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Record W4414379032 · doi:10.1113/jp289218

Determinants of maximal oxygen uptake in highly trained females and males: a mechanistic study of sex differences using advanced invasive methods

2025· article· en· W4414379032 on OpenAlexafffund
Øyvind Skattebo, Marcos Martín-Rincón, Bjarne Rud, Joachim Nielsen, Lars Henrik N. Hegg, Andreas V. Kleive, Niels Ørtenblad, Øyvind Sandbakk, Robert Boushel, Hans‐Christer Holmberg, José A. L. Calbet, Jostein Hallén

Bibliographic record

VenueThe Journal of Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
FundersAgencia Estatal de InvestigaciónUniversidad de Las Palmas de Gran CanariaNorges IdrettshøgskoleMinisterio de Ciencia e InnovaciónCentrum för idrottsforskningNatural Sciences and Engineering Research Council of Canada
KeywordsVO2 maxBlood flowLean body massVenous bloodCardiac outputHemodynamicsIncremental exerciseFemoral artery

Abstract

fetched live from OpenAlex

Abstract Females typically have lower body mass‐normalised maximal oxygen uptake () than males. However, whether this difference is solely due to body composition or also reflects sex‐based differences in cardiovascular and muscular capacities for O2 delivery and O2 extraction remains unclear. This study examined sex differences in the O2 transport chain when normalised to lean body mass (LBM). Twenty‐three highly trained cyclists and triathletes (10 females; 29 ± 6 years) performed incremental cycling to exhaustion on an ergometer with simultaneous assessment of cardiac output, leg blood flow (thermodilution), O2 delivery, and leg O2 extraction (arterial and femoral venous catheters). Mitochondrial (TEM) and capillary (immunohistochemistry) densities were assessed in the vastus lateralis. Maximal cardiac output was 26% lower in females than males (22 ± 3 vs. 30 ± 3 l min−1; P < 0.001). However, this difference disappeared when normalised to LBM (P = 0.375). Two‐leg blood flow was similar after normalisation to leg lean mass (LLM; P = 0.327). However, females had 10% lower haemoglobin concentration and arterial O2 content (177 ± 10 vs. 194 ± 15 ml l−1; P = 0.004), resulting in 11%–14% lower lean mass‐normalised systemic and leg O2 delivery. Leg O2 extraction (91 ± 3 vs. 92 ± 3%; P = 0.204) and mitochondria, cristae, and capillary densities were similar between sexes. Therefore, proportional to sex differences in O2 delivery, females had lower lean mass‐normalised pulmonary (63 ± 8 vs. 73 ± 4 ml min−1 kgLBM−1; P = 0.003) and leg (135 ± 14 vs. 160 ± 14 ml min−1 kgLLM−1; P = 0.002) . These findings demonstrate that highly trained females and males have similar muscle O2 extraction and perfusion per kg LBM. However, females’ 10% lower haemoglobin concentration results in lower LBM‐normalised O2 delivery and . image Key points Females and males differ substantially in body size and composition, with males having greater skeletal muscle mass and females a higher body fat percentage. During maximal exercise, the active skeletal muscles consume most of the body's oxygen uptake. Consequently, males exhibit higher absolute and body‐mass‐normalised maximal oxygen uptakes. Here, we show that the heart's capacity to pump blood and perfuse the exercising muscles is similar between sexes when scaled to muscle mass. Despite similar perfusion, oxygen delivery per exercising muscle mass is approximately 10% lower in females than males, caused by a 10% lower blood haemoglobin concentration and oxygen‐carrying capacity. Conversely, the fractional oxygen extraction by the skeletal muscles, along with their mitochondria and capillary densities, are similar between sexes. These findings demonstrate that sex differences in body composition and haemoglobin concentration are the primary mechanisms underpinning the lower body‐mass normalised maximal oxygen uptake in females compared to males.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.359
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2025
Admission routes2
Has abstractyes

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