Statistical analysis
REGRESSION, HYPERTENSION
After testing data for normality with the Kolmogorov-Smirnov test, quantitative variables were expressed as mean ± SD or median (IQR) and categorical variables were expressed as number (%). Chi-square test and ANOVA test were used to compare the results. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the metabolite concentrations between different groups. To normalize the results of amino acids and acylcarnitines, the natural logarithm of the data was calculated and the value of Z was estimated. The obtained P values were adjusted using Benjamini-Hochberg method. Logistic regression was used to examine the relationship between metabolites and different stages of hypertension. The correlation between metabolic variables was assessed by Pearson correlation, and a correlation matrix was constructed. Because of the high correlation between metabolites, principal component analysis (PCA) was used to generate independent factors, and the test adequacy was assessed using the Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests. The factors based on eigenvalues above 1 were constructed by varimax rotation with maximum probability. Logistic regression analyses were used to examine the relationship between each factor and the different stages of hypertension. All logistic regression analyses were performed in three models, including a univariate model (crude model), model 1 with adjustment for age, sex, and BMI, and model 2 with adjustment for age, sex, BMI, lipid profile (total cholesterol, triglyceride, HDL cholesterol), FPG, intake of oral glucose-lowering drugs, statins, and antihypertensive drugs. We tested the multicollinearity between total cholesterol, triglyceride, and HDL cholesterol and the results showed that the multicollinearity between them was low (