How do physiological networks respond to normobaric hypoxia and isometric exercise?

The dynamics of physiological systems are impacted by both exercise and hypoxia. Network models can be used to map the interactions between various physiological components in environmental physiology and exercise using the concepts of information theory. This cross-over study compared three normobaric conditions: control, simulated altitude of 2500 m (fraction of inspired oxygen: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>F</mml:mi> <mml:mrow><mml:mi>i</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:msub> <mml:annotation>${F_{{\mathrm{i}}{{\mathrm{O}}_2}}}$</mml:annotation></mml:semantics> </mml:math> ≈ 15.1%) and 3500 m ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>F</mml:mi> <mml:mrow><mml:mi>i</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:msub> <mml:annotation>${F_{{\mathrm{i}}{{\mathrm{O}}_2}}}$</mml:annotation></mml:semantics> </mml:math> ≈ 13.5%), and rest vs. isometric exercise through the lens of network physiology. The 12 participants (6 M and 6 F; 22.25 ± 2.42 years; 23.01 ± 3.24 kg/m²) spent ∼30 min in a tent coupled to an altitude simulator, whose last 3 min consisted of a series of nine unilateral isometric maximal contractions of quadriceps. A metabolic system in breath-by-breath mode was used to register cardiorespiratory variables. In-degree, out-degree, and transfer entropy (TE) were computed to capture the information flow between variables. A weighted Jaccard Similarity Index was used to assess network similarities. The increase of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:msub><mml:mover><mml:mi>V</mml:mi> <mml:mo>̇</mml:mo></mml:mover> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:msub> <mml:annotation>${\dot V_{{{\mathrm{O}}_2}}}$</mml:annotation></mml:semantics> </mml:math> in exercise over rest was slightly more prominent during hypoxia (P = 0.054, η² _p = 0.232). Normoxia-hypoxia networks were more similar during resting than exercise. Rest-exercise networks were less similar to each other during simulated altitude of ∼2500 m (P = 0.008, η² _p = 0.353). Neither TE during rest nor during exercise nor the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>S</mml:mi> <mml:mrow><mml:mi>p</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:msub> <mml:annotation>${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$</mml:annotation></mml:semantics> </mml:math> / <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>F</mml:mi> <mml:mrow><mml:mi>i</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:msub> <mml:annotation>${F_{{\mathrm{i}}{{\mathrm{O}}_2}}}$</mml:annotation></mml:semantics> </mml:math> ratio significantly predicted the occurrence of symptoms. Unexpectedly, compared to mild-grade hypoxia, low-grade hypoxia induced more changes in physiological connectivity, with the majority of the connections converging on putative hidden nodes that we suggest are oxygen delivery-dependent. Network approaches could offer new developments in exercise and environmental physiology.