By: Britt Mardis
Most people’s understanding of biological sex is that it is related to both your chromosomes and your physical attributes (i.e. genitalia). The version of sex that most of us learn at a relatively young age is that if you have two X chromosomes you’re a woman and if you have one X and one Y chromosome, you’re a man.
However, this version of sex isn’t exactly right. There are XY people who have ovaries and give birth, and XX people who have ‘male’ bodies and functional sperm. People like these examples who don’t perfectly fit the XX/XY categories are commonly called intersex. Some intersex people have physical attributes that don’t match their chromosomal category, but others exist in other chromosomal categories completely. There are several genetic occurrences that can lead to people with different numbers of sex chromosomes. For example, there are people who are XXX, XXY, X, Y, XYY, as well as those with deletions or insertions of genes that can cause different combinations of genitalia and secondary sex characteristics (breasts, facial hair, etc.). There are also many hormonal cases, in which things like androgen insensitivity or estradiol failure can lead to a person being intersex.
The next question you might ask is “well, if there are all these exceptions, why do we only ever talk about male and female?” The answer has a lot to do with scientific research. For all their social ramifications, categories make scientific data much easier to analyze. So to try and categorize all these people in a way that makes sense, you, as a scientist, might plot them on a graph. So you look at all the ways people differ, including genetic variables, responses to hormones, secondary sex characteristics, etc., you give everything a number, and then you graph it.
You would get something called a bimodal distribution, which looks like this. The two big peaks are what we’d call “male” and “female,” but you can see the more purple areas which we would call “intersex.” Now, this image is just an example. In reality, there is not as much overlap, and the data would look different depending on which specific characteristics you focused on. But the idea is that there is a continuum of observed characteristics.
So when you’re trying to examine scientific data, it’s often helpful to group things so you can look for patterns or commonalities among members of a group, or differences between groups. Traditionally, when scientists looked at their bimodal distribution, they decided to split it down the middle and make two groups: male and female. And for a while, this worked.
But as we’ve learned more about hormones and brain signaling, and started paying more attention to the outliers where ‘standard’ things didn’t work, we’ve discovered that a binary model doesn’t work very well.
So let’s say you’re a scientist again, and you’re studying the effects of 2,3,7,8-Tetrachlorodibenzo-P-dioxin (TCDD) on animals, and you start to realize that the binary model of sex doesn’t predict your results very well. So you repeat your experiment, and again, the results are not predicted very well. You might start to think that your model is wrong. But the model kind of mostly predicts things, and it worked for years…
Eventually some brave, clever person pipes up and says “well, remember how we made that big graph, but then we just split it into two categories?” and you’re like “…yeah?” and they say “well, what if we used that data?”
At this point you groan, because complicated data is hard, but you sit down and do the complicated math shit and lo and behold, the model starts to work again. Where TCDD was randomly turning some females into males and vice versa, now you can see there’s a subgroup of what you’d call “female” that responds like the male.
It’s important to note here that these folks weren’t males that you mislabeled as females. They are functionally female and can do all the “female” things like gestate babies and such. They just respond to this one endocrine disruptor in a “male” way. So you just create two new categories, call them “Male2” and “Female2,” and keep going, happy that your data works. You’ve got four sexes now, but you don’t really have to tell anyone that, right?
Except then you have those XY people who can gestate babies, so you add “Intersex 1”, and the XX people with penises… and ovaries. Ok, fine, you add “Intersex 2,” because all these groups respond differently with signalling and brains when you get into the nitty gritty of it.
So the more you look and study, the more you learn, and we’re going to be able to sort out more of these fine differences between people. Depending on what we’re doing, we can choose whether or not to care. If a doctor is giving you aspirin, it probably doesn’t matter. But if they’re using a steroid on you or treating you for dioxin poisoning, that could be really important!
The important thing to remember is that we’re not inventing ‘new’ sexes. The distribution has always looked like this, we were just using really broad categories. But it’s gotten to the point where it inhibits our medicine and our scientific understanding of sex.
It’s also worth noting that I didn’t touch on transgender people. Intersex is not the same as transgender. You can be intersex without being transgender, and vice versa. You could even be both! Current estimates are that about 2% of the population is intersex, and we know that’s low, because many variations of intersex people are not “visibly” intersex.
The point of all this is that intersex is not a condition or a disease. It is a natural part of the bimodal distribution of sex characteristics. Science not only supports this, it also suggests that ignoring intersex people makes your conclusions wrong. Long story short, there is no scientifically valid reason to medically or culturally force people into the ‘peaks.’
The links below contain more detailed explanations about the embryology of sex organs, genetics and how it all relates to intersex people:
https://www.who.int/genomics/gender/en/index1.htmlThis piece is adapted from a twitter thread by @ScienceVet2: https://twitter.com/sciencevet2/status/1035250518870900737?lang=en