A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population.

AIMS: The FINDRISC questionnaire is a screening tool to estimate the risks for type 2 diabetes as well as asymptomatic type 2 diabetes. We aimed to evaluate its performance to predict diabetes in a German population and to compare its predictive and detective ability in the same population. METHODS: A total of 552 subjects with increased risk of type 2 diabetes were investigated. All individuals completed the FINDRISC questionnaires and underwent an oral glucose tolerance test (OGTT). All individuals were followed for 3 years and underwent an OGTT again. The performance of the opportunistic screening was assessed with the area under the receiver operating characteristics curve (AUC). An intervention program was carried out for all diabetic and IFG/IGT patients at baseline. RESULTS: For identification, the asymptomatic type 2 DM was named Condition 1; prediction of type 2 DM risk in the follow-up survey as Condition 2; and diabetes risk predicting in a hypothetical case of survey without intervention program as Condition 3. The ROC-AUC in the three condition were AUC (FINDRISC1)=0.745, AUC (FINDRISC2)=0.789, and AUC (FINDRISC3)=0.775, respectively. A significant association between FINDRISC and evolution of disease was found, but the variation of plasma glucose during the three years follow-up was not associated with FINDRISC. People in the intervention group with an improvement of glucose tolerance had a smaller FINDRISC score than persons with an unchanged or progressive condition of disease. CONCLUSION: FINDRISC was validated in our study as a simple tool with high performance to predict diabetes risk and less efficient to identify asymptomatic type 2 diabetes. People with lower FINDRISC score will benefit easier from preventive intervention.     

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.     

A Novel Testing Model for Opportunistic Screening of Pre-Diabetes and Diabetes among U.S. Adults

Objective

The study aim was to evaluate the performance of a novel simultaneous testing model, based on the Finnish Diabetes Risk Score (FINDRISC) and HbA1c, in detecting undiagnosed diabetes and pre-diabetes in Americans.

Research Design and Methods

This cross-sectional analysis included 3,886 men and women (≥ 20 years) without known diabetes from the U.S. National Health and Nutrition Examination Survey (NHANES) 2005-2010. The FINDRISC was developed based on eight variables (age, BMI, waist circumference, use of antihypertensive drug, history of high blood glucose, family history of diabetes, daily physical activity and fruit & vegetable intake). The sensitivity, specificity, and the receiver operating characteristic (ROC) curve of the testing model were calculated for undiagnosed diabetes and pre-diabetes, determined by oral glucose tolerance test (OGTT).

Results

The prevalence of undiagnosed diabetes was 7.0% and 43.1% for pre-diabetes (27.7% for isolated impaired fasting glucose (IFG), 5.1% for impaired glucose tolerance (IGT), and 10.3% for having both IFG and IGT). The sensitivity and specificity of using the HbA1c alone was 24.2% and 99.6% for diabetes (cutoff of ≥6.5%), and 35.2% and 86.4% for pre-diabetes (cutoff of ≥5.7%). The sensitivity and specificity of using the FINDRISC alone (cutoff of ≥9) was 79.1% and 48.6% for diabetes and 60.2% and 61.4% for pre-diabetes. Using the simultaneous testing model with a combination of FINDRISC and HbA1c improved the sensitivity to 84.2% for diabetes and 74.2% for pre-diabetes. The specificity for the simultaneous testing model was 48.4% of diabetes and 53.0% for pre-diabetes.

Conclusions

This simultaneous testing model is a practical and valid tool in diabetes screening in the general U.S. population.     

Mobile Application-Based Seoul National University Prostate Cancer Risk Calculator: Development,Validation, and Comparative Analysis with Two Western Risk Calculators in Korean Men

Objectives

We developed a mobile application-based Seoul National University Prostate Cancer Risk Calculator (SNUPC-RC) that predicts the probability of prostate cancer (PC) at the initial prostate biopsy in a Korean cohort. Additionally, the application was validated and subjected to head-to-head comparisons with internet-based Western risk calculators in a validation cohort. Here, we describe its development and validation.

Patients and Methods

As a retrospective study, consecutive men who underwent initial prostate biopsy with more than 12 cores at a tertiary center were included. In the development stage, 3,482 cases from May 2003 through November 2010 were analyzed. Clinical variables were evaluated, and the final prediction model was developed using the logistic regression model. In the validation stage, 1,112 cases from December 2010 through June 2012 were used. SNUPC-RC was compared with the European Randomized Study of Screening for PC Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC). The clinical value was evaluated using decision curve analysis.

Results

PC was diagnosed in 1,240 (35.6%) and 417 (37.5%) men in the development and validation cohorts, respectively. Age, prostate-specific antigen level, prostate size, and abnormality on digital rectal examination or transrectal ultrasonography were significant factors of PC and were included in the final model. The predictive accuracy in the development cohort was 0.786. In the validation cohort, AUC was significantly higher for the SNUPC-RC (0.811) than for ERSPC-RC (0.768, p<0.001) and PCPT-RC (0.704, p<0.001). Decision curve analysis also showed higher net benefits with SNUPC-RC than with the other calculators.

Conclusions

SNUPC-RC has a higher predictive accuracy and clinical benefit than Western risk calculators. Furthermore, it is easy to use because it is available as a mobile application for smart devices.     

Immunoglobulin E and mast cell proteases are potential risk factors of human pre-diabetes and diabetes mellitus

Background

Recent studies have suggested that mast-cell activation and inflammation are important in obesity and diabetes. Plasma levels of mast cell proteases and the mast cell activator immunoglobulin E (IgE) may serve as novel inflammatory markers that associate with the risk of pre-diabetes and diabetes mellitus.

Methods and Results

A total of 340 subjects 55 to 75 years of age were grouped according to the American Diabetes Association 2003 criteria of normal glucose tolerance, pre-diabetes, and diabetes mellitus. The Kruskal-Wallis test demonstrated significant differences in plasma IgE levels (P = 0.008) among groups with different glucose tolerance status. Linear regression analysis revealed significant correlations between plasma levels of chymase (P = 0.030) or IgE (P = 0.022) and diabetes mellitus. Ordinal logistic regression analysis showed that IgE was a significant risk factor of pre-diabetes and diabetes mellitus (odds ratio [OR]: 1.674, P = 0.034). After adjustment for common diabetes risk factors, including age, sex, hypertension, body-mass index, cholesterol, homeostatic model assessment (HOMA) index, high-sensitivity C-reactive protein (hs-CRP), and mast cell chymase and tryptase, IgE remained a significant risk factor (OR: 1.866, P = 0.015). Two-variable ordinal logistic analysis indicated that interactions between hs-CRP and IgE, or between IgE and chymase, increased further the risks of developing pre-diabetes and diabetes mellitus before (OR: 2.204, P = 0.044; OR: 2.479, P = 0.033) and after (OR: 2.251, P = 0.040; OR: 2.594, P = 0.026) adjustment for common diabetes risk factors.

Conclusions

Both IgE and chymase associate with diabetes status. While IgE and hs-CRP are individual risk factors of pre-diabetes and diabetes mellitus, interactions of IgE with hs-CRP or with chymase further increased the risk of pre-diabetes and diabetes mellitus.     

Changes in the erythrocyte glutathione concentration in the course of diabetes mellitus

This study aims to evaluate the significance of the changes of erythrocyte reduced glutathione (GSH) in the course of diabetes mellitus including the pre-diabetes stage and cardiovascular disease co-morbidity. A total of 222 participants (female:male, 107:115) were selected and their erythrocyte GSH levels were measured. The participants were divided into four groups: (i) control; (ii) those with blood glucose level > or =5.6 mmol/l but < 6.9 mmol/l as pre-diabetes mellitus with no other pathology; (iii) diabetes without co-morbidity; and (iv) those with diabetes mellitus and cardiovascular disease. Statistical analysis was by ANOVA followed by a Fisher's LSD post hoc test. We observed that GSH concentration was significantly different between groups (P < 0.04). The Fisher's post hoc test indicated significant differences in erythrocyte GSH levels between the pre-diabetes mellitus and diabetes mellitus groups compared to control (P < 0.005 and P < 0.05, respectively). A statistically significant change (P < 0.001) involving an initial fall followed by a rise in erythrocyte GSH levels was observed when diabetes mellitus and diabetes mellitus+cardiovascular disease groups were combined and assessed with respect to period of diabetes. We conclude that oxidative stress is already present in the pre-diabetes stage as determined by the fall in GSH, representing the initial phase of oxidative stress in diabetes mellitus progression. This finding provides evidence that antioxidant markers such as GSH could be a useful tool for pre-diabetes mellitus screening.     

A Risk Assessment Model for Type 2 Diabetes in Chinese

Aims

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

Materials and Methods

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

Results

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

Discussion

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

Optimal Cut-Off Points of Fasting Plasma Glucose for Two-Step Strategy in Estimating Prevalence and Screening Undiagnosed Diabetes and Pre-Diabetes in Harbin,China

To identify optimal cut-off points of fasting plasma glucose (FPG) for two-step strategy in screening abnormal glucose metabolism and estimating prevalence in general Chinese population. A population-based cross-sectional study was conducted on 7913 people aged 20 to 74 years in Harbin. Diabetes and pre-diabetes were determined by fasting and 2 hour post-load glucose from the oral glucose tolerance test in all participants. Screening potential of FPG, cost per case identified by two-step strategy, and optimal FPG cut-off points were described. The prevalence of diabetes was 12.7%, of which 65.2% was undiagnosed. Twelve percent or 9.0% of participants were diagnosed with pre-diabetes using 2003 ADA criteria or 1999 WHO criteria, respectively. The optimal FPG cut-off points for two-step strategy were 5.6 mmol/l for previously undiagnosed diabetes (area under the receiver-operating characteristic curve of FPG 0.93; sensitivity 82.0%; cost per case identified by two-step strategy ¥261), 5.3 mmol/l for both diabetes and pre-diabetes or pre-diabetes alone using 2003 ADA criteria (0.89 or 0.85; 72.4% or 62.9%; ¥110 or ¥258), 5.0 mmol/l for pre-diabetes using 1999 WHO criteria (0.78; 66.8%; ¥399), and 4.9 mmol/l for IGT alone (0.74; 62.2%; ¥502). Using the two-step strategy, the underestimates of prevalence reduced to nearly 38% for pre-diabetes or 18.7% for undiagnosed diabetes, respectively. Approximately a quarter of the general population in Harbin was in hyperglycemic condition. Using optimal FPG cut-off points for two-step strategy in Chinese population may be more effective and less costly for reducing the missed diagnosis of hyperglycemic condition.     

Changes in the erythrocyte glutathione concentration in the course of diabetes mellitus

Abstract

This study aims to evaluate the significance of the changes of erythrocyte reduced glutathione (GSH) in the course of diabetes mellitus including the pre-diabetes stage and cardiovascular disease co-morbidity. A total of 222 participants (female:male, 107:115) were selected and their erythrocyte GSH levels were measured. The participants were divided into four groups: (i) control; (ii) those with blood glucose level ≥5.6 mmol/l but < 6.9 mmol/l as pre-diabetes mellitus with no other pathology; (iii) diabetes without co-morbidity; and (iv) those with diabetes mellitus and cardiovascular disease. Statistical analysis was by ANOVA followed by a Fisher's LSD post hoc test. We observed that GSH concentration was significantly different between groups (P < 0.04). The Fisher's post hoc test indicated significant differences in erythrocyte GSH levels between the pre-diabetes mellitus and diabetes mellitus groups compared to control (P < 0.005 and P < 0.05, respectively). A statistically significant change (P < 0.001) involving an initial fall followed by a rise in erythrocyte GSH levels was observed when diabetes mellitus and diabetes mellitus+cardiovascular disease groups were combined and assessed with respect to period of diabetes. We conclude that oxidative stress is already present in the pre-diabetes stage as determined by the fall in GSH, representing the initial phase of oxidative stress in diabetes mellitus progression. This finding provides evidence that antioxidant markers such as GSH could be a useful tool for pre-diabetes mellitus screening.     

Anthropometric variables accurately predict dual energy x-ray absorptiometric-derived body composition and can be used to screen for diabetes

The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations.     

Using Hemoglobin A1C as a Predicting Model for Time Interval from Pre-Diabetes Progressing to Diabetes

Objective

The early identification of subjects at high risk for diabetes is essential, thus, random rather than fasting plasma glucose is more useful. We aim to evaluate the time interval between pre-diabetes to diabetes with anti-diabetic drugs by using HbA1C as a diagnostic tool, and predicting it using a mathematic model.

Methods

We used the Taipei Medical University Affiliated Hospital Patient Profile Database (AHPPD) from January-2007 to June-2011. The patients who progressed and were prescribed anti-diabetic drugs were selected from AHPPD. The mathematical model used to predict the time interval of HbA1C value ranged from 5.7% to 6.5% for diabetes progression.

Results

We predicted an average overall time interval for all participants in between 5.7% to 6.5% during a total of 907 days (standard error, 103 days). For each group found among 5.7% to 6.5% we determined 1169.3 days for the low risk group (i.e. 3.2 years), 1080.5 days (i.e. 2.96 years) for the increased risk group and 729.4 days (i.e. 1.99 years) for the diabetes group. This indicates the patients will take an average of 2.49 years to reach 6.5%.

Conclusion

This prediction model is very useful to help prioritize the diagnosis at an early stage for targeting individuals with risk of diabetes. Using patients'' HbA1C before anti-diabetes drugs are used we predicted the time interval from pre-diabetes progression to diabetes is 2.49 years without any influence of age and gender. Additional studies are needed to support this model for a long term prediction.     

Depression in Persons with Diabetes by Age and Antidiabetic Treatment: A Cross-Sectional Analysis with Data from the Hordaland Health Study

BackgroundPersons with diabetes have increased risk of depression, however, studies addressing whether the risk varies by age and type of antidiabetic treatment have yielded conflicting results. The aim of this study was to investigate if the association between diabetes and depression varied by type of antidiabetic treatment in a large community based sample of middle-aged (40–47 years) and older adults (70–72 years).MethodsData from 21845 participants in the Hordaland Health Study (HUSK) were analyzed in a cross-sectional design. Diabetes was assessed by self-report and classified as un-medicated, treated by oral antidiabetic agents or by insulin. Depression was defined as a score ≥8 on the depression subscale of the Hospital Anxiety and Depression Scale and/or self-reported use of antidepressant agents. Associations between diabetes and depression were estimated using logistic regression.ResultsPersons in their forties with diabetes had a doubled prevalence of depression (OR: 1.96 (95% C.I.: 1.35, 2.83)) compared to persons without diabetes, while a lower and non-significant association was found among persons in their seventies. Persons in their forties with orally treated diabetes had about three times higher prevalence of depression (OR: 2.92 (95% C.I.: 1.48, 5.77)) after adjustment for gender, BMI, physical activity, alcohol consumption and education, compared to non-diabetic persons in the same age-group. No association between depression and insulin or un-medicated diabetes was found.ConclusionsClinicians should be aware that persons in their forties with orally treated diabetes are at a marked increased risk of depression.     

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.     

Combining predictors for classification using the area under the receiver operating characteristic curve

No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function-the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case-control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood-based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.     

Comparison of four established questionnaires to identify highway bus drivers at risk for obstructive sleep apnea in Turkey

In Turkey, all heavy-vehicle driver’s license applicants older than 45 years and with body mass index (BMI) >25 kg/m² are required to have polysomnography (PSG). However, this law is usually overlooked in practice due to the large number of applications. We aimed to assess the usefulness of four standardized questionnaires: Berlin, STOP, STOP-BANG and OSA50, in identifying the high-risk bus drivers for obstructive sleep apnea (OSA). Ninety highway bus drivers underwent polysomnography and completed four questionnaires. They also underwent otolaryngologic evaluation and blood testing for probable co-existing conditions such as diabetes and hypothyroidism. Neck circumference, BMI, waist circumference, prevalence of OSA and metabolic syndrome, oxygen desaturation index and duration of sleep associated with less than 90% saturation were significantly higher and mean oxygen saturation was significantly lower in drivers >45 years old than drivers <45 years old. STOP-BANG questionnaire had the highest sensitivity (87%) and the highest negative predictive value (NPV) (76%) in identifying high-risk for OSA. A cut off of 45 years old is suitable in screening highway bus drivers for OSA. Among the four questionnaires, STOP-BANG questionnaire had the highest sensitivity and negative predictive value (NPV) in identifying high risk patients for OSA in highway bus drivers and can be safely used as a screening test in this group.

     

A risk score to predict type 2 diabetes mellitus in an elderly Spanish Mediterranean population at high cardiovascular risk

Introduction

To develop and test a diabetes risk score to predict incident diabetes in an elderly Spanish Mediterranean population at high cardiovascular risk.

Materials and Methods

A diabetes risk score was derived from a subset of 1381 nondiabetic individuals from three centres of the PREDIMED study (derivation sample). Multivariate Cox regression model ß-coefficients were used to weigh each risk factor. PREDIMED-personal Score included body-mass-index, smoking status, family history of type 2 diabetes, alcohol consumption and hypertension as categorical variables; PREDIMED-clinical Score included also high blood glucose. We tested the predictive capability of these scores in the DE-PLAN-CAT cohort (validation sample). The discrimination of Finnish Diabetes Risk Score (FINDRISC), German Diabetes Risk Score (GDRS) and our scores was assessed with the area under curve (AUC).

Results

The PREDIMED-clinical Score varied from 0 to 14 points. In the subset of the PREDIMED study, 155 individuals developed diabetes during the 4.75-years follow-up. The PREDIMED-clinical score at a cutoff of ≥6 had sensitivity of 72.2%, and specificity of 72.5%, whereas AUC was 0.78. The AUC of the PREDIMED-clinical Score was 0.66 in the validation sample (sensitivity = 85.4%; specificity = 26.6%), and was significantly higher than the FINDRISC and the GDRS in both the derivation and validation samples.

Discussion

We identified classical risk factors for diabetes and developed the PREDIMED-clinical Score to determine those individuals at high risk of developing diabetes in elderly individuals at high cardiovascular risk. The predictive capability of the PREDIMED-clinical Score was significantly higher than the FINDRISC and GDRS, and also used fewer items in the questionnaire.     

Diabetes and Pre-Diabetes among Persons Aged 35 to 60 Years in Eastern Uganda: Prevalence and Associated Factors

Background

Our aim was to estimate the prevalence of abnormal glucose regulation (AGR) (i.e. diabetes and pre-diabetes) and its associated factors among people aged 35-60 years so as to clarify the relevance of targeted screening in rural Africa.

Methods

A population-based survey of 1,497 people (786 women and 711 men) aged 35-60 years was conducted in a predominantly rural Demographic Surveillance Site in eastern Uganda. Participants responded to a lifestyle questionnaire, following which their Body Mass Index (BMI) and Blood Pressure (BP) were measured. Fasting plasma glucose (FPG) was measured from capillary blood using On-Call® Plus (Acon) rapid glucose meters, following overnight fasting. AGR was defined as FPG ≥6.1mmol L^-1 (World Health Organization (WHO) criteria or ≥5.6mmol L^-1 (American Diabetes Association (ADA) criteria. Diabetes was defined as FPG >6.9mmol L^-1, or being on diabetes treatment.

Results

The mean age of participants was 45 years for men and 44 for women. Prevalence of diabetes was 7.4% (95%CI 6.1-8.8), while prevalence of pre-diabetes was 8.6% (95%CI 7.3-10.2) using WHO criteria and 20.2% (95%CI 17.5-22.9) with ADA criteria. Using WHO cut-offs, the prevalence of AGR was 2 times higher among obese persons compared with normal BMI persons (Adjusted Prevalence Rate Ratio (APRR) 1.9, 95%CI 1.3-2.8). Occupation as a mechanic, achieving the WHO recommended physical activity threshold, and higher dietary diversity were associated with lower likelihood of AGR (APRR 0.6, 95%CI 0.4-0.9; APRR 0.6, 95%CI 0.4-0.8; APRR 0.5, 95%CI 0.3-0.9 respectively). The direct medical cost of detecting one person with AGR was two US dollars with ADA and three point seven dollars with WHO cut-offs.

Conclusions

There is a high prevalence of AGR among people aged 35-60 years in this setting. Screening for high risk persons and targeted health education to address obesity, insufficient physical activity and non-diverse diets are necessary.     

A Random Forest Based Risk Model for Reliable and Accurate Prediction of Receipt of Transfusion in Patients Undergoing Percutaneous Coronary Intervention

Background

Transfusion is a common complication of Percutaneous Coronary Intervention (PCI) and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI.

Methods

Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC), with net reclassification improvement (NRI) used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories.

Results

Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70%) were randomly selected for training the models, and 30,966 (30%) for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877–0.899), reduced model AUC = 0.880 (95% CI, 0.868–0.892), p for difference 0.003, NRI = 2.77%, p = 0.007). Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort.

Conclusions

The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion). This risk prediction algorithm may prove useful for both bed side clinical decision making and risk adjustment for assessment of quality.     

HbA1c Alone Is a Poor Indicator of Cardiometabolic Risk in Middle-Aged Subjects with Pre-Diabetes but Is Suitable for Type 2 Diabetes Diagnosis: A Cross-Sectional Study

Objectives

Glycated haemoglobin A_1c (HbA_1c) measurement is recommended as an alternative to fasting plasma glucose (FPG) for the diagnosis of pre-diabetes and type 2 diabetes. However, evidence suggests discordance between HbA_1c and FPG. In this study we examine a range of metabolic risk features, pro-inflammatory cytokines, acute-phase response proteins, coagulation factors and white blood cell counts to determine which assay more accurately identifies individuals at increased cardiometabolic risk.

Materials and Methods

This was a cross-sectional study involving a random sample of 2,047 men and women aged 46-73 years. Binary and multinomial logistic regression were employed to examine risk feature associations with pre-diabetes [either HbA_1c levels 5.7-6.4% (39-46 mmol/mol) or impaired FPG levels 5.6-6.9 mmol/l] and type 2 diabetes [either HbA_1c levels >6.5% (>48 mmol/mol) or FPG levels >7.0 mmol/l]. Receiver operating characteristic curve analysis was used to evaluate the ability of HbA_1c to discriminate pre-diabetes and diabetes defined by FPG.

Results

Stronger associations with diabetes-related phenotypes were observed in pre-diabetic subjects diagnosed by FPG compared to those detected by HbA_1c. Individuals with type 2 diabetes exhibited cardiometabolic profiles that were broadly similar according to diagnosis by either assay. Pre-diabetic participants classified by both assays displayed a more pro-inflammatory, pro-atherogenic, hypertensive and insulin resistant profile. Odds ratios of having three or more metabolic syndrome features were also noticeably increased (OR: 4.0, 95% CI: 2.8-5.8) when compared to subjects diagnosed by either HbA_1c (OR: 1.4, 95% CI: 1.2-1.8) or FPG (OR: 3.0, 95% CI: 1.7-5.1) separately.

Conclusions

In middle-aged Caucasian-Europeans, HbA_1c alone is a poor indicator of cardiometabolic risk but is suitable for diagnosing diabetes. Combined use of HbA_1c and FPG may be of additional benefit for detecting individuals at highest odds of type 2 diabetes development.