Risk models and scores for type 2 diabetes: systematic review

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Background

Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences.

Methods

We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions.

Results

Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application.

Conclusions

Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

Electronic supplementary material

The online version of this article (doi:10.1186/1476-072X-13-47) contains supplementary material, which is available to authorized users.

Comparison of different aspects of BMI history to identify undiagnosed diabetes in Japanese men and women: Toranomon Hospital Health Management Center Study 12 (TOPICS 12)

AIMS

To examine current BMI and various aspects of BMI history as pre-screening tools for undiagnosed diabetes in Japanese individuals.

METHODS

This cross-sectional study included 16 226 men and 7026 women aged 30-75 years without a self-reported history of clinician-diagnosed diabetes. We estimated the probability of having undiagnosed diabetes (fasting glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 48 mmol⁄mol (≥ 6.5%) for the following variables: current BMI, BMI in the early 20s (BMI(20y)), lifetime maximum BMI (BMI(max)), change between BMI in the early 20s and current BMI (ΔBMI(20y-cur)), change between BMI in the early 20s and maximum BMI (ΔBMI(20y-max)), and change between lifetime maximum and current BMI (ΔBMI(max-cur)).

RESULTS

The prevalence of undiagnosed diabetes was 3.3% (771/23252) among participants. BMI(max) , ΔBMI(20y-max) and current BMI (1-sd increments) were more strongly associated with diabetes than the other factors (multivariate odds ratio 1.58 [95% CI 1.47-1.70] in men and 1.65 [95% CI 1.43-1.90] in women for BMI(max) ; multivariate odds ratio 1.47 [95% CI 1.37-1.58] in men and 1.61 [95% CI 1.41-1.84] in women for ΔBMI(20y-max) ; multivariate odds ratio 1.47 [95% CI 1.36-1.58] in men and 1.63 [95% CI 1.40-1.89] in women for current BMI). The probability of having diabetes was markedly higher in those with both the highest tertile of BMI(max) and greatest ΔBMI(20y-max) ; however, a substantially lower likelihood of diabetes was observed among individuals with the lowest and middle tertiles of current BMI (< 24.62 kg/m² in men and < 22.54 kg/m² in women).

CONCLUSIONS

Lifetime maximum BMI and BMI changes from early adulthood were strongly associated with undiagnosed diabetes. Adding BMI history to people's current BMI would improve the identification of individuals with a markedly higher probability of having undiagnosed diabetes.

External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination

OBJECTIVES

To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations.

STUDY DESIGN AND SETTING

We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates.

RESULTS

We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: -0.05 (P < 0.001) overall; -0.04 (P = 0.009) for validation by overlapping authors; -0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001).

CONCLUSION

External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

Medical devices and diagnostics for cardiovascular diseases in low-resource settings

Noncommunicable diseases (NCDs), including cardiovascular diseases and diabetes, have emerged as an underappreciated health threat with enormous economic and public health implications for populations in low-resource settings. In order to address these diseases, devices that are to be used in low-resource settings have to conform to requirements that are generally more challenging than those developed for traditional markets. Characteristics and issues that must be considered when working in low- and middle-income countries (LMICs) include challenging environmental conditions, a complex supply chain, sometimes inadequate operator training, and cost. Somewhat counterintuitively, devices for low-resource setting (LRS) markets need to be of at least as high quality and reliability as those for developed countries to be setting-appropriate and achieve impact. Finally, the devices need to be designed and tested for the populations in which they are to be used in order to achieve the performance that is needed. In this review, we focus on technologies for primary and secondary health-care settings and group them according to the continuum of care from prevention to treatment.

Ethnicity is an independent risk indicator when estimating diabetes risk with FINDRISC scores: a cross sectional study comparing immigrants from the Middle East and native Swedes

AIMS

This study sought to compare type 2 diabetes (T2D) risk indicators in Iraqi immigrants with those in ethnic Swedes living in southern Sweden.

METHODS

Population-based, cross-sectional cohort study of men and women, aged 30-75 years, born in Iraq or Sweden conducted in 2010-2012 in Malmö, Sweden. A 75g oral glucose tolerance test was performed and sociodemographic and lifestyle data were collected. T2D risk was assessed by the Finnish Diabetes Risk Score (FINDRISC).

RESULTS

In Iraqi versus Swedish participants, T2D was twice as prevalent (11.6 vs. 5.8%, p<0.001). A large proportion of the excess T2D risk was attributable to larger waist circumference and first-degree family history of diabetes. However, Iraqi ethnicity was a risk factor for T2D independently of other FINDRISC factors (odds ratio (OR) 2.5, 95% CI 1.6-3.9). The FINDRISC algorithm predicted that more Iraqis than Swedes (16.2 vs. 12.3%, p<0.001) will develop T2D within the next decade. The total annual costs for excess T2D risk in Iraqis are estimated to exceed 2.3 million euros in 2005, not accounting for worse quality of life.

CONCLUSIONS

Our study suggests that Middle Eastern ethnicity should be considered an independent risk indicator for diabetes. Accordingly, the implementation of culturally tailored prevention programs may be warranted.

Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups

CONTEXT/RATIONALE: Meta-analyses of genome-wide association studies have identified many single-nucleotide polymorphisms associated with various metabolic and cardiovascular traits, offering us the opportunity to learn about and capitalize on the links between cardiometabolic traits and type 2 diabetes (T2D).

DESIGN

In multiple datasets comprising over 30 000 individuals and 3 ethnic/racial groups, we calculated 17 genetic risk scores (GRSs) for glycemic, anthropometric, lipid, hemodynamic, and other traits, based on the results of recent trait-specific meta-analyses of genome-wide association studies, and examined associations with T2D risk. Using a training-testing procedure, we evaluated whether additional GRSs could contribute to risk prediction.

RESULTS

In European Americans, we find that GRSs for T2D, fasting glucose, fasting insulin, and body mass index are associated with T2D risk. In African Americans, GRSs for T2D, fasting insulin, and waist-to-hip ratio are associated with T2D. In Hispanic Americans, GRSs for T2D and body mass index are associated with T2D. We observed a trend among European Americans suggesting that genetic risk for hyperlipidemia is inversely associated with T2D risk. The use of additional GRSs resulted in only small changes in prediction accuracy in multiple independent validation datasets.

CONCLUSIONS

The analysis of multiple GRSs can shed light on T2D etiology and how it varies across ethnic/racial groups. Our findings using multiple GRSs are consistent with what is known about the differences in T2D pathogenesis across racial/ethnic groups. However, further work is needed to understand the putative inverse correlation of genetic risk for hyperlipidemia and T2D risk and to develop ethnic-specific GRSs.

Circulating peroxiredoxin 4 and type 2 diabetes risk: the Prevention of Renal and Vascular Endstage Disease (PREVEND) study

AIMS/HYPOTHESIS

Oxidative stress plays a key role in the development of type 2 diabetes mellitus. We previously showed that the circulating antioxidant peroxiredoxin 4 (Prx4) is associated with cardiometabolic risk factors. We aimed to evaluate the association of Prx4 with type 2 diabetes risk in the general population.

METHODS

We analysed data on 7,972 individuals from the Prevention of Renal and Vascular End-stage Disease (PREVEND) study (49% men, aged 28-75 years) with no diabetes at baseline. Logistic regression models adjusted for age, sex, smoking, waist circumference, hypertension and family history of diabetes were used to estimate the ORs for type 2 diabetes.

RESULTS

During a median follow up of 7.7 years, 496 individuals (288 men; 58%) developed type 2 diabetes. The median (Q1-Q3) Prx4 level was 0.84 (0.53-1.40) U/l in individuals who developed type 2 diabetes and 0.68 (0.43-1.08) U/l in individuals who did not develop type 2 diabetes. For every doubling of Prx4 levels, the adjusted OR (95% CI) for type 2 diabetes was 1.16 (1.05-1.29) in the whole population; by sex, it was 1.31 (1.14-1.50) for men and 1.03 (0.87-1.21) for women. Further adjustment for other clinical measures did not materially change the results. The addition of Prx4 to a validated diabetes risk score significantly improved the prediction of type 2 diabetes in men (p = 0.002 for reclassification improvement).

CONCLUSIONS/INTERPRETATION

Our findings suggest that elevated serum Prx4 levels are associated with a higher risk of incident type 2 diabetes. For men, taking Prx4 into consideration can improve type 2 diabetes prediction over a validated diabetes risk score; in contrast, there is no improvement in risk prediction for women.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study

AIMS

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database.

METHODS

For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures.

RESULTS

We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status.

CONCLUSIONS

In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes.

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.

Long-term mortality after community-acquired pneumonia--impacts of diabetes and newly discovered hyperglycaemia: a prospective, observational cohort study

OBJECTIVES

Community-acquired pneumonia is associated with a significant long-term mortality after initial recovery. It has been acknowledged that additional research is urgently needed to examine the contributors to this long-term mortality. The objective of the present study was to assess whether diabetes or newly discovered hyperglycaemia during pneumonia affects long-term mortality.

DESIGN

A prospective, observational cohort study.

SETTING

A single secondary centre in eastern Finland.

PARTICIPANTS

153 consecutive hospitalised patients who survived at least 30 days after mild-to-moderate community-acquired pneumonia.

INTERVENTIONS

Plasma glucose levels were recorded seven times during the first day on the ward. Several possible confounders were also recorded. The surveillance status and causes of death were recorded after median of 5 years and 11 months.

RESULTS

In multivariate Cox regression analysis, a previous diagnosis of diabetes among the whole population (adjusted HR 2.84 (1.35-5.99)) and new postprandial hyperglycaemia among the non-diabetic population (adjusted HR 2.56 (1.04-6.32)) showed independent associations with late mortality. New fasting hyperglycaemia was not an independent predictor. The mortality rates at the end of follow-up were 54%, 37% and 10% among patients with diabetes, patients without diabetes with new postprandial hyperglycaemia and patients without diabetes without postprandial hyperglycaemia, respectively (p<0.001). The underlying causes of death roughly mirrored those in the Finnish general population with a slight excess in mortality due to chronic respiratory diseases. Pneumonia was the immediate cause of death in just 8% of all late deaths.

CONCLUSIONS

A previous diagnosis of diabetes and newly discovered postprandial hyperglycaemia increase the risk of death for several years after community-acquired pneumonia. As the knowledge about patient subgroups with an increased late mortality risk is gradually gathering, more studies are needed to evaluate the possible postpneumonia interventions to reduce late mortality.

Fasting insulin sensitivity indices are not better than routine clinical variables at predicting insulin sensitivity among Black Africans: a clamp study in sub-Saharan Africans

BACKGROUND

We aimed to evaluate the predictive utility of common fasting insulin sensitivity indices, and non-laboratory surrogates [BMI, waist circumference (WC) and waist-to-height ratio (WHtR)] in sub-Saharan Africans without diabetes.

METHODS

We measured fasting glucose and insulin, and glucose uptake during 80/mU/m2/min euglycemic clamp in 87 Cameroonians (51 men) aged (SD) 34.6 (11.4) years. We derived insulin sensitivity indices including HOMA-IR, quantitative insulin sensitivity check index (QUICKI), fasting insulin resistance index (FIRI) and glucose-to-insulin ratio (GIR). Indices and clinical predictors were compared to clamp using correlation tests, robust linear regressions and agreement of classification by sex-specific thirds.

RESULTS

The mean insulin sensitivity was M = 10.5 ± 3.2 mg/kg/min. Classification across thirds of insulin sensitivity by clamp matched with non-laboratory surrogates in 30-48% of participants, and with fasting indices in 27-51%, with kappa statistics ranging from -0.10 to 0.26. Fasting indices correlated significantly with clamp (/r/=0.23-0.30), with GIR performing less well than fasting insulin and HOMA-IR (both p < 0.02). BMI, WC and WHtR were equal or superior to fasting indices (/r/=0.38-0.43). Combinations of fasting indices and clinical predictors explained 25-27% of variation in clamp values.

CONCLUSION

Fasting insulin sensitivity indices are modest predictors of insulin sensitivity measured by euglycemic clamp, and do not perform better than clinical surrogates in this population.

Diabetes risk scores and death: predictability and practicability in two different populations

The aim was to examine the capacity of commonly used type 2 diabetes mellitus (T2DM) risk scores to predict overall mortality. The US-based NHANES III (n = 3138; 982 deaths) and the Swiss-based CoLaus study (n = 3946; 191 deaths) were used. The predictive value of eight T2DM risk scores regarding overall mortality was tested. The Griffin score, based on few self-reported parameters, presented the best (NHANES III) and second best (CoLaus) predictive capacity. Generally, the predictive capacity of scores based on clinical (anthropometrics, lifestyle, history) and biological (blood parameters) data was not better than of scores based solely on clinical self-reported data. T2DM scores can be validly used to predict mortality risk in general populations without diabetes. Comparison with other scores could further show whether such scores also suit as a screening tool for quick overall health risk assessment.

Comparative characteristics of diabetes risk scores

The worldwide prevalence of diabetes among adults (aged 20?79 years) was 8.35% in 2013, and this is expected to increase by 55% (592 million adults) by 2035. To avoid the increase in the prevalence of diabetes, primary prevention and early diagnosis of prediabetes are required. It is important to identify individuals at a high risk of hyperglycaemia using inexpensive and available methods. At present, risk score is an alternative to identify the risk of developing diabetes. There are approximately 10 types of risk scores in the world, and further research for the development and adaptation of risk scores for various populations are being conducted. The use of risk score methods for prediction allows the setting of the level of total risk, identification of high-risk patients and prescription of necessary preventive measures. Actual validation of existing diabetes risk score for the Russian population is being conducted. Assessment of the risk of diabetes is simple, fast, inexpensive, non-invasive and reliable.

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.

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.

A psychosocial risk index for poor glycemic control in children and adolescents with type 1 diabetes

OBJECTIVE

The aim of this study was to develop and validate a psychosocial screening tool to predict risk for poor glycemic control in children with type 1 diabetes.

METHODS

Participants seen for psychological screening were 196 children aged 3-18 yr at diabetes diagnosis. A psychosocial risk index was developed to predict poor glycemic control [mean hemoglobin A1c (HbA1c) ≥ 9.5%; 80 mmol/mol] 1-4 yr post diagnosis. Cutoff scores were derived for multiple levels of risk from receiver operating characteristic (ROC) curves and likelihood ratios (LRs). Discrimination and calibration were examined in the sample, and validated in 1000 bootstrap samples. Ability to predict diabetes-related emergency-room (ER) visits and diabetic ketoacidosis (DKA) was also tested.

RESULTS

The risk index accounted for 16.2% of variance in mean HbA1c, discriminated between children with and without poor glycemic control [area under the receiver operating characteristic curve (AUC) = 0.814, 0.713-0.915; p < 0.001], ER visits (AUC = 0.655, 0.561-0.748; p = 0.001), and DKA(AUC = 0.709, 0.588-0.830; p = 0.001), and was well-calibrated. Every one-point increase in score was associated with an absolute increase in risk for poor glycemic control of approximately 10% (LRs = 1.7, 3.2, 5.8, and 9.3). Sensitivity and specificity were 0.68 (0.43-0.86) and 0.79 (0.72-0.84) for detecting patients at moderate risk, and 0.53 (0.29-0.75) and 0.91 (0.85-0.95) for detecting high-risk patients. The index performed equally well in validation samples.

CONCLUSIONS

This paper presents the first psychosocial risk index for poor glycemic control in children newly diagnosed with type 1 diabetes. It is brief, easily administered, and provides a single score that translates directly into an estimate of risk that can help guide routine diabetes care.

Supporting collaborative use of the Diabetes Population Risk Tool (DPoRT) in health-related practice: a multiple case study research protocol

Background

Health policy makers have stated that diabetes prevention is a priority; however, the type, intensity, and target of interventions or policy changes that will achieve the greatest impact remains uncertain. In response to this uncertainty, the Diabetes Population Risk Tool (DPoRT) was developed and validated to estimate future diabetes risk based on routinely collected population data. To facilitate use of DPoRT, we partnered with regional and provincial health-related decision makers in Ontario and Manitoba, Canada. Primary objectives include: i) evaluate the effectiveness of partnerships between the research team and DPoRT users; ii) explore strategies that facilitate uptake and overcome barriers to DPoRT use; and iii) implement and evaluate the knowledge translation approach.

Methods

This protocol reflects an integrated knowledge translation (IKT) approach and corresponds to the action phase of the Knowledge-to-Action (KtoA) framework. Our IKT approach includes: employing a knowledge brokering team to facilitate relationships with DPoRT users (objective 1); tailored training for DPoRT users; assessment of barriers and facilitators to DPoRT use; and customized dissemination strategies to present DPoRT outputs to decision maker audiences (objective 2). Finally, a utilization-focused evaluation will assess the effectiveness and impact of the proposed KtoA process for DPoRT application (objective 3). This research design utilizes a multiple case study approach. Units of analyses consist of two public health units, one provincial health organization, and one provincial knowledge dissemination team whereby we will connect with multiple regional health authorities. Evaluation will be based on analysis of both quantitative and qualitative data collected from passive (e.g., observer notes) and active (e.g., surveys and interviews) methods.

Discussion

DPoRT offers an innovative way to make routinely collected population health data practical and meaningful for diabetes prevention planning and decision making. Importantly, we will evaluate the utility of the KtoA cycle for a novel purpose – the application of a tool. Additionally, we will evaluate this approach in multiple diverse settings, thus considering contextual factors. This research will offer insights into how knowledge translation strategies can support the use of population-based risk assessment tools to promote informed decision making in health-related settings.

The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort

Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (p--value 2:09 x 10(-11)). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.

Improved diabetes screening using an extended predictive feature search

BACKGROUND

Screening entire populations for diabetes is not cost-effective. Hence, an efficient screening process must select those people who are at high risk for diabetes. In this study, we investigated whether screening procedures could be improved using an extended predictive feature search.

MATERIALS AND METHODS

In order to develop our model and identify persons with diabetes (prevalence) we used data from years of the National Health and Nutrition Examination Survey (2005-2010), which has not been explored for this purpose before. We calculated all combinations of predictors in order to identify the optimal subset, and we used a linear logistic classification model to predict diabetes. V-fold cross-validation was used for the process of including variables and for validating the final models. This new model was compared with two established models.

RESULTS

In total, 5,398 participants were included in this study. Among these, 478 participants had unidentified diabetes. The established models had a receiver operating characteristics curve for the area under the curve (AUC) of 0.74 and 0.71 compared with an AUC of 0.78 for the new model, showing a significant difference (P<0.05). A proposed cutoff point for the established models yielded respective sensitivities/specificities of 63%/72% and 40%/72% compared with the new model, which had a sensitivity/specificity of 70%/72%.

CONCLUSIONS

Our data indicate that simple healthcare and economic information such as ratio of family income to poverty can add value in deciding who is at risk of unknown diabetes by using extended investigations of predictor combinations.

Protocol for an HTA report: Does therapeutic writing help people with long-term conditions? Systematic review, realist synthesis and economic modelling

INTRODUCTION

Long-term medical conditions (LTCs) cause reduced health-related quality of life and considerable health service expenditure. Writing therapy has potential to improve physical and mental health in people with LTCs, but its effectiveness is not established. This project aims to establish the clinical and cost-effectiveness of therapeutic writing in LTCs by systematic review and economic evaluation, and to evaluate context and mechanisms by which it might work, through realist synthesis.

METHODS

Included are any comparative study of therapeutic writing compared with no writing, waiting list, attention control or placebo writing in patients with any diagnosed LTCs that report at least one of the following: relevant clinical outcomes; quality of life; health service use; psychological, behavioural or social functioning; adherence or adverse events. Searches will be conducted in the main medical databases including MEDLINE, EMBASE, PsycINFO, The Cochrane Library and Science Citation Index. For the realist review, further purposive and iterative searches through snowballing techniques will be undertaken. Inclusions, data extraction and quality assessment will be in duplicate with disagreements resolved through discussion. Quality assessment will include using Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Data synthesis will be narrative and tabular with meta-analysis where appropriate. De novo economic modelling will be attempted in one clinical area if sufficient evidence is available and performed according to the National Institute for Health and Care Excellence (NICE) reference case.

Diabetes in the Africa Region: an update

The Africa Region (AFR), where diabetes was once rare, has witnessed a surge in the condition. Estimates for type 1 diabetes suggest that about 39,000 people suffer from the disease in 2013 with 6.4 new cases occurring per year per 100,000 people in children <14 years old. Type 2 diabetes prevalence among 20-79-year-olds is 4.9% with the majority of people with diabetes <60 years old; the highest proportion (43.2%) is in those aged 40-59 years. Figures are projected to increase with the numbers rising from 19.8 million in 2013 to 41.5 million in 2035, representing a 110% absolute increase. There is an apparent increase in diabetes prevalence with economic development in AFR with rates of 4.4% in low-income, 5.0% in lower-middle income and 7.0% in upper-middle income countries. In addition to development and increases in life-expectancy, the likely progression of people at high risk for the development of type 2 diabetes will drive the expected rise of the disease. This includes those with impaired glucose tolerance, the prevalence of which is 7.3% among 20-79-year-olds in 2013. Mortality attributable to diabetes in 2013 in AFR is expected to be over half a million with three-quarter of these deaths occurring in those <60 years old. The prevalence of undiagnosed diabetes remains unacceptably high at 50.7% and is much higher in low income (75.1%) compared to lower- and upper-middle income AFR countries (46.0%). This highlights the inadequate response of local health systems which need to provide accessible, affordable and optimal care for diabetes.

Spousal diabetes as a diabetes risk factor: a systematic review and meta-analysis

BACKGROUND

Diabetes history in biologically-related individuals increases diabetes risk. We assessed diabetes concordance in spouses (that is, biologically unrelated family members) to gauge the importance of socioenvironmental factors.

METHODS

We selected cross-sectional, case-control and cohort studies examining spousal association for diabetes and/or prediabetes (impaired fasting glucose or impaired glucose tolerance), indexed in Medline, Embase or Scopus (1 January 1997 to 28 February 2013). Effect estimates (that is, odds ratios, incidence rate ratios, and so on) with body mass index (BMI) adjustment were pooled separately from those without BMI adjustment (random effects models) to distinguish BMI-dependent and independent concordance.

RESULTS

Searches yielded 2,705 articles; six were retained (n = 75,498 couples) for systematic review and five for meta-analysis. Concordance was lowest in a study that relied on women's reports of diabetes in themselves and their spouses (effect estimate 1.1, 95% CI 1.0 to 1.30) and highest in a study with systematic assessment of glucose tolerance (2.11, 95% CI 1.74 to 5.10). The random-effects pooled estimate adjusted for age and other covariates but not BMI was 1.26 (95% CI 1.08 to 1.45). The estimate with BMI adjustment was lower (1.18, 95% CI 0.97 to 1.40). Two studies assessing between-spouse associations of diabetes/prediabetes determined by glucose testing reported high concordance (OR 1.92, 95% CI 1.55 to 2.37 without BMI adjustment; 2.32, 95% CI 1.87 to 3.98 with BMI adjustment). Two studies did not distinguish type 1 and type 2 diabetes. However given that around 95% of adults is type 2, this is unlikely to have influenced the results.

CONCLUSIONS

Our pooled estimate suggests that a spousal history of diabetes is associated with a 26% diabetes risk increase. Recognizing shared risk between spouses may improve diabetes detection and motivate couples to increase collaborative efforts to optimize eating and physical activity habits.

Association between socio-economic status and hemoglobin A1c levels in a Canadian primary care adult population without diabetes

BACKGROUND

Hgb A1c levels may be higher in persons without diabetes of lower socio-economic status (SES) but evidence about this association is limited; there is therefore uncertainty about the inclusion of SES in clinical decision support tools informing the provision and frequency of Hgb A1c tests to screen for diabetes. We studied the association between neighborhood-level SES and Hgb A1c in a primary care population without diabetes.

METHODS

This is a retrospective study using data routinely collected in the electronic medical records (EMRs) of forty six community-based family physicians in Toronto, Ontario. We analysed records from 4,870 patients without diabetes, age 45 and over, with at least one clinical encounter between January 1st 2009 and December 31st 2011 and one or more Hgb A1c report present in their chart during that time interval. Residential postal codes were used to assign neighborhood deprivation indices and income levels by quintiles. Covariates included elements known to be associated with an increase in the risk of incident diabetes: age, gender, family history of diabetes, body mass index, blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting blood glucose.

RESULTS

The difference in mean Hgb A1c between highest and lowest income quintiles was -0.04% (p = 0.005, 95% CI -0.07% to -0.01%), and between least deprived and most deprived was -0.05% (p = 0.003, 95% CI -0.09% to -0.02%) for material deprivation and 0.02% (p = 0.2, 95% CI -0.06% to 0.01%) for social deprivation. After adjustment for covariates, a marginally statistically significant difference in Hgb A1c between highest and lowest SES quintile (p = 0.04) remained in the material deprivation model, but not in the other models.

CONCLUSIONS

We found a small inverse relationship between Hgb A1c and the material aspects of SES; this was largely attenuated once we adjusted for diabetes risk factors, indicating that an independent contribution of SES to increasing Hgb A1c may be limited. This study does not support the inclusion of SES in clinical decision support tools that inform the use of Hgb A1c for diabetes screening.

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.

Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39-1.95], P = 8.46 × 10(-9)) and was moderately heritable (h(2) = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34-2.11], P = 6.52 × 10(-6)) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27-2.75], P = 1 × 10(-3)). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

Nutritional modulation of cataract

Lens opacification or cataract reduces vision in over 80 million people worldwide and blinds 18 million. These numbers will increase dramatically as both the size of the elderly demographic and the number of those with carbohydrate metabolism-related problems increase. Preventative measures for cataract are critical because the availability of cataract surgery in much of the world is insufficient. Epidemiologic literature suggests that the risk of cataract can be diminished by diets that are optimized for vitamin C, lutein/zeaxanthin, B vitamins, omega-3 fatty acids, multivitamins, and carbohydrates: recommended levels of micronutrients are salutary. The limited data from intervention trials provide some support for observational studies with regard to nuclear - but not other types of - cataracts. Presented here are the beneficial levels of nutrients in diets or blood and the total number of participants surveyed in epidemiologic studies since a previous review in 2007.

New screening technologies for type 2 diabetes mellitus appropriate for use in tuberculosis patients

Type 2 diabetes mellitus (DM), which is epidemic in low- and middle-income countries (LMICs), may threaten gains made in tuberculosis (TB) control, as DM is both a major risk factor for developing active TB and it can lead to adverse TB treatment outcomes. Despite World Health Organization guidance that all TB patients should be screened for DM, most facilities in LMICs that manage TB patients do not currently perform screening for DM, due in part to the cost and complexity involved. DM screening is further complicated by the presentation of transient hyperglycemia in many TB patients, as well as differences in diabetes risk factors (e.g., body mass index) between TB patients and the general public. In this article, we review existing and new technologies for DM screening that may be more suitable for TB patients in LMICs. Such methods should be rapid, they should not require fasting, and they should allow the provider to differentiate between transient and longer-term hyperglycemia, using inexpensive tools that require little training and no specialized infrastructure. Several methods that are currently under development, such as point-of-care glycated hemoglobin and glycated albumin assays, non-invasive advanced glycation end-product readers, and sudomotor function-based screening devices, offer interesting performance characteristics and warrant evaluation in populations with TB.

Clinical and genetic determinants of progression of type 2 diabetes: a DIRECT study

OBJECTIVE

To identify the clinical and genetic factors that explain why the rate of diabetes progression is highly variable between individuals following diagnosis of type 2 diabetes.

RESEARCH DESIGN AND METHODS

We studied 5,250 patients with type 2 diabetes using comprehensive electronic medical records in Tayside, Scotland, from 1992 onward. We investigated the association of clinical, biochemical, and genetic factors with the risk of progression of type 2 diabetes from diagnosis to the requirement of insulin treatment (defined as insulin treatment or HbA1c ≥8.5% [69 mmol/mol] treated with two or more noninsulin therapies).

RESULTS

Risk of progression was associated with both low and high BMI. In an analysis stratified by BMI and HbA1c at diagnosis, faster progression was independently associated with younger age at diagnosis, higher log triacylglyceride (TG) concentrations (hazard ratio [HR] 1.28 per mmol/L [95% CI 1.15-1.42]) and lower HDL concentrations (HR 0.70 per mmol/L [95% CI 0.55-0.87]). A high Genetic Risk Score derived from 61 diabetes risk variants was associated with a younger age at diagnosis and a younger age when starting insulin but was not associated with the progression rate from diabetes to the requirement of insulin treatment.

CONCLUSIONS

Increased TG and low HDL levels are independently associated with increased rate of progression of diabetes. The genetic factors that predispose to diabetes are different from those that cause rapid progression of diabetes, suggesting a difference in biological process that needs further investigation.

Risk equations for the development of worsened glucose status and type 2 diabetes mellitus in a Swedish intervention program

Background

Several studies investigated transitions and risk factors from impaired glucose tolerance (IGT) to type 2 diabetes mellitus (T2D). However, there is a lack of information on the probabilities to transit from normal glucose tolerance (NGT) to different pre-diabetic states and from these states to T2D. The objective of our study is to estimate these risk equations and to quantify the influence of single or combined risk factors on these transition probabilities.

Methods

Individuals who participated in the VIP program twice, having the first examination at ages 30, 40 or 50 years of age between 1990 and 1999 and the second examination 10 years later were included in the analysis. Participants were grouped into five groups: NGT, impaired fasting glucose (IFG), IGT, IFG&IGT or T2D. Fourteen potential risk factors for the development of a worse glucose state (pre-diabetes or T2D) were investigated: sex, age, education, perceived health, triglyceride, blood pressure, BMI, smoking, physical activity, snus, alcohol, nutrition and family history. Analysis was conducted in two steps. Firstly, factor analysis was used to find candidate variables; and secondly, logistic regression was employed to quantify the influence of the candidate variables. Bootstrap estimations validated the models.

Results

In total, 29 937 individuals were included in the analysis. Alcohol and perceived health were excluded due to the results of the factor analysis and the logistic regression respectively. Six risk equations indicating different impacts of different risk factors on the transition to a worse glucose state were estimated and validated. The impact of each risk factor depended on the starting or ending pre-diabetes state. High levels of triglyceride, hypertension and high BMI were the strongest risk factors to transit to a worsened glucose state.

Conclusions

The equations could be used to identify individuals with increased risk to develop any of the three pre-diabetic states or T2D and to adapt prevention strategies.

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.

Inclusion of plasma lipid species improves classification of individuals at risk of type 2 diabetes

Background

A significant proportion of individuals with diabetes or impaired glucose tolerance have fasting plasma glucose less than 6.1 mmol/L and so are not identified with fasting plasma glucose measurements. In this study, we sought to evaluate the utility of plasma lipids to improve on fasting plasma glucose and other standard risk factors for the identification of type 2 diabetes or those at increased risk (impaired glucose tolerance).

Methods and Findings

Our diabetes risk classification model was trained and cross-validated on a cohort 76 individuals with undiagnosed diabetes or impaired glucose tolerance and 170 gender and body mass index matched individuals with normal glucose tolerance, all with fasting plasma glucose less than 6.1 mmol/L. The inclusion of 21 individual plasma lipid species to triglycerides and HbA1c as predictors in the diabetes risk classification model resulted in a statistically significant gain in area under the receiver operator characteristic curve of 0.049 (p<0.001) and a net reclassification improvement of 10.5% (p<0.001). The gain in area under the curve and net reclassification improvement were subsequently validated on a separate cohort of 485 subjects.

Conclusions

Plasma lipid species can improve the performance of classification models based on standard lipid and non-lipid risk factors.

Estimating the population prevalence of diagnosed and undiagnosed diabetes

OBJECTIVE

Health administrative data are frequently used for diabetes surveillance, but validation studies are limited, and undiagnosed diabetes has not been considered in previous studies. We compared the test properties of an administrative definition with self-reported diabetes and estimated prevalence of undiagnosed diabetes by measuring glucose levels in mailed-in capillary blood samples.

RESEARCH DESIGN AND METHODS

A stratified random sample of 6,247 individuals (Quebec province) was surveyed by telephone and asked to mail in fasting blood samples on filter paper to a central laboratory. An administrative definition was applied (two physician claims or one hospitalization for diabetes within a 2-year period) and compared with self-reported diabetes alone and with self-reported diabetes or elevated blood glucose level (≥7 mmol/L). Population-level prevalence was estimated with the use of the administrative definition corrected for its sensitivity and specificity.

RESULTS

Compared with self-reported diabetes, sensitivity and specificity were 84.3% (95% CI 79.3-88.5%) and 97.9% (97.4-98.4%), respectively. Compared with diabetes by self-report and/or glucose testing, sensitivity was lower at 58.2% (52.2-64.6%), whereas specificity was similar at 98.7% (98.0-99.3%). Adjusted for sampling weights, population-level prevalence of physician-diagnosed diabetes was 7.2% (6.3-8.0%). Prevalence of total diabetes (physician-diagnosed and undiagnosed) was 13.4% (11.7-15.0%), indicating that ∼40% of diabetes cases are undiagnosed.

CONCLUSIONS

A substantial proportion of diabetes cases are missed by surveillance methods that use health administrative databases. This finding is concerning because individuals with undiagnosed diabetes are likely to have a delay in treatment and, thus, a higher risk for diabetes-related complications.

Genetic information and the prediction of incident type 2 diabetes in a high-risk multiethnic population: the EpiDREAM genetic study

OBJECTIVE

To determine if 16 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2DM) in Europeans are also associated with T2DM in South Asians and Latinos and if they can add to the prediction of incident T2DM in a high-risk population.

RESEARCH DESIGN AND METHODS

In the EpiDREAM prospective cohort study, physical measures, questionnaires, and blood samples were collected from 25,063 individuals at risk for dysglycemia. Sixteen SNPs that have been robustly associated with T2DM in Europeans were genotyped. Among 15,466 European, South Asian, and Latino subjects, we examined the association of these 16 SNPs alone and combined in a gene score with incident cases of T2DM (n = 1,016) that developed during 3.3 years of follow-up.

RESULTS

Nine of the 16 SNPs were significantly associated with T2DM, and their direction of effect was consistent across the three ethnic groups. The gene score was significantly higher among subjects who developed incident T2DM (cases vs. noncases: 16.47 [2.50] vs. 15.99 [2.56]; P = 0.00001). The gene score remained an independent predictor of incident T2DM, with an odds ratio of 1.08 (95% CI 1.05-1.11) per additional risk allele after adjustment for T2DM risk factors. The gene score in those with no family history of T2DM was 16.02, whereas it was 16.19 in those with one parent with T2DM and it was 16.32 in those with two parents with T2DM (P trend = 0.0004). The C statistic of T2DM risk factors was 0.708 (0.691-0.725) and increased only marginally to 0.714 (0.698-0.731) with the addition of the gene score (P for C statistic change = 0.0052).

CONCLUSIONS

T2DM genetic associations are generally consistent across ethnic groups, and a gene score only adds marginal information to clinical factors for T2DM prediction.

Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review

AIMS

To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM).

METHODS

We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers.

RESULTS

We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these.

CONCLUSIONS

Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.

Role of HDL cholesterol and estimates of HDL particle composition in future development of type 2 diabetes in the general population: the PREVEND study

BACKGROUND AND AIMS

High-density lipoproteins (HDLs) may directly stimulate β-cell function and glucose metabolism. We determined the relationships of fasting high-density lipoprotein cholesterol (HDL-C), plasma apolipoprotein (apo) A-I and apoA-II, and HDL-C-to-apoA-I and HDL-C-to-apoA-II ratios, as estimates of HDL particle composition, with incident type 2 diabetes mellitus.

METHODS

A prospective study was carried out in the Prevention of Renal and Vascular End-Stage Disease (PREVEND) cohort after exclusion of subjects with diabetes at baseline (n = 6820; age, 28-75 years). The association of HDL-related variables with incident type 2 diabetes was determined by multivariate logistic regression analyses.

RESULTS

After a median follow-up of 7.7 years, 394 incident cases of type 2 diabetes mellitus were ascertained (5.8%). After adjustment for age, sex, family history of diabetes, body mass index, hypertension, alcohol, and smoking, odd ratios (ORs) for diabetes were 0.55 (95% confidence interval [CI], 0.47-0.64; P < .001), 0.81 (0.71-0.93; P = .002), 0.02 (0.01-0.06; P < .001), and 0.03 (0.01-0.060; P < .001) per 1-SD increase in HDL-C and apoA-I and in the HDL-C-to-apoA-I and the HDL-C-to-apoA-II ratios, respectively. In contrast, apoA-II was not related to incident diabetes (OR = 1.02; 95% CI, 0.90-1.16; P=0.71). The relationships of HDL-C and the ratios of HDL-C to apoA-I and HDL-C to apoA-II remained significant after further adjustment for baseline glucose and triglycerides (OR(HDL) = 0.74 [95% CI, 0.61-0.88], OR(HDL/APO A-I) = 0.14 [0.04-0.44], and OR(HDL/APOA-II) = 0.12 [0.04-0.36]; all P ≤ .001).

CONCLUSIONS

Higher HDL-C, as well as higher HDL-C-to-apoA-I and HDL-C-to-apoA-II ratios are strongly and independently related to a lower risk of future type 2 diabetes.

Hyperglycaemia during exacerbations of asthma and chronic obstructive pulmonary disease

INTRODUCTION

Hyperglycaemia is a well-known phenomenon among patients with an exacerbation of asthma or chronic obstructive pulmonary disease (COPD). It may be associated with increased risks of death and complications.

OBJECTIVES

To define the prevalence and determinants of hyperglycaemia in patients with an exacerbation of asthma or COPD.

METHODS

This was a prospective, cross-sectional study including 153 hospitalised patients with an exacerbation of asthma or COPD. All received inhaled beta-2-adrenergic bronchodilators and oral glucocorticoids in internationally recommend doses. Plasma glucose was measured seven times during the first day. Hyperglycaemia was defined as fasting glucose >6.9 mmol/L or postprandial glucose >11.1 mmol/L. In addition, the family history for diabetes and the Karnofsky performance score were assessed. Height, weight, waist circumference, oxygen saturation, blood pressure, temperature and heart rate were measured. Glycosylated haemoglobin A1c (gHbA1c), C-reactive protein, leucocytes, urea and arterial blood gas values were analysed.

RESULTS

Eighty-two per cent of the patients demonstrated hyperglycaemia, with similar prevalence between asthma and COPD. Of the 130 patients without a previous diagnosis of diabetes, 79% showed hyperglycaemia. In binary logistic regression analysis, high gHbA1c, high C-reactive protein and Karnofsky score less than 80% associated with the presence of fasting hyperglycaemia. High gHbA1c and current smoking associated with postprandial hyperglycaemia.

CONCLUSIONS

Hyperglycaemia is very common among hospitalised patients with an exacerbation of asthma or COPD. It is probably triggered by the medication and the patient's metabolic predisposition mainly determines its presence. Current smoking is the main treatable contributor to hyperglycaemia.

Skin autofluorescence based decision tree in detection of impaired glucose tolerance and diabetes

Aim

Diabetes (DM) and impaired glucose tolerance (IGT) detection are conventionally based on glycemic criteria. Skin autofluorescence (SAF) is a noninvasive proxy of tissue accumulation of advanced glycation endproducts (AGE) which are considered to be a carrier of glycometabolic memory. We compared SAF and a SAF-based decision tree (SAF-DM) with fasting plasma glucose (FPG) and HbA1c, and additionally with the Finnish Diabetes Risk Score (FINDRISC) questionnaire±FPG for detection of oral glucose tolerance test (OGTT)- or HbA1c-defined IGT and diabetes in intermediate risk persons.

Methods

Participants had ≥1 metabolic syndrome criteria. They underwent an OGTT, HbA1c, SAF and FINDRISC, in adition to SAF-DM which includes SAF, age, BMI, and conditional questions on DM family history, antihypertensives, renal or cardiovascular disease events (CVE).

Results

218 persons, age 56 yr, 128M/90F, 97 with previous CVE, participated. With OGTT 28 had DM, 46 IGT, 41 impaired fasting glucose, 103 normal glucose tolerance. SAF alone revealed 23 false positives (FP), 34 false negatives (FN) (sensitivity (S) 68%; specificity (SP) 86%). With SAF-DM, FP were reduced to 18, FN to 16 (5 with DM) (S 82%; SP 89%). HbA1c scored 48 FP, 18 FN (S 80%; SP 75%). Using HbA1c-defined DM-IGT/suspicion ≥6%/42 mmol/mol, SAF-DM scored 33 FP, 24 FN (4 DM) (S76%; SP72%), FPG 29 FP, 41 FN (S71%; SP80%). FINDRISC≥10 points as detection of HbA1c-based diabetes/suspicion scored 79 FP, 23 FN (S 69%; SP 45%).

Conclusion

SAF-DM is superior to FPG and non-inferior to HbA1c to detect diabetes/IGT in intermediate-risk persons. SAF-DM’s value for diabetes/IGT screening is further supported by its established performance in predicting diabetic complications.