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A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption
Many studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood. In a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed befo...
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Published in: | Frontiers in nutrition (Lausanne) 2023-03, Vol.10, p.1104685-1104685 |
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creator | Barbe, Valentin de Toro-Martín, Juan San-Cristobal, Rodrigo Garneau, Véronique Pilon, Geneviève Couture, Patrick Roy, Denis Couillard, Charles Marette, André Vohl, Marie-Claude |
description | Many studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.
In 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.
Two subgroups of participants, type 1 responders (
= 17) and type 2 responders (
= 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.
The 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. |
doi_str_mv | 10.3389/fnut.2023.1104685 |
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In 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.
Two subgroups of participants, type 1 responders (
= 17) and type 2 responders (
= 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.
The 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.</description><identifier>ISSN: 2296-861X</identifier><identifier>EISSN: 2296-861X</identifier><identifier>DOI: 10.3389/fnut.2023.1104685</identifier><identifier>PMID: 37125033</identifier><language>eng</language><publisher>Switzerland: Frontiers Media S.A</publisher><subject>clustering ; machine learning ; metabolic health ; metabolomics ; Nutrition ; precision nutrition ; raspberry</subject><ispartof>Frontiers in nutrition (Lausanne), 2023-03, Vol.10, p.1104685-1104685</ispartof><rights>Copyright © 2023 Barbe, de Toro-Martín, San-Cristobal, Garneau, Pilon, Couture, Roy, Couillard, Marette and Vohl.</rights><rights>Copyright © 2023 Barbe, de Toro-Martín, San-Cristobal, Garneau, Pilon, Couture, Roy, Couillard, Marette and Vohl. 2023 Barbe, de Toro-Martín, San-Cristobal, Garneau, Pilon, Couture, Roy, Couillard, Marette and Vohl</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c418t-11eb4851258cb566decf6b2829563bc0cc76cb797809e3c3afaff015ac1bc4453</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130762/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130762/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37125033$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Barbe, Valentin</creatorcontrib><creatorcontrib>de Toro-Martín, Juan</creatorcontrib><creatorcontrib>San-Cristobal, Rodrigo</creatorcontrib><creatorcontrib>Garneau, Véronique</creatorcontrib><creatorcontrib>Pilon, Geneviève</creatorcontrib><creatorcontrib>Couture, Patrick</creatorcontrib><creatorcontrib>Roy, Denis</creatorcontrib><creatorcontrib>Couillard, Charles</creatorcontrib><creatorcontrib>Marette, André</creatorcontrib><creatorcontrib>Vohl, Marie-Claude</creatorcontrib><title>A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption</title><title>Frontiers in nutrition (Lausanne)</title><addtitle>Front Nutr</addtitle><description>Many studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.
In 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.
Two subgroups of participants, type 1 responders (
= 17) and type 2 responders (
= 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.
The 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.</description><subject>clustering</subject><subject>machine learning</subject><subject>metabolic health</subject><subject>metabolomics</subject><subject>Nutrition</subject><subject>precision nutrition</subject><subject>raspberry</subject><issn>2296-861X</issn><issn>2296-861X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVUktr3DAQNqWlCWl-QC9Fx152o4clS6cSQh-BQC8t9CZGYylRsC1XsgObX19tdxOSk8R8D82Mvqb5yOhWCG0uwrQuW0652DJGW6Xlm-aUc6M2WrE_b1_cT5rzUu4ppUxw2bL2fXMiOsYlFeK0ebwkfSyY4xgnmBYCEwy7EgtJgcwDlBHI6BdwaUhjxEJCymS58wRK8aWMvkoq80iJSLIvc5pKfPBTxcmSaqUnGcrsfM47ghVcx3mJafrQvAswFH9-PM-a39--_rr6sbn5-f366vJmgy3Ty4Yx71ota8ManVSq9xiU45obqYRDitgpdJ3pNDVeoIAAIVAmAZnDtpXirLk--PYJ7u1cR4W8swmi_V9I-dZCXiIO3nKKEilFY5xpu-CNbjsFinYcRBBm7_Xl4DWvbvQ91vkzDK9MXyNTvLO36cGyun3aKV4dPh8dcvq7-rLYse7fDwNMPq3Fck01Z0IaU6nsQMWcSsk-PL_DqN1nwO4zYPcZsMcMVM2nlw0-K55-XPwDoruxiQ</recordid><startdate>20230323</startdate><enddate>20230323</enddate><creator>Barbe, Valentin</creator><creator>de Toro-Martín, Juan</creator><creator>San-Cristobal, Rodrigo</creator><creator>Garneau, Véronique</creator><creator>Pilon, Geneviève</creator><creator>Couture, Patrick</creator><creator>Roy, Denis</creator><creator>Couillard, Charles</creator><creator>Marette, André</creator><creator>Vohl, Marie-Claude</creator><general>Frontiers Media S.A</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20230323</creationdate><title>A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption</title><author>Barbe, Valentin ; de Toro-Martín, Juan ; San-Cristobal, Rodrigo ; Garneau, Véronique ; Pilon, Geneviève ; Couture, Patrick ; Roy, Denis ; Couillard, Charles ; Marette, André ; Vohl, Marie-Claude</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-11eb4851258cb566decf6b2829563bc0cc76cb797809e3c3afaff015ac1bc4453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>clustering</topic><topic>machine learning</topic><topic>metabolic health</topic><topic>metabolomics</topic><topic>Nutrition</topic><topic>precision nutrition</topic><topic>raspberry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barbe, Valentin</creatorcontrib><creatorcontrib>de Toro-Martín, Juan</creatorcontrib><creatorcontrib>San-Cristobal, Rodrigo</creatorcontrib><creatorcontrib>Garneau, Véronique</creatorcontrib><creatorcontrib>Pilon, Geneviève</creatorcontrib><creatorcontrib>Couture, Patrick</creatorcontrib><creatorcontrib>Roy, Denis</creatorcontrib><creatorcontrib>Couillard, Charles</creatorcontrib><creatorcontrib>Marette, André</creatorcontrib><creatorcontrib>Vohl, Marie-Claude</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Frontiers in nutrition (Lausanne)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Barbe, Valentin</au><au>de Toro-Martín, Juan</au><au>San-Cristobal, Rodrigo</au><au>Garneau, Véronique</au><au>Pilon, Geneviève</au><au>Couture, Patrick</au><au>Roy, Denis</au><au>Couillard, Charles</au><au>Marette, André</au><au>Vohl, Marie-Claude</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption</atitle><jtitle>Frontiers in nutrition (Lausanne)</jtitle><addtitle>Front Nutr</addtitle><date>2023-03-23</date><risdate>2023</risdate><volume>10</volume><spage>1104685</spage><epage>1104685</epage><pages>1104685-1104685</pages><issn>2296-861X</issn><eissn>2296-861X</eissn><abstract>Many studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.
In 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.
Two subgroups of participants, type 1 responders (
= 17) and type 2 responders (
= 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.
The 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.</abstract><cop>Switzerland</cop><pub>Frontiers Media S.A</pub><pmid>37125033</pmid><doi>10.3389/fnut.2023.1104685</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | clustering machine learning metabolic health metabolomics Nutrition precision nutrition raspberry |
title | A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption |
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