A multiple regression analyses adjusted for significant covariates demonstrated that YKL-40 was associated with albuminuria (r = 0.42, p < 0.001).
Odds ratios (OR) with 95% confidence intervals (CI) of medium versus short, long versus short and long versus medium spells, adjusted for other significant covariates in multiple logistic regression analysis (see text). 1) Short = only short spells.
In a multiple Cox regression model adjusting for significant covariates using a best subset selection criterion, sC5b-9 remasned an an independent predictor of CVEs (1.30 [1.02–1.66]; P = 0.04).
Only patient category (r = 0.22, p = 0.04) and LDL: HDL-cholesterol ratio (r = 0.31, p = 0.02) was significantly associated with levels of YKL-40 in a multiple regression model adjusting for the significant covariates.
In a multiple regression model adjusting for the significant covariates (UACR, A1C, serum creatinine, age, and diastolic blood pressure) and cholesterol, systolic blood pressure, and the presence of intermittent claudication and retinopathy, YKL-40 levels were significantly associated with the level of albuminuria (P < 0.001).
To examine the relationship between PCS/MCS and the significant covariates we used multiple regression analysis (p < 0.05) Afterwards, the model was employed on the MG subgroups with regard to antibodies to identify if the predictors played the same role in the subgroups.
Log-transformed data In a multiple regression model adjusting for the significant covariates (UACR, age, HOMA-IR, HbA1c, triglycerides, systolic blood pressure, duration of diabetes, total cholesterol and LDL-cholesterol), albuminuria was still found to be significantly associated with YKL-40 levels (r = 0,32, p = 0.006).
With the significant covariates established, we then used hierarchical multiple regression models to investigate whether genetic diversity predicted health (number of symptoms) after controlling for the covariates.
Results are presented as multiple R for the model and β coefficients for significant covariates.
A Cox multiple regression model was utilized to assess the influence of all significant covariates on overall survival in ESCC patients who underwent curative resections.
