A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption

Frontiers in Nutrition

Nutrition

Patrick Couture¹  Geneviève Pilon²  André Marette²  Marie-Claude Vohl³  Valentin Barbe³  Rodrigo San-Cristobal³  Juan de Toro-Martín³  Véronique Garneau³  Charles Couillard³  Denis Roy⁴ 

[1] Centre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada;Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada;Endocrinology and Nephrology Unit, CHU de Quebec Research Center, Québec City, QC, Canada;Centre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada;Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada;Québec Heart and Lung Institute (IUCPQ) Research Center, Québec City, QC, Canada;Centre Nutrition, santé et société (NUTRISS), Université Laval, Québec City, QC, Canada;Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada;School of Nutrition, Université Laval, Québec City, QC, Canada;Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada;

关键词: raspberry;    clustering;    machine learning;    metabolic health;    metabolomics;    precision nutrition;   

DOI  :  10.3389/fnut.2023.1104685

 received in 2022-11-21, accepted in 2023-03-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundMany studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.MethodsIn a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed before and after an 8-week red raspberry consumption. Based on partial least squares discriminant analysis (PLS-DA) on plasma xenobiotic levels, adherence to the intervention was first evaluated. A second PLS-DA followed by hierarchical clustering was used to classify individuals into response subgroups. Clinical immune and metabolic outcomes, including insulin resistance (HOMA-IR) and sensitivity (Matsuda, QUICKI) indices, during the intervention were assessed and compared between response subgroups.ResultsTwo subgroups of participants, type 1 responders (n = 17) and type 2 responders (n = 5), were identified based on plasma metabolite levels measured during the intervention. Type 1 responders showed neutral to negative effects on immune-metabolic clinical parameters after raspberry consumption, and type 2 responders showed positive effects on the same parameters. Changes in waist circumference, waist-to-hip ratio, fasting plasma apolipoprotein B, C-reactive protein and insulin levels as well as Matsuda, HOMA-IR and QUICKI were significantly different between the two response subgroups. A deleterious effect of two carotenoid metabolites was also observed in type 1 responders but these variables were significantly associated with beneficial changes in the QUICKI index and in fasting insulin levels in type 2 responders. Increased 3-ureidopropionate levels were associated with a decrease in the Matsuda index in type 2 responders, suggesting that this metabolite is associated with a decrease in insulin sensitivity for those subjects, whereas the opposite was observed for type 1 responders.ConclusionThe beneficial effects associated with red raspberry consumption are subject to inter-individual variability. Metabolomics-based clustering appears to be an effective way to assess adherence to a nutritional intervention and to classify individuals according to their immune-metabolic responsiveness to the intervention. This approach may be replicated in future studies to provide a better understanding of how interindividual variability impacts the effects of nutritional interventions on immune-metabolic health.

【 授权许可】

Unknown   
Copyright © 2023 Barbe, de Toro-Martín, San-Cristobal, Garneau, Pilon, Couture, Roy, Couillard, Marette and Vohl.