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Background: Hyperuricemia is now regarded as a risk factor for cardiovascular disease. Micro-albuminuria is associated with increased risk for cardiovascular disease and chronic kidney disease. We hypothesized that elevated serum uric acid (UA) is associated with development of micro-albuminuria in the general population. Methodology/Principal Findings: We conducted a community-based prospective cohort study. A total of 1862 subjects from southern Taiwan, all older than 40 years, were screened and 993 of these participants without micro-albuminuria were followed for 4 years. Urinary albumin-to-creatinine ratio was measured two times per year. A multiple linear regression model indicated that serum UA was independently associated with ln(ACR) after adjustment for 8 factors (age, sex, and 6 metabolic metrics) (β = 0.194, p<0.01). Logistic regression analysis indicated that each 1 mg/dL increase of UA was associated with a 1.42-fold increased risk of micro-albuminuria after adjustment for the same 8 factors (OR = 1.42, 95% CI: 1.27–1.59, p<0.01). A Cox regression model using subjects with serum UA less than 5 mg/dL as reference group indicated higher hazard ratios (HRs) only found in subjects with serum UA more than 7 mg/dL (HR = 3.54, 95% CI: 2.11–5.93, p<0.01) and not in subjects with serum UA of 5 to 7 mg/dL (HR = 1.30, 95% CI: 0.82–2.07, p = 0.15). Conclusion: Hyperuricemia is significantly associated with micro-albuminuria in middle-aged and elderly males and females from a general population in Taiwan. Elevated serum UA is an independent predictor for development of micro-albuminuria in this population. [ABSTRACT FROM AUTHOR]

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine. [ABSTRACT FROM AUTHOR]