Dysmenorrhea is a prevalent pain condition among women and a risk factor for other chronic pain conditions. Individuals vary in dysmenorrhea pain severity, the number of painful sites, and co-occurring gastrointestinal symptoms. Three dysmenorrhea symptom-based phenotypes were previously identified using latent class analysis; however, there is a need to validate these in an independent sample, so they can be used in mechanistic and interventional research. There is also a need to further characterize dysmenorrhea symptom-based phenotypes in terms of demographic, clinical, and psychobehavioral characteristics so they can be used to inform precision dysmenorrhea treatment.
The study objectives were to (a) determine whether the same dysmenorrhea symptom-based phenotypes would be found in a new sample; (b) determine whether including demographic, clinical, and psychobehavioral covariates in latent class analyses would change individuals’ phenotype memberships; and (c) investigate relationships between dysmenorrhea symptom-based phenotypes and demographic, clinical, and psychobehavioral characteristics.
This cross-sectional survey study included 678 women (aged 14–42 years) with dysmenorrhea. Participants reported dysmenorrhea symptom severity, demographic, clinical (comorbid chronic pain and gynecological conditions), and psychobehavioral characteristics (perceived stress, anxiety, depression, sleep disturbance, and pain catastrophizing). We used latent class analysis to identify symptom-based phenotypes. We compared analyses with and without covariates (i.e., demographic, clinical, and psychobehavioral characteristics) to determine if individuals’ phenotype memberships changed. We then examined associations between phenotypes and demographic, clinical, and psychobehavioral characteristics.
We reproduced three dysmenorrhea symptom-based phenotypes: the “mild localized pain” phenotype (characterized by mild abdominal cramps), the “severe localized pain” phenotype (characterized by severe abdominal cramps), and the “multiple severe symptoms” phenotype (characterized by severe pain at multiple locations and gastrointestinal symptoms). Analyses with and without covariates had little effect on individuals’ phenotype membership. Race, comorbid chronic pain condition, endometriosis, and pain catastrophizing were significantly associated with the dysmenorrhea phenotypes.
Findings provide a foundation to further study mechanisms of dysmenorrhea symptom heterogeneity and develop dysmenorrhea precision treatments. The three dysmenorrhea symptom-based phenotypes were validated in a second sample. Demographic, clinical, and psychobehavioral factors were associated with dysmenorrhea symptom-based phenotypes.