Department of Ophthalmology, Stony Brook University, Stony Brook, New York, USA.
Departments of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, New York, USA.
Curr Eye Res. 2021 May;46(5):622-629. doi: 10.1080/02713683.2020.1830115. Epub 2021 Jan 14.
To evaluate whether principal component analysis (PCA) can assess various diagnostic tests of dry eye disease (DED), providing a simplified, more informative measure of disease status than individual clinical test parameters (ICTP).
ICTP were analyzed using PCA in two groups of normal rabbits (Groups 1 and 2). Group 3, not truly normal, was also assessed. DED was induced in Group 1 by complete dacryoadenectomy; in Groups 2 and 3 by injection of concanavalin A. Tear break up time, tear osmolarity, Schirmer's tear test and rose bengal staining were the ICTP measured in all groups. Statistical analysis including descriptive statistics, t test, correlation coefficients and PCA was done. PCA using ICTP data from Group 1 generated axes; Group 2 and 3 were plotted over these axes.
All groups had induction of DED. Correlations for all ICTP were in the correct direction and were strongest for Group 1 and weakest in Group 3. PCA clearly separated DED and normal eyes. Principal component (PC) 1, made up of nearly equal contributions from the four clinical tests, explained 73% of the variation and provided a means to separate normal from DED. PC 1 values under 0.52 can be mathematically defined as DED. Of all pairwise comparisons, PC 1 vs PC 2 and PC 1 vs PC 3 were the most informative providing excellent spatial separation and additional information regarding DED status.
PCA proved useful for evaluating DED providing a simpler, more comprehensive assessment than ICTP. PC 1 is a valuable, clinically relevant, and informative metric for DED status and severity having superior diagnostic value and statistical strength compared to ICTP. Spatial information on biplots of PC 1 vs PC 3 is also informative. PCA, and specifically PC 1, has the potential to serve as a biomarker for DED.
评估主成分分析（PCA）是否可以评估各种干眼症（DED）的诊断测试，提供一种比个体临床测试参数（ICTP）更简单、更具信息量的疾病状态衡量方法。
使用 PCA 对两组正常兔（第 1 组和第 2 组）的 ICTP 进行分析。第 3 组并非真正正常，也进行了评估。第 1 组通过完全泪腺切除术诱导 DED；第 2 组和第 3 组通过注射刀豆球蛋白 A 诱导 DED。在所有组中，都测量了泪膜破裂时间、泪液渗透压、泪液分泌试验和玫瑰红孟加拉染色。进行了包括描述性统计、t 检验、相关系数和 PCA 的统计分析。使用第 1 组的 ICTP 数据进行 PCA 生成轴；将第 2 组和第 3 组绘制在这些轴上。
所有组均诱导产生 DED。所有 ICTP 的相关性均指向正确方向，第 1 组最强，第 3 组最弱。PCA 清楚地区分了 DED 和正常眼。由四个临床测试的几乎相等贡献组成的主成分（PC）1 解释了 73%的变异，并提供了一种将正常与 DED 区分开来的方法。PC 1 值小于 0.52 可以通过数学定义为 DED。在所有两两比较中，PC 1 与 PC 2 和 PC 1 与 PC 3 的相关性最强，提供了出色的空间分离和有关 DED 状态的额外信息。
PCA 被证明对评估 DED 非常有用，提供了比 ICTP 更简单、更全面的评估。PC 1 是一种有价值的、临床相关的、信息量丰富的 DED 状态和严重程度指标，与 ICTP 相比具有更高的诊断价值和统计强度。PC 1 与 PC 3 的双变量图的空间信息也很有意义。PCA，特别是 PC 1，有可能成为 DED 的生物标志物。
