Upasna Gupta (Centre of Biomedical Research (CBMR) & Lucknow and Academy of Scientific and Innovative Research(AcSIR), India)
LinkedIn: @Upasna Gupta; X: @Upasnagupta30
Abstract: The progressive illness known as chronic kidney disease (CKD) can often be challenging to diagnose in its early stages with conventional diagnostic approaches such as serum creatinine and albumin assessment. Identifying possible biomarkers for early detection and personalized treatment, as well as physiological changes linked to early CKD—an area that hasn’t been fully investigated before—is the goal of this study to address this gap.
We performed a metabolomic analysis using ¹H NMR on 115 human serum samples (24 healthy controls, 91 patients with early-stage CKD). MetaboAnalyst 6.0 was used for data pre-processing and statistical analyses (PCA, PLS-DA, OPLS-DA, ANOVA, and Wilcoxon Mann-Whitney test). Strong differentiation between CKD stages was shown by random forest modelling. The KEGG database was used to perform pathway enrichment, and ROC analysis evaluated the diagnostic value of important metabolites.
Across CKD stages, significant changes in ten different metabolites: myo-inositol, glycerol, pyruvate, carnitine, phenylalanine, tyrosine, histidine, TMAO, 2-hydroxyisobutyrate, and 3-hydroxyisobutyrate (p 1). AUC values > 0.7 from ROC curves demonstrated its potential for diagnosis. Pathway analysis revealed significant dysregulation in metabolism of inositol phosphate, tyrosine, histidine, pyruvate, and biosynthesis of phenylalanine, tryptophan and tyrosine.
This comprehensive metabolomics investigation identified potential early-stage CKD biomarkers in addition to significant metabolic abnormalities. These findings could help provide individualized care for CKD early management.
-
Thanks for a nice presentation. I have following questions regarding the same:
1. In the stack plot showing the 1D NMR spectra shown gradual variation of creatinine and format in different groups but these two do not show up in the contributing metabolic factors of group deafferentation. What can be possible explanation?2. Similarly, my-inositol does not seem to vary much in the 1D plots but its there in contributing factors of group differentiations. What can be the reason?
Thanks again.-
Thank you, sir.
1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.
2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.
-
-
Hey, very interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? what kind of NMR pulse sequences did you use? Is there any evidence of these proteins to be a biomarker of the disease?
-
Thank You, Dr. Marco Schiavina
Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.
-
-
Thank you, sir.
1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.
2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.
-
Thank You, Dr. Marco Schiavina
Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.
Leave a Reply