This study showed that inflammatory and metabolomic profiles, together with gut microbiome taxonomy, of QFS patients and CFS patients are comparable, and both groups clearly differ from HC (with CFS patients showing a larger difference than QFS patients). In addition, QFS patients exhibited more of an inflammatory profile than CFS patients. These findings are important, as they indicate that QFS patients and CFS patients show a common denominator in the long term, i.e., alterations in inflammatory and metabolomic profiles, together with gut microbiome taxonomy, regardless of the precipitating event that started the complaints. It also corroborates our previous findings that QFS patients exhibit more of an inflammatory profile that CFS patients (and healthy controls) (12).
The profile of important characteristics such as microbiome and metabolome is very similar in QFS and CFS, but differences are seen in their inflammatory profiles (5, 12). One could speculate that the microbial origin of QFS plays a role in this low-grade persistent inflammation. Together with previous findings on differences in fatigue-perpetuating factors and response to cognitive behavioural therapy (CBT) (42-44), one could advocate that QFS should be seen as a separate, more inflammatory, fatigue syndrome entity that requires a different diagnostic (27, 28) and therapeutic (44, 45) approach. These findings argue for a ‘splitting’ rather than a ‘lumping’ approach to chronic fatigue (46).
Inflammatory markers 4E-BP1, AXIN1, and MMP-1 showed the potential to differentiate both QFS and CFS patients from HC and might therefore be associated with fatigue in general as this is the common denominator between these groups. We further elaborated on these findings by using a machine-learning approach showing that both 4E-BP1 and AXIN1 are good candidate biomarkers for predicting/diagnosing chronic fatigue. The eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1) represses mRNA translation downstream of the mammalian target of rapamycin (mTOR). The latter is known to phosphorylate and inactivate 4E-BP1 (47). Several upstream stimuli, e.g., growth factors and cytokines, can regulate downstream processes, e.g., cell growth, cell proliferation, and cell plasticity, through mTOR (47). Dennis et al. showed that the 4E-BP1 phosphorylation was inhibited when intracellular adenosine triphosphate (ATP) levels were lowered (48). Interestingly, chronic fatigue has previously been associated with a decrease in cell metabolism (15, 18, 49, 50), and PBMCs of CFS patients showed a decrease in mitochondrial function compared to PBMCs of HC when stressed (51-53). Axis inhibition protein (AXIN1), negatively regulates the Wnt signalling pathway by downregulation of β-catenin (54), but has also been identified as a scaffold protein that activates TGF-β signalling (55). Especially the latter finding is of interest as elevated levels of TGF-β have frequently been associated with CFS (10). However, it should be noted that results on TGF-β levels must be interpreted with great caution as measuring TGF-β in plasma has some noteworthy, pre-analytic, pitfalls (56). Matrix metalloproteinase 1 (MMP-1) is a collagen cleaving protease that has been associated with inflammation in infections such as HIV (57, 58), but has also shown to have a negative association with the risk of being a CFS patient (59). Exactly how, and how strong, 4E-BP1, AXIN1, and MMP-1 relate to chronic fatigue warrants further investigation in independent cohorts.
Comparing CFS patients to HC, studies on metabolomic profiles consistently found differences between these groups (17-20). Armstrong et al. found that CFS patients show lower levels of glutamine and ornithine compared to HC (20). Germain et al. found pathway abnormalities in taurine, glycerophospholipid, primary bile acid, glyoxylate, dicarboxylate, and fatty acid metabolism (19). Naviaux et al. suggested that CFS patients exhibit a hypometabolic state and found pathway abnormalities in sphingolipid, phospholipid, purine, cholesterol, microbiome, pyrroline-5-carboxylate, riboflavin, branch chain amino acid, peroxisomal, and mitochondrial metabolism (18). Our study shows enrichment similarities in sphingolipid and primary bile acid biosynthesis pathways. As the sphingolipid pathway is altered in both QFS and CFS, these pathway alterations might be specific for chronic fatigue in general, whereas the primary bile acid biosynthesis pathway might be more specific for QFS.
Additionally, several of these metabolites, e.g., l-cysteine and l-cysteinylglycine, appear to positively correlate with various inflammatory proteins, e.g., MCP-1, but also 4E-BP1 and MMP-1. PBMCs stimulated with l-cysteinylglycine produced significantly more MCP-1 compared to PBMCs that are stimulated with the negative control RPMI. A similar trend was observed for l-cysteine. This shows us that some of these metabolites might have the potential to initiate a more (anti-)inflammatory environment. One could speculate that such a mechanism contributes to changes in inflammation in QFS patients and CFS patients, and that the observed inflammation is secondary to metabolic alterations. As our group recently showed that monocytes of QFS patients and CFS patients exhibit a decreased expression of MDP-coding genes MT-RNR1 and MT-RNR2 compared to HC (15), it would be interesting to investigate the role of these MDP-coding peptides in these metabolic and inflammatory alterations.
Previous studies on gut microbiome composition compared CFS patients to HC and found differences between these groups. Unfortunately, many of the differences are inconsistent. Giloteaux et al., showed that the gut microbiome of CFS patients has less bacterial diversity with the balance shifting towards more pro-inflammatory species (22). Sheedy et al., showed that CFS patients have more aerobic microbial flora, with more Gram-positives, and an abundance of E. faecalis and S. sanguinis compared to HC (60). Armstrong et al., found an increase in Clostridium spp. and a decrease in total bacteria, total anaerobic bacteria, and Bacteroides spp. In CFS patients compared to HC (61). Fremont et al., found that both Belgian and Norwegian CFS patients had an increase in Lactinofacter compared to HC (62). Shukla et al., found a decreased mean relative abundance of Actinobacteria in CFS patients compared to HC (63). Our study found a similar decrease in Bacteroides spp. when comparing CFS patients to HC. Interestingly, this genus appears to be increased when comparing QFS patients to HC. Furthermore, we conflictingly find an increase in Actinobacteria when comparing CFS patients, but a decrease when comparing QFS patients, to HC. Our most important observation, however, is that the taxonomy of QFS patients and CFS patients is quite similar, while both groups appear to differ quite profoundly from HC (with CFS patients showing a larger difference than QFS patients). This is similar to our findings in inflammatory and metabolomic profiles and functionally reflected by highly significant upregulation of pathways, like urate biosynthesis/inosine 5’-phosphate degradation and CMP-3-deoxy-D-manno-octulosonate biosynthesis, when comparing QFS and CFS patients to HC. When one compares QFS patients to CFS patients, less significant upregulation of pathways, like L-lysine biosynthesis III and VI, is found. Exactly how gut microbiome dysbiosis plays part in the pathophysiology of chronic fatigue remains unclear but likely involve the microbiome-brain-axis, and/or subsequent systemic low-grade inflammation. A recent systematic review confirmed that even though independent studies do report differences, these differences are inconsistent (23). Such inconsistencies are likely to occur if control groups are not representative and/or in- and exclusion criteria for patients are not strictly adhered to. Further investigation of the gut microbiome, using strict in- and exclusion criteria together with adequate and representative control groups (64), in patients with chronic fatigue is definitely of interest.
Although our study lacks a replication cohort, the observed differential patterns among QFS, CFS and HC are consistent across three omics layers. A batch effect across different (control) groups is unlikely but should be kept in mind when interpreting these data. Because systematic assessment of multi-omics data is still limited, our detailed datasets are an important reference for improving our understanding of the molecular processes leading to a state of chronic fatigue.