Biomedical science has historically been a male-dominated world — not just for the scientists, but also for their research subjects. Even most lab mice were male. But now, a new study shows that researchers are starting to include more females — from mice to humans — in their work.
In 2019, 49 percent of
articles surveyed in biomedical science used both male and female subjects, almost
twice as many as a decade before,
according to findings published June 9 in eLife.
A study of articles published
in 2009 across 10 biomedical disciplines showed a
dismal picture. Only 28 percent of 841
research studies included both males and female subjects. The results were
published in 2011 in Neuroscience and Biobehavioral Reviews.
The scientific world took
note. In 2016, the U.S. National Institutes of Health instituted the Sex as a Biological Variable policy in an effort to correct the imbalance.
Scientists had to use both males and females in NIH-funded research unless they
could present a “strong justification” otherwise.
Annaliese Beery, a
neuroscientist at Smith College in Northhampton, Mass., conducted the original study
showing the extent of sex bias in research. In 2019, she and Nicole Woitowich,
a chemist at Northwestern University in Evanston, Ill., wanted to see if sex
bias was still as strong as it was in 2009.
Have things improved? After
scanning another 720 articles across nine of the 10 original disciplines, Beery
and Woitowich have shown that yes, they have, with nearly half of all journal articles
including both males and females. Behavioral research was the most inclusive,
with both sexes in 81 percent of studies. Overall, six out of nine fields
surveyed showed a significant increase in studies that included both sexes.
But it’s not all good news.
Most studies that used only one sex offered no rationale for doing so. In
addition, many of the studies that used both sexes did not state whether they
had analyzed the results for sex differences.
Science News talked with Beery about her current findings, and the
changes underway in biomedical science. The interview has been edited for
length and clarity.
SN: Why is it important to study both males and females in biomedical research?
you only study one sex, [you don’t know if] that information you learn applies
to the other sex. But by studying both sexes, you can learn, is this shared? Is
this uncommon? Is this one of the areas in which there isn’t a sex difference,
or is there something different happening here between the sexes?
SN: Why weren’t people including females in their scientific studies a decade ago?
Beery: Researchers were
making an active choice to exclude females from their studies. One
rationale for this is that a lot of people assume that females are more variable
than males [due to their
hormone cycles]. There
have now been several papers that have looked explicitly at that question
and shown that no, females aren’t more variable than males.
bias also gets historically entrenched. If everyone in your field has studied
males, and the body of knowledge that’s been built up has always used male-only
subjects, then you might be inclined to continue studying male-only subjects…. I
think that’s been part of perpetuating the male bias for a long time.
SN: Were you surprised by the difference that took place over the last decade?
Beery: I was
pleasantly surprised by the increase in female inclusion. I expected that it
would be there, but it was substantial.
SN: On the downside, the studies frequently don’t analyze if there were differences between the sexes. Why?
pretty stumped, honestly. I really can’t think of a good statistical argument to not include sex as a factor in your analysis. If
it matters, it’s really important. And if it doesn’t matter, that seems like a
really nice thing to be able to contribute to the literature.
SN: Why is it important to keep track of sex bias in preclinical research?
Beery: I think it’s important to know what we know. And I think that’s why the 2011 paper had the reception that it did. Everybody knew that there was a male bias in the field. To be able to say “this is how bad it is” can make an important contribution to both measuring whether it’s getting better and really understanding what the limits of our knowledge are.