Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form
D-Scholarship @ Pitt Banner and Links to Homepage

Metabolomic Analysis Of Chronic Kidney Disease

Fan, Li (2024) Metabolomic Analysis Of Chronic Kidney Disease. Master's Thesis, University of Pittsburgh. (Unpublished)

[img] PDF
Restricted to University of Pittsburgh users only until 18 December 2026.
Download (2MB) | Request a Copy

Abstract

Chronic kidney disease (CKD) is a progressive condition characterized by the gradual loss of kidney function, significantly impacting metabolic homeostasis. However, the differential abundance of metabolites in different stages of CKD are unknown. This study aimed to identify differentially abundant (DA) metabolites in individuals without CKD and across four stages of CKD, and in doing so, uncover potential biomarkers and understand disease-related metabolic alterations. I also tried to classify individuals CKD into unaffected or into stage I–IV using DA metabolites or clinical features. I identified 15 DA metabolites between controls and early-stage CKD cases, 40 between early-stage CKD and late-stage CKD cases, and 79 between controls and late-stage CKD cases. There was enrichment of DA metabolites in 24 pathways in controls vs and early-stage CKD cases, 19 in controls vs late-stage CKD cases, and 40 in early-stage vs late-stage CKD cases. Machine learning models that attempted to classify individuals based on their DA metabolites had greater than 80% accuracy when classifying between controls and early-stage cases or early-stage cases and late-stage cases and greater than 88% accuracy when classifying between controls and late-stage cases. However, multi-outcome predictions only performed modestly well. The results also indicate that classification using metabolites performs better than classification with clinical features. This study identified stage-specific metabolic alterations, underscores the promise of precision medicine in CKD diagnosis, which can help the early treatment of CKD. However, we did not identify a solid biomarker, which requires the need for further research. The insights in DA metabolites of CKD can help improve the quality of care and reduce global healthcare disparities by improving screening programs, especially for at-risk populations, and supporting the design of cost-effective interventions to slow the progression of chronic renal failure.

Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Fan, Lilif45@pitt.edulif450000-0002-8764-3687
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee MemberChernus, Jonathanjonchernus@pitt.edujonchernus
Committee MemberMinster, Ryanrminster@pitt.edurminster
Committee ChairSilvia, Liushl96@pitt.edushl96
Date: 18 December 2024
Date Type: Publication
Defense Date: 6 December 2024
Approval Date: 18 December 2024
Submission Date: 13 December 2024
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 96
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Human Genetics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Chronic kidney disease; Metabolomic
Date Deposited: 18 Dec 2024 19:40
Last Modified: 18 Dec 2024 19:40
URI: http://d-scholarship.pitt.edu/id/eprint/47276

Metrics

Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item