Objective: To investigate a potential association between gender identity disorders (GID) and subsequent multiple sclerosis (MS) risk.
Background: Sex hormones may be important in mediating gender differences in MS, but their influence on MS risk remains largely uncharacterised. We hypothesize that an altered balance of sex hormones in males with GID will both inherently, and secondary to treatment in undergoing male-to-female conversion (which typically involves taking feminizing hormones, anti-androgens and/or sex reassignment surgery), increase MS risk.
Methods: We analysed linked English national Hospital Episode Statistics from 1999-2011. A cohort of males with GID and a cohort of females with GID were constructed by identifying the first episode of day-case care or hospital admission in which a GID or sexual transformation procedure was coded. A reference cohort was constructed from individuals admitted for various minor medical conditions. We searched for any subsequent day-case care or inpatient admission for, or death from, MS in these cohorts. We calculated rates for MS, stratified and then standardized by age, calendar year of first recorded admission, region of residence, and socio-economic status.
Results: There were 1157 males and 2390 females in the GID cohorts, and 4.6 million males and 3.4 million females in the respective reference cohorts. The adjusted rate ratio (RR) of MS following GID in males was 6.63 (95% confidence interval (95%CI) 1.81-17.01, p=0.0002), based on 4 observed cases and 0.6 expected. The adjusted RR of MS following GID in females was 1.44 (95%CI 0.47-3.37), p=0.58), based on 5 observed cases and 3.5 expected.
Conclusions: We report a positive association (a near seven-fold elevation of rates) between GID and subsequent MS in males. Our findings support a postulated association between low testosterone and MS risk, and highlight a need for further exploration of the influence of feminizing sex hormones on MS risk.
CoI: The building of the linked datasets, and the development of the analytical software used to study disease associations, was funded by the English National Institute for Health Research. This study had no specific funding.