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Record W4388600827 · doi:10.1093/eurheartj/ehad655.752

Sex differences in the peripheral determinants of oxygen uptake in heart failure with preserved ejection fraction

2023· article· en· W4388600827 on OpenAlexaff
Rachel J. Skow, Denis J. Wakeham, Mitchel Samels, Zachary T. Martin, Damith Nandadeva, N. Balmain, James P. MacNamara, Tiffany Brazile, Michael D. Nelson, Tony G. Babb, B. D. Levine, Satyam Sarma, Paul J. Fadel, Christopher M. Hearon, Mark J. Haykowsky

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Alberta
FundersAmerican Heart Association
KeywordsMedicineHeart failure with preserved ejection fractionCardiologyInternal medicineExercise intoleranceVO2 maxEjection fractionHeart failureCardiac outputAerobic exercisePeripheralBlood pressureHemodynamicsHeart rate

Abstract

fetched live from OpenAlex

Abstract Background A hallmark feature of heart failure with preserved ejection fraction (HFpEF) is exercise intolerance. Data from non-invasive, whole-body cardiopulmonary exercise testing indicates that females with HFpEF have reduced VO2 secondary to a to lower cardiac output and arterial-to-venous oxygen content difference (∆a-vO2) at maximal effort compared to males with HFpEF [1]. Thus, in addition to cardiac limitations, peripheral/non-cardiac limitations may be important contributors to reduced peak aerobic power (VO2) in females with HFpEF; however, these peripheral mechanisms of exercise intolerance are not fully understood. Given that HFpEF disproportionately affects older females, identifying sex differences in the limitations to exercise is especially important. Purpose Using dynamic single leg knee extension (SLKE) exercise to isolate peripheral determinants of oxygen transport and utilization, we sought to test the hypothesis that females with HFpEF have lower leg VO2 at peak SLKE resulting from smaller increases Δa-vO2 and muscle oxygen diffusive conductance (DMO2) compared to males. Methods Eighteen females (69 ± 7 years) and 13 males (73 ± 6 years) with HFpEF performed a maximal SLKE exercise test on a custom ergometer. LBF (duplex Doppler ultrasound) was measured at the common femoral artery and leg VO2 was determined as the product of LBF and ∆a-vO2 (femoral venous catheter). DMO2 was calculated as leg VO2/2x leg venous oxygen pressure (PvO2). Thigh lean and fat mass (TLM and TFM, respectively) was measured using dual energy x-ray absorptiometry. Group differences between the two sexes were compared using an unpaired t-test and significance was set at p<0.05. Results Body mass, body mass index, and TLM were not different between groups (p > 0.05 for all), however TFM was higher in females (p<0.001; Table 1). At rest, ∆a-vO2 was lower in females (p = 0.001), whereas heart rate, mean arterial blood pressure, femoral LBF, and leg VO2 were not different between groups (p>0.05 for all; Table 1). Peak workload was lower in females compared to males (13 ± 5 vs 20 ± 7 watts, p = 0.011). At peak SLKE, leg VO2 was lower in females (185 ± 57 vs 286 ± 91 ml/min, Figure 1) due to lower ∆a-vO2 (9.7 ± 1.8 vs 12.3 ± 2.5 ml O2/dl blood, p = 0.003) and LBF (1918 ± 576 vs 2382 ± 872 ml/min, p = 0.084). DMO2 at peak SLKE was also lower in females (3.6 ± 1.2 vs 6.3 ± 1.8 ml/min/mmHg, Figure 1) suggesting sex differences in both convective and diffusive oxygen transport. Moreover, when normalized to TLM, the difference in peak leg VO2 between the sexes persisted (28.2 ± 6.5 vs 41.5 ± 10.9 ml/kg/min; p = 0.004). Conclusion These data highlight that that the lower peak oxygen extraction (∆a-vO2) and diffusion (DMO2) in females with HFpEF contribute to sex-dependent differences in exercise intolerance. Further studies investigating potential sex-specific impairments in the muscle-vascular interface in HFpEF are warranted.Table 1Figure 1

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.042
GPT teacher head0.289
Teacher spread0.247 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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