The Association of Dietary Patterns with Bone Mineral Density in Middle-aged Women: A Cohort of Korean Genome Epidemiology Study / 대한지역사회영양학회지

Several nutrients are known to affect bone mineral density (BMD). However, these nutrients are combined with food intake and dietary patterns and little is known about the association of dietary patterns and BMD. The objective of this study was to investigate the association of dietary patterns with BMD in Korea Genome Epidemiology Study subjects. Among 2,884 women (40-69 yr) recruited at baseline study (2001), 861 subjects with BMD measurements at baseline and a 4-year follow up study (2005) completed the semi-quantitative food frequency questionnaire. BMD was measured by the Quantitative Ultrasound method. One hundred three food items were combined into 17 food groups and 4 dietary patterns were identified by factor analysis. Cluster analysis using factor score classified each subject into one of three dietary pattern groups named 'Rice and kimchi eating' (n = 617), 'Contented eating' (n = 124), and 'Healthy and light eating' (n = 120). The 'Healthy and light eating' group, characterized by higher intake of fruit, vegetables, fish, milk and dairy products, and younger age, more exercise, higher education, and higher income than other groups. The tibia BMD of the 'Healthy and light eating' group was higher than the other groups after adjusting for the age. After the adjustment for the age BMI and exercise, the 'Healthy and light eating' group showed significantly lower odds of tibia osteopenia/osteoporosis risk compared to the 'Rice and kimchi eating' group both at the baseline [OR(95% CI) : 0.50(0.30-0.84)] and follow-up [OR(95% CI) : 0.59(0.36-0.97)] examinations. The dietary pattern with low calorie and high intakes of fruit, vegetables, fish, milk and dairy products may have beneficial effects on BMD in middle-aged women.

Comparative Statistic Module (CSM) for Significant Gene Selection

Comparative Statistic Module(CSM) provides more reliable list of significant genes to genomics researchers by offering the commonly selected genes and a method of choice by calculating the rank of each statistical test based on the average ranking of common genes across the five statistical methods, i.e. t-test, Kruskal-Wallis (Wilcoxon signed rank) test, SAM, two sample multiple test, and Empirical Bayesian test. This statistical analysis module is implemented in Perl, and R languages.