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Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults
Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored. We...
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Published in: | The Journal of nutrition 2020-01, Vol.150 (1), p.149-158 |
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description | Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored.
We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs).
This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R.
We identified 5 DPs and 7 BCPs. The “bread, margarine, and processed meat” and “fruiting vegetables and vegetable oils” DPs were positively associated with the “amino acids” (β = 0.35; 95% CI: 0.02, 0.69;P = 0.03) and “fatty acids” MPs (β = 0.45; 95% CI: 0.16, 0.74;P= 0.01), respectively. The “tea and miscellaneous” was inversely associated with the “amino acids” (β = –0.28; 95% CI: –0.52, –0.05;P = 0.02) and “amino acid derivatives” MPs (β = –0.21; 95% CI: –0.39, –0.02;P = 0.03). One BCP negatively modified the association between the “bread, margarine, and processed meat” DP and the “amino acids” MP (P–interaction = 0.01).
In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP–MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria. |
doi_str_mv | 10.1093/jn/nxz194 |
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We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs).
This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R.
We identified 5 DPs and 7 BCPs. The “bread, margarine, and processed meat” and “fruiting vegetables and vegetable oils” DPs were positively associated with the “amino acids” (β = 0.35; 95% CI: 0.02, 0.69;P = 0.03) and “fatty acids” MPs (β = 0.45; 95% CI: 0.16, 0.74;P= 0.01), respectively. The “tea and miscellaneous” was inversely associated with the “amino acids” (β = –0.28; 95% CI: –0.52, –0.05;P = 0.02) and “amino acid derivatives” MPs (β = –0.21; 95% CI: –0.39, –0.02;P = 0.03). One BCP negatively modified the association between the “bread, margarine, and processed meat” DP and the “amino acids” MP (P–interaction = 0.01).
In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP–MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.</description><identifier>ISSN: 0022-3166</identifier><identifier>EISSN: 1541-6100</identifier><identifier>DOI: 10.1093/jn/nxz194</identifier><identifier>PMID: 31504715</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Adults ; Aged ; Amino acids ; Bacteria ; Biomarkers ; Biomarkers - blood ; Bread ; Cross-Sectional Studies ; DataSHIELD ; Diet ; Dietary intake ; dietary intake patterns ; Edible oils ; Fatty acids ; Feeding Behavior ; Female ; Food - classification ; Food intake ; Gastrointestinal Microbiome ; Gene sequencing ; gut bacteria compositional patterns ; Humans ; Male ; Margarine ; Meat ; Metabolites ; Middle Aged ; Nutrition ; Nutritional Epidemiology ; Oils & fats ; rRNA 16S ; serum metabolite patterns ; Statistical models ; Tea ; treelet transform analysis ; Vegetable oils ; Vegetables</subject><ispartof>The Journal of nutrition, 2020-01, Vol.150 (1), p.149-158</ispartof><rights>2020 American Society for Nutrition.</rights><rights>Copyright © American Society for Nutrition 2019. 2019</rights><rights>Copyright © American Society for Nutrition 2019.</rights><rights>Copyright American Institute of Nutrition Jan 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-e23f54e8f9cabab04ba1ef3c0fdc381dfbef196d257f1202a1bd76a9b003e8373</citedby><cites>FETCH-LOGICAL-c476t-e23f54e8f9cabab04ba1ef3c0fdc381dfbef196d257f1202a1bd76a9b003e8373</cites><orcidid>0000-0002-9454-5970 ; 0000-0002-5789-2252</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022316622020089$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,3549,27924,27925,45780</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31504715$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oluwagbemigun, Kolade</creatorcontrib><creatorcontrib>Foerster, Jana</creatorcontrib><creatorcontrib>Watkins, Claire</creatorcontrib><creatorcontrib>Fouhy, Fiona</creatorcontrib><creatorcontrib>Stanton, Catherine</creatorcontrib><creatorcontrib>Bergmann, Manuela M</creatorcontrib><creatorcontrib>Boeing, Heiner</creatorcontrib><creatorcontrib>Nöthlings, Ute</creatorcontrib><title>Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults</title><title>The Journal of nutrition</title><addtitle>J Nutr</addtitle><description>Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored.
We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs).
This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R.
We identified 5 DPs and 7 BCPs. The “bread, margarine, and processed meat” and “fruiting vegetables and vegetable oils” DPs were positively associated with the “amino acids” (β = 0.35; 95% CI: 0.02, 0.69;P = 0.03) and “fatty acids” MPs (β = 0.45; 95% CI: 0.16, 0.74;P= 0.01), respectively. The “tea and miscellaneous” was inversely associated with the “amino acids” (β = –0.28; 95% CI: –0.52, –0.05;P = 0.02) and “amino acid derivatives” MPs (β = –0.21; 95% CI: –0.39, –0.02;P = 0.03). One BCP negatively modified the association between the “bread, margarine, and processed meat” DP and the “amino acids” MP (P–interaction = 0.01).
In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP–MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.</description><subject>Adult</subject><subject>Adults</subject><subject>Aged</subject><subject>Amino acids</subject><subject>Bacteria</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Bread</subject><subject>Cross-Sectional Studies</subject><subject>DataSHIELD</subject><subject>Diet</subject><subject>Dietary intake</subject><subject>dietary intake patterns</subject><subject>Edible oils</subject><subject>Fatty acids</subject><subject>Feeding Behavior</subject><subject>Female</subject><subject>Food - classification</subject><subject>Food intake</subject><subject>Gastrointestinal Microbiome</subject><subject>Gene sequencing</subject><subject>gut bacteria compositional patterns</subject><subject>Humans</subject><subject>Male</subject><subject>Margarine</subject><subject>Meat</subject><subject>Metabolites</subject><subject>Middle Aged</subject><subject>Nutrition</subject><subject>Nutritional Epidemiology</subject><subject>Oils & fats</subject><subject>rRNA 16S</subject><subject>serum metabolite patterns</subject><subject>Statistical models</subject><subject>Tea</subject><subject>treelet transform analysis</subject><subject>Vegetable oils</subject><subject>Vegetables</subject><issn>0022-3166</issn><issn>1541-6100</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNp9kctuFDEQRS0EIpPAgh9AlmARFk386OcGaQgwjBQUJMLactvljEfd9sR2B8I38NEYOgwPIVZe1LlH5boIPaLkOSUdP9m6E_f5C-3KO2hBq5IWNSXkLloQwljBaV0foMMYt4QQWnbtfXTAaUXKhlYL9PWVhSTDDX4vU4LgIl4GwMsYvbIygcafbNrgDxCmEb_LZO8Hm-AXLZ3GFxuwYZ-x3uF1xGtnhgmcyor-Bq-mhF9KlTNWYjl6d4nPBw0BryCM0uGlnoYUH6B7Rg4RHt6-R-jjm9cXp2-Ls_PV-nR5VqiyqVMBjJuqhNZ0SvayJ2UvKRiuiNGKt1SbHgztas2qxlBGmKS9bmrZ9YRwaHnDj9CL2bub-hG0ApeCHMQu2DGfQnhpxZ8TZzfi0l-Luivrtmuz4PhWEPzVBDGJ0UYFwyAd-CkKxtq2oW1Fy4w--Qvd-im4_D3BOO8I6-qKZ-rZTKngYwxg9stQIr53LLZOzB1n9vHv2-_Jn6Vm4OkM-Gn3Xw-fMcinvrYQRFT2R2M2gEpCe_uP1Df7AcRh</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Oluwagbemigun, Kolade</creator><creator>Foerster, Jana</creator><creator>Watkins, Claire</creator><creator>Fouhy, Fiona</creator><creator>Stanton, Catherine</creator><creator>Bergmann, Manuela M</creator><creator>Boeing, Heiner</creator><creator>Nöthlings, Ute</creator><general>Elsevier Inc</general><general>Oxford University Press</general><general>American Institute of Nutrition</general><scope>6I.</scope><scope>AAFTH</scope><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9454-5970</orcidid><orcidid>https://orcid.org/0000-0002-5789-2252</orcidid></search><sort><creationdate>20200101</creationdate><title>Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults</title><author>Oluwagbemigun, Kolade ; Foerster, Jana ; Watkins, Claire ; Fouhy, Fiona ; Stanton, Catherine ; Bergmann, Manuela M ; Boeing, Heiner ; Nöthlings, Ute</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-e23f54e8f9cabab04ba1ef3c0fdc381dfbef196d257f1202a1bd76a9b003e8373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Adults</topic><topic>Aged</topic><topic>Amino acids</topic><topic>Bacteria</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Bread</topic><topic>Cross-Sectional Studies</topic><topic>DataSHIELD</topic><topic>Diet</topic><topic>Dietary intake</topic><topic>dietary intake patterns</topic><topic>Edible oils</topic><topic>Fatty acids</topic><topic>Feeding Behavior</topic><topic>Female</topic><topic>Food - classification</topic><topic>Food intake</topic><topic>Gastrointestinal Microbiome</topic><topic>Gene sequencing</topic><topic>gut bacteria compositional patterns</topic><topic>Humans</topic><topic>Male</topic><topic>Margarine</topic><topic>Meat</topic><topic>Metabolites</topic><topic>Middle Aged</topic><topic>Nutrition</topic><topic>Nutritional Epidemiology</topic><topic>Oils & fats</topic><topic>rRNA 16S</topic><topic>serum metabolite patterns</topic><topic>Statistical models</topic><topic>Tea</topic><topic>treelet transform analysis</topic><topic>Vegetable oils</topic><topic>Vegetables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oluwagbemigun, Kolade</creatorcontrib><creatorcontrib>Foerster, Jana</creatorcontrib><creatorcontrib>Watkins, Claire</creatorcontrib><creatorcontrib>Fouhy, Fiona</creatorcontrib><creatorcontrib>Stanton, Catherine</creatorcontrib><creatorcontrib>Bergmann, Manuela M</creatorcontrib><creatorcontrib>Boeing, Heiner</creatorcontrib><creatorcontrib>Nöthlings, Ute</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Open Access: Oxford University Press Open Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Journal of nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oluwagbemigun, Kolade</au><au>Foerster, Jana</au><au>Watkins, Claire</au><au>Fouhy, Fiona</au><au>Stanton, Catherine</au><au>Bergmann, Manuela M</au><au>Boeing, Heiner</au><au>Nöthlings, Ute</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults</atitle><jtitle>The Journal of nutrition</jtitle><addtitle>J Nutr</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>150</volume><issue>1</issue><spage>149</spage><epage>158</epage><pages>149-158</pages><issn>0022-3166</issn><eissn>1541-6100</eissn><abstract>Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored.
We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs).
This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R.
We identified 5 DPs and 7 BCPs. The “bread, margarine, and processed meat” and “fruiting vegetables and vegetable oils” DPs were positively associated with the “amino acids” (β = 0.35; 95% CI: 0.02, 0.69;P = 0.03) and “fatty acids” MPs (β = 0.45; 95% CI: 0.16, 0.74;P= 0.01), respectively. The “tea and miscellaneous” was inversely associated with the “amino acids” (β = –0.28; 95% CI: –0.52, –0.05;P = 0.02) and “amino acid derivatives” MPs (β = –0.21; 95% CI: –0.39, –0.02;P = 0.03). One BCP negatively modified the association between the “bread, margarine, and processed meat” DP and the “amino acids” MP (P–interaction = 0.01).
In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP–MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>31504715</pmid><doi>10.1093/jn/nxz194</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-9454-5970</orcidid><orcidid>https://orcid.org/0000-0002-5789-2252</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Adults Aged Amino acids Bacteria Biomarkers Biomarkers - blood Bread Cross-Sectional Studies DataSHIELD Diet Dietary intake dietary intake patterns Edible oils Fatty acids Feeding Behavior Female Food - classification Food intake Gastrointestinal Microbiome Gene sequencing gut bacteria compositional patterns Humans Male Margarine Meat Metabolites Middle Aged Nutrition Nutritional Epidemiology Oils & fats rRNA 16S serum metabolite patterns Statistical models Tea treelet transform analysis Vegetable oils Vegetables |
title | Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults |
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