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IntroductionElite breath-hold divers (BHD) possess several oxygen conserving adaptations to endure long dives similar to diving mammals. During dives, Bottlenose Dolphins may increase the alveolar ventilation (VA) to perfusion (Q) ratio to increase alveolar oxygen delivery. We hypothesized that BHD possess similar adaptive mechanisms during apnea.Methods and resultsPulmonary blood volume (PBV) was determined by echocardiography, 15O-H2O PET/CT, and cardiac MRi, (n = 6) during and after maximum apneas. Pulmonary function was determined by body box spirometry and compared to matched controls. After 2 min of apnea, the PBV determined by echocardiography and 15O-H2O-PET/CT decreased by 26% and 41%, respectively. After 4 min of apnea, the PBV assessed by echocardiography and cardiac MRi decreased by 48% and 67%, respectively (n = 6). Fractional saturation (F)O2Hb determined by arterial blood-gas-analyses collected after warm-up and a 5-minute pool-apnea (n = 9) decreased by 43%. Compared to matched controls (n = 8), spirometry revealed a higher total and alveolar-lung-capacity in BHD (n = 9), but a lower diffusion-constant.ConclusionOur results contrast with previous studies, that demonstrated similar lung gas transfer in BHD and matched controls. We conclude that elite BHD 1) have a lower diffusion constant than matched controls, and 2) gradually decrease PBV during apnea and in turn increase VA/Q to increase alveolar oxygen delivery during maximum apnea. We suggest that BHD possess pulmonary adaptations similar to diving mammals to tolerate decreasing tissue oxygenation.New and noteworthyThis manuscript addresses novel knowledge on tolerance to hypoxia during diving, which is shared by elite breath-hold divers and adult diving mammals: Our study indicates that elite breath-hold divers gradually decrease pulmonary blood volume and in turn increase VA/Q, to increase alveolar oxygen delivery during maximum apnea to tolerate decreasing oxygen levels similar to the Bottlenose Dolphin.

IntroductionElite breath-hold divers (BHD) enduring apneas of more than 5 min are characterized by tolerance to arterial blood oxygen levels of 4.3 kPa and low oxygen-consumption in their hearts and skeletal muscles, similar to adult seals. Adult seals possess an adaptive higher hemoglobin-concentration and Bohr effect than pups, and when sedated, adult seals demonstrate a blood shift from the spleen towards the brain, lungs, and heart during apnea. We hypothesized these observations to be similar in human BHD. Therefore, we measured hemoglobin- and 2,3-biphosphoglycerate-concentrations in BHD (n = 11) and matched controls (n = 11) at rest, while myocardial mass, spleen and lower extremity volumes were assessed at rest and during apnea in BHD.Methods and resultsAfter 4 min of apnea, left ventricular myocardial mass (LVMM) determined by 15O-H2O-PET/CT (n = 6) and cardiac MRI (n = 6), was unaltered compared to rest. During maximum apnea (∼6 min), lower extremity volume assessed by DXA-scan revealed a ∼268 mL decrease, and spleen volume, assessed by ultrasonography, decreased ∼102 mL. Compared to age, BMI and VO2max matched controls (n = 11), BHD had similar spleen sizes and 2,3- biphosphoglycerate-concentrations, but higher total hemoglobin-concentrations.ConclusionOur results indicate: 1) Apnea training in BHD may increase hemoglobin concentration as an oxygen conserving adaptation similar to adult diving mammals. 2) The blood shift during dry apnea in BHD is 162% more from the lower extremities than from the spleen. 3) In contrast to the previous theory of the blood shift demonstrated in sedated adult seals, blood shift is not towards the heart during dry apnea in humans.

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.