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Chronic kidney disease is one of the diseases which threaten the world, and patients' quality of life could be deteriorated with the disease progression. The pathogenesis of CKD involves inflammation, immune disorders, and oxidative stress. In fact, the specific pathogenesis of CKD has not been fully elaborated. It is of great significance to clarify the pathogenesis of CKD for the development and prognosis of the disease. Indolelactic acid is a protein-bound uric acid solute from tryptophan metabolism that belongs to the indole uremic solute family. Indolelactic acid is an agonist of the transcription factor aryl hydrocarbon receptor, promotes vascular inflammation, oxidative stress, and atherosclerosis, and plays a role in cardiovascular disease. Accumulation of urine toxins usually affects disease progression and prognosis in patients with CKD. Urine toxin is associated with multiple complications in patients with CKD and may be involved in cardiovascular events in patients with CKD, which has certainly raised our concerns about the accumulation of toxins in patients with CKD. At present, metabolomics methods are commonly used in clinical practice for the detection of urine toxins. This study intends to explore the changes of plasma and urine Indolelactic acid in patients with CKD and healthy people through LC-MS, and explore the relationship of inflammation and Indolelactic acid in patients with CKD and healthy people
Patients and Methods: A total of 47 patients with CKD and 30 healthy people were included. Clinical parameters were recorded. One-way ANOVA was performed for variables which had normal distributions and homogeneous variance. The rank sum test was performed for variables which had non-normal distributions. Pearson or Spearman correlation analysis was used for the correlations analysis. Binary logistic regression and ordinal logistic regression were used for the independent relationship of CKD. Receiver operating characteristic curve was used for the accuracy of diagnosis Youden’s index was calculated for the best cut-off of the Receiver operating characteristic curve.
The control group had higher levels of hemoglobin and albumin, and lower levels of Ln (creatinine) and Ln (BUN). Plasma and urine ILA had significant differences between the patients with CKD and control group, and showed an increased trend with the progression of renal function. Plasma ILA and urine ILA were positively correlated (r = 0.51, P < 0.01). Plasma ILA had positive correlations with age, BMI, creatinine, BUN, triglycerides, and uric acid, and negative correlations with hemoglobin. Urine ILA had positive correlations with age, creatinine, BUN, and uric acid, and a negative correlation with hemoglobin and albumin. Before adjustment for confounding, CKD was associated with plasma ILA (OR = 5.20, P < 0.01) and urine ILA (OR = 5.24, P < 0.01). After adjustment for confounding, ordinal logistic regression indicated that CKD had a significant relationship with plasma ILA (OR = 4.49, P < 0.01), urine ILA (OR = 2.14, P < 0.01), BUN (OR = 1.43, P < 0.01), and hemoglobin (OR = 0.95, P < 0.05). ROC curve showed that plasma ILA and urine ILA were reliable predictions of CKD. CKD was associated with three inflammatory factors: plasma ILA (OR = 5.92, P < 0.01), urine ILA (OR = 2.79, P < 0.01), and Hs-CRP (OR = 2.45, P < 0.01).
Plasma and urine ILA have potential use as biomarkers for CKD, and inflammation status.