Machine Learning Metabolomics Profiling of Dietary Interventions from a Six-Week Randomised Trial

Author:

Kouraki Afroditi12ORCID,Nogal Ana34ORCID,Nocun Weronika1,Louca Panayiotis35,Vijay Amrita1,Wong Kari6,Michelotti Gregory A.6,Menni Cristina3ORCID,Valdes Ana M.127ORCID

Affiliation:

1. Academic Unit of Injury, Recovery and Inflammation Sciences, Rheumatology, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK

2. NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham NG7 2UH, UK

3. Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK

4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

5. Human Nutrition and Exercise Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK

6. Metabolon Inc., Research Triangle Park, Morrisville, NC 27560, USA

7. Pain Centre Versus Arthritis, University of Nottingham, Nottingham NG5 1PB, UK

Abstract

Metabolomics can uncover physiological responses to prebiotic fibre and omega-3 fatty acid supplements with known health benefits and identify response-specific metabolites. We profiled 534 stool and 799 serum metabolites in 64 healthy adults following a 6-week randomised trial comparing daily omega-3 versus inulin supplementation. Elastic net regressions were used to separately identify the serum and stool metabolites whose change in concentration discriminated between the two types of supplementations. Random forest was used to explore the gut microbiome’s contribution to the levels of the identified metabolites from matching stool samples. Changes in serum 3-carboxy-4-methyl-5-propyl-2-furanpropanoate and indoleproprionate levels accurately discriminated between fibre and omega-3 (area under the curve (AUC) = 0.87 [95% confidence interval (CI): 0.63–0.99]), while stool eicosapentaenoate indicated omega-3 supplementation (AUC = 0.86 [95% CI: 0.64–0.98]). Univariate analysis also showed significant increases in indoleproprionate with fibre, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, and eicosapentaenoate with omega-3. Out of these, only the change in indoleproprionate was partly explained by changes in the gut microbiome composition (AUC = 0.61 [95% CI: 0.58–0.64] and Rho = 0.21 [95% CI: 0.08–0.34]) and positively correlated with the increase in the abundance of the genus Coprococcus (p = 0.005). Changes in three metabolites discriminated between fibre and omega-3 supplementation. The increase in indoleproprionate with fibre was partly explained by shifts in the gut microbiome, particularly Coprococcus, previously linked to better health.

Publisher

MDPI AG

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