A total of 73,047 apparently healthy subjects were recruited for the study. The mean age was 41.73 ± 8.4 years, [n = 44,118 men (41.9 ± 8.1-years) and n = 28,929 women (41.4 ± 8.7-years)]. Subjects participated in a routine health check-up program that was held at the Health Promotion Center of Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea in 2008. The medical health checkup program was developed to improve the health of employees. Most subjects were employees, or family members, from various industrial companies across the country. The cost of medical examinations was predominantly paid for by the employers, and most subjects underwent a health check annually or biannually. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki as reflected by a priori approval from our institution’s Human Research Committee.
The health check consisted of a full medical history and comprehensive blood test evaluation. Participants’ height and weight were measured barefoot and in light clothing. BMI was calculated as weight in kilograms divided by height in meters squared. Laboratory examinations were obtained after an overnight fast. An enzymatic calorimetric test was used to measure TC and TG concentrations. The selective inhibition method was used to measure HDLc, and a homogeneous enzymatic calorimetric method was used to measure the concentration of LDLc (Advia 1650 Autoanalyzer, Bayer Diagnostics, Leverkusen, Germany). Apo B100 and apoA1 concentrations were determined by rate nephelometry (IMMAGE system; Beckman Coulter).
Descriptive statistics for continuous variables are presented as means, standard deviations (SDs), medians and inter-quartile ranges (Q1, Q3). Categorical variable are presented as proportions (percentages).
The study sample was randomly divided into a training set for prediction model building and a validation set of equal size. Multivariable linear regression analysis was used to develop a prediction model equation and to validate the developed model. Natural log transformation was used to normalize the distribution of HDLc, TG, age and BMI. Analysis of residuals was used to check assumptions for multivariable linear regression modeling. The accuracy of the prediction model equation was evaluated using concordance correlation coefficient (CCC) analysis (Lin (1989)) that allowed comparison between prediction modeling results and the direct biochemical measurement of apo B100. In all tests, p-values < 0.05 were considered significant. Statistical analyses were performed using SAS 9.1.3 (SAS Institute Inc, Cary, NC) and R 2.13.2 (Vienna, Austria).
We conducted subgroup analyses by sex, glucose (7.0 mmol/l or 126 mg/dl), BMI (25kg/m2) and apoB quartile, in order to examine whether the derived equation was appropriate for specific subpopulation.