Multilevel metabolic adaptation to exercise training

Aerobic exercise training engages compensatory physiological and behavioral mechanisms that constrain energy expenditure. Reductions in basal metabolism, improved movement efficiency, and selective remodeling of metabolically active organs may collectively limit exercise-induced weight loss.

Authors

Institutions

Publication Details

Journal
Communications Medicine
Published
2026-03-11
DOI
https://doi.org/10.1038/s43856-026-01502-z
Primary Topic
Adipose Tissue and Metabolism
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multilevel metabolic adaptation to exercise training

Carmit Levy, David Peled, Tzachi Knaan, Paulee Manich et al.
Communications Medicine
Adipose Tissue and Metabolism
article

Multilevel metabolic adaptation to exercise training

Carmit Levy, David Peled, Tzachi Knaan, Paulee Manich, Gal Aziel, Gal Dubnov-Raz, Irit Markus, Daniel Barazany, Maayan Ramati, Edward L. Melanson, Yftach Gepner, Eylam Ziv-Av, Chen Luxenburg
article en

Abstract

Aerobic exercise training engages compensatory physiological and behavioral mechanisms that constrain energy expenditure. Reductions in basal metabolism, improved movement efficiency, and selective remodeling of metabolically active organs may collectively limit exercise-induced weight loss.

Communications Medicine
Bar-Ilan University (IL), Tel Aviv University (IL), Sheba Medical Center (IL), University of Colorado Anschutz Medical Campus (US)
Zero hunger
Openalex Percentile: Top 26%
Adipose Tissue and Metabolism
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.