Evidence map›Paper›PMID 41889866›Full record

ArticlebioRxiv : the preprint server for biology2026

Diverse high-fat diets drive multi-omic reprogramming that persists after dietary reversal.

Andrew G Van Camp, Jiwoon Park, Elif Ozcelik, Onur Eskiocak, Kadir A Ozler, Katie Papciak, Santhilal Subhash, Hanan Alwaseem, Ilgin Ergin, Charlie Chung and 19 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Andrew G Van CampDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7923-0460
Jiwoon ParkDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0003-0045-1429
Elif OzcelikDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
Onur EskiocakCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Graduate Program in Genetics, Stony Brook University, Stony Brook, NY, USA.
Kadir A OzlerCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Katie PapciakCell Biology Program, Sloan Kettering Institute, New York, NY, USA.
Santhilal SubhashDepartment of Biosciences and Bioengineering, Indian Institute of Technology Jammu, Jammu, India.ORCID 0000-0002-0077-4597
Hanan AlwaseemRockefeller University, New York, NY 10065, USA.ORCID 0000-0002-4946-1436
Ilgin ErginCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0002-0007-6502
Charlie ChungCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0003-1566-9498
Vyom ShahCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0001-6874-5285
Brian YuehCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0003-1098-0578
Aybuke AliciCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Miriam R FeinCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0003-3403-8798
Ceyda DurmazDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-7050-3653
Christopher MozsaryDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0001-5116-4767
Ece KilicCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0009-0005-7991-2495
Namita DamleDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.
Deena NajjarDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0009-0009-7950-2866
Theodore M NelsonDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0002-8600-0444
Krista A RyonDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0001-9440-2729
Daniel J ButlerDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-3687-8419
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-8756-8525
Christoph A ThaissDepartment of Pathology, Stanford University, Stanford, CA, USA.ORCID 0000-0001-8226-7718
Kivanc BirsoyLaboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.ORCID 0000-0002-7579-9895
Christopher E MasonDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-1850-1642
Cem MeydanDepartment of Medicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0002-0663-6216
Braden T TierneyDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7533-8802
Semir BeyazCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID 0000-0003-4730-4012

Funding

Single-Cell Biology Shared ResourceP30CA045508 · NCI · COLD SPRING HARBOR LABORATORY · PI David A Tuveson · 1987 to 2026
$118.9M
Translational Science Laboratory Shared ResourceP30CA138313 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI John J Lemasters · 2009 to 2026
$42.7M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Weill Cornell/Rockefeller/Sloan Kettering MST ProgramT32GM152349 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI KATHARINE C HSU · 2024 to 2026
$6.6M
RESEARCH PILOT PROJECTS PROGRAMP30GM103339 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI OGRETMEN, BESIM · 2012 to 2016
$5.4M
Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
Cataloging multi-ancestry 'omic readouts of the environmental and genetic determinants of type 2 diabetesR01DK137993 · NIDDK · HARVARD MEDICAL SCHOOL · PI ARJUN KUMAR MANRAI, Josep Maria Mercader · 2024 to 2026
$2.0M
MUSC FACIL EXPANSION &RENOV: SLE C06RR015455 · NCRR · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI CROUCH, ROSALIE K · 2002 to 2002
$2.0M
Metabolic reprogramming to boost the fitness of anti-tumor immunity against metastatic colon cancerR37CA292807 · NCI · COLD SPRING HARBOR LABORATORY · PI Semir Beyaz · 2025 to 2026
$1.3M
NCI NIH HHS P30 CA045508NCI NIH HHS P30 CA138313NCI NIH HHS R37 CA292807NCRR NIH HHS C06 RR015455NHGRI NIH HHS T32 HG002295NIDDK NIH HHS R01 DK137993NIEHS NIH HHS R01 ES032470NIGMS NIH HHS P30 GM103339NIGMS NIH HHS T32 GM152349
6 · The paper itself

Abstract

Dietary fat composition modulates host physiology and the gut microbiome, but the long-term effects of specific fat sources and the extent to which these changes resolve after dietary reversal remain incompletely defined. Here, we present a longitudinal multi-omic resource of mice maintained for one year on a purified control diet, seven high-fat diets differing in predominant fat source, or reversal regimens in which animals were switched from high-fat to control diet after 4 or 9 months. We further incorporated two cohorts with distinct pre-existing microbiome configurations to determine how baseline community structure shapes diet-induced remodeling of the gut microbiome ecosystem. By integrating longitudinal phenotyping, fecal metagenomics, fecal metabolomics, plasma metabolomics and lipidomics, and intestinal single-cell RNA sequencing, we defined the shared and dietary fat-specific responses across host and microbiome compartments. Baseline microbiome composition strongly influenced microbial responses to diet, indicating that pre-existing community structure is a major determinant of dietary ecosystem remodeling. Although many altered features shifted toward baseline after dietary reversal, only approximately half of diet-associated microbial changes recovered within the study window. A subset of taxa exhibited persistent alterations, including sustained depletion of

Identifiers

PMID41889866
PMCPMC13015288

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.