Article

Daily cytokine fluctuations, driven by leptin, are associated with fatigue severity in chronic fatigue syndrome: evidence of inflammatory pathology

Journal of Translational Medicine (Impact Factor: 3.99). 04/2013; 11(1):93. DOI: 10.1186/1479-5876-11-93
Source: PubMed

ABSTRACT Background
Chronic fatigue syndrome (CFS) is a debilitating disorder characterized by persistent fatigue that is not alleviated by rest. The lack of a clearly identified underlying mechanism has hindered the development of effective treatments. Studies have demonstrated elevated levels of inflammatory factors in patients with CFS, but findings are contradictory across studies and no biomarkers have been consistently supported. Single time-point approaches potentially overlook important features of CFS, such as fluctuations in fatigue severity. We have observed that individuals with CFS demonstrate significant day-to-day variability in their fatigue severity.

Methods
Therefore, to complement previous studies, we implemented a novel longitudinal study design to investigate the role of cytokines in CFS pathophysiology. Ten women meeting the Fukuda diagnostic criteria for CFS and ten healthy age- and body mass index (BMI)-matched women underwent 25 consecutive days of blood draws and self-reporting of symptom severity. A 51-plex cytokine panel via Luminex was performed for each of the 500 serum samples collected. Our primary hypothesis was that daily fatigue severity would be significantly correlated with the inflammatory adipokine leptin, in the women with CFS and not in the healthy control women. As a post-hoc analysis, a machine learning algorithm using all 51 cytokines was implemented to determine whether immune factors could distinguish high from low fatigue days.

Results
Self-reported fatigue severity was significantly correlated with leptin levels in six of the participants with CFS and one healthy control, supporting our primary hypothesis. The machine learning algorithm distinguished high from low fatigue days in the CFS group with 78.3% accuracy.

Conclusions
Our results support the role of cytokines in the pathophysiology of CFS.

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Available from: Jarred Younger, Aug 14, 2015
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    • "Inflammatory markers have been associated with specific symptoms common in CFS; chronic fatigue, heart rate variability, sleep quality, cognitive problems and post-exertional malaise [9] [10] [11] [12] [13] [14] [15] [16] [17]. Changes in cytokine profiles have been suggested as biomarkers of CFS [18] [19] [20] [21] [22]. "
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    ABSTRACT: Recent evidence suggests immune and inflammatory alterations are important in chronic fatigue syndrome (CFS). This study was done to explore the association of functionally important genetic variants in inflammation and immune pathways with CFS. Peripheral blood DNA was isolated from 50 CFS and 121 non-fatigued (NF) control participants in a population-based study. Genotyping was performed with the Affymetrix Immune and Inflammation Chip that covers 11K single nucleotide polymorphisms (SNP) following the manufacturer's protocol. Genotyping accuracy for specific genes was validated by pyrosequencing. Golden Helix SVS software was used for genetic analysis. SNP functional annotation was done using SPOT and GenomePipe programs. CFS was associated with 32 functionally important SNPs: 11 missense variants, 4 synonymous variants, 11 untranslated regulatory region (UTR) variants and 6 intronic variants. Some of these SNPs were in genes within pathways related to complement cascade (SERPINA5, CFB, CFH, MASP1 and C6), chemokines (CXCL16, CCR4, CCL27), cytokine signaling (IL18, IL17B, IL2RB), and toll-like receptor signaling (TIRAP, IRAK4). Of particular interest is association of CFS with two missense variants in genes of complement activation, rs4151667 (L9H) in CFB and rs1061170 (Y402H) in CFH. A 5'UTR polymorphism (rs11214105) in IL18 also associated with physical fatigue, body pain and score for CFS case defining symptoms. This study identified new associations of CFS with genetic variants in pathways including complement activation providing additional support for altered innate immune response in CFS. Additional studies are needed to validate the findings of this exploratory study. Copyright © 2015. Published by Elsevier Inc.
    Human immunology 06/2015; 77. DOI:10.1016/j.humimm.2015.06.014 · 2.28 Impact Factor
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    [Show abstract] [Hide abstract]
    ABSTRACT: Background Chronic fatigue syndrome (CFS) is a debilitating disorder characterized by persistent fatigue that is not alleviated by rest. The lack of a clearly identified underlying mechanism has hindered the development of effective treatments. Studies have demonstrated elevated levels of inflammatory factors in patients with CFS, but findings are contradictory across studies and no biomarkers have been consistently supported. Single time-point approaches potentially overlook important features of CFS, such as fluctuations in fatigue severity. We have observed that individuals with CFS demonstrate significant day-to-day variability in their fatigue severity. Methods Therefore, to complement previous studies, we implemented a novel longitudinal study design to investigate the role of cytokines in CFS pathophysiology. Ten women meeting the Fukuda diagnostic criteria for CFS and ten healthy age- and body mass index (BMI)-matched women underwent 25 consecutive days of blood draws and self-reporting of symptom severity. A 51-plex cytokine panel via Luminex was performed for each of the 500 serum samples collected. Our primary hypothesis was that daily fatigue severity would be significantly correlated with the inflammatory adipokine leptin, in the women with CFS and not in the healthy control women. As a post-hoc analysis, a machine learning algorithm using all 51 cytokines was implemented to determine whether immune factors could distinguish high from low fatigue days. Results Self-reported fatigue severity was significantly correlated with leptin levels in six of the participants with CFS and one healthy control, supporting our primary hypothesis. The machine learning algorithm distinguished high from low fatigue days in the CFS group with 78.3% accuracy. Conclusions Our results support the role of cytokines in the pathophysiology of CFS.
    Journal of Translational Medicine 04/2013; 11(1):93. DOI:10.1186/1479-5876-11-93 · 3.99 Impact Factor
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    ABSTRACT: Considerable controversy has transpired regarding the core features of myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). Current case definitions differ in the number and types of symptoms required. This ambiguity impedes the search for biological markers and effective treatments. This study sought to empirically operationalize symptom criteria and identify which symptoms best characterize the illness. Patients (n=236) and controls (n=86) completed the DePaul Symptom Questionnaire, rating the frequency and severity of 54 symptoms. Responses were compared to determine the threshold of frequency/severity ratings that best distinguished patients from controls. A Classification and Regression Tree (CART) algorithm was used to identify the combination of symptoms that most accurately classified patients and controls. A third of controls met the symptom criteria of a common CFS case definition when just symptom presence was required; however, when frequency/severity requirements were raised, only 5% met criteria. Employing these higher frequency/severity requirements, the CART algorithm identified three symptoms that accurately classified 95.4% of participants as patient or control: fatigue/extreme tiredness, inability to focus on multiple things simultaneously, and experiencing a dead/heavy feeling after starting to exercise. Minimum frequency/severity thresholds should be specified in symptom criteria to reduce the likelihood of misclassification. Future research should continue to seek empirical support of the core symptoms of ME and CFS to further progress the search for biological markers and treatments.
    01/2014; 2(1):40-56. DOI:10.1080/21641846.2013.862993
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