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The more interesting story behind the heading.


A recent study in mice made it into the NEWS by addressing the fungal component of the gut microbiome and how it is influenced by diet (1). It is well known that various fungi are part of the normal intestinal ecosystem. In some circles in particular Candida species became famous as an unfavorable inhabitant selectively “fed” by sucrose in the diet. In the present paper it is shown that when animals are shifted from a chow diet (which is not defined and varies considerably by the raw material used) to a chemically defined diet (here called WESTERN diet) containing 70% carbohydrates and around 10% fat (but no added dietary fibre) changed the microbiome and “fungiome” but also affected the metabolic phenotype. That all is no surprise. From my own experience I can add that you always get the largest diet effects if you use chow as a reference because it not only contains the nutrients but - depending on the raw material used – an endless number of secondary components.

What makes this paper so charming and valuable is another finding. The authors got the C57Bl6 mice from 4 different vendors with quite impressive differences in the microbiota. When animals were kept in the animal facility under standardized conditions (on chow diet) they preserved partly their vendor-specific features. And when animals were then shifted to the WESTERN diet, they changed again phenotype as well as microbiome and fungi, but still showed a remarkable vendor-specific phenotype despite the assumed identical genetic background and the given identical environment and diet.  Just to highlight some differences: up to 2-fold for plasma triglycerides, 3-fold in plasma leptin levels or 4-fold in ghrelin levels (but also in body weight, body fat ect.). These findings show nicely the VENDOR´s signature can not be overruled by standardized environmental measures. The paper thus adds another factor to the long list of variables that make mouse studies so difficult to interpret and difficult to reproduce. A mouse is thus not only not a human but also not even a MOUSE.    

  1. Mims TS et al.  The gut mycobiome of healthy mice is shaped by the environment and correlates with metabolic outcomes in response to diet. Communications Biology volume 4, Article number: 281 (2021)


Another example of the CHICKEN or EGG problem in microbiome research

Cause or effect – that is the question. A recent paper by a team of the Quadram Institute in Norwich describes the changes in the gut microbiome (fecal samples) of patients (from different cohorts) suffering from Parkinson disease (1). The findings received considerable public interest and when reading some statements in public press you get the impression that the consequence of a dysbiotic gut microbiome (microbiome imbalance) is Parkinson disease (PD).

And there is it again the CHICKEN OR EGG THING as a common problem in microbiome research with endless numbers of associations reported. The current study indeed suggests some rearrangement in the microbiota – despite a huge variability between studies and patients – BUT, what comes first? It is interesting to read that the authors bring in constipation as they observed an enrichment in Akkermansia (considered as a beneficial genus) and that is often found enriched in people with constipation. It is well known that PD patients have various intestinal problems characterized by dysphagia, delayed intestinal transit time and up to half of them show constipation and that all can be detected up to 20 years prior to PD diagnosis (2). Moreover, there is convincing evidence that the intestinal transit time per se is a major effector of the diversity of the gut microbiota as is the water content of the intestinal contents and the feces (3) and any alteration of transit time (my means of drugs for example) alters the quantity of bacteria found in feces and changes the bacterial composition. Taken together, it is more than likely that the observed (modest) changes in microbiome composition in PD patients is a consequence of a slow-down of the intestinal transit time as an early event in the pathogenesis. Since it is usually not recorded how often people pass stool, this is the “dark side” of microbiome research.   


  1. Romano S et al. (2021) Meta-analysis of the Parkinson’s disease gut microbiome suggests alterations linked to intestinal inflammation. npj Parkinson’s Disease DOI: 10.1038/s41531-021-00156-z

  2. Travagli, RA, Browning KN, Camilleri M. (2020) Parkinson disease and the gut: new insights into pathogenesis and clinical relevance. Nature Reviews Gastroenterology & Hepatology 17, 673–685

  3. Roager HM et al. (2016) Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut. Nat Microbiol1(9):1 6093

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Einige der letzten Originalpublikationen

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations, and Experiences.

Holzapfel C, Waldenberger M, Lorkowski S, Daniel H; Working Group “Personalized Nutrition” of the German Nutrition Society.Mol Nutr Food Res. 2022 Sep;66(17):e2200077. doi: 10.1002/mnfr.202200077. 


Gut Microbiome Analysis for Personalized Nutrition: The State of Science.

Simon MC, Sina C, Ferrario PG, Daniel H; Working Group “Personalized Nutrition” of the German Nutrition Society.Mol Nutr Food Res. 2023 Jan;67(1):e2200476. doi: 10.1002/mnfr.202200476. 


Dynamic patterns of postprandial metabolic responses to three dietary challenges.

Weinisch P, Fiamoncini J, Schranner D, Raffler J, Skurk T, Rist MJ, Römisch-Margl W, Prehn C, Adamski J, Hauner H, Daniel H, Suhre K, Kastenmüller G.Front Nutr. 2022 Sep 22;9:933526. doi: 10.3389/fnut.2022.933526. eCollection 2022.

Chronic High Phosphate Intake in Mice Affects Macronutrient Utilization and Body Composition.

Ugrica M, Gehring N, Giesbertz P, Pastor-Arroyo EM, Daniel H, Wagner CA, Rubio-Aliaga I.Mol Nutr Food Res. 2022 May;66(9):e2100949. doi: 10.1002/mnfr.202100949

Dynamics and determinants of human plasma bile acid profiles during dietary challenges.

Fiamoncini J, Rist MJ, Frommherz L, Giesbertz P, Pfrang B, Kremer W, Huber F, Kastenmüller G, Skurk T, Hauner H, Suhre K, Daniel H, Kulling SE.Front Nutr. 2022 Jul 28;9:932937. doi: 10.3389/fnut.2022.932937. eCollection 2022.

Associations between dietary patterns, FTO genotype and obesity in adults from seven European countries.

Livingstone KM, Brayner B, Celis-Morales C, Moschonis G, Manios Y, Traczyk I, Drevon CA, Daniel H, Saris WHM, Lovegrove JA, Gibney M, Gibney ER, Brennan L, Martinez JA, Mathers JC.Eur J Nutr. 2022 Sep;61(6):2953-2965. doi: 10.1007/s00394-022-02858-3. Epub 2022 Mar 21.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test.

Fiamoncini J, Donado-Pestana CM, Duarte GBS, Rundle M, Thomas EL, Kiselova-Kaneva Y, Gundersen TE, Bunzel D, Trezzi JP, Kulling SE, Hiller K, Sonntag D, Ivanova D, Brennan L, Wopereis S, van Ommen B, Frost G, Bell J, Drevon CA, Daniel H.Front Nutr. 2022 Jun 14;9:898782. doi: 10.3389/fnut.2022.898782. eCollection 2022.


Fetal sex modulates placental microRNA expression, potential microRNA-mRNA interactions, and levels of amino acid transporter expression and substrates: INFAT study subpopulation analysis of n-3 LCPUFA intervention during pregnancy and associations with offspring body composition. Sedlmeier EM, Meyer DM, Stecher L, Sailer M, Daniel H, Hauner H, Bader BL. BMC Mol Cell Biol. 2021 Mar 3;22(1):15. doi: 10.1186/s12860-021-00345-x.


Diet and the gut microbiome: from hype to hypothesis.

Daniel H. Br J Nutr. 2020 Sep 28;124(6):521-530. doi: 10.1017/S0007114520001142. Epub 2020 Apr 2.


Characteristics of participants who benefit most from personalised nutrition: findings from the pan-European Food4Me randomised controlled trial.  Livingstone KM, Celis-Morales C, Navas-Carretero S, San-Cristobal R, Forster H, Woolhead C, O'Donovan CB, Moschonis G, Manios Y, Traczyk I, Gundersen TE, Drevon CA, Marsaux CFM, Fallaize R, Macready AL, Daniel H, Saris WHM, Lovegrove JA, Gibney M, Gibney ER, Walsh M, Brennan L, Martinez JA, Mathers JC. Br J Nutr. 2020 Jun 28;123(12):1396-1405. doi: 10.1017/S0007114520000653.


Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers. Mack CI, Ferrario PG, Weinert CH, Egert B, Hoefle AS, Lee YM, Skurk T, Kulling SE, Daniel H. Mol Nutr Food Res. 2020 May;64(9):e1901190. doi: 10.1002/mnfr.201901190.


Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study. Riedl A, Hillesheim E, Wawro N, Meisinger C, Peters A, Roden M, Kronenberg F, Herder C, Rathmann W, Völzke H, Reincke M, Koenig W, Wallaschofski H, Daniel H, Hauner H, Brennan L, Linseisen J. Mol Nutr Food Res. 2020 Apr;64(8):e1900918. doi: 10.1002/mnfr.201900918.


Proteomic and Metabolite Profiling Reveals Profound Structural and Metabolic Reorganization of Adipocyte Mitochondria in Obesity.

Schöttl T, Pachl F, Giesbertz P, Daniel H, Kuster B, Fromme T, Klingenspor M. Obesity (Silver Spring). 2020 Mar;28(3):590-600. doi: 10.1002/oby.22737.


Calcium-sensing receptor regulates intestinal dipeptide absorption via Ca2+ signaling and IKCa activation. Xu J, Zeug A, Riederer B, Yeruva S, Griesbeck O, Daniel H, Tuo B, Ponimaskin E, Dong H, Seidler U. Physiol Rep. 2020 Jan;8(1):e14337. doi: 10.14814/phy2.14337.


Specificity, Dose Dependency, and Kinetics of Markers of Chicken and Beef Intake Using Targeted Quantitative LC-MS/MS: A Human Intervention Trial. Giesbertz P, Brandl B, Lee YM, Hauner H, Daniel H, Skurk T. Mol Nutr Food Res. 2020 Mar;64(5):e1900921. doi: 10.1002/mnfr.201900921.


Transport Versus Hydrolysis: Reassessing Intestinal Assimilation of Di- and Tripeptides by LC-MS/MS Analysis. Rohm F, Daniel H, Spanier B. Mol Nutr Food Res. 2019 Nov;63(21):e1900263. doi: 10.1002/mnfr.201900263.

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