Ritchey Jamie, Zhang Hongmei, Karmaus Wilfried, Steck Susan E, Sabo-Attwood Tara
University of South Carolina, Department of Epidemiology and Biostatistics, 800 Sumter Street, Columbia, SC 29208, United States; Inter Tribal Council of Arizona, Tribal Epidemiology Center, 2214 N Central Ave., Phoenix, AZ 85004, United States.
University of South Carolina, Department of Epidemiology and Biostatistics, 800 Sumter Street, Columbia, SC 29208, United States.
Steroids. 2014 Apr;82:23-8. doi: 10.1016/j.steroids.2013.12.006. Epub 2014 Jan 9.
It has been hypothesized that racial disparities among several diseases are explained by differences in testosterone (T), 17-β estradiol (E), sex hormone binding globulin (SHBG) and albumin (A) levels, yet epidemiologic results have been mixed. Statistical advice regarding appropriate adjustment methods for carrier proteins of sex steroid hormones in the literature is scant. Therefore, we investigated different adjustment methods for carrier proteins.
Data for 1496 men, >17 years from the Third National Health and Nutrition Examination Survey (NHANES III) 1988-91 were used to analyze linearity between sex hormones and carrier proteins by examining correlation, plots, and regression models. The statistical importance of age, body mass index (BMI), and race/ethnicity were examined for changes in results by the adjustment method.
T was weakly correlated with SHBG and A (r-squared, 0.25, 0.13, respectively) and E was weakly negatively correlated with A (p<0.0001), but not SHBG (p<0.1799). Based on the model residual plots and r-squared, the categorical model performed better than linear models. Regression coefficients for age, BMI, and race/ethnicity groups using quotient (e.g., T/A and E/A) models differed from continuous and categorical models.
Choosing an appropriate adjustment for carrier proteins is important to prevent bias in analyses and inconsistency in findings across studies. Linearity between variables should not be assumed when adjusting models, and should be conducted and reported. An independent categorical carrier protein variable is recommended in analysis exploring factors predicting sex hormone levels, although statistical testing should always be employed.
有假设认为，几种疾病之间的种族差异可由睾酮（T）、17-β雌二醇（E）、性激素结合球蛋白（SHBG）和白蛋白（A）水平的差异来解释，但流行病学结果却参差不齐。文献中关于性类固醇激素载体蛋白适当调整方法的统计学建议很少。因此，我们研究了载体蛋白的不同调整方法。
使用1988 - 1991年第三次全国健康和营养检查调查（NHANES III）中1496名年龄大于17岁男性的数据，通过检查相关性、图表和回归模型来分析性激素与载体蛋白之间的线性关系。通过调整方法检查年龄、体重指数（BMI）和种族/族裔对结果变化的统计学重要性。
T与SHBG和A呈弱相关（决定系数分别为0.25和0.13），E与A呈弱负相关（p<0.0001），但与SHBG无相关性（p<0.1799）。基于模型残差图和决定系数，分类模型比线性模型表现更好。使用商数模型（如T/A和E/A）时，年龄、BMI和种族/族裔组的回归系数与连续模型和分类模型不同。
选择合适的载体蛋白调整方法对于防止分析中的偏差和研究结果的不一致很重要。调整模型时不应假设变量之间的线性关系，应进行并报告相关分析。在探索预测性激素水平因素的分析中，建议使用独立的分类载体蛋白变量，尽管应始终进行统计检验。
