Phannee Pidetcha

Prevalence of Endemic Distal Renal Tubular Acidosis and Renal Stone in the Northeast of Thailand

We have previously reported a large group of patients with endemic distal renal tubular acidosis (EdRTA) admitted to the hospitals in the northeast of Thailand. Since large number of patients were identified in a relatively short period of time, and in an area whose population is homogeneous, we were led to investigate the prevalence of the condition in the area. A survey was conducted in five villages (total population of 3,606) within the northeast of Thailand. 3,013 villagers were examined for urinary citrate concentration and short acid loading test was performed in those with low urinary citrate. 2.8% of the population (2.2-3.4%, 95% confidence interval) failed to lower their urine pH after acid loading; within this group, 0.8% of the population had serum potassium less than or equal to 3.5 mEq/l. In addition a large number of villagers were found to have low urinary citrate concentration and there was concurrent high prevalence of renal stone. The prevalence of EdRTA and renal stone was higher in villagers with poorer socioeconomic status, suggesting that environmental factors play a major role in their pathogenesis. Villagers with acidification defect have 2.4 times the chance of having renal stone and/or nephrocalcinosis. EdRTA is therefore one of the important factors responsible for the high prevalence of renal stone in the area. In conclusion we have confirmed the high prevalence of EdRTA in the northeast of Thailand and provided data showing high prevalence of renal stone and hypocitraturia in the same population.

Identification of metabolic syndrome using decision tree analysis

This study employs decision tree as a decision support system for rapid and automated identification of individuals with metabolic syndrome (MS) among a Thai population. Results demonstrated strong predictivity of the decision tree in classification of individuals with and without MS, displaying an overall accuracy in excess of 99%.

Decreased paraoxonase 1 activity and increased oxidative stress in low lead−exposed workers

Paraoxonase 1 (PON1) has been proposed as an antioxidant enzyme. Although lead-inhibited PON1 activity has been demonstrated mostly based on in vitro experiments, it is uncertain whether this phenomenon is relevant in pathogenesis of lead-induced oxidative stress in the lead exposure. We examined associations of blood lead levels (BLL) and PON1 activity along with oxidative stress parameters in lead exposure workers. We determined malondialdehyde (MDA), conjugated diene (CD), total peroxides (TP), total antioxidant status (TAS), the oxidative stress index (OSI), and PON1 activity in earthenware factory workers (n = 60) and control subjects (n = 65). The lead-exposed group significantly increased lipid peroxidation parameters and OSI compared to the control group (p < 0.001). The lead-exposed group had significantly decreased PON1 activity and TAS levels compared to the control group (p < 0.001). Multiple linear regression analysis revealed that BLL were significantly correlated with decreased TAS (r = −0.496) and PON1 activity (r = −0.434), but with increased CD (r = 0.694), TP (r = 0.614), MDA (r = 0.788), and OSI (r = 0.722). Interestingly, BLL at 10 µg/dL significantly decreased PON1 activity and increased oxidative stress parameters with insignificant changes in other biochemical and hematological parameters. Altogether, the reduction of PON1 activity may associate in an imbalance in pro-oxidants and antioxidants, leading to oxidative damage in lead-exposed workers even at low BLL.

Determining the optimal cutoff points for waist circumference and body mass index for identification of metabolic abnormalities and metabolic syndrome in urban Thai population

This study describes the prevalence and optimal waist circumference (WC) and body mass index (BMI) cutoff point for metabolic abnormalities and metabolic syndrome (MS) from urban Thai population. The optimal BMI/WC cutoff has been used for identifying and evaluating metabolic abnormalities for screening individuals having risk factor of MS.

Determining a new formula for calculating low-density lipoprotein cholesterol: data mining approach

Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDL-C (LDL-Cal) level was performed using Friedewalds equation for triglyceride (TG) level less than 400 mg/dL. Therefore, the aim of this study is to generate a new formula for LDL-Cal and validate the correlation coefficient between LDL-Cal and LDL-C directly measured (LDL-Direct). A data set of 1786 individuals receiving annual medical check-ups from the Faculty of Medical Technology, Mahidol University, Thailand in 2008 was used in this study. Lipid profiles including total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C) and LDL-C were determined using Roche/Hitachi modular system analyzer. The estimated LDL-C was obtained using Friedewalds equation and the homogenous enzymatic method. The level of TG was divided into 6 groups (TG<200, <300, <400, <500, <600 and < 1000 mg/dL) for constructing the LDL-Cal formula. The pace regression model was used to construct the candidate formula for the LDL-Cal and determine the correlation coefficient (r) with the LDL-Direct. The candidate LDL-Cal formula was generated for 6 groups of TG levels that displayed well correlation between LDL-Cal and LDL-Direct. Interestingly, The TG level was less than 1000 mg/dL, the regression model was able to generate the equation as shown as strong r of 0.9769 with LDL-Direct. Furthermore, external data set (n = 666) with TG measurement (36-1480 mg/dL) was used to validate new formula which displayed high r of 0.971 between LDL-Cal and LDL-direct. This study explored a new formula for LDL-Cal which exhibited higher r of 0.9769 and far beyond the limitation of TG more than 1000 mg/dL and potential used for estimating LDL-C in routine clinical laboratories.

Predicting Metabolic Syndrome Using the Random Forest Method

Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

Coincidence of obesity associated with cardio-renal abnormalities in Thais obese population

AIM To investigate the patterns and relationship among the metabolic syndrome (MS), coronary heart disease (CHD) and kidney function. METHODS A cross-sectional secondary data set of 9359 individuals, age 30-74 years, receiving annual health check-up in 2012 were used in this studied. Identification of MS and CHD development was determined by International Diabetes Federation criteria and Framingham risk score, respectively, while kidney function was assessed by using the estimate glomerulus filtration rate (eGFR) and chronic kidney disease epidemiology (CKD-EPI) formula. RESULTS The prevalence of MS was 16.1%. The majority pattern of MS in male displayed abnormalities of body mass index (BMI) plus triglyceride and blood pressure (BP). Most of them had high risk of CHD, and kidney function in stage 1 and 2. Furthermore, abnormalities of BMI plus BP and blood glucose were the main components related to high risk of CHD, and stage 1 of kidney function in female. CONCLUSION This finding showed the cleared pattern of the sequential abnormality factors which potentially use for setting the activity and empowerment team to prevention, promotion, and treatment strategy in MS patients. Particularly, BMI is the first assessment and then follow by blood pressure and blood sugar which could be used as the guideline for reducing MS associated with CHD and kidney disorder in Thai population.