For RS_-2hPG, 7 independent predictors, exclusive of 2hPG and smoking but inclusive of elevated triglycerides (TG) comparing to RS_Full, were selected. FH, male sex, and higher age, SBP and HbA1c were independent predictors in RS_NI. In the validation cohort, C-statistic (95%CI) of RS_Full, RS_-2hPG and RS_NI were 0.80 (0.76-0.84), 0.75 (0.70-0.78) and 0.68 (0.63-0.72), respectively, which were significantly different from each other (P <0.01). Absolute percentage difference between predicted probability and observed diabetes were 1.9%, 0.7% and 0.9%, by the three scores, respectively, and not significantly different from each other. In conclusion, diabetes defined by the current criteria was predicted by the new diabetes risk scores with reasonable accuracy. Nonetheless, RS_Full with a postchallenge glucose value performed superior to RS_-2hPG and RS_NI.

Type 2 Diabetes Prevention: Implications of Hemoglobin A1c Genetics

Hemoglobin A1c (HbA1c) is a biomarker used for population-level screening of type 2 diabetes (T2D) and risk stratification. Large-scale, genome-wide association studies have identified multiple genomic loci influencing HbA1c. We discuss the challenges of classifying these genomic loci as influencing HbA1c through glycemic or nonglycemic pathways, based on their probable biology and pleiotropic associations with erythrocyte traits. We show that putative nonglycemic genetic variants have a measurable, albeit small, impact on the classification of T2D status by HbA1c in white and Asian populations. Accounting for their effect on HbA1c may be relevant when screening populations with higher frequencies of nonglycemic HbA1c-altering alleles. As carriers of such HbA1c-altering alleles have HbA1c levels that may not accurately reflect overall glycemia, we describe how accounting for genotype may improve the performance of HbA1c in T2D prediction models and risk stratification, allowing for lifestyle intervention strategies to be directed towards those who are truly at elevated risk for developing T2D. In a Mendelian randomization framework, genetic variants can be used as instrumental variables to estimate causal relationships between HbA1c and T2D-related complications. This approach may help to support or refute HbA1c as an appropriate biomarker for long-term health outcomes in the general population.

Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study

OBJECTIVE

Risk models and scores have been developed to predict incidence of type 2 diabetes in Western populations, but their performance may differ when applied to non-Western populations. We developed and validated a risk score for predicting 3-year incidence of type 2 diabetes in a Japanese population.

METHODS

Participants were 37,416 men and women, aged 30 or older, who received periodic health checkup in 2008-2009 in eight companies. Diabetes was defined as fasting plasma glucose (FPG) ≥ 126 mg/dl, random plasma glucose ≥ 200 mg/dl, glycated hemoglobin (HbA1c) ≥ 6.5%, or receiving medical treatment for diabetes. Risk scores on non-invasive and invasive models including FPG and HbA1c were developed using logistic regression in a derivation cohort and validated in the remaining cohort.

RESULTS

The area under the curve (AUC) for the non-invasive model including age, sex, body mass index, waist circumference, hypertension, and smoking status was 0.717 (95% CI, 0.703-0.731). In the invasive model in which both FPG and HbA1c were added to the non-invasive model, AUC was increased to 0.893 (95% CI, 0.883-0.902). When the risk scores were applied to the validation cohort, AUCs (95% CI) for the non-invasive and invasive model were 0.734 (0.715-0.753) and 0.882 (0.868-0.895), respectively. Participants with a non-invasive score of ≥ 15 and invasive score of ≥ 19 were projected to have >20% and >50% risk, respectively, of developing type 2 diabetes within 3 years.

CONCLUSIONS

The simple risk score of the non-invasive model might be useful for predicting incident type 2 diabetes, and its predictive performance may be markedly improved by incorporating FPG and HbA1c.

Utilizing Genetic Predisposition Score in Predicting Risk of Type 2 Diabetes Mellitus Incidence: A Community-based Cohort Study on Middle-aged Koreans

Contribution of genetic predisposition to risk prediction of type 2 diabetes mellitus (T2DM) was investigated using a prospective study in middle-aged adults in Korea. From a community cohort of 6,257 subjects with 8 yr' follow-up, genetic predisposition score with subsets of 3, 18, 36 selected single nucleotide polymorphisms (SNPs) (genetic predisposition score; GPS-3, GPS-18, GPS-36) in association with T2DM were determined, and their effect was evaluated using risk prediction models. Rs5215, rs10811661, and rs2237892 were in significant association with T2DM, and hazard ratios per risk allele score increase were 1.11 (95% confidence intervals: 1.06-1.17), 1.09 (1.01-1.05), 1.04 (1.02-1.07) with GPS-3, GPS-18, GPS-36, respectively. Changes in AUC upon addition of GPS were significant in simple and clinical models, but the significance disappeared in full clinical models with glycated hemoglobin (HbA1c). For net reclassification index (NRI), significant improvement observed in simple (range 5.1%-8.6%) and clinical (3.1%-4.4%) models were no longer significant in the full models. Influence of genetic predisposition in prediction ability of T2DM incidence was no longer significant when HbA1c was added in the models, confirming HbA1c as a strong predictor for T2DM risk. Also, the significant SNPs verified in our subjects warrant further research, e.g. gene-environmental interaction and epigenetic studies.

Excess body weight affects HbA1c progression irrespective of baseline HbA1c levels in Japanese individuals: a longitudinal retrospective study

PURPOSE/AIM

Obese individuals with normal HbA1c levels and low-body-weight individuals with high-normal HbA1c levels are frequently encountered in clinical settings, but the effects of these phenotypes on the onset of diabetes are poorly understood. Therefore, we addressed this issue in a longitudinal study.

MATERIALS AND METHODS

We analyzed clinical parameters, including body mass index (BMI) and HbA1c levels, in 5325 non-diabetic Japanese people aged 20-75 years who underwent four medical checkups between 1999 (baseline) and 2007. The subjects were then classified into six baseline BMI categories, each of which was divided into two HbA1c groups, resulting in a total of 12 groups.

RESULTS

In 405 obese subjects with a normal baseline HbA1c (BMI ≥ 27.0 kg/m(2), HbA1c 5.2-5.6%), the mean HbA1c level increased during the study period, and 50.9% developed prediabetes/diabetes. In contrast, in 77 low-body-weight subjects with a high-normal baseline HbA1c (BMI ≤ 18.9 kg/m(2), HbA1c 5.7-6.4%), the mean HbA1c level remained constant. Similar changes occurred in the other groups during the study, resulting in a linear increase in HbA1c levels with increasing BMI.

CONCLUSION

Our results suggest that approximately half of the obese individuals with HbA1c in the normal range develop prediabetes or diabetes within 8 years, whereas low-body-weight individuals with high-normal HbA1c are less likely to exhibit worsening in glycemia. Thus, excess body weight may be the primary therapeutic target to prevent the early onset of diabetes, regardless of the individual's HbA1c.

Development of a new risk score for incident type 2 diabetes using updated diagnostic criteria in middle-aged and older chinese

Type 2 diabetes mellitus (T2DM) reaches an epidemic proportion among adults in China. However, no simple score has been created for the prediction of T2DM incidence diagnosed by updated criteria with hemoglobin A1c (HbA1c) ≥6.5% included in Chinese. In a 6-year follow-up cohort in Beijing and Shanghai, China, we recruited a total of 2529 adults aged 50–70 years in 2005 and followed them up in 2011. Fasting plasma glucose (FPG), HbA1c, and C-reactive protein (CRP) were measured and incident diabetes was identified by the recently updated criteria. Of the 1912 participants without T2DM at baseline, 924 were identified as having T2DM at follow-up, and most of them (72.4%) were diagnosed using the HbA1c criterion. Baseline body mass index, FPG, HbA1c, CRP, hypertension, and female gender were all significantly associated with incident T2DM. Based upon these risk factors, a simple score was developed with an estimated area under the receiver operating characteristic curve of 0.714 (95% confidence interval: 0.691, 0.737), which performed better than most of existing risk score models developed for eastern Asian populations. This simple, newly constructed score of six parameters may be useful in predicting T2DM in middle-aged and older Chinese.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

Development of a screening score for undiagnosed diabetes and its application in estimating absolute risk of future type 2 diabetes in Japan: Toranomon Hospital Health Management Center Study 10 (TOPICS 10)

OBJECTIVE

The objective of the study was to develop a screening score for undiagnosed diabetes by eliciting information on noninvasive clinical markers and to assess its effectiveness for identifying the presence of diabetes and predicting future diabetes.

DESIGN, SETTING, AND PARTICIPANTS

A screening score was cross-sectionally developed for 33 335 Japanese individuals aged 18-88 years without known diabetes who underwent a health examination. We validated its utility and compared it with existing screening tools in an independent population (n = 7477). After initial assessment of the instrument, 7332 nondiabetic individuals were followed up for a mean 4.0 years.

RESULTS

Prevalence of undiagnosed diabetes (fasting plasma glucose ≥ 7.0 mmol/L or glycated hemoglobin ≥ 6.5%) was 2.9% (n = 965). Diabetes score included age, sex, family history of diabetes, current smoking habit, body mass index, and hypertension with an area under the receiver-operating characteristics curve of 0.771. Screening with 8 or more points yielded a sensitivity of 72.7% and a specificity of 68.1%. In the validation cohort, the area under the receiver-operating characteristics curve was 0.806. The developed score with 8 or more points had better positive predictive value (9.6%) and positive likelihood ratio (2.52) compared with existing tools (positive predictive value, from 6.9% to 9.4%; positive likelihood ratio, from 1.77 to 2.46) in which each tool's highest combination of sensitivity and specificity was observed. The 4-year cumulative risk of developing diabetes gradually escalated in association with higher screening scores at the initial examination.

CONCLUSIONS

Our algorithm could serve as a self-assessment tool for undiagnosed diabetic patients needing timely medical care and as a prognostic tool for individuals without present diabetes who must be closely followed up to prevent future diabetes.