Risk scores to predict decreased glomerular filtration rate at 10 years in an Asian general population

Abstract Background Asians have among the highest prevalence of chronic kidney disease (CKD) or end-stage renal disease in the world. A risk score capable of identifying high risk individuals at the primary care level could allow targeted therapy to prevent future development of CKD. Risk scores for new CKD have been developed in US general populations, but the impact of various risks factors for development of CKD may differ in Asian subjects. In this study, we aimed to develop risk models and simplified risk scores to predict the development of decreased glomerular filtration rate (GFR) at 10 years in an Asian general population using readily obtainable clinical and laboratory parameters. Methods Employees of EGAT (The Electric Generating Authority of Thailand) were studied prospectively. Multivariable logistic regression models were used to assess risk factors and used to derive risk models and risk scores for developing decreased GFR at 10 years: Model 1 (Clinical only), Model 2 (Clinical + Limited laboratory tests), and Model 3 (Clinical + Full laboratory tests). The performance of the risk models or risk scores to predict incident cases with decreased GFR were evaluated by tests of calibration and discrimination. Results Of 3186 subjects with preserved GFR (eGFR ≥60) at baseline, 271 (8.5%) developed decreased GFR (eGFR < 60) at 10 years. Model 1 (Age, sex, systolic blood pressure, history of diabetes, and waist circumference) had good performance (χ2 = 9.02; AUC = 0.72). Model 2 (Age, Sex, systolic blood pressure, diabetes, glomerular filtration rate) had better discrimination (χ2 = 10.87, AUC = 0.79) than Model 1. Model 3 (Model 2+ Uric acid, Hemoglobin) did not provide significant improvement over Model 2. Based on these findings, simplified categorical risk scores were developed for Models 1 and 2. Conclusions Clinical or combined clinical and laboratory risk models or risk scores using tests readily available in a resource-limited setting had good accuracy and discrimination power to estimate the 10-year probability of developing decreased GFR in a Thai general population. The benefits of the risk scores in identifying high risk individuals in the Thai or other Asian communities for special intervention requires further studies

Association between Inflammatory Marker, Environmental Lead Exposure, and Glutathione S-Transferase Gene

A number of studies suggested that lead is related to the induction of oxidative stress, and alteration of immune response. In addition, modifying these toxic effects varied partly by GST polymorphism. The objectives of this study were to assess the association between the lead-induced alteration in serum hs-CRP, with GSTM1, GSTT1, and GSTP1 Val105Ile genetic variations and the health consequence from environmental lead exposure. The 924 blood samples were analyzed for blood lead, CRP, and genotyping of three genes with real-time PCR. Means of blood lead and serum hs-CRP were 5.45 μg/dL and 2.07 mg/L. Both CRP and systolic blood pressure levels were significantly higher for individuals with blood lead in quartile 4 (6.48–24.63 μg/dL) compared with those in quartile 1 (1.23–3.47 μg/dL, P6.47 μg/dL the adjusted odds ratio (OR) of CRP levels for individuals with GSTP1 variants allele, GSTM1 null, GSTT1 null, double-null GSTM1, and GSTT1 compared with wild-type allele was 1.46 (95% CI; 1.05–2.20), 1.32 (95% CI; 1.03–1.69), 1.65 (95% CI; 1.17–2.35), and 1.98 (95% CI; 1.47–2.55), respectively. Our findings suggested that lead exposure is associated with adverse changes in inflammatory marker and SBP. GST polymorphisms are among the genetic determinants related to lead-induced inflammatory response

Long-term air pollution exposure and serum lipids and blood sugar: A longitudinal cohort study from the electricity generating authority of Thailand study

Only a few studies have investigated the association between long-term exposure to air pollution and alterations of serum lipids and blood sugar level in developing countries. The present longitudinal study examined associations between long-term air pollution exposure and serum lipids [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)] and fasting glucose (FG) in workers of the Electricity Generating Authority of Thailand (EGAT) in the Bangkok metropolitan region (BMR) of Thailand. We performed secondary analyses using the data obtained from 1, 839 participants (mean age, 58.3 years as of 2002) of the EGAT1 cohort study (2002–2012). The average concentration of each air pollutants (PM₁₀, O₃, NO₂, SO₂, and CO) at the sub-district level in BMR from 2002 to 2012 were estimated using the ordinary kriging method. Exposure periods were averaged to 3 months prior to laboratory testing. Linear mixed effects models were used to estimate associations between air pollution and serum lipids and blood sugar. After controlling for potential confounders, an interquartile range increment of PM₁₀, SO₂, and CO was associated with elevated LDL-C [6.6% (95%CI: 4.3, 9.0), 11.1% (7.2, 15.2), and 1.9% (1.1, 2.7), respectively] and FG [2.8% (1.5, 4.2), 6.8% (4.5, 9.1), and 1.1% (0.6, 1.5), respectively]. In addition, PM10, SO2, and CO were inversely associated with HDL-C [-1.8% (−3.7, 0.1), −3.3% (−6.2, −0.3), and −1.1 (−1.7, −0.5), respectively]. O₃ was negatively associated with TC, LDL-C, TG, and FG. These findings suggest inhalation of air pollutants may increase the risk of impaired metabolism of glucose and lipids

Additional file 1: Table S1. of Risk scores to predict decreased glomerular filtration rate at 10Â years in an Asian general population

Baseline characteristics of the participants with serum creatinine at both baseline (2002-2003) and follow-up (2012-2013) compared to all subjects with serum creatinine at the baseline visit. Table S2. Alternative clinical model (Model 1) with body mass index. Table S3. Clinical Model (Model 1) with proteinuria. Table S4. Baseline characteristics of EGAT 3 participants in the Validation dataset. (DOCX 20 kb