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@ARTICLE{Klier:281359,
      author       = {Klier, Kristin and Mehrjerd, Ameneh and Fässler, Daniel
                      and Franck, Maximilien and Weihs, Antoine and Budde, Kathrin
                      and Bahls, Martin and Frost, Fabian and Henning, Ann-Kristin
                      and Heinken, Almut and Völzke, Henry and Dörr, Marcus and
                      Nauck, Matthias and Grabe, Hans Jörgen and Friedrich, Nele
                      and Hertel, Johannes},
      title        = {{I}ntegrating population-based metabolomics with
                      computational microbiome modelling identifies methanol as a
                      urinary biomarker for protective diet-microbiome-host
                      interactions.},
      journal      = {Food $\&$ function},
      volume       = {16},
      number       = {18},
      issn         = {2042-6496},
      address      = {Cambridge},
      publisher    = {RSC},
      reportid     = {DZNE-2025-01106},
      pages        = {7067 - 7081},
      year         = {2025},
      abstract     = {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.},
      keywords     = {Humans / Biomarkers: urine / Methanol: urine / Methanol:
                      metabolism / Metabolomics / Male / Gastrointestinal
                      Microbiome: physiology / Female / Middle Aged / Diet / Adult
                      / Host Microbial Interactions / Aged / Biomarkers (NLM
                      Chemicals) / Methanol (NLM Chemicals)},
      cin          = {AG Hoffmann / AG Grabe},
      ddc          = {610},
      cid          = {I:(DE-2719)1510600 / I:(DE-2719)5000001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40856313},
      doi          = {10.1039/D5FO00761E},
      url          = {https://pub.dzne.de/record/281359},
}