The metabolomic profile of a high starch versus no starch diet in athletic horses

Feeding a high amount of starch-rich grains is common practice for performance horses even though the horse has evolved to eat a grass based, i.e. low starch diet. To our knowledge, there are no studies using metabolomics to investigate the effects of a high-starch diet in horses. In this study we investigated differences in the plasma metabolic profile of 6 Standardbred horses fed a no-starch, forage-only (F) diet or a high-starch forage-concentrate (FC) diet for 29 days, respectively in a cross-over design. Postprandial plasma samples were collected on the morning of day 25 of each dietary period. Metabolomics analysis of plasma using a targeted 1H NMR resulted in the quantification of 52 metabolites. Both a univariate and multivariate analysis of metabolites was performed. The univariate analysis found increased (p < 0.05) plasma concentrations of 2-hydroxybutyrate, citrate, dimethyl sulfone, hippurate, methionine, myo-inositol and proline in diet F and higher concentrations of glycine in diet FC. A PLS-DA analysis could discriminate between diets with good predictive power (Q2 (cum) = 0.745, p = 0.032 in CV-ANOVA). We conclude that diet F was strongest identified by metabolites originating from host-microbial co-metabolism and that the clear metabolomic profile discrimination between diets may have implications for health, performance and behaviour.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-10-13
DOI
https://doi.org/10.1038/s41598-025-23422-z
Primary Topic
Muscle metabolism and nutrition
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The metabolomic profile of a high starch versus no starch diet in athletic horses

Ali A. Moazzami, Anna Jansson, Jan Erik Lindberg, Emma Nilsson
Scientific Reports
Muscle metabolism and nutrition
article

The metabolomic profile of a high starch versus no starch diet in athletic horses

Ali A. Moazzami, Anna Jansson, Jan Erik Lindberg, Emma Nilsson
article en

Abstract

Feeding a high amount of starch-rich grains is common practice for performance horses even though the horse has evolved to eat a grass based, i.e. low starch diet. To our knowledge, there are no studies using metabolomics to investigate the effects of a high-starch diet in horses. In this study we investigated differences in the plasma metabolic profile of 6 Standardbred horses fed a no-starch, forage-only (F) diet or a high-starch forage-concentrate (FC) diet for 29 days, respectively in a cross-over design. Postprandial plasma samples were collected on the morning of day 25 of each dietary period. Metabolomics analysis of plasma using a targeted 1H NMR resulted in the quantification of 52 metabolites. Both a univariate and multivariate analysis of metabolites was performed. The univariate analysis found increased (p < 0.05) plasma concentrations of 2-hydroxybutyrate, citrate, dimethyl sulfone, hippurate, methionine, myo-inositol and proline in diet F and higher concentrations of glycine in diet FC. A PLS-DA analysis could discriminate between diets with good predictive power (Q2 (cum) = 0.745, p = 0.032 in CV-ANOVA). We conclude that diet F was strongest identified by metabolites originating from host-microbial co-metabolism and that the clear metabolomic profile discrimination between diets may have implications for health, performance and behaviour.

Scientific ReportsVol. 15(1)
Swedish University of Agricultural Sciences (SE)
Sveriges Lantbruksuniversitet
Openalex Percentile: Top 70%
Muscle metabolism and nutrition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.