Abstract
Background
Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) is a complex, heterogenous disease. It has been suggested that subgroups of people with ME/CFS exist, displaying a specific cluster of symptoms. Investigating symptom-based clusters may provide a better understanding of ME/CFS. Therefore, this study aimed to identify clusters in people with ME/CFS based on the frequency and severity of symptoms.
Methods
Members of the Dutch ME/CFS Foundation completed an online version of the DePaul Symptom Questionnaire version 2. Self-organizing maps (SOM) were used to generate symptom-based clusters using severity and frequency scores of the 79 measured symptoms. An extra dataset (n = 252) was used to assess the reproducibility of the symptom-based clusters.
Results
Data of 337 participants were analyzed (82% female; median (IQR) age: 55 (44–63) years). 45 clusters were identified, of which 13 clusters included ≥ 10 patients. Fatigue and PEM were reported across all of the symptom-based clusters, but the clusters were defined by a distinct pattern of symptom severity and frequency, as well as differences in clinical characteristics. 11% of the patients could not be classified into one of the 13 largest clusters. Applying the trained SOM to validation sample, resulted in a similar symptom pattern compared the Dutch dataset.
Conclusion
This study demonstrated that in ME/CFS there are subgroups of patients displaying a similar pattern of symptoms. These symptom-based clusters were confirmed in an independent ME/CFS sample. Classification of ME/CFS patients according to severity and symptom patterns might be useful to develop tailored treatment options.
https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-023-03946-6
Background
Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) is a complex, heterogenous disease. It has been suggested that subgroups of people with ME/CFS exist, displaying a specific cluster of symptoms. Investigating symptom-based clusters may provide a better understanding of ME/CFS. Therefore, this study aimed to identify clusters in people with ME/CFS based on the frequency and severity of symptoms.
Methods
Members of the Dutch ME/CFS Foundation completed an online version of the DePaul Symptom Questionnaire version 2. Self-organizing maps (SOM) were used to generate symptom-based clusters using severity and frequency scores of the 79 measured symptoms. An extra dataset (n = 252) was used to assess the reproducibility of the symptom-based clusters.
Results
Data of 337 participants were analyzed (82% female; median (IQR) age: 55 (44–63) years). 45 clusters were identified, of which 13 clusters included ≥ 10 patients. Fatigue and PEM were reported across all of the symptom-based clusters, but the clusters were defined by a distinct pattern of symptom severity and frequency, as well as differences in clinical characteristics. 11% of the patients could not be classified into one of the 13 largest clusters. Applying the trained SOM to validation sample, resulted in a similar symptom pattern compared the Dutch dataset.
Conclusion
This study demonstrated that in ME/CFS there are subgroups of patients displaying a similar pattern of symptoms. These symptom-based clusters were confirmed in an independent ME/CFS sample. Classification of ME/CFS patients according to severity and symptom patterns might be useful to develop tailored treatment options.
https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-023-03946-6