Machine Learning Algorithm Finds Links Between Adverse Childhood Experiences, Rheumatic Diseases

Adverse childhood experiences appear to increase a person’s risk of developing fibromyalgia, according to a new analysis based on patient surveys and machine learning algorithms.

The study also showed that an agreeable personality and somatic disorder are also predictive of rheumatic disease. The findings were published in Rheumatology International.

Corresponding author Germano Vera Cruz, of the University of Tours, in France, and colleagues wanted to better understand the extent to which psychological and psychopathological factors, as well as adverse childhood experiences and socio-demographic characteristics, might contribute to the development of certain rheumatic diseases. To find out, they asked 165 French women with fibromyalgia, rheumatoid arthritis, spondyloarthritis, and Sjögren’s syndrome to complete a questionnaire that probed topics such as personality traits, psychopathological conditions, fatigue, and pain. In their analysis, they drew a line between inflammatory rheumatic and musculoskeletal diseases and fibromyalgia, which is noninflammatory and still somewhat controversial, they said.

The data from the questionnaires were analyzed by random forest and logistic regression machine-learning algorithms to see what patterns emerged.

Mistreatment during childhood, the “agreeableness” personality trait, and somatic disorders were the most likely to be associated with rheumatic musculoskeletal diseases. Childhood mistreatment and agreeableness were more strongly associated with fibromyalgia than the inflammatory disorders (odds ratios of 18.92 and 6.11, respectively).

“Overall, adverse childhood experiences seem relatively more important than personality traits, psychopathological or demographic variables,” the authors said. “The results of this study suggest that traumatic childhood experiences may lead to psychopathological disorders in adulthood, which in turn might underlie, at least in part, the development of [fibromyalgia].”

Vera Cruz and colleagues noted that other findings have suggested an association between childhood adversity and fibromyalgia, and in particular sexual abuse.

“This seems to support the hypothesis that [fibromyalgia] could be the consequence of early psychological trauma that may affect the hypothalamic pituitary adrenal axis,” they wrote. “This early stress is associated with abnormal production of cortisol resulting in hypersensitivity to stress and negative affect.”

However, the authors cautioned that these factors should be characterized as risk factors, not “determining factors.”

Moreover, Vera Cruz and colleagues pointed out that some of the other risk factors identified in their study, such as somatic disorders, depression, agreeableness, and neuroticism, could also also be linked with early trauma.

In terms of socio-demographic factors, the authors noted that some earlier research has suggested that education level could be linked with rheumatoid arthritis risk. The current study did not find such an association. The investigators said it is too soon to draw firm conclusions one way or the other as to whether education level can be linked with a risk of rheumatic diseases.

The authors said their findings were limited by the relatively small size of the survey participants. They said it can be difficult to recruit a large number of patients due to the relatively small patient population of fibromyalgia, and the difficulty of convincing people to participate.

In their conclusion, Vera Cruz and colleagues argued that diagnosis of fibromyalgia would benefit from more research into the pathology of the disease.

Reference

Vera Cruz G, Bucourt E, Réveillère C, et al. Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases. Rheumatol Int. Published online June 14, 2021. doi:10.1007/s00296-021-04916-1