Risk models and scores for type 2 diabetes: systematic review

Lifestyle interventions in preventing new type 2 diabetes in Asian populations

The aim of this study was to review current evidence on interventional studies aimed at the prevention of type 2 diabetes in Asian population with lifestyle interventions. Prevalence of type 2 diabetes sharply increased in most Asian countries during the last decades. This issue has now also relevant implication for Europe where different surveys are also consistently revealing an higher prevalence of type 2 diabetes and other and major CVD risk factors among subjects originating from Asian Countries than in the native population. Nutrition and lifestyle transition seem to play a role in disclosing the predisposition for the development of type 2 diabetes and great interest is now shown toward the possibility to intervene with lifestyle intervention on at risk populations. A meta-analysis of Randomized Controlled Trials showed that lifestyle interventions are highly effective also in the Asian population. All studies were, however, conducted with an individual approach based on the identification of high-risk individuals. When ethnic minority groups have to be addressed, an approach directed to the community rather than to the individual might, however, be more effective. This review reinforces the importance for policy-makers to consider the involvement of the whole community of minority immigrant groups with lifestyle intervention programs.

Model for individual prediction of diabetes up to 5 years after gestational diabetes mellitus

Aims

To identify predictors of diabetes development up to 5 years after gestational diabetes mellitus (GDM) and to develop a prediction model for individual use.

Methods

Five years after GDM, a 75-g oral glucose tolerance test (OGTT) was performed in 362 women, excluding women already diagnosed with diabetes at 1- to 2-year follow-up or later (n = 45). All but 21 women had results from follow-up at 1–2 years, while 84 women were lost from that point. Predictive variables were identified by logistic regression analysis.

Results

Five years after GDM, 28/362 women (8 %) were diagnosed with diabetes whereas 187/362 (52 %) had normal glucose tolerance (NGT). Of the latter, 139/187 (74 %) also had NGT at 1- to 2-year follow-up. In simple regression analysis, using NGT at 1–2 years and at 5 years as the reference, diabetes at 1- to 2-year follow-up or later was clearly associated with easily assessable clinical variables, such as BMI at 1- to 2-year follow-up, 2-h OGTT glucose concentration during pregnancy, and non-European origin (P < 0.0001). A prediction model based on these variables resulting in 86 % correct classifications, with an area under the receiver-operating characteristic curve of 0.91 (95 % CI 0.86–0.95), was applied in a function-sheet line diagram illustrating the individual effect of weight on diabetes risk.

Conclusions

The results highlight the importance of BMI as a potentially modifiable risk factor for diabetes after GDM. Our proposed prediction model performed well, and should encourage validation in other populations in future studies.

Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data

OBJECTIVE

To develop and validate a model of incident type 2 diabetes based solely on administrative data.

DATA SOURCES/STUDY SETTING

Optum Labs Data Warehouse (OLDW), a national commercial administrative dataset.

STUDY DESIGN

HealthImpact model was developed and internally validated using nested case-control study design; n = 473,049 in training cohort and n = 303,025 in internal validation cohort. HealthImpact was externally validated in 2,000,000 adults followed prospectively for 3 years. Only adults ≥18 years were included.

DATA COLLECTION/EXTRACTION METHODS

Patients with incident diabetes were identified using HEDIS rules. Control subjects were sampled from patients without diabetes. Medical and pharmacy claims data collected over 3 years prior to index date were used to build the model variables.

PRINCIPAL FINDINGS

HealthImpact, scored 0-100, has 48 variables with c-statistic 0.80815. We identified HealthImpact threshold of 90 as identifying patients at high risk of incident diabetes. HealthImpact had excellent discrimination in external validation cohort (c-statistic 0.8171). The sensitivity, specificity, positive predictive value, and negative predictive value of HealthImpact >90 for new diagnosis of diabetes within 3 years were 32.35, 94.92, 22.25, and 96.90 percent, respectively.

CONCLUSIONS

HealthImpact is an efficient and effective method of risk stratification for incident diabetes that is not predicated on patient-provided information or laboratory tests.

Racial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study

OBJECTIVE

In 2010, the American Diabetes Association (ADA) added hemoglobin A1c (A1C) to the guidelines for diagnosing type 2 diabetes. However, existing models for predicting diabetes risk were developed prior to the widespread adoption of A1C. Thus, it remains unknown how well existing diabetes risk prediction models predict incident diabetes defined according to the ADA 2010 guidelines. Accordingly, we examined the performance of an existing diabetes prediction model applied to a cohort of African American (AA) and white adults from the Coronary Artery Risk Development Study in Young Adults (CARDIA).

RESEARCH DESIGN AND METHODS

We evaluated the performance of the Atherosclerosis Risk in Communities (ARIC) diabetes risk prediction model among 2,456 participants in CARDIA free of diabetes at the 2005-2006 exam and followed for 5 years. We evaluated model discrimination, calibration, and integrated discrimination improvement with incident diabetes defined by ADA 2010 guidelines before and after adding baseline A1C to the prediction model.

RESULTS

In the overall cohort, re-estimating the ARIC model in the CARDIA cohort resulted in good discrimination for the prediction of 5-year diabetes risk (area under the curve [AUC] 0.841). Adding baseline A1C as a predictor improved discrimination (AUC 0.841 vs. 0.863, P = 0.03). In race-stratified analyses, model discrimination was significantly higher in whites than AA (AUC AA 0.816 vs. whites 0.902; P = 0.008).

CONCLUSIONS

Addition of A1C to the ARIC diabetes risk prediction model improved performance overall and in racial subgroups. However, for all models examined, discrimination was better in whites than AA. Additional studies are needed to further improve diabetes risk prediction among AA.

Cost-Effectiveness of a Short Message Service Intervention to Prevent Type 2 Diabetes from Impaired Glucose Tolerance

Aims. To investigate the costs and cost-effectiveness of a short message service (SMS) intervention to prevent the onset of type 2 diabetes mellitus (T2DM) in subjects with impaired glucose tolerance (IGT). Methods. A Markov model was developed to simulate the cost and effectiveness outcomes of the SMS intervention and usual clinical practice from the health provider's perspective. The direct programme costs and the two-year SMS intervention costs were evaluated in subjects with IGT. All costs were expressed in 2011 US dollars. The incremental cost-effectiveness ratio was calculated as cost per T2DM onset prevented, cost per life year gained, and cost per quality adjusted life year (QALY) gained. Results. Within the two-year trial period, the net intervention cost of the SMS group was $42.03 per subject. The SMS intervention managed to reduce 5.05% onset of diabetes, resulting in saving $118.39 per subject over two years. In the lifetime model, the SMS intervention dominated the control by gaining an additional 0.071 QALY and saving $1020.35 per person. The SMS intervention remained dominant in all sensitivity analyses. Conclusions. The SMS intervention for IGT subjects had the superiority of lower monetary cost and a considerable improvement in preventing or delaying the T2DM onset. This trial is registered with ClinicalTrials.gov NCT01556880.

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.

Ethnic differences in prediabetes and diabetes in the Suriname Health Study

BACKGROUND

Diabetes is increasing worldwide, and information on risk factors to develop targeted interventions is limited. Therefore, we analyzed data of the Suriname Health Study to estimate the prevalence of prediabetes and diabetes. We also explored whether ethnic differences in prediabetes or diabetes risk could be explained by biological, demographic, lifestyle, anthropometric, and metabolic risk factors.

METHOD

The study was designed according to the WHO Steps guidelines. Fasting blood glucose levels were measured in 3393 respondents, aged 15-65 years, from an Amerindian, Creole, Hindustani, Javanese, Maroon or Mixed ethnic background. Prediabetes was defined by fasting blood glucose levels between 6.1 and 7.0 mmol/L and diabetes by fasting blood glucose levels ≥7.0 mmol/L or 'self-reported diabetes medication use.' For all ethnicities, we analyzed sex, age, marital status, educational level, income status, employment, smoking status, residence, physical activity, body mass index, waist circumference, hypertension, and the levels of triglyceride, total cholesterol, high-density lipoprotein-cholesterol and low-density lipoprotein-cholesterol.

RESULTS

The prevalence of prediabetes was 7.4%, while that of diabetes was 13 0%. From these diabetes cases, 39.6% were not diagnosed previously. No ethnic differences were observed in the prevalence of prediabetes. For diabetes, Hindustanis (23.3%) had twice the prevalence compared to other ethnic groups (4.7-14.2%). The associations of the risk factors with prediabetes or diabetes varied among the ethnic groups. The differences in the associations of ethnic groups with prediabetes or diabetes were partly explained by these risk factors.

CONCLUSIONS

The prevalence of diabetes in Suriname is high and most elevated in Hindustanis. The observed variations in risk factors among ethnic groups might explain the ethnic differences between these groups, but follow-up studies are needed to explore this in more depth.

Prognostic models in obstetrics: available, but far from applicable

Health care provision is increasingly focused on the prediction of patients' individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given. We searched MEDLINE (up to July 2012) for articles developing prognostic models in obstetrics. We identified 177 papers that reported the development of 263 prognostic models for 40 different outcomes. The most frequently predicted outcomes were preeclampsia (n = 69), preterm delivery (n = 63), mode of delivery (n = 22), gestational hypertension (n = 11), and small-for-gestational-age infants (n = 10). The performance of newer models was generally not better than that of older models predicting the same outcome. The most important measures of predictive accuracy (ie, a model's discrimination and calibration) were often (82.9%, 218/263) not both assessed. Very few developed models were validated in data other than the development data (8.7%, 23/263). Only two-thirds of the papers (62.4%, 164/263) presented the model such that validation in other populations was possible, and the clinical applicability was discussed in only 11.0% (29/263). The impact of developed models on clinical practice was unknown. We identified a large number of prognostic models in obstetrics, but there is relatively little evidence about their performance, impact, and usefulness in clinical practice so that at this point, clinical implementation cannot be recommended. New efforts should be directed toward evaluating the performance and impact of the existing models.

Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults

Metabolomic studies have identified several metabolites associated with type 2 diabetes (T2D) in populations of European ancestry. East Asians, a population of particular susceptibility to T2D, were generally not included in previous studies. We examined the associations of plasma metabolites with risk and prevalence of T2D in 976 Chinese men and women (40-74 years of age) who were participants of two prospective cohort studies and had no cardiovascular disease or cancer at baseline. Sixty-eight prevalent and 73 incident T2D cases were included. Non-targeted metabolomics was conducted that detected 689 metabolites with known identities and 690 unknown metabolites. Multivariable logistic and Cox regressions were used to evaluate the associations of standardized metabolites with diabetes risk and prevalence. We identified 36 known metabolites and 10 unknown metabolites associated with prevalent and/or incident T2D at false discovery rate <0.05. The known metabolites are involved in metabolic pathways of glycolysis/gluconeogenesis, branched-chain amino acids, other amino acids, fatty acids, glycerophospholipids, androgen, and bradykinin. Six metabolites showed independent associations with incident T2D: 1,5-anhydroglucitol, mannose, valine, 3-methoxytyrosine, docosapentaenoate (22:5n3), and bradykinin-hydroxy-pro(3). Each standard deviation increase in these metabolites was associated with a 40-150 % change in risk of developing diabetes (30-80 % after further adjustment for glucose). Risk prediction was significantly improved by adding these metabolites in addition to known T2D risk factors, including central obesity and glucose. These findings suggest that hexoses, branched-chain amino acids, and yet to be validated novel plasma metabolites may improve risk prediction and mechanistic understanding of T2D in Chinese populations.

Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT)

Background

Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours.

Methods

Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82 259 Ontarians who were followed for a median of 8.6 years (688 000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28 605 respondents (median 4.2 years follow-up).

Results

We observed 3 236 incident stroke events (1 551 resulting in hospitalization; 1 685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards.

Conclusion

Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention.

Metabolomics and its application to the evaluation of the efficacy and toxicity of traditional Chinese herb medicines

Traditional Chinese herb medicines (TCHMs) have been used in the treatment of a variety of diseases for thousands of years in Asian countries. The active components of TCHMs usually exert combined synergistic therapeutic effects on multiple targets, but with less potential therapeutic effect based on routine indices than Western drugs. These complex effects make the assessment of the efficacy of TCHMs and the clarification of their underlying mechanisms very challenging, and therefore hinder their wider application and acceptance. Metabolomics is a crucial part of systems biology. It allows the quantitative measurement of large numbers of the low-molecular endogenous metabolites involved in metabolic pathways, and thus reflects the fundamental metabolism status of the body. Recently, dozens of metabolomic studies have been devoted to prove the efficacy/safety, explore the underlying mechanisms, and identify the potential biomarkers to access the action targets of TCHMs, with fruitful results. This article presents an overview of these studies, focusing on the progress made in exploring the pharmacology and toxicology of various herbal medicines.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Association of a Dietary Score with Incident Type 2 Diabetes: The Dietary-Based Diabetes-Risk Score (DDS)

Background

Strong evidence supports that dietary modifications may decrease incident type 2 diabetes mellitus (T2DM). Numerous diabetes risk models/scores have been developed, but most do not rely specifically on dietary variables or do not fully capture the overall dietary pattern. We prospectively assessed the association of a dietary-based diabetes-risk score (DDS), which integrates optimal food patterns, with the risk of developing T2DM in the SUN (“Seguimiento Universidad de Navarra”) longitudinal study.

Methods

We assessed 17,292 participants initially free of diabetes, followed-up for a mean of 9.2 years. A validated 136-item FFQ was administered at baseline. Taking into account previous literature, the DDS positively weighted vegetables, fruit, whole cereals, nuts, coffee, low-fat dairy, fiber, PUFA, and alcohol in moderate amounts; while it negatively weighted red meat, processed meats and sugar-sweetened beverages. Energy-adjusted quintiles of each item (with exception of moderate alcohol consumption that received either 0 or 5 points) were used to build the DDS (maximum: 60 points). Incident T2DM was confirmed through additional detailed questionnaires and review of medical records of participants. We used Cox proportional hazards models adjusted for socio-demographic and anthropometric parameters, health-related habits, and clinical variables to estimate hazard ratios (HR) of T2DM.

Results

We observed 143 T2DM confirmed cases during follow-up. Better baseline conformity with the DDS was associated with lower incidence of T2DM (multivariable-adjusted HR for intermediate (25–39 points) vs. low (11–24) category 0.43 [95% confidence interval (CI) 0.21, 0.89]; and for high (40–60) vs. low category 0.32 [95% CI: 0.14, 0.69]; p for linear trend: 0.019).

Conclusions

The DDS, a simple score exclusively based on dietary components, showed a strong inverse association with incident T2DM. This score may be applicable in clinical practice to improve dietary habits of subjects at high risk of T2DM and also as an educational tool for laypeople to help them in self-assessing their future risk for developing diabetes.

Different type 2 diabetes risk assessments predict dissimilar numbers at 'high risk': a retrospective analysis of diabetes risk-assessment tools

BACKGROUND

Use of a validated risk-assessment tool to identify individuals at high risk of developing type 2 diabetes is currently recommended. It is under-reported, however, whether a different risk tool alters the predicted risk of an individual.

AIM

This study explored any differences between commonly used validated risk-assessment tools for type 2 diabetes.

DESIGN AND SETTING

Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, South Wales.

METHOD

Retrospective analysis of 676 individuals (389 females and 287 males) who participated in a workplace-based diabetes risk-assessment initiative. Ten-year risk of type 2 diabetes was predicted using the validated QDiabetes(®), Leicester Risk Assessment (LRA), FINDRISC, and Cambridge Risk Score (CRS) algorithms.

RESULTS

Differences between the risk-assessment tools were apparent following retrospective analysis of individuals. CRS categorised the highest proportion (13.6%) of individuals at 'high risk' followed by FINDRISC (6.6%), QDiabetes (6.1%), and, finally, the LRA was the most conservative risk tool (3.1%). Following further analysis by sex, over one-quarter of males were categorised at high risk using CRS (25.4%), whereas a greater percentage of females were categorised as high risk using FINDRISC (7.8%).

CONCLUSION

The adoption of a different valid risk-assessment tool can alter the predicted risk of an individual and caution should be used to identify those individuals who really are at high risk of type 2 diabetes.

Sleep disturbances compared to traditional risk factors for diabetes development: Systematic review and meta-analysis

Sleep disturbances [short (<6 h) and long (>8 h) sleeping time, insomnia (initiating or maintaining sleep), obstructive sleep apnea (OSA) and abnormal sleep timing] have been associated with increased diabetes risk but the effect size relative to that of traditional risk factors is unknown. We conducted a systematic review and meta-analysis to compare the risk associated with sleep disturbances to traditional risk factors. Studies were identified from Medline and Scopus. Cohort studies measuring the association between sleep disturbances and incident diabetes were eligible. For traditional risk factors (i.e., overweight, family history, and physical inactivity), systematic reviews with or without meta-analysis were included. Thirty-six studies (1,061,555 participants) were included. Pooled relative risks (RRs) of sleep variables were estimated using a random-effect model. Pooled RRs of sleeping ≤5 h, 6 h, and ≥9 h/d were respectively 1.48 (95%CI:1.25,1.76), 1.18 (1.10,1.26) and 1.36 (1.12,1.65). Poor sleep quality, OSA and shift work were associated with diabetes with a pooled RR of 1.40 (1.21,1.63), 2.02 (1.57, 2.61) and 1.40 (1.18,1.66), respectively. The pooled RRs of being overweight, having a family history of diabetes, and being physically inactive were 2.99 (2.42,3.72), 2.33 (1.79,2.79), and 1.20 (1.11,1.32), respectively. In conclusion, the risk of developing diabetes associated with sleep disturbances is comparable to that of traditional risk factors. Sleep disturbances should be considered in clinical guidelines for type 2 diabetes screening.

Air pollution as a risk factor for type 2 diabetes

Recent studies in both humans and animals suggest that air pollution is an important risk factor for type 2 diabetes mellitus (T2DM). However, the mechanism by which air pollution mediates propensity to diabetes is not fully understood. While a number of epidemiologic studies have shown a positive association between ambient air pollution exposure and risk for T2DM, some studies have not found such a relationship. Experimental studies in susceptible disease models do support this association and suggest the involvement of tissues involved in the pathogenesis of T2DM such as the immune system, adipose, liver, and central nervous system. This review summarizes the epidemiologic and experimental evidence between ambient outdoor air pollution and T2DM.

Adapting existing diabetes risk scores for an Asian population: a risk score for detecting undiagnosed diabetes in the Mongolian population

Background

Most of the commonly used diabetes mellitus screening tools and risk scores have been developed with American or European populations in mind. Their applicability, therefore, to low and middle-income countries remains unquantified. Simultaneously, low and middle-income countries including Mongolia are currently witnessing rising diabetes prevalence. This research aims to develop and validate a diabetes risk score for the screening of undiagnosed type 2 diabetes mellitus in the Mongolian adult population.

Methods

Blood glucose measurements from 1018 Mongolians, as well as information on demography and risk factors prevalence was drawn from 2009 STEPS data. Existing risk scores were applied, measuring sensitivity using area under ROC-curves. Logistic regression models were used to identify additional independent predictors for undiagnosed diabetes. Finally, a new risk score was developed and Hosmer-Lemeshow tests were used to evaluate the agreement between the observed and predicted prevalence.

Results

The performance of existing risk scores to identify undiagnosed diabetes was moderate; with the area under ROC curves between 61–64 %. In addition to well-established risk factors, three new independent predictors for undiagnosed diabetes were identified. Incorporating these into a new risk score, the area under ROC curves increased to 77 % (95 % CI 71 %–82 %).

Conclusions

Existing European or American diabetes risk tools cannot be adopted in Asian countries without prior validation in the specific population. With this in mind, a low-cost, reliable screening tool for undiagnosed diabetes was developed and internally validated for Mongolians. The potential for cost and morbidity savings could be significant.

Why screening for type 2 diabetes is necessary even in poor resource settings

Screening for type 2 diabetes (T2DM) remains controversial, in spite of the explosive increase in the prevalence of the disorder and the morbidity and mortality associated with its complications. In this review, we attempt to show that T2DM is an ideal candidate disease for screening, and why screening is needed to improve clinical outcomes and prevent complications. We also suggest that screening can be made more cost-effective by adopting a targeted approach and utilizing low-cost tools. We conclude that screening for T2DM is warranted even in resource-constrained settings, and provide examples from rural India showing that such an approach is feasible with meticulous planning and judicious allocation of resources.

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.

New risk-scoring system including non-alcoholic fatty liver disease for predicting incident type 2 diabetes in East China: Shanghai Baosteel Cohort

Aims/Introduction

The present study aimed to explore the incidence of type 2 diabetes, and to develop a risk‐scoring model for predicting diabetes among the adult health check‐up population in East China.

Materials and Methods

Participants from the Shanghai Baosteel Cohort (age ≥20 years) without diabetes at baseline were recruited in a 6‐year follow‐up study. In order to explore risk factors for diabetes, this cohort was categorized into two groups: new diabetes and no diabetes. Three models were developed by Cox regression analysis. The model accuracy was assessed using the area under the receiver operating characteristic curve.

Results

A total of 6,542 individuals were included in the Shanghai Baosteel Cohort Study. Of them, 368 (5.6%) developed type 2 diabetes at the end of the follow‐up period. Cox regression analysis found a close association between incident type 2 diabetes and several risk factors including non‐alcoholic fatty liver diseases at baseline. The Shanghai Baosteel Score including advanced age (2 points), hypertriglyceridemia (2 points), obesity (2 points), non‐alcoholic fatty liver diseases (2 points) and impaired fasting glucose (3 points) had a good diagnostic performance with estimated area under the receiver operating characteristic curve (0.724), sensitivity (57.9%) and specificity (72.2%) at a cut‐off point of >3.

Conclusions

A risk‐scoring system including non‐alcoholic fatty liver diseases can help identify individuals at a high risk of diabetes in the East Chinese population.

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.

Validating prediction scales of type 2 diabetes mellitus in Spain: the SPREDIA-2 population-based prospective cohort study protocol

INTRODUCTION

The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. When diagnosed, many patients already have organ damage or advance subclinical atherosclerosis. An early diagnosis could allow the implementation of lifestyle changes and treatment options aimed at delaying the progression of the disease and to avoid cardiovascular complications. Different scores for identifying undiagnosed diabetes have been reported, however, their performance in populations of southern Europe has not been sufficiently evaluated. The main objectives of our study are: to evaluate the screening performance and cut-off points of the main scores that identify the risk of undiagnosed T2DM and prediabetes in a Spanish population, and to develop and validate our own predictive models of undiagnosed T2DM (screening model), and future T2DM (prediction risk model) after 5-year follow-up. As a secondary objective, we will evaluate the atherosclerotic burden of the population with undiagnosed T2DM.

METHODS AND ANALYSIS

Population-based prospective cohort study with baseline screening, to evaluate the performance of the FINDRISC, DANISH, DESIR, ARIC and QDScore, against the gold standard tests: Fasting plasma glucose, oral glucose tolerance and/or HbA1c. The sample size will include 1352 participants between the ages of 45 and 74 years.

ANALYSIS

sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio positive, likelihood ratio negative and receiver operating characteristic curves and area under curve. Binary logistic regression for the first 700 individuals (derivation) and last 652 (validation) will be performed. All analyses will be calculated with their 95% CI; statistical significance will be p<0.05.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Research Ethics Committee of the Carlos III Hospital (Madrid). The score performance and predictive model will be presented in medical conferences, workshops, seminars and round table discussions. Furthermore, the predictive model will be published in a peer-reviewed medical journal to further increase the exposure of the scores.

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.

Guiding diabetes screening and prevention: rationale, recommendations and remaining challenges

Advances made in diabetes management are not sufficient to reduce morbidity, mortality and cost without making prevention efforts at various levels imperative for substantial impact. Research has demonstrated the efficacy of lifestyle intervention and medications in preventing type 2 diabetes among diverse high-risk groups commonly identified with oral glucose tolerance testing. Efficacy, sustainability and safety data are most comprehensive for lifestyle and metformin, with other medications also demonstrating efficacy and potential in the pharmacoprevention of diabetes. Subsequent implementation studies have demonstrated feasibility of lifestyle intervention programs at health centers, communities, and at local and national government levels. Challenges remain in widespread translation and reaching and engaging at-risk individuals and populations.

Genetics of Type 2 Diabetes and Clinical Utility

A large proportion of heritability of type 2 diabetes (T2D) has been attributed to inherent genetics. Recent genetic studies, especially genome-wide association studies (GWAS), have identified a multitude of variants associated with T2D. It is thus reasonable to question if these findings may be utilized in a clinical setting. Here we briefly review the identification of risk loci for T2D and discuss recent efforts and propose future work to utilize these loci in clinical setting-for the identification of individuals who are at particularly high risks of developing T2D and for the stratification of specific health-care approaches for those who would benefit most from such interventions.

Spatial modelling of type II diabetes outcomes: a systematic review of approaches used

With the rising incidence of type II diabetes mellitus (DM II) worldwide, methods to identify high-risk geographical areas have become increasingly important. In this comprehensive review following Cochrane Collaboration guidelines, we outline spatial methods, outcomes and covariates used in all spatial studies involving outcomes of DM II. A total of 1894 potentially relevant citations were identified. Studies were included if spatial methods were used to explore outcomes of DM II or type I and 2 diabetes combined. Descriptive tables were used to summarize information from included studies. Ten spatial studies conducted in the USA, UK and Europe met selection criteria. Three studies used Bayesian generalized linear mixed modelling (GLMM), three used classic generalized linear modelling, one used classic GLMM, two used geographic information systems mapping tools and one compared case:provider ratios across regions. Spatial studies have been effective in identifying high-risk areas and spatial factors associated with DM II outcomes in the USA, UK and Europe, and would be useful in other parts of the world for allocation of additional services to detect and manage DM II early.

Triglyceride-Increasing Alleles Associated with Protection against Type-2 Diabetes

Elevated plasma triglyceride (TG) levels are an established risk factor for type-2 diabetes (T2D). However, recent studies have hinted at the possibility that genetic risk for TG may paradoxically protect against T2D. In this study, we examined the association of genetic risk for TG with incident T2D, and the interaction of baseline TG with TG genetic risk on incident T2D in 13,247 European-Americans (EA) and 3,238 African-Americans (AA) from three prospective cohort studies. A TG genetic risk score (GRS) was calculated based on 31 validated single nucleotide polymorphisms (SNPs). We considered several baseline covariates, including body- mass index (BMI) and lipid traits. Among EA and AA, we find, as expected, that baseline levels of TG are strongly positively associated with incident T2D (p<2 x 10-(10)). However, the TG GRS is negatively associated with T2D (p=0.013), upon adjusting for only race, in the full dataset. Upon additionally adjusting for age, sex, BMI, high-density lipoprotein cholesterol and TG, the TG GRS is significantly and negatively associated with T2D incidence (p=7.0 x 10(-8)), with similar trends among both EA and AA. No single SNP appears to be driving this association. We also find a significant statistical interaction of the TG GRS with TG (pi(nteraction) = 3.3 x 10-(4)), whereby the association of TG with incident T2D is strongest among those with low genetic risk for TG. Further research is needed to understand the likely pleiotropic mechanisms underlying these findings, and to clarify the causal relationship between T2D and TG.

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.

Sixty-five common genetic variants and prediction of type 2 diabetes

We developed a 65 type 2 diabetes (T2D) variant-weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38-99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12-3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58-0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10(-7)). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m(2), 27.6% [95% CI 17.7-37.5], P = 4.82 × 10(-8); 24.5-27.5 kg/m(2), 11.6% [95% CI 5.8-17.4], P = 9.88 × 10(-5); >27.5 kg/m(2), 2.6% [95% CI -1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D.

The NHS Health Check programme: implementation in east London 2009-2011

OBJECTIVES

To describe implementation and results from the National Health Service (NHS) Health Check programme.

DESIGN

Three-year observational open cohort study: 2009-2011.

PARTICIPANTS

People of age 40-74 years eligible for an NHS Health Check.

SETTING

139/143 general practices in three east London primary care trusts (PCTs) serving an ethnically diverse and socially disadvantaged population.

METHOD

Implementation was supported with education, IT support and performance reports. Tower Hamlets PCT additionally used managed practice networks and prior-stratification to call people at higher cardiovascular (CVD) risk first.

MAIN OUTCOMES MEASURES

Attendance, proportion of high-risk population on statins and comorbidities identified.

RESULTS

Coverage 2009, 2010, 2011 was 33.9% (31,878/10,805), 60.6% (30,757/18,652) and 73.4% (21,194/28,890), respectively. Older people were more likely to attend than younger people. Attendance was similar across deprivation quintiles and was in accordance with population distributions of black African/Caribbean, South Asian and White ethnic groups. 1 in 10 attendees were at high-CVD risk (20% or more 10-year risk). In the two PCTs stratifying risk, 14.3% and 9.4% of attendees were at high-CVD risk compared to 8.6% in the PCT using an unselected invitation strategy. Statin prescription to people at high-CVD risk was higher in Tower Hamlets 48.9%, than in City and Hackney 23.1% or Newham 20.2%. In the 6 months following an NHS Health Check, 1349 new cases of hypertension, 638 new cases of diabetes and 89 new cases of chronic kidney disease (CKD) were diagnosed. This represents 1 new case of hypertension per 38 Checks, 1 new case of diabetes per 80 Checks and 1 new case of CKD per 568 Checks.

CONCLUSIONS

Implementation of the NHS Health Check programme in these localities demonstrates limited success. Coverage and treatment of those at high-CVD risk could be improved. Targeting invitations to people at high-CVD risk and managed practice networks in Tower Hamlets improved performance.

Random blood glucose: a robust risk factor for type 2 diabetes

CONTEXT

Although random blood glucose (RBG) values are common in clinical practice, the role of elevated RBG values as a risk factor for type 2 diabetes is not well described.

OBJECTIVE

This study aimed to examine nondiagnostic, RBG values as a risk factor for type 2 diabetes

DESIGN

This was a cross-sectional study of National Health and Nutrition Examination Surveys (NHANES) participants (2005-2010).

PARTICIPANTS

Nonfasting NHANES participants (n = 13 792) without diagnosed diabetes were included.

PRIMARY OUTCOME

The primary outcome was glycemic status (normal glycemia, undiagnosed prediabetes, or undiagnosed diabetes) using hemoglobin HbA1C as the criterion standard.

ANALYSIS

Multinomial logistic regression examined associations between diabetes risk factors and RBG values according to glycemic status. Associations between current U.S. screening strategies and a hypothetical RBG screening strategy with undiagnosed diabetes were examined.

RESULTS

In unadjusted analyses, a single RBG ≥ 100 mg/dL (5.6 mmol/L) was more strongly associated with undiagnosed diabetes than any single risk factor (odds ratio [OR], 31.2; 95% confidence interval [CI], 21.3-45.5) and remained strongly associated with undiagnosed diabetes (OR, 20.4; 95% CI, 14.0-29.6) after adjustment for traditional diabetes risk factors. Using RBG < 100 mg/dL as a reference, the adjusted odds of undiagnosed diabetes increased significantly as RBG increased. RBG 100-119 mg/dL (OR 7.1; 95% CI 4.4-11.4); RBG 120-139 mg/dL (OR 30.3; 95% CI 20.0-46.0); RBG ≥ 140 mg/dL (OR 256; 95% CI 150.0-436.9). As a hypothetical screening strategy, an elevated RBG was more strongly associated with undiagnosed diabetes than current United States Preventative Services Task Force guidelines (hypertension alone; P < .0001) and similar to American Diabetes Association guidelines (P = .12).

CONCLUSIONS

A single RBG ≥ 100 mg/dL is more strongly associated with undiagnosed diabetes than traditional risk factors. Abnormal RBG values are a risk factor for diabetes and should be considered in screening guidelines.

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

BACKGROUND

Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken.

OBJECTIVES

The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM).

PATIENTS AND METHODS

A population of 6647 individuals without diabetes, aged ≥ 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures.

RESULTS

In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) ≥ 30 kg/m(2), family history of diabetes, wrist circumference > 16.5 cm and waist to height ≥ 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio ≥ 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence.

CONCLUSIONS

Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone.

Body mass index, waist circumference, hip circumference, waist-hip-ratio and waist-height-ratio: which is the better discriminator of prevalent screen-detected diabetes in a Cameroonian population?

BACKGROUND

The link between measures of adiposity and prevalent screen-detected diabetes (SDM) in Africa has been less well investigated. We assessed and compared the strength of association and discriminatory capability of measures of adiposity including body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-hip-ratio (WHR) and waist-height-ratio (WHtR) for prevalent SDM risk in a sub-Saharan African population.

METHODS

Participants were 8663 adults free of diagnosed type 2 diabetes, who took part in the nationally representative Cameroon Burden of Diabetes (CAMBoD) 2006 survey. Logistic regression models were used to compute the odd ratio (OR) and 95% confidence interval (95%CI) for a standard deviation (SD) higher level of BMI (7.3), WC (12.5), HC (11.7), WHR (0.19) and WHtR (0.08) with prevalent SDM risk. Assessment and comparison of discrimination used C-statistic and relative integrated discrimination improvement (RIDI, %).

RESULTS

The adjusted OR and 95%CI for prevalent SDM with each SD higher adipometric variable were: 1.05 (0.98-1.13) for BMI, 1.30 (1.16-1.46) for WC, 1.18 (1.05-1.34) for HC, 1.05 (1.00-1.16) for WHR and 1.26 (1.11-1.39) for WHtR. C-statistic comparisons and RIDI analyses showed a trend toward a significant superiority of WC over other adipometric variables in multivariable models. Combining adiposity variables did not improve discrimination beyond multivariable models with WC alone.

CONCLUSION

WC was the best predictors and to some extent WHtR of prevalent SDM in this population, while BMI and WHR were less effective.

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

En España, según datos del estudio Di@bet.es, un 13,8% de la población adulta padece diabetes y un 14,8% algún tipo de prediabetes (intolerancia a la glucosa, glucemia basal alterada o ambas). Puesto que la detección precoz de la prediabetes puede facilitar la puesta en marcha de medidas terapéuticas que eviten su progresión a diabetes, consideramos que las estrategias de prevención en las consultas de atención primaria y especializada deberían consensuarse. La detección de diabetes y prediabetes mediante un cuestionario específico (test de FINDRISC) y/o la determinación de la glucemia basal en pacientes de riesgo permiten detectar los pacientes con riesgo de desarrollar la enfermedad y es necesario considerar cómo debe ser su manejo clínico. La intervención sobre los estilos de vida puede reducir la progresión a diabetes o hacer retroceder un estado prediabético a la normalidad y es una intervención coste-efectiva. Algunos fármacos, como la metformina, también se han mostrado eficaces en reducir la progresión a diabetes aunque no son superiores a las intervenciones no farmacológicas. Finalmente, aunque no hay pruebas sólidas que apoyen la eficacia del cribado en términos de morbimortalidad, sí que se ha observado una mejora de los factores de riesgo cardiovascular.

El Grupo de Trabajo de Consensos y Guías Clínicas de la Sociedad Española de Diabetes, ha elaborado unas recomendaciones que han sido consensuadas con la Sociedad Española de Endocrinología y Nutrición, la Sociedad Española de Endocrinología Pediátrica, la Sociedad Española de Farmacia Comunitaria, la Sociedad Española de Medicina Familiar y Comunitaria, la Sociedad Española de Médicos Generales, la Sociedad Española de Médicos de Atención Primaria, la Sociedad Española de Medicina Interna y la Asociación de Enfermería Comunitaria y la Red de Grupos de Estudio de la Diabetes en Atención Primaria.

The influence of family history of diabetes on disease prevalence and associated metabolic risk factors among Sri Lankan adults

AIMS

To describe the influence of family history on diabetes prevalence and associated metabolic risk factors in a nationally representative sample from Sri Lanka.

METHODS

A cross sectional national survey was conducted among 5000 adults in Sri Lanka. Family history was evaluated at three levels: (1) parents, (2) grandparents (paternal and maternal) and (3) siblings. A binary-logistic regression analysis controlling for confounders (age, gender, BMI and physical activity) was performed in all patients with 'presence of diabetes' as the dichotomous dependent variable and using family history in father, mother, maternal grandmother/grandfather, paternal grandmother/grandfather, siblings and children as binary independent variables.

RESULTS

The sample size was 4485, mean age was 46.1 ± 15.1 years and 39.5% were males. In all adults, the prevalence of diabetes was significantly higher in patients with a family history (23.0%) than those without (8.2%) (P < 0.001). When family history was present in both parents, the prevalence of diabetes was 32.9%. Presence of a family history significantly increased the risk of diabetes [odds ratio (OR): 3.35, 95% confidence interval (CI): 2.78-4.03], obesity (OR: 2.45, 95% CI: 1.99-2.99), hypertension (OR: 1.25, 95% CI: 1.08-1.45) and metabolic syndrome (OR: 2.28, 95% CI: 1.97-2.63). In all adults, the presence of a family history of diabetes in a father (OR: 1.29, 95% CI: 1.02-1.63), mother (OR: 1.23, 95% CI: 1.11-1.36), paternal grandfather (OR: 1.27, 95% CI: 1.14-1.41), siblings (OR: 4.18, 95% CI: 3.34-5.22) and children (OR: 5.47, 95% CI: 2.93-10.19) was associated with a significantly increased risk of developing diabetes.

CONCLUSIONS

Family history and diabetes had a graded association in the Sri Lankan population, because the prevalence increased with the increasing number of generations affected. Family history of diabetes was also associated with the prevalence of obesity, metabolic syndrome and hypertension. Individuals with a family history of diabetes form an easily identifiable group who may benefit from targeted interventions.

The performance of diabetes risk prediction models in new populations: the role of ethnicity of the development cohort

It is believed that diabetes risk scores need to be ethnic specific. However, this prerequisite has not been tested. We examined the performance of several risk models, developed in various populations, in a Europid and a South Asian population. The performance of 14 published risk prediction models were tested in two prospective studies: the Australian Diabetes, Obesity and Lifestyle (AusDiab) study and the Mauritius non-communicable diseases survey. Eight models were developed in Europid populations; the remainder in various non-Europid populations. Model performance was assessed using area under the receiver operating characteristic curves (discrimination), Hosmer-Lemeshow tests (goodness-of-fit) and Brier scores (accuracy). In both AusDiab and Mauritius, discrimination was highest for a model developed in a mixed population (non-Hispanic white and African American) and lowest for a model developed in a Europid population. Discrimination for all scores was higher in AusDiab than in Mauritius. For almost all models, goodness-of-fit was poor irrespective of the ethnicity of the development cohort, and accuracy was higher in AusDiab compared to Mauritius. Our results suggest that similarity of ethnicity or similarity of diabetes risk may not be the best way of identifying models that will perform well in another population. Differences in study methodology likely account for much of the difference in the performance. Thus, identifying models which use measurements that are clearly described and easily reproducible for both research and clinical settings may be more important.

Low-energy diets differing in fibre, red meat and coffee intake equally improve insulin sensitivity in type 2 diabetes: a randomised feasibility trial

AIMS/HYPOTHESIS

Epidemiological studies have found that a diet high in fibre and coffee, but low in red meat, reduces the risk for type 2 diabetes. We tested the hypothesis that these nutritional modifications differentially improve whole-body insulin sensitivity (primary outcome) and secretion.

METHODS

Inclusion criteria were: age 18-69 years, BMI ≥ 30 kg/m(2), type 2 diabetes treated with diet, metformin or acarbose and known disease duration of ≤ 5 years. Exclusion criteria were: HbA1c >75 mmol/mol (9.0%), type 1 or secondary diabetes types and acute or chronic diseases including cancer. Patients taking any medication affecting the immune system or insulin sensitivity, other than metformin, were also excluded. Of 59 patients (randomised using randomisation blocks [four or six patients] with consecutive numbers), 37 (54% female) obese type 2 diabetic patients completed this controlled parallel-group 8-week low-energy dietary intervention. The participants consumed either a diet high in cereal fibre (whole grain wheat/rye: 30-50 g/day) and coffee (≥ 5 cups/day), and free of red meat (L-RISK, n = 17) or a diet low in fibre (≤ 10 g/day), coffee-free and high in red meat (≥ 150 g/day) diet (H-RISK, n = 20). Insulin sensitivity and secretion were assessed by hyperinsulinaemic-euglycaemic clamp and intravenous glucose tolerance tests with isotope dilution. Whole-body and organ fat contents were measured by magnetic resonance imaging and spectroscopy.

RESULTS

Whole-body insulin sensitivity increased in both groups (mean [95% CI]) (H-RISK vs L-RISK: 0.8 [0.2, 1.4] vs 1.0 [0.4, 1.7]mg kg(-1) min(-1), p = 0.59), while body weight decreased (-4.8% [-6.1%, -3.5%] vs -4.6% [-6.0%, -3.3%], respectively). Hepatic insulin sensitivity remained unchanged, whereas hepatocellular lipid content fell in both groups (-7.0% [-9.6%, -4.5%] vs -6.7% [-9.5%, -3.9%]). Subcutaneous fat mass (-1,553 [-2,767, -340] cm(3) vs -751 [-2,047; 546] cm(3), respectively) visceral fat mass (-206 [-783, 371] cm(3) vs -241 [-856, 373] cm(3), respectively) and muscle fat content (-0.09% [-0.16%, -0.02%] vs -0.02% [-0.10%, 0.05%], respectively) decreased similarly. Insulin secretion remained unchanged, while the proinflammatory marker IL-18 decreased only after the L-RISK diet.

CONCLUSIONS/INTERPRETATION

No evidence of a difference between both low-energy diets was identified. Thus, energy restriction per se seems to be key for improving insulin action in phases of active weight loss in obese type 2 diabetic patients, with a potential improvement of subclinical inflammation with the L-RISK diet.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01409330.

FUNDING

This study was supported by the Ministry of Science and Research of the State of North Rhine-Westphalia (MIWF NRW), the German Federal Ministry of Health (BMG), the Federal Ministry for Research (BMBF) to the Center for Diabetes Research (DZD e.V.) and the Helmholtz Alliance Imaging and Curing Environmental Metabolic Diseases (ICEMED).

A history of diabetes but not hyperglycaemia during exacerbation of obstructive lung disease has impact on long-term mortality: a prospective, observational cohort study

OBJECTIVE

Hyperglycaemia is very common during exacerbations of asthma and chronic obstructive pulmonary disease (COPD). However, its clinical significance is not clear. The objective of the present study was to assess whether exacerbation-associated hyperglycaemia affects long-term mortality in these patients.

DESIGN

A prospective, observational cohort study.

SETTING

A single hospital in eastern Finland.

PARTICIPANTS

153 consecutive patients who were hospitalised due to mild to moderate obstructive lung disease exacerbation (110 with asthma and 43 with COPD) and who survived at least 30 days.

INTERVENTIONS

Plasma glucose levels were recorded seven times during the first day on the ward. Several possible confounders were also recorded. The median follow-up time was 6 years and 2 months.

RESULTS

During the follow-up, 57 (37%) of the patients died. Previously diagnosed diabetes was strongly associated with elevated mortality (adjusted HR (aHR) 3.03 (1.28 to 7.18). The highest fasting glucose value (aHR 1.10 (1.01 to 1.20) per 1 mmol/L) and the highest postprandial glucose value ((aHR 1.07 (1.00 to 1.16)) were also associated with late mortality. However, the associations between highest glucose values and mortality vanished when the diagnosis of diabetes was included in the same model. Within the patients without diabetes, neither fasting (aHR 0.92 (0.42 to 2.02)) nor postprandial ((aHR 1.04 (0.50 to 2.12)) hyperglycaemia was associated with late mortality. There were no statistically significant differences in the underlying causes of death between the patients with and without diabetes.

CONCLUSION

A history of diabetes but not hyperglycaemia during exacerbation of obstructive lung disease has impact on long-term mortality.

16S gut community of the Cameron County Hispanic Cohort

BACKGROUND

Obesity and type 2 diabetes (T2D) are major public health concerns worldwide, and their prevalence has only increased in recent years. Mexican Americans are disproportionately afflicted by obesity and T2D, and rates are even higher in the United States-Mexico border region. To determine the factors associated with the increased risk of T2D, obesity, and other diseases in this population, the Cameron County Hispanic Cohort was established in 2004.

RESULTS

In this study, we characterized the 16S gut community of a subset of 63 subjects from this unique cohort. We found that these communities, when compared to Human Microbiome Project subjects, exhibit community shifts often observed in obese and T2D individuals in published studies. We also examined microbial network relationships between operational taxonomic units (OTUs) in the Cameron County Hispanic Cohort (CCHC) and three additional datasets. We identified a group of seven genera that form a tightly interconnected network present in all four tested datasets, dominated by butyrate producers, which are often increased in obese individuals while being depleted in T2D patients.

CONCLUSIONS

Through a combination of increased disease prevalence and relatively high gut microbial homogeneity in the subset of CCHC members we examined, we believe that the CCHC may represent an ideal community to dissect mechanisms underlying the role of the gut microbiome in human health and disease. The lack of CCHC subject gut community segregation based on all tested metadata suggests that the community structure we observe in the CCHC likely occurs early in life, and endures. This persistent 'disease'-related gut microbial community in CCHC subjects may enhance existing genetic or lifestyle predispositions to the prevalent diseases of the CCHC, leading to increased attack rates of obesity, T2D, non-alcoholic fatty liver disease, and others.

Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts

AIMS/HYPOTHESIS

The relevance of the OGTT in predicting type 2 diabetes is unclear. We assessed the performance of 14 OGTT glucose traits in type 2 diabetes prediction.

METHODS

We studied 2,603 and 2,386 Europeans from the Botnia study and Malmö Prevention Project (MPP) cohorts with baseline OGTT data. Over a follow-up period of 4.94 years and 23.5 years, 155 (5.95%) and 467 (19.57%) participants, respectively, developed type 2 diabetes. The main outcome was incident type 2 diabetes.

RESULTS

One-hour plasma glucose (1h-PG) was a fair/good predictor of incident type 2 diabetes in the Botnia study and MPP (AUC for receiver operating characteristic [AUCROC] 0.80 [0.77, 0.84] and 0.70 [0.68, 0.73]). 1h-PG alone outperformed the prediction model of multiple clinical risk factors (age, sex, BMI, family history of type 2 diabetes) in the Botnia study and MPP (AUCROC 0.75 [0.72, 0.79] and 0.67 [0.64, 0.70]). The same clinical risk factors added to 1h-PG modestly increased prediction for incident type 2 diabetes (Botnia, AUCROC 0.83 [0.80, 0.86]; MPP, AUCROC 0.74 [0.72, 0.77]). 1h-PG also outperformed HbA1c in predicting type 2 diabetes in the Botnia cohort. A 1h-PG value of 8.9 mmol/l and 8.4 mmol/l was the optimal cut-point for initial screening and selection of high-risk individuals in the Botnia study and MPP, respectively, and represented 30% and 37% of all participants in these cohorts. High-risk individuals had a substantially increased risk of incident type 2 diabetes (OR 8.0 [5.5, 11.6] and 3.8 [3.1, 4.7]) and captured 75% and 62% of all incident type 2 diabetes in the Botnia study and MPP.

CONCLUSIONS/INTERPRETATION

1h-PG is a valuable prediction tool for identifying adults at risk for future type 2 diabetes.

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.