#367 - Tylenol, pregnancy, and autism: What recent studies show and how to interpret the data

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Welcome to a special episode of The Drive, everyone.

If you've been following the headlines recently, you may have seen, of course, stories linking acetaminophen or Tylenol use during pregnancy to autism.

Not surprisingly, those headlines have generated a lot of questions, a lot of controversy, and a lot of confusion.

I've heard about this a lot from every direction.

My patients, listeners of the podcast, friends, family members, people writing in through the website.

Basically, it's like I'm sure many people in the space, we've all been inundated by it.

And the more I thought about it, the more I realized this was a great opportunity to, I think, maybe put forth a framework for how to think about these things critically.

While we initially thought we would just do this in the newsletter last week, once we got into it, we realized, no, this doesn't really lend itself to an article or even a short video.

It really...

commands effectively the discipline of what we do in the AMAs, the Ask Me Anythings.

Of course, unlike the normal AMAs, this is going to be made available to everybody.

So before we dive in, though, I want to kind of lay out some groundwork.

We're going to unpack some of the points in more detail that I'm going to lay out below, but I also want to make sure we're starting from a place of reference.

I want to start out with a few important observations.

Okay, so the first is autism rates have risen dramatically over the past generation.

Now, we're going to talk about why that might be, but it's very important to state up front that there is unlikely to be a single cause.

Why?

Because complex conditions usually don't have simple explanations.

This is true of obesity, despite what some people would have you believe that it's just this one thing or just this one thing and whatever.

But the reality of it is complex conditions require multiple things typically.

So anytime we look at a possible contributing factor, we need to kind of resist the temptation to assume it's the sole cause.

Now, that doesn't diminish the interest in identifying a bunch of potential causes.

Okay.

Second point I want to make here, and it's kind of weird that I have to make it, but I do, science is supposed to be apolitical.

Unfortunately, that's not the case.

And for reasons that I don't think I'm smart enough to understand, autism happens to be one of those examples.

But so are many other topics we've discussed, like nutrition or protein, which has become remarkably political.

My goal here is not to have a political debate, but rather to examine the evidence as carefully and objectively as I can.

Third, we do need to realize something that I think is very hard to accept, and that is that as humans, we are not wired to think scientifically.

I want to restate that because it sounds condescending, but it's simply an observation of how we have evolved.

We are not wired for critical and scientific thought.

This is something I've written about, and we're going to actually link to a piece I wrote over 10 years ago that I think synthesizes that point really well.

But again, it really comes down to the fact that we should understand that the scientific method and critical thought are human inventions.

They're wonderful inventions, and I would argue they are the single most important invention our species has ever put forth.

And without this, nothing else would exist.

We'd still be living in caves.

But that doesn't mean that it comes naturally, and it doesn't mean we're wired to do it.

So just keep that in mind as you catch yourself, as I catch myself, falling into non-scientific thought.

We're going to rely on a framework at some point during this discussion, which is very helpful when considering epidemiology, which is the branch of science we're going to be talking primarily about today.

And it's called the Bradford-Hill criteria.

These are nine principles that were laid out in the mid-60s to help us determine whether an observed observation is likely to be causal.

So this framework looks at things like strength of association, consistency across multiple studies, biologic plausibility, temporality, and things like that.

They're not a checklist per se, but they provide a disciplined disciplined way to try to make sense of correlations and interpret which ones have a higher probability of being causal from those that don't.

Another thing I want to point out is we're going to be talking about medications.

We're going to be talking about pregnancy.

And I think it should be obvious, and I'm sure anyone listening to this or watching this who has gone through pregnancy will understand.

that the bar is very high when we are talking about medications to be used during pregnancy.

Most physicians, myself included, though I don't treat very many many pregnant women, think about drugs and supplements very differently in the setting of pregnancy.

Of course, because we are typically not treating patients with life-threatening conditions, our mantra is during pregnancy, women should basically stop all medications and supplements beyond the obvious ones, such as prenatal vitamins or hormones such as thyroid hormone, which can be essential.

But anything that's even in a gray area or probably okay, we tend to just avoid.

Now, since the late 70s, the FDA has used a very simple letter system to classify drugs by their risk during pregnancy.

These categories go by A, B, C, D, and X.

And basically, each letter refers to a level of evidence, mostly from animal and human studies, about the potential harm of the drug to the fetus.

So for more than, I don't know, 35 years or so, this was the framework physicians relied on.

About 10 years ago, the FDA replaced it by a framework that is called the Pregnancy and Lactation Labeling Rule, or the PLLR.

The idea was to move away from single letters and instead provide a more descriptive guidance.

And in theory, that's an improvement, but in practice, it's been kind of slow to roll out.

And frankly, I'm a little guilty of generally thinking about it in the ABCDX category.

And that's what I'm going to refer to a little bit.

So I'm going to stick with that older category.

And while it's imperfect, it's widely understood.

It is still a clear framework.

And I just want to share with you as we begin this so you have a broad sense of how drugs fit into this.

Okay, so category A means that there is no demonstrated risk in well-controlled human studies.

Again, that's pretty unusual because that's a hard thing to do.

And that's reflected in the proportion of total drugs and supplements out there that fit in this category.

And it's somewhere between 2% and 5%.

Okay, so what does that mean?

That means that is completely safe.

We have definitive evidence that women can take these things during pregnancy.

And obviously, as reflected by the numbers, virtually nothing fits in that category.

By the way, the examples I gave earlier of thyroid hormone and prenatal vitamins do fit in that category.

Then you have category B, which says there's no evidence of risk in humans, but animal data might show signals in some studies.

And so these are generally thought of as safe, but exercise caution, basically.

And this is 15 to 25%.

Then you have category C.

This is is the biggest one.

This says risk can't be ruled out.

We don't have evidence that there's risk, but we don't have evidence that it's safe.

And most drugs sit here.

It's a big range, somewhere between 60 and 75%.

Category D says, actually, we do have some positive evidence of human fetal risk.

And the drugs that sit in this category, and I'm going to come back to this and give you examples of each of the drugs.

The drugs that sit in this category, you might say, well, why would a woman ever take a drug if there's some evidence?

of risk to the fetus.

And that is only if the risk to the mother not taking it is greater.

So the classic example here are seizure medications.

So if you have a woman who is going to be debilitated by seizures and this is the only treatment she can have, then a physician will typically make that decision.

And again, very few drugs fit in this category.

It's typically 5% to 8%.

And then you have category X, which are drugs that have definitively been proven, as much as you can prove anything in biology, to cause significant harm to the fetus, regardless of benefit to the mother.

And again, these are pretty rare and this is 1% to 3%.

So again, just keep in mind.

Now, where does Tylenol or acetaminophen fit into that?

Well, it fits into category B.

And to be clear, for the last 10 years, there has been some concern about does Tylenol belong in category B?

Should it belong in category C?

The other thing to keep in mind with Tylenol is you always have to ask yourself about the switching cost or the alternative choices.

And of course, a very common alternative choice for Tylenol would be something like ibuprofen or an NSAID.

Now, ibuprofen, Advil, Alev, for example, are considered category B in the first two trimesters, but bump to category D in the third trimester for reasons I don't necessarily need to get into, but for those who are interested, it has to do with the premature closure of a very small blood vessel that connects the aorta and the pulmonary artery.

And if that closes prematurely, it leads to premature delivery and all sorts of things like that.

So anyway, I just want you to kind of keep in mind why these categories exist.

And the the reason we're walking through all of this now is to just sort of set the stage for this discussion.

So the goal today is not just to look at the potential link between acetaminophen and autism, but also to put it in context so that we can hopefully end with, I think, the question that at least some of you are asking, which is, okay, science aside, Peter, what's the bottom line?

If a woman is pregnant, should she be taking Tylenol?

And I'm going to resist the urge to just give you that answer right now.

because I think it undermines the process of thought.

So, of course, to answer that question, you have to not only take into account the possible effect of Tylenol exposure on the baby, but also the health and the well-being of the mother, and also the possible effects of going without Tylenol in the case of fever or inflammation, which is also associated with negative health outcomes for babies that are exposed to those conditions in utero.

We're going to take the same structured approach that I basically try to recommend and utilize anytime I've confronted with an association between exposure X and condition Y.

And that's true if condition Y is positive or negative, good or bad.

So I want to lay this out right now so that you know what we're going to do and how we're going to land this plane.

The first thing you want to be able to do is confirm that there is indeed an association statistically.

Okay, so a lot of times people say there's an association, but there might actually not be.

So you actually want to document that statistically there is an exposure.

So you want to verify that.

The second question you're asking is, of course, this is the hardest one, is the first one's pretty easy.

If there is a statistical association, you want to determine the likelihood that the association is causal.

Now, included in this would be sensitivity analyses, falsification tests, and things of that nature.

Now, notice I said you're not trying to prove if the association is causal.

Why?

Because as I'm sure many of you heard me say before, there are no proofs in biology.

It's not like mathematics.

You don't get to write QED at the end of your work here.

What we're really dealing with here is probabilities, and we're trying to determine the likelihood of causality.

Now, if the association is believed to be more likely causal than not, then we have to ask the final question, which factors into what do you do, which is we have to understand the effect size.

So you could have things that are causal, but the effect size is so small that it doesn't matter, in which case your behavior is going to be quite different.

So final point before kind of jumping into this.

It's very important to remember that we're discussing the state of science today.

And science is not about being right or wrong in an absolute sense.

It's really about constantly updating our priors, understanding the probability of something as new evidence becomes emergent.

And that's how we should really work.

So as more and more data come online, we might have to revise whatever views and conclusions I've come to here.

Doing so is not a weakness, although tragically, it has become viewed as a weakness.

Certainly, if politicians change their mind about things, that's viewed as waffling.

But as scientists or as communicators of science, we shouldn't be afraid of that.

We should be open and acknowledge that as of today, this might be how we view things.

And in the presence of new information, we should be very receptive to changing that mind.

So I know that was a lot of background, normally far more than I would at the outset of a podcast, but I think it's really important to have a shared foundation of knowledge and an understanding of the framework before we wade into a topic that is not only scientifically complex, but obviously very emotionally and politically charged.

That's the lens I'm hoping to use for this discussion.

Now, with all of that said, I thought rather than just continue a monologue, it would be great to have my co-host, Nick Stenson from the AMAs, join me and basically lay out this discussion with me in the form of a QA such that it really reflects questions we're hearing and creates a bit of a storyline through this.

So Nick, thanks very much for joining me on very short notice.

Peter, I think as we get started, it would be helpful first to just even look at and lay the foundation of what exactly are the claims being made about acetaminophen and autism.

The basic gist of the scientific claim is that maternal use of acetaminophen during pregnancy is associated with an increased risk of autism in the exposed child.

And this has prompted the government to respond by asking the FDA to issue warnings to physicians and change the labels on acetaminophen products, with obviously Tylenol being the most common and the most familiar, to reflect the possible risk during pregnancy.

But it's important to note that both the FDA and the scientific community agree that we don't yet have evidence to assert that the apparent correlations between prenatal acetaminophen exposure and autism risk reflect a causal relationship.

In other words, no authoritative sources are claiming that we can conclude from the existing body of evidence that acetaminophen actually causes an increase in autism risk, though some argue that a causal relationship is plausible and others argue that a causal relationship is very likely and that acetamin should therefore therefore be avoided during pregnancy or used at most with strong precaution.

You mentioned at the offset that there could be a lot of reasons why we're seeing autism rates increase and not just a single thing.

What do we know about why there are so many things being linked to autism these days?

So actually, there's two parts to your question, Nick.

Because on the one hand, you're asking me, why are autism rates going up?

We can't deny that.

That's sort of like saying, is the sun coming up every morning?

But your second question is, why are there so many things being linked to it these days?

So I'm going to answer that question first, because I think that's the more jugular question at the moment.

I promise I will get to that second question.

So why are so many things being linked to the enormous uptick in autism these days?

And it really comes down to a very, very understandable, rational, and logical desire, which is a very strong motivation to look for the triggers of autism.

We are looking for culprits.

Okay.

So autism rates have risen dramatically, both nationally and even globally over the past few decades.

So according to the CDC, the prevalence of autism increased from 6.7 cases per thousand children in the year 2000, just 25 years ago, to 32.2 cases per thousand children just three years ago.

That's a five-fold increase.

Lots of explanations for this, which we will get to, but understandably, that's not a subtle increase.

And again, while some of that increase is due to an expanding diagnostic definition and increased awareness, which we'll get into more later, as I said, there is no doubt that some residual increase, even after accounting for these changes, is out there.

And therefore, in an effort to find these potential causes, a lot of research has been done to find potential associations between autism and countless other variables.

Now, all of that sounds great and all of that makes sense, but it poses a significant statistical problem.

And this is known as the multiple comparisons problem.

If you look at enough variables, you are bound to find statistically significant associations.

This is the first example that I'm going to pull forth from what I stated at the outset, which is we are not wired to think scientifically.

If anybody out there thinks they are smart enough that they can understand p-values out of the womb, More power to you.

And I majored in mathematics.

I spent my life doing math and stats.

This idea is not that intuitive until it is explained to you.

So it's understandable why what I'm about to say doesn't necessarily jump to your mind as the explanation for this.

Now, let me use an example.

Imagine if you're trying to detect if someone has psychic powers by having them guess the outcome of coin flips.

You create the rules such that if they guess correctly on at least seven out of 10 flips, which by the way, that's a 5% chance somebody would do that based on pure luck with a fair coin.

So if I had a fair coin and I flipped it 10 times, each of those has a 50-50 shot of heads or tails.

But if you can guess correctly, 7 out of 10, 8 out of 10, 9 out of 10, or 10 out of 10, there's only a 5% chance of doing that.

I'm going to declare you a psychic.

So that game is basically like a single hypothesis test with a significance level of 0.5.

So you see where I'm going with this.

Now I'm setting this up like a single hypothesis test with a significance level of 0.05 or a p-value of 0.05.

Now, suppose instead of just testing one person, I'm going to test 100 random people, all with fair coins.

And let's, of course, just assume for the purpose of illustration, there are no real psychics.

I realize there's going to be a little hatred directed toward me from people who believe they're psychics.

Well, by chance alone, I'm going to identify five people out of 100 who are going to pass the psychic test, and they're going to look like psychics.

But the probability that at least one person passes the test isn't just 5%.

It's much higher because randomness can hit anywhere across that group.

So if you keep scaling this up to thousands of tests, like scanning genes for diseases and links and running marketing experiments, I mean, anytime you're running massive amounts of experiments, the odds of finding at least one false hit approach near certainty.

So you're essentially trolling through noise until patterns emerge by accident, like seeing faces in clouds or winning a lottery if you buy enough tickets.

There's a great website called Spurious Correlations.

I've been playing with this website for a long time and went back to it recently that shares examples of how easy it is to find significant correlations, even very strong correlations.

between variables that clearly have nothing to do with each other, provided you're willing to look at enough different combinations of variables.

So, for example, one very silly one is a 98.5% correlation between the per capita consumption of margarine and the divorce rate in Maine.

One of my personal favorites, if you look at the number of physicists in the state of California and the ranking of Michael Schumacher when he was driving in F1 between the year 2003 and 2012, the correlation was 0.971, 97.1%.

But what's most interesting about this is that the site also demonstrates how easy it is to come up with plausible sounding stories for why two clearly unrelated variables might be related.

So they ask AI to come up with a train of logic linking the variables.

If you consider the example of the California physicists and Michael Schumacher's success, AI explains that by saying that the rising number of physicists in California drive innovation in the automotive industry, which leads to faster and more effective race cars that propelled Michael Schumacher to higher rankings, which of course is ridiculous.

Peter, what do we know about why these ideas about associations with autism tend to persist, even if the evidence can be shaky?

I think it comes down to the fact that it is literally impossible to disprove the link between any variable and autism, the way that you can disprove other things such as the Earth being flat or even things that are really complicated like risveratrol extending mammalian life, where you have the luxury of doing randomized controlled experiment after randomized controlled experiment after randomized controlled experiment, all of which fail.

You have such a high degree of probability that you've effectively disproved it.

But we can't do that in epidemiology.

And I think that's why these ideas persist.

Going back to the recent news, what do we know about?

Was there anything in particular that triggered the recent concern around acetaminophen and autism?

Not really, other than a publication that we'll talk about.

But the idea that autism might be linked to prenatal acetaminophen exposure isn't new.

A handful of studies have reported very small associations between that exposure and outcome over the past decade, more or less.

But the recent alarm was triggered by a systematic review of earlier research, which was published in late August in a journal called BMC Environmental Health.

Now, importantly, and I was a little surprised to see this, this publication was not a meta-analysis.

So they didn't pool the data from the studies to reevaluate the overall association or perform any new statistical tests.

The authors of this paper just collected all the relevant studies they could find on the relationship between prenatal acetaminophen exposure and the risk of autism.

They also looked at ADHD and some other neurodevelopmental disorders in non-overlapping human cohorts, and they shared the basic study details and results in one place and added some additional commentary.

So basically, you can think of it as sort of a review article.

And so I think now it would be just helpful to just break down this paper in more detail for people.

So let's start with what did the paper show?

Yeah, so in the case of autism, there were six observational studies that met their criteria for inclusion.

And the authors reported that these six studies, quote, consistently reported a positive association between prenatal acetaminophen use and ASD, autism spectrum disorder, with an exposure-response relationship observed in four of the five studies that evaluated the relationship.

But this isn't actually true.

In actuality, two of the six studies showed no significant association between use of acetaminophen use during pregnancy and the risk of autism in the offspring.

And only three of the included studies directly examined the potential dose-response relationship, while the fourth, by authors Xi and others, attempted to assess dose dividing the participants into turtiles, groups of thirds based on acetaminophen detected in a single blood test.

This method kind of had the advantage of using a quantitative biomarker instead of potentially biased patient questionnaires that try to get at recall.

But since the measurement was based on just one sample taken during birth, it's a very poor indicator of overall exposure during pregnancy.

Acetaminophen is almost completely eliminated from the body within 24 hours.

So all the blood tests from the G study really tell us is whether or not a woman happened to take Tylenol in the 24 hours leading up to delivery.

Of course, a woman who had none before delivery might have taken Tylenol for weeks on end earlier in her pregnancy, or a woman could have taken Tylenol with delivery could have had none up until that point.

So again, it's an interesting study, the G study, which we're going to look at all of them in a moment, but I just want to point out that most of these studies rely on questionnaires.

And this study attempted to look at this biomarker, but obviously it has significant limitations.

Another point I'd say is that in the largest study examining a dose-response relationship, this was a study by lead author Alkfest, who the senior author on that was Lee, and I'm going to come back to that in a second because Dr.

Lee was interviewed this week by JAMA.

The dose response was only present in a partially adjusted statistical model, where it disappeared in what was called the fully adjusted model.

And I'll talk about this in a moment so you understand what I mean.

But this suggests that the dose-dependent association between acetaminophen and autism, which is actually very important, was actually due to confounding variables that weren't accounted for in the partially adjusted model, but were accounted for in the fully adjusted model.

So to take a look at this, I want you to look at the figure sitting next to my head here, which is an analysis that my team pulled together by plotting the risk ratios from the studies included in this analysis.

I was surprised that this figure was not in the paper because almost all review articles would do this and certainly a meta-analysis would have.

But nevertheless, they didn't.

And so we've done this and you can feel free to check us if you like.

But we've taken all of the data out of their tables and simply put them into a pooled table.

And then the one thing we did at the end was pool it.

So that's what's shown in red here.

So let me just orient you to this figure.

So you've got all the names of the studies.

And by the way, the ALCA study is referred to as the Swedish study.

So you've got at the very top, you've got the sibling-controlled version of the Swedish study, followed by the full cohort of the Swedish study, followed by a set of other studies.

And then the G, you're seeing two versions of this.

You're seeing the third tertile compared to the first tertile and the second tertile compared to the first tertile.

You You then have a couple of these other studies, and then you can see the summary in red is where we're showing the pool data.

Now, let me remind you how to interpret these lines and bars and things like that.

The unity line represents absolutely no risk.

Anything to the right of the unity line would represent an increase in risk.

Anything to the left of the unity line would represent a decrease in risk.

And the bars represent the 95% confidence intervals.

In an ideal world, you're looking to see dots that would be quite far from the unity line one way or the other.

Peter, can you walk people through what this chart is showing us?

Because one, a lot of people haven't looked at or interpreted charts like this before.

And two, we also have people listening, not watching.

So for those people, this will be in the show notes, which will be available to all as well.

But can you walk through what the big insights that you and the team had from this chart?

In this chart, you can see that the overall association between acetaminophen use and autism is very small, corresponding to just a 5% increase in relative risk between exposed and unexposed children.

But there are a few other details that jump out at you when you're looking at this.

So the most obvious feature, at least to me, is that there is a very strong association coming from one very small study.

In fact, the smallest study here, which is the G study from 2020.

But as discussed a minute ago, this study was done very different from the others.

So instead of comparing comparing the risk between children who were versus were not exposed to acetaminophen during gestation, they then just divided the participants into three groups or tertiles based on the concentration of acetaminophen that was detected in the samples of umbilical cord blood.

So then you could compare the risk in the first to the second and the first to the third tertiles, respectively.

So again, this might eliminate the issue of recall bias, which of course is a real issue as well, where you have to ask a woman after she has the baby, how much Tylenol did you take during your pregnancy, when, et cetera.

But again, it still has a pretty big issue, which is acetaminophen does not stick around very long in the blood.

And therefore, we don't really know how much the acetaminophen levels identified in cord blood at the time of delivery really tell us anything about the amount of acetaminophen that the woman took during pregnancy.

Now, some have pointed out that this isn't a concern since a study published this past January by another group reported a positive correlation between three levels of self-reported acetaminophen used throughout pregnancy.

So they called it non-use, less than 14 days or more than 14 days, and the level of acetaminophen detected in cord blood at birth.

However, the correlation was not that great.

It was 72%

and involved a completely distinct cohort.

So there's certainly a lot of room for error based on that.

Additionally, the G study mentions that all cord blood samples contained detectable levels of acetaminophen, but they don't actually report how those levels differed across the tertiles, either averages or thresholds.

So we have no idea how much acetaminophen we're actually talking about here.

However, they do report that 70% of the samples had no detectable levels of acetamin metabolites.

which would strongly suggest that the majority of participants had very minimal levels of acetaminophen exposure, such as what you might see through drinking water.

This essentially means that the comparison between the second and first tertiles was comparing sub-therapeutic exposure to sub-therapeutic exposure, telling us virtually nothing.

And as further evidence of this, that paper I just referenced that looked at comparing cord blood levels to recall, remember it divided them into nothing up to 14 days, more than 14 days, it found acetaminophen in all cord blood samples, yet it showed that tertiles one and two were statistically identical.

The suggestion here is there's some low level of hermetic Tylenol or Tylenol metabolite that we're probably all exposed to that doesn't really constitute an exposure.

Another issue, and I actually think this is the single biggest issue, by the way, seems to be the biased participant inclusion.

So the participants in the G study were enrolled in 1998 and followed for another 20 years.

And anybody who dropped out before the end of that 20-year mark was excluded.

But think about that for a moment.

People are more likely to stay in an extended longitudinal study if they have a personal interest in the results.

For instance, if they find their child has autism.

This is likely to affect studies on this subject, but it seems especially pronounced in the G paper, as this study, which was based in the U.S., reported rates of autism in the participants at roughly 11%.

So, meaning, when you look at all of the participants who completed that study, 11% of the kids had autism.

But for context, the general population currently at that 5x increase is 3%.

And at the time of enrollment, it was 0.7%.

The enrollment was between 98 and 2004.

So the cohort used for this, the G study, was the Boston Birth Cohort, which originally enrolled almost 9,000 mother-child pairs.

But among them, preterm births were quite overrepresented, over 35%.

By 2018, approximately 3,000 dyads remained in the active follow-up cohort, presumably due to loss on follow-up.

And of those eligible dyads, slightly less than 1,000 had available umbilical cord plasma samples and complete outcome data.

So again, we have kind of a concentration, it would seem, of cases due to all the reasons I just stated.

Now, but one of the most important things to take away from this graph is the fact that the risk estimate summarized from all the studies, what we're sort of calling the pooled one here in red, is virtually identical to the risk estimate derived solely from the 2024 Alkfist study.

which was done in Sweden, and so it's commonly referred to, and you've probably heard of it referred to this way as the Swedish cohort study.

And the reason they're essentially identical is that that Swedish study included more than 10 times the number of participants than all other studies combined.

I know that's a lot there.

I think it's actually worth just summarizing that again before we move on to the Swedish study, because if you looked at this graph that we put together, you could very easily come to the conclusion that the G study is indeed the smoking gun.

But again, you have to remember the limitations of this.

And there are several, right?

One is the sample size is incredibly small relative to the others.

Two, you have the concentration effect, where based on the nature of the study, you concentrated and disproportionately counted cases of autism versus non.

And then of course you have the collection methods where using this single sample of cord blood, which may have some association with maternal use during pregnancy, but is very unlikely.

to account for the actual nuanced differences in dose and exposure during pregnancy.

Again, think about that through the lens of any other thing.

If I could only measure how many doughnuts you ate on your birthday, birthday, it would be very difficult for me to impute how many donuts you eat over the course of a year.

Would it have a correlation?

Probably, but it wouldn't be strong enough to take to the bank if I was trying to use donut consumption as a marker of predicting heart disease.

So again, it's very tempting when you look at these meta-analyses, or in this case, even just a review article to think more is better.

But remember, a thousand sow's ears makes not a pearl necklace, quoting the great James Yang, who used to be one of my mentors in the lab.

Given how big the Swedish cohort study was, I do think it's worth spending some time to really break that down.

So can you walk people through that study and what it found in more detail?

Yeah, the Swedish study was a very large, prospective cohort study, and the general results indicate a small correlation between acetaminophen use by the mother during pregnancy and later life ASD in the offspring.

So there were just under 2.5 million Swedish children included in the full cohort, and the primary exposure metric was ever use of acetaminophen in pregnancy, with dose serving as a secondary metric.

So again, the primary outcome is binary.

Either you ever used acetaminophen or you did not.

So acetaminophen use was determined through a combination of prescriptions, because again, single healthcare systems, they have access to all the prescriptions, and also through maternal interview with midwife or physician throughout the pregnancy.

They don't specify the number of interviews, and it probably varied across participants.

So over a median follow-up of about 13 and a half years, the general cohort showed a very small but statistically significant positive association between prenatal acetaminophen exposure and autism.

The hazard ratio is 1.05

and the confidence interval was 1.02 to 1.08.

So what does that mean?

That means it showed a 5%

increase in relative risk and the confidence interval of 95% confidence was significant because it did not cross the unity line.

So anytime the error bars do not cross the unity line, it's statistically significant.

Remember, just going back to the studying study stuff we talk about, we always like to calculate an absolute risk exposure if we can.

So the relative risk was a 5% increase.

The absolute risk increase was 0.09.

percent increase at 10 years.

So that's a very small absolute risk increase, increase, less than 1 tenth of 1%.

So the researchers then examined risk specifically in a cohort subset that was composed of matched sets of full biologic siblings.

So the authors examined sibling pairs that were discordant in acetaminophen exposure and found no significant difference in risk for autism between exposure and lack of exposure.

So why do this?

The logic here is similar to any matched cohort study.

But instead of merely matching based on general characteristics like age or sex, each exposed individual is matched to an unexposed sibling.

This means that exposed and unexposed groups on the whole should be relatively evenly matched in terms of several confounding variables related to home environment.

and even many genetic factors.

Each person in one group could be mirrored by somebody in the control group.

So there shouldn't be any systematic differences between the groups.

Can you walk people through what happened when they did the more detailed sibling analysis?

Yeah, so when they did that concordant-discordant analysis, the correlation was entirely abolished when they compared and controlled for family, environment, and genetics as best as you could.

Remember, this was not an identical twin comparison.

It was just siblings.

But obviously, this is the best control you could get.

And this suggests that the apparent link observed in the full cohort was likely due to confounding factors.

Given these results, the authors of the Swedish study came to the same conclusion.

And they stated, quote, results of this study indicate that the association between acetaminophen use during pregnancy and neurodevelopmental disorders is a non-causal association.

Associations observed in models without sibling control may be attributable to confounding.

Now, it's important to note that the review article that came out in August, in their analysis, they state that the Swedish study only included siblings that were discordant for both exposure and outcome.

But this was not the case according to the Swedish study's senior author.

And such a design would introduce what's known as a collider bias, where the selection criteria create a situation where the exposure and outcome are already related in some way.

To illustrate why this double discordance selection doesn't work, consider a very extreme example.

Imagine autism can only occur with acetaminophen exposure, but that acetaminophen exposure does not guarantee autism.

So in biological parlance, we would say acetaminophen is necessary, but not sufficient.

If you select only pairs that are discordant for both the exposure and the outcome, you would exclude all cases in which acetaminophen exposure did not result in autism, even if those sibling pairs accounted for the majority of sibling pairs discordant for the exposure.

In other words, you would falsely conclude 100% risk.

Therefore, I believe that Lee, the senior author of the Swedish study, is correct in his assessment, which is that once you correct for genetics and home environmental exposures, the risk of autism as it pertains to acetaminophen exposure is not causal, stated another way, acetaminophen exposure prenatally in the Swedish cohort does not appear to be causally related to autism.

Looking at what you just looked at, is there any reason to question?

those results and question what that study said.

Another way to think about it is, do people who argue in favor of a potential link between acetaminophen and autism have anything to say about those results?

Yes, you should question everything.

So I would say that one of the criticisms that's been leveled against the Swedish study is that the overall rates of acetaminophen use in that study were much lower than are observed here in the U.S.

or elsewhere in the world.

Only about 7.5%

of the participating mothers in the Swedish study were consumers of acetaminophen, whereas some studies have reported up to 50% of mothers using acetaminophen during pregnancy.

And again, given this discrepancy, some have argued that the generalizability of the Swedish study is limited, which is interesting.

And as it happens, you couldn't make this up.

Another large cohort study with a similar nested sibling analysis was just published a couple of weeks ago.

This was after the August publication of the review article, and it supports the findings of the Swedish study.

The new study was conducted in a nationwide Japanese population and consisted of almost 220,000 children, of which almost 40% were exposed to acetaminophen during gestation.

So very similar to the rates we see in the United States and in some of the other high-exposure studies.

The associations reported from this Japanese Japanese cohort were similar to those in the Swedish study.

In the general cohort, so unadjusting for siblings, prenatal exposure to acetaminophen was associated with a 6% uptick in autism rates.

Recall in the Swedish study, it was 5%.

But in the Japanese study, this did not reach statistical significance.

So the confidence interval, the 95% confidence interval, crossed the unity line.

It was 0.98 to 1.15.

But when they did the sibling analysis, even this small trend towards an increased risk was completely abolished.

So when you take this Japanese study of nearly 220,000 children and pair it with the Swedish study of 2.5 million children, and both of them, when done by this method, abolish any causality, it's very difficult to make a strong case for causality.

We've talked about this before, but I think anytime we're this deep into science, it's good for people to kind of step back.

And so can you walk people through why it's so hard to make assumptions about causality based on just observational data?

Yes.

Again, I think you can think back to the sort of spurious correlation site that I was talking about earlier.

It really comes down to the potential influence of confounding variables that we are blind to.

That's basically what it comes down to.

Dr.

Lee, the senior author of the Swedish paper, was interviewed by JAMA this week.

It's a great interview.

I think it's worth reading.

We'll link to it.

He talks about a great example that I'm sure many people have heard.

I'd certainly heard it before in a statistics class, but it's worth repeating.

It's the example of the strong correlation between ice cream consumption and drowning.

So as we see rates of ice cream consumption go up, we see drowning deaths go up.

And as one falls, the other falls.

Obviously, if you were being cheeky, you would say somehow eating ice ice cream is causing people to drown.

But of course, there's a confounding variable, and the confounding variable is heat.

The warmer it gets, the more people are likely to eat ice cream, and separately, the more people are likely to swim.

And therefore, it's this confounding variable that isn't immediately obvious that explains both of these things.

I think that's really the challenge of epidemiology.

And I don't say that as a knock on epidemiology.

I say it's the legitimate challenge.

It's that you can never, ever, ever identify all of the confounders.

And therefore, you are always at the mercy of wondering, is there something I'm not seeing here that is what is actually explaining the causality?

The only way to show causation, unfortunately, is through randomized trials.

That's the only way you can really be as close to 100% sure that you've established.

causality by doing a well-controlled randomized control trial.

But unfortunately, some questions do not lend themselves to that for either ethical or logistical reasons.

And clearly, this question, the use of acetaminophen and autism, is one of those tricky questions.

So we're not going to get an RCT to do this.

And instead, we're going to have to glean what we can, as best we can, from epidemiology.

And that's where I think we get to this set of guidelines that I talked about at the top of the show here called the Bradford-Hill criteria.

So the Bradford-Hill criteria are a set of nine principles used to assess whether an observed association is likely to reflect a true causal relationship.

So Sir Austin Bradford Hill in 1965 put forth these criteria to help epidemiologic researchers examine their data when RCTs were not available.

So they consider factors like strength, consistency, specificity, temporality, biological gradient, biological plausibility, coherence, experimental evidence, and analogy.

So Peter, I think what would be helpful now is let's just go through the state of the evidence for each of those criteria.

So looking at acetaminophen and autism, what's first on the list?

Yeah, so let's just start with strength.

How large is the effect?

And the larger the effect, the more likely it is to be causal.

Again, we can talk about a few obvious and famous examples.

The example of smoking.

is perhaps most notable.

I've never met a person who doesn't understand or disputes the exposure relationship between cigarette smoke and lung cancer.

There's nobody out there making the case that we need an RCT to determine that.

We don't have an RCT.

And why is it?

It's because if you run the smoking lung cancer data through the Bradford-Hill criteria, it pops on many levels, but effect size is probably the biggest.

We're talking about an effect size of 10x.

So 10x is just a magnitude beyond what we normally would find in most biologic associations.

By comparison, the effect size here is 1.05x.

That's what a 5% relative risk increase is.

So this is smaller than associations that have been reported for many other things that we actually know are probably almost assuredly not causal based on more well-to-do data, such as the association between red meat consumption and type 2 diabetes, which is a 1.10

or a 10% relative risk increase per 100 gram per day increase in red meat.

Of course, we've argued ad nauseum that those associations are almost assuredly picking up a confounder, which is healthy user bias, or even poultry consumption and the risk of type 2 diabetes, 1.08.

Again, both of these are stronger associations here.

An even clearer example would be the meta-analysis of observational studies that reported that a higher leisure time physical activity was linked to a 5% higher increase in prostate cancer.

Again, we know that that is completely nonsensical, but that is what you get when you go trolling for signal in a sea of noise.

You will eventually find it.

In other words, for this, the effect size is very weak.

In other words, when the effect size is weak, and here we're defining weak as a subset of the type of epidemiology we're looking at.

And in this case, we're looking at pharmacoepidemiology versus, say, nutritional epidemiology or toxicology epidemiology.

Weak is generally regarded as 1.5 for pharmacoepidemiology.

And the reason for it is just based on the pervasiveness of bias throughout these studies.

So when you're showing up at 1.05

and the threshold for interesting is 1.5, you're well below it.

I would say we don't do very well on the strength here.

Moving on to the next piece of the criteria, consistency.

How consistent are the data?

linking autism and acetaminophen?

I would say actually they're reasonably consistent.

So consistency obviously just means how often does this show up across multiple studies and maybe even across multiple populations and different methodologies.

I would say a handful of prospective cohort studies have reported this positive association.

But remember, these associations tend to go away when you control for family environment or genetics, as we've seen in the two largest studies here that we've talked about.

So in general population studies, i.e.

no sibling control, the reported associations have varied somewhat in magnitude.

Some have shown little to no association, but I would say more often there is some association.

Moving on to specificity.

What do we know about specificity?

Yeah, so specificity is asking the question basically, how specific is the cause-effect relationship?

So if an exposure is associated with only one outcome or an outcome associated with only one exposure, a causal relationship is more likely.

So here it's very non-specific because there are many variables that have been linked linked to autism risk, many with much stronger lines of evidence than acetaminophen.

For example, advanced paternal age, premature birth, air pollution exposure, and heavy metal exposure.

So these are all variables that have much stronger associations with autism.

So we're not dealing with a one versus one.

And some lack of specificity also exists in the other direction.

There's observational studies that have also reported associations between acetaminophen use and ADHD and language development.

So again, what you really want to look for if you want to check the specificity box is a one-to-one mapping.

It's not a deal-breaker not to have it.

Cigarette smoking can cause lung cancer and other cancer and heart disease, and that doesn't necessarily by itself at all diminish the fact that it causes lung cancer.

Moving next on the list, we have temporality.

Does the exposure precede the reported effects in this case?

It does, and certainly to a first order it does, meaning acetaminophen use comes before autism.

But there really hasn't been a consensus on the impact of timing of exposure or critical windows in which gestational exposure might be more problematic than others.

And if you look at the two researchers most known for their belief that acetaminophen exposure raises the risk of ASD, Andrea Bacharelli and William Parker, actually have conflicting views on the critical window of acetaminophen exposure.

Baccharelli, who is the author, by the way, of the paper that came out in August that we've been talking about, he believes that maternal use during any part of pregnancy increases the risk to the fetus, while Parker believes that prenatal exposure carries relatively little risk provided the mother has a healthy liver to process the drug.

Parker instead argues that the greatest risk comes with exposure in the neonatal period.

or even during birth itself, basically starting from the time the umbilical cord is clamped and onward.

What do we know about dose dependency in this case?

Yeah, so dose dependency, which you could also think of as biological gradient, says the more you have the exposure, the more you should see the outcome.

And some studies have reported modest dose dependency based on the amount of time over which the mother was taking acetaminophen during pregnancy.

But the results, again, have been pretty inconsistent.

Is there a plausible biological mechanism for exposure that might cause an effect?

The mechanism of action for acetaminophen is generally pretty poorly understood.

It's kind of amazing that we don't understand how such a ubiquitous drug actually lowers temperature and alleviates pain.

So therefore, we don't really have much clarity on how it might ultimately lead to autism.

That said, its effects are mediated at least in part through inhibition of the synthesis.

of prostaglandins, which are molecules that contribute to pain and the inflammatory response.

And since prostaglandins also play a role in neurodevelopment, some researchers have argued that acetaminophen leads to autism by disrupting neurodevelopmental pathways.

So again, there's no clear evidence of this, but there is at least what we would call biological plausibility, even if at best it might be a little bit hand-wavy.

This, of course, also leads to another criteria, the next one, which is analogy.

where we compare the current body of evidence to another similar intervention with a more established effect.

And I think here we can look at the effect of another prostaglandin inhibitor in the CNS, which is aspirin, which is shown to have modest protective effects against autism-like symptoms in animal studies.

So, this potential protective effect was also seen in the Swedish cohort study that we talked about earlier, in which sibling analyses showed a small but statistically significant reduction in autism risk with prenatal aspirin exposure.

This was about a 13%

relative risk reduction.

In other words, the analogy criteria would actually argue against it based on the dual inhibition of prostaglandins between both of these drugs.

And then wrapping this section, looking at the Bradford Health criteria, do you want to cover the last two?

Yeah, the last two don't really help us much here because one of them is on whether or not we have intervention-based evidence to support these conclusions, but obviously we don't.

We don't have randomized control trials that can point to sub-analyses here.

As an example of where we would be able to use this, if you're looking at exercise epidemiology or nutrition epidemiology, you might not be able to answer the meta question with epidemiology, but you could do short-term, well-controlled studies to show that, for example, like six months of exercise improved blood pressure.

Then you'd be more likely to believe that exercise could reduce the risk of cardiovascular disease if that's what the large EPI showed.

But again, we can't do the short-term studies here.

And then of course, the final metric here is called coherence, which is how do we tie the observational data with the in vitro and in vivo testing.

And while we have some data here, they're very inconsistent to the question.

There are some studies that involve pre- or perinatal acetaminophen exposure in mice and rats that have reported a few neurodevelopmental abnormalities.

but they've been very inconsistent in the nature of the effect, and many have aligned quite poorly with the characteristics of autism.

For example, one study showed minor alterations in spatial learning and locomotor activity, which aren't typically associated with ASD, but not anxiety-like behaviors, which often do accompany ASD.

Additionally, some of the studies have used extreme doses far exceeding the therapeutic doses used in humans as adults or children at all.

What I think might be helpful is if you could just quickly summarize all the information we just talked about as it relates to acetaminophen and autism in looking looking at the Bradford Hill criteria.

Okay, so let's go through them one by one.

Strength, definitely weak.

Consistency, moderate.

Specificity, weak.

Temporality, I would say modest and probably even strong.

Biological gradient, I would say moderate.

Plausibility, I would say weak.

Analogy actually provides evidence against this effect.

And then obviously experiment and coherence.

We don't really have meaningful data, but if we do, I would classify coherence as probably somewhat weak.

Based on that, where do you land on looking at autism and acetaminophen?

Well, again, to the first question I posed, which is, is there even a statistical association?

I would say possibly.

Obviously, there is in an uncorrected or unadjusted analysis, but I'm really trying to refer to these adjusted analyses.

So I would say, yes, there's probably some association between acetaminophen and Tylenol.

It's not particularly large, but let's assume it is there.

The important question and the only question that really matters here is, what is the probability that that association is causal?

And based on everything we've just talked about, inclusive of the running of the Bradford-Hill criteria, I would say the probability that the association between acetaminophen use by a mother and the development of autism of her child is a very low probability event to be causal.

Again, let me restate that.

What that means is I think the probability that if a woman takes Tylenol during pregnancy, it's going to increase the probability that her child has autism is very low.

And I'm sorry for using the word probability twice in one sentence, but that's the challenge of trying to talk about this thing technically and accurately.

I hope that makes sense.

I'll clarify it if it doesn't.

No, I think it does.

And I think what would be helpful now is kind of stepping back.

So early on, you mentioned that one of the things we do know is that there is an increase in cases of autism.

Let's assume there is causality causality here.

Is it enough to explain what we opened with, which is a five-fold increase in the prevalence of autism today?

I think the answer is unquestionably no.

That's a much more confident thing that we can say, that there is essentially zero chance that maternal Tylenol use is the thing, quote unquote, the thing in quotes, responsible for the rise in autism.

So if it plays a role, it would be a very small role, and it would have to be in the setting of another susceptibility.

Again, I still would argue that it is not playing a measurable role based on everything we've discussed.

And so Peter, I think now would be a good time to like take a look at some of those important risk factors.

So if you look at autism, What are some of the most important risk factors when it comes to that?

Well, this is something I started looking into probably

three or four four years ago.

So it was kind of actually nice to kind of go back and brush up on this literature and see what had been updated.

But the long and short of it is genetics play a much larger role in autism risk than all other variables combined and account for an estimated 80 to 90 percent of the interindividual variability in autism risk.

The term for that is heritability.

So the heritability of a trait can be assessed through studies that compare monozygotic twins, so identical twins, and dizygotic twins.

So these are fraternal twins.

This can also be done comparing what are called concordant-discordant identical twins or monodozygotic twins where you take identical twins that are raised in different environments.

So there's lots of elegant ways to do this.

What do we know?

We know that monozygotic twins are obviously genetically identical, whereas dizygotic twins are genetically no more closely related than any other pair of siblings.

However, all twins are exposed to the same in utero environment and in most cases also raised in the same environment.

I mentioned that there are some studies that do look at identical twins raised apart, but let's put that off to the side.

This means that you can assume that dizygotic twins differ mostly in genetics, whereas monozygotic twins don't really differ at all.

You have very elegant what we call natural experiment.

So if we see that a given trait is highly correlated between monozygotic twins, but is often discordant between dizygotic twins, it must have a very significant impact from genes.

So I just want to pause before I go any further, because so much of what I'm about to say hinges on that.

So Nick, did that make sense?

Do you want me to explain this beautiful natural experimental tool that we have?

Yeah, I think in this case, it is worth double checking and just reconfirming things so people understand because it is such an important point.

And it's come up on so many podcasts we've done in the past.

I can think off the top of my head of three guests we've had on the drive over the past five or six years where we have talked about the heritability of various things.

They're almost always neuropsychiatric.

So the heritability of bipolar disorder, schizophrenia, major depressive disorder.

Okay, so how are they figuring this stuff out?

If you have identical twins, they are in the mother at the same time, therefore they are exposed to all of the same things while the mother is carrying them.

And let's just, again, limit this to all twins that are raised together, which most are, then they come out and they're also exposed to the same environment.

If you have dizygotic twins, they're just siblings.

They're genetically, obviously similar, but not identical, but they were exposed to the exact same environment inside the mother, but then once they're born, they're exposed to comparable things outside.

So if we see that a trait is highly correlated only in the monozygotic twins, but the correlation is nowhere near as strong in the dizygotic twins, then we know that genetics are playing the role.

So let's talk about two things that everyone will appreciate.

Height.

and body weight.

Height has approximately an 80% heritability.

This shouldn't be surprising to people.

We understand that on average, tall parents have tall kids and short parents have short kids.

Is it perfect?

Not at all, but it's 80% heritable.

Body weight, also quite heritable, though not as much.

It's about 60% heritable.

So that's what we mean by heritability.

Now, in one of the studies that was included in the August paper, the review paper, and this was the Leopard paper, the primary study actually focused on how acetaminophen used during pregnancy correlated with the mother's genetic predisposition for autism.

And they didn't find any significant association.

But if they had, it might suggest that a woman's genetic predisposition towards autism might be the real variable behind the apparent association.

between acetaminophen and autism within the offspring.

If the mother is predisposed towards autism, then the child is also likely at a higher than average risk of autism based solely on genetics.

But if a genetic predisposition also increases the likelihood that the mother might use Tylenol during pregnancy, which is entirely possible given that autism is related to sensory perception, which is in turn related to pain sensing, then it would appear as if acetaminophen use and the child's risk of autism were related, even though both associations might actually be explained by genetics.

And this is what I referred to above when I talked about a sort of middle confounding variable, the example I gave earlier about the temperature being the thing that relates ice cream consumption and drowning.

In other words, genetics would constitute a confounding variable that influences both autism risk and acetaminophen use, just as temperature

is the confounding variable that influences both ice cream consumption and drowning.

If so much of autism risk is genetics, what can we say about genetics explaining the increase in autism rates over the past few decades?

They definitely don't because genetics do not shift enough over those kinds of time scales to explain this five, six, or potentially even seven-fold increase in autism diagnoses that we've seen over basically, let's just call it two generations if you want to go back enough.

Now, some cases of autism do involve de novo mutations, but the majority of this increase seems to be explained by the increased awareness and expanded diagnostic definitions.

So, let's review a little bit of history here.

There has been a progressive expansion of the diagnostic criteria for autism over the last 40 years.

In 1987, the DSM-3 made a revision, which expanded from a strict infantile autism diagnosis, or definition, where the symptoms must occur between 30 months of age to something called autistic disorder which was defined by a checklist of symptoms that could manifest well beyond infancy.

Then in the 1990s and into the 2000s, a series of revisions in the DSM-4 and the ICD-10 created something called the Pervasive Developmental Disorder Family, the PDD family, which encompassed autistic disorder, Asperger's disorder, something called PDD not otherwise specified, which I talked about on the podcast with Trina.

It sort of became the all-else bucket, Rhett's disorder, and then something called childhood disintegrative disorder, where kids actually go on to lose an already acquired skill.

So, if they acquire a language skill, but then go on to lose it.

So, further expands this recognition, but with very inconsistent boundaries between the subtypes.

The age of onset was typically before three years of age.

Then, in 2013, the DSM-5

collapsed all the PDD subtypes into a single diagnosis called Autism Spectrum Disorder or ASD.

It also relaxed the before age 3 requirement to symptoms in the early developmental period.

and it introduced certain specifiers with or without intellectual or language impairment.

And the severity levels were based on needed support.

Other changes that were also made to some of the checklist criteria, but the main issue is that an array of disorders are now lumped together under this ASD umbrella, which has vastly increased the number of individuals who fall under that umbrella.

Now, the estimates for how much this dramatically increasing diagnostic aperture has contributed to the increase in prevalence vary, but the analyses that have looked and attempted to assess this directly report that the expanded criteria account for 40 to 60%

of the increase.

And furthermore, increased awareness accounts for 20 to 30%

of the increase in diagnoses.

So a 2009 study found that roughly 26% of the increase in autism diagnoses in California between 92 and 2005 were attributable specifically to cases in which children had previously been diagnosed with mental retardation and were then subsequently screened for autism.

So again, when I hear people say, oh yes, but even the cases of severe autism are increasing, not necessarily.

It could be that kids that we now think have severe autism, for example, being nonverbal, were actually previously diagnosed as something else.

Racial and socioeconomic disparities in autism diagnoses have narrowed or reversed over the last 30 years, which the CDC and others suggest is evidence of more widespread awareness and screening.

So take those two together, Nick, 40 to 60%, 20 to 30 percent.

That's really kind of the lion's share of what explains this increase.

But that also still leaves room for other factors.

And so do we know what else might be accounting for the increase, not what we just covered?

Yeah, I think the next clearest contributor is advancing parental age, both both in mothers and fathers, although the paternal age probably seems to play a greater role.

This is seen mostly in the U.S.

and other high-income countries, and various studies have put this at about 5 to 15%

of the increase in autism prevalence.

So paternal age has advanced in the U.S.

from 27.6 years when I was born to 31.1 years 10 years ago, that number seems to be going up.

The proportion of fathers with more advanced paternal age has also increased.

So fathers over 40 at the time of offspring birth has more than doubled and going from 4.1% to 8.9%.

And fathers over 50 has also doubled going from 0.5% to 0.9%.

Trends in maternal age have also increased during the same average period by about three years.

And the CDC reports reports that between 2016 and 23, the proportion of births in women aged 35 and over has increased from 10 to 12 and a half percent.

Furthermore, there are other factors such as maternal obesity, metabolic disease, preterm birth, and air pollution that are also widely recognized to contribute to the remaining 15%

of unattributable factors.

So let's talk about these just briefly, right?

Maternal health, including metabolic health, is an important factor, And there's no question that obesity rates among women at the time of conception have risen steadily.

A meta-analysis of global data reports obesity rates in pregnancy have more than tripled in the last three decades from a pre-1990 rate of 4.7%

to 16.3% in the decade from 2010 to 2020.

And rates in the United States are even higher than those averages.

According to a 2024 CDC CDC report, rates of preterm, so under 37 weeks, and early term, 37 to 38 week births rose during that period as well.

So preterm births rose from 7.74%

of all singletons in 2014 to 8.67%

in 2020, while early term birth rates rose from 24.31 to 29.07%.

Sources that track earlier years indicate a steady rise in preterm birth from at least 1980 to 2005, after which rates dip slightly before beginning to rise again in the 2010s.

Some of this, of course, could be attributed to advanced maternal age, which is in and of itself a risk factor for preterm birth.

Finally, I would say globally air pollution has been increasing.

We've talked about this a lot on the podcast.

We talk a lot about the PM 2.5s.

Truthfully, we've always talked about it more through the risk of all-cause mortality and cancer mortality, but here is yet another issue.

So we've seen a 38% increase in PM 2.5s.

Again, just for folks maybe not familiar with that content, these are particles that are sub-2.5 microns in the air.

Obviously, you can't see these things.

You don't feel these things.

But because of how small they are, when inhaled, these particles can go all the way into the bloodstream because of their ability to go straight down into the most distal part of the air sacs of the lung and cross the diffusion barrier where oxygen and CO2 are transmitted.

So seeing this enormous increase in pollution, driven largely by the industrialization of China and India, is another part of this.

And while air pollution in the U.S.

has been coming down, we've seen in the last decade an uptick in this, mostly attributed to wildfires.

Looking at what you just covered and those three buckets, in that last bucket of environmental factors, is it possible that acetaminophen could be in that bucket as well?

Yes, it is possible.

Nothing I have discussed today, none of the analysis we've done or anybody has done has shown dispositively that we can disprove the role, the causal role between acetaminophen and pregnancy and the elevated autism risk.

Yes, it is possible.

Again, as I've stated a couple of times, it is impossible to disprove anything.

We can't disprove anything here.

That's the nature of what we're doing epidemiologically.

But the point here is look at how many other variables we have that have a either demonstrated, i.e.

genetically, or much, much stronger associations.

That even if acetaminophen plays some causal role, it is going to be very, very low.

Think back to what we talked about on the absolute risk increase.

This was a 0.09 absolute risk increase with a 5% relative increase.

So even if you assume that to be causal, which again, I don't, because when the twin analysis was done, all of that vanished in addition to everything else we've talked about.

This would be a very, very, very small contributor relative to other modifiable things, such as maternal obesity, metabolic health, air pollution, paternal and maternal age.

So I think there are many things we should be looking at before this.

As we finish this podcast, I think one thing you mentioned early on, which I think is really helpful and ultimately what a lot of people are curious about is based on everything we just talked about, what advice would you give to women who are pregnant about the use of acetomedaphin?

As a general rule, I would advise women to stop taking medications when they get pregnant, but medications aren't the only potential threat to the unborn child.

The health of the mother is also important to the unborn child.

The medical conditions that these medications are intended to treat can sometimes also create problems indirectly or directly, but we have to balance that against the use of the medication and what's already being addressed.

Let's take an example.

If a woman has an elevated APO B, she should be taking a lipid-lowering medication.

But does she need to take that during pregnancy?

I would argue no.

Why?

Because nine more months of additional APOB exposure are not a meaningful threat to a young woman's life, whereas there may be some downside in suppressing her cholesterol synthesis if we're talking about a statin.

Conversely, when we think about something like thyroid hormone, where we've established actually quite safe use during pregnancy, if a woman is requiring thyroid hormone because she has hypothyroid, to withhold that from her during pregnancy would pose enormous risk to her and, by extension, to the child.

Now, it gets gets interesting when we talk about other classes of drugs.

So, for example, GLP-1 drugs.

They're very common.

And of course, the question is, should women stop these during pregnancy?

Well, I don't think I have enough data to comment, but I can tell you how one would have to think about this.

If a woman's taking a GLP-1 receptor agonist is the difference between her having gestational diabetes and not, maybe it's considered.

Of course, we would typically turn to something like metformin as a first-line therapy there, where we have much more ongoing safety data.

But the point here is you have to be able to consider this in a nuanced way, which is the single most important thing for the healthy development of a fetus is a healthy environment in utero.

And sometimes that may actually require the mother taking a medication.

With that kind of background, might be worth going back to the historical FDA risk categories and just kind of walking through what they are again.

And then even highlighting a few few different medications that are included in each category.

So people just have a much better idea of how this is done in practice.

Yeah, again, the good news is you don't have to guess here.

You should be talking about this with your doctor.

So again, that FDA category, category A, which is pretty small.

We've only got about 2% to 5% of drugs here.

We have controlled studies in humans that demonstrate no risk.

to the fetus in any trimester.

So again, the two most obvious here are T3, T4, prenatal vitamins, that kind of stuff.

Then you have category B, so animal studies that for the most part show no risk or animal risk not confirmed, adequate human epidemiology that generally shows safety.

Again, 15 to 25% of risk.

We see a number of antibiotics in here, things like Benadryl, as I mentioned.

Tylenol is in here, as is metformin.

Then you go to category C.

We have animal studies that show some adverse effects, but no real adequate human studies.

And here, these are drugs that are supposed to be used provided there's enough benefit for the mother to justify it.

Again, this is most drugs fit in this category, 60 to 70%.

So you have something like gabapentin, amlodipine, which is a blood pressure medication, trazodone for sleep.

GLP-1 agonists are in here, certain SSRIs or antidepressants, and even very short-term use of narcotic pain medication.

Then you go to category D.

So we have positive human fetal risk data, but in some cases, the benefits might outweigh it.

So for example, a couple of seizure medications, valproic acid and phenytoin, also lithium, which would be used to treat bipolar disorder, NSAIDs, which in the third trimester should be discontinued for the reasons I talked about earlier, and even long-term use of narcotics.

And then finally, we have category X.

We have drugs where there's simply no reason for women to take these during pregnancy.

Statins would be in this category, methotrexate, and drugs that also are known to cause teratogenic defects in the child.

One of the other things you talked about early on in the beginning was not only do you have to look at the risk of taking the medication, but you also have to balance that in terms of what else could be going on during pregnancy.

And so how do you think about the use of acetaminophen in terms of balancing the benefits that it can also cause for people who are pregnant?

Yeah, I think we need to look at the other side of the equation.

What's the risk of not taking Tylenol during pregnancy?

In many cases, maybe the trade-off is just an annoying headache or some other discomfort that the mother can sort of power through.

And in those instances, maybe she's just better off skipping the Tylenol and trying to get to bed.

But we can't discount the mother's well-being and the importance of that as well, not just for herself, but her well-being in the context of how important it is for the unborn child.

And we certainly shouldn't trivialize the likelihood and presence of more intense debilitating pain with pregnancy.

If the pain is bad enough that she's unable to get out of bed for several days on end, that in and of itself poses a risk to the child.

Let's not forget Tylenol is also used to reduce fever.

And for this purpose, current evidence would suggest that the scales clearly tip in favor of using Tylenol since exposure to fever itself carries a number of known risk factors to a developing fetus.

So for example, children born to mothers who experience fevers during pregnancy, especially during the first trimester, are at a significantly higher risk of certain birth defects than children who weren't exposed to fever in utero.

Various analyses have reported anywhere from 25 to 200% higher risk for cleft palate or neural tube defects.

In fact, prenatal exposure to fever and maternal infection are also separate risk factors for autism and other neurodevelopmental disorders.

So several studies have reported that exposure to maternal infection is associated with an increase in autism risk by 25 to 40 percent, while exposure to maternal fever is typically associated with an even greater risk, up to 200% across most analyses.

So for some, research has reported that these risks are attenuated when the mothers actually take a fever-reducing medicine like Tylenol.

All of these associations between infection and fever exposure and autism may actually be contributing to the apparent correlation that we see.

between autism and autism risk, given that acetaminophen is by far the safest option for reducing fever and pain relief during pregnancy, because remember, NSAIDs and opioids are category D.

If a woman does have an infection during pregnancy, there's a good chance she might try to ease the fever or the aches with Tylenol.

And in other words, it could be that it's the infection that is the issue and the signal we're picking up and measuring is the acetaminophen use.

As we finish and wrap this podcast, Is there anything else based on what we covered you want to share with listeners and viewers?

Look, there are some people who might be wondering, why did you just take so long to explain all this to us?

Why didn't you just give us the answer?

Like, I just want the soundbite, man.

And it's like, if you just want sound bites, you're never going to learn.

Honestly, if you just want soundbites, this isn't the podcast for you.

But if you actually want to be able to learn to think for yourself, then that's what we're here to do.

And that's the reason we killed ourselves over the past week.

to put together the most thorough gathering of all the data we could find and the most intense night weekend analysis possible.

It's because we want to help you think about this stuff because this is not going away.

This is going to be a forever game of whack-a-mole.

There is always going to be a bad guy.

I'm not going to be here every time to do a two-hour podcast on helping you think through why exposure X leads to disease Y.

I don't want to sound like a scolding teacher, but the truth of the matter is we live in a world today where people don't want to think.

People use stupid vehicles like social media to get their information and they don't want to read the fine print.

And even if they do read the fine print, they just want to outsource thinking to somebody else.

So I appreciate that those of you who are still watching this have outsourced your thinking for the past couple of hours to me.

But honestly, like we're never going to get out of this rut of people not knowing how to think critically unless everybody starts taking steps to try practicing this on their own.

We're going to include amazing show notes to this podcast like we do for every podcast, although for this episode, it'll be not behind a paywall.

Normally our show notes are only there for our subscribers, but I would encourage you to go through this and follow the logic as I've laid it out here with the help of my team.

And when the next thing comes up, because it will come up, whether it's this drug or that drug or this intervention, it's just going to keep happening over and over again.

You've got to be able to kind of go through this type of thinking.

If you don't want to, that's fine.

It is hard, but then I think you've sort of forfeited your right to have an opinion on it.

So again, I don't mean to sound like a crotchety old man, but honestly, I think on a day like today, I kind of feel like it.

So let me just put a bow on this and let's land this plane.

I think the upshot here is that any one potential risk can't be considered in isolation.

You have to look at the full picture, the risk of a given intervention like Tylenol, as well as the potential risks of not taking Tylenol.

as well as the nature and magnitude of those risks.

For minor aches and pains, maybe it's best to just err on the side of caution and skip the acetaminophen.

Whereas when the pain becomes really a nuisance and it might interfere with you doing things that are otherwise going to help you provide the best environment for your fetus, then judicious use of acetaminophen can help with the oversight of your physician.

I think for maternal fever, the balance is clearly leaning towards the use of acetaminophen.

But I want people to understand that the strength of these associations is very small and in many cases vanishes altogether when you apply some rigorous statistical corrections that look at the most important variables that we should be considering here which is genetic and environmental.

So I hope that this exercise has indeed provided benefit to all of you not just as we consider this particular question but as we consider the onslaught of questions that we're going to see in the future.

Thank you for listening to this week's episode of the Drive.

Head over to peteratiamd.com forward slash show notes if you want to dig deeper into this episode.

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