Meta-analysis data of skeletal muscle slow fiber content across mammalian species

Skeletal muscle slow fiber (MyHC-I) content varies across muscles and taxa and is one of the traits that distinguishes humans from other apes, yet no study to date has compiled this interspecific data into a single, usable format. Thus, the goal of this study was to collate mammalian skeletal muscle slow fiber composition data from published, peer-reviewed articles into a single, open-access Excel sheet for interspecific comparison and analysis (as in Queeno et al., 2023).

A systematic literature search and review was conducted between June 1 2021 and November 30 2022 following a structure similar to PRISMA. Terms relating to mammalian skeletal muscle fiber composition were queried using academic search systems (e.g. Google Scholar) and library databases for relevant primary articles. Reference lists in relevant articles were thoroughly investigated for eligible studies. In total, 269 primary articles were deemed eligible for inclusion in the meta-analysis (i.e. these studies provided skeletal muscle fiber composition data from mammalian species that were not subjected to experimental manipulations). Relevant metadata (e.g. taxonomic information, sex, age, fiber-typing methodology, average body mass, and average percent slow fiber content) was then extracted from the text, figures, tables, and supplementary materials of eligible studies when available.

Muscle fiber composition data was collected from more than 200 skeletal muscles across 174 mammalian species, which will be of immense value to those interested in muscle physiology, interspecific muscle comparisons, and connections between muscle physiology, taxonomy, body mass, ecomorphology and locomotor strategy (among others). These data highlight certain species, taxonomic orders, and muscles for which fiber composition data is lacking and needs investigation. Hopefully, these data will spark interest in gathering muscle fiber composition data from underrepresented species and muscles, and generate interest in pursuing questions relating to muscle physiology and evolution, as well as analyses based on interspecific datasets.