Patients
In our cross-sectional study, we investigated 143 LN patients diagnosed through renal biopsy who had been admitted to Xiangya Hospital of Central South University in Changsha, China during the June 2012–December 2016 period. The exclusion criteria included the coexistence of another autoimmune disease or having been diagnosed with thyroid disease prior to LN. All patients were informed of the objectives of this study, and each provided signed written consent prior to enrolment. As this research did not affect patient treatment, as per Central South University policies, ethics board approval was not required.
Collection of clinical data
Data on patient characteristics, clinical symptoms, and laboratory results were retrospectively collected from each patient’s medical records. These included: (1) general information, including age and sex; (2) clinical symptoms, including course of disease, hypertension, fever, cutaneous manifestations, alopecia, oral ulcer, malar rash, renal dysfunction (proteinuria), and haematological disease; and (3) laboratory results, including white blood cell count, haemoglobin (Hb) concentration, concentration of total protein (TP), serum lipid, erythrocyte sedimentation rate, C-reactive protein, C3, C4, and antibodies to dsDNA, simth, SSA, SSB, anti-U1 ribonucleoprotein, and ribosomal P protein. Patients’ SLE disease activity (i.e., SLEDAI) scores were collected from medical records and calculated by an experienced clinician.
Statistical analysis
Values herein are expressed as mean (standard deviation), median, and interquartile range, or as a number and percentage. We undertook comparisons between categorical variables by using the χ2 test, and between continuous variables in two independent groups by using the t-test. In cases where we were unable to establish a normal distribution for a variable, we performed the Mann–Whitney U-test.
We performed PCA by using SPSS software (a factor analysis package), to determine the interplay of clinical variables among LN patients with and without hypothyroidism. We achieved convergence during an Oblimin rotation with Kaiser normalization. In the final PCA iteration, we covered nine clinical variables in the patient group analysed. To be considered a PC, a variable’s eigenvalue had to exceed 1, and PC1 represents the group of variables that induced the greatest amount of variation in the data. We used logistic regression to further screen clinically significant eigenvalues and scrutinize critical factors that affect outcomes among LN patients.
We performed the analysis in three stages. First, we performed a monofactor analysis to examine differences between LN patients with and without hypothyroidism. Second, we performed PCA with regard to all the serology, immunology, and biochemistry variables of LN patients. We truncated those data by rotational reorientation to maximize variance along the new axis (i.e., PC) while concurrently preserving the relationship and order among the data points; the PCs could then be used in further classification, as they retain information from the original data. Third, the absolute majority of cumulative contribution (> 2/3) was used to extract PCs as independent variables, and the clinical outcome was used as a dependent variable for logistic regression modelling. In this way, we were able to obtain the PCs that significantly correlated with certain clinical outcomes. We generated an ROC of multivariate observations to assess the PCA—logistic regression model’s performance. Statistical analysis was performed using SPSS (version 19), and all p-values less than 0.05 were considered statistically significant.