Screening For Undiagnosed Diabetes Research Articles

Prevalence and risk factors associated with prediabetes and undiagnosed diabetes in France: The national CONSTANCES cohort

AimsTo assess the prevalence of prediabetes and diabetes in France between 2013 and 2014 using data from the CONSTANCES cohort, and to identify factors associated with prediabetes and undiagnosed diabetes. MethodsThe study population comprised participants recruited in 2013–2014 in CONSTANCES, an ongoing French national prospective cohort following participants aged 18–69 years who are covered by France's general health insurance scheme. Participants completed a questionnaire at baseline and underwent a medical examination which included providing blood samples. Undiagnosed diabetes was defined as a fasting plasma glucose (FPG) ≥ 7 mmol/l and diagnosed diabetes as self-report or identification of reimbursements for anti-diabetics. Prediabetes was defined as a FPG ≥ 6 mmol/l but < 7 mmol/l. Results25,137 participants were included in the analyses. The overall prevalence of prediabetes was 7.2% [95% confidence interval: 6.7–7.7], 1.6% [1.4–1.9] for undiagnosed diabetes, and 4.0% [3.6–4.4] for diagnosed diabetes. These rates were significantly higher in men, in older persons, in persons with obesity, and in those with lower education levels. In multivariate regression models, excessive corpulence was the variable most strongly associated with undiagnosed diabetes (adjusted Odds Ratio=9.31) and prediabetes (aOR=3.85). Additionally, male sex, older age, family history of diabetes, at-risk alcohol use, and lower education level were all positively associated with undiagnosed diabetes and prediabetes. ConclusionDiabetes and prediabetes prevention together with screening for undiagnosed diabetes must be strengthened for persons with low socioeconomic status and for those with obesity or overweight.

68-OR: Using Machine Learning to Improve Screening for Undiagnosed Diabetes among Emergency Department Patients

Introduction: Screening for previously undiagnosed diabetes in the emergency department (ED) can enhance early detection, which may particularly benefit patients with barriers to accessing primary care. To optimize use of limited ED resources, there is a need to identify ED patients who are at high risk. Objectives: To improve identification of ED patients with undiagnosed diabetes using machine learning (ML) . Methods: Diabetes screening by HbA1c was implemented in four EDs at an academic medical center. Patients aged 40 or older, with BMI &amp;gt; 25, no history of diabetes and no HbA1c result within 6 months were eligible for screening. Patients were stratified by screening result and compared across demographic and clinical characteristics, and local geographic measures of health and socioeconomic variables. ML classifier models predicting a positive screening result were trained, and were validated on a prospectively withheld subset of the cohort. Results: In the first weeks after implementation, 2423 eligible patients were identified, and 1518 (62.6%) were screened, with 114 (7.5% of those screened) found to have previously undiagnosed diabetes. Patients with diabetes more often identified as male, Hispanic or Latino, Black, or Asian, had higher BMIs and blood pressures at triage, resided in neighborhoods with higher diabetes prevalence and poverty rates, and had less prior healthcare utilization, especially in the outpatient setting. The best performing classifier models were logistic regression with elastic net regularization (AUC 0.81) and gradient-boosted trees (AUC 0.77) . These models identified a high-risk subgroup consisting of 30% of eligible patients who accounted for over 80% of newly detected cases of diabetes. Conclusions: ML models can reliably identify ED patients at high risk of having undiagnosed diabetes based on demographic, clinical, and local geographic risk factors, and could form the basis of a selective screening program. Disclosure I.Bohart: None. J.Caldwell: None. J.Swartz: None. P.E.Rosen: None. N.Genes: None. C.A.Koziatek: None. D.B.Neill: None. D.C.Lee: None.

Indian Diabetes Risk Score (IDRS): An effective tool to screen undiagnosed diabetes

Background: Diabetes is an insidious public health problem. India has the second largest number of adults living with diabetes worldwide (77 million). Indian Diabetes Risk Score (IDRS) is a simple, cost-effective and feasible tool for mass screening programme at community level. Aim &amp; Objective: To assess diabetes risk in adults aged 30 years and above and to identify high risk subjects for screening undiagnosed diabetes in an urban population of Meerut. Settings and Design: Community based cross-sectional study. Methods and Material: All adults who were ?30 years of age and non-diabetic were interviewed using pre-designed, pre-tested questionnaire for their socio-demographic profile and lifestyle. Fasting Blood glucose of all study subjects were done to screen undiagnosed diabetics. Statistical analysis used: Centers for Disease Control (CDC), Epi Info TM 7.2.3.1 was used. Pearson’s Chi Square were applied. Results: 33.4% were found to have high diabetes risk. Risk of diabetes increases with age. 7.6% of the study subjects were found to be diabetic and were unaware of their diabetic status. Physical inactivity and increasing waist circumference were found to be significantly associated with risk of diabetes. Diabetes risk was also significantly associated with positive family history. Conclusions: Screening and early identification of high risk individuals would help in early diagnosis and treatment to prevent or to delay the onset of diabetes mellitus and its complications.

Diabetes, even newly defined by HbA1c testing, is associated with an increased risk of in-hospital death in adults with COVID-19

BackgroundDiabetes is associated with poor coronavirus disease 2019 (COVID-19) outcomes. However, little is known on the impact of undiagnosed diabetes in the COVID-19 population. We investigated whether diabetes, particularly undiagnosed diabetes, was associated with an increased risk of death from COVID-19.MethodsThis retrospective study identified adult patients with COVID-19 admitted to Tongji Hospital (Wuhan) from January 28 to April 4, 2020. Diabetes was determined using patients’ past history (diagnosed) or was newly defined if the hemoglobin A1c (HbA1c) level at admission was ≥6.5% (48 mmol/mol) (undiagnosed). The in-hospital mortality rate and survival probability were compared between the non-diabetes and diabetes (overall, diagnosed, and undiagnosed diabetes) groups. Risk factors of mortality were explored using Cox regression analysis.ResultsOf 373 patients, 233 were included in the final analysis, among whom 80 (34.3%) had diabetes: 44 (55.0%) reported a diabetes history, and 36 (45.0%) were newly defined as having undiagnosed diabetes by HbA1c testing at admission. Compared with the non-diabetes group, the overall diabetes group had a significantly increased mortality rate (22.5% vs. 5.9%, p < 0.001). Moreover, the overall, diagnosed, and undiagnosed diabetes groups displayed lower survival probability in the Kaplan-Meier survival analysis (all p < 0.01). Using multivariate Cox regression, diabetes, age, quick sequential organ failure assessment score, and D-dimer ≥1.0 μg/mL were identified as independent risk factors for in-hospital death in patients with COVID-19.ConclusionsThe prevalence of undiagnosed pre-existing diabetes among patients with COVID-19 is high in China. Diabetes, even newly defined by HbA1c testing at admission, is associated with increased mortality in patients with COVID-19. Screening for undiagnosed diabetes by HbA1c measurement should be considered in adult Chinese inpatients with COVID-19.

The Interaction of Diabetes and Tuberculosis: Translating Research to Policy and Practice.

Diabetes Mellitus increases the risk of developing Tuberculosis (TB) disease by about three times; it also doubles the risk of death during TB treatment and other poor TB treatment outcomes. Diabetes may increase the risk of latent infection with Mycobacterium tuberculosis (LTBI), but the magnitude of this effect is less clear. Whilst this syndemic has received considerable attention, most of the published research has focussed on screening for undiagnosed diabetes in TB patients or observational follow-up of TB treatment outcomes by diabetes status. There are thus substantial research and policy gaps, particularly with regard to prevention of TB disease in people with diabetes and management of patients with TB–diabetes, both during TB treatment and after successful completion of TB treatment, when they likely remain at high risk of TB recurrence, mortality from TB and cardiovascular disease. Potential strategies to prevent development of TB disease might include targeted vaccination programmes, screening for LTBI and preventive therapy among diabetes patients or, perhaps ideally, improved diabetes management and prevention. The cost-effectiveness of each of these, and in particular how each strategy might compare with targeted TB prevention among other population groups at higher risk of developing TB disease, is also unknown. Despite research gaps, clinicians urgently need practical management advice and more research evidence on the choice and dose of different anti-diabetes medication and effective medical therapies to reduce cardiovascular risks (statins, anti-hypertensives and aspirin). Substantial health system strengthening and integration may be needed to prevent these at risk patients being lost to care at the end of TB treatment.

Evaluation of the value of diabetes risk scores in screening for undiagnosed diabetes and prediabetes: a community-based study in southwestern China

ABSTRACT Objectives To evaluate the performance and cost-effectiveness of existing diabetes risk scores (DRSs) to screen for undiagnosed diabetes mellitus (UDM) and prediabetes (PD) in a community-based southwestern Chinese population. Methods Participants in TIDE-Chengdu survey with requisite data and without known diabetes were included. Five Chinese-derived DRSs and six non-Chinese-derived DRSs were included for evaluation. Their performance in detecting UDM and UMD or PD (UDM/PD) was assessed using the C-statistic. The cost-effectiveness of the optimal DRS was compared with that of capillary fasting blood glucose (CFBG). Results Of the 1,692 TIDE-Chengdu survey participants included, 177 (10.5%) had UDM and 339 (20.0%) had PD. The rural participants (N = 737) were more likely to have UDM (13.4% vs. 8.2%) and PD (24.8% vs. 16.3%) than their urban counterparts (N = 955) (P < 0.0001). In the full population, the included DRSs all showed good discrimination in detecting UDM (C-statistic: 0.699 to 0.762) and UDM/PD (C-statistic: 0.717 to 0.769), but the New Chinese DRS (NCDRS) performed best for both UDM and UDM/PD. The DRSs evaluated all showed better performance in urban participants than rural participants for both UDM (C-statistic: 0.718 to 0.795 vs. 0.642 to 0.720) and UDM/PD (C-statistic: 0.729 to 0.793 vs. 0.682 to 0.726) (all P < 0.05). The mean cost per UDM/PD case identified was lower with NCDRS at score 25 (¥503.3($71.9)) and 27 (¥490.5 ($70.1)) than CFBG at 5.0, 5.1, 5.2, or 5.3 mmol/L (¥631.7 ($90.2), ¥611.8 ($87.4), ¥579.2 ($82.7) and ¥551.9 ($78.8)), whereas the mean costs per UDM case identified was higher with NCDRS at score 25 (¥1379.3 ($197.0)) and 27 (¥1315.1 ($187.9)) than CFBG at 5.3, 5.4, or 5.5 mmol/L (¥1301.7 ($186.0), ¥1247.7 ($178.2) and ¥1173.3 ($167.6)). Conclusion The NCDRS represents a valid and cost-effective tool for use in southwestern China to identify high-risk patients with UDM or PD who need a diagnostic test.

Can gingival crevicular blood effectively screen for diabetes in Chinese patients with moderate to severe periodontitis? A pilot study

Background/purposeThe early diagnosis of diabetes is essential for the prevention of complications. Periodontitis has been identified as the sixth complication of diabetes and chair-side screening may improve diagnosis of diabetes. This study evaluated whether gingival crevicular blood (GCB) available during routine periodontal examination can be used to screen for diabetes in Chinese patients with moderate to severe periodontitis. Materials and methodsFinger-stick blood (FSB) and GCB were collected from patients (18 with diabetes and 42 without diabetes) during routine periodontal probing and analyzed for glucose and hemoglobin A1c (HbA1c) levels. ResultsIn the diabetic group, the mean glucose levels in GCB and FSB were 12.21 ± 3.86 and 12.61 ± 4.19 mmol/L respectively, while those of the non-diabetic group were 6.14 ± 0.85 and 6.15 ± 0.87 mmol/L, respectively. The average HbA1c values of the diabetic group were 7.72% ± 1.71% and 7.89% ± 1.78% in GCB and FSB, respectively, while those of the non-diabetic group were 5.28% ± 0.31% and 5.23% ± 0.32%, respectively. Highly significant correlations were found between GCB and FSB glucose levels (r = 0.993 for the diabetic group, and r = 0.977 for the non-diabetic group) and between GCB and FSB HbA1c levels (r = 0.977 for the diabetic group, and r = 0.829 for the non-diabetic group). ConclusionOur study results indicate that GCB available during routine periodontal examination may be acceptable for the analyses of blood glucose and HbA1c levels. The approach is suitable for screening undiagnosed diabetes in a dental setting.

The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China.

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P < 0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional study

BackgroundThe terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data.MethodsThis multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20–70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model.ResultsThe overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 67.3% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects).ConclusionThe non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.

PD155: Screening for Undiagnosed Diabetes and Pre‐Diabetes among Dental Patients in Singapore

PD155: Screening for Undiagnosed Diabetes and Pre‐Diabetes among Dental Patients in Singapore

Validation of simplified Indian Diabetes Risk Score for screening undiagnosed diabetes in an urban setting of Haryana

Background and objectivesIdentification of at risk individuals using simple screening tools like Indian Diabetes Risk Score (IDRS) and appropriate life style interventions could greatly help in preventing or delaying the onset of diabetes and thus reducing the burden of disease. Material and method450 individuals >20years who consented to participate were selected randomly from three anganwadis of urban area of Rohtak city. Demographic characteristics and anthropometric measurements such as weight, height and waist circumference were taken. BMI was calculated. IDRS developed by Mohan et al. was applied for categorizing the individuals as high risk, moderate risk and low risk. World Health Organization (WHO) criterion of fasting blood glucose ≥126mg/dl was used for diagnosing diabetes. Results54% of individuals were categorized as high risk followed by 37.6% as moderate risk as per IDRS risk score. Prevalence of diabetes was more in ≥50years age group (28.6%) as compared to 35–49 years (14.2%) and 20–35 years age group (6.0%). Individuals with no physical activity had significantly high prevalence of diabetes as compared to mild or moderate physical activity (P 0.004). Similarly, males with waist circumference ≥100cm and females with ≥90cm had significantly high prevalence of diabetes (P 0.000 and P 0.005). On multiple logistic regression analysis age, family history of diabetes and BMI were independent predictors of diabetes. ConclusionDiabetes is a rising epidemic. Early screening and appropriate interventions at the start are needed for control of disease and risk factor modifications.

Performance of Indian Diabetes Risk Score (IDRS) as screening tool for diabetes in an urban slum

BackgroundIndia is diabetic capital of world, with maximum number of diabetic patients. There is large burden of undetected diabetic cases in community. There is increasing risk of diabetes in urban slum, because of illiteracy, lack of awareness, low socioeconomic status and unhealthy life style. Madras Diabetes Research Foundation (MDRF) has developed Indian Diabetes Risk Score (IDRS) to detect undiagnosed Type 2 diabetes. The aim of this article is to study the performance of IDRS as screening tool for undiagnosed cases of Type 2 diabetes and to find the prevalence of undiagnosed Type 2 diabetes in an urban slum. MethodsScreening for diabetes was carried out in an urban slum. The sample size was 155 (assumed prevalence of undiagnosed diabetes 9%). IDRS tool comprising of two modifiable (waist circumference, physical activity) and two non-modifiable risk factors (age, family history) for diabetes was used to assess the risk of diabetes anthropometry data was obtained. Conformation of diabetes was done using blood sugar levels on fasting venous sample. ResultsMean and SD for age of study subjects were 49.68±14.80 years, BMI 26.60±8.51kg/m2, waist hip ratio (females) 0.87±0.06cm, waist hip ratio (males) 0.95±0.06cm, waist circumference (females) 89.99±10.95cm, waist circumference (males) 89.44±10.9cm. IDRS predicted the risk of diabetes mellitus with sensitivity of 95.12% and specificity of 28.95% in individuals with score >60. ConclusionIDRS can be used as an effective tool for screening undiagnosed diabetes in the community.

Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK).

BackgroundScreening for undiagnosed diabetes is not widely undertaken due to the high costs and invasiveness of blood sampling. Simple non-invasive tools to identify high risk individuals can facilitate screening. The main objectives of this study are to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores.MethodsData were available from a representative sample of 4276 adults without diagnosed diabetes. In a jack-knife approach two thirds of the sample was used for the development of the risk score and the remainder for the validation. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity and osmotic symptoms were significantly associated with undiagnosed diabetes (age most to osmotic symptoms least). Individual scores were generated for these factors using the beta coefficient values obtained in multiple logistic regression. A cut-off value of sum = 31 was determined by ROC curve analysis.ResultsThe area under the ROC curve of the risk score for prevalent diabetes was 0.78 (CI 0.73–0.82). In the sample 36.3 % were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3 % respectively. For Sri Lankans the AUC for the CRS and IDRS were 0.72 and 0.66 repectively.ConclusionsThis simple non-invasive screening tool can identify 80 % of undiagnosed diabetes by selecting 40 % of Sri Lankan adults for confirmatory blood investigations.

Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study.

BackgroundVarious diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters.MethodsThe data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis.ResultsFor the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264).ConclusionsThe non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes.

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.

Reliability of gingival blood sample to screen diabetes in dental hospital

Background:Early detection and treatment of diabetes mellitus may reduce the burden of diabetes and its complications. Screening of undiagnosed diabetes with gingival blood sample in patients attending to the dental hospital and to check the reliability with standard method.Methods:Five hundred and fifty new patients age ranged from 30 to 50 years were randomly selected. Of 550 patients examined, gingival blood samples of 454 patients were collected from bleeding site and analyzed with self-monitoring device. Blood glucose values were recorded. In addition, all the patients were advised for the second visit with overnight fasting for fasting Blood glucose assessment. Among them, 442 patients returned for fasting blood glucose assessment in laboratory with blood glucose analyzer.Results:In the total of 454 patients gingival blood glucose assessment with self-monitoring device, 64 patients showed ≥200 mg/dL, which indicates diabetes, and the other 390 patients showed ≤200 mg/dL, which indicates patients are not diabetic. Whereas, the results of the laboratory blood glucose analysis, 24 (5.43%) patients showed ≥126 mg/dL, 36 (8.14%) patients showed 100–125 mg/dL and the other 382 patients are not diabetic. Comparison of blood glucose measurements by two methods showed sensitivity 96.66% and specificity 99.47%.Conclusions:The results of the present study showed blood obtained from periodontal pocket probing is a reliable sample to screen diabetes in periodontal disease population. Early diagnosis of diabetes in the dental hospitals can help improve the patient's oral health and overall health status by helping patients avoid or reduce complications from diabetes.

Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010.

ObjectiveTo evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race.MethodsThis cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999–2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity.ResultsAmong the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes.ConclusionsFINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults.

Optimal Cut-Off Points for Two-Step Strategy in Screening of Undiagnosed Diabetes: A Population-Based Study in China

To identify optimal cut-off points of fasting plasma glucose for two-step strategy in screening of undiagnosed diabetes in Chinese people, data were selected from two cross-sectional studies of Metabolic Syndrome in Zhejiang Province of China, Zhejiang Statistical Yearbook (2010), and published literatures. Two-step strategy was used among 17437 subjects sampled from population to screen undiagnosed diabetes. Effectiveness (proportion of cases identified), costs (including medical and non-medical costs), and efficiency (cost per case identified) of these different two-step screening strategies were evaluated. This study found the sensitivities of all the two-step screening strategies with further Oral Glucose Tolerance Test (OGTT) at different Fasting Plasma Glucose (FPG) cut-off points from 5.0 to 7.0 (mmol/L) ranged from 0.66 to 0.91. For the FPG point of 5.0 mmol/L, 91 percent of undiagnosed cases were identified. The total cost of detecting one undiagnosed diabetes case ranged from 547.1 to 1294.5 CNY/case, and the strategy with FPG at cut-off point of 6.1 (mmol/L) resulted in the least cost. Considering both sensitivity and cost of screening diabetes, FPG cut-off point at 5.4 mmol/L was optimized for the two-step strategy. In conclusion, different optimal cut-off points of FPG for two-step strategy in screening of undiagnosed diabetes should be used for different screening purposes.

Dental Clinics as Potent Sources for Screening Undiagnosed Diabetes and Prediabetes

IntroductionThis pilot study investigated the efficacy of dental clinics as potent sources for screening diabetes and prediabetes in undiagnosed individuals. MethodsData were randomly collected from 385 patients (aged 40 years and older) visiting dental clinics. Patients already having a diagnosis of diabetes and/or prediabetes were excluded. Demographic data, body mass index and family and dental histories were recorded. Signs and symptoms of diabetes were investigated. Random blood glucose levels (RBGLs) were recorded. Individuals with RBGL ≥110mg/dL underwent the oral glucose tolerance test and the glycosylated hemoglobin test (HbA1c). ResultsOf the 385 patients, 60% (232) had RBGL <110mg/dL, whereas 40% (153) had RBGL ≥110mg/dL. Prevalence of confirmed diabetes and prediabetes among the study participants was 16.4% and 15.8%, respectively. Body mass index was significantly higher among patients with diabetes and prediabetes as compared with healthy controls. HbA1c level was statistically significantly higher among patients with diabetes than among patients with prediabetes. Symptoms of polyuria and polydipsia were significantly higher among patients with diabetes than in those without diabetes. ConclusionsA high percentage of undiagnosed type 2 diabetes and prediabetes among patients visiting dental clinics was found compared with that reported in the medical literature. Further studies with a lager sample size are needed to confirm these results.

Evaluation of Indian Diabetic Risk Score for Screening Undiagnosed Diabetes Subjects in the Community

A diabetic screening camp was conducted in villages around Tagore Medical College medical college, Chennai in India. Four simple questions along with one measurement for waist circumference and fasting capillary blood sugar and/or post prandial blood sugar tests in individuals with Indian Diabetic Risk Score (IDRS) ≥60 were employed. Results indicated that IDRS (consisting of factors like age, abdominal obesity, physical activity and family history) predicted the risk of diabetes mellitus with sensitivity of 100% and specificity of 17.6% in individuals with score ≥60 and can be used as an effective tool for screening undiagnosed diabetics in the community.