In Vivo Investigation on Mitochondrial Dysfunction in Post-COVID Fatigue and Cancer Fatigue.

Overview

To date, little is understood regarding post-COVID fatigue or cancer fatigue though it is known to affect a large proportion of patients (10-70% depending on the population). This study aims to investigate potential mitochondrial function and metabolic changes in brain to provide further information regarding the etiology of these changes leading to fatigue. This study hypothesized that Post-COVID fatigue is ensued by perturbations in metabolism and mitochondrial function in the brain. This is a case-control study. In this study, 30 patients (experimental group) complaining of persistent fatigue lasting longer than 4 weeks after recovering from SARS-CoV-2 infection and the age/gender-matched control of 30 healthy subjects (control group 1) and 30 patients suffering from cancer-related fatigue patients longer than 4 weeks after remission (control group 2). Both the experimental group (post-COVID fatigue) and control group 2 (cancer fatigue) will be recruited from NUH outpatient clinic will undergo a session of MRI, 3D Arterial Spin Labelling (ASL) and 1H magnetic resonance spectroscopy (MRS) and the Chalder Fatigue Scale, Health Questionnaire (EQ-5D-5L) and Hamilton Depression Rating Scale at baseline and follow-up visit.

The clinical manifestations of post-COVID fatigue are comparable with chronic fatigue syndrome or myalgic encephalomyelitis (CFS/ME) whose clinical features comprise substantial decrease in function persisting for more than 6 months, post-exertional malaise, unrefreshing sleep, cognitive impairment or orthostatic intolerance (Blomberg, 2021; Logue, 2021). From the clinical perspective, there is high plausibility of abnormalities of energy production or mitochondrial function as the etiology of CFS/ME (Tomas, 2017; Filler, 2014). The similar clinical features were observed in other infections such as EBV, Ebola, lime disease, etc., which led to coining a terminology, post-infectious syndrome. Considering the recent study showing SARS-CoV-2 reduces mitochondrial proteins uniquely, known as Complex One, to quiet the cell's metabolic output (Miller, 2021), the etiological relevance of post-COVID fatigue and CFS/ME should be scrutinized. By the way, fatigue is prevalent in cancer survivors even after achieving the complete remission, which is described as "paralyzing" and ensues physical or mental exhaustion that may not reversed by taking rest or sleep. This cancer related fatigue may compound the effect of chemo brain or chemo fog which denotes cancer-related cognitive dysfunction lingering for months or years after cancer treatment. As such, there are many analogous clinical features shared by post-COVID fatigue and cancer fatigue. The comparison of the metabolic changes between post-COVID fatigue and cancer fatigue may provide hints to understand the mechanism of fatigue better. It has been challenging technically to verify the role of mitochondria in CFS/ME. A commercially available test, e mitochondrial energy score protocol, failed to show the reliability and reproducibility required of a diagnostic test (Thomas, 2019), which implies the practical limitations of in vitro test to evaluate mitochondrial function given the intrinsic and extrinsic factors affecting the test result of target metabolites. That said, proton Magnetic Resonance spectroscopy (MRS) can be a useful tool which detects the major brain metabolites such as N-acetylaspartate (NAA), creatinine (Cr), myo-inositol (MI) and lactate (Kantarci, 2003). The MRS spectra including abnormal positive lactate peaks correlate well with other clinical markers in mitochondriopathies (Lin, 2003). The level of organic acid (including 3-methylglutacoric acid) in urine is associated with compromised mitochondrial energy metabolism (Ikon N, 2016), which will be used as an adjunct index. To date, little is understood regarding post-COVID fatigue or cancer fatigue though it is known to affect a large proportion of patients (10-70% depending on the population). This study aims to investigate potential mitochondrial function and metabolic changes in brain to provide further information regarding the etiology of these changes leading to fatigue. This study aims: 1\) to determine whether there are changes in the NAA/MI indicating perturbed bioenergetic role in neuronal mitochondrial function in post-COVID fatigue and 2) to prove changes of cerebral blood flow (CBF) assessed by 3D Arterial Spin Labelling (ASL) sequence for brain in post-COVID fatigue. This study hypothesized that Post-COVID fatigue is ensued by perturbations in metabolism and mitochondrial function in the brain. This study will include 30 patients (experimental group) complaining of persistent fatigue lasting longer than 4 weeks after recovering from SARS-CoV-2 infection and the age/gender-matched control of 30 healthy subjects (control group 1) and 30 patients suffering from cancer-related fatigue patients longer than 4 weeks after remission (control group 2). Both the experimental group (post-COVID fatigue) and control group 2 (cancer fatigue) will be recruited from NUH outpatient clinic. The healthy control will be recruited from NUH employees from various work groups utilizing a recruitment flyer and word of mouth. Screening Visits and Procedures: (Visit 1) The study details will be explained clearly to the prospective participants and consent will be taken from the participants prior to commencing study procedures which includes screening, accessing and extracting data from their medical records, administering the study questionnaires (Chalder fatigue scale) and arranging for the study participants to undergo MRI, 3D Arterial Spin Labelling (ASL) and 1H magnetic resonance spectroscopy (MRS) at the NUS-Center for transitional magnetic resonance (TMR). Eligible subjects will then proceed to the following visits. Visit 2 which will take place within 28 days of the first visit and will involve MRI/MRS/ASL measurements to assess metabolic and mitochondrial perturbations in post-COVID fatigue or cancer fatigue (details below). The urine sample (10ml) will be collected from all subjects to measure changes of organic acid level against normal range which may reflect mitochondrial dysfunction. Final Study Visit (3rd visit or phone call). For the participants in the post-COVID fatigue group and the cancer fatigue group, the study team will apply the same questionnaires including Chalder Fatigue Scale, Health Questionnaire (EQ-5D-5L) and Hamilton Depression Rating Scale through a phone call or in person, which will coincide with their follow up visit with their treating physician. For healthy normal subjects without fatigue symptoms, the questionnaires will be administered via a phone call and do not need to attend Visit 3 in person, which is set to monitor the clinical course of post-COVID fatigue and cancer fatigue. This is a case-control study. With a sample size of 30 per group (90 in total for 3 groups), the study will have 90% power to detect a standard difference of 0.95 and the mean difference in the primary endpoints equal to 95% of the SD of the endpoint. As such, the minimum sample size will be at least 27 per group. Since the two primary endpoints of the study are NAA/MI measured by proton MRS and cerebral blood flow (CBF) assessed by 3D Arterial Spin Labelling (ASL) sequence for brain, the significant level will be adjusted to 0.0125 (for 2 primary endpoints and 2 controls, the significant level will be 0.05/4). Those numerical variables measured in MRI/MRS will be compared with either healthy control or disease control (cancer fatigue patients). 2 sample T test or Mann-Whitney U test, whichever is more appropriate for the hypothesis testing will be used. Linear regression can be applied to adjust for other confounders or covariates. The Null hypothesis: the mean NAA/MI and CBF of study group equals to the mean NAA/MI and CBF of disease control group (cancer fatigue) and healthy control group. The alternative hypothesis: the mean NAA/MI and CBF of study group is different from the mean NAA/MI and CBF of disease control group and healthy control group. Type I error: 0.0125 Type II error: 0.1