Presence of depression and anxiety with distinct patterns of pharmacological treatments before the diagnosis of chronic fatigue syndrome: a population-based study in Taiwan
Journal of Translational Medicine volume 21, Article number: 98 (2023)
An increased prevalence of psychiatric comorbidities (including depression and anxiety disorder) has been observed among patients with chronic fatigue syndrome (CFS). However, few studies have examined the presence of depression and anxiety disorder before the diagnosis of CFS. This study aimed to clarify the preexisting comorbidities and treatments associated with patients with subsequent CFS diagnosis in a population-based cohort in Taiwan.
An analysis utilizing the National Health Insurance Research Database of Taiwan was conducted. Participants included were 6303 patients with CFS newly diagnosed between 2000 and 2010 and 6303 age-/sex-matched controls.
Compared with the control group, the CFS group had a higher prevalence of depression and anxiety disorder before the diagnosis of CFS. Sampled patients who took specific types of antidepressants, namely, selective serotonin reuptake inhibitors (adjusted odds ratio [aOR] = 1.21, 95% confidence interval [CI] 1.04–1.39), serotonin antagonists and reuptake inhibitors (SARI; aOR = 1.87, 95% CI 1.59–2.19), and tricyclic antidepressants (aOR = 1.46, 95% CI 1.09–1.95), had an increased risk of CFS. CFS risk was also higher among participants taking benzodiazepine, muscle relaxants, and analgesic drugs. A sub-group analysis revealed that SARI use was related to an increased risk of CFS in the depression, anxiety disorder, male, and female groups. In the depression and anxiety disorder groups, analgesic drug use was associated with an increased CFS risk. Nonpharmacological treatment administration differed between men and women.
This population-based retrospective cohort study revealed an increased risk of CFS among populations with preexisting depression and anxiety disorder, especially those taking SARI and analgesic drugs.
Patients with chronic fatigue syndrome (CFS) experience prolonged and disabling fatigue that cannot be explained with the existing state of medical knowledge. The prevalence of CFS differs widely depending on the diagnostic criteria, assessment method, and studied population, with its numbers ranging from 0.2% to 6.41% [1, 2]. A systematic review of 46 studies in 2020 estimated a CFS prevalence rate of 0.89% on the basis of the commonly used Centers for Disease Control (CDC)-1994 definition of CFS [3, 4]. The aforementioned review also reported a sex difference, with female individuals having prevalence rates that were 1.5 to 2 times higher than those of male individuals.
In addition to fatigue, several accompanying symptoms were also frequently reported, specifically muscle pain, multiple joint pain, poor sleep, anxiety, and depression . Musculoskeletal pain and insomnia were included in the CDC-1994 diagnostic criteria. Furthermore, mood and anxiety disorders were reported to be more prevalent in individuals with CFS relative to the general population . CFS, which is also known as myalgic encephalomyelitis, had found to be potentially related with immune processes such as inflammation and infection . Recent comparisons between the similarities of CFS and the potential COVID-19 long-term effects, including persistent fatigue, postexertional malaise and pain, had underlined the critical role of the immune response in such conditions [8, 9]. On the other hand, the systemic inflammation may be the mediator of CFS and its psychiatric comorbidities [10, 11]. It is notable that the relationship between CFS and psychiatric comorbidities might be bidirectional as an abnormal immune response has also been demonstrated among the patients with depression or anxiety disorder [12,13,14]. A study investigated patients with CFS and reported that the prevalence rates of concurrent anxiety and depression were 42.2% and 33.3%, respectively . However, few large-scale epidemiological investigations of psychiatric comorbidities, especially those that focused on Asian populations, have been conducted.
With a focus on CFS, depression, and anxiety, this population-based retrospective cohort study investigated and analyzed the data from the Taiwan National Health Insurance Research Database (NHIRD). The treatments received by participants were also further analyzed by sex, age, and comorbidities.
The dataset used in this study were derived from the National Health Insurance Research Database (NHIRD) in Taiwan. The National Health Insurance (NHI) program was launched on March 1, 1995, by Taiwan’s government. NHIRD has contained details concerning the demographic characteristics, dates of admission and discharge, prescriptions, surgical procedures, and diagnostic codes for approximately 99% of the entire population of the 23 million people residing in Taiwan. We used the 2000 Longitudinal Health Insurance Database (LHID) which was established by NHIRD. LHID 2000 was created and released to the public by NHIRD and includes all the original claim data and registration files between 2000 and 2013 for one million individuals randomly sampled from the Registry for beneficiaries of the NHI program in 2000 in Taiwan. The diseases are defined according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).
Cases of CFS were identified using two outpatient records or one admission record with a diagnosis of ICD-9-CM code 780.71. The date of the first diagnosed record of chronic fatigue syndrome was defined as the index date. For each chronic fatigue syndrome case, we used a frequency matching method and randomly selected one control without chronic fatigue syndrome diagnosis. The dataset for the control population of 1 million samples was randomly selected from the LHI dataset, and individuals without a diagnosis of CFS were selected as the control population with the same sex, age, and index date. (Fig. 1.) We excluded the participants aged below 18 years or with missing information on sex. In the ICD-9-CM, the diagnosis of CFS is mainly based on the CDC-1994 diagnostic criteria noted in the ICD-9-CM Coordination and Maintenance Committee Meeting in 2011. The CDC-1994 diagnostic criteria specifically defined the patients receiving appropriate treatment for depression or anxiety, the diagnosis could still be made among patients with premorbid depression or anxiety .
Exposure assessment and comorbidities
For this study, we examined the exposure of pharmaceutical and non-pharmaceutical treatments. We accounted the exposure to pharmaceutical treatments of SSRI drugs (ATC code N06AB10, N06AB06, N06AB03, and N06AB08), SNRI drugs (ATC code N06AX21, and N06AX16), SARI drugs (ATC code N06AX05), norepinephrine and dopamine reuptake inhibitor (NDRI) drug (ATC code N06AX12), noradrenergic and specific serotonergic antidepressants (NaSSA) drug (N06AX11), TCAs drugs (ATC code N06AA09 and N06CA01), BZD drugs (ATC code N03AE01, N05BA06, N05BA12, N05BA01, N05BA17, N05BA22, N05CD04, N05CD05, N05CD03, N05CD09, N05CD01, N05CD08), muscle relaxant (ATC code M03BX08), analgesic drugs which including acetaminophen, NSAIDs, pregabalin, gabapentin (ATC code M02AA, D11AX18, M01A, M01B, N03AX16, and N03AX12) and non-pharmaceutical of support psychotherapy, supportive group psychotherapy, deep psychotherapy, in-depth group psychotherapy, special psychotherapy, special group psychotherapy, behavioral therapy evaluation, behavioral therapy plan, supportive psychosocial consultation for family members/caregivers, stretching exercise, exercise therapy, breathing exercise, induced deep breathing exercise, rehabilitation exercise, multiple physical examinations of sleep, brainwave examination, sleep or wakefulness, and brainwave examination for sleep disorders. Study participants were categorized based on their pharmaceutical and non-pharmaceutical exposure status. Patients exposed to pharmaceutical or non-pharmaceutical were classified as users or non-users. We adjusted for the potentially confounding effects of other comorbidities, including depression (ICD-9-CM code 296.2, 296.3, 926.82, 300.4, 309.0, 309.1, and 311), anxiety disorder (ICD-9-CM code 300.0–300.3, 300.5–300.9, 309.2–309.4, 309.81, and 313.0), insomnia (ICD-9-CM code 307.41, 307.42, 780.50, and 780.52), suicide (ICD-9-CM code E950-E959), Crohn’s disease (ICD-9-CM code 555), ulcerative colitis (ICD-9-CM code 555–556), renal disease (ICD-9-CM code 580–589), diabetes mellitus (ICD-9-CM code 250 and A181), obesity (ICD-9-CM code 278), gout (ICD-9-CM code 274), dyslipidemia (ICD-9-CM code 272), malignancy (ICD-9-CM code 140–208), HIV (ICD-9-CM code 042–044), rheumatoid arthritis (ICD-9-CM code 714), psoriasis (ICD-9-CM code 696.x), ankylosing spondylitis (ICD-9-CM code 720.0), lymphadenopathy (ICD-9-CM code 289.1–289.3, 686, and 785.6), Hashimoto's thyroiditis (ICD-9-CM code 245.2), Sjogren's syndrome (ICD-9-CM code 710.2), irritable bowel syndrome (ICD-9-CM code 564.1), SLE (ICD-9-CM code 710.0), celiac disease (ICD-9-CM code 579.00, and herpes zoster (ICD-9-CM code 053) prior to the index date were evaluated as part of the analysis.
Descriptive statistics of CFS and controls are reported, including demographic characteristics, comorbid diseases, and exposure to potentially confounding treatments. The chi-square test was used to compare categorical variables, whereas the Student’s t-test was used to compare continuous variables between chronic fatigue syndrome cohort and comparison cohort as necessary. We used conditional logistic regression to assess the risk of CFS according to each category of pharmaceutical and non-pharmaceutical. The odds ratio (ORs) and 95% confidence intervals (CIs) for CFS were calculated as an unadjusted incidence rate, and then subsequently adjusted for covariates including age, sex, comorbidities, pharmaceutical and non-pharmaceutical. Bonferroni correction was performed for the correction of multiple comparisons. Analyses were performed using SAS software (version 9.4 for windows; SAS Institute, Cary, NC, USA) for Windows 10. All statistical significance levels were set at a p < 0.05.
This study included 6306 patients with CFS and 6306 patients without, all of whom were identified from the NHIRD between January 1, 2000, and December 31, 2013. The demographic and clinical characteristics of the study participants are presented in Table 1. Among the participants, 52.9 were female, and most were between 25 and 64 years old; the mean age of the participants was 50.6 years. With regard to the prevalence of comorbidities, participants with CFS had higher numbers of psychiatric disorders (depression, anxiety disorder, and insomnia), irritable bowel syndrome, inflammatory bowel diseases (Crohn’s disease and ulcerative colitis), autoimmune disorders (rheumatoid arthritis, and Sjogren’s syndrome), metabolic disorders (type 2 diabetes mellitus, gout, and dyslipidemia), and renal disease (all p < 0.005).
Table 2 and Fig. 2 shows the pharmacological and no-pharmacological treatment received before the diagnosis of CFS. Participants taking certain types of antidepressants, including SSRI, SARI, and TCA, had higher odds of CSF, with the adjusted odds ratio (aORs) of 1.21 (95% CI 1.04–1.39), 1.87 (95% CI 1.59–2.19), and 1.46 (95% CI 1.09–1.95). Other drugs with increased aORs of CFS included BZDs (1.60, 95% CI 1.46–1.76), muscle relaxants (1.74, 95% CI 1.39–2.19), and analgesics (3.56, 95% CI 3.16–4). As for the non-pharmacological treatments and examinations received by the participants, undergoing brainwave examination had a significantly increased odds ratio (1.6, 95% CI 1.44–1.77) of CFS but an insignificant aOR after being adjusted with demographic data and comorbidities.
Table 3 and Fig. 3 presents the treatment received before the diagnosis of chronic fatigue syndrome with comorbidity sub-classification by having depression or anxiety disorder. The aORs of SARI usages and analgesic drug usages increased in both groups with depression and anxiety disorders. Among the participants with depression who received supportive individual psychotherapy, the aORs of risk of CFS was 1.85 (95% CI 1.02–3.35). As for the participants with anxiety disorder, the aORs of risk of CFS was 1.55 (95% CI 1.03–2.31) in those who also take muscle relaxants.
As presented in Table 4 and Fig. 4, the analysis with sub-classification by age also demonstrates different patterns of medications used across different ages. BZD, muscle relaxants, and analgesic drug usages were indicated on increased aORs of risks of CFS in all the age groups. In contrast, the usages of SSRI, SARI, and TCA among participants aging from 35 to 64 years old had aORs of 1.24 (95% CI 1.04–1.47), 1.90 (95% CI 1.56–2.31), and 1.80 (95% CI 1.26–2.58), respectively. Among participants aging over 65 years old, the use of serotonin and norepinephrine reuptake inhibitor (SNRI) and SARI, with aORs being 2.15 (95% CI 1.22–3.81) and 1.93 (95% CI 1.46–2.57), respectively.
In Table 5 and Fig. 5, we present the therapeutic options received by the patients with CFS and controls with sex specific sub-classification. In female patients, the adjusted odds ratio of risk of CFS were 1.22 (95% CI 1.01–1.48), 1.69 (95% CI 1.37–2.08), 1.72 (95% CI 1.17–2.53), 1.66 (95% CI 1.45–1.9), 1.56 (95% CI 1.16–2.1), 3.23 (95% CI 2.72–3.84), 1.36 (95% CI 1.08–1.72), 1.38 (95% CI 1.09–1.76), and 1.26 (95% CI 1.02–1.54), folds with SSRI use, SARI use, TCA use, BZD use, muscle relaxant use, analgesic drug use, supportive individual psychotherapy, re-educative psychotherapy, and stretching exercise. In male patients, the adjusted odds ratio risk of CFS were 1.92 (95% CI 1.19–3.08), 2.20 (95% CI 1.70–2.84), 1.55 (95% CI 1.36–1.76), 2.07 (95% CI 1.45–2.97), and 3.90 (95% CI 3.31–4.59) folds with SNRI use, SARI use, BZD use, muscle relaxant use, and analgesic drug use.
Our nationwide population-based study revealed that sampled patients with CFS experienced more comorbidities, such as depression and anxiety. These findings are consistent with those of previous studies. Furthermore, the treatments received by the participants before their diagnosis of CFS were also explored, and the results indicated that the use of specific types of antidepressants (e.g., SSRI, SARI, and TCA) was related to an increased risk of a subsequent diagnosis of CFS. In addition, a subgroup analysis also revealed that the treatment received differed by comorbidities, age, and sex.
Notably, no clear male or female predominance was observed in the present study. Other studies have reported that the prevalence of CFS among female individuals was approximately two-fold higher than that among male individuals [1, 4, 16]. However, several studies from East Asia, including Japan and China, have reported almost 1:1 sex ratios with respect to CFS prevalence [17, 18]. Different definitions of cases led to the variations in the prevalence and the incidence of CFS. We defined CFS using the CDC-1994 criteria in this study since it is the most common one that may resulted in recruit more cases [4, 19]. Cross-cultural differences in diagnostic practices for CFS and other conditions, especially neurasthenia, could explain the aforementioned differences in reported findings [20, 21], and this could ultimately lead to partly dissimilar populations being diagnosed. Another possible cause is the accessibility of the healthcare systems in Taiwan, as the National Health Insurance had covered over 99.9% of the civilians . The increased accessibility could decrease the numbers of undetected cases. It therefore highlights the importance of the detection of male patients with CFS who might potentially be neglected.
The demographic data (Table 1) of the participants of the present study indicated higher comorbidity rates of depression, anxiety, inflammatory bowel diseases (IBD; Crohn’s disease and ulcerative colitis), autoimmune diseases, and metabolic disorders relative to the general population. Studies have reported an association between metabolic syndrome and CFS and identified altered fatty acid levels and lipid metabolism in individuals with CFS through further plasma metabolic profiling [23,24,25]. Other studies have suggested the presence of a shared pathophysiological process in CFS, autoimmune rheumatic diseases, and inflammatory bowel diseases because of the reported associations among the conditions and their similar symptomatology [25,26,27,28]. The role of the immune system in CFS could also be highlighted by our previous findings of the correlation between CFS and infectious diseases, indicating the involvement of post-infection dysregulated immune response [29, 30]. These findings highlight the complexity of CFS and its potential causes.
The greater prevalence of depression and anxiety disorder among individuals with CFS is an extensively studied topic. In both adult and adolescent populations, a high comorbidity of depression and anxiety has been reported in the literature [6, 31, 32]. Similarly, our analysis revealed an almost two times higher prevalence of depression and anxiety disorders in addition to insomnia among the participants diagnosed with CFS (Table 1). The causal relationship between CFS and concurrent psychiatric disorders remains unclear. Several neuroimaging studies have produced similar findings (including decreased cortical glutathione levels and altered resting-state functional connectivity in the anterior cingulate cortex) in both individuals with CFS and individuals with depression [33,34,35,36], suggesting a shared pathophysiology.
The increased use of multiple types of antidepressants, especially SARI (mainly trazodone), has been observed before the diagnosis of CFS even after adjustments for clinical covariates, such as depression, anxiety, and insomnia (Tables 2 and 3.). In the diagnostic criteria for CFS, the applicable duration for defining unexplainable fatigue is a period in excess of 6 months , thus the prescription received by a patient at the point of diagnosis may correspond to the ongoing symptoms of CFS itself. Therefore, the medications prescribed during the aforementioned period may also provide us with a general overview of a patient’s status at the beginning of the clinical course of CFS.
In a clinical setting, trazodone is not only used as an antidepressant but also an efficacious treatment for insomnia at a low dose. Trazodone has been demonstrated to improve perceived sleep quality and reduce the number of early awakenings . In Taiwan, trazodone is the fifth most frequently prescribed psychotropic drug in the outpatient clinics and has usually been used as a hypnotic . In addition, it is also used off-label for anxiety and fibromyalgia in limited clinical settings . SARI is speculated to be prescribed more frequently in such populations because of the accompanying subclinical symptoms of CFS, which include depression, anxiety, insomnia, and muscle pain . This viewpoint is further supported by our finding regarding the increased pre diagnostic use of BZD, muscle relaxants, and analgesic drugs across all age groups in the participants with CFS (Table 4). Among the aforementioned symptoms, depression, and pain have been reported to be associated with decreased quality of life and physical functioning [40, 41]. Our data revealed that these disabling symptoms may occur in the early stage of the clinical course of CFS, and physicians must thus be aware of them.
With regard to sex, the pattern of antidepressant use differed between male and female participants with CFS. Before receiving a diagnosis of CFS, female participants were more likely to be taking SSRI and TCAs, whereas male participants were more likely to be taking SNRIs. This could be related to the sex-specific symptomatology in CFS, such as the higher prevalence of insomnia in female individuals relative to male individuals , which could lead to the prescription of sedative medications (e.g., TCAs and specific SSRIs) . Higher ORs for receiving psychotherapy and rehabilitation were also observed in female individuals relative to male individuals, which could indicate a higher rate of engagement with medical services among female individuals with CFS and an insufficient awareness of CFS among male individuals. Similar sex differences have also been observed for other conditions, such as posttraumatic stress disorder and depression [44,45,46].
It is noticeable that, in younger groups, an increased risk of CFS is mainly associated with the usage of muscle relaxants and analgesics, rather than anti-depressants (shown in Table 5 and Fig. 5). Muscle pain is a common symptom of CFS, and some researchers even describe that CFS is “old muscle in young body .” Furthermore, adolescents with CFS were indicated to have lower pain thresholds . In the present study, CFS patients suffer from muscle pain symptoms more than control participants do, so the increased use of muscle relaxants and analgesics before diagnosis in CFS was noted. We further analyzed whether there was a gender difference in this group (age < 34y) and found that in younger females, the use of BZD and analgesics was related to subsequent CFS diagnosis (Additional file 2: Table S1 and Additional file 1: Figure S1). In males, in addition to BZD and analgesics, SNRI and muscle relaxants were also related to an increased risk of subsequent CFS. The phenomena suggest that compared to young females, young males have more diverse symptoms before CFS onset, leading to more varieties of medications being prescribed.
Our previous study analyzed both pharmacological and nonpharmacological treatments administered after the diagnosis of CFS. In contrast to the present study, we noted an increased use of antidepressants with dual-targeting mechanisms (serotonin– noradrenaline reuptake inhibitors and norepinephrine–dopamine reuptake inhibitors) after a diagnosis . Such medications have relatively well-established effects on fatigue and pain under multiple conditions [50,51,52,53,54]. As for nonpharmacological treatments, the number of patients receiving supportive psychotherapy, re-educative group psychotherapy, stretching exercise, and therapeutic exercise significantly increased after, but not before, diagnosis of CFS . The contrast between these two studies indicates the extensive and multimodal approach taken in the Taiwanese health care system in treating CFS.
Studies have increasingly demonstrated the long-term postinfection symptoms of COVID-19, a phenomenon termed long COVID. The symptoms include persistent fatigue, pain, postexertional malaise, and appetite loss [55, 56]. Because the symptomatology of long COVID indicates certain similarities to that of CFS, a shared pathophysiology may be possible, such as alterations in oxidative stress or the hypothalamic–pituitary–adrenal (HPA) axis [57,58,59]. Our results may also contribute to investigations into identifying populations that are at high risk of long COVID. One study showed that female sex is a risk factor for long COVID . Another preliminary study focusing on patients with multiple sclerosis demonstrated that pre-existing depression and anxiety were associated with increased risk of long COVID . These findings accord with our findings regarding CFS. The increased susceptibility to CFS and long COVID among these populations might be related to depression-related or anxiety-related increases in oxidative stress [61, 62] or HPA axis dysregulation [63, 64]. Because the research on this topic is limited, further studies should compare the mechanisms of CFS and long COVID and investigate the implications for prevention and treatment.
This study has several limitations. First, the associations between CFS and the severity of depression and anxiety were not classified. Furthermore, due to the nature of the datasets from the NHIRD, the characteristics and the severity of the symptomatology in the patients were not recorded. The detailed associations between the medications prescribed and the severity of clinical symptoms couldn’t be investigated. As a results, the study aimed to speculate the corresponding symptomatology of the patients according to the genre of medications they received. Further prospective clinical studies focusing on the causal relationship and subgroup analysis were therefore warranted. Second, the present study could only examine a limited sample because the CDC-1994 diagnosis criteria for CFS (ICD-9-CM 780.71) were adopted for this study. These criteria mainly center on neurologic and neurocognitive symptoms; however, it did not incorporate other common accompanying symptoms, such as orthostatic intolerance, anorexia, and motor disturbance [65, 66], which are included in other newly proposed diagnostic criteria . Therefore, the differences and similarities in the patterns of psychiatric comorbidities in CFS under different diagnostic criteria should be examined in future studies. Third, ethnic or geographic differences could not be clarified because the population examined in the present study mostly comprised East Asian individuals.
This study is the first nationwide population-based study to report a higher risk of CFS in patients with depression and anxiety disorder, especially those taking SSRIs, SARIs, and TCAs. In addition, BZD, muscle relaxants, and analgesic drugs were also revealed to be indicators of an elevated risk of CFS. These findings can increase the awareness of clinicians regarding high-risk populations and extend our current understanding of CFS.
Availability of data and materials
The data underlying this study is from the National Health Insurance Research database (NHIRD). Interested researchers can obtain the data through formal application to the Ministry of Health and Welfare, Taiwan.
Nacul LC, Lacerda EM, Pheby D, Campion P, Molokhia M, Fayyaz S, Leite JC, Poland F, Howe A, Drachler ML. Prevalence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in three regions of England: a repeated cross-sectional study in primary care. BMC Med. 2011;9:1–12.
Yiu Y-M, Qiu M-Y. A preliminary epidemiological study and discussion on traditional Chinese medicine pathogenesis of chronic fatigue syndrome in Hong Kong. J Chinese Int Med. 2005;3:359–62.
Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A. The chronic fatigue syndrome: a comprehensive approach to its definition and study. Ann Intern Med. 1994;121:953–9.
Lim E-J, Ahn Y-C, Jang E-S, Lee S-W, Lee S-H, Son C-G. Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). J Transl Med. 2020;18:1–15.
Afari N, Buchwald D. Chronic fatigue syndrome: a review. Am J Psychiatry. 2003;160:221–36.
Janssens KA, Zijlema WL, Joustra ML, Rosmalen JG. Mood and anxiety disorders in chronic fatigue syndrome, fibromyalgia, and irritable bowel syndrome: results from the Lifelines cohort study. Psychosom Med. 2015;77:449–57.
Bansal A, Bradley A, Bishop K, Kiani-Alikhan S, Ford B. Chronic fatigue syndrome, the immune system and viral infection. Brain Behav Immun. 2012;26:24–31.
Komaroff AL, Lipkin WI. Insights from myalgic encephalomyelitis/chronic fatigue syndrome may help unravel the pathogenesis of postacute COVID-19 syndrome. Trends Mol Med. 2021;27:895–906.
Al-Jassas HK, Al-Hakeim HK, Maes M. Intersections between pneumonia, lowered oxygen saturation percentage and immune activation mediate depression, anxiety, and chronic fatigue syndrome-like symptoms due to COVID-19: a nomothetic network approach. J Affect Disord. 2022;297:233–45.
Maes M, Twisk FN, Ringel K. Inflammatory and cell-mediated immune biomarkers in myalgic encephalomyelitis/chronic fatigue syndrome and depression: inflammatory markers are higher in myalgic encephalomyelitis/chronic fatigue syndrome than in depression. Psychother Psychosom. 2012;81:286–95.
Milrad SF, Hall DL, Jutagir DR, Lattie EG, Czaja SJ, Perdomo DM, Fletcher MA, Klimas N, Antoni MH. Depression, evening salivary cortisol and inflammation in chronic fatigue syndrome: a psychoneuroendocrinological structural regression model. Int J Psychophysiol. 2018;131:124–30.
Mehta ND, Haroon E, Xu X, Woolwine BJ, Li Z, Felger JC. Inflammation negatively correlates with amygdala-ventromedial prefrontal functional connectivity in association with anxiety in patients with depression: preliminary results. Brain Behav Immun. 2018;73:725–30.
Peirce JM, Alviña K. The role of inflammation and the gut microbiome in depression and anxiety. J Neurosci Res. 2019;97:1223–41.
Beurel E, Toups M, Nemeroff CB. The bidirectional relationship of depression and inflammation: double trouble. Neuron. 2020;107:234–56.
Daniels J, Brigden A, Kacorova A. Anxiety and depression in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME): examining the incidence of health anxiety in CFS/ME. Psychol Psychother Theory Res Pract. 2017;90:502–9.
Cho HJ, Menezes PR, Hotopf M, Bhugra D, Wessely S. Comparative epidemiology of chronic fatigue syndrome in Brazilian and British primary care: prevalence and recognition. Br J Psychiatry. 2009;194:117–22.
Hamaguchi M, Kawahito Y, Takeda N, Kato T, Kojima T. Characteristics of chronic fatigue syndrome in a Japanese community population. Clin Rheumatol. 2011;30:895–906.
Shi J, Shen J, Xie J, Zhi J, Xu Y. Chronic fatigue syndrome in Chinese middle-school students. Medicine. 2018. https://doi.org/10.1097/MD.0000000000009716.
Lim E-J, Son C-G. Review of case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). J Transl Med. 2020;18:1–10.
Starcevic V. Neurasthenia: cross-cultural and conceptual issues in relation to chronic fatigue syndrome. Gen Hosp Psychiatry. 1999;21:249–55.
Ware NC, Kleinman A. Culture and somatic experience: the social course of illness in neurasthenia and chronic fatigue syndrome. Psychosom Med. 1992;54:546–60.
Lee Y-C, Huang Y-T, Tsai Y-W, Huang S-M, Kuo KN, McKee M, Nolte E. The impact of universal national health insurance on population health: the experience of Taiwan. BMC Health Serv Res. 2010;10:1–8.
Germain A, Ruppert D, Levine SM, Hanson MR. Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism. Mol BioSyst. 2017;13:371–9.
Maloney EM, Boneva RS. Lin J-MS, Reeves WC: Chronic fatigue syndrome is associated with metabolic syndrome: results from a case-control study in Georgia. Metabolism. 2010;59:1351–7.
Castro-Marrero J, Faro M, Aliste L, Sáez-Francàs N, Calvo N, Martínez-Martínez A, de Sevilla TF, Alegre J. Comorbidity in chronic fatigue syndrome/myalgic encephalomyelitis: a nationwide population-based cohort study. Psychosomatics. 2017;58:533–43.
Ali S, Matcham F, Irving K, Chalder T. Fatigue and psychosocial variables in autoimmune rheumatic disease and chronic fatigue syndrome: a cross-sectional comparison. J Psychosom Res. 2017;92:1–8.
Overman CL, Kool MB, Da Silva JA, Geenen R. The prevalence of severe fatigue in rheumatic diseases: an international study. Clin Rheumatol. 2016;35:409–15.
Tsai S-Y, Chen H-J, Lio C-F, Kuo C-F, Kao A-C, Wang W-S, Yao W-C, Chen C, Yang T-Y. Increased risk of chronic fatigue syndrome in patients with inflammatory bowel disease: a population-based retrospective cohort study. J Transl Med. 2019;17:1–8.
Yang T-Y, Lin C-L, Yao W-C, Lio C-F, Chiang W-P, Lin K, Kuo C-F, Tsai S-Y. How mycobacterium tuberculosis infection could lead to the increasing risks of chronic fatigue syndrome and the potential immunological effects: a population-based retrospective cohort study. J Transl Med. 2022;20:1–9.
Tsai S-Y, Yang T-Y, Chen H-J, Chen C-S, Lin W-M, Shen W-C, Kuo C-N, Kao C-H. Increased risk of chronic fatigue syndrome following herpes zoster: a population-based study. Eur J Clin Microbiol Infect Dis. 2014;33:1653–9.
Loades ME, Read R, Smith L, Higson-Sweeney NT, Laffan A, Stallard P, Kessler D, Crawley E. How common are depression and anxiety in adolescents with chronic fatigue syndrome (CFS) and how should we screen for these mental health co-morbidities? A clinical cohort study. Eur Child Adolesc Psychiatry. 2021;30:1733–43.
Skapinakis P, Lewis G, Mavreas V. Unexplained fatigue syndromes in a multinational primary care sample: specificity of definition and prevalence and distinctiveness from depression and generalized anxiety. Am J Psychiatry. 2003;160:785–7.
Gay CW, Robinson ME, Lai S, O’Shea A, Craggs JG, Price DD, Staud R. Abnormal resting-state functional connectivity in patients with chronic fatigue syndrome: results of seed and data-driven analyses. Brain connectivity. 2016;6:48–56.
Godlewska BR, Near J, Cowen PJ. Neurochemistry of major depression: a study using magnetic resonance spectroscopy. Psychopharmacology. 2015;232:501–7.
Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiat. 2007;62:429–37.
Godlewska BR, Williams S, Emir UE, Chen C, Sharpley AL, Goncalves AJ, Andersson MI, Clarke W, Angus B, Cowen PJ. Neurochemical abnormalities in chronic fatigue syndrome: a pilot magnetic resonance spectroscopy study at 7 Tesla. Psychopharmacology. 2022;239:163–71.
Yi X-Y, Ni S-F, Ghadami MR, Meng H-Q, Chen M-Y, Kuang L, Zhang Y-Q, Zhang L, Zhou X-Y. Trazodone for the treatment of insomnia: a meta-analysis of randomized placebo-controlled trials. Sleep Med. 2018;45:25–32.
Dai Y-X, Chen M-H, Chen T-J, Lin M-H. Patterns of psychiatric outpatient practice in Taiwan: a nationwide survey. Int J Environ Res Public Health. 2016;13:955.
Bossini L, Casolaro I, Koukouna D, Cecchini F, Fagiolini A. Off-label uses of trazodone: a review. Expert Opin Pharmacother. 2012;13:1707–17.
Winger A, Kvarstein G, Wyller VB, Ekstedt M, Sulheim D, Fagermoen E, Småstuen MC, Helseth S. Health related quality of life in adolescents with chronic fatigue syndrome: a cross-sectional study. Health Qual Life Outcomes. 2015;13:1–9.
Strand EB, Mengshoel AM, Sandvik L, Helland IB, Abraham S, Nes LS. Pain is associated with reduced quality of life and functional status in patients with myalgic encephalomyelitis/chronic fatigue syndrome. Scand J Pain. 2019;19:61–72.
Faro M, Sàez-Francás N, Castro-Marrero J, Aliste L, de Sevilla TF, Alegre J. Gender differences in chronic fatigue syndrome. Reumatología clínica (English edition). 2016;12:72–7.
Steffens DC, Krishnan KRR, Helms MJ. Are SSRIs better than TCAs? comparison of SSRIs and TCAs: a meta-analysis. Depress Anxiety. 1997;6:10–8.
Johnson JL, Oliffe JL, Kelly MT, Galdas P, Ogrodniczuk JS. Men’s discourses of help-seeking in the context of depression. Sociol Health Illn. 2012;34:345–61.
Swift JK, Greenberg RP. Premature discontinuation in adult psychotherapy: a meta-analysis. J Consult Clin Psychol. 2012;80:547.
Valenstein-Mah H, Kehle-Forbes S, Nelson D, Danan ER, Vogt D, Spoont M. Gender differences in rates and predictors of individual psychotherapy initiation and completion among Veterans Health Administration users recently diagnosed with PTSD. Psychol Trauma Theory Res Pract Policy. 2019;11:811.
Pietrangelo T, Fulle S, Coscia F, Gigliotti PV, Fanò-Illic G. Old muscle in young body: an aphorism describing the Chronic Fatigue Syndrome. Eur J Trans Myol. 2018. https://doi.org/10.4081/ejtm.2018.7688.
Winger A, Kvarstein G, Wyller VB, Sulheim D, Fagermoen E, Småstuen MC, Helseth S. Pain and pressure pain thresholds in adolescents with chronic fatigue syndrome and healthy controls: a cross-sectional study. BMJ Open. 2014;4:e005920.
Leong K-H, Yip H-T, Kuo C-F, Tsai S-Y. Treatments of chronic fatigue syndrome and its debilitating comorbidities: a 12-year population-based study. J Transl Med. 2022;20:1–19.
Papakostas GI, Nutt DJ, Hallett LA, Tucker VL, Krishen A, Fava M. Resolution of sleepiness and fatigue in major depressive disorder: a comparison of bupropion and the selective serotonin reuptake inhibitors. Biol Psychiat. 2006;60:1350–5.
Cooper JA, Tucker VL, Papakostas GI. Resolution of sleepiness and fatigue: a comparison of bupropion and selective serotonin reuptake inhibitors in subjects with major depressive disorder achieving remission at doses approved in the European Union. J Psychopharmacol. 2014;28:118–24.
Arnold LM, Blom TJ, Welge JA, Mariutto E, Heller A. A randomized, placebo-controlled, double-blinded trial of duloxetine in the treatment of general fatigue in patients with chronic fatigue syndrome. Psychosomatics. 2015;56:242–53.
Bates D, Schultheis BC, Hanes MC, Jolly SM, Chakravarthy KV, Deer TR, Levy RM, Hunter CW. A comprehensive algorithm for management of neuropathic pain. Pain Med. 2019;20:S2–12.
Ferreira GE, McLachlan AJ, Lin CWC, Zadro JR, Abdel-Shaheed C, O’Keeffe M, Maher CG. Efficacy and safety of antidepressants for the treatment of back pain and osteoarthritis: systematic review and meta-analysis. Bmj. 2021. https://doi.org/10.1136/bmj.m4825.
Fernández-de-Las-Peñas C, Martín-Guerrero JD, Pellicer-Valero ÓJ, Navarro-Pardo E, Gómez-Mayordomo V, Cuadrado ML, Arias-Navalón JA, Cigarán-Méndez M, Hernández-Barrera V, Arendt-Nielsen L. Female sex is a risk factor associated with long-term post-COVID related-symptoms but not with COVID-19 symptoms: the LONG-COVID-EXP-CM multicenter study. J Clin Med. 2022;11:413.
Twomey R, DeMars J, Franklin K, Culos-Reed SN, Weatherald J, Wrightson JG. Chronic fatigue and postexertional malaise in people living with long COVID: an observational study. Physical Therapy. 2022;102:4.
Wood E, Hall KH, Tate W. Role of mitochondria, oxidative stress and the response to antioxidants in myalgic encephalomyelitis/chronic fatigue syndrome: a possible approach to SARS-CoV-2 ‘long-haulers’? Chronic Diseases Trans Med. 2021;7:14–26.
Bansal R, Gubbi S, Koch CA. COVID-19 and chronic fatigue syndrome: an endocrine perspective. J Clin Transl Endocrinol. 2022;27:100284.
Tsai SY, Lin CL, Shih SC, Hsu CW, Leong KH, Kuo CF, Lio CF, Chen YT, Hung YJ, Shi L. Increased risk of chronic fatigue syndrome following burn injuries. J Transl Med. 2018;16(1):342. https://doi.org/10.1186/s12967-018-1713-2.
Garjani A, Middleton RM, Nicholas R, Evangelou N. Pre-existing anxiety, depression, and neurological disability are associated with long COVID: a prospective and longitudinal cohort study of the United Kingdom multiple sclerosis register. medRxiv. 2021;27:4.
Fedoce AdG, Ferreira F, Bota RG, Bonet-Costa V, Sun PY, Davies KJ. The role of oxidative stress in anxiety disorder: cause or consequence? Free Radical Res. 2018;52:737–50.
Bhatt S, Nagappa AN, Patil CR. Role of oxidative stress in depression. Drug Discovery Today. 2020;25:1270–6.
Menke A. Is the HPA axis as target for depression outdated, or is there a new hope? Front Psych. 2019;10:101.
Tafet GE, Nemeroff CB. Pharmacological treatment of anxiety disorders: the role of the HPA axis. Front Psych. 2020;11:443.
Carruthers BM, Jain AK, De Meirleir KL, Peterson DL, Klimas NG, Lerner AM, Bested AC, Flor-Henry P, Joshi P, Powles AP. Myalgic encephalomyelitis/chronic fatigue syndrome: clinical working case definition, diagnostic and treatment protocols. J Chronic Fatigue Syndrome. 2003;11:7–115.
Carruthers BM, van de Sande MI, De Meirleir KL, Klimas NG, Broderick G, Mitchell T, Staines D, Powles AP, Speight N, Vallings R. Myalgic encephalomyelitis: international consensus criteria. J Intern Med. 2011;270:327–38.
We would like to extend acknowledgment to Dr. Jung-Nien Lai’s and Miss. Yu-Chi Yang's material support, and the listed institutes and Department of Medical Research at Mackay Memorial Hospital, and Mackay Medical College for funding support.
This work was supported by the Taiwan Ministry of Health and Welfare Clinical Trial Center (MOHW109-TDU-B-212–114004), MOST Clinical Trial Consortium for Stroke (MOST 109-2321-B-039-002), China Medical University Hospital (DMR-109-231), Tseng-Lien Lin Foundation, Taichung, Taiwan, Mackay Medical College (1082A03), Department of Medical Research at Mackay Memorial Hospital (MMH-109-79; MMH-109-103).
Ethics approval and consent to participate
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. This study was approved by the Research Ethics Committee of the China Medical University Hospital (CMUH-104-REC2-115) and the Institutional Review Board of Mackay Memorial Hospital (16MMHIS074).
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Figure S1. Forest plot of conditional logical regression measured odds ratios and 95% confidence interval of chronic fatigue syndrome with different treatments stratified by sex in participants younger than 34 years old. CFS chronic fatigue syndrome, CI confidence interval, SSRI selective serotonin reuptake inhibitor, SNRI serotonin and norepinephrine reuptake inhibitor, SARI serotonin antagonist and reuptake inhibitor, TCA tricyclic antidepressants, BZD benzodiazepine; *P < .05, **P < .01, ***P < .001.
Table S1. Conditional logical regression measured odds ratios and 95% confidence interval of chronic fatigue syndrome with different treatments stratified by sex in participants younger than 34 years old.
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Chen, C., Yip, HT., Leong, KH. et al. Presence of depression and anxiety with distinct patterns of pharmacological treatments before the diagnosis of chronic fatigue syndrome: a population-based study in Taiwan. J Transl Med 21, 98 (2023). https://doi.org/10.1186/s12967-023-03886-1