PCA uses a mathematical algorithm to determine a smaller number of new variables called principal components (PCs), which are linear functions of those in the original dataset. Principal component analysis (PCA) is a powerful method by which to explore intricate datasets that feature multiple variables. However, if the variables therein are collinear, the regression equation will be unstable and its results difficult to predict. Logistic regression is widely used to analyse the relationship between individual risk/protective factors and outcomes. Furthermore, the availability of multiple indicators and of large relevant datasets makes it difficult to analyse clinical data directly therefore, the precise nature of these mechanisms remains unknown. However, the pathological and physiological mechanisms underlying SLE with hypothyroidism are sophisticated. Accordingly, analysis of the associations between LN and hypothyroidism and a determination of relevant risk factors would greatly aid in diagnosis and disease management. Studies indicate that the prevalence of hypothyroidism is much higher in SLE, and especially among LN patients, than in the general population additionally, the risk of subsequent cardiovascular events and renal impairment is higher among LN patients with thyroid dysfunction.
Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease, and lupus nephritis (LN) is a frequently occurring and serious complication of SLE. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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