Today, we are excited to share a new publication by Fereshteh Jahaniani, Ph.D., Ron Davis, Ph.D., & colleagues: “Longitudinal cytokine and multi-modal health data of an extremely severe ME/CFS patient with HSD reveals insights into immunopathology, and disease severity.” This study is supported by OMF and the EDS Society. Read the full publication here!
From the Desk of the ME/CFS Collaborative Research Center at Stanford University
This study investigates Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) through a four-year examination of an individual with hypermobility spectrum disorder (HSD). Given the high prevalence of comorbidities between ME/CFS and connective tissue disorders like HSD, we integrated longitudinal cytokine data spanning four years with clinical and health data spanning over two decades from disease onset to conduct an extensive analysis of this individual. This comprehensive approach allowed us to explore the dynamic nature of symptoms, severity, and environmental factors influencing ME/CFS.
Our primary objective was to emphasize the diverse spectrum of ME/CFS severity and the dynamic, temporal evolution of disease manifestations by following a patient from potential onset through 20 years of the illness. Through this investigation, we aimed to highlight the variability and heterogeneity of symptoms existing between and within patients throughout the course of the condition and their impact on all aspects of the patient’s life (personal, occupation, education, and social). We observed both abrupt onset cases with high severity and those with a gradual onset and progressive worsening over time due to factors such as misdiagnosis and lack of appropriate therapeutic options. Furthermore, patients with mild symptoms can experience severe or very severe ME/CFS episodes, complicating our attempts at severity classification.
To better capture the dynamic range of symptomatology, severity, and their impact on patients’ lives, we proposed a modified severity assessment framework. This framework emphasizes the need for a personalized severity scale that accounts for individual fluctuations in symptomatology during their illness and in response to medical intervention.
In addition, to address the challenges posed by the subjective nature of severity assessment and the lack of systematic tools for symptom recording and treatment response tracking. To fill this gap, we developed innovative tracking tools such as the ME-CFSTrackerApp and LexiTime to provide ME/CFS patients with a means to accurately monitor symptom severity and management over time, facilitating better disease management and communication with healthcare providers.
To better capture the experiences of patients facing profound levels of impairment, we introduced the concept of an “extremely severe” category. This classification, based on research observations and patient feedback, sheds light on patients’ experiences beyond the existing severity categories found in the ICC Criteria.
Moreover, our integrative longitudinal cytokine study implicates mononucleosis infection as an environmental risk factor triggering ME/CFS. Our study underscores the significance of Th2-type cytokines and their interactions with mast cells and eosinophils, suggesting potential shared mechanisms with other ME/CFS comorbidities such as HSD and Postural Tachycardia Syndrome (POTS). We identified key pathways such as BCL6 and TP53, emphasizing the need for further investigation into medication efficacy and drug interactions. Our findings also support the value of CCL11 as a biomarker for assessing ME/CFS disease severity, particularly for cognitive impairment and sensorial intolerance.
Additionally, we investigated the association between medications and supplements and disease severity. Our findings emphasize the need for better monitoring of adverse drug reactions. We also suggested investigating low-dose partial agonists such as Abilify and Naltrexone in ME/CFS clinical trials.
In light of our findings, we advocate for the integration of multi-omics data and advanced techniques such as artificial intelligence (AI) to tailor treatments to individual patients and improve healthcare outcomes for those affected by ME/CFS. While our study is a case study and limited in sample size, our findings align with previous research findings. However, these findings might be applied to a subset of patients. We also acknowledge that our modified severity scale might not be applicable to all patients. Overall, our study contributes to a deeper understanding of ME/CFS and lays the groundwork for future research and clinical practice aimed at addressing the complex challenges posed by this debilitating condition.
Fereshteh Jahaniani, PharmD/PhD
We invite you to read the full study to explore the data in detail and learn more about this research.
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