Non-clinical symptoms associated with polycystic ovary syndrome: evidence from a comparative study in Bangladesh

Polycystic ovary syndrome (PCOS) is a common endocrine disorder that occurs in women of reproductive age and is linked to pregnancy, metabolic, and psychological complications. Although it is very prevalent, a significant percentage of the cases are not diagnosed, because of poor awareness and access to diagnostic services. Identifying non-clinical symptom patterns associated with PCOS may support the development of symptom-based risk assessment approaches and facilitate earlier clinical evaluation of women at increased risk of the condition. The objective of the study was to examine differences in non-clinical, self-reported, and observable symptom profiles between women with and without PCOS in Bangladesh.

A cross-sectional study was carried out on 546 women between the ages of 15 and 30 years in Bangladesh, 273 with physician-diagnosed PCOS and 273 controls. χ² tests were conducted on bivariate analyses and multivariate logistic regression was conducted to determine the possible symptoms of PCOS with age and body mass index as confounding variables.

There were a number of symptoms that were significantly different in the two groups. The strongest associations with PCOS in multivariate analysis were irregular menstrual cycles (odds ratio (OR) = 4.93), unexpected weight gain (OR = 4.32), and hair growth in the jawline (OR = 4.25). The condition was also significantly related to self-reported sleep difficulty (OR = 2.62) and waist circumference greater than hips (OR = 2.47). Conversely, the symptoms that were frequently reported, including acne, oily skin, loss of hair, anxiety or stress, hair growth in upper lip, thigh, and upper arm, and family history of PCOS were not statistically significant after adjustments.

Several observable and self-reported symptoms were associated with PCOS among Bangladeshi women. These findings provide a basis for future research aimed at developing and validating symptom-based risk assessment approaches for PCOS.