Symptoms-Based Phenotypes Among Women With Dysmenorrhea: A Latent Class Analysis

Chen Chen, Susan Ofner, Giorgos Bakoyannis, Kristine L. Kwekkeboom, Janet Carpenter

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Dysmenorrhea is highly prevalent and may increase women’s risk for developing other chronic pain conditions. Although it is highly variable, symptom-based dysmenorrhea phenotypes have not been identified. The aims of the study were to identify symptom-based dysmenorrhea phenotypes and examine their relationships with demographic and clinical characteristics. In a cross-sectional study, 762 women with dysmenorrhea rated severity of 14 dysmenorrhea-related symptoms. Using latent class analysis, we identified three distinctive phenotypes. Women in the “mild localized pain” phenotype (n = 202, 26.51%) had mild abdominal cramps and dull abdominal pain/discomfort. Women in the “severe localized pain” phenotype (n = 412, 54.07%) had severe abdominal cramps. Women in the “multiple severe symptoms” phenotype (n = 148, 19.42%) had severe pain at multiple locations and multiple gastrointestinal symptoms. Race, ethnicity, age, and comorbid chronic pain conditions were significantly associated with phenotypes. Identification of these symptom-based phenotypes provides a foundation for research examining genotype–phenotype associations, etiologic mechanisms, and/or variability in treatment responses.

Original languageEnglish (US)
JournalWestern Journal of Nursing Research
DOIs
StateAccepted/In press - Sep 1 2017

Fingerprint

Dysmenorrhea
Phenotype
Colic
Pain
Chronic Pain
Abdominal Pain
Cross-Sectional Studies
Demography

Keywords

  • chronic pain
  • dysmenorrhea
  • menstruation
  • pelvic pain
  • phenotype

ASJC Scopus subject areas

  • Nursing(all)

Cite this

Symptoms-Based Phenotypes Among Women With Dysmenorrhea : A Latent Class Analysis. / Chen, Chen; Ofner, Susan; Bakoyannis, Giorgos; Kwekkeboom, Kristine L.; Carpenter, Janet.

In: Western Journal of Nursing Research, 01.09.2017.

Research output: Contribution to journalArticle

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