I have just an article from the Annals of Clinical Biochemistry about establishing reference ranges for transgender patients. It does seem that finally evidence is being collected in order to address the problem rather than just assuming that existing male and female ranges would be appropriate.
The data they collect is on a limited number of patients but as these patients have recruited from a transgender clinic, I’m not sure what else they could do to reliably recruit more patients. Only patients on hormone treatment are included in their analysis. I don’t remember if these patients were screened to ensure that only patients who have been on treatment for a prolonged period of time. I would like to think the data was collected on a stable population.
The paper mentions the various treatments that patients might be under e.g. different medications and doses. I wouldn’t like to assume that each medication would have the exact same effect, especially when the discussion section discuss the biochemical impacts of the different medications. If the dataset was sufficiently big enough to break down into different treatment groups, I’m not sure how that information would ever be captured and passed onto the lab.
I suppose I approach the problem from a lab perspective. At the moment, identifying transgender patients isn’t a simple process of ticking a box. HL7 is the standard way of moving healthcare information between a PAS system and the LIMS. At the moment, there is no concept of sex and gender within the HL7 specification. Should the sex at birth be recorded or the transitioned sex? A transgender patient is allowed to have a completely new NHS number and doesn’t have to disclose they have transitioned. So whilst studies are being down to identify appropriate ranges, the challenge then becomes in the implementation of that range.
This particular study was performed on a particular analyser platform. So whilst is possible that it highlights analyses where they might be differences, it wouldn’t be appropriate to just apply these ranges on results being generated on different platforms. Just look at any EQA data and you will notice the difference between various analysers.
So whilst this study does indeed start to address the lack of data, I feel I must make a similar conclusion to the authors that more studies are required to complete. Hopefully some of these studies would replicate earlier findings as it seems that some studies are finding the same thing.