A community-based validation study of the short-form 36 version 2 philippines (tagalog) in two cities in the philippines

10.1371/journal.pone.0083794PLoS ONE812-POLN

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

10.1186/1471-2288-12-48BMC Medical Research Methodology12

Data management for prospective research studies using SAS® software

<p>Abstract</p> <p>Background</p> <p>Maintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created.</p> <p>Methods</p> <p>Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. The data management procedures are described.</p> <p>Results</p> <p>Study data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated.</p> <p>Conclusion</p> <p>Data management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.</p

Ethnic differences in the association of fat and lean mass with bone mineral density in the Singapore population

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APOE genotype influences insulin resistance, apolipoprotein CII and CIII according to plasma fatty acid profile in the Metabolic Syndrome

Metabolic markers associated with the Metabolic Syndrome (MetS) may be affected by interactions between the APOE genotype and plasma fatty acids (FA). In this study, we explored FA-gene interactions between the missense APOE polymorphisms and FA status on metabolic markers in MetS. Plasma FA, blood pressure, insulin sensitivity and lipid concentrations were determined at baseline and following a 12-week randomized, controlled, parallel, dietary FA intervention in 442 adults with MetS (LIPGENE study). FA-APOE gene interactions at baseline and following change in plasma FA were assessed using adjusted general linear models. At baseline E4 carriers had higher plasma concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apo B) compared with E2 carriers; and higher TC, LDL-C and apo B compared with E3/E3. Whilst elevated plasma n-3 polyunsaturated FA (PUFA) was associated with a beneficially lower concentration of apo CIII in E2 carriers, a high proportion of plasma C16:0 was associated with insulin resistance in E4 carriers. Following FA intervention, a reduction in plasma long-chain n-3 PUFA was associated with a reduction in apo CII concentration in E2 carriers. Our novel data suggest that individuals with MetS may benefit from personalized dietary interventions based on APOE genotype