Risk assessment tools for identifying individuals at risk of developing type 2 diabetes

Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting

Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey (n = 2,472) and the CRONICAS Cohort Study (n = 2,945). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62-0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61-0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

Metabolomics in diabetes, a review

Metabolomics is a promising approach for the identification of chemical compounds that serve for early detection, diagnosis, prediction of therapeutic response and prognosis of disease. Moreover, metabolomics has shown to increase the diagnostic threshold and prediction of type 2 diabetes. Evidence suggests that branched-chain amino acids, acylcarnitines and aromatic amino acids may play an early role on insulin resistance, exposing defects on amino acid metabolism, β-oxidation, and tricarboxylic acid cycle. This review aims to provide a panoramic view of the metabolic shifts that antecede or follow type 2 diabetes. Key messages BCAAs, AAAs and acylcarnitines are strongly associated with early insulin resistance. Diabetes risk prediction has been improved when adding metabolomic markers of dysglycemia to standard clinical and biochemical factors.

Evaluation of the Finnish Diabetes Risk Score to predict type 2 diabetes mellitus in a Colombian population: A longitudinal observational study

AIM: To assess the performance of the Finnish Diabetes Risk Score (FINDRISC) questionnaire for detecting and predicting type 2 diabetes mellitus (DM2) in a Colombian population.

METHODS: This is a longitudinal observational study conducted in Floridablanca, Colombia. Adult subjects (age ≥ 35 years) without known diabetes, were included. A modified version of FINDRISC was completed, and the glycemia values from all the subjects were collected from the hospital’s database. Firstly, a cross-sectional analysis was performed and then, the subsample of prediabetic participants was followed for diabetes incidence.

RESULTS: A total of 772 subjects were suitable for the study. The overall prevalence of undiagnosed DM2 was 2.59%, and the incidence of DM2 among the prediabetic participants was 7.5 per 100 person-years after a total of 265257 person-years follow-up. The FINDRISC at baseline was significantly associated with undiagnosed and incident DM2. The area under receiver operating characteristics curve of the FINDRISC score for detecting undiagnosed DM2 in both men and women was 0.7477 and 0.7175, respectively; and for predicting the incidence of DM2 among prediabetics was 71.99% in men and 67.74% in women.

CONCLUSION: The FINDRISC questionnaire is a useful screening tool to identify cross-sectionally unknown DM2 and to predict the incidence of DM2 among prediabetics in the Colombian population.

Evaluation of Major Online Diabetes Risk Calculators and Computerized Predictive Models

Classical paper-and-pencil based risk assessment questionnaires are often accompanied by the online versions of the questionnaire to reach a wider population. This study focuses on the loss, especially in risk estimation performance, that can be inflicted by direct transformation from the paper to online versions of risk estimation calculators by ignoring the possibilities of more complex and accurate calculations that can be performed using the online calculators. We empirically compare the risk estimation performance between four major diabetes risk calculators and two, more advanced, predictive models. National Health and Nutrition Examination Survey (NHANES) data from 1999-2012 was used to evaluate the performance of detecting diabetes and pre-diabetes. American Diabetes Association risk test achieved the best predictive performance in category of classical paper-and-pencil based tests with an Area Under the ROC Curve (AUC) of 0.699 for undiagnosed diabetes (0.662 for pre-diabetes) and 47% (47% for pre-diabetes) persons selected for screening. Our results demonstrate a significant difference in performance with additional benefits for a lower number of persons selected for screening when statistical methods are used. The best AUC overall was obtained in diabetes risk prediction using logistic regression with AUC of 0.775 (0.734) and an average 34% (48%) persons selected for screening. However, generalized boosted regression models might be a better option from the economical point of view as the number of selected persons for screening of 30% (47%) lies significantly lower for diabetes risk assessment in comparison to logistic regression (p < 0.001), with a significantly higher AUC (p < 0.001) of 0.774 (0.740) for the pre-diabetes group. Our results demonstrate a serious lack of predictive performance in four major online diabetes risk calculators. Therefore, one should take great care and consider optimizing the online versions of questionnaires that were primarily developed as classical paper questionnaires.

Systematic Review and Meta-Analysis of Response Rates and Diagnostic Yield of Screening for Type 2 Diabetes and Those at High Risk of Diabetes

Background

Screening for type 2 diabetes (T2DM) and individuals at risk of diabetes has been advocated, yet information on the response rate and diagnostic yield of different screening strategies are lacking.

Methods

Studies (from 1998 to March/2015) were identified through Medline, Embase and the Cochrane library and included if they used oral glucose tolerance test (OGTT) and WHO-1998 diagnostic criteria for screening in a community setting. Studies were one-step strategy if participants were invited directly for OGTT and two, three/four step if participants were screened at one or more levels prior to invitation to OGTT. The response rate and diagnostic yield were pooled using Bayesian random-effect meta-analyses.

Findings

47 studies (422754 participants); 29 one-step, 11 two-step and seven three/four-step were identified. Pooled response rate (95% Credible Interval) for invitation to OGTT was 65.5% (53.7, 75.6), 63.1% (44.0, 76.8), and 85.4% (76.4, 93.3) in one, two and three/four-step studies respectively. T2DM yield was 6.6% (5.3, 7.8), 13.1% (4.3, 30.9) and 27.9% (8.6, 66.3) for one, two and three/four-step strategies respectively. The number needed to invite to the OGTT to detect one case of T2DM was 15, 7.6 and 3.6 in one, two, and three/four-step strategies. In two step strategies, there was no difference between the response or yield rates whether the first step was blood test or risk-score. There was evidence of substantial heterogeneity in rates across study populations but this was not explained by the method of invitation, study location (rural versus urban) and developmental index of the country in which the study was performed.

Conclusions

Irrespective of the invitation method, developmental status of the countries and or rural/urban location, using a multi-step strategy increases the initial response rate to the invitation to screening for diabetes and reduces the number needed to have the final diagnostic test (OGTT in this study) for a definite diagnosis.

Association between CETP gene polymorphism, insulin resistance and risk of diabetes mellitus in patients with vascular disease

INTRODUCTION

Genetic inhibition of Cholesteryl Ester Transfer Protein (CETP) might be associated with insulin resistance and incident type 2 diabetes mellitus (T2DM). This study investigated the relation between a genetic variant in the CETP gene and measures of insulin resistance and incident T2DM in patients with manifest cardiovascular disease (CVD). Furthermore the effect on risk of recurrent cardiovascular events was investigated.

METHODS

SMART is a prospective cohort study performed in 5601 patients with clinically manifest CVD. We selected a variant (rs3764261) associated with reduced CETP activity and increased levels of HDL cholesterol (HDL-C). Patients were divided in three groups: 2640 wild type patients (GG), 2420 heterozygotes for rs3764261 (GT) and 541 homozygotes for rs3764261 (TT). Regression analyses were performed using an additive model.

RESULTS

The study population consisted of 4656 patients without T2DM and 945 patients with T2DM at baseline. Presence of rs3764261 was associated with increased HDL-C in patients without T2DM (β 0.106, 95%CI 0.083-0.128) and with T2DM (β 0.043, 95%CI 0.007-0.078). During a median follow up of 7.2 years (IQR 4.7-10.2) 427 incident T2DM occurred. Presence of rs3764261 was not related to incident T2DM (HR 0.96, 95%CI 0.83-1.11) in patients without T2DM at baseline. Furthermore, presence of rs3764261 was not related to insulin resistance (glucose, insulin, HOMA-IR, HbA1c) or recurrent CVD (HR 0.92, 95%CI 0.84-1.02).

CONCLUSION

Presence of CETP SNP rs3764261 is not associated with insulin resistance and incident T2DM in patients with clinically manifest vascular disease. Furthermore, no effect of rs3764261 on the risk of recurrent CVD was observed.

The product of triglycerides and glucose in comparison with fasting plasma glucose did not improve diabetes prediction

AIMS

Previous study has reported that triglycerides-glucose (TyG) index, a product of triglycerides and fasting plasma glucose (FPG), might be useful in the prediction of incident type 2 diabetes (T2D). We evaluated the ability of the TyG index compared to FPG and OGTT as possible diabetes predictor in nondiabetic first-degree relatives (FDRs) of patients with T2D.

METHODS

A total of 1,488 FDRs without diabetes of consecutive patients with T2D 30-70 years old (361 men and 1,127 women) were examined and followed for a mean (SD) of 6.9 (1.7) years for diabetes incidence. We examined the incidence of diabetes across quartiles of the TyG index and plotted a receiver operating characteristic (ROC) curve to assess discrimination. At baseline and through follow-up, participants underwent a standard 75-g two-hour oral glucose tolerance test.

RESULTS

During 10,124 person-years of follow-up, 41 men and 154 women developed T2D. Those in the top quartile of TyG index were 3.4 times more likely to develop T2D than those in the bottom quartile (odds ratio 3.36; 95 % CI 1.83, 6.19). On ROC curve analysis, a higher area under the ROC was found for FPG (76.2; 95 % CI 71.9, 80.6), 1-hPG (81.0, 95 % CI 77.2, 84.9) and 2-hPG (76.5; 95 % CI 72.3, 80.8) than for TyG index (65.1; 95 % CI 60.5, 69.7).

CONCLUSIONS

TyG index is predicted T2D in high-risk individuals in Iran but FPG, 1-hPG and 2-hPG appeared to be more robust predictor of T2D in our study population.

Primary care screening for individuals with impaired glucose metabolism with focus on impaired glucose tolerance

AIM

To evaluate the utility of three short questions (the Skövde Form) combined with a random plasma glucose, and HbA1c as alternative tools for detection of individuals with impaired glucose metabolism (IGM), and particularly impaired glucose tolerance (IGT).

METHODS

Three questions concerning BMI ≥ 25 kg/m(2), heredity for type 2 diabetes, and known hypertension were asked in a random population of 573 individuals. All with two positive answers or one positive answer and a random plasma glucose > 7.2 mmol/l were invited for an oral glucose tolerance test and an HbA1c examination. FINDRISC was completed for comparison.

RESULTS

The positive predictive value (PPV) for IGM, using the Skövde Form, was 31% while sensitivity and specificity were 59% and 73%, respectively. Corresponding values for IGT were 11%, 50% and 69%. Using HbA1c ≥ 42 mmol/mol, the PPV for IGM was 64% while sensitivity and specificity were 28% and 97%, respectively. The corresponding values for IGT were 15%, 16% and 94%.

CONCLUSION

The Skövde Form combined with a random plasma glucose may be used as an alternative tool for detection of individuals with IGM and IGT in particular. HbA1c may be used to identify individuals with type 2 diabetes but fails to detect most individuals with prediabetes.

[Consensus on the detection and management of prediabetes. Consensus and Clinical Guidelines Working Group of the Spanish Diabetes Society]

In Spain, according to the Di@bet.es study, 13.8% of the adult population suffers from diabetes and 14.8% from some form of prediabetes (impaired glucose tolerance, impaired fasting glucose or both). Since early detection of prediabetes can facilitate the implementation of therapeutic measures to prevent its progression to diabetes, we believe that preventive strategies in primary care and specialized clinical settings should be agreed. Screening for diabetes and prediabetes using a specific questionnaire (FINDRISC) and/or the measurement of fasting plasma glucose in high risk patients leads to detecting patients at risk of developing diabetes and it is necessary to consider how they should be managed. The intervention in lifestyle can reduce the progression to diabetes and reverse a prediabetic state to normal and is a cost-effective intervention. Some drugs, such as metformin, have also been shown effective in reducing the progression to diabetes but are not superior to non-pharmacological interventions. Finally, an improvement in some cardiovascular risk factors has been observed although there is no strong evidence supporting the effectiveness of screening in terms of morbility and mortality. The Consensus and Clinical Guidelines Working Group of the Spanish Diabetes Society has issued some recommendations that have been agreed by the Sociedad Española de Endocrinología y Nutrición, Sociedad Española de Endocrinología Pediátrica, Sociedad Española de Farmacia Comunitaria, Sociedad Española de Medicina Familiar y Comunitaria, Sociedad Española de Médicos Generales, Sociedad Española de Médicos de Atención Primaria, Sociedad Española de Medicina Interna, Asociación de Enfermería Comunitaria and Red de Grupos de Estudio de la Diabetes en Atención Primaria.

Screening for people with abnormal glucose metabolism in the European DE-PLAN project

AIMS

The aim of this report is to describe the application of the FINDRISC in clinical practice within the DE-PLAN project as a step to screen for Type 2 diabetes.

METHODS

Nine out of 24 possible centers were included. Six centers used opportunistic screening methods for participant recruitment whereas three centers provided study participants of a random population sample. Men (n=1621) and women (n=2483) were evaluated separately. In order to assess the prevalence of abnormal glucose tolerance (AGT) disorders across different risk categories, the FINDRISC was used. Anthropometric measurements included blood pressure, height, weight, and waist circumference. Blood lipids and an oral glucose tolerance test were performed in all participants. The primary outcome was identified risk of AGT and type 2 diabetes.

RESULTS

There was no difference in the prevalence of smoking between the FINDRISC categories, people with a FINDRISC below 15 points tend to be more physically active and to eat more frequently fruits and vegetables. Men with a FINDRISC from 15 to 19 points had a prevalence of abnormal glucose tolerance of approximately 60% and women 50%. The prevalence for men and women with a FINDRISC >20 points was 80%. 30% of men and 20% of women with a FINDRISC between 15 and 19 points had Type 2 diabetes. Among people with a FINDRISC more than 20 points, 50% had previously undiagnosed Type 2 diabetes.

CONCLUSIONS

The FINDRISC may be a practical tool to be used in primary health-care systems throughout the European population.

Predictive modeling for incident and prevalent diabetes risk evaluation

With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.

Threshold-free measures for assessing the performance of medical screening tests

Background

The area under the receiver operating characteristic curve (AUC) is frequently used as a performance measure for medical tests. It is a threshold-free measure that is independent of the disease prevalence rate. We evaluate the utility of the AUC against an alternate measure called the average positive predictive value (AP), in the setting of many medical screening programs where the disease has a low prevalence rate.

Methods

We define the two measures using a common notation system and show that both measures can be expressed as a weighted average of the density function of the diseased subjects. The weights for the AP include prevalence in some form, but those for the AUC do not. These measures are compared using two screening test examples under rare and common disease prevalence rates.

Results

The AP measures the predictive power of a test, which varies when the prevalence rate changes, unlike the AUC, which is prevalence independent. The relationship between the AP and the prevalence rate depends on the underlying screening/diagnostic test. Therefore, the AP provides relevant information to clinical researchers and regulators about how a test is likely to perform in a screening population.

Conclusion

The AP is an attractive alternative to the AUC for the evaluation and comparison of medical screening tests. It could improve the effectiveness of screening programs during the planning stage.

Integrated genomic and BMI analysis for type 2 diabetes risk assessment

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.

Genomic-based tools for the risk assessment, management, and prevention of type 2 diabetes

Type 2 diabetes (T2D) is a common and serious disorder and is a significant risk factor for the development of cardiovascular disease, neuropathy, nephropathy, retinopathy, periodontal disease, and foot ulcers and amputations. The burden of disease associated with T2D has led to an emphasis on early identification of the millions of individuals at high risk so that management and intervention strategies can be effectively implemented before disease progression begins. With increasing knowledge about the genetic basis of T2D, several genomic-based strategies have been tested for their ability to improve risk assessment, management and prevention. Genetic risk scores have been developed with the intent to more accurately identify those at risk for T2D and to potentially improve motivation and adherence to lifestyle modification programs. In addition, evidence is building that oral antihyperglycemic medications are subject to pharmacogenomic variation in a substantial number of patients, suggesting genomics may soon play a role in determining the most effective therapies. T2D is a complex disease that affects individuals differently, and risk prediction and treatment may be challenging for health care providers. Genomic approaches hold promise for their potential to improve risk prediction and tailor management for individual patients and to contribute to better health outcomes for those with T2D.

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND

Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans.

METHODS

Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment.

RESULTS

Seven hundred thirty-seven participants (27 % male), mean age, 52.2 years, were included, among whom 130 (17.6 %) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95 % confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment.

CONCLUSIONS

The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings.

Challenges of diabetes prevention in the real world: results and lessons from the Melbourne Diabetes Prevention Study

OBJECTIVE

To assess effectiveness and implementability of the public health programme Life! Taking action on diabetes in Australian people at risk of developing type 2 diabetes.

RESEARCH DESIGN AND METHODS

Melbourne Diabetes Prevention Study (MDPS) was a unique study assessing effectiveness of Life! that used a randomized controlled trial design. Intervention participants with AUSDRISK score ≥15 received 1 individual and 5 structured 90 min group sessions. Controls received usual care. Outcome measures were obtained for all participants at baseline and 12 months and, additionally, for intervention participants at 3 months. Per protocol set (PPS) and intention to treat (ITT) analyses were performed.

RESULTS

PPS analyses were considered more informative from our study. In PPS analyses, intervention participants significantly improved in weight (-1.13 kg, p=0.016), waist circumference (-1.35 cm, p=0.044), systolic (-5.2 mm Hg, p=0.028) and diastolic blood pressure (-3.2 mm Hg, p=0.030) compared with controls. Based on observed weight change, estimated risk of developing diabetes reduced by 9.6% in the intervention and increased by 3.3% in control participants. Absolute 5-year cardiovascular disease (CVD) risk reduced significantly for intervention participants by 0.97 percentage points from 9.35% (10.4% relative risk reduction). In control participants, the risk increased by 0.11 percentage points (1.3% relative risk increase). The net effect for the change in CVD risk was -1.08 percentage points of absolute risk (p=0.013).

CONCLUSIONS

MDPS effectively reduced the risk of diabetes and CVD, but the intervention effect on weight and waist reduction was modest due to the challenges in recruiting high-risk individuals and the abbreviated intervention.

Understanding perceived risk of type 2 diabetes in healthy middle-aged adults: a cross-sectional study of associations with modelled risk, clinical risk factors, and psychological factors

AIMS

To determine the perceived risk of type 2 diabetes in a sample of healthy middle-aged adults and examine the association between perceived risk and modelled risk, clinical risk factors, and psychological factors theorised to be antecedents of behaviour change.

METHODS

An exploratory, cross-sectional analysis of perceived risk of type 2 diabetes (framed according to time and in comparison with peers) was conducted using baseline data collected from 569 participants of the Diabetes Risk Communication Trial (Cambridgeshire, UK). Type 2 diabetes risk factors were measured during a health assessment and the Framingham Offspring Diabetes Risk Score was used to model risk. Questionnaires assessed psychological factors including anxiety, diabetes-related worry, behavioural intentions, and other theory-based antecedents of behaviour change. Multivariable regression analyses were used to examine associations between perceived risk and potential correlates.

RESULTS

Participants with a high perceived risk were at higher risk according to the Framingham Offspring Diabetes Risk Score (p<0.001). Higher perceived risk was observed in those with a higher body fat percentage, lower self-rated health, higher diabetes-related worry, and lower self-efficacy for adhering to governmental recommendations for physical activity (all p<0.001). The framing of perceived risk according to time and in comparison with peers did not influence these results.

CONCLUSIONS

High perceived risk of type 2 diabetes is associated with higher risk of developing the disease, and a decreased likelihood of engagement in risk-reducing health behaviours. Risk communication interventions should target high-risk individuals with messages about the effectiveness of prevention strategies.

Management of NCD in low- and middle-income countries

Noncommunicable disease (NCD), comprising cardiovascular disease, stroke, diabetes, and chronic obstructive pulmonary disease, are increasing in incidence rapidly in low- and middle-income countries (LMICs). Some patients have access to the same treatments available in high-income countries, but most do not, and different strategies are needed. Most research on noncommunicable diseases has been conducted in high-income countries, but the need for research in LMICs has been recognized. LMICs can learn from high-income countries, but they need to devise their own systems that emphasize primary care, the use of community health workers, and sometimes the use of mobile technology. The World Health Organization has identified "best buys" it advocates as interventions in LMICs. Non-laboratory-based risk scores can be used to identify those at high risk. Targeting interventions to those at high risk for developing diabetes has been shown to work in LMICs. Indoor cooking with biomass fuels is an important cause of chronic obstructive pulmonary disease in LMICs, and improved cookstoves with chimneys may be effective in the prevention of chronic diseases.

Role of various indices derived from an oral glucose tolerance test in the prediction of conversion from prediabetes to type 2 diabetes

AIMS

The clinical implications of prediabetes for development of type 2 diabetes may differ for Asian ethnicity. We investigated various indices derived from a 2-h oral glucose tolerance test (OGTT) in people with prediabetes to predict their future risk of diabetes.

METHODS

We recruited 406 consecutive subjects with prediabetes from 2005 to 2006 and followed them up every 3-6 months for up to 9 years. Prediabetes was defined as isolated impaired fasting glucose (IFG), isolated impaired glucose tolerance (IGT), combined glucose intolerance (CGI), or isolated elevated HbA1c (5.7-6.4%, 39-46 mmol/mol) without IFG or IGT. The rate of diabetes conversion was compared between prediabetes categories. The association of glycemic indices with development of diabetes was also investigated.

RESULTS

Eighty-one patients were diagnosed with diabetes during the 9-year follow-up (median 46.0 months). The rate of diabetes conversion was higher in subjects with CGI (31.9%), or isolated IGT (18.5%) than in those with isolated IFG (15.2%) or isolated elevated HbA1c (10.9%). Surrogate markers reflecting β-cell dysfunction were more closely associated with diabetes conversion than insulin resistance indices. Subjects with a 30-min postload glucose ≥ 165 mg/dL and a 30-min C-peptide < 5 ng/mL had 8.83 times greater risk (95% confidence interval 2.98-26.16) of developing diabetes than other prediabetic subjects.

CONCLUSIONS

In Asians, at least Koreans, β-cell dysfunction seems to be the major determinant for diabetes conversion. A combination of high glucose and low C-peptide levels at 30 min after OGTT may be a good predictor for diabetes conversion in this population.

Excess body iron and the risk of type 2 diabetes mellitus: a nested case–control in the PREDIMED (PREvention with MEDiterranean Diet) study

A prospective nested case–control study within the PREvention with MEDiterranean Diet (PREDIMED) was conducted to evaluate the relationship between excess body Fe (measured as serum ferritin (SF), soluble transferrin receptor (sTfR) and sTfR:ferritin ratio) and the risk of type 2 diabetes mellitus (T2DM) in a Mediterranean population at a high risk of CVD, without T2DM at the start of the study. The study contained 459 subjects, 153 with incident T2DM (cases) and 306 without incident T2DM (controls). The follow-up period was for 6·0 (interquartile range 3·9–6·5) years. For each incident diabetic subject, two subjects were selected as controls who were matched broadly for age as well as for sex, intervention group and BMI. We observed a relationship between SF values >257 μg/l in males and >139 μg/l in females and the risk of T2DM, following adjustment in the conditional logistic regression model for high-sensitivity C-reactive protein, fasting glucose and other components of the metabolic syndrome (OR 3·62, 95 % CI 1·32, 19·95; P= 0·022). We also found an association between low sTfR:ferritin ratio levels and the incidence of T2DM (OR 3·02, 95 % CI 1·09, 8·39; P= 0·042), but no association with sTfR (OR 1·29, 95 % CI 0·51, 3·23; P= 0·722). Oxidative stress has been hypothesised to contribute to the development of insulin resistance and β-cell dysfunction, the two key events in the clinical development of T2DM. Following adjustment for other risk factors for T2DM, excess body Fe (measured as SF and sTfR:ferritin ratio) was associated with an increased risk of developing T2DM in a Mediterranean population at a high risk of CVD.

Preventing type 2 diabetes mellitus: a call for personalized intervention

In parallel with the rising prevalence of obesity worldwide, especially in younger people, there has been a dramatic increase in recent decades in the incidence and prevalence of metabolic consequences of obesity, in particular prediabetes and type 2 diabetes mellitus (DM2). Although approximately one-third of US adults now meet one or more diagnostic criteria for prediabetes, only a minority of those so identified as being at risk for DM2 actually progress to diabetes, and some may regress to normal status. Given the uncertain prognosis of prediabetes, it is not clear who is most likely to benefit from lifestyle change or medication interventions that are known to reduce DM2 risk. We review the many factors known to influence risk of developing DM2 and summarize treatment trials demonstrating the possibility of preventing DM2. Applying the concepts of personalized medicine and the potential of "big data" approaches to analysis of massive amounts of routinely gathered clinical and laboratory data from large populations, we call for the development of tools to more precisely estimate individual risk of DM2.

New insight into improvement of cardiovascular outcomes with intensive glycemic control in patients with metabolic syndrome and type 2 diabetes mellitus included

The epidemiology of metabolic syndrome (MetS) is very concerning since this is a widespread chronic disease in adult and elderly population. Different epidemiological studies confirmed strong relationship between type 2 diabetes mellitus (DM2) inclusive MetS and the occurrence of microvascular and macrovascular complications in these patients. Therefore, we are discussing molecular mechanisms and pathways recently introduced and intensively researched. These molecules have been held responsible for direct and indirect correlative trends important in the assessment of potential benefit of intensive glycemic control. Novel substances may improve patient's outcome in recent future regarding cardiovascular complications though their acting mechanisms have not been completely elucidated. We are reviewing whether it would intensive glycemic control be a reasonable approach in patients with MetS when DM2 is included and which recommendations are currently widely applied. The aim was therefore to emphasize current need for further investigations in the field since the utility of intensive glycemic control in all DM/MetS patients as mean for reduction of cardiovascular complications still remains controversial.

A risk assessment model for type 2 diabetes in Chinese

Aims

To develop a risk assessment model for persons at risk from type 2 diabetes in Chinese.

Materials and Methods

The model was generated from the cross-sectional data of 16246 persons aged from 20 years old and over. C4.5 algorithm and multivariate logistic regression were used for variable selection. Relative risk value combined with expert decision constructed a comprehensive risk assessment for evaluating the individual risk category. The validity of the model was tested by cross validation and a survey performed six years later with some participants.

Results

Nine variables were selected as risk variables. A mathematical model was established to calculate the average probability of diabetes in each cluster's group divided by sex and age. A series of criteria combined with relative RR value (2.2) and level of risk variables stratified individuals into four risk groups (non, low, medium and high risk). The overall accuracy reached 90.99% evaluated by cross-validation inside the model population. The incidence of diabetes for each risk group increased from 1.5 (non-risk group) to 28.2(high-risk group) per one thousand persons per year with six years follow-up.

Discussion

The model could determine the individual risk for type 2 diabetes by four risk degrees. This model could be used as a technique tool not only to support screening persons at different risk, but also to evaluate the result of the intervention.

BMI and waist circumference are associated with impaired glucose metabolism and type 2 diabetes in normal weight Chinese adults

AIMS

To examine the associations of BMI and waist circumference with glucose metabolism and (pre)diabetes among adults with BMI < 25 kg/m².

METHODS

We conducted a cross-sectional study in a nationally representative sample (10,098 men and 17,454 women) of Chinese adults aged ≥ 20 years with BMI < 25 kg/m². Glucose levels after at least 10 hours of overnight fasting, at 30 minutes and at 120 minutes after a standard 75-g oral glucose load were measured. Associations of BMI and waist circumference with outcomes were examined by general linear models for continuous outcomes and by logistic regression models for dichotomous outcomes.

RESULTS

Among those with BMI < 25 kg/m², 18.8% of men and 17.1% of women had abnormal glucose metabolism, including 4.9% of men and 3.8% of women with undiagnosed type 2 diabetes. For each SD increase in BMI (2.1 kg/m²) and waist circumference (8.3 cm), fasting glucose levels increased by 0.128 and 0.170 mmol/L in men, and by 0.112 and 0.167 mmol/L in women, respectively; the corresponding increases for 2-hour post-load glucose levels were 0.121 and 0.217 mmol/L in men, and 0.241 and 0.362 mmol/L in women. When simultaneously included in the same model, these associations with waist circumference were stronger than with BMI.

CONCLUSION

Obesity measures are associated with abnormal glucose metabolism and diabetes, with central obesity playing a more prominent role than general obesity in Chinese population with BMI < 25 kg/m². Chinese diabetes prevention and treatment programs should incorporate targeting of normal weight adults with central obesity.

Risk assessment tools for detecting those with pre-diabetes: a systematic review

AIM

To describe and evaluate risk assessment tools which detect those with pre-diabetes defined as either impaired glucose tolerance or impaired fasting glucose using an OGTT or as a raised HbA1c.

METHODS

Tools were identified through a systematic search of PubMed and EMBASE for articles which developed a risk tool to detect those with pre-diabetes. Data were extracted using a standardised data extraction form.

RESULTS

Eighteen tools met the inclusion criteria. Eleven tools were derived using logistic regression, six using decision trees and one using support vector machine methodology. Age, body mass index, family history of diabetes and hypertension were the most frequently included variables. The size of the datasets used and the number of events per variable considered were acceptable in all the tools. Missing data were not discussed for 8 (44%) of the tools, 10 (91%) of the logistic tools categorised continuous variables, external validation was carried out for only 7 (39%) of the tools and only 3 tools reported calibration levels.

CONCLUSIONS

Several risk scores are available to identify those with pre-diabetes. Before these are used in practice, the level of calibration and validity of the tools in the population of interest should be assessed.

How diabetes risk assessment tools are implemented in practice: a systematic review

This review aimed to explore the extent of the use of diabetes risk assessment tools and to determine influential variables associated with the implementation of these tools. CINAHL, Google Scholar, ISI Citation Indexes, PubMed, and Scopus were searched from inception to January 2013. Studies that reported the use of diabetes risk assessment tools to identify individuals at risk of diabetes were included. Of the 1719 articles identified, 24 were included. Follow-up of high risk individuals for diagnosis of diabetes was conducted in 5 studies. Barriers to the uptake of diabetes risk assessment tools by healthcare practitioners included (1) attitudes toward the tools; (2) impracticality of using the tools and (3) lack of reimbursement and regulatory support. Individuals were reluctant to undertake self-assessment of diabetes risk due to (1) lack of perceived severity of type 2 diabetes; (2) impracticality of the tools; and (3) concerns related to finding out the results. The current use of non-invasive diabetes risk assessment scores as screening tools appears to be limited. Practical follow up systems as well as strategies to address other barriers to the implementation of diabetes risk assessment tools are essential and need to be developed.

Update of the German Diabetes Risk Score and external validation in the German MONICA/KORA study

AIMS

Several published diabetes prediction models include information about family history of diabetes. The aim of this study was to extend the previously developed German Diabetes Risk Score (GDRS) with family history of diabetes and to validate the updated GDRS in the Multinational MONItoring of trends and determinants in CArdiovascular Diseases (MONICA)/German Cooperative Health Research in the Region of Augsburg (KORA) study.

METHODS

We used data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study for extending the GDRS, including 21,846 participants. Within 5 years of follow-up 492 participants developed diabetes. The definition of family history included information about the father, the mother and/or sibling/s. Model extension was evaluated by discrimination and reclassification. We updated the calculation of the score and absolute risks. External validation was performed in the MONICA/KORA study comprising 11,940 participants with 315 incident cases after 5 years of follow-up.

RESULTS

The basic ROC-AUC of 0.856 (95%-CI: 0.842-0.870) was improved by 0.007 (0.003-0.011) when parent and sibling history was included in the GDRS. The net reclassification improvement was 0.110 (0.072-0.149), respectively. For the updated score we demonstrated good calibration across all tenths of risk. In MONICA/KORA, the ROC-AUC was 0.837 (0.819-0.855); regarding calibration we saw slight overestimation of absolute risks.

CONCLUSIONS

Inclusion of the number of diabetes-affected parents and sibling history improved the prediction of type 2 diabetes. Therefore, we updated the GDRS algorithm accordingly. Validation in another German cohort study showed good discrimination and acceptable calibration for the vast majority of individuals.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study

BACKGROUND

Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.

METHODS AND FINDINGS

The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.

CONCLUSIONS

The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

The effectiveness of screening for diabetes and cardiovascular disease risk factors in a community pharmacy setting

Risk factors for cardiovascular disease including diabetes have seen a large rise in prevalence in recent years. This has prompted interest in prevention through the identifying individuals at risk of both diabetes and cardiovascular disease and has seen increased investment in screening interventions taking place in primary care. Community pharmacies have become increasingly involved in the provision of such interventions and this systematic review and meta-analysis aims to gather and analyse the existing literature assessing community pharmacy based screening for risk factors for diabetes and those with a high cardiovascular disease risk.

Methods

We conducted systematic searches of electronic databases using MeSH and free text terms from 1950 to March 2012. For our analysis two outcomes were assessed. They were the percentage of those screened who were referred for further assessment by primary care and the uptake of this referral.

Results

Sixteen studies fulfilled our inclusion criteria comprising 108,414 participants screened. There was significant heterogeneity for all included outcomes. Consequently we have not presented summary statistics and present forest plots with I² and p values to describe heterogeneity. We found that all included studies suffered from high rates of attrition between pharmacy screening and follow up. We have also identified a strong trend towards higher rates for referral in more recent studies.

Conclusions

Our results show that pharmacies are feasible sites for screening for diabetes and those at risk of cardiovascular disease. A significant number of previously unknown cases of cardiovascular disease risk factors such as hypertension, hypercholesterolemia and diabetes are identified, however a significant number of referred participants at high risk do not attend their practitioner for follow up. Research priorities should include methods of increasing uptake to follow up testing and early intervention, to maximise the efficacy of screening interventions based in community pharmacies.

Depression and type 2 diabetes: a causal association?

There is a controversy regarding whether depression and type 2 diabetes are causally linked. To assess this issue, we review key findings for the association between depression and diabetes, and its underlying mechanisms. Findings from meta-analyses of cohort studies show a modestly sized bidirectional association between depression and type 2 diabetes (ie, depression predicts diabetes onset and diabetes predicts future depression). However, depression-related biological alterations in the hypothalamic-pituitary-adrenal cortex axis and the sympathetic nervous system, and subclinical inflammation, are not consistently linked with increased diabetes risk. The evidence for an association between depression and glycaemic traits (eg, glucose, insulin, insulin sensitivity, and insulin secretion) is also mixed. Diabetes increases the risk of depression to the same extent as do other chronic disorders (eg, cardiac diseases, osteoarthritis, lung disease, and poor hearing). At present, the available evidence suggests that pathophysiological changes preceding the onset of type 2 diabetes might not cause depression, nor might depression directly increase the risk of developing type 2 diabetes. Despite insufficient robust causal evidence, treating physicians should be aware of the co-occurrence of depression and type 2 diabetes.

Risk distribution and its influence on the population targets for diabetes prevention

OBJECTIVE

To quantify the influence of type 2 diabetes risk distribution on prevention benefit and apply a method to optimally identify population targets.

METHODS

We used data from the 2011 Canadian Community Health Survey (N=45,040) and the validated Diabetes Population Risk Tool to calculate 10-year diabetes risk. We calculated the Gini coefficient as a measure of risk dispersion. Intervention benefit was estimated using absolute risk reduction (ARR), number-needed-to-treat (NNT), and number of cases prevented.

RESULTS

There is a wide variation of diabetes risk in Canada (Gini=0.48) and with an inverse relation to risk (r=-0.99). Risk dispersion is lower among individuals meeting an empirically derived risk cut-off (Gini=0.18). Targeting prevention based on a risk cut-off (10-year risk ≥ 16.5%) resulted in a greater number of cases prevented (340 thousand), higher ARR (7.7%) and lower NNT (13) compared to targeting individuals based on risk factor targets.

CONCLUSIONS

This study provides empirical evidence to demonstrate that risk variability is an important consideration for estimating the prevention benefit. Prioritizing target populations using an empirically derived cut-off based on a multivariate risk score will result in greater benefit and efficiency compared to risk factor targets.

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.

Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

BACKGROUND

The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.

METHODS

We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).

FINDINGS

We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.

INTERPRETATION

Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.

FUNDING

The European Union.

Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis

BACKGROUND

Risk scores calculated from electronic patient records can be used to predict the risk of adults developing diabetes in the future.

AIM

To use a risk-prediction model on GPs' electronic health records in three inner-city boroughs, and to map the risk of diabetes by locality for commissioners, to guide possible interventions for targeting groups at high risk.

DESIGN AND SETTING

Cross-sectional analysis of electronic general practice records from three deprived and ethnically diverse inner-city boroughs in London.

METHOD

A cross-sectional analysis of 519 288 electronic primary care records was performed for all people without diabetes aged 25-79 years. A validated risk score, the QDScore, was used to predict 10-year risk of developing type 2 diabetes. Descriptive statistics were generated, including subanalysis by deprivation and ethnicity. The proportion of people at high risk (≥20% risk) per general practice was geospatially mapped.

RESULTS

Data were obtained from 135 out of 145 general practices (91.3%); 1 in 10 people in this population were at high risk (≥20%) of developing type 2 diabetes within 10 years. Of those with known cardiovascular disease or hypertension, approximately 50% were at high risk. Male sex, increasing age, South Asian ethnicity, deprivation, obesity, and other comorbidities increased the risk. Geospatial mapping revealed hotspots of high risk.

CONCLUSION

Individual risk scores calculated from electronic records can be aggregated to produce population risk profiles to inform commissioning and public health planning. Specific localities were identified (the 'East London diabetes belt'), where preventive efforts should be targeted. This method could be used for other diseases and risk states, to inform targeted commissioning and preventive research.

Risk predictive modelling for diabetes and cardiovascular disease

Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

Communicating genetic risk information for common disorders in the era of genomic medicine

Communicating genetic risk information in ways that maximize understanding and promote health is increasingly important given the rapidly expanding availability and capabilities of genomic technologies. A well-developed literature on risk communication in general provides guidance for best practices, including presentation of information in multiple formats, attention to framing effects, use of graphics, sensitivity to the way numbers are presented, parsimony of information, attentiveness to emotions, and interactivity as part of the communication process. Challenges to communicating genetic risk information include deciding how best to tailor it, streamlining the process, deciding what information to disclose, accepting that communications may have limited influence, and understanding the impact of context. Meeting these challenges has great potential for empowering individuals to adopt healthier lifestyles and improve public health, but will require multidisciplinary approaches and collaboration.

Finnish Diabetes Risk Score to predict type 2 diabetes in the Isfahan diabetes prevention study

AIM

The strong association between the Finnish Diabetes Risk Score (FINDRISC) and risk of diabetes reported in European populations cannot necessarily be generalized to other populations. The aim of this study was to evaluate the ability of FINDRISC to predict progression to diabetes in an Iranian population without diabetes.

METHODS

A total of 1537 first-degree relatives (FDR) without diabetes of consecutive people with type 2 diabetes 30-70 years old (376 men and 1161 women) were examined and followed for a mean (SD) of 7.8 (1.7) years for diabetes incidence. We examined the incidence of diabetes across quartiles of FINDRISC and plotted a receiver operating characteristic (ROC) curve to assess discrimination. At baseline and through follow-up, participants underwent a standard 75-g 2-h oral glucose tolerance test. Data for the FINDRISC were available from each participant.

RESULTS

During 12,046 person-years of follow-up, 41 men and 154 women developed diabetes. The incidence of type 2 diabetes was 14.0 per 1000 person-years in men and 16.9 in women. Those in the top quartile of FINDRISC were 21.7 times more likely to develop diabetes than those in the bottom quartile (relative risk 21.7; 95% CI 9.90, 47.39). The area under the ROC was 75.1% (95% CI 71.3, 78.8).

CONCLUSIONS

The results of this study show that FINDRISC is a robust predictor of type 2 diabetes in high-risk individuals in Iran.

Risco para diabetes mellitus tipo 2 e fatores associados

OBJETIVO: Avaliar o risco para diabetes mellitus tipo 2 e sua associação com variáveis clínicas e sociodemográficas. MÉTODOS: Estudo transversal realizado com 419 usuários da Estratégia Saúde da Família. O instrumento de pesquisa foi um questionário validado. RESULTADOS: Verificou-se que 25,3% dos usuários tinham idades ≥45 anos; 59,7% estavam com excesso de peso e 84,0% com obesidade abdominal; 83,3% eram sedentários; 53,7% não comiam frutas/verduras diariamente; 12,9% tomavam anti-hipertensivos; 5,3% mencionaram história prévia de glicose alta e 47% história familiar de diabetes. Foram classificados como de baixo risco 24,6% dos usuários; 63,5% como de risco moderado e 11,7% de risco alto. CONCLUSÃO: Houve associação significante entre o risco para desenvolver diabetes mellitus tipo 2 e as variáveis clínicas: índice de massa corporal, circunferência abdominal, alimentação, uso de anti-hipertensivos, história de glicose alta e história familiar, e as variáveis sociodemográficas gênero e idade.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.