Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Saman Ghazvini; Saji Uthaman; Lilly Synan; Eugene C. Lin; Soumik Sarkar; Mark K. Santillan; Donna A. Santillan; Rizia Bardhan

doi:10.1002/btm2.10595

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Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Journal article

Open access

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Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Saman Ghazvini, Saji Uthaman, Lilly Synan, Eugene C. Lin, Soumik Sarkar, Mark K. Santillan, Donna A. Santillan and Rizia Bardhan

Bioengineering & translational medicine, Vol.9(1), e10595

01/2024

DOI: 10.1002/btm2.10595

PMCID: PMC10771567

PMID: 38193120

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Abstract

Abstract Preeclampsia is a life‐threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at‐risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

Details

Title: Subtitle: Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy
Creators: Saman Ghazvini - Iowa State University
Saji Uthaman - Iowa State University
Lilly Synan - Iowa State University
Eugene C. Lin - National Chung Cheng University
Soumik Sarkar - Iowa State University
Mark K. Santillan - University of Iowa
Donna A. Santillan - University of Iowa
Rizia Bardhan - Iowa State University
Resource Type: Journal article
Publication Details: Bioengineering & translational medicine, Vol.9(1), e10595
DOI: 10.1002/btm2.10595
PMID: 38193120
PMCID: PMC10771567
NLM abbreviation: Bioeng Transl Med
ISSN: 2380-6761
eISSN: 2380-6761
Grant note: DOI: 10.13039/100000968, name: American Heart Association, award: 15SFRN23480000; DOI: 10.13039/501100004663, name: Ministry of Science and Technology, Taiwan, award: 109‐2113‐M‐194‐010‐MY3; DOI: 10.13039/100000002, name: National Institutes of Health, award: 3UL1TR002537‐02S1, R01EB029756‐01A1, R01HD089940, R21HD100685‐01
Language: English
Electronic publication date: 08/31/2023
Date published: 01/2024
Academic Unit: Radiology; Obstetrics and Gynecology
Record Identifier: 9984464481902771

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