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Deriving Biological Meaning and Clinical Application for Pediatric Sepsis with Data-driven Analysis

Qin, Yidi (2024) Deriving Biological Meaning and Clinical Application for Pediatric Sepsis with Data-driven Analysis. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Pediatric sepsis is a life-threatening syndrome characterized by abnormal immune response to infection, resulting in organ failure and mortality. However, the success of regular therapies of pediatric sepsis has been hindered by the unavoidable heterogeneity within the patient population. To enable advanced precision medicine treatment, it is of great importance to identify patients at high risk and unravel the potential biological mechanisms driving the heterogeneity. In line with this need, this study leveraged clinical, genetic, and epigenetic data to first identify pediatric sepsis patients at high risk of severe outcomes and then detect biological markers associated with the phenotype of interest.
Beyond the conventional empirical pediatric sepsis phenotypes, Aim 1 of this study applied a machine learning approach to bedside clinical features and derived four computable pediatric sepsis phenotypes, PedSep-A, B, C, and D, which exhibited distinct infection resources and sites, inflammations, metabolisms, organ failures, and mortalities. Among them, PedSep-D was distinguished by significantly more severe outcomes compared to other phenotypes. Following this discovery, gene-based analysis in Aim 2 identified several deleterious variants in one exome-wide significant (LTBP4, p < 5E-8) and two suggestive (PLA2G4E and CCDC157, p < 5E-7) genes associated with PedSep-D, demonstrating the contribution of rare variants in pediatric sepsis severity. Finally, epigenome-wide association analysis in Aim 3 identified one genome-wide significant (cg16704797, p < 9E-8) and 24 suggestive significant (p < 1E-5) differentially methylated CpGs (DMCs), and one significant differentially methylated region (DMR) associated with PedSep-D. Functional analysis of the identified DMCs indicated their roles in regulating gene expression, immune cell activation, and lipid metabolisms.
This study has promoted our current knowledge of heterogeneity in pediatric sepsis and forwarded our understanding of disease pathology from perspectives of genetics and epigenetics. Furthermore, the accomplishment of this work contributed to addressing several gaps between current results from established studies and future applications in clinical programs to inform better development of precision medicine. The public health significance of findings gained from this study is particularly profound, offering the potential to revolutionize the way sepsis is diagnosed and treated in children, ultimately leading to more effective and efficient healthcare interventions.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Qin, Yidiyiq22@pitt.eduyiq220000-0002-7860-2517
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairPark, Hyun Junghyp15@pitt.eduhyp15
Committee MemberWeeks, Danielweeks@pitt.eduweeks
Committee MemberShaffer, Johnjohn.r.shaffer@pitt.edujohn.r.shaffer
Committee MemberCarcillo,
Date: 16 May 2024
Date Type: Publication
Defense Date: 5 April 2024
Approval Date: 16 May 2024
Submission Date: 25 April 2024
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 195
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Human Genetics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: pediatric sepsis, machine learning, genetics, epigenetics
Date Deposited: 16 May 2024 17:43
Last Modified: 16 May 2024 17:43


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