A simple clinical score for type 2 diabetes mellitus screening in the Canary Islands

A systematic review of diabetes risk assessment tools in sub-Saharan Africa

Objectives

To systematically review all current studies on diabetes risk assessment tools used in SSA to diagnose diabetes in symptomatic and asymptomatic patients.

Methods

Tools were identified through a systematic search of PubMed, Ovid, Google Scholar, and the Cochrane Library for articles published from January 2010 to January 2020. The search included articles reporting the use of diabetes risk assessment tool to detect individuals with type 2 diabetes in SSA. A standardized protocol was used for data extraction (registry #177726).

Results

Of the 825 articles identified, 39 articles met the inclusion criteria, and three articles reported tools used in SSA population but developed for the Western population. None was validated in SSA population. All but three articles were observational studies (136 and 58,657 study participants aged between the ages of 15 and 85 years). The Finnish Medical Association risk tool, World Health Organization (WHO) STEPS instrument, General Practice Physical Activity Questionnaire (GPPAQ), Rapid Eating and Activity Assessment for Patients (REAP), and an anthropometric tool were the most frequently used non-invasive tools in SSA. The accuracy of the tools was measured using sensitivity, specificity, or area under the receiver operating curve. The anthropometric predictor variables identified included age, body mass index, waist circumference, positive family of diabetes, and activity levels.

Conclusions

This systematic review demonstrated a paucity of validated diabetes risk assessment tools for SSA. There remains a need for the development and validation of a tool for the rapid identification of diabetes for targeted interventions.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study

Background:

Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups.

Objective(s):

We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands.

Methods:

A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one.

Conclusions:

Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.

Validation of DIABSCORE in screening for Type 2 Diabetes and prediabetes in Tunisian population

AIMS

To perform a validation of DIABSCORE in a sample of Tunisian adults and find out the optimal cut-off point for screening of Type 2 diabetes (T2D) and prediabetes.

METHODS

225 adults 18-75 years and a subgroup of 138 adults (18-54 years), with undiagnosed T2D from the region of Cap-Bon, Tunisia were included in the present study. The DIABSCORE was calculated based on: age, waist/height ratio, family history of T2D and gestational diabetes. Receiver operating characteristics (ROC) curves and areas under curve (AUC) were obtained. The T2D and prediabetes prevalences odds ratios (OR) between patients exposed and not exposed to DIABSCORE≥90 and DIABSCORE≥80, respectively were calculated in both age ranges.

RESULTS

For screening of T2D the best value was DIABSCORE = 90 with a highest sensitivity (Se), negative predictive value (NPV) and lower negative likelihood ratio in participants aged 18-75 yr (Se = 97%; NPV = 97%) when compared to participants aged 18-54 yr (Se = 95%; NPV = 97%); for prediabetes, the best Se and NPV were for DIABSCORE = 80 in both age groups, but it showed a disbalanced sensitivity-specificity. The ROC curves for T2D showed a similar AUC in both age ranges (AUC = 0.62 and AUC = 0.61 respectively). The ROC curves for prediabetes showed a highest AUC in those aged 18-54 years than the older ones (AUC = 0.62 and AUC = 0.57, respectively). The prevalences OR of T2D for DIABSCORE≥90 was higher than for DIABSCORE≥80 in both age ranges. Nevertheless, the prevalences OR of prediabetes for DIABSCORE≥90 was half of the detected for DIABSCORE≥80 in both age ranges.

CONCLUSION

The DIABSCORE is a simple clinical tool and accurate method in screening for T2D and prediabetes in the adult Tunisian population.

Clinical applicability and cost-effectiveness of DIABSCORE in screening for type 2 diabetes in primary care

AIMS

To evaluate the applicability and cost-effectiveness of a clinical risk score (DIABSCORE) to screen for type 2 diabetes in primary care patients.

METHODS

Multicenter cross-sectional study of 10,508 adult no previously diagnosed with diabetes, in 2 Spanish regions (Canary Islands and Valencian Community). The variables comprising DIABSCORE were age, waist to height ratio, family history of diabetes and gestational diabetes. ROC curves were obtained; the diabetes prevalences odds ratios (HbA1c ≥6.5%) between patients exposed and not exposed to DIABSCORE ≥100, and to fasting blood glucose ≥126mg/dL were calculated. The opinions of both the professionals and the patients concerning DIABSCORE were collected, and a cost-effectiveness analysis was performed.

RESULTS

In both regions, the valid cut-off point for diabetes (DIABSCORE=100), showed an area under the curve >0.80. The prevalences odds ratio of diabetes for DIABSCORE ≥100 was 9.5 (3.7-31.5) in Canarian and 18.3 (8.0-51.1) in Valencian; and for glucose ≥126mg/dL it was, respectively, 123.0 (58.8-259.2) and 303.1 (162.5-583.8). However, glucose ≥126mg/dL showed a low sensitivity (below 48% in both communities) as opposed to DIABSCORE ≥100 (above 90% in both regions). Professionals (100%) and patients (75%) satisfaction was greater when using DIABSCORE rather than glucose measurement for diabetes screening. The cost of each case of diabetes identified was lower with DIABSCORE ≥100 (7.6 € in Canarian and 8.3 € in Valencian) than glucose ≥126mg/dL (10.8 € and 10.5 €, respectively).

CONCLUSIONS

DIABSCORE is an applicable and cost-effective screening method for type 2 diabetes in primary care.

Identifying risk for type 2 diabetes in different age cohorts: does one size fit all?

OBJECTIVE

To estimate age-specific risk equations for type 2 diabetes onset in young, middle-aged, and older US adults, and to compare the performance of simple equations based on readily available demographic information alone, against enhanced equations that require both demographic and clinical information (fasting plasma glucose, high-density lipoprotein, and triglyceride levels).

RESEARCH DESIGN AND METHODS

We estimated the probability of developing diabetes by age group using data from the Coronary Artery Risk Development in Young Adults (for ages 18-40 years), Atherosclerosis Risk in Communities (for ages 45-64 years), and the Cardiovascular Health Study (for ages 65 years and older). Simple and enhanced equations were estimated using logistic regression models, and performance was compared by age group. Thresholds based on these risk equations were evaluated using split-sample bootstraps and calibrating the constant of one age cohort to others.

RESULTS

Simple risk equations had an area under the receiver-operating curve (AUROC) of 0.72, 0.79, 0.75, and 0.69 for age groups 18-30, 28-40, 45-64, and 65 and older, respectively. The corresponding AUROCs for enhanced equations were 0.75, 0.85, 0.85, and 0.81. Risk equations based on younger populations, when applied to older cohorts, underpredict diabetes incidence and risk. Conversely, risk equations based on older populations overpredict the likelihood of diabetes in younger cohorts.

CONCLUSIONS

In general, risk equations are more successful in middle-aged adults than in young and old populations. The results demonstrate the importance of applying age-specific risk equations to identify target populations for intervention. While the predictive capacity of equations that include biomarkers is better than of those based solely on self-reported variables, biomarkers are more important in older populations than in younger ones.

Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

BACKGROUND

The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non-diabetic subjects based on non-laboratory- and laboratory-based risk algorithms.

METHODS

Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non-diabetic subjects aged 18-70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non-laboratory- and laboratory-based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample (n = 839). The Hosmer-Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms.

RESULTS

Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2-h post-load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non-laboratory-based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory-based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM.

CONCLUSION

A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms.

Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search

BACKGROUND AND PURPOSE

Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa.

DESIGN

systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors.

DATA EXTRACTION

Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population.

RESULTS

Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context.

CONCLUSION

Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate.

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.

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.

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.

Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review

OBJECTIVE

To systematically review published primary research on the development or validation of risk scores that require only self-reported or available clinical data to identify undiagnosed Type 2 Diabetes Mellitus (T2DM).

METHODS

A systematic literature search of Medline and EMBASE was conducted until January 2011. Studies focusing on the development or validation of risk scores to identify undiagnosed T2DM were included. Risk scores to predict future risk of T2DM were excluded.

RESULTS

Thirty-one studies were included; 17 developed a new risk score, 14 validated existing scores. Twenty-six studies were conducted in high-income countries. Age and measures of body mass/fat distribution were the most commonly used predictor variables. Studies developing new scores performed better than validation studies, with 11 reporting an AUC of >0.80 compared to one validation study. Fourteen validation studies reported sensitivities of <80%. The performance of scores did not differ by the number of variables included or the country setting.

CONCLUSIONS

There is a proliferation of newly developed risk scores using similar variables, which sometimes perform poorly upon external validation. Future research should explore the recalibration, validation and applicability of existing scores to other settings, particularly in low/middle income countries, and on the utility of scores to improve diabetes-related outcomes.

Survey of diabetes risk assessment tools: concepts, structure and performance

The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.

Prospective validation of American Diabetes Association risk tool for predicting pre-diabetes and diabetes in Taiwan-Taichung community health study

BACKGROUND

A simple diabetes risk tool that does not require laboratory tests would be beneficial in screening individuals at higher risk. Few studies have evaluated the ability of these tools to identify new cases of pre-diabetes. This study aimed to assess the ability of the American Diabetes Association Risk Tool (ADART) to predict the 3-year incidence of pre-diabetes and diabetes in Taiwanese.

METHODS

This was a 3-year prospective study of 1021 residents with normoglycemia at baseline, gathered from a random sample of residents aged 40-88 years in a metropolitan city in Taiwan. The areas under the curve (AUCs) of three models were compared: ADART only, ADART plus lifestyle behaviors at baseline, and ADART plus lifestyle behaviors and biomarkers at baseline. The performance of ADART was compared with that of 16 tools that had been reported in the literature.

RESULTS

The AUCs and their 95% confidence intervals (CIs) were 0.60 (0.54-0.66) for men and 0.72 (0.66-0.77) for women in model 1; 0.62 (0.56-0.68) for men and 0.74 (0.68-0.80) for women in model 2; and 0.64 (0.58-0.71) for men and 0.75 (0.69-0.80) for women in model 3. The AUCs of these three models were all above 0.7 in women, but not in men. No significant difference in either women or men (p = 0.268 and 0.156, respectively) was observed in the AUC of these three models. Compared to 16 tools published in the literature, ADART had the second largest AUC in both men and women.

CONCLUSIONS

ADART is a good screening tool for predicting the three-year incidence of pre-diabetes and diabetes in females of a Taiwanese population. The performance of ADART in men was similar to the results with other tools published in the literature. Its performance was one of the best among the tools reported in the literature.

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

BACKGROUND

The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.

METHODS

We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.

RESULTS

Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).

CONCLUSIONS

We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

Screening for type 2 diabetes and dysglycemia

Type 2 diabetes mellitus (T2DM) and dysglycemia (impaired glucose tolerance and/or impaired fasting glucose) are increasingly contributing to the global burden of diseases. The authors reviewed the published literature to critically evaluate the evidence on screening for both conditions and to identify the gaps in current understanding. Acceptable, relatively simple, and accurate tools can be used to screen for both T2DM and dysglycemia. Lifestyle modification and/or medication (e.g., metformin) are cost-effective in reducing the incidence of T2DM. However, their application is not yet routine practice. It is unclear whether diabetes-prevention strategies, which influence cardiovascular risk favorably, will also prevent diabetic vascular complications. Cardioprotective therapies, which are cost-effective in preventing complications in conventionally diagnosed T2DM, can be used in screen-detected diabetes, but the magnitude of their effects is unknown. Economic modeling suggests that screening for both T2DM and dysglycemia may be cost-effective, although empirical data on tangible benefits in preventing complications or death are lacking. Screening for T2DM is psychologically unharmful, but the specific impact of attributing the label of dysglycemia remains uncertain. Addressing these gaps will inform the development of a screening policy for T2DM and dysglycemia within a holistic diabetes prevention and control framework combining secondary and high-risk primary prevention strategies.

A Chinese diabetes risk score for screening of undiagnosed diabetes and abnormal glucose tolerance

BACKGROUND

This study is aimed at developing and evaluating a diabetes risk score (DRS) to predict incident diabetes and screen for undiagnosed diabetes and abnormal glucose tolerance in the Chinese population.

METHODS

Three DRS instruments were respectively developed and validated based on the data collected from a 10-year longitudinal health checkup-based population of 1,851 individuals without diabetes at baseline. The efficiency on glucose abnormality screening was evaluated based on the testing of a cross-sectional sample of 699 individuals without known diabetes.

RESULTS

The DRS consisting of age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, serum triglycerides, and serum high-density lipoprotein-cholesterol had the best prediction properties (area under curve [AUC] = 0.734 [95% confidence interval 0.702-0.766] and 0.759 [0.686-0.831] in exploratory and validation cohorts, respectively). The DRS had a sensitivity of 64.5% and 72.9%, respectively, and a specificity of 71.6% and 63.9%, respectively, with an optimal cutoff of 4. AUCs were 0.828 (0.797-0.860) and 0.909 (0.884-0.933) for detecting abnormal glucose tolerance and diabetes, respectively, through cross-sectional screening. Performance of the oral glucose tolerance test (OGTT) in selected subjects with DRS ≥ 4 led to the identification of 76.2% cases of abnormal glucose tolerance and 100% cases of diabetes, while avoiding an OGTT in 52.8% of the study group.

CONCLUSIONS

The DRS instrument including age, hypertension, history of high blood glucose, body mass index, fasting plasma glucose, triglycerides, and high-density lipoprotein-cholesterol is practical and effective in predicting incident diabetes and screening glucose abnormality in the Chinese population.

A simple Chinese risk score for undiagnosed diabetes

AIMS

A diabetes risk score for screening undiagnosed diabetes was constructed and validated in Chinese adults.

METHODS

Two consecutive population-based diabetes surveys among Chinese adults aged 20-74 years were conducted in 2002 (n = 1986) and 2006 (n = 4336). Demographic and anthropometric measures were collected following similar procedures. Standard 2-h 75-g oral glucose tolerance tests (OGTTs) were performed to diagnose diabetes in both surveys. Fasting capillary plasma glucose (FCG) and glycated haemoglobin (HbA(1c)) were also measured together with the OGTTs on the same day of the 2006 survey. Beta coefficients estimated using logistic regression analysis derived from data of the 2002 survey were used to develop the risk assessment algorithm. The performance of the algorithm was validated in the study population of the 2006 survey.

RESULTS

Of all the variables tested, waist circumference, age and family history of diabetes were significant predictors of diabetes and were used to construct the risk assessment score. The score, ranging from 3 to 32, performed well when applied to the study population of the 2006 survey. The area under the receiver operating characteristic curve was 67.3% (95% CI, 64.9-69.7%) for the score, while it was 76.3% (73.5-79.0%) for FCG alone and 67.8% (64.9-70.8%) for HbA(1c) alone. At a cut-off point of 14, the sensitivity and specificity of the risk score were 84.2% (81.0-87.5%) and 39.8% (38.2-41.3%).

CONCLUSIONS

The risk score based on age, waist circumference and family history of diabetes is efficient as a layperson-oriented diabetes screening tool for health promotion and for population-based screening programmes.

Inverse association between serum resistin and insulin resistance in humans

AIM

To determine how serum concentrations of resistin are distributed in humans in relation to insulin resistance, type 2 diabetes, and obesity.

METHODS

Cross-sectional, descriptive study carried out in a random sample (n=713, 43% men, 18-75 years) of general population of inhabitants of the Canary Islands (Spain). Serum resistin concentration, HOMA2-IR, anthropometric parameters, drug consumption and physical activity were recorded.

RESULTS

There were no differences in resistin concentration between participants with and without diabetes (3.1+/-0.2 vs. 3.2+/-0.1ng/mL; p=0.566), or between obese and non-obese participants (3.1+/-0.1 vs. 3.2+/-0.1ng/mL; p=0.803). Individuals with abdominal obesity (waist-hip ratio [WHR] >or=1 in men or >or=0.9 in women) had lower concentrations of resistin (3.0+/-0.13 vs. 3.4+/-0.1ng/mL; p<0.001). The correlations between resistin and HOMA2-IR (r=-0.231; p<0.001) and between resistin and WHR (r=-0.202; p<0.001) were inverse. Multivariate analysis corroborated the inverse association of this cytokine with HOMA2-IR, WHR and, in women, also retained in the model the direct association between resistin and physical activity and the inverse association between resistin and antihypertensive agents.

CONCLUSIONS

In this population resistin is inversely associated with insulin resistance and abdominal obesity.