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

Strategies to improve the efficiency of celiac disease diagnosis in the laboratory

The demand for testing to detect celiac disease (CD) autoantibodies has increased, together with the cost per case diagnosed, resulting in the adoption of measures to restrict laboratory testing. We designed this study to determine whether opportunistic screening to detect CD-associated autoantibodies had advantages compared to efforts to restrict testing, and to identify the most cost-effective diagnostic strategy. We compared a group of 1678 patients in which autoantibody testing was restricted to cases in which the test referral was considered appropriate (G1) to a group of 2140 patients in which test referrals were not reviewed or restricted (G2). Two algorithms A (quantifying IgA and Tissue transglutaminase IgA [TG-IgA] in all patients), and B (quantifying only TG-IgA in all patients) were used in each group, and the cost-effectiveness of each strategy was calculated. TG-IgA autoantibodies were positive in 62 G1 patients and 69 G2 patients. Among those positive for tissue transglutaminase IgA and endomysial IgA autoantibodies, the proportion of patients with de novo autoantibodies was lower (p=0.028) in G1 (11/62) than in G2 (24/69). Algorithm B required fewer determinations than algorithm A in both G1 (2310 vs 3493; p<0.001) and G2 (2196 vs 4435; p<0.001). With algorithm B the proportion of patients in whom IgA was tested was lower (p<0.001) in G2 (29/2140) than in G1 (617/1678). The lowest cost per case diagnosed (4.63 euros/patient) was found with algorithm B in G2. We conclude that opportunistic screening has advantages compared to efforts in the laboratory to restrict CD diagnostic testing. The most cost-effective strategy was based on the use of an appropriate algorithm. •In celiac disease screening in the laboratory, opportunistic detection has advantages over restricted testing.•Use of a suitable algorithm is more cost-effective than simply opting for the cheapest test kit.•A well-defined algorithm reaches the greatest efficiency when the strengths and limitations of every technique are known.•This algorithm can be optimized further by using a combination of techniques to reduce costs and response time

Inverse association of resistin with physical activity in the general population

<div><p>Aim</p><p>Resistin is a cytokine related with inflammation and ischemic heart disease. Physical activity (PA) prevents chronic inflammation and ischemic heart disease. We studied the relationship of serum concentration of resistin with HDL cholesterol, a known biomarker of PA, and with different measures of PA, in a large sample of the general adult population in the Canary Islands.</p><p>Methods</p><p>Cross-sectional study of 6636 adults recruited randomly. We analyzed the correlation of resistin and HDL cholesterol with PA (as metabolic equivalent level [MET]), and fitted the results with linear and logistic regression models using adjustment for age, alcohol consumption and smoking.</p><p>Results</p><p>Mean resistin level was higher in women (p<0.001), correlated inversely with age, HDL cholesterol (p<0.001) and alcohol consumption (p<0.001 in men), and correlated directly with smoking (p<0.001). Resistin correlated inversely with the duration of leisure time PA (p<0.001), leisure time MET (p<0.001) and moderate leisure time PA (p<0.001), with some differences between sexes. Men (OR = 0.78 [0.61–0.99; p<0.05]) and women (OR = 0.75 [0.61–0.92; p<0.01]) in the upper quintile of leisure time PA had a lower risk of elevated resistin. In contrast, a high degree of sedentarism was associated with an increased risk elevated resistin in women (OR = 1.24 [1.04–1.47; p<0.05] and in men (OR = 1.40 [1.01–1.82; p<0.05]).</p><p>Conclusions</p><p>In our sample of the general population, resistin was inversely associated with measures and levels of PA and HDL cholesterol. The association of resistin with PA was stronger than the association of HDL cholesterol with PA, making resistin a potentially useful biomarker of PA.</p></div

Standardized regression coefficients obtained in linear regression models.^$.

<p>Standardized regression coefficients obtained in linear regression models.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182493#t003fn001" target="_blank">^$</a>.</p

Spearman nonparametric coefficients correlation of resistin and HDL cholesterol with different measures of physical activity.

<p>Spearman nonparametric coefficients correlation of resistin and HDL cholesterol with different measures of physical activity.</p