Why Male Fertility Is Declining Drastically | Dr. Shanna Swan & Dr. Andrew Huberman

00:00:00.000 | - And when we hear that sperm counts are going down,

00:00:05.000 | are they going down to the point

00:00:06.520 | where fertility is impacted?

00:00:08.780 | That's really the one of the, I think, functional questions.

00:00:11.960 | - And when you have 45 to 50 million per milliliter

00:00:15.480 | and below, it matters a lot what your sperm count is.

00:00:20.040 | I mean, people say, "Does it matter?"

00:00:21.640 | Yeah, if you get in this range

00:00:23.580 | where the probability of conception

00:00:25.080 | is dropping off really rapidly, it matters a lot.

00:00:28.760 | And then around 45 to 50, it starts to level off.

00:00:32.720 | And then after that, after certainly after a hundred,

00:00:36.280 | probably 75, it doesn't matter at all.

00:00:38.440 | So when people say,

00:00:39.780 | "Does sperm count matter for fertility?"

00:00:42.560 | Yes, it matters a lot if it's low.

00:00:45.440 | And no, it doesn't matter at all if it's high.

00:00:49.200 | So we just have too many sperm.

00:00:51.600 | I mean, I don't, but humans, you know, and there's-

00:00:55.400 | - Nature runs a probability game, overproduce sperm.

00:00:58.120 | - Right.

00:00:58.960 | - And sperm counts range anywhere from, you know,

00:01:03.200 | it could be low eight, nine, 10 million per milliliter

00:01:07.000 | in the very low situation.

00:01:08.480 | - It could be zero.

00:01:09.320 | - It could be zero in some people, right?

00:01:10.560 | All the way up to 400 million.

00:01:13.120 | There's a huge range.

00:01:14.200 | - That's right.

00:01:15.040 | - And that's a function of age.

00:01:15.920 | It's a function of genetics.

00:01:17.100 | It's a function of presumably phthalate exposure.

00:01:20.100 | - I was asked to join a committee

00:01:22.640 | of the National Academy of Sciences.

00:01:25.920 | And that committee was assembled to look at the question

00:01:30.920 | of whether hormonally active chemicals,

00:01:35.000 | endocrine disrupting chemicals in the environment

00:01:38.200 | posed a threat to human health.

00:01:39.940 | Because at that time it was like,

00:01:42.880 | well, yeah, we hear about this, but should we care, right?

00:01:46.560 | And so that committee wanted to consider a study

00:01:53.600 | that had come out of Denmark a few years earlier,

00:01:56.840 | which claimed that sperm count had dropped 50% in 50 years.

00:02:01.840 | - Wow, that's a huge drop.

00:02:05.840 | - That's what, we're seeing worse than that by the way now.

00:02:08.440 | So, okay.

00:02:10.160 | They said to me, I was the only statistician on the panel,

00:02:13.240 | would you look at this and see if we need to consider this

00:02:16.400 | in our deliberations?

00:02:18.840 | And as I mentioned, I'm skeptical and I looked at it

00:02:22.520 | and I thought, eh, I don't think so.

00:02:25.800 | That was my initial reaction.

00:02:27.280 | And that was because, first of all,

00:02:30.480 | I didn't know who had written this,

00:02:32.240 | I just saw it in a journal and it was not very big

00:02:36.120 | and not very many figures, not very much data.

00:02:40.000 | And I thought of it and I thought,

00:02:41.640 | that's a big claim for a little paper.

00:02:45.440 | But I'll look at it 'cause it's important.

00:02:49.760 | And so, what I did then was to think about

00:02:53.240 | all the factors that we epidemiologists call confounders,

00:02:58.240 | things that might have caused that decline

00:03:03.000 | if it wasn't real biologically, right?

00:03:06.600 | And so, we could think of some of them together,

00:03:09.880 | maybe the method of counting sperm had changed

00:03:13.680 | so that later methods counted fewer sperm

00:03:16.720 | in the same sample.

00:03:18.000 | That's certainly possible, right?

00:03:20.000 | But it turned out that wasn't the case

00:03:22.960 | 'cause they actually had all used the same method.

00:03:26.240 | And maybe the men had changed.

00:03:28.240 | So, maybe you can't get a sperm count at random,

00:03:31.800 | you have to get somebody to volunteer, right?

00:03:34.320 | So, who were these men?

00:03:35.240 | Or were they very different in the early part of the study

00:03:38.440 | and the late part of the study in a way that maybe

00:03:40.520 | in the late part of the study,

00:03:41.800 | there were men with lower sperm count

00:03:44.040 | and they were more concerned?

00:03:47.560 | Maybe they were more obese.

00:03:49.760 | That's pretty plausible.

00:03:50.960 | Obesity is related to sperm count, fertility.

00:03:55.000 | And maybe they smoked more and maybe,

00:03:58.400 | and so on and so forth, right?

00:04:00.000 | So, what I did was to get the 61 studies,

00:04:05.000 | go through them and try to extract information

00:04:09.160 | on all the factors that could explain the decline.

00:04:13.280 | So, I created a multivariable model and ran that model.

00:04:18.120 | And to my astonishment, when I was done,

00:04:24.320 | the slope of the decline was exactly the same

00:04:28.760 | to the first decimal place.

00:04:30.440 | It had not explained anything.

00:04:35.520 | I was like, oh my God, this looks like it might be real.

00:04:39.800 | - This is really important to,

00:04:42.080 | because I think what we're talking about here

00:04:43.560 | in parallel to the main conversation

00:04:45.240 | is how to do really great science.

00:04:47.400 | - So, when I saw that and actually did another study

00:04:52.400 | to select my own studies and not accept her 61 studies

00:04:56.960 | that had been published,

00:04:57.880 | that Elizabeth Carlson had published.

00:05:01.200 | So, new studies came up to more recent times,

00:05:04.360 | went back further, did it again,

00:05:06.800 | found exactly the same thing.

00:05:08.160 | Okay, so there were three looks at that.

00:05:10.760 | And I thought, okay, I'm gonna accept this now.

00:05:13.040 | This is, sperm count is declining.

00:05:15.240 | And why?

00:05:18.080 | I turned to the why.

00:05:20.560 | Okay, because up and down now,

00:05:22.120 | we hadn't said anything about why.

00:05:23.440 | We just said, is it doing that?

00:05:25.400 | Yes.

00:05:26.240 | Okay, now we believe it is declining.

00:05:28.960 | Why?

00:05:29.800 | And so then I thought quite a lot and talked to people

00:05:32.920 | and ruled out genetics because it was too fast.

00:05:35.760 | It's two generations, it's too fast.

00:05:38.320 | 50 years, two generations.

00:05:39.840 | So, if it's not genetics, then it's environment.

00:05:42.800 | And so what is it about the environment that could do this?

00:05:47.200 | So, I asked, okay, in the environment,

00:05:52.040 | there could be things that are making sperm decline.

00:05:56.680 | So, if you think about how you might look at that,

00:06:00.480 | you might design the study that I designed next,

00:06:02.520 | which was another study.

00:06:04.440 | And by the way, this preceded the AGD.

00:06:07.680 | So, we had four cities in the United States

00:06:11.080 | that we picked with different environments.

00:06:13.200 | And then we got men to come in

00:06:15.560 | and we used the same equipment at each place.

00:06:19.880 | We used the same method of selecting the men.

00:06:23.800 | The technicians were trained centrally at UC Davis.

00:06:27.920 | We had very good quality control.

00:06:29.520 | So, samples were sent around every quarter

00:06:32.160 | to make sure that everybody was measuring things

00:06:34.200 | the same way.

00:06:35.880 | We didn't want drift, right?

00:06:38.080 | And then we got their urine.

00:06:41.160 | And that's how I had those urine samples.

00:06:43.040 | So, if you wanted to do this study

00:06:45.400 | and you wanted to get a representative sample of men,

00:06:48.040 | where would you go?

00:06:50.960 | Because you can't, I can't ask a guy in the street

00:06:53.520 | to give me a semen sample, right?

00:06:55.200 | I mean, it's not something you'd get very, you know.

00:06:58.080 | So, I thought, how can I get a representative sample,

00:07:02.000 | and which would teach me something

00:07:04.360 | about a larger population called the parent population.

00:07:07.280 | So, here's a sample, it should represent the parent.

00:07:09.440 | So, how do I ensure that?

00:07:11.320 | And what I decided was to sample partners of pregnant women.

00:07:16.320 | Because pregnant women all come to medical care, almost all.

00:07:20.680 | And if their partners will give a semen sample,

00:07:23.800 | then we have a representative sample.

00:07:26.160 | And we know what we're looking at.

00:07:27.520 | So, that's what we did.

00:07:28.720 | So, this is a, the semen study

00:07:31.360 | is the study of partners of pregnant women.

00:07:34.880 | And of course, they'll have slightly higher semen quality

00:07:38.200 | 'cause they got their partner pregnant, but.

00:07:41.120 | And so, we had their urine, we had their blood,

00:07:45.760 | and we looked at their semen quality.

00:07:50.760 | And then we decided to look at pesticides.

00:07:54.560 | And the reason we look at pesticides

00:07:56.480 | was because there was a lot of gradation

00:08:00.280 | across our four centers in pesticide use.

00:08:03.000 | And what we found was really extraordinary

00:08:06.960 | that men who were living in central Missouri,

00:08:09.480 | where I was living at the time,

00:08:11.760 | who were in the middle of a agricultural belt

00:08:15.200 | where there was spraying all the time

00:08:17.040 | for soybeans and so on.

00:08:19.080 | Those men had half as many moving sperm

00:08:24.840 | as men in Minneapolis.

00:08:26.640 | - Whoa.

00:08:28.760 | - Whoa.

00:08:30.600 | Huge, right?

00:08:32.440 | And then we went one step further.

00:08:35.240 | And within Missouri, we looked at a sample of men

00:08:39.520 | who had very high sperm parameters

00:08:43.080 | and very low sperm parameters

00:08:44.840 | and showed that five pesticides were significantly higher

00:08:49.000 | in the men with the low sperm parameters.

00:08:51.560 | That include motility, morphology, you know.

00:08:54.560 | - So, these are pesticides

00:08:55.600 | that are being sprayed in the air on crops.

00:08:57.480 | You mentioned soybeans, what other types of crops?

00:09:00.560 | - I don't know.

00:09:01.880 | I don't remember.

00:09:02.720 | - So, plant and fruit crops, presumably.

00:09:06.520 | - Yeah, whatever they were growing

00:09:08.720 | in Columbia, Missouri at that time.

00:09:10.640 | - And just to make sure I understand,

00:09:14.320 | it's not that the men need- - Soybeans, corn and soybeans.

00:09:16.480 | - Corn and soybeans.

00:09:17.320 | But we're not talking about eating corn and soybeans.

00:09:19.560 | We're talking about living in an area

00:09:21.200 | where pesticides are being used by,

00:09:24.520 | what is it called?

00:09:25.360 | Is it still called dust crop?

00:09:26.560 | - Yeah, we didn't go into how they got these.

00:09:29.160 | We just looked in their urine

00:09:30.680 | and there were the metabolites.

00:09:31.880 | The metabolites don't get in their urine

00:09:33.880 | unless they were exposed.

00:09:35.280 | - Exposed through the air

00:09:36.520 | or exposed by eating corn and soybeans?

00:09:38.320 | We don't know.

00:09:39.160 | - We don't know.

00:09:40.000 | - Okay. - We don't know.

00:09:41.080 | But this was not a particularly,

00:09:43.560 | you know, we didn't sample farmers only

00:09:45.840 | or anything like that.

00:09:46.680 | So, whoever came into the,

00:09:49.080 | you remember how we got these men?

00:09:50.640 | Their wives were pregnant.

00:09:51.960 | They were having prenatal care

00:09:53.560 | at the University of Missouri.

00:09:55.440 | So, that's where we got them.

00:09:56.600 | Whoever happened to come in to the prenatal clinic

00:09:59.440 | and agreed to be in our study,

00:10:01.240 | their, the male, you know, urine,

00:10:04.600 | male's urine was measured for these pesticides.

00:10:07.520 | - I'm sure a number of people,

00:10:09.000 | including myself, are wondering

00:10:10.920 | in what other products are these five pesticides present?

00:10:17.160 | Are these commonly used pesticides

00:10:21.360 | or is it something about-

00:10:22.440 | - They're called the triazine pesticides.

00:10:25.240 | Atrazine is, was the most widely used

00:10:27.360 | and it's a huge use around the world.

00:10:30.560 | I mean, it's highly, you know,

00:10:32.560 | one of the most, the largest commercial pesticides.

00:10:35.960 | So, these were very big players in the pesticide field.

00:10:39.680 | (upbeat music)

00:10:43.280 | (upbeat music)

00:10:45.880 | you

Why Male Fertility Is Declining Drastically | Dr. Shanna Swan & Dr. Andrew Huberman

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