Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

Takafumi Yamauchi, Daisuke Ochi, Naomi Matsukawa, Daisuke Saigusa, Mami Ishikuro, Taku Obara, Yoshiki Tsunemoto, Satsuki Kumatani, Riu Yamashita, Osamu Tanabe, Naoko Minegishi, Seizo Koshiba, Hirohito Metoki, Shinichi Kuriyama, Nobuo Yaegashi, Masayuki Yamamoto, Masao Nagasaki, Satoshi Hiyama, Junichi Sugawara

Research output: Contribution to journalArticlepeer-review

Abstract

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

Original languageEnglish
Article number17777
JournalScientific reports
Volume11
Issue number1
DOIs
Publication statusPublished - 2021 Dec

ASJC Scopus subject areas

  • General

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