The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans
Open accessAbstract
SUMMARY A carbohydrate-restricted diet is a widely recommended intervention for non-alcoholic fatty liver disease (NAFLD), but a systematic perspective on the multiple benefits of this diet is lacking. Here, we performed a short-term intervention with an isocaloric low-carbohydrate diet with increased protein content in obese subjects with NAFLD and characterized the resulting alterations in metabolism and the gut microbiota using a multi-omics approach. We observed rapid and dramatic reductions of liver fat and other cardiometabolic risk factors paralleled by (1) marked decreases in hepatic de novo lipogenesis; (2) large increases in serum β-hydroxybutyrate concentrations, reflecting increased mitochondrial β-oxidation; and (3) rapid increases in folate-producing Streptococcus and serum folate concentrations. Liver transcriptomic analysis on biopsy samples from a second cohort revealed downregulation of the fatty acid synthesis pathway and upregulation of folate-mediated one-carbon metabolism and fatty acid oxidation pathways. Our results highlight the potential of exploring diet-microbiota interactions for treating NAFLD.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Diet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
Donnelly, K. L., Smith, C. I., Schwarzenberg, S. J., Jessurun, J., Boldt, M. D., & Parks, E. J. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23621 · Cited by 3,604
One-Carbon Metabolism in Health and Disease
Ducker, G. S., & Rabinowitz, J. D. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.009 · Cited by 2,239
A Randomized Trial of a Low-Carbohydrate Diet for Obesity
Foster, G. D., Wyatt, H. R., Hill, J. O., McGuckin, B. G., Brill, C., Mohammed, B. S., Szapary, P. O., Rader, D. J., Edman, J. S., & Klein, S. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022207 · Cited by 1,794
Badman, M. K., Pissios, P., Kennedy, A. R., Koukos, G., Flier, J. S., & Maratos-Flier, E. (2007) · Cell Metabolism · doi:10.1016/j.cmet.2007.05.002 · Cited by 1,495
Cited by
The impact of dietary fiber on gut microbiota in host health and disease
Makki, K., Deehan, E. C., Walter, J., & Bäckhed, F. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.05.012 · Cited by 2,517
Self-citationThe intestinal microbiota fuelling metabolic inflammation
Tilg, H., Zmora, N., Adolph, T. E., & Elinav, E. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-019-0198-4 · Cited by 1,089
You Are What You Eat—The Relationship between Diet, Microbiota, and Metabolic Disorders—A Review
Moszak, M., Szulińska, M., & Bogdański, P. (2020) · Nutrients · doi:10.3390/nu12041096 · Cited by 397
Dietary carbohydrates and fats in nonalcoholic fatty liver disease
Yki-Järvinen, H., Luukkonen, P. K., Hodson, L., & Moore, J. B. (2021) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-021-00472-y · Cited by 294
Causes and consequences of hypertriglyceridemia
Packard, C. J., Boren, J., & Taskinen, M.-R. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00252 · Cited by 239
Self-citationMicrobiome-derived ethanol in nonalcoholic fatty liver disease
Meijnikman, A. S., Davids, M., Herrema, H., Aydin, O., Tremaroli, V., Rios-Morales, M., Levels, H., Bruin, S., de Brauw, M., Verheij, J., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02016-6 · Cited by 215
Self-citationThe Roles of ApoC-III on the Metabolism of Triglyceride-Rich Lipoproteins in Humans
Borén, J., Packard, C. J., & Taskinen, M.-R. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00474 · Cited by 196
“In line, a two-week intervention using an isocaloric low-carbohydrate diet ( 30 g carbohydrates/day) induced an almost 50% reduction of plasma apoC-III levels in obese subjects with non-alcoholic fatty liver disease (NAFLD) (115).”
MethodSelf-citationLuukkonen, P. K., Qadri, S., Ahlholm, N., Porthan, K., Männistö, V., Sammalkorpi, H., Penttilä, A. K., Hakkarainen, A., Lehtimäki, T. E., Gaggini, M., et al. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2021.10.013 · Cited by 170
Self-citationRisk assessment with gut microbiome and metabolite markers in NAFLD development
Leung, H., Long, X., Ni, Y., Qian, L., Nychas, E., Siliceo, S. L., Pohl, D., Hanhineva, K., Liu, Y., Xu, A., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk0855 · Cited by 152
Hepatocyte-Macrophage Acetoacetate Shuttle Protects Against Tissue Fibrosis
Puchalska, P., Martin, S. E., Huang, X., Lengfeld, J. E., Daniel, B., Graham, M. J., Han, X., Nagy, L., Patti, G. J., & Crawford, P. A. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.10.015 · Cited by 138
The Ketogenic Diet: Evidence for Optimism but High-Quality Research Needed
Ludwig, D. S. (2020) · The Journal of Nutrition · doi:10.1093/jn/nxz308 · Cited by 133
Matikainen, N., Söderlund, S., Björnson, E., Pietiläinen, K., Hakkarainen, A., Lundbom, N., Taskinen, M., & Borén, J. (2019) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.13487 · Cited by 129
Self-citationHansen, C. D., Gram-Kampmann, E.-M., Hansen, J. K., Hugger, M. B., Madsen, B. S., Jensen, J. M., Olesen, S., Torp, N., Rasmussen, D. N., Kjærgaard, M., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m22-1787 · Cited by 110
Impacts of carbohydrate‐restricted diets on micronutrient intakes and status: A systematic review
Churuangsuk, C., Griffiths, D., Lean, M. E. J., & Combet, E. (2019) · Obesity Reviews · doi:10.1111/obr.12857 · Cited by 92
Thomsen, M. N., Skytte, M. J., Samkani, A., Carl, M. H., Weber, P., Astrup, A., Chabanova, E., Fenger, M., Frystyk, J., Hartmann, B., et al. (2022) · Diabetologia · doi:10.1007/s00125-021-05628-8 · Cited by 90
Yulug, B., Altay, O., Li, X., Hanoglu, L., Cankaya, S., Lam, S., Velioglu, H. A., Yang, H., Coskun, E., Idil, E., et al. (2023) · Translational Neurodegeneration · doi:10.1186/s40035-023-00336-2 · Cited by 79
Self-citationRisi, R., Rossini, G., Tozzi, R., Pieralice, S., Monte, L., Masi, D., Castagneto-Gissey, L., Gallo, I. F., Strigari, L., Casella, G., et al. (2022) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2022.03.022 · Cited by 68
Low carbohydrate dietary approaches for people with type 2 diabetes-a narrative review
Wheatley, S. D., Deakin, T. A., Arjomandkhah, N. C., Hollinrake, P. B., & Reeves, T. E. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.687658 · Cited by 50
Vilar-Gomez, E., Athinarayanan, S. J., Adams, R. N., Hallberg, S. J., Bhanpuri, N. H., McKenzie, A. L., Campbell, W. W., McCarter, J. P., Phinney, S. D., Volek, J. S., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-023597 · Cited by 50
Volek, J. S., Kackley, M. L., & Buga, A. (2024) · Current Nutrition Reports · doi:10.1007/s13668-024-00548-6 · Cited by 48
Type 2 Diabetes Prevention Focused on Normalization of Glycemia: A Two-Year Pilot Study
McKenzie, A. L., Athinarayanan, S. J., McCue, J. J., Adams, R. N., Keyes, M., McCarter, J. P., Volek, J. S., Phinney, S. D., & Hallberg, S. J. (2021) · Nutrients · doi:10.3390/nu13030749 · Cited by 37
Costabile, G., Della Pepa, G., Salamone, D., Luongo, D., Naviglio, D., Brancato, V., Cavaliere, C., Salvatore, M., Cipriano, P., Vitale, M., et al. (2022) · Nutrients · doi:10.3390/nu14102178 · Cited by 27
Della Pepa, G., Vetrani, C., Brancato, V., Vitale, M., Monti, S., Annuzzi, G., Lombardi, G., Izzo, A., Tommasone, M., Cipriano, P., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001342 · Cited by 22
Karimi, M., Akhgarjand, C., Houjaghani, H., Nejad, M. M., Sohrabpour, A. A., Poustchi, H., Mohammadi, H., Chamari, M., & Imani, H. (2025) · Clinical Therapeutics · doi:10.1016/j.clinthera.2025.01.007 · Cited by 12
De Luca, A., Delaye, J.-B., Fauchier, G., Bourbao-Tournois, C., Champion, H., Bourdon, G., Dupont, J., Froment, P., Dufour, D., & Ducluzeau, P.-H. (2023) · Obesity Surgery · doi:10.1007/s11695-023-06702-3 · Cited by 11
Metadata
- Authors
- Mardinoglu, A., Wu, H., Bjornson, E., Zhang, C., Hakkarainen, A., Räsänen, S. M., Lee, S., Mancina, R. M., Bergentall, M., Pietiläinen, K. H., Söderlund, S., Matikainen, N., Ståhlman, M., Bergh, P.-O., Adiels, M., Piening, B. D., Granér, M., Lundbom, N., Williams, K. J., Romeo, S., Nielsen, J., Snyder, M., Uhlén, M., Bergström, G., Perkins, R., Marschall, H.-U., Bäckhed, F., Taskinen, M.-R., & Borén, J.
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 27
- Issue
- 3
- Pages
- 559-571.e5
- ISSN
- 1550-4131 (ISSN-L)
- OpenAlex
- W2788488000
- Cited by
- 453
- References
- 23
- FWCI
- 32.22
- Type
- journal article
- Language
- en
- Open access
- bronze
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
