Sex-based differences in hematological values after normalization to body mass or fat-free mass in adults matched for aerobic fitness
Bibliographic record
Abstract
Blood properties influence aerobic exercise performance. While vascular volumes and hemoglobin mass (Hbmass) are elevated in trained individuals, evidence of sex differences in vascular volumes is equivocal due to inadequate matching of aerobic fitness between males and females. This cross-sectional study aimed to compare hematological values normalized to body mass (BM) and fat-free mass (FFM) between males ( n = 45) and females ( n = 34) matched for aerobic fitness (V̇O2max) normalized to FFM (mL∙kg FFM−1∙min− 1). Data included body composition measured by dual-energy X-ray absorptiometry (DXA), V̇O2max from an incremental test, and hematological values derived from a CO rebreathe test. Fat mass was unrelated to blood volume (BV; R2 = 0.02, P = 0.26) and Hbmass ( R2 = 0.03, P = 0.16), while FFM was the strongest predictor of both ( R2 = 0.75 and R2 = 0.83, respectively, P < 0.001). Females exhibited higher FFM-normalized BV (+4%, P < 0.05) and plasma volume (PV) (+14%, P < 0.001) and lower red blood cell volume (RBCV) (−8%, P < 0.001) and Hbmass (−8%, P < 0.001) compared to males. Positive correlations between aerobic fitness and relative Hbmass and BV were observed in both sexes when normalized to BM and FFM (0.48 < r < 0.71; P < 0.003). Stepwise multiple regression models, including FFM, V̇O2max, height, and [Hb], provided accurate predictions of Hbmass ( R2 = 0.91) and BV ( R2 = 0.85). Overall, sex differences persist in relative Hbmass, BV, PV, and RBCV after matching of aerobic fitness, though relative BV and PV were greater in females. These findings suggest sex-specific strategies in oxygen delivery and/or extraction, and they underscore the importance of carefully selecting normalization practices when assessing sex-based differences in hematological variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".