Inefficient energy consumption is related to post exertional malaise during cardiopulmonary exercise testing in long COVID

Inefficient energy consumption is related to post exertional malaise during cardiopulmonary exercise testing in long COVID | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inefficient energy consumption is related to post exertional malaise during cardiopulmonary exercise testing in long COVID Leonardo Tamariz, Brian Garnet, Santiago Avecillas, Elizabeth Bast, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8072121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Dyspnea, fatigue and post-exertional malaise (PEM) are hallmark features of long Covid and emerging evidence suggests that abnormal energy metabolism may contribute to these symptoms. A cardiopulmonary exercise test (CPET) provides a detailed physiologic assessment of ventilatory and cardiovascular function and can offer insights into metabolic substrate utilization energy at rest and during exertion. Our aim was to evaluate patterns of energy metabolism at rest and during exercise during a CPET in patients with long Covid. Methods We conducted a cross-sectional study of consecutive non-selected patients that had been referred for a CPET. We included two groups: a long COVID and a control group. The CPET was performed on a cycle ergometer and we measured standard variables including oxygen uptake (V̇O₂), respiratory exchange ratio (RER), breathing reserve, heart rate, O2 pulse, and anaerobic threshold. We used RER to calculate indirect calorimetry estimating the use of carbohydrates and fat at rest and exertion. We analyzed the association between long COVID symptom severity symptoms including fatigue and post-exertional malaise (PEM) with patterns of energy consumption. We used logistic regression and area under the receiver operating characteristic curve to determine which CPET variables were most associated with long COVID. Results CPET results were analyzed for 50 patients who met the definition of long COVID and 45 patients controls. Long COVID patients and controls had similar peak V̇O₂, heart rate on exertion and V̇O₂ at anaerobic threshold. Seventy-three percent of patients with long COVID had predominant energy use of carbohydrates rather than fat at rest compared to 20% of controls. In multivariable models the odds ratio of using fat as energy source at rest was 0.99; 95% CI 0.99–0.99; p = 0.04. Patients with long COVID and severe fatigue as well as severe PEM had higher usage of carbohydrates (p < 0.01) and similar use of fat. Conclusion Patients with long COVID use energy inefficiently and this pattern could serve as a diagnostic feature in certain presentations of long COVID. Figures Figure 1 Background Long COVID (LC) is a complex disease affecting between 5 and 30% of the US population and presenting with over 200 reported symptoms. [ 1 ] The most disabling symptoms include fatigue, sleep disturbance, dyspnea and post exertional malaise (PEM). PEM is defined as a delayed but acute worsening of one or more symptoms after a physical, emotional and/or mental exertion. [ 2 ] There is growing evidence that impaired oxygen extraction, inefficient ventilation and a metabolic shift causing inefficient energy production are key mediators of these long COVID symptoms. [ 3 ][ 4 ][ 5 ] [ 6 ] In this context, cardiopulmonary exercise test (CPET) testing is useful in the evaluation of unexplained fatigue, dyspnea and PEM and can provide important information about exercise capacity and limitations.[ 7 ] In ME/CFS, a condition with similar presentation and pathophysiology to long COVID [ 8 ], CPET has found chronotropic intolerance due to autonomic dysfunction [ 9 ] and inefficient exercise ventilation during exercise [ 10 ] to be causes of exercise limitation. More recently, an invasive CPET study revealed abnormally low oxygen extraction suspected to be due to peripheral microvascular shunting as an additional mediator of reduced exercise capacity [ 11 ]. A systematic review among LC patients reported that deconditioning, abnormal oxygen extraction and chronotropic incompetence were commonly seen.[ 12 ] However, several publications have refuted the notion that LC is primarily caused by deconditioning[ 13 ] and rather describe biological pathways, emphasizing neurological, vascular and immunological mechanisms as culprits of intolerance to physical activity, fatigue and other symptoms. [ 14 ][ 5 ] These publications collectively emphasize biological, not psychological or deconditioning, as explanations for this complex condition. Yet, a clear understanding of how these mechanisms interact to produce fatigue and PEM is still limited. Thus, evaluating metabolic parameters at rest and in response to exercise in LC can provide important information regarding energy consumption. Invasive CPET testing has limited availability, but indirect calorimetry may offer diagnostic insight into this condition that is related to the pathophysiologic mechanisms of LC. Therefore, our aims are two-fold: to evaluate the relationship between self-reported symptoms and energy consumption on a CPET and to compare the energy consumption patterns between long COVID and a control group. Methods Study setting The Miami VAHS long COVID clinic was established in 2021 as a multidisciplinary clinic that offers hybrid services (remote or in person). Patients are referred by primary care providers or screened via a national VAHS digital screening pilot. The clinic consists of 6 providers and serves 250 patients per month. Our clinical protocol includes a combination of interventions supported by a multidisciplinary team.[ 15 ] These include the use of evidence-based approaches to manage postural tachycardia or hypotension, sleep disturbances, and pain. In addition, we use low dose naltrexone, low histamine diet, mindfulness strategies to improve autonomic balance and pacing with long COVID oriented physical rehabilitation. [ 16 ] As part of the rehabilitation process, we refer patients for CPET to inform the individualized exercise protocol. Study design and study population We conducted a cross-sectional study of consecutive non-selected patients that had been referred for a CPET. We included two groups: a LC and a control group. The LC group was referred from our long COVID clinic. We defined long COVID based on the WHO criteria[ 17 ] and our criteria for CPET referral was unexplained fatigue and dyspnea that was not improving with standards of care and/or evaluation of exercise tolerance in preparation for a physical rehabilitation prescription. For this analysis we excluded patients with a prior diagnosis of ME/CFS, fibromyalgia or gulf war illness as metabolism shifts have already been reported. [ 18 ]The control group were patients referred for CPET from the pulmonary clinic for the evaluation of unexplained dyspnea. In both the LC and control groups, pulmonary function testing was performed as part of the evaluation of dyspnea prior to referral for CPET. The study was approved by the institutional review board at the Miami VA in accordance to the Declaration of Helsinki. Our study was approved via expedited mechanisms as a retrospective chart review and did not require consent. Cardiopulmonary exercise test Patients underwent a symptom-limited CPET on a cycle ergometer with respiratory gas exchange analysis and cardiopulmonary monitoring. [ 7 ] The CPET protocol started with a two-minute resting recording followed by a 2-minute unloaded cycling as warm-up at a cadence of 60 rpm. The exercise portion utilized a continuous ramp (5–25 watts/minute) targeting predicted peak work rate at 10 minutes. The test proceeded until the patient reported volitional fatigue or dyspnea based on a Borg score of 9–10/10, reported other limiting symptoms such as chest pain or dizziness, experienced oxygen desaturation to < 80%, or slowed down to a cadence of < 50 rpm. Electrocardiograms were continuously monitored to record heart rate and blood pressure was measured at baseline and every 2 minutes using an automated blood pressure cuff. Blood pressure was repeated manually by the technician if the measurement exceeded 200mmHg systolic or 90mmHg diastolic to confirm. Borg dyspnea and fatigue scores were assessed every 2 minutes at the time of blood pressure measurement. Pulse oximetry was measured continuously using a finger pulse oximeter. Standard CPET variables including ventilation (V̇E), end-tidal carbon dioxide (ETCO2) was measured using SentrySuite Software and Vyntus One CPX cart using breath by breath analysis with 30s averages updated every 10s (“rolling 30s”). The software was used to calculate oxygen consumption (V̇O₂), expired carbon dioxide (V̇CO₂), V̇E /V̇O₂, V̇E /V̇CO₂, RER. Calibration of the system with gravimetric quality gases was performed before each test. A standard CPET mask with a viral filter was used to connect to the pneumotach with custom adjustment for the dead space of the mask and filter. Peak V̇O₂ was determined at the conclusion of the test as the highest 30 second average V̇O₂ during the exercise portion of the test and was expressed in ml/kg/min and as a percent of predicted based on age, sex, and weight using Jones and colleagues reference equations(1). Other peak variables including heart rate, V̇E, blood pressure were determined by the system during the same 30 second period as the peak V̇O₂. Breathing reserve was defined as 100% (1 – peak V̇E / 35 x FEV1). V̇E /V̇CO₂ nadir was taken as the lowest 30 second average during exercise. RER (V̇CO₂ / V̇O₂) was used to assess effort and to calculate indirect calorimetry as detailed below. We considered the test to be maximal if the RER was ≥ 1.15.[19] CPET indirect calorimetry We evaluated indirect calorimetry to determine the energy derived from carbohydrate and fat from the CPET. We collected the energy derived both at rest and during maximal exercise using three methods. First, we collected the RER on a breath-by-breath basis from the CPET to obtain the percentage of energy produced by carbohydrate and fat.[ 19 ] Second, we estimated the amount of fat and carbohydrate used as kilocalories per day for each time point to determine the predominant energy source used. Third, we also collected fat (FATox) and carbohydrate (CHOox) oxidation as defined the stoichiometric equations by Fryan and colleagues.[ 20 , 21 ][ 22 ] Long COVID symptoms We evaluated LC symptoms using the modified COVID-19 Yorkshire Rehabilitation scale (C19-YRSm) and the COMPASS-31, a scale of autonomic dysfunction. The C19-CYRSm is a 17-item instrument with 4 subscales (scores): symptom severity (0–30), functional disability (0–15), other symptoms (0–25), and overall health (0–10). The higher the score the more symptom burden or dysfunction, however overall health is best at a 10 and worst imaginable at a 0. The scale has been validated in long COVID[ 23 ]. We also used the C19-YRSm answers for fatigue and post-exertional malaise and classified them as mild, moderate and severe. We defined fatigue and PEM as having at least mild fatigue or PEM in the C19-YRSm.The COMPASS‐31 is validated and widely used questionnaire to quantify autonomic symptom severity. It consists of 31 questions that fall into six domains of dysautonomia: orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, and pupillomotor. An answer was scored as zero when it was not assigned a point. A raw domain score was obtained by adding together points within each domain. The total score within each domain was weighted and then added together to give a total score ranging from 0 to 100.[ 24 ] A total COMPASS‐31 of > 28.6 was used to suggest initial autonomic nervous system dysfunction, as reported in earlier studies.[ 25 ] Covariates We collected demographic and clinical characteristics to include in our analysis. From the electronic health record (EHR) we collected age, gender and race/ethnicity defined as Non-Hispanic White, Black or Hispanic. We also collected clinical conditions known to be associated with dysautonomia symptoms. These included the comorbidities and body mass index (BMI). We also collected results from the most recent pulmonary function test or the spirometry performed during the CPET. Statistical analysis We report baseline characteristics as mean with standard deviation and percentages. We compared baseline and CPET characteristics using t-test and chi-square. To compare levels of fat and carbohydrate energy used we used the t-test. To determine CPET predictors of having long COVID we created a multivariable model with long COVID as the dependent variable. We calculated the odds ratio (OR) and the corresponding 95% confidence interval of each predictor adjusted for age, gender and race. To evaluate the strength of the relationship we also calculated the area under the receiver operating characteristic curve (aROC) using long COVID as the reference variable. The fitness of the data was assessed using the deviance ratio. Analyses were performed using STATA version 17 (College Station, Texas), and all significance tests were two-tailed. Results Baseline characteristics Table 1 shows the baseline characteristics of the included patients. We collected information from 95 patients who underwent a CPET at the Miami VA Hospital. We included 50 patients who met the definition of LC and 45 patients who were controls. LC patients were younger and more likely to be female when compared to the control group. Both groups had similar body mass index. The pulmonary function tests of the long COVID and controls were similar (appendix table 1) and the most common pulmonary diagnosis of the patients in the control group included asthma, OSA and no pulmonary diagnosis (appendix Fig. 1). The V̇E/V̇CO₂ and ETCO2 were similar between both groups (appendix table 3). COVID disease characteristics of the long COVID group Table 2 of the appendix shows the COVID disease baseline characteristics of the long COVID group. Patients with LC had a mild acute COVID-19 infection as they did not have pneumonia and had no residual lung disease, however, their C19-YRSm was 25.4+/-3.1 and their COMPASS-31 was 42.7+/-20. More than half (58%) reported severe fatigue and 42% reported severe post-exertional malaise. CPET findings for long COVID and controls LC patients and controls had similar peak V̇O₂, respiratory exchange ratio, heart rate on exertion, heart rate, load and anaerobic threshold. The breathing reserve was higher in the long COVID group (p < 0.01) (Table 2 ). CPET Indirect calorimetry as a predictor of long COVID Seventy two percent of patients with LC had predominant energy use of carbohydrates rather than fat at rest compared to 20% of controls (p < 0.01; Fig. 2 appendix). At rest carbohydrate use was higher and fat was lower in LC patients compared to controls (p < 0.01; Fig. 1 ) During exertion fat oxidation was higher in the control group at 50 and 150 watts while carbohydrate oxidation was lowest at 150 watts in the LC group (p < 0.01). Lower use of fat at rest was the only CPET predictor of long COVID on the multivariable model (p < 0.05) (Table 3 ). The highest aROC in predicting long COVID was seen with the breathing reserve and having carbohydrate as the main source of energy during rest (appendix table 4). The area under the curve for using carbohydrates as the main source of energy at rest had a aROC of 76% (appendix Fig. 2) At rest patients with long COVID and severe fatigue had higher usage of carbohydrates (p < 0.01) and similar use of fat (appendix Fig. 3) than those with moderate fatigue while long COVID patients with severe PEM had higher use of carbohydrates and lower use of fat (p < 0.01; Fig. 4 ) than those with moderate PEM. Discussion Our study found that LC patients preferentially use carbohydrates as an energy source at rest and lower fat use during exertion when compared to a control group without LC. This carbohydrate energy preference was even more profound in patients with PEM. We also found that out of all CPET predictors, use of carbohydrate at rest was predictive of having LC. The strengths of this paper include the use of well-characterized consecutive LC patients referred to CPET, the inclusion of a control group without long COVID and the extensive calorimetric analysis. Our study found that peak Vo2 and the anaerobic threshold were similar while breathing reserve was different when comparing long COVID and controls. A systematic review comparing LC patients and controls found a 4.9 ml/kg/min lower mean peak Vo2, chronotropic incompetence and low oxygen extraction as a potential mechanisms for the deconditioning seen in LC. [ 26 ] A more recent study by Meza et al. also found a lower predicted peak Vo2 and autonomic imbalance. [ 27 ] A potential explanation for the differences in the findings between our study and others could be that other studies used historical controls while we used patients with unexplained dyspnea with prior COVID infections. The differential use of energy sources, previously reported [ 18 ] has been suspected in LC. [ 28 ] Prior research suggests that patients with long COVID and ME/CFS experience a metabolic shift toward increased anaerobic glycolysis and reduced oxidative phosphorylation through the Krebs cycle,[ 29 ] a phenomenon similar to the Warburg effect observed in cancer cells. Studies demonstrate that these patients' cells show increased lactate production and decreased pyruvate dehydrogenase activity, indicating a preferential use of anaerobic glycolysis over aerobic respiration.[ 30 ] This metabolic switch is particularly inefficient, as anaerobic glycolysis only produces 2 ATP molecules per glucose molecule, compared to the 36 ATP molecules generated through complete aerobic respiration via the Krebs cycle and electron transport chain. This energy deficit contributes directly to the profound fatigue and PEM characteristic of these conditions, as cells struggle to meet energy demands during physical or cognitive exertion. The resulting accumulation of lactate and potential mitochondrial dysfunction further exacerbate these symptoms, creating a vicious cycle of energy depletion and impaired recovery. During the acute phase of COVID-19, SARS-CoV2 can directly interact with mitochondria and cause structural damage, shown to lead to abnormal production of ATP[ 31 ]. In muscle biopsies of patients with LC compared to controls who recovered fully from Covid-19 there were similar amounts of SARS-CoV-2 nucleocapsid protein, but increased CD68 + macrophages and muscle cell necrosis. [ 5 ]The inflammatory mechanism of ongoing muscle dysfunction in Long Covid remains undetermined. The diagnosis of LC is challenging as it is primarily symptom based. In this study, we found that a preference for carbohydrate metabolism at rest was strongly associated with long COVID, suggesting its potential as a supportive diagnostic marker at least for a sub-type of long COVID characterized by fatigue and post exertional malaise. Cardiopulmonary exercise testing can also provide valuable measures in patients with LC with unexplained fatigue, PEM or dyspnea. A CPET can help differentiate the cause of exercise limitation in LC patients by diagnosing alternative etiologies of symptoms including reactive airway disease, pulmonary vascular disease, cardiac disease, dysautonomia, or deconditioning. During invasive CPET we could identify a higher peak venous oxygen content diagnostic of reduced oxygen extraction and suggestive of microvascular shunting in the muscle, as it has been seen previously [ 11 ]in LC [ 4 ]. Our study adds to the literature by confirming a significant difference in energy sources during rest between LC and a sick control group. This measure could be helpful, particularly among highly symptomatic LC patients to diagnose and monitor progress. Additional CPET measures such as the time to anaerobic threshold and Borg scale of perceived exertion at the time of reaching the anaerobic threshold, can be used to guide the rehabilitation prescription and progression. A potential draw back of a CPET is the precipitation of a post-exertional malaise episode, particularly with the recent proposal of two-day CPET [ 32 ]. This study suggests that all CPETs should include calorimetric measurement during the rest period to ascertain the level and extent of metabolic intracellular abnormalities and potentially monitor only this value for those at highest risk of disabling PEM. In Applebaum’s study [ 5 ]that included muscle biopsies, there was evidence of large increases in inflammation and muscle cell necrosis following a CPET that supports caution in performing two-day CPETs and highlights the importance of developing exercise prescriptions that will avoid causing further muscle damage. Our study has several limitations that deserve mention. First, our sample size was small, however, despite the small sample size we were able to identify clinical and statistically significant findings. Second, the LC patients and controls were different in their demographic distribution and because of a small sample size we are unable to do matching, however, in multivariable age adjusted analysis, carbohydrate energy dependence at rest was consistently a predictor. Third, our control group was not a healthy control but rather patients with unexplained dyspnea as an indication for the CPET and could explain why metrics such as peak V̇O₂ and heart rate at exertion were similar between the two compared groups and could be underestimating the real scope of differences in CPET performance between LC and healthy individuals, as have previously been demonstrated by Appelman et al and Singh et al.[5][4] Fourth, the cross-sectional design does not allow us to determine the trajectory and duration of this energy utilization imbalance. Fifth, we did not have any dietary measurements which could also partially explain the results. In conclusion our study demonstrates evidence that indirect calorimetry showing carbohydrate metabolism at rest may support the diagnosis of LC and may be reflective of the pathophysiology of chronic fatigue and PEM in these patients. Future studies should explore how different rehabilitation strategies improve energy use and production at rest, modify the metabolic and inflammatory response to exercise, and alter the risk of ongoing PEM in prospective studies. Declarations Disclosures: None. There was no funding for this study. Author Contribution Conceptualization- LT, EB, JC, ND, AP, NKData collection- LT,SA,BGData analysis- LT, JCManuscript writing- LT, BG, SA, EB, JC, NDCritical revision- AP, NK Data Availability Our data is newly generated and have not used data from repositories. We are unable to share our data as our privacy rule does not allow. References Al-Aly Z, Davis H, McCorkell L, et al. Long COVID science, research and policy. 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Mitochondrial dysfunction in long COVID: Mechanisms, consequences, and potential therapeutic approaches. Geroscience. 2024;46(5):5267–86. 10.1007/s11357-024-01165-5 . Gattoni C, Abbasi A, Ferguson C, et al. Two-day cardiopulmonary exercise testing in long COVID post-exertional malaise diagnosis. Respir Physiol Neurobiol. 2025;331:104362. 10.1016/j.resp.2024.104362 . Tables Table 1: Baseline characteristics of the long COVID and control groups. Characteristics Long COVID (n=50) Controls (n=45) p-value Mean Age, SD 50.0+/-6.6 59.1+/-10.1 <0.01 Male gender, % 81 93 <0.01 Minority, % 50 27 0.17 Mean body mass index, SD 28.4+/-6.4 28.9+/-4.4 0.90 Diabetes, % 19 20 0.66 Mean ejection fraction, SD 56.7+/-2.8 54.3+/-2.5 <0.01 SD: standard deviation Table 2: CPET characteristics between the long COVID and control groups Characteristics Long COVID (n=50) Controls (n=45) p-value Peak V̇O₂ (ml/kg/min) 20.2+/-6.7 17.9+/-6.3 0.45 Peak V̇O₂ (% predicted) 70.5+/-17.9 70.6+/-19.6 0.85 Breathing reserve (%) 45.3+/-14.6 29.5+/-17.7 <0.01 V̇O₂ at anaerobic threshold (ml/kg/min) 13.1+/-4.5 13.1+/-5.6 0.98 V̇O₂ at anaerobic threshold (% predicted) 50.4+/-14.5 56.3+/-15.5 0.24 Peak heart rate (bpm) 134.1+/-20.0 129.2+/-18.7 0.41 Peak heart rate (% predicted) 74.9+/-13.8 79.2+/-13.8 0.30 Maximum load (watts) 137.2+/-71.5 117.2+/-55.1 0.23 Mean use of carbohydrates at peak rest 2750.3+/-1055 1606+/-1002 0.01 Mean use of carbohydrates on exertion 9201+/-3275 8653+/-2387 0.61 Mean use of fat at rest 1734+/-635 2753+/-1000 <0.01 Mean use of fat at peak exertion 3593+/-2187 3906+/-1642 0.67 Mean time to RER of 1 or more during exercise (minutes) 5.10+/-2.37 5.89+/-2.26 0.29 Respiratory exchange ratio at rest 0.93+/-0.09 0.87+/-0.05 0.03 Values are mean +/- standard deviation Table 3: Multivariable predictors of long COVID Long COVID predictor Odds ratio 95% confidence interval p-value Peak carbohydrate use at rest (continuos) 1.0 0.99-1.00 0.55 Peak fat use at rest (continuos) 0.99 0.99-0.99 0.03 Peak carbohydrate use on exertion (continuos) 1.0 0.99-1.00 0.75 Peak fat use on exertion (continuos) 1.0 0.99-100 0.55 Age 0.89 0.79-0.97 0.01 Peak V̇O₂ 0.89 0.72-1.10 0.30 Respiratory exchange ratio at rest 1.1 0.90-1.25 0.28 Breathing reserve 1.05 0.99-1.10 0.06 Anaerobic threshold 0.94 0.76-1.15 0.56 Time to RER of 1 0.82 0.62-1.09 0.18 Using carbohydrates at rest (categorical) 11.3 2.30-22.1 <0.01 Resting RER 0.97 0.95-0.99 0.02 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":37989,"visible":true,"origin":"","legend":"\u003cp\u003eCHOox (A) and FATox (B) during the CPET\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8072121/v1/f436c722159f5572b27362ae.png"},{"id":108976860,"identity":"949cf8dc-2a39-4ee1-bc0d-2464e32a1e86","added_by":"auto","created_at":"2026-05-11 11:29:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":308453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8072121/v1/a844f161-3c7c-4239-917e-64182f6c2166.pdf"},{"id":98245823,"identity":"6cadecd5-2695-4847-a8da-f7ca0fbdf456","added_by":"auto","created_at":"2025-12-15 16:18:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":154210,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8072121/v1/2760726f49c9d74a7796b0e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inefficient energy consumption is related to post exertional malaise during cardiopulmonary exercise testing in long COVID","fulltext":[{"header":"Background","content":"\u003cp\u003eLong COVID (LC) is a complex disease affecting between 5 and 30% of the US population and presenting with over 200 reported symptoms. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The most disabling symptoms include fatigue, sleep disturbance, dyspnea and post exertional malaise (PEM). PEM is defined as a delayed but acute worsening of one or more symptoms after a physical, emotional and/or mental exertion. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] There is growing evidence that impaired oxygen extraction, inefficient ventilation and a metabolic shift causing inefficient energy production are key mediators of these long COVID symptoms. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn this context, cardiopulmonary exercise test (CPET) testing is useful in the evaluation of unexplained fatigue, dyspnea and PEM and can provide important information about exercise capacity and limitations.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] In ME/CFS, a condition with similar presentation and pathophysiology to long COVID [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], CPET has found chronotropic intolerance due to autonomic dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and inefficient exercise ventilation during exercise [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] to be causes of exercise limitation. More recently, an invasive CPET study revealed abnormally low oxygen extraction suspected to be due to peripheral microvascular shunting as an additional mediator of reduced exercise capacity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA systematic review among LC patients reported that deconditioning, abnormal oxygen extraction and chronotropic incompetence were commonly seen.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] However, several publications have refuted the notion that LC is primarily caused by deconditioning[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and rather describe biological pathways, emphasizing neurological, vascular and immunological mechanisms as culprits of intolerance to physical activity, fatigue and other symptoms. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThese publications collectively emphasize biological, not psychological or deconditioning, as explanations for this complex condition. Yet, a clear understanding of how these mechanisms interact to produce fatigue and PEM is still limited. Thus, evaluating metabolic parameters at rest and in response to exercise in LC can provide important information regarding energy consumption. Invasive CPET testing has limited availability, but indirect calorimetry may offer diagnostic insight into this condition that is related to the pathophysiologic mechanisms of LC. Therefore, our aims are two-fold: to evaluate the relationship between self-reported symptoms and energy consumption on a CPET and to compare the energy consumption patterns between long COVID and a control group.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy setting\u003c/h2\u003e\u003cp\u003eThe Miami VAHS long COVID clinic was established in 2021 as a multidisciplinary clinic that offers hybrid services (remote or in person). Patients are referred by primary care providers or screened via a national VAHS digital screening pilot. The clinic consists of 6\u003c/p\u003e\u003cp\u003eproviders and serves 250 patients per month. Our clinical protocol includes a combination of interventions supported by a multidisciplinary team.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] These include the use of evidence-based approaches to manage postural tachycardia or hypotension, sleep disturbances, and pain. In addition, we use low dose naltrexone, low histamine diet, mindfulness strategies to improve autonomic balance and pacing with long COVID oriented physical rehabilitation. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] As part of the rehabilitation process, we refer patients for CPET to inform the individualized exercise protocol.\u003c/p\u003e\u003cp\u003eStudy design and study population\u003c/p\u003e\u003cp\u003eWe conducted a cross-sectional study of consecutive non-selected patients that had been referred for a CPET. We included two groups: a LC and a control group. The LC group was referred from our long COVID clinic. We defined long COVID based on the WHO criteria[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and our criteria for CPET referral was unexplained fatigue and dyspnea that was not improving with standards of care and/or evaluation of exercise tolerance in preparation for a physical rehabilitation prescription. For this analysis we excluded patients with a prior diagnosis of ME/CFS, fibromyalgia or gulf war illness as metabolism shifts have already been reported. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]The control group were patients referred for CPET from the pulmonary clinic for the evaluation of unexplained dyspnea. In both the LC and control groups, pulmonary function testing was performed as part of the evaluation of dyspnea prior to referral for CPET. The study was approved by the institutional review board at the Miami VA in accordance to the Declaration of Helsinki. Our study was approved via expedited mechanisms as a retrospective chart review and did not require consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCardiopulmonary exercise test\u003c/h3\u003e\n\u003cp\u003ePatients underwent a symptom-limited CPET on a cycle ergometer with respiratory gas exchange analysis and cardiopulmonary monitoring. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The CPET protocol started with a two-minute resting recording followed by a 2-minute unloaded cycling as warm-up at a cadence of 60 rpm. The exercise portion utilized a continuous ramp (5\u0026ndash;25 watts/minute) targeting predicted peak work rate at 10 minutes. The test proceeded until the patient reported volitional fatigue or dyspnea based on a Borg score of 9\u0026ndash;10/10, reported other limiting symptoms such as chest pain or dizziness, experienced oxygen desaturation to \u0026lt;\u0026thinsp;80%, or slowed down to a cadence of \u0026lt;\u0026thinsp;50 rpm. Electrocardiograms were continuously monitored to record heart rate and blood pressure was measured at baseline and every 2 minutes using an automated blood pressure cuff. Blood pressure was repeated manually by the technician if the measurement exceeded 200mmHg systolic or 90mmHg diastolic to confirm. Borg dyspnea and fatigue scores were assessed every 2 minutes at the time of blood pressure measurement. Pulse oximetry was measured continuously using a finger pulse oximeter. Standard CPET variables including ventilation (V̇E), end-tidal carbon dioxide (ETCO2) was measured using SentrySuite Software and Vyntus One CPX cart using breath by breath analysis with 30s averages updated every 10s (\u0026ldquo;rolling 30s\u0026rdquo;). The software was used to calculate oxygen consumption (V̇O₂), expired carbon dioxide (V̇CO₂), V̇E /V̇O₂, V̇E /V̇CO₂, RER. Calibration of the system with gravimetric quality gases was performed before each test. A standard CPET mask with a viral filter was used to connect to the pneumotach with custom adjustment for the dead space of the mask and filter.\u003c/p\u003e\u003cp\u003ePeak V̇O₂ was determined at the conclusion of the test as the highest 30 second average V̇O₂ during the exercise portion of the test and was expressed in ml/kg/min and as a percent of predicted based on age, sex, and weight using Jones and colleagues reference equations(1). Other peak variables including heart rate, V̇E, blood pressure were determined by the system during the same 30 second period as the peak V̇O₂. Breathing reserve was defined as 100% (1 \u0026ndash; peak V̇E / 35 x FEV1). V̇E /V̇CO₂ nadir was taken as the lowest 30 second average during exercise. RER (V̇CO₂ / V̇O₂) was used to assess effort and to calculate indirect calorimetry as detailed below. We considered the test to be maximal if the RER was \u0026ge; 1.15.[19]\u003c/p\u003e\n\u003ch3\u003eCPET indirect calorimetry\u003c/h3\u003e\n\u003cp\u003eWe evaluated indirect calorimetry to determine the energy derived from carbohydrate and fat from the CPET. We collected the energy derived both at rest and during maximal exercise using three methods. First, we collected the RER on a breath-by-breath basis from the CPET to obtain the percentage of energy produced by carbohydrate and fat.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Second, we estimated the amount of fat and carbohydrate used as kilocalories per day for each time point to determine the predominant energy source used. Third, we also collected fat (FATox) and carbohydrate (CHOox) oxidation as defined the stoichiometric equations by Fryan and colleagues.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eLong COVID symptoms\u003c/h3\u003e\n\u003cp\u003eWe evaluated LC symptoms using the modified COVID-19 Yorkshire Rehabilitation scale (C19-YRSm) and the COMPASS-31, a scale of autonomic dysfunction. The C19-CYRSm is a 17-item instrument with 4 subscales (scores): symptom severity (0\u0026ndash;30), functional disability (0\u0026ndash;15), other symptoms (0\u0026ndash;25), and overall health (0\u0026ndash;10). The higher the score the more symptom burden or dysfunction, however overall health is best at a 10 and worst imaginable at a 0. The scale has been validated in long COVID[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We also used the C19-YRSm answers for fatigue and post-exertional malaise and classified them as mild, moderate and severe. We defined fatigue and PEM as having at least mild fatigue or PEM in the C19-YRSm.The COMPASS‐31 is validated and widely used questionnaire to quantify autonomic symptom severity. It consists of 31 questions that fall into six domains of dysautonomia: orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, and pupillomotor. An answer was scored as zero when it was not assigned a point. A raw domain score was obtained by adding together points within each domain. The total score within each domain was weighted and then added together to give a total score ranging from 0 to 100.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] A total COMPASS‐31 of \u0026gt;\u0026thinsp;28.6 was used to suggest initial autonomic nervous system dysfunction, as reported in earlier studies.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eWe collected demographic and clinical characteristics to include in our analysis. From the electronic health record (EHR) we collected age, gender and race/ethnicity defined as Non-Hispanic White, Black or Hispanic. We also collected clinical conditions known to be associated with dysautonomia symptoms. These included the comorbidities and body mass index (BMI). We also collected results from the most recent pulmonary function test or the spirometry performed during the CPET.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe report baseline characteristics as mean with standard deviation and percentages. We compared baseline and CPET characteristics using t-test and chi-square. To compare levels of fat and carbohydrate energy used we used the t-test.\u003c/p\u003e\u003cp\u003eTo determine CPET predictors of having long COVID we created a multivariable model with long COVID as the dependent variable. We calculated the odds ratio (OR) and the corresponding 95% confidence interval of each predictor adjusted for age, gender and race. To evaluate the strength of the relationship we also calculated the area under the receiver operating characteristic curve (aROC) using long COVID as the reference variable.\u003c/p\u003e\u003cp\u003eThe fitness of the data was assessed using the deviance ratio. Analyses were performed using STATA version 17 (College Station, Texas), and all significance tests were two-tailed.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the included patients. We collected information from 95 patients who underwent a CPET at the Miami VA Hospital. We included 50 patients who met the definition of LC and 45 patients who were controls. LC patients were younger and more likely to be female when compared to the control group. Both groups had similar body mass index. The pulmonary function tests of the long COVID and controls were similar (appendix table 1) and the most common pulmonary diagnosis of the patients in the control group included asthma, OSA and no pulmonary diagnosis (appendix Fig. 1). The V̇E/V̇CO₂ and ETCO2 were similar between both groups (appendix table 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCOVID disease characteristics of the long COVID group\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e of the appendix shows the COVID disease baseline characteristics of the long COVID group. Patients with LC had a mild acute COVID-19 infection as they did not have pneumonia and had no residual lung disease, however, their C19-YRSm was 25.4+/-3.1 and their COMPASS-31 was 42.7+/-20. More than half (58%) reported severe fatigue and 42% reported severe post-exertional malaise.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCPET findings for long COVID and controls\u003c/h2\u003e\n \u003cp\u003eLC patients and controls had similar peak V̇O₂, respiratory exchange ratio, heart rate on exertion, heart rate, load and anaerobic threshold. The breathing reserve was higher in the long COVID group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCPET Indirect calorimetry as a predictor of long COVID\u003c/h2\u003e\n \u003cp\u003eSeventy two percent of patients with LC had predominant energy use of carbohydrates rather than fat at rest compared to 20% of controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e appendix). At rest carbohydrate use was higher and fat was lower in LC patients compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) During exertion fat oxidation was higher in the control group at 50 and 150 watts while carbohydrate oxidation was lowest at 150 watts in the LC group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Lower use of fat at rest was the only CPET predictor of long COVID on the multivariable model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe highest aROC in predicting long COVID was seen with the breathing reserve and having carbohydrate as the main source of energy during rest (appendix table 4). The area under the curve for using carbohydrates as the main source of energy at rest had a aROC of 76% (appendix Fig. 2)\u003c/p\u003e\n \u003cp\u003eAt rest patients with long COVID and severe fatigue had higher usage of carbohydrates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and similar use of fat (appendix Fig. 3) than those with moderate fatigue while long COVID patients with severe PEM had higher use of carbohydrates and lower use of fat (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) than those with moderate PEM.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study found that LC patients preferentially use carbohydrates as an energy source at rest and lower fat use during exertion when compared to a control group without LC. This carbohydrate energy preference was even more profound in patients with PEM. We also found that out of all CPET predictors, use of carbohydrate at rest was predictive of having LC. The strengths of this paper include the use of well-characterized consecutive LC patients referred to CPET, the inclusion of a control group without long COVID and the extensive calorimetric analysis.\u003c/p\u003e\n\u003cp\u003eOur study found that peak Vo2 and the anaerobic threshold were similar while breathing reserve was different when comparing long COVID and controls. A systematic review comparing LC patients and controls found a 4.9 ml/kg/min lower mean peak Vo2, chronotropic incompetence and low oxygen extraction as a potential mechanisms for the deconditioning seen in LC. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] A more recent study by Meza et al. also found a lower predicted peak Vo2 and autonomic imbalance. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] A potential explanation for the differences in the findings between our study and others could be that other studies used historical controls while we used patients with unexplained dyspnea with prior COVID infections.\u003c/p\u003e\n\u003cp\u003eThe differential use of energy sources, previously reported [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] has been suspected in LC. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] Prior research suggests that patients with long COVID and ME/CFS experience a metabolic shift toward increased anaerobic glycolysis and reduced oxidative phosphorylation through the Krebs cycle,[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] a phenomenon similar to the Warburg effect observed in cancer cells. Studies demonstrate that these patients\u0026apos; cells show increased lactate production and decreased pyruvate dehydrogenase activity, indicating a preferential use of anaerobic glycolysis over aerobic respiration.[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] This metabolic switch is particularly inefficient, as anaerobic glycolysis only produces 2 ATP molecules per glucose molecule, compared to the 36 ATP molecules generated through complete aerobic respiration via the Krebs cycle and electron transport chain. This energy deficit contributes directly to the profound fatigue and PEM characteristic of these conditions, as cells struggle to meet energy demands during physical or cognitive exertion. The resulting accumulation of lactate and potential mitochondrial dysfunction further exacerbate these symptoms, creating a vicious cycle of energy depletion and impaired recovery.\u003c/p\u003e\n\u003cp\u003eDuring the acute phase of COVID-19, SARS-CoV2 can directly interact with mitochondria and cause structural damage, shown to lead to abnormal production of ATP[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. In muscle biopsies of patients with LC compared to controls who recovered fully from Covid-19 there were similar amounts of SARS-CoV-2 nucleocapsid protein, but increased CD68\u0026thinsp;+\u0026thinsp;macrophages and muscle cell necrosis. [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]The inflammatory mechanism of ongoing muscle dysfunction in Long Covid remains undetermined.\u003c/p\u003e\n\u003cp\u003eThe diagnosis of LC is challenging as it is primarily symptom based. In this study, we found that a preference for carbohydrate metabolism at rest was strongly associated with long COVID, suggesting its potential as a supportive diagnostic marker at least for a sub-type of long COVID characterized by fatigue and post exertional malaise. Cardiopulmonary exercise testing can also provide valuable measures in patients with LC with unexplained fatigue, PEM or dyspnea. A CPET can help differentiate the cause of exercise limitation in LC patients by diagnosing alternative etiologies of symptoms including reactive airway disease, pulmonary vascular disease, cardiac disease, dysautonomia, or deconditioning. During invasive CPET we could identify a higher peak venous oxygen content diagnostic of reduced oxygen extraction and suggestive of microvascular shunting in the muscle, as it has been seen previously [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]in LC [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our study adds to the literature by confirming a significant difference in energy sources during rest between LC and a sick control group. This measure could be helpful, particularly among highly symptomatic LC patients to diagnose and monitor progress. Additional CPET measures such as the time to anaerobic threshold and Borg scale of perceived exertion at the time of reaching the anaerobic threshold, can be used to guide the rehabilitation prescription and progression. A potential draw back of a CPET is the precipitation of a post-exertional malaise episode, particularly with the recent proposal of two-day CPET [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. This study suggests that all CPETs should include calorimetric measurement during the rest period to ascertain the level and extent of metabolic intracellular abnormalities and potentially monitor only this value for those at highest risk of disabling PEM. In Applebaum\u0026rsquo;s study [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]that included muscle biopsies, there was evidence of large increases in inflammation and muscle cell necrosis following a CPET that supports caution in performing two-day CPETs and highlights the importance of developing exercise prescriptions that will avoid causing further muscle damage.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations that deserve mention. First, our sample size was small, however, despite the small sample size we were able to identify clinical and statistically significant findings. Second, the LC patients and controls were different in their demographic distribution and because of a small sample size we are unable to do matching, however, in multivariable age adjusted analysis, carbohydrate energy dependence at rest was consistently a predictor. Third, our control group was not a healthy control but rather patients with unexplained dyspnea as an indication for the CPET and could explain why metrics such as peak V̇O₂ and heart rate at exertion were similar between the two compared groups and could be underestimating the real scope of differences in CPET performance between LC and healthy individuals, as have previously been demonstrated by Appelman et al and Singh et al.[5][4] Fourth, the cross-sectional design does not allow us to determine the trajectory and duration of this energy utilization imbalance. Fifth, we did not have any dietary measurements which could also partially explain the results.\u003c/p\u003e\n\u003cp\u003eIn conclusion our study demonstrates evidence that indirect calorimetry showing carbohydrate metabolism at rest may support the diagnosis of LC and may be reflective of the pathophysiology of chronic fatigue and PEM in these patients. Future studies should explore how different rehabilitation strategies improve energy use and production at rest, modify the metabolic and inflammatory response to exercise, and alter the risk of ongoing PEM in prospective studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosures:\u003c/h2\u003e\u003cp\u003eNone. There was no funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization- LT, EB, JC, ND, AP, NKData collection- LT,SA,BGData analysis- LT, JCManuscript writing- LT, BG, SA, EB, JC, NDCritical revision- AP, NK\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eOur data is newly generated and have not used data from repositories. We are unable to share our data as our privacy rule does not allow.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Aly Z, Davis H, McCorkell L, et al. Long COVID science, research and policy. 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Geroscience. 2024;46(5):5267\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11357-024-01165-5\u003c/span\u003e\u003cspan address=\"10.1007/s11357-024-01165-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGattoni C, Abbasi A, Ferguson C, et al. Two-day cardiopulmonary exercise testing in long COVID post-exertional malaise diagnosis. Respir Physiol Neurobiol. 2025;331:104362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resp.2024.104362\u003c/span\u003e\u003cspan address=\"10.1016/j.resp.2024.104362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Baseline characteristics of the long COVID and control groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eLong COVID (n=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eControls (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMean Age, SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e50.0+/-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e59.1+/-10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMale gender, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMinority, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMean body mass index, SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.4+/-6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.9+/-4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDiabetes, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMean ejection fraction, SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e56.7+/-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e54.3+/-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: standard deviation\u003c/p\u003e\n\u003cp\u003eTable 2: CPET characteristics between the long COVID and control groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003eLong COVID (n=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003cp\u003e(n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003ePeak V̇O₂\u0026nbsp;(ml/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e20.2+/-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e17.9+/-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003ePeak V̇O₂\u0026nbsp;(% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e70.5+/-17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e70.6+/-19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eBreathing reserve (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e45.3+/-14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e29.5+/-17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eV̇O₂\u0026nbsp;at anaerobic threshold (ml/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e13.1+/-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e13.1+/-5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eV̇O₂\u0026nbsp;at anaerobic threshold (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e50.4+/-14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e56.3+/-15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003ePeak heart rate (bpm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e134.1+/-20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e129.2+/-18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003ePeak heart rate (% predicted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e74.9+/-13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e79.2+/-13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMaximum load (watts)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e137.2+/-71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e117.2+/-55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMean use of carbohydrates at peak rest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e2750.3+/-1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e1606+/-1002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMean use of carbohydrates on exertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e9201+/-3275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e8653+/-2387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMean use of fat at rest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e1734+/-635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e2753+/-1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMean use of fat at peak exertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e3593+/-2187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e3906+/-1642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eMean time to RER of 1 or more during exercise (minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e5.10+/-2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e5.89+/-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7692%;\"\u003e\n \u003cp\u003eRespiratory exchange ratio at rest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e0.93+/-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.0385%;\"\u003e\n \u003cp\u003e0.87+/-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1154%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are mean +/- standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Multivariable predictors of long COVID\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eLong COVID predictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e95% confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePeak carbohydrate use at rest (continuos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePeak fat use at rest (continuos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePeak carbohydrate use on exertion (continuos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePeak fat use on exertion (continuos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.79-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePeak V̇O₂\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.72-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eRespiratory exchange ratio at rest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.90-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBreathing reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.99-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAnaerobic threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.76-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTime to RER of 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.62-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUsing carbohydrates at rest (categorical)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2.30-22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eResting RER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.95-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8072121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8072121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDyspnea, fatigue and post-exertional malaise (PEM) are hallmark features of long Covid and emerging evidence suggests that abnormal energy metabolism may contribute to these symptoms. A cardiopulmonary exercise test (CPET) provides a detailed physiologic assessment of ventilatory and cardiovascular function and can offer insights into metabolic substrate utilization energy at rest and during exertion. Our aim was to evaluate patterns of energy metabolism at rest and during exercise during a CPET in patients with long Covid.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional study of consecutive non-selected patients that had been referred for a CPET. We included two groups: a long COVID and a control group. The CPET was performed on a cycle ergometer and we measured standard variables including oxygen uptake (V̇O₂), respiratory exchange ratio (RER), breathing reserve, heart rate, O2 pulse, and anaerobic threshold. We used RER to calculate indirect calorimetry estimating the use of carbohydrates and fat at rest and exertion. We analyzed the association between long COVID symptom severity symptoms including fatigue and post-exertional malaise (PEM) with patterns of energy consumption. We used logistic regression and area under the receiver operating characteristic curve to determine which CPET variables were most associated with long COVID.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCPET results were analyzed for 50 patients who met the definition of long COVID and 45 patients controls. Long COVID patients and controls had similar peak V̇O₂, heart rate on exertion and V̇O₂ at anaerobic threshold. Seventy-three percent of patients with long COVID had predominant energy use of carbohydrates rather than fat at rest compared to 20% of controls. In multivariable models the odds ratio of using fat as energy source at rest was 0.99; 95% CI 0.99\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.04. Patients with long COVID and severe fatigue as well as severe PEM had higher usage of carbohydrates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and similar use of fat.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePatients with long COVID use energy inefficiently and this pattern could serve as a diagnostic feature in certain presentations of long COVID.\u003c/p\u003e","manuscriptTitle":"Inefficient energy consumption is related to post exertional malaise during cardiopulmonary exercise testing in long COVID","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 16:14:09","doi":"10.21203/rs.3.rs-8072121/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30e3d5df-9484-4c30-bcd9-a7935b865652","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-09T10:28:10+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T10:40:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 16:14:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8072121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8072121","identity":"rs-8072121","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}