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000281359 1001_ $$00009-0003-3098-5867$$aKlier, Kristin$$b0
000281359 245__ $$aIntegrating population-based metabolomics with computational microbiome modelling identifies methanol as a urinary biomarker for protective diet-microbiome-host interactions.
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000281359 520__ $$aBackground: Diet-microbiome interactions are core to human health, in particular through bacterial fibre degradation pathways. However, biomarkers reflective of these interactions are not well described. Methods: Using the population-based SHIP-START-0 cohort (n = 4017), we combined metabolome-wide screenings with elastic net machine learning models on 33 food items captured using a food frequency questionnaire (FFQ) and 43 targeted urine nuclear magnetic resonance (NMR) metabolites, identifying methanol as a marker of plant-derived food items. We utilised the independent SHIP-START-0 cohort for the replication of food-metabolite associations. Moreover, constraint-based microbiome community modelling using the Human Microbiome data (n = 149) was performed to predict and analyse the contribution of the microbiome to the human methanol pools through bacterial fibre degradation. Finally, we employed prospective survival analysis in the SHIP-START-0 cohort, testing urinary methanol on its predictive value for mortality. Results: Among 21 metabolites associated with 17 dietary FFQ variables after correction for multiple testing, urinary methanol emerged as the top hit for a range of plant-derived food items. In line with this, constraint-based community modelling demonstrated that gut microbiomes can produce methanol via pectin degradation with the genera Bacteroides (68.9%) and Faecalibacterium (20.6%) being primarily responsible. Moreover, microbial methanol production capacity was a marker of high microbiome diversity. Finally, prospective survival analysis in SHIP-START-0 revealed that higher urinary methanol is associated with lower all-cause mortality in fully adjusted Cox regressions. Conclusion: Integrating population-based metabolomics and computational microbiome modelling identified urinary methanol as a promising biomarker for protective diet-microbiome interactions linked to microbial pectin degradation.
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000281359 650_7 $$2NLM Chemicals$$aBiomarkers
000281359 650_7 $$0Y4S76JWI15$$2NLM Chemicals$$aMethanol
000281359 650_2 $$2MeSH$$aHumans
000281359 650_2 $$2MeSH$$aBiomarkers: urine
000281359 650_2 $$2MeSH$$aMethanol: urine
000281359 650_2 $$2MeSH$$aMethanol: metabolism
000281359 650_2 $$2MeSH$$aMetabolomics
000281359 650_2 $$2MeSH$$aMale
000281359 650_2 $$2MeSH$$aGastrointestinal Microbiome: physiology
000281359 650_2 $$2MeSH$$aFemale
000281359 650_2 $$2MeSH$$aMiddle Aged
000281359 650_2 $$2MeSH$$aDiet
000281359 650_2 $$2MeSH$$aAdult
000281359 650_2 $$2MeSH$$aHost Microbial Interactions
000281359 650_2 $$2MeSH$$aAged
000281359 7001_ $$aMehrjerd, Ameneh$$b1
000281359 7001_ $$aFässler, Daniel$$b2
000281359 7001_ $$aFranck, Maximilien$$b3
000281359 7001_ $$0P:(DE-2719)9002604$$aWeihs, Antoine$$b4$$udzne
000281359 7001_ $$aBudde, Kathrin$$b5
000281359 7001_ $$aBahls, Martin$$b6
000281359 7001_ $$aFrost, Fabian$$b7
000281359 7001_ $$aHenning, Ann-Kristin$$b8
000281359 7001_ $$aHeinken, Almut$$b9
000281359 7001_ $$aVölzke, Henry$$b10
000281359 7001_ $$aDörr, Marcus$$b11
000281359 7001_ $$aNauck, Matthias$$b12
000281359 7001_ $$0P:(DE-2719)2811781$$aGrabe, Hans Jörgen$$b13$$udzne
000281359 7001_ $$aFriedrich, Nele$$b14
000281359 7001_ $$aHertel, Johannes$$b15
000281359 773__ $$0PERI:(DE-600)2578152-2$$a10.1039/D5FO00761E$$gVol. 16, no. 18, p. 7067 - 7081$$n18$$p7067 - 7081$$tFood & function$$v16$$x2042-6496$$y2025
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