| 001 | 281359 | ||
| 005 | 20251008102405.0 | ||
| 024 | 7 | _ | |a 10.1039/D5FO00761E |2 doi |
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| 024 | 7 | _ | |a 2042-6496 |2 ISSN |
| 024 | 7 | _ | |a 2042-650X |2 ISSN |
| 037 | _ | _ | |a DZNE-2025-01106 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Klier, Kristin |0 0009-0003-3098-5867 |b 0 |
| 245 | _ | _ | |a Integrating population-based metabolomics with computational microbiome modelling identifies methanol as a urinary biomarker for protective diet-microbiome-host interactions. |
| 260 | _ | _ | |a Cambridge |c 2025 |b RSC |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1759835871_17321 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Background: 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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| 650 | _ | 7 | |a Biomarkers |2 NLM Chemicals |
| 650 | _ | 7 | |a Methanol |0 Y4S76JWI15 |2 NLM Chemicals |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Biomarkers: urine |2 MeSH |
| 650 | _ | 2 | |a Methanol: urine |2 MeSH |
| 650 | _ | 2 | |a Methanol: metabolism |2 MeSH |
| 650 | _ | 2 | |a Metabolomics |2 MeSH |
| 650 | _ | 2 | |a Male |2 MeSH |
| 650 | _ | 2 | |a Gastrointestinal Microbiome: physiology |2 MeSH |
| 650 | _ | 2 | |a Female |2 MeSH |
| 650 | _ | 2 | |a Middle Aged |2 MeSH |
| 650 | _ | 2 | |a Diet |2 MeSH |
| 650 | _ | 2 | |a Adult |2 MeSH |
| 650 | _ | 2 | |a Host Microbial Interactions |2 MeSH |
| 650 | _ | 2 | |a Aged |2 MeSH |
| 700 | 1 | _ | |a Mehrjerd, Ameneh |b 1 |
| 700 | 1 | _ | |a Fässler, Daniel |b 2 |
| 700 | 1 | _ | |a Franck, Maximilien |b 3 |
| 700 | 1 | _ | |a Weihs, Antoine |0 P:(DE-2719)9002604 |b 4 |u dzne |
| 700 | 1 | _ | |a Budde, Kathrin |b 5 |
| 700 | 1 | _ | |a Bahls, Martin |b 6 |
| 700 | 1 | _ | |a Frost, Fabian |b 7 |
| 700 | 1 | _ | |a Henning, Ann-Kristin |b 8 |
| 700 | 1 | _ | |a Heinken, Almut |b 9 |
| 700 | 1 | _ | |a Völzke, Henry |b 10 |
| 700 | 1 | _ | |a Dörr, Marcus |b 11 |
| 700 | 1 | _ | |a Nauck, Matthias |b 12 |
| 700 | 1 | _ | |a Grabe, Hans Jörgen |0 P:(DE-2719)2811781 |b 13 |u dzne |
| 700 | 1 | _ | |a Friedrich, Nele |b 14 |
| 700 | 1 | _ | |a Hertel, Johannes |b 15 |
| 773 | _ | _ | |a 10.1039/D5FO00761E |g Vol. 16, no. 18, p. 7067 - 7081 |0 PERI:(DE-600)2578152-2 |n 18 |p 7067 - 7081 |t Food & function |v 16 |y 2025 |x 2042-6496 |
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