No Signs of Neuroinflammation in Women With Chronic Fatigue Syndrome or Q Fever Fatigue Syndrome Using the TSPO Ligand [11C]-PK11195

Background and ObjectivesThe pathophysiology of chronic fatigue syndrome (CFS) and Q fever fatigue syndrome (QFS) remains elusive. Recent data suggest a role for neuroinflammation as defined by increased expression of translocator protein (TSPO). In the present study, we investigated whether there are signs of neuroinflammation in female patients with CFS and QFS compared with healthy women, using PET with the TSPO ligand 11C-(R)-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinoline-carbox-amide ([11C]-PK11195).MethodsThe study population consisted of patients with CFS (n = 9), patients with QFS (n = 10), and healthy subjects (HSs) (n = 9). All subjects were women, matched for age (±5 years) and neighborhood, aged between 18 and 59 years, who did not use any medication other than paracetamol or oral contraceptives, and were not vaccinated in the last 6 months. None of the subjects reported substance abuse in the past 3 months or reported signs of underlying psychiatric disease on the Mini-International Neuropsychiatric Interview. All subjects underwent a [11C]-PK11195 PET scan, and the [11C]-PK11195 binding potential (BPND) was calculated.ResultsNo statistically significant differences in BPND were found for patients with CFS or patients with QFS compared with HSs. BPND of [11C]-PK11195 correlated with symptom severity scores in patients with QFS, but a negative correlation was found in patients with CFS.DiscussionIn contrast to what was previously reported for CFS, we found no significant difference in BPND of [11C]-PK11195 when comparing patients with CFS or QFS with healthy neighborhood controls. In this small series, we were unable to find signs of neuroinflammation in patients with CFS and QFS.Trial Registration InformationEudraCT number 2014-004448-37.

No signs of neuroinflammation in women with chronic fatigue syndrome or Q fever fatigue syndrome using the TSPO ligand [11C]-PK11195

Background The pathophysiology of chronic fatigue syndrome (CFS) and Q fever fatigue syndrome (QFS) remains elusive. Recent data suggest a role for neuroinflammation as defined by increased expression of translocator protein (TSPO). In the present study we investigated neuroinflammation in female CFS and QFS patients compared with healthy women, using Positron Emission Tomography (PET) with the TSPO ligand [11C]-PK11195. Methods The study population consisted of CFS patients (n = 9), QFS patients (n = 10), and healthy controls (n = 9). All subjects were women, matched for age (± 5 years) and neighbourhood, between 18 and 59 years of age, who did not use any medication other than paracetamol or oral contraceptives, and were not vaccinated in the last six months. None of the subjects reported substance abuse in the past 3 months or reported signs of underlying psychiatric disease on the Mini-International Neuropsychiatric Interview (MINI). All subjects underwent a [11C]-PK11195 PET scan and the [11C]-PK11195 binding potential (BPND) was calculated. Results No statistically significant differences in BPND were found for CFS patients or QFS patients when compared to healthy controls. BPND of [11C]-PK11195 positively correlated with symptom severity scores in QFS patients, but a negative correlation was found in CFS patients. Conclusions In contrast to what was previously reported for CFS, we found no significant difference in BPND of [11C]-PK11195 when comparing CFS or QFS patients to healthy neighbourhood controls. In this small series we were unable to find signs of neuroinflammation in patients with CFS and QFS. Trial registration EudraCT number: 2014-004448-37

Could Cognitive Behavioural Therapy Be an Effective Treatment for Long COVID and Post COVID-19 Fatigue Syndrome? Lessons from the Qure Study for Q-Fever Fatigue Syndrome

An increasing number of young and previously fit and healthy people who did not require hospitalisation continue to have symptoms months after mild cases of COVID-19. Rehabilitation clinics are already offering cognitive behavioural therapy (CBT) as an effective treatment for long COVID and post-COVID-19 fatigue syndrome based on the claims that it is effective for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)—the most common post-infectious syndrome—as no study into the efficacy of CBT for post-COVID-19 fatigue syndrome has been published. Re-analyses of these studies, however, showed that CBT did not lead to objective improvements in heterogeneous groups of ME/CFS patients, nor did it restore the ability to work. The group of patients with long COVID and post-COVID-19 fatigue syndrome, on the other hand, is homogeneous. We therefore analysed the Dutch Qure study, as it studied the efficacy of CBT in a homogeneous group of patients who developed Q-fever fatigue syndrome—which affects up to 30% of patients—after the largest reported outbreak of Q-fever, to see if CBT might potentially be an effective treatment for long-haulers after COVID-19 infection. Our reanalysis found that the Qure study suffered from many serious methodological problems, which included relying on one subjective primary outcome in a study without a control group for the non-blinded CBT treatment group, using a post hoc definition of improvement, waiting 2 years before publishing their objective actometer results and ignoring the null effect of said results. Moreover, only 10% of participants achieved a clinically meaningful subjective improvement in fatigue as a result of CBT according to the study’s own figures. Consequently, CBT has no subjective clinically meaningful effect in nine out of every ten patients that are treated with it. Additionally, the subjective improvement in fatigue was not matched by an improvement in disability, even though the disability was fatigue related according to the researchers. On top of this, CBT did not lead to an objective improvement in physical performance. Therefore, it cannot be said that CBT is an effective treatment for Q-fever fatigue syndrome either. It seems therefore unlikely that CBT will reduce disability or lead to objective improvement in long COVID or in post-COVID-19 fatigue syndrome.

Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome

Background Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results Inflammatory markers, including 4E-BP1 (P = 9.60–16 and 1.41–7) and MMP-1 (P = 7.09–9 and 3.51–9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. Conclusions We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome

Background: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found.Conclusions: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

Impact of Q-fever fatigue syndrome on patients’ work status

Background Chronic illnesses can increase the risk of unemployment, but evidence on the specific impact of Q-fever fatigue syndrome (QFS) on work is lacking. Aims The aim of this study was to describe and quantify the impact of QFS on work. Methods Changes in work status from 1 year prior to 4 years after acute Q-fever infection of QFS patients were retrospectively collected with a self-report questionnaire measuring employment status and hours of paid work per week. In addition, information on work ability, job satisfaction and need for recovery after work was collected in 2016. Data were compared to participants from the general population. Results The proportion of employed QFS patients from 1 year prior to 4 years after acute infection decreased from 78 to 41%, while remaining relatively constant in the general population (82 to 78%). Working QFS patients showed a decrease in mean hours of paid work from 35 to 22 h per week, which is significantly steeper compared to the general population (31–28 h per week) (P < 0.001). QFS patients showed a significantly lower work ability (P < 0.001), lower job satisfaction (P = 0.006) and greater need for recovery (P < 0.001) compared to the general population. Conclusions The number of QFS patients with paid work decreased over the years, while patients who continue to work experience lower work ability, job satisfaction and increased need for recovery. Occupational physicians should be aware of the occurrence and severity of the impact of QFS on work, even after many years.

Multi-omics Examination of Q Fever Fatigue Syndrome Identifies Similarities with Chronic Fatigue Syndrome

Background: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). Methods: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. Results: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC, and QFS patients show more of an inflammatory profile than CFS patients and HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobateria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. Conclusions: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.