Abstract
Diet composition shapes tissue function and disease risk by modulating nutrient availability, metabolic state and cellular dynamics1. In the gastrointestinal tract, obesogenic high-fat diets enhance small-intestinal stem cell activity and tumorigenesis2. However, the impact of ketogenic diets (KDs), which contain even higher lipid content but reduce circulating insulin and induce ketogenesis, remains poorly understood3. This is particularly relevant for patients with familial adenomatous polyposis who face a high risk of small-intestinal tumours4. Here we combine dietary, genetic and metabolic manipulations in mouse models of spontaneous intestinal adenoma formation to dissect the role of systemic and epithelial ketogenesis in intestinal cancer. We show that KD accelerates tumour burden and shortens survival, independent of ketone metabolites. Through genetic manipulation of the ketogenic pathway, we modulate the production of local and systemic ketone metabolites; however, neither inhibition nor augmentation of the ketogenic enzyme 3-hydroxy-3-methylglutaryl-coenzyme A synthase 2 nor disruption of ketolysis altered tumorigenesis. Combined intestinal loss of PPARα/δ/γ attenuates KD-driven intestinal stem cell expansion, proliferation and clonogenicity, whereas inhibition of downstream fatty acid oxidation through CPT1A loss limits adenoma formation specifically under KD, linking tumour initiation to fatty acid oxidation of dietary lipids rather than lipid accumulation. These findings reveal that dietary lipid content, through fatty acid oxidation rather than ketone metabolism, influences intestinal tumorigenesis and highlight the need for nuanced consideration of dietary strategies for cancer prevention in genetically susceptible populations.
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Data availability
The scRNA-seq dataset generated in this study is available with interactive visualizations at the Broad Institute Single Cell Portal (project ID SCP3661). The same scRNA-seq dataset is also available at the Gene Expression Omnibus repository (GSE334093). The lipidomics data generated and used for this project are available at Metabolomics Workbench (project ID PR003051; https://doi.org/10.21228/M89G3N). Any further information required to reanalyse the data reported in this paper is available on request. Source data are provided with this paper.
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Acknowledgements
We thank the Swanson Biotechnology Center at the Koch Institute, including the Flow Cytometry, Histology and ES Cell and Transgenics core facilities; the Department of Comparative Medicine for mouse husbandry support; S. Holder and members of the Hope Babette Tang (1983) Histology Facility for substantial histological support; C.-W. Cheng and V. Butty for initial scRNA-seq samples collection; M. Mana and A. Hussey for generation of the PPAR-3KO mice; all the members of the Yilmaz laboratory for discussions; A. Hussey for laboratory management and H. Whalen for administrative assistance. Generative artificial intelligence tools (Claude from Anthropic and ChatGPT from OpenAI) were used for language editing and prose refinement. All scientific content and conclusions are the sole responsibility of the authors.
Funding
Ö.H.Y. is supported by the US National Institutes of Health (grant nos. R01CA245314, R01CA211184, R01CA034992, R01CA257523, R01DK140310, P30CA014051 and R01DK126545), a Pew-Stewart Trust scholar award, the Kathy and Curt Marble cancer research award, a Koch Institute–Dana-Farber/Harvard Cancer Center Bridge project grant and AFAR; and receives support from the MIT Stem Cell Initiative, Fondation MIT. This work was supported in part by the Koch Institute Support (core) grant (no. NCI P30-CA14051). F.C. was a Damon Runyon Fellow supported by the Damon Runyon Cancer Research Foundation (grant no. DRG-2463-22) and is at present supported by NIH K99/R00 award (grant no. K99DK146116). J.E.S.S. is supported by an American Gastroenterological Association Research Scholar Award (grant no. AGA2025-13-05). C.N.T. is supported by the Biswas Postdoctoral Fellows Program. A.K.S. and C.N.T. receive support from the MIT Stem Cell Initiative through Fondation MIT.
Author information
Author notes
These authors contributed equally: Jessica E. S. Shay, Fangtao Chi
Authors and Affiliations
David H. Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Jessica E. S. Shay, Fangtao Chi, Constantine N. Tzouanas, Shixun Han, Xiao Zhang, Seda Neptun, Tolga Sever, Isabela Fuentes, Sangeeta N. Bhatia, Gizem Calibasi-Kocal, Matthew G. Vander Heiden, Alex K. Shalek & Ömer H. Yilmaz
Gastrointestinal Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Jessica E. S. Shay
Division of Gastroenterology, Hepatology and Nutrition, University of Utah School of Medicine, Salt Lake City, UT, USA
Jessica E. S. Shay
Broad Institute of MIT and Harvard, Cambridge, MA, USA
Constantine N. Tzouanas, Sangeeta N. Bhatia, Matthew G. Vander Heiden, Alex K. Shalek & Ömer H. Yilmaz
Ragon Institute of MGB and MIT, Cambridge, MA, USA
Constantine N. Tzouanas & Alex K. Shalek
Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
Constantine N. Tzouanas & Alex K. Shalek
UCLA Metabolomics Center, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA
Johanna Ten Hoeve
UCLA Lipidomics Core, University of California, Los Angeles, Los Angeles, CA, USA
Kevin J. Williams
The Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
Sangeeta N. Bhatia
Harvard-MIT Program in Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
Sangeeta N. Bhatia & Alex K. Shalek
Howard Hughes Medical Institute, Chevy Chase, MD, USA
Sangeeta N. Bhatia
Dokuz Eylul University, Institute of Oncology, Department of Translational Oncology, Izmir, Turkey
Gizem Calibasi-Kocal
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Matthew G. Vander Heiden
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
Alex K. Shalek
Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
Ömer H. Yilmaz
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Contributions
J.E.S.S. and F.C. initiated the project, conceived, designed, performed and interpreted all experiments, and wrote the paper with help from Ö.H.Y. C.N.T., S.H., X.Z., J.T.H., K.J.W., S.N., T.S., I.F. and G.C.-K. performed experiments and assisted with data collection and interpretation. C.N.T., S.N.B. and A.K.S. assisted with sequencing data analysis. J.T.H. and M.G.V.H. assisted with intestinal tissue metabolomic analysis. K.J.W. assisted with intestinal crypts lipidomic analysis. All authors assisted in interpreting experiments, writing and editing the paper.
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Competing interests
Ö.H.Y. holds equity and is a scientific advisory board member in Ava Lifesciences, Jumble Therapeutics and AI Proteins; received research support from Microbial Machines; and is a consultant for Nestlé. M.G.V.H. is a scientific advisor for Agios Pharmaceuticals, iTeos Therapeutics, Faeth Therapeutics, Sage Therapeutics, Lime Therapeutics, Pretzel Therapeutics and Auron Therapeutics. A.K.S. reports compensation for consulting and/or SAB membership from Honeycomb Biotechnologies, Cellarity, Ochre Bio, Bio-Rad Laboratories, Relation Therapeutics, IntrECate Biotherapeutics, Parabalis Medicines, Quotient Therapeutics, Passkey Therapeutics, Danaher, Senda Biosciences and Dahlia Biosciences unrelated to this work. S.N.B. reports interests in Ropirio Therapeutics, Earli Inc., Sunbird Bio, Satellite Bio, Catalio Capital, Port Therapeutics, Matrisome Bio, Xilio Therapeutics, Ochre Bio, Impilo Therapeutics, Vertex Pharmaceuticals, Moderna, Johnson & Johnson and Owlstone, which were not involved in this study. S.N.B.’s interests are reviewed and managed under MIT’s policies for potential conflicts of interest. The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Metabolic and histological changes following ketogenic diet exposure
(a) Composition of the control diet (CD), 60% lard-based high fat diet (HFD) and 80% lard-based ketogenic diet (KD). (b-e) Body weight (b), circulating glucose (c), BHB (d) and insulin (e) in male and female mice fed CD, HFD, or KD over the experimental timeline. For b, CD, n = 8 mice; HFD, n = 8; KD, n = 9. For c, CD, n = 10; HFD, n = 10; KD, n = 8. For d, CD, n = 14; HFD, n = 8; KD, n = 17. For e, CD, n = 8; HFD, n = 8; KD, n = 8. (f) Kaplan-Meier survival curves with log-rank Mantel-Cox test for CD-, HFD- and KD-fed Vil-CreERT2; Apcfl/wt mice cohorts. CD, n = 27 mice; HFD, n = 20; KD, n = 20. (g-i) Quantification of tumour burden (g-h) and representative images (i) in CD-, HFD-, and KD-fed Vil-CreERT2; Apcfl/wt mice cohorts. CD, n = 9 mice; HFD, n = 8; KD, n = 7. Significance was determined by one-way ANOVA with Tukey’s multiple comparisons test (b, c, d, e, g, h) and Mantel-Cox test (f). Data (b, c, d, e, g, h) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 2 KD enhances IS cell activity and tumorigenesis
(a) Schematic of Lgr5-eGFP-IRES-CreERT2 mice fed CD or KD. (b) Immunoblot of isolated crypts from CD- and KD-fed mice. Experiments were repeated three times independently, three mice per condition each time. (c-d) Representative image (c) and quantification (d) of Paneth cell numbers in crypts from CD- and KD-fed mice. CD, n = 4 mice; KD, n = 3 mice. Scale bar, 20 μm. (e-f) Representative sorting plot (e) of LGR5-IRES-EGFP from mice fed CD or KD and quantification (f) of Paneth Paneth, GFPhi, and GFPlo populations. CD, n = 5 mice; KD, n = 7 mice. (g) In vitro clonogenicity of GFP+ ISCs isolated from CD- and KD-fed mice. CD, n = 5 mice; KD, n = 7 mice. (h) Schematic of the 90-day Vil-CreERT2; Apcfl/wt tumour model. (i-k) Representative H&E images (i) and quantification of tumour number (j) and size (k) in CD- and KD-fed mice. CD, n = 7 mice; KD, n = 14 mice. (l-n) Correlation of tumour burden with circulating BHB (l), glucose (m), and NEFAs (n) at sacrifice. BHB, n = 34 mice; glucose, n = 34 mice; NEFAs, n = 33 mice. Significance was determined by unpaired two-tailed Student’s t-test (d, j, k), two-way ANOVA with uncorrected Fisher’s LSD (f, g) and simple linear regression (l, m, n). Data (d, f, g, j, k) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 3 HCAR2 expression in intestinal tissues and early adenomas
(a) Serum acetoacetate (AcAc) and BHB levels across two independent cohorts comprising 289 patients with colorectal adenomas and carcinomas, including a test cohort (n = 100) and a validation cohort (n = 189). Significance was determined by Non-parametric Mann-Whitney U test, two-sided. (b) Serum lipid abundances in 289 participants with colorectal adenomas or carcinomas from Gao et al. The training cohort included 100 participants: 34 healthy controls, 31 with adenomas and 35 with carcinomas. The validation cohort included 189 participants: 76 healthy controls and 113 with carcinomas. Triangles indicate Benjamini–Hochberg-adjusted P < 0.05 in the validation cohort. (c) Immunofluorescence co-staining for β-catenin and HCAR2 in early adenomas from CD- and KD-fed mice. Experiments were repeated three times independently, five mice per condition each time. Scale bar, 25 μm. (d) Representative immunohistochemistry for HCAR2 in early intestinal adenomas from CD- and KD-fed mice. Experiments were repeated three times independently, five mice per condition each time. Scale bar, 200 μm. (e) UMAP of mouse intestinal cell populations from the Aliluev dataset.(f-i) Hcar2 expression across annotated cell populations (f), corresponding marker-gene dot plot (g), number of detected genes (h), and UMI counts (i), stratified by cell type. Data (a, b) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians. Whiskers extend 1.5×interquartile range above and below the box.
Extended Data Fig. 4 Characterization of ketogenesis-deficient mouse models
(a) Quality control metrics for scRNAseq datasets with marker gene dotplot visualization. (b-c) Gene set enrichment analysis (GSEA) of the most upregulated (b) and downregulated (c) pathways in ISCs from CD- and KD-fed mice. Gene set enrichment analysis p-values calculated with fgsea package, two-sided, with Benjamini-Hochberg adjustment for multiple testing comparison. (d) Schematic of HMGCS2 iKO mice maintained on KD. (e) Lipidomic profiling of intestinal crypts from WT and HMGCS2 iKO mice under KD conditions. WT, n = 4 mice; iKO, n = 5 mice. (f) Schematic of hepatocyte-specific HMGCS2 knockout (LiKO) mice. (g-i) Characterization of LiKO mice, including body weight (g), circulating glucose (h), and insulin (i). WT, n = 5 mice; LiKO, n = 8 mice. Significance was determined by two-way ANOVA with Šidák’s multiple comparison test (e) and unpaired two-tailed Student’s t-test (g, h, i). Data (e, g, h, i) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 5 Characterization of ketolysis-deficient mouse models
(a) Schematic of BDH1- and OXCT1-mediated ketolysis. (b-d) Immunoblot (b) and immunohistochemistry (c-d) validation of BDH1 iKO and OXCT1 iKO cohorts. Experiments were repeated three times independently, with three mice per condition each time. Scale bar, 50 µm.(e) Circulating BHB in BDH1 iKO mice on CD and KD. CD, n = 6 mice; KD, n = 8 mice. (f) Circulating BHB in OXCT1 iKO mice on CD and KD. CD, n = 7 mice; KD, n = 6 mice. (g) Breeding scheme for BDH1 iKO and OXCT1 iKO tumour cohorts. (h-k) Quantification of tumour number (h) and size (i), with representative H&E and β-catenin images (j-k), in WT, BDH1 iKO, and OXCT1 iKO mice on CD or KD. WT-CD, n = 9 mice; BDH1 iKO-CD, n = 10 mice; OXCT1 iKO-CD, n = 15 mice; WT-KD, n = 12 mice; BDH1 iKO-KD, n = 8 mice; OXCT1 iKO-KD, n = 6 mice. The CD tumour studies in h-i were shared with the CD-treated WT cohort shown in Fig. 1i,j. Significance was determined by unpaired two-tailed Student’s t-test (e, f) and two-way ANOVA with Tukey’s multiple comparison test (h, i). Data (e, f, h, i) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 6 Characterization of intestinal HMGCS2 overexpression model
(a) Schematic of Rosa26 targeting and the Hmgcs2 iOE construct. (b) Immunoblot of iOE organoids treated with 4-hydroxytamoxifen in vitro. Experiments were repeated three times independently, with two mice per condition each time. (c) Lipidomic profiling of small-intestinal crypts from WT and iOE mice on CD. WT, n = 4 mice; iOE, n = 4 mice. Significance was determined by two-way ANOVA with Šidák’s multiple comparison test (c). Data (c) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 7 KD suppresses colonic tumorigenesis independently of ketones
(a) Immunohistochemistry of HMGCS2 in small intestine (SI), proximal colon (pCO), and distal colon (dCO) from CD- and KD-fed mice. Experiments were repeated three times independently, with five mice per condition each time. Scale bar, 50 μm. (b) RNA in situ hybridization of Hmgcs2 in pCO and dCO from CD- and KD-fed mice. Experiments were repeated three times independently, with five mice per condition each time. Scale bar, 50 µm. (c-d) Lipidomic profiling of colonic crypts (c) and small-intestinal crypts (d) from CD- and KD-fed mice. CD, n = 5 mice; KD, n = 5 mice. (e) Whole-colon BHB levels in WT mice fed CD, HFD, or KD, and in CD-fed HMGCS2 iOE mice. CD, HFD, and KD, n = 5 mice each; CD-iOE, n = 8 mice. (f) Immunoblot of snap-frozen pCO and dCO from WT, HMGCS2 iKO, and HMGCS2 iOE mice. Experiments were repeated three times independently, with three mice per condition each time. (g) Normalized BHB levels in whole pCO and dCO from WT and HMGCS2 iOE mice. n = 5 mice per group. (h-i) Quantification of total tumour number (h) and tumour area (i) in colons from CD-, HFD-, and KD-fed mice. CD, n = 10 mice; HFD, n = 9 mice; KD, n = 7 mice. (j) Total colonic tumour number in WT mice on CD or KD, HMGCS2 iKO and dKO mice on KD, and HMGCS2 iOE mice on CD. WT-CD, n = 10 mice; WT-KD, n = 7 mice; iKO-KD, n = 7 mice; iOE-CD, n = 14 mice; LiKO-KD, n = 8 mice. (k) Representative H&E images corresponding to (h–j). Significance was determined by two-way ANOVA with Šidák’s multiple comparison test (c, d, g) and ordinary one-way ANOVA with Tukey’s multiple comparison test (h, I, j). Data (c, d, e, g, h, i, j) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
Source data
Extended Data Fig. 8 PPARs mediate KD-induced IS cell enhancement
(a-b) Representative immunohistochemistry images (a) and quantification (b) of OLFM4+ cells in WT and PPAR-3KO mice on CD or KD. WT-CD, n = 5 mice; WT-KD, n = 5 mice; PPAR-3KO-CD, n = 6; PPAR-3KO-KD, n = 7. Scale bar, 10 μm. (c-d) Representative in situ hybridization images (c) and quantification (d) of Lgr5+ cells in WT and PPAR-3KO mice on CD or KD. WT-CD, n = 5 mice; WT-KD, n = 5; PPAR-3KO-CD, n = 6; PPAR-3KO-KD, n = 7. Scale bar, 10 μm.(e-f) Representative images (e) and quantification (f) of organoid clonogenicity from crypts isolated from WT and PPAR-3KO mice on CD or KD. n = 10 treatment per group. Scale bar, 2 mm. Significance was determined by two-way ANOVA with uncorrected Fisher’s LSD (b, d, f). Data (b, d, f) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
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Extended Data Fig. 9 CPT1A loss does not affect small-intestinal tumorigenesis on control diet
(a) Quantification of OLFM4+ crypt length of KD-fed WT and CPT1A iKO mice. WT, n = 8 mice; CPT1A iKO, n = 6 mice. (b) Schematic of inducible CPT1A knockout tumour model on control diet. (c-d) Quantification of tumour number (c) and tumour size (d) in WT and CPT1A iKO mice on CD. WT, n = 10 mice; CPT1A iKO, n = 14 mice. (e) Representative H&E and β-catenin images of tumours from CD-fed WT and CPT1A iKO mice. WT, n = 10 mice; CPT1A iKO, n = 14 mice. (f) Lipid species composition of the Class I and Class II clusters identified in Fig. 5c. Significance was determined by unpaired two-tailed Student’s t-test (a, c, d). Data (a, c, d) are shown as box-and-whisker plots: boxes indicate the 25th–75th percentiles, central lines indicate medians, and whiskers indicate minimum to maximum values. Individual data points are overlaid.
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Supplementary Fig. 1.ts shown in the main and Extended Data figures. Supplementary Fig. 2. Flow-cytometry gating strategy. Flow-cytometry gating strategy for LGR5hi stem cells, LGR5low progenitor cells and Paneth cells from control and KD-fed mice, shown in Extended Data Fig. 2e–g. Supplementary Table 1. Composition of the CD, 60% lard-based HFD and 80% lard-based KD shown in Extended Data Fig. 1a
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Shay, J.E.S., Chi, F., Tzouanas, C.N. et al. Ketogenic diet mediates intestinal tumorigenesis through lipids not ketones.
Nature (2026). https://doi.org/10.1038/s41586-026-10779-y
Received:05 June 2025
Accepted:08 June 2026
Published:15 July 2026
Version of record:15 July 2026
DOI
:https://doi.org/10.1038/s41586-026-10779-y
