Evidence map›Paper›PMID 41482230›Full record

ArticleThe Journal of nutrition2026

Invited: Longitudinal Assessment of Diets with Varying Carbohydrate-to-Fat Ratios and Fiber Supplementation on Immunometabolic Markers, Liver Function, and Gut Microbiome.

Umesh K Goand, Devendra Paudel, Anthony M Koehle, Fuhua Hao, Loi V Nguyen, Gopi Yalavarthi, Sangshan Tian, Sumudu Rajakaruna, Chloé Em Robert, Inês V da Silva and 4 more

Abstract read
In one paragraph

Article in The Journal of nutrition, 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

14 authors.

Umesh K GoandDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Devendra PaudelDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
Anthony M KoehleDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
Fuhua HaoDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Loi V NguyenDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
Gopi YalavarthiDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Sangshan TianDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Sumudu RajakarunaDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Chloé Em RobertMicrobiome-Host Interactions, Institut Pasteur, INSERM U1306, CNRS UMR6047, Université Paris Cité, Paris, France.
Inês V da SilvaDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal; Department of Pharmaceutical Sciences and Medicines, Faculty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal.
Benoit ChassaingMicrobiome-Host Interactions, Institut Pasteur, INSERM U1306, CNRS UMR6047, Université Paris Cité, Paris, France.
Andrew D PattersonDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.
Rita CastroDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal; Department of Pharmaceutical Sciences and Medicines, Faculty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal.
Vishal SinghDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States; One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States. Electronic address: vxs28@psu.edu.

Funding

Integrative Analysis of Metabolic Phenotypes (IAMP) Predoctoral Training ProgramT32DK120509 · NIDDK · PENNSYLVANIA STATE UNIVERSITY, THE · PI PATTERSON, ANDREW, PERDEW, GARY H. · 2020 to 2024
$781k
NIDDK NIH HHS T32 DK120509
6 · The paper itself

Abstract

backgroundThe proportions of macronutrients and fiber in the diet influence host metabolism and the development of metabolic and fatty liver disease.

objectivesWe aimed to investigate the temporal changes in metabolic and liver function markers that occur in response to diets with markedly different proportions of carbohydrates and fats, such as the ketogenic diet (KD) and a high-carbohydrate diet (HCD). We further examined whether these diets exert differential effects on immunometabolic markers under obese physiological conditions. Additionally, we evaluated whether the incorporation of prebiotic fiber modifies the metabolic effects of the KD.

methodsThis study conducted longitudinal assessments of immunometabolic and liver function markers in both lean and obese C57BL/6 mice.

resultsAssessments at 2, 4, 8, and 16 wks post-intervention in lean mice revealed that diets rich in fat (high fat (HFD) and KD) induced obesity and hyperglycemia compared to the baseline chow diet. KD instigated nutritional ketosis as early as two-wk post-feeding; however, it impaired metabolic and liver function starting from wk 2. Following the 16-wk intervention, we observed that the fat-rich diets (HFD and KD), but not the HCD, promoted hepatic steatosis, inflammation, and fibrosis, as assessed by

conclusionsOur findings demonstrate that KD affects metabolic and liver health differently in lean versus obese states. While the whole-grain-based diet and HCD were the most effective overall, the fiber-enriched KD (KD-F) outperformed the standard KD in promoting recovery from HFD-induced metabolic and hepatic dysfunctions. This suggests that incorporating fiber enhances the therapeutic potential of KD while preserving the metabolic benefits of ketogenesis.

Indexed as

Diet, KetogenicGastrointestinal MicrobiomeLiverNon-alcoholic Fatty Liver DiseaseObesityAnimalsBiomarkersDietary CarbohydratesDietary FatsDietary FiberDiet, High-FatLipid MetabolismLiver Function TestsMaleMiceMice, Inbred C57BLBiomarkersDietary CarbohydratesDietary FatsDietary Fibergrain-based diethigh carbohydrate dietketogenic dietprebiotic fibersteatohepatitis

Identifiers

PMID41482230
PMCPMC13551834

What Socratic holds

Textmetadata
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 Socratic graph.