Projection of gut microbiome pre- And post-bariatric surgery to predict surgery outcome

Meirav Ben Izhak, Adi Eshel, Ruti Cohen, Liora Madar-Shapiro, Hamutal Meiri, Chaim Wachtel, Conrad Leung, Edward Messick, Narisra Jongkam, Eli Mavor, Shimon Sapozhnikov, Nitsan Maharshak, Subhi Abu-Abeid, Avishai Alis, Ilanit Mahler, Aviel Meoded, Shai Meron Eldar, Omry Koren, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

Abstract

Bariatric surgery is often the preferred method to resolve obesity and diabetes, with ;800,000 cases worldwide yearly and high outcome variability. The ability to predict the long-term body mass index (BMI) change following surgery has important implications for individuals and the health care system in general. Given the tight connection between eating habits, sugar consumption, BMI, and the gut microbiome, we tested whether the microbiome before any treatment is associated with different treatment outcomes, as well as other intakes (high-density lipoproteins [HDL], triglycerides, etc.). A projection of the gut microbiome composition of obese (sampled before and after bariatric surgery) and lean patients into principal components was performed, and the relation between this projection and surgery outcome was studied. The projection revealed three different microbiome profiles belonging to lean, obese, and obese individuals who underwent bariatric surgery, with the postsurgery microbiome more different from the lean microbiome than the obese microbiome. The same projection allowed for a prediction of BMI loss following bariatric surgery, using only the presurgery microbiome. The microbial changes following surgery were an increase in the relative abundance of Proteobacteria and Fusobacteria and a decrease in Firmicutes. The gut microbiome can be decomposed into main components depicting the patient's development and predicting in advance the outcome. Those may be translated into the better clinical management of obese individuals planning to undergo metabolic surgery.

Original languageEnglish
Article numbere01367-20
JournalmSystems
Volume6
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • Bariatric surgery
  • Machine learning
  • Obesity

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