Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions.

BACKGROUND: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. METHOD: We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). RESULTS: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. CONCLUSIONS: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Approaching biomarkers of membranous nephropathy from a murine model to human disease.

BACKGROUND: Membranous glomerulonephropathy (MN) is the most prevalent cause of nephrotic syndrome in adult humans. However, the specific biomarkers of MN have not been fully elucidated. We examined the alterations in gene expression associated with the development of MN. METHODS: Murine MN was induced by cationic bovine serum albumin (cBSA). After full-blown MN, cDNA microarray analysis was performed to identify gene expression changes, and highly expressed genes were evaluated as markers both in mice and human kidney samples. RESULTS: MN mice revealed clinical proteinuria and the characteristic diffuse thickening of the glomerular basement membrane. There were 175 genes with significantly different expressions in the MN kidneys compared with the normal kidneys. Four genes, metallothionein-1 (Mt1), cathepsin D (CtsD), lymphocyte 6 antigen complex (Ly6), and laminin receptor-1 (Lamr1), were chosen and quantified. Mt1 was detected mainly in tubules, Lamr1 was highly expressed in glomeruli, and CtsD was detected both in tubules and glomeruli. The high expressions of Lamr1 and CtsD were also confirmed in human kidney biopsies. CONCLUSION: The murine MN model resembled the clinical and pathological features of human MN and may provide a tool for investigating MN. Applying cDNA microarray analysis may help to identify biomarkers for human MN.