Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases
Open accessAbstract
Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity1. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available2, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality3, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10−5), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings. Mendelian randomization analyses using genotyping data, gut metagenomic sequence and fecal short-chain-fatty-acid levels from 952 individuals combined with GWAS data show evidence of a causal effect of the gut microbiome on metabolic traits.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prebiotics have the potential to improve glycemic control through gut microbiome modulation.
“Prebiotics have the potential to improve glycemic control through gut microbiome modulation, though this mechanism was not directly assessed in the present study.”
From Prebiotic soda lowers postprandial glucose compared to traditional soda pop: a randomized controlled trial (2025) · cites this paper as [24]
The paper’s discussion of prebiotics (section labeled Abstract; no page given) reports that fructo-oligosaccharides reduced butyrate-producing bacteria and had an adverse effect on glucose metabolism after an oral glucose tolerance test. Although the paper links other microbiome changes to improved insulin response, its specific prebiotic example points in the opposite direction from the attributed claim. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIncreased intake of dietary fiber reduces the risk for developing metabolic diseases, possibly by changing gut microbiome composition and diversity with increased production of SCFAs.
“Further, epidemiological and experimental studies have demonstrated that increased intake of dietary fiber reduces the risk for developing metabolic diseases (188–190), possibly by changing gut microbiome composition and diversity with increased production of SCFAs (187–189).”
From The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication (2020) · cites this paper as [187]
The paper’s Abstract says gut bacteria produce SCFAs by fermenting undigested food and reports that a shift toward butyrate-producing bacteria benefits insulin response. This supports a possible microbiome–SCFA mechanism, but the paper does not establish that increased dietary fiber changes microbiome diversity or reduces the risk of developing metabolic disease; it reports no detectable effect on type 2 diabetes risk for the butyrate-producing shift. The indexed passages provide no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA shift in the microbiome composition away from species able to produce butyrate was one consistent finding in type 2 diabetes subjects.
“Despite differences in the identification of specific microbiome features responsible for these effects, a shift in the microbiome composition away from species able to produce butyrate was one consistent finding in type 2 diabetes subjects (187).”
From The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication (2020) · cites this paper as [187]
A recent combination analysis using GWAS of the microbiome and metagenomic sequencing discovered a causal effect of the gut microbiome on metabolic traits, suggesting increased gut butyrate production is associated with improved insulin response after an oral glucose-tolerance test, while errors in production or absorption of propionate are causally related to enhanced risk of type II diabetes.
“The most recent combination analysis using GWAS of the microbiome and metagenomic sequencing has discovered a causal effect of the gut microbiome on metabolic traits, suggesting increased gut butyrate production associated with improved insulin response after an oral glucose-tolerance test, but errors in production or absorption of propionate causally related to enhanced risk of type II diabetes (”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Sanna et al., 2019
Intestinal microbe composition and corresponding byproducts [e.g., short-chain fatty acids, (SCFA)] are associated with diabetes risk in clinical observations and have been shown to improve glycemic control in pre-clinical models.
“Intestinal microbe composition and corresponding byproducts [e.g., short-chain fatty acids, (SCFA)] are associated with diabetes risk in clinical observations (24) and have been shown to improve glycemic control in pre-clinical models (25).”
From Prebiotic soda lowers postprandial glucose compared to traditional soda pop: a randomized controlled trial (2025) · cites this paper as [24]
Bacteria that produce short-chain fatty acids are important regulators of metabolic homeostasis.
“Bacteria that produce short-chain fatty acids (SCFAs) are important regulators of metabolic homeostasis.87, 88”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [87]
A bidirectional Mendelian randomization analysis provided genetic evidence that increased intestinal short-chain fatty acid production improves insulin responses and reduces abnormalities in short-chain fatty acid production or absorption and risk of type 2 diabetes.
“A bidirectional Mendelian randomization analysis provided genetic evidence that increased intestinal production of SCFAs improves patient responses to insulin and reduces abnormalities in the production or absorption of SCFAs and risk of type 2 diabetes.87”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [87]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591 · see citing works
Obesity alters gut microbial ecology
Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D., & Gordon, J. I. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504978102 · Cited by 6,176 · see citing works
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
den Besten, G., van Eunen, K., Groen, A. K., Venema, K., Reijngoud, D.-J., & Bakker, B. M. (2013) · Journal of Lipid Research · doi:10.1194/jlr.r036012 · Cited by 5,048 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
Self-citationGut metagenome in European women with normal, impaired and diabetic glucose control
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115 · see citing works
Formation of propionate and butyrate by the human colonic microbiota
Louis, P., & Flint, H. J. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13589 · Cited by 3,028 · see citing works
Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults
Larsen, N., Vogensen, F. K., van den Berg, F. W. J., Nielsen, D. S., Andreasen, A. S., Pedersen, B. K., Al-Soud, W. A., Sørensen, S. J., Hansen, L. H., & Jakobsen, M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009085 · Cited by 2,952 · see citing works
Environment dominates over host genetics in shaping human gut microbiota
Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., Costea, P. I., Godneva, A., Kalka, I. N., Bar, N., et al. (2018) · Nature · doi:10.1038/nature25973 · Cited by 2,859 · see citing works
Self-citationA reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836 · see citing works
Variance component model to account for sample structure in genome-wide association studies
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817 · see citing works
Microbiota and SCFA in lean and overweight healthy subjects
Schwiertz, A., Taras, D., Schäfer, K., Beijer, S., Bos, N. A., Donus, C., & Hardt, P. D. (2010) · Obesity · doi:10.1038/oby.2009.167 · Cited by 2,457 · see citing works
Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes
Zhao, L., Zhang, F., Ding, X., Wu, G., Lam, Y. Y., Wang, X., Fu, H., Xue, X., Lu, C., Ma, J., et al. (2018) · Science · doi:10.1126/science.aao5774 · Cited by 2,304 · see citing works
Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health
Ríos-Covián, D., Ruas‐Madiedo, P., Margollés, A., Gueimonde, M., Reyes‐Gavilán, C. G. D. L., & Salazar, N. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00185 · Cited by 2,215 · see citing works
Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity
Zhernakova, A., Kurilshikov, A., Bonder, M. J., Tigchelaar, E. F., Schirmer, M., Vatanen, T., Mujagic, Z., Vila, A. V., Falony, G., Vieira-Silva, S., et al. (2016) · Science · doi:10.1126/science.aad3369 · Cited by 1,914 · see citing works
Self-citationSpecies-level functional profiling of metagenomes and metatranscriptomes
Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., Lipson, K. S., Knight, R., Caporaso, J. G., Segata, N., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0176-y · Cited by 1,693 · see citing works
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580 · see citing works
Self-citationEfficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507 · see citing works
Chambers, E. S., Viardot, A., Psichas, A., Morrison, D. J., Murphy, K. G., Zac-Varghese, S. E. K., MacDougall, K., Preston, T., Tedford, C., Finlayson, G. S., et al. (2015) · Gut · doi:10.1136/gutjnl-2014-307913 · Cited by 1,424 · see citing works
The microbiology of butyrate formation in the human colon
Pryde, S. E., Duncan, S. H., Hold, G. L., Stewart, C. S., & Flint, H. J. (2002) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2002.tb11467.x · Cited by 1,320 · see citing works
Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans
Vatanen, T., Kostic, A. D., d’Hennezel, E., Siljander, H., Franzosa, E. A., Yassour, M., Kolde, R., Vlamakis, H., Arthur, T. D., Hämäläinen, A.-M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.007 · Cited by 1,289 · see citing works
Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity
Schirmer, M., Smeekens, S. P., Vlamakis, H. C., Jaeger, M., Oosting, M., Franzosa, E. A., ter Horst, R., Jansen, T., Jacobs, L., Bonder, M. J., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.020 · Cited by 1,159 · see citing works
Self-citationGenetic determinants of the gut microbiome in UK twins
Goodrich, J. K., Davenport, E. R., Beaumont, M., Jackson, M. A., Knight, R., Ober, C., Spector, T. D., Bell, J. T., Clark, A. G., & Ley, R. E. (2016) · Cell Host & Microbe · doi:10.1016/j.chom.2016.04.017 · Cited by 1,048 · see citing works
Kootte, R. S., Levin, E., Salojärvi, J., Smits, L. P., Hartstra, A. V., Udayappan, S. D., Hermes, G., Bouter, K. E., Koopen, A. M., Holst, J. J., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.09.008 · Cited by 1,039 · see citing works
Bowden, J., Del Greco M, F., Minelli, C., Zhao, Q., Lawlor, D. A., Sheehan, N. A., Thompson, J., & Davey Smith, G. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy258 · Cited by 995 · see citing works
Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance
Zhang, X., Shen, D., Fang, Z., Jie, Z., Qiu, X., Zhang, C., Chen, Y., & Ji, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071108 · Cited by 946 · see citing works
The effect of host genetics on the gut microbiome
Bonder, M. J., Kurilshikov, A., Tigchelaar, E. F., Mujagic, Z., Imhann, F., Vila, A. V., Deelen, P., Vatanen, T., Schirmer, M., Smeekens, S. P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3663 · Cited by 819 · see citing works
Self-citationManning, A. K., Hivert, M., Scott, R. A., Grimsby, J., Bouatia‐Naji, N., Chen, H., Rybin, D. V., Liu, C.-T., Bielak, L. F., Prokopenko, I., et al. (2012) · Nature Genetics · doi:10.1038/ng.2274 · Cited by 806 · see citing works
An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans
Scott, R. A., Scott, L. J., Mägi, R., Marullo, L., Gaulton, K. J., Kaakinen, M., Pervjakova, N., Pers, T. H., Johnson, A. D., Eicher, J. D., et al. (2017) · Diabetes · doi:10.2337/db16-1253 · Cited by 736 · see citing works
Self-citationGenetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge
Saxena, R., the MAGIC investigators, Hivert, M., Langenberg, C., Tanaka, T., Pankow, J. S., Vollenweider, P., Lyssenko, V., Bouatia‐Naji, N., Dupuis, J., et al. (2010) · Nature Genetics · doi:10.1038/ng.521 · Cited by 621 · see citing works
Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality
Evans, D. M., & Smith, G. D. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-050016 · Cited by 524 · see citing works
Peng, L., He, Z., Chen, W., Holzman, I. R., & Lin, J. (2007) · Pediatric Research · doi:10.1203/01.pdr.0000250014.92242.f3 · Cited by 518 · see citing works
Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank
Eastwood, S. V., Mathur, R., Atkinson, M. D., Brophy, S. T., Sudlow, C. L. M., Flaig, R., de Lusignan, S., Allen, N. E., & Chaturvedi, N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162388 · Cited by 475 · see citing works
Association of host genome with intestinal microbial composition in a large healthy cohort
GEM Project Research Consortium, Turpin, W., Espin-Garcia, O., Xu, W., Silverberg, M. S., Kevans, D., Smith, M. I., Guttman, D. S., Griffiths, A., Panaccione, R., et al. (2016) · Nature Genetics · doi:10.1038/ng.3693 · Cited by 460 · see citing works
Soranzo, N., Sanna, S., Wheeler, E., Gieger, C., Radke, D., Dupuis, J., Bouatia‐Naji, N., Langenberg, C., Prokopenko, I., Stolerman, E., et al. (2010) · Diabetes · doi:10.2337/db10-0502 · Cited by 445 · see citing works
Self-citationDuncan, S. H., Hold, G. L., Barcenilla, A., Stewart, C. S., & Flint, H. J. (2002) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/00207713-52-5-1615 · Cited by 368 · see citing works
Brunkwall, L., & Orho‐Melander, M. (2017) · Diabetologia · doi:10.1007/s00125-017-4278-3 · Cited by 360 · see citing works
Strawbridge, R. J., Dupuis, J., Prokopenko, I., Barker, A., Ahlqvist, E., Rybin, D., Petrie, J. R., Travers, M. E., Bouatia-Naji, N., Dimas, A. S., et al. (2011) · Diabetes · doi:10.2337/db11-0415 · Cited by 356 · see citing works
Self-citationA Functional Genomics Approach to Understand Variation in Cytokine Production in Humans
Li, Y., Oosting, M., Smeekens, S. P., Jaeger, M., Aguirre-Gamboa, R., Le, K. T. T., Deelen, P., Ricaño-Ponce, I., Schoffelen, T., Jansen, A. F. M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.017 · Cited by 324 · see citing works
Self-citationHost Genetics and Gut Microbiome: Challenges and Perspectives
Kurilshikov, A., Wijmenga, C., Fu, J., & Zhernakova, A. (2017) · Trends in Immunology · doi:10.1016/j.it.2017.06.003 · Cited by 309 · see citing works
Self-citationLiu, F., Li, P., Chen, M., Luo, Y., Prabhakar, M., Zheng, H., He, Y., Qi, Q., Long, H., Zhang, Y., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10722-2 · Cited by 297 · see citing works
Garcı́a-Villalba, R., Giménez‐Bastida, J. A., García‐Conesa, M., Tomás-Barberán, F. A., Espı́n, J. C., & Larrosa, M. (2012) · Journal of Separation Science · doi:10.1002/jssc.201101121 · Cited by 291 · see citing works
Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis
Rücker, G., Schwarzer, G., Carpenter, J. R., Binder, H., & Schumacher, M. C. (2010) · Biostatistics · doi:10.1093/biostatistics/kxq046 · Cited by 284 · see citing works
Pingitore, A., Chambers, E. S., Hill, T., Ruz‐Maldonado, I., Liu, B., Bewick, G. A., Morrison, D. J., Preston, T., Wallis, G. A., Tedford, C. M., et al. (2016) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12811 · Cited by 280 · see citing works
Tigchelaar, E. F., Zhernakova, A., Dekens, J. A. M., Hermes, G. D. A., Baranska, A., Mujagic, Z., Swertz, M. A., Contreras, A. M. M., Deelen, P., Cénit, M. C., et al. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-006772 · Cited by 248 · see citing works
Self-citationHost genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes
Kreznar, J. H., Keller, M. P., Traeger, L. L., Rabaglia, M. E., Schueler, K. L., Stapleton, D. S., Zhao, W., Vivas, E. I., Yandell, B. S., Broman, A. T., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.01.062 · Cited by 177 · see citing works
Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative
Wang, J., Kurilshikov, A., Radjabzadeh, D., Turpin, W., Croitoru, K., Bonder, M. J., Jackson, M. A., Medina‐Gómez, C., Frost, F., Homuth, G., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0479-3 · Cited by 177 · see citing works
Self-citationA Central Role for GRB10 in Regulation of Islet Function in Man
Prokopenko, I., Poon, W., Mägi, R., Prasad, R. B., Salehi, A. A., Almgren, P., Osmark, P., Bouatia‐Naji, N., Wierup, N., Fall, T., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004235 · Cited by 174 · see citing works
Self-citationTaylor, A. E., Davies, N. M., Ware, J. J., VanderWeele, T., Smith, G. D., & Munafò, M. R. (2014) · Economics & Human Biology · doi:10.1016/j.ehb.2013.12.002 · Cited by 166 · see citing works
Bowden, J., Fabiola Del Greco, M., Minelli, C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/159442 · Cited by 102 · see citing works
Pleiotropic Effects of Lipid Genes on Plasma Glucose, HbA1c, and HOMA-IR Levels
Li, N., van der Sijde, M. R., LifeLines Cohort Study Group, Bakker, S. J. L., Dullaart, R. P. F., van der Harst, P., Gansevoort, R. T., Elbers, C. C., Wijmenga, C., Snieder, H., et al. (2014) · Diabetes · doi:10.2337/db13-1800 · Cited by 70 · see citing works
Self-citationJakobsdottir, G., Bjerregaard, J. H., Skovbjerg, H., & NYMAN, M. E. (2013) · Scandinavian Journal of Gastroenterology · doi:10.3109/00365521.2013.786128 · Cited by 55 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Cryan, J. F., O'Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., et al. (2019) · Physiological Reviews · doi:10.1152/physrev.00018.2018 · Cited by 5,286 · see citing works
Gut microbiota in human metabolic health and disease
Fan, Y., & Pedersen, O. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-020-0433-9 · Cited by 4,724 · see citing works
The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication
Silva, Y. P., Bernardi, A., & Frozza, R. L. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00025 · Cited by 3,043 · see citing works
“Despite differences in the identification of specific microbiome features responsible for these effects, a shift in the microbiome composition away from species able to produce butyrate was one consistent finding in type 2 diabetes subjects (187).”
BackgroundProbiotics and prebiotics in intestinal health and disease: from biology to the clinic
Sanders, M. E., Merenstein, D. J., Reid, G., Gibson, G. R., & Rastall, R. A. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0173-3 · Cited by 2,059 · see citing works
Large-scale association analyses identify host factors influencing human gut microbiome composition
Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R., Radjabzadeh, D., Wang, J., Demirkan, A., Le Roy, C. I., Raygoza Garay, J. A., Finnicum, C. T., Liu, X., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00763-1 · Cited by 1,786 · see citing works
Self-citationShort-chain fatty acids: Linking diet, the microbiome and immunity
Mann, E. R., Lam, Y. K., & Uhlig, H. H. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-024-01014-8 · Cited by 1,595 · see citing works
The integrative biology of type 2 diabetes
Roden, M., & Shulman, G. I. (2019) · Nature · doi:10.1038/s41586-019-1797-8 · Cited by 1,167 · see citing works
Ghosh, T. S., Rampelli, S., Jeffery, I. B., Santoro, A., Neto, M., Capri, M., Giampieri, E., Jennings, A., Candela, M., Turroni, S., et al. (2020) · Gut · doi:10.1136/gutjnl-2019-319654 · Cited by 991 · see citing works
Human postprandial responses to food and potential for precision nutrition
Berry, S. E., Valdes, A. M., Drew, D. A., Asnicar, F., Mazidi, M., Wolf, J., Capdevila, J., Hadjigeorgiou, G., Davies, R., Al Khatib, H., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0934-0 · Cited by 932 · see citing works
Nogal, A., Valdes, A. M., & Menni, C. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1897212 · Cited by 917 · see citing works
Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer
Song, M., Chan, A. T., & Sun, J. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.06.048 · Cited by 752 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Self-citationA robust and efficient method for Mendelian randomization with hundreds of genetic variants
Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M. (2020) · Nature Communications · doi:10.1038/s41467-019-14156-4 · Cited by 681 · see citing works
Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project
Lopera-Maya, E. A., Kurilshikov, A., van der Graaf, A., Hu, S., Andreu-Sánchez, S., Chen, L., Vila, A. V., Gacesa, R., Sinha, T., Collij, V., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00992-y · Cited by 553 · see citing works
Self-citationWalter, J., Armet, A. M., Finlay, B. B., & Shanahan, F. (2020) · Cell · doi:10.1016/j.cell.2019.12.025 · Cited by 596 · see citing works
Qin, Y., Havulinna, A. S., Liu, Y., Jousilahti, P., Ritchie, S. C., Tokolyi, A., Sanders, J. G., Valsta, L., Brożyńska, M., Zhu, Q., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00991-z · Cited by 586 · see citing works
Microbiota-Gut-Brain Axis: New Therapeutic Opportunities
Long-Smith, C., O'Riordan, K. J., Clarke, G., Stanton, C., Dinan, T. G., & Cryan, J. F. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023628 · Cited by 419 · see citing works
Kim, C. H. (2023) · Cellular & Molecular Immunology · doi:10.1038/s41423-023-00987-1 · Cited by 420 · see citing works
Chen, L., Zhernakova, D. V., Kurilshikov, A., Andreu-Sánchez, S., Wang, D., Augustijn, H. E., Vich Vila, A., Study, L. C., Weersma, R. K., Medema, M. H., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02014-8 · Cited by 327 · see citing works
Self-citationHe, F.-F., & Li, Y.-M. (2020) · Journal of Ovarian Research · doi:10.1186/s13048-020-00670-3 · Cited by 289 · see citing works
Inter-organ crosstalk during development and progression of type 2 diabetes mellitus
Xourafa, G., Korbmacher, M., & Roden, M. (2024) · Nature Reviews Endocrinology · doi:10.1038/s41574-023-00898-1 · Cited by 274 · see citing works
Zhuang, Z., Yang, R., Wang, W., Qi, L., & Huang, T. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-01961-8 · Cited by 220 · see citing works
Deciphering the Chemical Lexicon of Host–Gut Microbiota Interactions
Nicolas, G. R., & Chang, P. V. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.04.006 · Cited by 195 · see citing works
Perraudeau, F., McMurdie, P., Bullard, J., Cheng, A., Cutcliffe, C., Deo, A., Eid, J., Gines, J., Iyer, M., Justice, N., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001319 · Cited by 186 · see citing works
Christensen, L., Vuholm, S., Roager, H. M., Nielsen, D. S., Krych, L., Kristensen, M., Astrup, A., & Hjorth, M. F. (2019) · The Journal of Nutrition · doi:10.1093/jn/nxz198 · Cited by 162 · see citing works
Liddicoat, C., Sydnor, H., Cando-Dumancela, C., Dresken, R., Liu, J., Gellie, N. J. C., Mills, J. G., Young, J. M., Weyrich, L. S., Hutchinson, M. R., et al. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134684 · Cited by 140 · see citing works
Microbiota–neuroepithelial signalling across the gut–brain axis
Ohara, T. E., & Hsiao, E. Y. (2025) · Nature Reviews Microbiology · doi:10.1038/s41579-024-01136-9 · Cited by 147 · see citing works
Mazhar, M., Zhu, Y., & Qin, L. (2023) · Foods · doi:10.3390/foods12051023 · Cited by 119 · see citing works
Masse, K. E., & Lu, V. B. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1169624 · Cited by 120 · see citing works
From taxonomy to metabolic output: what factors define gut microbiome health?
Wilmanski, T., Rappaport, N., Diener, C., Gibbons, S. M., & Price, N. D. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1907270 · Cited by 73 · see citing works
Gut microbiota predicts severity and reveals novel metabolic signatures in acute pancreatitis
Ammer-Herrmenau, C., Antweiler, K. L., Asendorf, T., Beyer, G., Buchholz, S. M., Cameron, S., Capurso, G., Damm, M., Dang, L., Frost, F., et al. (2024) · Gut · doi:10.1136/gutjnl-2023-330987 · Cited by 67 · see citing works
Vetrani, C., Maukonen, J., Bozzetto, L., Della Pepa, G., Vitale, M., Costabile, G., Riccardi, G., Rivellese, A. A., Saarela, M., & Annuzzi, G. (2020) · Acta Diabetologica · doi:10.1007/s00592-020-01494-9 · Cited by 64 · see citing works
Fecal microbiota transplantation in obesity metabolism: A meta analysis and systematic review
Zecheng, L., Donghai, L., Runchuan, G., Yuan, Q., Qi, J., Yijia, Z., Shuaman, R., Xiaoqi, L., Yi, W., Ni, M., et al. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110803 · Cited by 69 · see citing works
Zhang, Q., Linke, V., Overmyer, K. A., Traeger, L. L., Kasahara, K., Miller, I. J., Manson, D. E., Polaske, T. J., Kerby, R. L., Kemis, J. H., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01326-w · Cited by 43 · see citing works
Robinson, J. M., & Breed, M. F. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137626 · Cited by 30 · see citing works
Zhao, J., Pan, X., Hao, D., Zhao, Y., Chen, Y., Zhou, S., Peng, H., & Zhuang, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1190230 · Cited by 20 · see citing works
Analysis of gut microbiota and depression and anxiety: Mendelian randomization from three datasets
Lai, Y., & Xiong, P. (2025) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2025.03.012 · Cited by 20 · see citing works
Tao, W., Yu, Y., Tan, D., Huang, X., Huang, J., Lin, C., & Yu, R. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1603442 · Cited by 9 · see citing works
McKenna, C. F., Voreades, N., Weiser, M. J., McCormick, C., Valdes, A. M., Blonquist, T., Kaden, V., Mah, E., & Cook, C. (2025) · doi:10.1101/2025.04.18.25326077
- Authors
- Sanna, S., van Zuydam, N. R., Mahajan, A., Kurilshikov, A., Vich Vila, A., Võsa, U., Mujagic, Z., Masclee, A. A. M., Jonkers, D. M. A. E., Oosting, M., Joosten, L. A. B., Netea, M. G., Franke, L., Zhernakova, A., Fu, J., Wijmenga, C., & McCarthy, M. I.
- Affiliations
- University Medical Center Groningen (NL)
- Centre for Human Genetics (GB)
- Maastricht University Medical Centre (NL)
- Radboud University Medical Center (NL)
- Corresponding author
- Sanna, Serena
- Wijmenga, Cisca
- McCarthy, Mark I.
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-02-18
- Volume
- 51
- Issue
- 4
- Pages
- 600-605
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6441384
- OpenAlex
- W2913914265
- Semantic Scholar
- 1c6ce10bc7c9676f9506eaa8f595a029ac38be90
- Cited by
- 1,539
- References
- 56
- Citations by year
- 2026: 160 · 2025: 236 · 2024: 335 · 2023: 258 · 2022: 172 · 2021: 183 · 2020: 140 · 2019: 53
- FWCI
- 45.60
- Type
- journal article
- Language
- en
- Open access
- green
- research.rug.nl
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion)
- Funders
- Wellcome Trust (106130/Z/14/Z)
- National Institute for Health and Care Research (FP7/2007–2013, NF-SI-0611-10099, NF-SI-0617-10090)
- Rijksuniversiteit Groningen (CVON2012-03)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (FP7 2007-2013, # 016.176.006, 864.13.013, SPI 94- 212, 016.Vidi.178.056, SPI 92-266)
- Novo Nordisk
- Universitair Medisch Centrum Groningen
- Novo Nordisk Fonden (NNF15CC0018486)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK105535)
- H2020 European Research Council (715772)
- FP7 Ideas: European Research Council (310372, 322698)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- gut microbiota, short-chain fatty acids, butyrate, propionate, type 2 diabetes, obesity, insulin response, glucose tolerance, metabolic phenotypes, causal inference, bidirectional Mendelian randomization, host genetics, gut metagenomics
- MeSH terms
- Gastrointestinal Microbiome, Adolescent, Adult, Aged, Aged, 80 and over, Diabetes Mellitus, Type 2, Fatty Acids, Volatile, Female, Genotype, Glucose Tolerance Test, Humans, Male, Metabolic Diseases, Middle Aged, Netherlands, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
