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Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy

Hypertension Research volume 46, pages 2513–2526 (2023)Cite this article

A Comment to this article was published on 11 October 2023

Abstract

Hypertensive disorders of pregnancy (HDP) result in major maternal and fetal complications. Our study aimed to find a panel of protein markers to identify HDP by applying machine-learning models. The study was conducted on a total of 133 samples, divided into four groups, healthy pregnancy (HP, n = 42), gestational hypertension (GH, n = 67), preeclampsia (PE, n = 9), and ante-partum eclampsia (APE, n = 15). Thirty circulatory protein markers were measured using Luminex multiplex immunoassay and ELISA. Significant markers were screened for potential predictive markers by both statistical and machine-learning approaches. Statistical analysis found seven markers such as sFlt-1, PlGF, endothelin-1(ET-1), basic-FGF, IL-4, eotaxin and RANTES to be altered significantly in disease groups compared to healthy pregnant. Support vector machine (SVM) learning model classified GH and HP with 11 markers (eotaxin, GM-CSF, IL-4, IL-6, IL-13, MCP-1, MIP-1α, MIP-1β, RANTES, ET-1, sFlt-1) and HDP with 13 markers (eotaxin, G-CSF, GM-CSF, IFN-gamma, IL-4, IL-5, IL-6, IL-13, MCP-1, MIP-1β, RANTES, ET-1, sFlt-1). While logistic regression (LR) model classified PE with 13 markers (basic FGF, IL-1β, IL-1ra, IL-7, IL-9, MIP-1β, RANTES, TNF-alpha, nitric oxide, superoxide dismutase, ET-1, PlGF, sFlt-1) and APE by 12 markers (eotaxin, basic-FGF, G-CSF, GM-CSF, IL-1β, IL-5, IL-8, IL-13, IL-17, PDGF-BB, RANTES, PlGF). These markers may be used to diagnose the progression of healthy pregnant to a hypertensive state. Future longitudinal studies with large number of samples are needed to validate these findings.

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Acknowledgements

The authors sincerely acknowledge the Department of Pharmaceuticals (DoP) under the Ministry of Chemicals and Fertilizers, Government of India, and Dr. USN Murty, Director, NIPER-G for their extensive support.

Funding

This study is funded by the Institutional Core Grant, NIPER-Guwahati, Department of Pharmaceuticals (DoP), Ministry of Chemicals and Fertilizers, Government of India.

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Authors and Affiliations

  1. Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Guwahati, Sila katamur Village, Changsari, Assam, India

    Bincy Varghese, Chippy Anna Joy & Ramu Adela

  2. Applied Biology Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, India

    Jhansi Venkata Nagamani Josyula, Shraddha Jangili & Srinivas Rao Mutheneni

  3. Department of Obstetrics and Gynecology, Gauhati Medical College, Guwahati, India

    R. K. Talukdar

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Contributions

Conceived and designed the study: RA, and BV. Statistical and Bioinformatic analysis: JVNJ, SJ, SRM, BV and RA. Clinical study: BV, CAJ, RA and RKT. Draft manuscript preparation: BV, RA, JVNJ, SJ, and SRM. Final manuscript preparation: BV and RA.

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Varghese, B., Joy, C.A., Josyula, J.V.N. et al. Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy. Hypertens Res 46, 2513–2526 (2023). https://doi.org/10.1038/s41440-023-01348-1

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