Li Y, et al. Transl Clin Pharmacol. 2021 Dec;29(4):216-225. https://doi.org/10.12793/tcp.2021.29.e19

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

The understanding of brain structural abnormalities across different clinical forms of dystonia and their contribution to clinical characteristics remains unclear. The objective of this study is to investigate shared and specific gray matter volume (GMV) abnormalities in various forms of isolated idiopathic dystonia. We collected imaging data from 73 isolated idiopathic dystonia patients and matched them with healthy controls to explore the GMV alterations in patients and their correlations with clinical characteristics using the voxel-based morphometry (VBM) technique. In addition, we conducted an activation likelihood estimation (ALE) meta-analysis of previous VBM studies. Our study demonstrated widespread morphometry alterations in patients with idiopathic dystonia. Multiple systems were affected, which mainly included basal ganglia, sensorimotor, executive control, and visual networks. As the result of the ALE meta-analysis, a convergent cluster with increased GMV was found in the left globus pallidus. In subgroup VBM analyses, decreased putamen GMV was observed in all clinic forms, while the increased GMV was observed in parahippocampal, lingual, and temporal gyrus. GD demonstrated the most extensive GMV abnormalities in cortical regions, and the aberrant GMV of the posterior cerebellar lobe was prominent in CD. Moreover, trends of increased GMV regions of the left precuneus and right superior frontal gyrus were demonstrated in the moderate-outcome group compared with the superior-outcome group. Results of our study indicated shared pathophysiology of the disease-centered on the dysfunction of the basal ganglia-thalamo-cortical circuit, impairing sensorimotor integration, high-level motor execution, and cognition of patients. Dysfunction of the cerebello-thalamo-cortical circuit could also be involved in CD especially. Finally, the frontal-parietal pathway may act as a potential marker for predicting treatment outcomes such as deep brain stimulation.

Introduction: WK0202, a β-lapachone derivative under clinical development, activates NAD(P)H quinone dehydrogenase 1 (NQO1), acting as a detoxifying and antioxidant agent. In this study, a metabolomics investigation of β-lapachone derivatives in humans is performed to characterize drug-induced alterations in endogenous metabolic pathways. Objectives: This study investigated metabolic alterations induced by WK0202 administration and their potential association with its therapeutic mechanism and efficacy. Using targeted and untargeted metabolomics approaches, we identified potential pharmacodynamic biomarker candidates that may reflect the drug’s activity and metabolic effects. Methods: Plasma samples from healthy subjects who received multiple doses of WK0202 were compared with a placebo control group. The metabolomic profiles were compared pre- and post-dose to identify significant metabolic changes. Significant metabolites were identified using statistical analyses, focusing on key metabolic pathways. To further investigate NQO1 genotype effects, Spearman correlation analysis was performed between post/pre-dose concentration ratios and genotypes. Results: Following WK0202 administration, significant changes were observed in the alanine, aspartate and glutamate metabolism, arginine biosynthesis, and lipid metabolism. Although most metabolites were not strongly dependent on NQO1 genotype or dose group, they exhibited an overall consistent trend. These alterations were indicative of Nrf2 pathway activation, possibly by NQO1-mediated drug activity. Conclusion: These metabolic alterations highlight the potential of endogenous metabolites as surrogate markers for identifying novel therapeutic targets and assessing the efficacy of WK0202 in future clinical studies.

Pioglitazone is class of thiazolidinediones that activates peroxisome proliferator‐activated receptors (PPARs) in adipocytes to improve glucose metabolism and insulin sensitivity and has been used as a treatment for type 2 diabetes. However, the underlying mechanisms of associated pioglitazone‐induced effects remain unclear. Our study aimed to investigate endogenous metabolite alterations associated with pioglitazone administration in healthy male subjects using an untargeted metabolomics approach. All subjects received 30 mg of pioglitazone once daily in the assigned sequence and period. Urine samples were collected before pioglitazone administration and for 24 h after 7 days of administration. A total of 1465 compounds were detected and filtered using a coefficient of variance below 30% and 108 metabolites were significantly altered upon pioglitazone administration via multivariate statistical analysis. Fourteen significant metabolites were identified using authentic standards and public libraries. Additionally, pathway analysis revealed that metabolites from purine and beta‐alanine metabolisms were significantly altered after pioglitazone administration. Further analysis of quantification of metabolites from purine metabolism, revealed that the xanthine/hypoxanthine and uric acid/xanthine ratios were significantly decreased at post‐dose. Pioglitazone‐dependent endogenous metabolites and metabolic ratio indicated the potential effect of pioglitazone on the activation of PPAR and fatty acid synthesis. Additional studies involving patients are required to validate these findings.