Predictors for attending annual eye screening for diabetic retinopathy amongst patients with diabetes in an urban community of Beijing

AIM： To gain a better understanding of possible factors that may influence the decision of diabetes persons to participate in annual eye screening in an urban community setting of China.METHODS： A structured interview including questions on attendance of eye screening, knowledge and awareness of diabetic retinopathy was conducted. The presence and degree of retinopathy were assessed using two field nonmydriatic retinal photography. RESULTS： Totally 720 diabetes persons were recruited and 519 were enrolled in this cross-sectional study. In this urban setting of Beijing, among diabetes patients of average of 10 y duration, 77% confirmed having undergone at least one eye examination and 61% reported having at least one eye examination with dilated pupil. As for the last 12 mo, the number decreased to 210（47%） and 131（30%） separately. Most of the participants（95%） were aware that diabetes could affect their vision and that regular eye examination was necessary. Very few of them（12%） however were aware that the early stages of diabetic retinopathy presented without symptoms of vision loss. Having attended patient education on diabetes was effective in building awareness about diabetic eye disease and was a significant positive predictor for attending eye screening [education in a year, Adj. OR=0.47（0.29-0.74）, P〈0.001, education years ago, Adj. OR=0.56（0.33-0.96）, P=0.036]. The duration of disease also increasedthe likelihood of having undergone eye screening（Adj. OR=0.96, P〈0.05）. CONCLUSION： Being exposed to education about the complications of diabetes increases the probability of attending diabetic eye screening. An appropriate patient knowledge building strategy should be made available to patients from the time of diagnosis.

A Better Instrument for Screening Diabetes in Rural Areas of China: An Equation Developed from Multivariate Logistic Regression or a Simplified Scoring Form

Diabetes has become a major public health concern in the world and the total number of diabetic patients is considered to soar to 366 million in 2030. Emerging evidence has suggested that earlier detection of type 2 diabetes, glycaemic control improvement and intensified risk factor management may result in clinically important improvements in diabetes-related morbidity and mortality. According to the World Health Organization （WHO）p strategies such as clinical questionnaires, urine glucose, blood glucose, glycated haemoglobin A1C （HbA1C） and combinations of the above tests can be used for screening diabetes.

Detection of Undiagnosed Prediabetes and Diabetes in Dental Patients: A Proposal of a Dental-Office-Friendly Diabetes Screening Tool

Objective: This study was designed to develop a dental-office-friendly diabetes self-screening tool for diabetes mellitus (DM) and prediabetes (PreDM). Methods: Consecutive dental patients, aged 18 years or older, without history of DM or PreDM, completed a 14-question questionnaire without assistance. They subsequently underwent onsite finger-sticks for capillary blood collection for glycohemoglobin (A1c) measurement. Results: Of the total 500 patients who completed the study, 302 were women (60.4%) and 198 were men (39.6%), with a collective mean age of 47.8 (±16.8) years old. The prevalence of PreDM and DM was 19.2% and 1.2%, respectively. Predictors of PreDM or DM included age, >10% above ideal body weight, waist size above 40” for men or 35” for women, reported hypertension, reported abnormal lipids, tingling of hands or feet, and visual symptoms or conditions (blurring, cataracts, glaucoma). Conclusions: This study introduces a newly developed, user-friendly, PreDM and DM self-screening tool, abbreviated as DiDDO (Diabetes detection in the dental office). This screening tool requires no body weighing or BMI calculation (undesirable by dentists) nor laboratory tests or blood pressure measurement, allowing dentists to identify patients at moderate and high risk for DM/PreDM, and perform (or refer for) diagnostic A1c testing. This dental-office-friendly self-screening tool is proposed for validation in other dental populations.

Performance of Fasting Plasma Glucose and Postprandial Urine Glucose in Screening for Diabetes in Chinese High-risk Population

Background： The conventional approaches to diabetes screening are potentially limited by poor compliance and laboratory demand. This study aimed to evaluate the performance of fasting plasma glucose （FPG） and postprandial urine glucose （PUG） in screening for diabetes in Chinese high-risk population. Methods： Nine hundred and nine subjects with high-risk factors of diabetes underwent oral glucose tolerance test after an overnight fast. FPG, hemoglobin A 1 c, 2-h plasma glucose （2 h-PG）, and 2 h-PUG were evaluated. Diabetes and prediabetes were defined by the American Diabetes Association criteria. The area under the receiver operating characteristic （ROC） curve was used to evaluate the diagnostic accuracy of 2 h-PUG, and the optimal cut-off determined to provide the largest Youden index. Spearman correlation was used for relationship analysis. Results： Among 909 subjects, 33.4% （304/909） of subjects had prediabetes, and 17.2% （156/909） had diabetes. The 2 h-PUG was positively related to FPG and 2 h-PG （r = 0.428 and 0.551, respectively, both P 〈 0.001）. For estimation of 2 h-PG 〉 7.8 mmol/L and 2 h-PG ≥ 11.I mmol/L using 2 h-PUG, the area under the ROC curve were 0.772 （95% confidence interval [CI]： 0.738-0.806） and 0.885 （95% CI：0.850-0.921 ）, respectively. The corresponding optimal cut-offs for 2 h-PUG were 5.6 mmol/L and 7.5 retool/L, respectively. Compared with FPG alone, FPG combined with 2 h-PUG had a higher sensitivity fbr detecting glucose abnormalities （84.1% vs. 73.7%, P〈 0.001） and diabetes （82.7% vs. 48.1%, P〈 0.001）. Conclusion： FPG combined with 2 h-PUG substantially improves the sensitivity in detecting prediabetes and diabetes relative to FPG alone, and may represent an efficient layperson-oriented diabetes screening method.

UTILITY OF ALBUSURE TEST IN SCREENING FOR EARLY STAGE OF DIABETIC NEPHROPATHY

all diabetic patients should be screened for the early stage of diabetic nephropathy,because microalbuminuria has a great prognostic significance.The albusure test(AT),alatex agglutination nephelometric immunoassay,is a rapid and low cost test for the detection of microalbuminurai of 30mg/L or more.

Confirmation of susceptibility gene loci on chromosome 1 in Northern China Han families with type 2 diabetes

Objective To confirm previous effort to identify type 2 diabetes susceptibility genes in a Northern Chinese population by conducting a new genome scan with both an increased number of type 2 diabetes families and a new set of microsatellite markers within the previously localized regions.Methods A genome scan method was applied. After multiplexed PCR, electrophoreses, genescan and genotyping analysis, we obtained size information for all loci , and then a further study was done by both parametric and non-parametric linkage analysis to investigate the P values and Z values of these loci.Results We surveyed 34 microsatellite markers which distributed within 5 regions along chromosome 1, and a total of 12?000 genotypes were screened. Evidence of linkage with diabetes was identified for 8 of the 34 loci. All P values of the 8 loci were lower than 0.05, and the highest Z value was 2.17. A very interesting finding is that all 5 markers at the p- terminal 1p36.3-1p36.23 region, spanning a long range of 16.9?cM, were identified to have a low P value of less than 0.05, which suggests that this region may contain multiple susceptibility genes. Regions 4 and 5 also confirmed the previous findings, and we narrowed these two regions to a 2.7?cM and 2.5?cM regions, respectively.Conclusions We further confirmed the results gained in the previous genome-wide scan using an increased number of NIDDM families and a new set of microsatellite markers lying within the initially localized regions. The fact that all 5 loci at the p- terminal region displayed a low P value of less than 0.05 suggests that more than 1 susceptibility gene may reside in this region.

Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early-onset type 2 diabetes

Background: Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods: A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results: A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion: The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.