FEmale Metabolic Risk and Androgens: an Irish Longitudinal (FEMAIL) Study

Androgen excess is the cardinal biochemical feature of polycystic ovary syndrome (PCOS), a lifelong metabolic disorder affecting 10% of women. Serum testosterone correlates with insulin resistance in women, however, there is an urgent need to improve our understanding of the association between androgens and the risk of type 2 diabetes. Recently, a new subclass of androgenic steroids known as 11-oxygenated androgens has been identified. Utilising highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques, our group has recently demonstrated that 11-oxygenated steroids are the predominant androgens in both health controls and women with PCOS, and that these correlate closely with markers of insulin resistance. The bioactive 11-oxygenated androgen 11-ketotestosterone (11KT) binds and activates the androgen receptor with equal affinity to testosterone, yet nothing is known about its impact on metabolism or glucose homeostasis. Intriguingly, unlike testosterone, 11-oxygenated androgens do not decline with age in women, and, therefore, may mediate an increased risk of T2DM in women across their life course. Therefore, this previously ignored androgen class is likely of major importance in female metabolic health, and may represent a novel metabolic risk factor and biomarker. However, 11-oxygenated androgens are not currently measured in routine clinical practice. To date, no population-based or human in vivo physiology studies have examined the association between 11-oxygenated androgens, glucose metabolism and diabetes risk in women, despite the high prevalence of PCOS in the female population. There is emerging evidence, even in women without a confirmed history of PCOS, that the levels of androgens over time correlate with their likelihood of developing metabolic and cardiovascular disease. This has not been studied to date in a prospective manner in healthy women in the background population using long term follow up data.