A Chemical Hunger – Interlude G: Li+


Let’s talk about some of the new stuff we’ve learned about lithium.

Lithium Grease

Our first post on lithium mentioned lithium grease, which is used on all kinds of heavy machinery. We found this interesting because professions that work closely with cars, trucks, planes, and trains tend to be more obese than average, and if lithium causes weight gain, lithium grease might be able to explain this pattern.

We knew that lithium grease was a modern invention, but we recently found out that the timeline for lithium greases matches the timeline of the obesity epidemic even better than we realized — it was introduced in the 1940s, but only started seeing serious use in the 1980s. Here’s the story per The Society of Tribologists and Lubrication Engineers:

Greases made with simple lithium soap thickeners first appeared in the 1940s, starting with Clarence Earle’s 1942 patent (U.S. 2,274,675). Users found that these greases resisted water better than greases made with sodium soaps, and they performed better at high temperatures than calcium soap greases did. Lithium soap greases resist shearing, and they exhibit good pumpability properties, although they require the addition of antioxidants. This combination of advantages outweighed the extra manufacturing expense compared with calcium and sodium thickeners, and lithium soap greases (notably lithium 12-hydroxystearate formulations) quickly claimed a large share of the market (9). 

Lester McClennan patented the first lithium complex grease in 1947 (U.S. 2,417,428), but lithium complex greases did not become popular commercially until the early 1980s (9). For the past 20-30 years, manufacturers have been shifting away from thickeners based on simple lithium soaps to lithium complex thickeners because of the latter’s better performance at high temperatures, Waynick says. 

With this in mind, you can imagine our reaction when we saw an email drop into our inbox with the subject line, “Repeatedly eating lithium grease”. This email turned out to be from reader Emily Conn, sharing the following anecdote about a guy she used to work with: 

I used to work in HVAC repair with a guy named A—, and he was sort of famous in our company for eating lithium grease. The reason he did this was so that he could identify the sort of grease that had been used somewhere and then apply the same type. He could even taste differences between brands. We all touched the stuff, which I thought at the time was safe, but he was the only one who ever ate it.

Well, all the other health problems the guys in the company had, he had 10 times over. Not only was he extremely fat, he was also frequently out for medical issues, although I never knew what they were.

Another note for the story – I’ve never seen anyone else care about matching brands of lithium grease, not sure why A— thought it was important. Also, I never validated his claim that he could taste the difference between brands – that was what he said he could do but none of us ever checked.

This is just an anecdote and only a sample size of one, and may not be representative of all cases. Nevertheless, we strongly recommend that you avoid eating lithium grease.

What about the Middle East?

In our first post about lithium, we speculated that many Middle Eastern countries have high rates of obesity because they get a lot of their drinking water from desalination. Desalination removes all trace elements from seawater, but because distilled water is bad for pipes and trace elements are important for your health, the desalinated water is remineralized by blending it with some of the original sea water. 

Seawater has more than a bit of lithium in it, and lithium levels in the Persian Gulf are on the high end for seawater. Based on measurements of seawater in the gulf, we calculated that desalinated seawater in the Middle East could end up carrying a pretty big dose of lithium, somewhere in the range of 10-100 ng/mL.

(Also notable is that desalination produces lots of toxic brine, which is itself a problem.)

It’s true that 100 ng/mL is a pretty high level of lithium to find in your drinking water, but it’s not a total outlier — we see lithium levels this high or higher in places like Texas, some Greek Islands, and some parts of Austria. So 100 ng/mL is concerning, but it’s still surprising that Middle Eastern countries ended up so obese when there are other places that get similar doses.

Some Reddit comments recently introduced us to the Pima people of the Gila River Valley, who had very high levels of obesity way before the obesity epidemic started for the rest of the world, as high as 40% obese in 1970. In the course of looking into this, we learned that the Pima were exposed to very high levels of lithium in their food and water quite early on, because “in the Gila River Valley, deep petroleum exploration boreholes were drilled during the early 1900’s through the thick layers of gypsum and salty clay found throughout the valley. Although oil was not found, salt brines are now discharging to the land surface through improperly sealed abandoned boreholes, and the local water quality has been degraded.” Fun!

This led us to the literature on oil-field brines, which tend to contain huge amounts of lithium. How huge? Well first we need some comparisons. In drinking water, a low level of lithium is about 0.5 ng/mL, and high levels are 100 or 200 ng/mL. In some parts of Chile, however, drinking water can have up to 700 ng/mL, and in Argentina, up to 1000 ng/mL. In seawater, lithium concentrations are usually in the range of 100 to 1000 ng/mL. The highest concentrations of all are found in extreme locations like the Dead Sea, with 14,000 ng/mL of lithium, and 24,880 ng/mL in the headwaters of one river in Chile. 

All of these are dwarfed by the levels possible in oil-field brines. Here’s a quick review from a USGS paper released in May 2021:

Produced water from oil and gas wells can have extremely high concentrations of lithium (Dresel and Rose, 2010; Blondes et al., 2018). For example, waters from shale gas wells in the U.S. had reported lithium concentrations ranging from 10 to 634,000 [ng/mL] (median 25,000 [ng/mL]) and those from conventional oil and gas wells had concentrations ranging from <10 to 1,730,000 [ng/mL] (median 5000 [ng/mL]) (Blondes et al., 2018). Lithium and associated components of such brines may be accidentally or intentionally released to the surface or groundwater in certain locations (Tasker et al., 2018; McDevitt et al., 2019).

Another point of comparison is this report from 2019 on recovering lithium from “oil and gas produced water”. This includes a number of interesting observations, including that Smackover brines from the Smackover Formation, which extends from Texas to Florida, contain between 50,000 ng/mL and 572,000 ng/mL of lithium. This source is currently being considered for exploration by the Arkansas Smackover Lithium Project.

Some of these levels are very very extremely high indeed. The highest number we reported in our original post was 24,880 ng/mL in parts of Chile. In comparison, the MEDIAN concentration in waters from shale gas wells is 25,000 ng/mL. Many of these brines have lithium concentrations in the range of 100,000 ng/mL, and some contain more than 1,000,000 ng/mL!

The USGS report also mentions that lithium concentrations are higher in arid regions. All together, this seems pretty interesting because the Middle East is especially famous for 1) being very arid and 2) having lots and lots of oil wells.

In theory, these wells should all be sealed and/or the brines should be injected deep underground where they can never contaminate anything humans come into contact with. This is important because these brines are not only as salty as hell, but also radioactive. Fortunately, the oil and natural gas industry doesn’t make mistakes.

It seems quite clear that the Pima were exposed to lithium from brines leaking out of improperly sealed boreholes, so there is at least one example of accidental exposure. And the USGS report we quoted above acknowledges that “such brines may be accidentally or intentionally released to the surface or groundwater”. Is it possible that the Middle East is especially obese because of lithium leaking in from petroleum mining, rather than (or in addition to) lithium exposure from desalination? 

If this were true, one thing we might see would be especially high rates of obesity in oil field workers. There are some indications that this is the case. One paper looked at Kuwait Oil Company employees in 1999-2000, and found that 28.8% of field workers were obese, compared to only 25.1% of office workers. Still, both of these groups were less obese than Kuwait in general at the time (about 29% obese). There are also reports like this one, this one, and this one, that suggest that obesity is a particular issue for oil and gas workers, though the reports are frustratingly short on details.

We’ll also note that some of these reports focus on offshore oil workers in particular, and it seems that offshore oil rigs get much of their water from desalination (e.g. see here, here). 

“The mining industry,” says a paper from 2017 suggested by a reader, “has the highest proportion (76%) of overweight and obese employees in Australia.” This is in comparison to 62.8% of Australian adults in general. Tsai et al. (2008) looked at “4153 Shell Oil Company employees from three refineries, one each in Texas, Louisiana, California” in the years between 1994 and 2003. “For study subjects actively employed in 1994,” they report, “the most current examination data before 1994 were used. For employees hired after 1994, data were derived from the preemployment exams.” They found that 44.6% of employees were overweight, and an additional 29.0% were obese. For comparison, the general rate of obesity in the US was about 21% in 1994 and about 28% in 2003. (Though note that all these samples are majority male, often about 80% male, which increases the mean somewhat.)

It’s easy to see how this could be a problem in the Middle East, where there’s so much oil drilling. But is it a problem elsewhere? Is it a problem in the US? And could these brines really be getting into the average person’s groundwater?

The first thing to keep in mind is that the United States is one of the top three oil producing countries in the world, and as of this writing holds the #1 spot for most oil produced per day. We produce a lot of oil, and that means we also produce a lot of brine. 

The second thing to keep in mind is that we seem to be pretty bad at not spilling our brine everywhere. You’ll recall that the USGS report we quoted above said, “lithium and associated components of such brines may be accidentally or intentionally released to the surface or groundwater in certain locations.” This sounds bad enough, but the bird’s-eye view really obscures some of the horrible details. So let’s look at some sources for these horrible details. In no particular order: 

This report from North Dakota State University, which mentions that “the average well in North Dakota produces 18 barrels of brine per barrel of oil and three barrels of brine per barrel of gas,” and goes into some detail about “commonly used methods” for responding to brine spills. 

This report “characteriz[ing] the major and trace element chemistry and isotopic ratios … of surface waters (n = 29) in areas impacted by oil and gas wastewater spills in the Bakken region of North Dakota”, which is full of interesting tidbits. For example, we can see that there appear to be more leaks pretty much every year, and we can see that in this sample 46.7% of the brine leakage by volume came from pipeline leaks. We even have lithium measurements — we can see that in “Type A Spills” (whatever those are), the first sample contained 3,244 ng/mL lithium, the second sample contained 3,490 ng/mL, the third sample contained 478 ng/mL… you get the idea.

This report of an ACME Environmental response to an event where “500 barrels of produced water and 50 barrels of oil spilled into a drainage gully which directly flowed into a creek” in Central Oklahoma. The spill was vacuumed up and then the area was flushed with freshwater until “the salinity levels reached an acceptable level.”

This article from certifiedcropadviser.org, that tries to sound optimistic, but includes a number of concerning statistics. “North Dakota’s oil boom can have a salty side-effect,” it begins. “Wastewater from oil drilling and hydraulic fracturing – or fracking – is often laden with salts and can spill, contaminating soils. In 2014, for example, 42 such brine spills per week, on average, were recorded in North Dakota.” They discuss a new method for cleaning up such spills, but the method appears to remove less than half of the salts. “Other methods,” they tell us, “attempt to push the salts below the level plant roots can reach.”

This article from Rolling Stone, which documents some cases of brine trucks crashing and spilling thousands of gallons of brine into drinking water, discusses a case where a hauling company had been dumping brine into abandoned mine shafts for six years, and mentions that “brine has even been used in commercial products sold at hardware stores and is spread on local roads as a de-icer.” If they’re spreading it on the roads, that would uh, that would be a clear reason why it’s ended up everywhere:

Radioactive oil-and-gas waste is purposely spread on roadways around the country. The industry pawns off brine — offering it for free — on rural townships that use the salty solution as a winter de-icer and, in the summertime, as a dust tamper on unpaved roads. … In 2016 alone, 11 million gallons of oil-field brine were spread on roads in Pennsylvania … Much of the brine is spread for dust control in summer, when contractors pick up the waste directly at the wellhead, says Lawson, then head to Farmington to douse roads. On a single day in August 2017, 15,300 gallons of brine were reportedly spread.

This article from the Dallas Morning News, which documents some seriously concerning spills. Really this one is just worth quoting directly. Here are some choice excerpts: 

Five years ago, a broken pipe soaked the land with as much as 420,000 gallons of wastewater, a salty drilling byproduct that killed the shrubs and grass. It was among dozens of spills that have damaged the Johnsons’ grazing lands and made them worry about their groundwater.

An Associated Press analysis of data from leading oil- and gas-producing states found more than 180 million gallons of wastewater spilled from 2009 to 2014 in incidents involving ruptured pipes, overflowing storage tanks and even deliberate dumping. There were at least 21,651 individual spills. The numbers are incomplete because many releases go unreported.

Though oil spills get more attention, wastewater spills can be more damaging. Microbes in soil eventually degrade spilled oil. Not so with wastewater — also known as brine, produced water or saltwater. Unless thoroughly cleansed, salt-saturated land dries up. Trees die. Crops cannot take root.

“Oil spills may look bad, but we know how to clean them up,” said Kerry Sublette, a University of Tulsa environmental engineer. “Brine spills are much more difficult.”

The AP obtained data from Texas, North Dakota, California, Alaska, Colorado, New Mexico, Oklahoma, Wyoming, Kansas, Utah and Montana — states that account for more than 90 percent of U.S. onshore oil production. In 2009, there were 2,470 reported spills in the 11 states; by 2014, the total was 4,643. The amount spilled doubled from 21.1 million gallons in 2009 to 43 million in 2013.

The spills usually occur as oil and gas are channeled to metal tanks for separation from the wastewater, and the water is delivered to a disposal site — usually an injection well that pumps it back underground. Pipelines, tank trucks and pits are involved.

Equipment malfunctions or human error cause most spills, according to state reports reviewed by the AP. Though no full accounting of damage exists, the scope is sketched out in a sampling of incidents:

•In North Dakota, a spill of nearly 1 million gallons in 2006 caused a massive die-off of fish and plants in the Yellowstone River and a tributary. Cleanup costs approached $2 million. Two larger spills since then scoured vegetation along an almost 2-mile stretch.

•Wastewater from pits seeped beneath a 6,000-acre cotton and nut farm near Bakersfield, Calif., and contaminated groundwater. Oil giant Aera Energy was ordered in 2009 to pay $9 million to grower Fred Starrh, who had to remove 2,000 acres from production.

•Brine leaks exceeding 40 million gallons on the Fort Peck Indian Reservation in Montana polluted a river, private wells and the municipal water system in Poplar. “It was undrinkable,” said resident Donna Whitmer. “If you shook it up, it’d look all orange.” Under a 2012 settlement, oil companies agreed to monitor the town’s water supply and pay $320,000 for improvements, including new wells.

•In Fort Stockton in West Texas, officials in February accused Bugington Energy of illegally dumping 3 million gallons of wastewater in pastures. The Middle Pecos Groundwater Conservation District levied a $130,000 fine, alleging a threat to groundwater, but the company hasn’t paid, contending the district overstepped its authority.

•A pipeline joint failure caused flooding on Don Stoker’s ranch near Snyder in West Texas in November 2012, turning his hackberry shade trees into skeletons. Vacuum trucks sucked up some saltwater and the oil company paid damages, but Stoker said his operation was in turmoil. “I had to stay out there three days and watch them while they were getting the saltwater out, to make sure they didn’t totally destroy the whole area.”

So yeah, maybe these brines are sometimes ending up in the groundwater.

No one is drinking these brines directly. For one thing, doing so would almost certainly kill you. But the lithium concentrations in these brines are so high that even a small amount leaching into your drinking water could have big consequences. 

Let’s imagine that a groundwater source containing no lithium mixes with a brine that contains 100,000 ng/mL lithium. If the brine mixes at 1%, the water source ends up containing 1,000 ng/mL lithium, higher than pretty much any lithium levels we’ve seen in drinking water. If the brine mixes at 0.1%, the water source still ends up containing about 100 ng/mL lithium, which is pretty high. But we see about 100 ng/mL in the data for the Pima (coming up below), and that’s probably from a leak from an old well, so 100 ng/mL looks pretty plausible for a brine leak. Even if it were only mixing at 0.05%, that would still be 50 ng/mL in your drinking water, which is quite a bit.

Return to Gila River Valley

A while ago, we looked at the case of the Pima people of the Gila River Valley. During our investigation, we became interested in a paper by Sievers & Cannon (1973). Sources suggested that this paper contained a lot of detail about lithium contamination in the Gila River Valley in the early ‘70s, but we weren’t able to track it down.

But in recent developments, commenter Ralph Waldo Porcupine found the original paper and sent us a copy. Thanks Ralph!

This paper is short, but contains a number of interesting details, so let’s jump right in. To begin with, the obvious pull quote is:

It is tempting to postulate that the lithium intake of Pimas may relate 1) to apparent tranquility and rarity of duodenal ulcer and 2) to relative physical inactivity and high rates of obesity and diabetes mellitus.

Native Americans have higher rates of obesity and diabetes than Caucasians, so we should expect relatively high rates among the Pima either way. However, the paper makes it clear that the rates of both diseases among the Pima are “extraordinary”, even for Native American populations at the time, “the highest ever reported”. For full context:

Most southwestern tribes greatly exceed whites in prevalence of diabetes mellitus. Observations at the PIMC reveal that the Pima tribe has the highest diabetic rate—45% of adults (12). Population studies by Bennett confirm an extraordinary frequency of diabetes mellitus in Pima Indians—the highest ever reported and 10 to 15 times that for the general population (2).

Most American Indian tribes have a very high prevalence of obesity. … The Pima tribe has an especially high rate of marked obesity.

The obvious question is, how much lithium was in their water back in the 1970s? The paper doesn’t name a specific number, but judging from Figure 1, the median lithium level in their drinking water was around 100 ng/mL. You’ll recall that this is pretty high even compared to the modern water sources we reviewed in our first post on lithium.

This figure is a little hard to read, so let’s orient you. Each “chemical constituent” is a vertical bar showing the range of concentrations in 77 samples, and a small triangle indicates the median value. We can see that the triangle for lithium is hovering around 10-1 ppm, which works out to 100 ng/mL.

The diagonal line cutting through the figure indicates the median for each constituent in municipal waters, and the horizontal location of each of the bars tells you how much of each constituent is normally in “municipal waters”. This may actually be the most important aspect of the figure, because it answers a longstanding question: back in the day, how much lithium was in the average water supply? We know that in the early 1970s the Pima were getting about 100 ng/mL, but how much was everyone else getting? 

Figure 1 suggests that back in the early 1970s, the average concentration of lithium in “municipal waters” was just slightly more than 1 ng/mL. This seems like a pretty big deal — the Pima were not just being exposed to a pretty high dose in their water, their dose was 50-100x higher than the median dose in American municipal water sources at the time! 

But we can actually do one better, because Sievers & Cannon cite their source for this diagonal line, a 1964 paper called Public water supplies of the 100 largest cities in the United States by Durfor & Becker. This whole paper is available on Google Books where you can also download a copy for yourself as a PDF.

Just like the title says, this paper looks at the public water supplies of the 100 largest cities in the United States in 1964, and looks at what was in their water. Unlike most modern sources, lithium was on their list — so amazingly, we have pretty good records of how much lithium was in American drinking water all the way back in 1964. 

The records are quite clear. The median level of lithium in their samples was only 2.0 ng/mL, 50 times less than the median for the Pima and quite low compared to modern sources.

The minimum level detected was “not detected at all”, and perusing the numbers from individual cities, this makes a lot of sense. As you flip through the 100 cities, you see that lithium levels are generally quite low — often around 1 ng/mL, but sometimes no lithium at all. 

Occasionally lithium levels are higher, especially for cities in arid locations like Arizona and Texas. The maximum level detected was 170 ng/mL (in Texas), which is a lot — but this makes sense given what we see with the Pima. We already knew that some water supplies were contaminated with lithium that far back, and it’s no surprise that these water supplies are in Arizona and Texas! In fact, while it’s hard to read from Figure 1, it looks like the maximum value recorded in the Gila River Valley was more than 170 ng/mL, probably closer to 250 ng/mL. But notably, not all water supplies from arid locations had high concentrations of lithium, not even in Texas.

This also matches the other historical sources we’ve been able to find, though most of them have a much more limited scope. 

Municipal Drinking Water and Cardiovascular Death Rates from 1966 looked at water from 88 cities. They don’t give much detail, but suggest that the lithium levels in city drinking water were somewhere in the range of 16.8 ng/mL to 5.3 ng/mL. This may seem maddeningly vague to you — it also seems vague to us, so we’re more inclined to trust the 100-cities paper from above, since it is much clearer and provides more detail.

This paper from 1970 looks at lithium levels in the drinking water of 27 Texas communities in summer and fall 1968. They found that lithium was present in measurable amounts in the drinking water of 22 of the 27 communities, and found levels above 11 ng/mL in 15 of the 27. Matching what we saw in the 100-cities paper, some sources were found to contain as much as 160 ng/mL. Texas wasn’t universally contaminated back then, but like Arizona, it had some extreme cases.

Mood and Lithium in Drinking Water from 1976 looked at 384 drinking water samples from Washington County, Maryland. They found that 37.5% of the samples had lithium levels below 1.9 ng/mL and 75.2% had lithium levels below 5.9 ng/mL. About 90% of samples contained less than 10 ng/mL, and the highest recorded level was a mere 32 ng/mL.


Sievers & Cannon also found that trace elements in the groundwater accumulate in some plants but not in others. Lithium in particular seems to concentrate in specific plants to an incredible degree:

Vegetation is low in most trace elements but some food plants concentrate particular ones. Mesquite beans accumulate strontium; cabbage accumulates sulfate; beans concentrate molybdenum and wolfberry contains an extraordinary 1120 ppm lithium in the dry weight.

Figure 2 shows the chemical content of produce and plants that grow on the sandy alluvium of the Gila River. This vegetation has low concentrations of most trace metals but has high levels of lithium. The median level of lithium for Pima Reservation plants is 1.4 ppm (general average elsewhere, 1.0 ppm) but the mean is much higher due to abnormal accumulation by the wolfberry (or squawberry; Lycium californium).

High concentrations of particular elements in certain vegetations become important to human health if the plants are consumed in quantity. Some unusual accumulations in edible plants are shown in Table I. The wolfberry, with an extraordinary lithium content of 1120 ppm, is used occasionally for jelly. 

They’re not kidding when they say “extraordinary” — 1,120 ppm is equivalent to 1,120,000 ng/mL! 

Let’s unpack this a little. Elsewhere in the paper they say, “Pima Indians drink about 1.6 liters per day (9) of hard water.” Since the level of lithium in the water seems to have been around 100 ng/mL, which is the same as 100 µg/L, this suggests that they got about 160 µg of lithium per day from their drinking water.

Lycium californium

In comparison, they say that the wolfberry is “occasionally” used for jelly. Let’s say that this means an average of one tablespoon of wolfberry jelly per day, and let’s assume that the dose is the same in the jelly as it is in the wolfberry itself. A tablespoon is about 14 milliliters, so with a lithium concentration of 1,120,000 ng/mL, they would be getting 15,680,000 ng or 15,680 µg of lithium from the jelly. If these numbers are anywhere near correct, then the Pima were getting around 100 times more lithium from wolfberry jelly than directly from their drinking water. 

This might help us square the fact that lithium seems to be associated with weight gain, but the trace amounts in water are so much smaller than the therapeutic doses given by psychiatrists. By the calculations we did in our first post on lithium, the “minimum efficacious” therapeutic dose is about 600,000 µg per day, which would take about 40 tablespoons of wolfberry jelly (2 ½ cups, or slightly more than one jar of jelly). Alternatively, 3,750 liters of Gila River Valley water. It would be hard to drink that much water, but you could definitely get a therapeutic dose if you ate enough jelly. We don’t know what dose of lithium you need before the weight gain starts kicking in, but whatever it is, you can get there a lot faster if lithium is concentrating in the plants you eat.

Let’s imagine that corn accumulates lithium similarly to the wolfberry. (Just to be clear, this is purely a hypothetical — we have no reason to suspect corn in particular.) If your corn is irrigated with pure water containing no lithium, then the corn also contains no lithium. But if the corn is irrigated with water containing 100 ng/mL lithium, like the water in the Gila River Valley, then the corn accumulates a lot of lithium, maybe as much as 1,120 ppm / 1,120,000 ng/mL like the wolfberry does. Certain corn products might contain even more, if they concentrate the lithium further. If the corn is irrigated with water somewhere in between those two doses, then the corn ends up containing some lithium, but probably not as much.

This is complicated even further by the fact that (again just picking on corn as an example, this is true of most crops) there are many different varieties of corn. We treat them all as simply “corn”, but there can be important differences between different varieties, and some varieties might end up concentrating more lithium than others. This means that it’s impossible or sort of even meaningless to ask a question like, “how much lithium is in corn?” Well, what kind of corn? Where is it from? How much lithium was the irrigation water when it was being grown? 

So in the future, we need to keep an eye out for plants that might be concentrating lithium. Even if they are only exposed to trace amounts in their water, they could end up concentrating levels around 10,000 times higher! We grow a lot of crops — corn, soybeans, wheat, grapes, rice, almonds, peanuts, apples, etc. — which of these concentrate lithium from their water? Which are commonly exposed? 

This complicates things somewhat, but the good news is that if this is the case, we wouldn’t have to worry too much about lithium in our actual drinking water. Instead, we would want to make sure that the water we use to irrigate crops is as low in lithium as possible, which seems much more manageable.

A Chemical Hunger – Interlude F: Demographics



The stereotype is that poor people are more obese than rich people, but rich countries are definitely more obese on average than poor countries:

This same trend of wealth being related to obesity is also mirrored within many countries. In poor countries, upper-class people are generally more likely to be obese than lower-class people. For example, in India rich people are fatter than poor people.

We see that the general pattern between countries is that wealth is associated with obesity, and we see the pattern within most poor countries is also that wealth is associated with obesity. Given this, it would be kind of surprising if the relationship ran the other way around in wealthy countries. 

Still, common-sense beliefs say that — in America at least — poor people are more obese than rich people, maybe a lot more obese. But evidence for this idea is pretty elusive. 

The National Health and Nutrition Examination Survey (NHANES) is an ongoing project by the CDC where every year they take a nationally representative sample of about 5,000 Americans and collect a bunch of information about their health and lifestyle and so on. In 2010 a NCHS team led by Cynthia Ogden examined the NHANES data from 2005-2008. They wanted to find out if there was any relationship between socioeconomic status and obesity, the exact same question we have in this post.

The results of their analysis were mixed, but there certainly wasn’t a strong relationship between socioeconomic status and obesity. Their key findings were: 

Among men, obesity prevalence is generally similar at all income levels, however, among non-Hispanic black and Mexican-American men those with higher income are more likely to be obese than those with low income.

Higher income women are less likely to be obese than low income women, but most obese women are not low income.

There is no significant trend between obesity and education among men. Among women, however, there is a trend, those with college degrees are less likely to be obese compared with less educated women.

Between 1988–1994 and 2007–2008 the prevalence of obesity increased in adults at all income and education levels.

Cynthia Ogden got to do it again in 2017, this time looking at the NHANES data from 2011-2014, trying to figure out the same thing. Again the picture was complicated — in some groups there is a relationship between socioeconomic status and obesity, but it sure ain’t universal. This time her team concluded:

Obesity prevalence patterns by income vary between women and men and by race/Hispanic origin. The prevalence of obesity decreased with increasing income in women (from 45.2% to 29.7%), but there was no difference in obesity prevalence between the lowest (31.5%) and highest (32.6%) income groups among men. Moreover, obesity prevalence was lower among college graduates than among persons with less education for non-Hispanic white women and men, non-Hispanic black women, and Hispanic women, but not for non-Hispanic Asian women and men or non-Hispanic black or Hispanic men. The association between obesity and income or educational level is complex and differs by sex, and race/non-Hispanic origin.

If you don’t trust us but do trust the Washington Post, here’s their 2018 article on Ogden’s work.

The studies that do find a relationship between income and obesity tend to qualify it pretty heavily. For example, this paper from 2018 finds a relationship between obesity and income in data from 2015, but not in data from 1990. This suggests that any income-obesity connection, if it exists, is pretty new, and this matches the NHANES analysis above, which found some evidence for a connection 2011-2014 but almost no evidence 2005-2008. Here’s a pull quote and relevant figure:

Whereas by 2015 these inverse correlations were strong, these correlations were non-existent as recently as 1990. The inverse correlations have evolved steadily over recent decades, and we present equations for their time evolution since 1990.

Another qualifier can be found in this meta-analysis from 2018. This paper argues that while there seems to be a relationship between income and obesity, it’s not that being poor makes you obese, it’s that being obese makes you poor. “Obesity is considered a cause for lower income,” they say, “when obese people drift into lower-income jobs due to labour–market discrimination and public stigmatisation.” 

Anyone who is familiar with how we treat obese people should find this theory plausible. But we don’t even have to bring discrimination into it — being obese can lead to fatigue and health complications, both of which might hurt your ability to find or keep a good job. 

This may explain why Cynthia Ogden found a relationship between income and obesity for women but not for men. It’s not that rich women tend to stay thin; it’s that thin women tend to become rich. A thin woman will get better job offers, is more likely to find a wealthy partner, is more likely to find a partner quickly, etc. Meanwhile, there’s a double standard for how men are expected to look, and so being overweight or even obese hurts a man’s financial success much less. This kind of discrimination could easily lead to the differences we see.

But the biggest qualifier is the relationship between race and income. If you’re at all familiar with race in America, you’ll know that white people make more money, have more opportunities, etc. than black people do. Black Americans also have slightly higher rates of obesity. The NHANES data we mentioned earlier contain race data and are publicly available, so we decided to take a look. In particular, we now have complete data up to 2017-2018, so we decided to update the analysis.

Sure enough, when we look at the correlation between BMI and household income, we see a small negative relationship, where people with more income weigh less. But we have to emphasize, this relationship is MEGA WEAK, only r = -.037. Another way to put this is that household income explains only one-tenth of a percent of the variance in BMI! Because the sample size is so huge, this is statistically significant — but not by much, p = .011. And as soon as we control for race, the effect of income disappears entirely.

We see the same thing with the relationship between BMI and family income. A super weak relationship of only r = -.031, explaining only 0.07% of the variance in BMI, p = .032. As soon as we control for race, the effect of income disappears.

We see the same thing with the relationship between BMI and education. Weak-ass correlation, r = -032, p = .022, totally vanishes as soon as we control for race. 

Any income effect needs to take into account the fact that African-Americans have higher BMIs and make less than whites do, and the fact that Asian-Americans have lower BMIs and make slightly more than whites do.

We don’t see much of a connection between income and obesity. If there is a link, it’s super weak and/or super idiosyncratic. Even if the connection exists, it could easily be that being obese makes you poorer, not that being poor makes you obese. 


Race actually doesn’t explain all that much about BMI either. A simple model shows that in the 2017-2018 data at least, race/ethnicity explains only 4.5% of the variance in BMI. The biggest effect isn’t that African-Americans are heavier than average, it’s that Asian-Americans are MUCH leaner than everyone else. In this sample, 42% of whites are obese (BMI > 30), 49% of African-Americans are obese, but only 16% of Asian-Americans are obese! 

On the topic of race, some readers have tried to argue that race can explain the altitude and/or watershed effects we see in the Continental United States. But we don’t think that’s the case, so let’s take a closer look. Here’s the updated map based on data from 2019:

US Adults

This map is for all adults, and things have not changed much in 2019. Colorado is still the leanest state; the states along the Mississippi river are still among the most obese. Now, it’s true that a lot of African-Americans do live in the south. But race can’t explain this because the effect is pretty similar for all races. 

For non-hispanic white Americans, Colorado is still one of the leanest states (second-leanest after Hawaii) and states like Mississippi are still the most obese:

Non-Hispanic White Adults

For non-hispanic black Americans, Colorado is still one of the leanest states, and while you can’t see it on this map because the CDC goofed with the ranges, states like Mississippi and Alabama are still the most obese: 

Non-Hispanic Black Adults

In fact, here’s a hasty photoshop with extended percentile categories: 

Non-Hispanic Black Adults

If the overall altitude pattern were the result of race, we wouldn’t see the same pattern for both white and black and Americans — but we do, so it isn’t.

A Chemical Hunger – Part IX: Anorexia in Animals


A natural prediction of the idea that anorexia is the result of a paradoxical reaction to the same contaminants that cause obesity is that we should observe anorexia nervosa in animals as well as in humans. 

We’ve previously reviewed the evidence that pets, lab animals, and even wild animals have gotten more obese over the past several decades. We’ve also argued that anorexia is a paradoxical reaction of the compound or compounds that cause obesity. Since nonhuman animals are getting more obese when exposed to these contaminants, we should expect that some of them will experience a paradoxical reaction and become anorexic instead, just like humans do.

All the animals we have data on are getting fatter, but some species are gaining weight faster than others. It’s very likely that there will also be major differences in the rate and degree of paradoxical reactions. It would be very surprising if these contaminants affect mice in the exact same way they affect lizards or stingrays.

When we look at obesity data for animals, we see that primates appear to be gaining more weight than other species, and this makes sense. Primates are more closely related to humans than other animals are, so anything that causes obesity in humans is more likely to cause obesity in primates than in other mammals, and more likely cause obesity in mammals than in non-mammals, etc. As a result, we expect that anorexia is also most likely to be found in other primates.

Testing this prediction is a bit tricky. A wild animal that develops anorexia will likely die. As a result it won’t be around for us to observe, and won’t end up in our data. While pets and lab animals receive a higher standard of care, they may not survive either. 

As far as we can tell, when veterinarians notice that an animal is underweight and not eating, they don’t generally record this as an instance of an eating disorder. Instead, when a young animal doesn’t eat and eventually wastes away, this is often classified as “failure to thrive.” This is further complicated by the fact that veterinarians use the term anorexia to refer to any case where an animal isn’t eating, treating it as a symptom rather than a disorder. For example, a dog might not eat because it has an ulcer, or has accidentally consumed a toxic substance, and this would be recorded as anorexia. In humans, we would call this something like loss of appetite, which is itself a symptom of many disorders — including anorexia nervosa. (We’d love to hear from any vets with expertise in this area.)

As a consequence of all this, we don’t expect to find much direct evidence for anorexia in different species of animals. We do however expect there to be plenty of statistical evidence, because there are many statistical signatures that we can look for.

One thing we can look for is increased variance in body weights. Everyone knows that the average BMI has been going up for decades, but what is less commonly known is that the variance of BMI has also increased since 1975. When expressed in standard deviation, it has almost doubled in many countries. As correctly noted in The Lancet, this “contributed to an increase in the prevalence of people at either or both extremes of BMI.”

We should expect that animals today will have higher variation of body weights than they did in the past, just like humans do. We can similarly expect that animals that live in captivity will have higher variation of body weights than animals that live in the wild.

A particularly telling sign of this will be that while animals today (or in captivity) will on average be fatter than animals in the past (or in the wild), the leanest animals will actually be in the modern (or captive) group. We may not see animals with recognizable anorexia, but we should expect to see animals that are thinner than they would be naturally, which is presumably thinner than is healthy for them.

We might also expect to see different patterns by sex. In humans, women have higher variance of body weights than men do, which may explain why anorexia is more common in women than in men. This may not be the case in all species — it may even reverse. But a gender effect is what we see in humans and so we might also expect to see it in other animals as well.

Obesity in English Adults. Note that the distribution for women has a higher variance, which leads to more underweight AND more morbidly obese women than men.

Long-Tail Macaques 

In nonhuman animals, we use BMI equivalents. Sterck and colleagues developed a weight-for-height index for long-tail macaques which they called WHI2.7, which can function much like BMI does for humans.

For BMI in humans, values above 25 are considered overweight and values below 18.5 are considered underweight. For WHI2.7, the authors suggest that values above 62 indicate the macaque is overweight and values below 39 indicate the macaque is underweight.

Sterck and colleagues developed this measure by looking at macaques in their current population of research subjects, but they also compared the measurements of their research population to the measurements of the founder generation at Utrecht University from 1987 to 1989, and to some measurements of wild macaques from Indonesia in 1989.

Consistent with other observations of lab animals, we see that the macaques in the research population in 2019 are quite a bit fatter than the wild macaques in the 1980s (see table & figure below). The current population has an average WHI2.7 of 53.95, while the wild macaques had an average WHI2.7 of only 38.26. The current macaques are also quite a bit fatter than their ancestors, the founder group from the 1980s, who had an average WHI2.7 of 48.76.

WHI2.7 means and standard deviations for the three populations of long-tailed macaques described in Sterck et al., 2019

When we look at the standard deviations of these weight-height indexes, we find that the wild macaques in 1989 had a standard deviation of only 3.35, while the current population in 2019 had a standard deviation of 8.68! The founder population was somewhere in between, with a standard deviation of 8.07 (and this is slightly inflated by one extreme outlier). As macaques in captivity become more overweight and obese, the variance in their weight also increases. We can note that the standard deviation more than doubled between wild macaques and the current research population, and this is similar to the change in the standard deviation of human BMIs from 1975 to now, which approximately doubled.

WHI2.7 means and standard deviations for the three populations of long-tailed macaques described in Sterck et al., 2019

The wild monkeys were the leanest on average, with most of the wild females slightly underweight by the WHI2.7 measure. But the very leanest monkeys are actually in the current population, just as predicted. The leanest wild macaque had a WHI2.7 of 34.0, but the two leanest monkeys overall are both in the current population, and had WHI2.7 of 33.8 and 31.0. All of these leanest individuals were female.

As these observations suggest, there are consistent sex effects. In all three groups, male macaques have higher average WHI2.7 scores than females. In the wild group, the distributions barely overlap at all — the leanest male has a score just barely below that of the heaviest female.

Taking sex into account, the change in variance is even more pronounced. The wild macaques had a standard deviation in WHI2.7 scores of 3.35, but because the male and female distributions were largely separate, the standard deviation for males was 2.48 and the standard deviation for females was only 1.80.

This means that for the female macaques, the standard deviation of body composition scores increased by a factor of more than 4.5x, from 1.80 in the wild population to 8.14 in the current population.

We can use these data to make reasonable inferences about what we would see with a larger population. Weight and adiposity tend to be approximately normally distributed, and when we look at the distribution for WHI2.7 in these data, we see that the scores are indeed approximately normally distributed.

For these analyses, we’ll limit ourselves to the female macaques exclusively. Every underweight macaque in this dataset is female — not a single male macaque is classified as underweight. In every group, the mean WHI2.7 is higher for males than it is for females. Just as in humans, being underweight seems to be more of a concern for females than for males.

We could use this information to estimate what percent of macaques are underweight (WHI2.7 of 39 or less). But this doesn’t make sense because we already know that the wild macaques are underweight on average (mean WHI2.7 of 38.26). This is because that threshold, a WHI2.7 of 39, is based on the body fat percentage observed in these same wild macaques.

(This is quite similar to humans who don’t live a western lifestyle. On the Trobriand Islands, the average BMI was historically around 20 for men and around 18 among women, technically underweight by today’s standards.)

The authors also suggest that a WHI2.7 of 37 is perfectly healthy. Even though some of the macaques have WHI2.7 scores below 37, all macaques were examined by veterinarians as part of the study, and seem to be perfectly healthy (99% had BCS scores above 2.5, which indicates “lean” but not thin and certainly not emaciated). Other sources suggest that macaques can still be healthy even when they are thinner than this. Essentially, the threshold of 39 or even 37 isn’t appropriate for our analysis, because macaques appear to be largely healthy in this range.

While it’s hard to determine what WHI2.7 would indicate that a macaque is dangerously underweight, we’ve based our analysis on the leanest macaques we have data for. All the macaques we have data for have WHI2.7 scores above 30. We know that they were all surviving at this weight and the leanest were rated by the vets as merely thin, not emaciated. As a result, 30 seems like a good cutoff, and we can calculate approximately how many macaques would have a WHI2.7 below 30 in a larger population.

The wild female macaques have an average WHI2.7 of 36.16 with a standard deviation of 1.80. Based on this, in a larger population about 0.03% of wild female macaques would have a WHI2.7 below 30.0.

The female macaques from the current research population have an average WHI2.7 of 53.14 with a standard deviation of 8.14. Based on this, in a larger research population about 0.22% of current macaques would have a WHI2.7 below 30.0.

Theoretical distribution of WHI2.7 scores for female macaques in two distributions.

This shows an increase in the mean WHI2.7 and an enormous increase in the variation, just what we would expect to see if anorexia were the result of a paradoxical reaction. In addition, we see that the increase in variation also leads to an increase in the number of extremely underweight macaques (see below). If we tentatively describe a WHI2.7 of 30 or below as anorexic, then the rate of anorexia in female macaques in the current population is about ten times higher than the rate of anorexia in the wild population. The prevalence in the current female research macaques, 0.22%, is also notably similar to the prevalence of anorexia in humans, which is usually estimated to be in the range of 0.1% to 1.0% among women.

Lower tail of the theoretical distribution of WHI2.7 scores for female macaques in two distributions.

Another way to put this is that if we had a group of 10,000 wild macaques, we would expect about 7 wild macaques with a WHI2.7 of 30, 1 wild macaque with a WHI2.7 of 29, and no wild macaques with a WHI2.7 of 28 or below. In comparison if we had 10,000 macaques from a contemporary research population, we would expect about 8 macaques with a WHI2.7 of 30, about 6 macaques with a WHI2.7 of 29, about 4 macaques with a WHI2.7 of 28, about 3 macaques with a WHI2.7 of 27, about 2 macaques with a WHI2.7 of 26, about 1 macaque with a WHI2.7 of 25, about 1 macaque with a WHI2.7 of 24, and probably no macaques with WHI2.7 scores of 23 or below.

A different cutoff wouldn’t change the effect. For any arbitrary threshold, there will be more modern macaques at the extreme ends of the distribution. Based on what we know about healthy weights for these animals, 30 is a conservative cutoff, and the disparity only increases if we look at lower WHI2.7 scores.

Expected number of macaques with various extremely underweight WHI2.7 scores in different populations of 10,000 macaques.

It seems clear that a macaque with a score of 25 would be an extremely underweight animal, and from a simple analysis of the distributions, we should only expect to see these animals in a modern research population. In short, it’s clear that modern captive macaques have higher rates of anorexia than wild macaques from the 1980s, just the kind of paradoxical reaction this theory predicts.


A Chemical Hunger – Part VIII: Paradoxical Reactions


We come up with theories to try to make sense of the world around us, and we start by trying to come up with a theory that can explain as much of the available evidence as possible.

But one of the known problems with coming up with theories is that sometimes you are overenthusiastic, and connect together lots of things that aren’t actually related. It can be very tempting to cherry-pick evidence to support an idea, and leave out evidence that doesn’t fit the picture. It’s possible to make this mistake honestly — you get excited that things seem to fit together and don’t even notice all the evidence that is stacked against your theory.

But sometimes noticing that things seem to fit together is how an important insight comes to light. The theory of continental drift was invented when Alfred Wegener was looking through a friend’s new atlas and noticed that South America and Africa seemed to have matching coastlines, “like a couple spooning in bed”. He wasn’t even a geologist — at the time, he was an untenured lecturer in meteorology — but he thought that it was important, so he followed up on the idea. “Why should we hesitate to toss the old views overboard?” he said when his father-in-law suggested that he be cautious in his theorizing. He was criticized by geologists in Germany, Britain, and America, in part because he couldn’t describe a mechanism with the power to shuffle the continents around the globe. But in the end, Wegener was right.

The true power of a theory is its ability to make testable predictions. One obvious prediction of the theory that obesity is caused by a contaminant in our environment is that we should also expect to see paradoxical reactions to that contaminant.

Predicting Paradoxical Reactions

Sometimes drugs have what’s called a paradoxical reaction, where the drug does the opposite of the thing it normally does. For example, amphetamines are usually a stimulant, but in a small percent of cases, they make people drowsy instead. Antidepressants usually make people less suicidal, but sometimes they make people more suicidal.

Normally when we talk about paradoxical reactions, we’re talking about the intended effect of the drug, not the side effects. But from the drug’s point of view, there’s no such thing as side effects — all effects are just effects. As a result, we should expect to sometimes see paradoxical reactions in side effects as well.

And in fact, we do. A common side effect of the sedative alprazolam is rapid weight gain. But another common side effect is rapid weight loss. Clinical trials show both side effects regularly. One trial of 1,388 people found that 27% of patients experienced weight gain and 23% of patients experienced weight loss. In those who do lose weight, weight loss is correlated with the dose (r = .35, p = .006).

Severe weight gain is a common side effect of psychiatric drug clozapine. People can and do regularly gain ten or twenty pounds on this drug. But some people actually lose weight on clozapine instead.

Lithium increases leptin levels in most patients, and this is presumably part of the mechanism that causes people to gain weight on lithium. But in some patients, lithium reduces leptin levels instead.

Normally the weight loss from these paradoxical reactions is pretty limited. But occasionally people lose huge amounts. People can gain 4 lbs (1.8 kg) over only 17 days on alprazolam. In comparison, anecdotal reports from admitted abusers suggest that high doses of alprazolam can lead you to eventually lose 10 or even 40 lbs.

On clozapine, people usually gain 10-15 lbs. But some people lose huge amounts of weight instead, up to 50% (!!!) of their body weight. One patient, a woman in her 30s, went from about 148 lbs (67 kg) to about 75 lbs (34 kg) on clozapine.

AGRP neurons are a population of neurons closely related to feeding. One of the ways researchers established this connection was by showing that activating these neurons in mice led to “voracious feeding within minutes.” Another way they showed this connection was by destroying these neurons, a process called ablation. “AGRP neuron ablation in adult mice,” reviews one paper, “leads to anorexia.”

If weight gain is the main effect of a drug, the paradoxical reaction is weight loss. If the obesity epidemic is caused by one or more contaminants that cause weight gain, we should expect that there will be some level of paradoxical reaction as well. If obesity is the condition, the paradoxical condition would be anorexia.

If it’s possible to turn the lipostat up, leading to serious weight gain, it’s certainly possible to turn the lipostat down as well, leading to serious weight loss. For most people, these environmental contaminants cause weight gain. But just like with other drugs, in some people there’s a paradoxical reaction instead.

This is biologically plausible. People with anorexia have extremely low leptin levels, and some reports suggest that leptin levels are correlated with symptoms other than just BMI. Anorexia risk is genetically heritable and some of the genes involved have already been identified. The authors of one genetic analysis close by saying,

Low BMI has traditionally been viewed as a consequence of the psychological features of anorexia nervosa (that is, drive for thinness and body dissatisfaction). This perspective has failed to yield interventions that reliably lead to sustained weight gain and psychological recovery. Fundamental metabolic dysregulation may contribute to the exceptional difficulty that individuals with anorexia nervosa have in maintaining a healthy BMI (even after therapeutic renourishment). Our results encourage consideration of both metabolic and psychological drivers of anorexia nervosa when exploring new avenues for treating this frequently lethal illness.

Brain lesions alone can cause anorexia nervosa, complete with the characteristic psychopathologies like fear of fatness, drive for thinness, and body image disturbance. Many cases present as “typical” anorexia nervosa, complete with weight and shape preoccupations. When tumors are surgically removed, these symptoms go away and the patients return to a healthy weight.

Brain lesions are not the only purely biological issue that can cause anorexia. In some cases, it appears to be closely related to the gut microbiome. In one case study a patient with anorexia had a BMI of only 15 even after undergoing cognitive-behavioral therapy, medication, and short-term force feeding, and despite maintaining a diet of 2,500 calories per day. Physicians gave her a fecal microbiota transplant from an unrelated donor with a BMI of 25. Following the transplant she gained 6.3 kg (13.9 lbs) over the next 36 weeks, despite not increasing her calorie intake at all. This is only one case, but the authors indicate that they are planning to conduct a randomized controlled trial to investigate the effects of fecal transplants in individuals suffering with anorexia. To the best of our knowledge this next study has not yet been published, but we look forward to seeing the results.

Eating and maintaining weight is a central cognitive problem. “The lipostat does much more than simply regulate appetite,” says Stephan Guyenet, “It’s so deeply rooted in the brain that it has the ability to hijack a broad swath of brain functions, including emotions and cognition.”

Remember those children we mentioned in Part II, who were born without the ability to produce leptin? Unlike normal teenagers, they aren’t interested in dating, films, or music. All they want to talk about is food. “Everything they do, think about, talk about, has to do with food,” says one of the lead researchers in the field. A popular topic of conversation among these teens is recipes.

These teenagers have a serious genetic disorder. But if you put average people in a similar situation, they behave the same way. The Minnesota Starvation Experiment put conscientious objectors on a diet of 1,560 calories per day. Naturally, these volunteers became very hungry, and soon found themselves unable to socialize, think clearly, or open heavy doors.

As they lost weight, these men developed a remarkable obsession with food. The researchers came to call this “semistarvation neurosis”. Volunteers’ thoughts, conversations, dreams, and fantasies all centered on food. They became fascinated by the paraphernalia of eating. “We not only cleaned our plates, we licked them,” recalled one volunteer. “Talked about food, thought about it. Some people collected as many as 25 or 30 cookbooks” (one such collection is pictured below). Others collected cooking utensils. “What we enjoyed doing was to see other people eat,” he continued. “We would go into a restaurant and order just a cup of coffee and sit and watch other people eat.”

Subjects became overwhelmingly preoccupied with food, and some collected dozens of cookbooks, like the collection shown above. 

These are the neuroses of people whose bodies believe that they are dangerously thin, either correctly (as in the starvation experiment) or incorrectly (as in the teenagers with leptin deficiency). The same thing happens when your mind, correctly or incorrectly, believes that you are dangerously fat. You become obsessed with food and eating, only in this case, you become obsessed with avoiding both. A classic symptom of anorexia is “preoccupations and rituals concerning food”. If that doesn’t describe the behavior above, I’m not sure what would.

But avoiding food and collecting cookbooks isn’t the lipostat’s only method for controlling body weight. It has a number of other tricks up its sleeve.

Many people burn off extra calories through a behavior called “non-exercise activity thermogenesis” (NEAT). This is basically a fancy term for fidgeting. When a person has consumed more calories than they need, their lipostat can boost calorie expenditure by making them fidget, make small movements, and change posture frequently. It’s largely involuntary, and most people aren’t aware that they’re burning off extra calories in this way. Even so, NEAT can burn off nearly 700 calories per day.

When most people eat less than they need, they become sluggish and fatigued, like the volunteers in the Minnesota Starvation Experiment. But people with anorexia fidget like crazy. A classic symptom of anorexia is excessive physical activity, even in the most severe stages of the illness. When one group measured fidgeting with a highly accurate shoe-based accelerometer, they found that anorexics fidget almost twice as much as healthy controls.

This kind of fidgeting is the classic response in people whose bodies are fatter than they want to be. In studies where people were overfed until they were 10% heavier than their baseline, NEAT increased dramatically. All of this is strong evidence that people with anorexia have lipostats that mistakenly think they desperately need to lose weight.

Of course, this does sound a little far-fetched. If anorexia were really a paradoxical reaction to the same contaminants that cause obesity, then in the past we would see almost no anorexia in the population, up to a sharp spike around 1980…

While there’s not as much historical data as we would like, the pattern we observe is just about that (see figure below). Cases were quite low until about 1970, when prevalence suddenly shot up. When we look at specific sections of historical data, finding evidence of an increasing trend (often only in young women) is pretty common.

Registered yearly incidence of anorexia nervosa in mental healthcare in northern Europe in the 20th century

In general the data is pretty scattered and spotty. Rarely does a study look at rates in the same area for more than five years. When there are such comparisons, they are usually for periods before 1980. For example, van’t Hof and Nicolson, writing in 1996 and arguing that rates of anorexia are not increasing, at one point cite studies that showed no increase from 1935-1979, 1935-1940, 1975-1980, and 1955-1960. But data from the Global Health Data Exchange (GHDx) shows that rates of eating disorders have been increasing worldwide since 1990, from about 0.185% to 0.215%. This trend is small but reliable — 87.5% of countries saw their rates of eating disorders increase since 1990.

(If that’s not enough for you, we can mention that in 1985 the New York Times reported, “before the 1970’s, most people had never heard of anorexia nervosa.” Writing in the 1980s, presumably they would know.)

There are other ways to look at the relationship. For example, we can compare the most obese countries to the countries with the highest rates of eating disorders:

Share of Adults that are Obese, 2016. Reproduced from ourworldindata.org under the CC BY 4.0 license.
Share of Population with an Eating Disorder, 2016. Reproduced from ourworldindata.org under the CC BY 4.0 license. 

With the exception of a few notable outliers (genetically homogeneous South Korea and Japan), these match up really well. The fit isn’t perfect, but we shouldn’t expect it to be. There are large genetic differences and differences in healthcare practices between these countries. They may use different criteria to diagnose eating disorders. But even given these concerns, we still see pretty strong associations — Chile, Argentina, and Uruguay are the most obese countries in South America, and they also have the highest rates of eating disorders.

We can go one step further. Looking at the data, we see that these are statistically related. In 2016, rates of eating disorders were correlated with obesity in the 185 countries where we have measures for both, r = .33, p < .001. If we remove the five tiny island nations with abnormally high (> 45%) obesity (Kiribati, Marshall Islands, Micronesia, Samoa, and Tonga), all of them with populations of less than 200,000 people, the correlation is r = .46:

Prevalence of eating disorders and obesity, 2016. Kiribati, Marshall Islands, Micronesia, Samoa, and Tonga not shown.

We see the same correlation between rates of obesity and rates of eating disorders when we look at the data from 1990, r = .37, p < .001.

Perhaps most compelling, we find that the rate of change in obesity between 1990 and 2016 is correlated with the rate of change in eating disorders between 1990 and 2016. The correlation is r = .26, p = .0004, and it’s r = .30 if we kick out Equatorial Guinea, a country where the rates of eating disorders tripled between 1990 and 2016, when none of the other countries even had their rates double. You can see those data (minus Equatorial Guinea) below:

 Increase in the prevalence of eating disorders and obesity, 1990-2016. Equatorial Guinea not shown.

That’s no joke. The countries that are becoming more obese are also having higher and higher rates of eating disorders.

We even see signs of a paradoxical reaction in some of the contaminants we reviewed earlier. You’ll remember that when mice are exposed to low doses of PFOA in-utero, they are fatter as adults — but when mice are exposed to high doses as adults, they lose weight instead. The dose and the stage of development at exposure seems to matter, at least in mice. It’s notable that anorexia most often occurs in teenagers and young adults, especially young women. Are young women being exposed to large doses all of a sudden, just as they start going through puberty? Where would these huge doses come from? It may not be that much of a stretch — PFAS are included in many cosmetics.

In one study of 3M employees, higher PFOS levels led to a higher average BMI, but also to a wider range in general. The lightest people in the study had some of the highest levels of PFOS in their blood. The quartile with the least PFOS in their blood had an average BMI of 25.8 and a range of BMIs from 19.2 to 40. The quartile with the most PFOS in their blood had an average BMI of 27.2 and a range of BMIs from 17.8 to 45.5. Remember, a BMI of below 18.5 is considered underweight.

In the study of newborn deliveries in Baltimore that we mentioned earlier, researchers found that obese mothers had babies with higher levels of PFOS than mothers of a healthy weight. But underweight mothers also had babies with higher levels of PFOS. In fact, babies from underweight mothers had the highest levels of PFOS exposure, 5.9 ng/mL, compared to 5.4 ng/mL in obese mothers, and 4.8 ng/mL in mothers of normal weight. “The finding that levels were higher among obese and underweight mothers is interesting,” they say, “but does not have an obvious explanation.” Knowing what we know now, the obvious explanation is that PFOS usually causes weight gain, but like all drugs, it sometimes has a paradoxical reaction, resulting in weight loss instead.


A Chemical Hunger – Interlude E: Bad Seeds


People have been asking us if we’re going to review the literature on seed oils. 

We weren’t aware this was such a popular theory when we wrote the series, so we didn’t think to address it. Briefly, this theory says that some of our food oils — usually soybean, corn, canola, cottonseed, and safflower oils, but sometimes others — are behind the modern meteoric rise in obesity rates. Clearly this is relevant to our interests. 

People are split over exactly what to call the culprit — seed oils, industrial seed oils, vegetable oil, etc. These terms may mean slightly different things to different people, but ultimately these are all a closely related set of theories. In this post, we will go with the term “seed oils” because it is the shortest. 

Here is just a small subset of the sources people sent us:

We haven’t read everything ever written on seed oils because there are a truly enormous number of pieces on this idea. But we do think it’s worthwhile to do our own review of the theory, so here goes. 


There are a number of things that make this hypothesis attractive. For one, these oils truly are in everything

One of the authors of this blog went on a couple dates with a girl who had a serious corn allergy. Unfortunately for her, corn oils and other corn byproducts are all over the damn place. She couldn’t eat out at restaurants or buy most prepared foods. She couldn’t have chocolate. She couldn’t even eat citrus, because corn proteins are a component in the pesticides/preservatives sprayed on citrus trees and fruit. She could eat apples if she peeled them, but even that was a risk. She mostly lived on rice, rice noodles, a small number of safe vegetables, and potatoes (which, to be fair, are the prince of food). 

And this is just corn! We’re looking for a factor that is omnipresent in the environment and difficult to escape regardless of one’s diet, and seed oils do seem to fit the bill.

We should also note that the contaminants theory more or less predicts something like seed oils. Any contaminant that causes obesity probably bioaccumulates in plants and animals. If it bioaccumulates in plants, then highly concentrated byproducts of those plants might contain higher concentrations of the contaminant. This would vary based on the species of plant (some will bioaccumulate more than others) but we would expect to see especially high levels of contaminants in some extremely concentrated food products. 

Seed oils are also highly processed (check out the cool/disturbing video at that link) so they have many opportunities to come into contact with industrial contaminants. “I thought it was going well,” says YouTube commenter Sam De Francesco, “up to the bit about using a chemical solvent to remove the remaining oil from the canola cake. It went downhill from there quickly.”

The two theories are complementary, and it’s possible that both could be true. We can even look at specific contaminants to see if there might be a connection.

There is some work on measuring the levels of PFAS in cooking oils, and they do record some PFAS being detected in “Edible Oil Samples from Markets in Beijing, China, in 2013”, including corn oil, soybean oil, etc. But these levels of contamination are in about the same range as PFAS found in other foods, like milk, not thousands of times higher.

We can’t find any actual measurements of lithium levels in different food oils. This paper finds no relationship between vegetable oil consumption and lithium in the blood plasma of 922 Germans, but the food consumption measure was all self-reported, and people are probably not fully aware of how much vegetable oil is in many foods. Some secondary sources (here, here) say that lithium is most concentrated in vegetables, cereals, and grains, which would sort of line up with the seed oil hypothesis, but we can’t find the primary sources for these claims, so take this one with a grain of salt for sure. 

Glyphosate’s Hail Mary

In a previous post, we took a look at glyphosate and concluded that it doesn’t seem like glyphosate could be a primary driver of obesity, and probably doesn’t contribute to obesity at all. There is one longshot hypothesis, however, of how glyphosate might play a role in the obesity epidemic, or at least the increase we’ve seen since the 1990s. 

When reviewing the list of Roundup Ready crops, varieties designed to be sprayed with huge doses of glyphosate, we couldn’t help but notice that soybeans, corn, canola, and cotton are all on the list (though not safflower). As a result, these crops are sprayed with much more glyphosate than normal. This seems like kind of a weird coincidence. You’ll also recall that in 2016, the EPA found glyphosate in 63.1% of corn samples and 67.0% of soybean samples they tested

The level of contamination was quite low in all of these samples, but seed oils are highly concentrated, so the levels of contamination in corn oil could potentially be hundreds of times higher. That said, we haven’t been able to find any data on glyphosate levels in any of these oils, so it’s hard to tell. There’s some evidence that glyphosate is in more foods than officially recognized (thanks to commenter Louis for finding this one!), but that’s not a direct connection to seed oils per se. We also found one claim that glyphosate “does not mix with oil, so there are negligible glyphosate residues in vegetable oil from treated crops such as soybeans, canola and corn”, but this is from gmoanswers.com.


The seed oil account of obesity also has several weaknesses, which make the hypothesis seem less likely.

First of all, it seems unlikely that there is a problem with the sources of the oils themselves. Soybeans and corn have been eaten for centuries without a wave of obesity. Rapeseed (you know this as the source of canola oil) is yet another Brassica, which people have been eating forever. 

(Ok rapeseed is a Brassica but not a Brassica Oleracea, just let us have this one.)

It could all be in the dose, though. These seed oils are highly concentrated — hundreds or thousands of servings of the relevant vegetable are needed to make one serving of the oil — so if there is some corn enzyme that is harmless at a small dose but dangerous when the dose is 100x higher, that could lead to corn being harmless but corn oil being an issue. A common saying among seed oil proponents is “the dose makes the poison,” and that certainly might be the case.

We notice that the seed oils under suspicion were developed around 1900-1930, which seems a little too early to match the timeline for obesity. People have been eating more and more of them over time, but this trend took off around 1940 and doesn’t show a clear inflection point around 1980. This isn’t damning evidence, but it doesn’t quite fit the pattern. 

Image courtesy of Jeff Nobbs, reproduced with permission of the author

In addition, seed oils face the same problems faced by every food-based explanation for the obesity epidemic.

Seed oils have a hard time accounting for patterns like obesity being related to altitude, and wild animals also becoming obese. Maybe wild animals are eating scraps of human food out of dumpsters behind the 7-11, but it’s not clear why people at low altitudes would be eating more seed oils than people at high altitudes.

Seed oils also seem unable to account for the big differences between obesity rates in different professions. It doesn’t seem like people in different professions are likely to eat different amounts of seed oils, but they do seem likely to be exposed to different contaminants at work.

Similarly, there is a large amount of variance in obesity rates between countries, and there has been for a long time. Why is the Middle East so obese? Why has Kuwait always been one of the most obese countries in the world? Why was Kuwait 20% obese in 1979, back when the obesity rate for the US was only 13%? It doesn’t seem like seed oils could be behind the high rates of obesity in the Middle East, which limits the possible role they could play in driving the obesity epidemic.

Health Claims

Of course, what we’re really interested in is whether or not seed oils are related to weight gain and obesity.

It’s unfair to look through a literature and start with some random papers. You want to let the theory’s supporters point you to the evidence they feel is strongest, the evidence they think is most important. We wanted to look at the strongest possible argument, the sort of case that only a supporter could put together. So we decided to start by looking at one person’s case against seed oils.

For this purpose, we decided to focus on work by Jeff Nobbs, because his series was the best of the many arguments we saw, and we want to engage with the strongest version of the theory. Nobbs cites lots of primary sources, links directly to them, and tells you which parts of the findings he thinks are relevant. He doesn’t hit you in the face with a bunch of biochem claims right out of the gate. He doesn’t engage in name-calling and doesn’t try to scare the reader by going on about how dirty or unnatural seed oils are, something we can’t say about some of the other sources we saw. He just shares the evidence that convinced him and explains why he thinks it’s important.

In Part 1 of his series, Nobbs does a really great job describing how levels of chronic disease and obesity are increasing, and reviewing the evidence for just how weird this is. He hits a number of the same points we made in our review of the mysteries surrounding obesity, and often he makes a more compelling case for their weirdness than we did. In particular, he does a better job describing how exercise rates are increasing. We especially like this figure: 

Image courtesy of Jeff Nobbs, reproduced with permission of the author

Part 2, called Death by Vegetable Oil: What the Studies Say, reviews a number of studies that suggest that seed oils (Nobbs prefers the term vegetable oil, but it’s essentially the same thing) contribute to health conditions like cancer, dementia, and weight gain. In this series we are primarily interested in obesity, so we will stick to the weight gain results.

In this post, Nobbs references four studies showing an obesity-seed oil connection:

In one study, rats were divided into different groups receiving diets identical in fat, protein, and carbohydrate calories but differing in the source of the fats. The rats in the group receiving fat from safflower oil had a 12.3% increase in total body weight compared to the rats eating traditional fats [6].

In a randomized trial on rabbits, three groups of rabbits were given access to identical foods, with only one difference: the first group of rabbits was fed unheated vegetable oil, the second group was fed vegetable oil that had been heated once, and the third group was fed vegetable oil that had been repeatedly heated multiple times. Everything else about their diets was kept the same [7].

The outcome? Compared to the group of rabbits eating unheated oil, the group eating single heated oil gained 6% more weight, and the group eating repeatedly heated oil gained 45% more weight!

A 2020 study in mice showed that consumption of soybean oil leads not only to weight gain, but also to gene dysregulation that could cause higher rates of neurological conditions like autism, Alzheimer’s disease, anxiety, and depression [8].

In another mouse study, feeding mice the equivalent of 2 tablespoons of canola oil per day is associated with worsened memory, learning ability and weight gain, along with “considerable neuronal damage” and increased formation of beta-amyloid plaques, the signature of Alzheimer’s disease [10].

Links 8 and 10 are to news sites, not to the original research papers, but as far as we can tell, 8 is probably this paper and 10 appears to be this paper.

We do want to mention at this point that all of these studies were conducted on animals, not humans. (Nobbs also knows this, of course.) We recognize the value of animal studies and we often cite them ourselves, but it’s curious that there seem to be no studies showing that seed oils cause weight gain in humans — more on this in a bit.

These sources describe what Nobbs says they do, showing connections between seed oil consumption and weight gain. And in fact there are many other studies that also show this connection — this study shows soybean oil led to more weight gain than other oils (in mice), this study shows soybean oil led to more weight gain than fish oil and palm oil (in rats), and this study shows safflower oil led to higher levels of leptin (but not body weight) compared to animal-based fats (also in rats).

However, there are also studies that show essentially no difference between seed oils and other oils in terms of weight gain. For example, in this study soybean oil led to an equal amount of weight gain as lard and palm oil, and canola oil led to less weight gain than the other three (still in rats), and in this study lard, sunflower oil, and palm olein oil led to nearly identical weights (mercifully, in vervets this time). 

Many other studies find the exact opposite, that seed oils cause less weight gain than other fats, including animal fats. For example, this paper found that a butter-rich diet led to more weight gain than a canola-rich diet (in rats), this paper found that a lard diet led to greater weight gain than safflower oil (in mice), and mice that were started on a lard diet and then switched to safflower oil actually started losing weight, this paper reviewed the literature and found that pretty much any high fat diet leads to some weight gain (in rats and mice), and the paper Body Fat Accumulation Is Greater in Rats Fed a Beef Tallow Diet than in Rats Fed a Safflower or Soybean Oil Diet found what it says on the tin. 

Even this is only a small selection of the large number of papers on this topic. Some of these papers are probably better than others — some we should take seriously, and others we should discount. Probably a bunch of these studies are crap. If someone wants to do a deep dive into these papers, and all the other ones out there, to try to figure out the state of the literature, we would be interested in reading that piece. But even if someone did a deep dive, we doubt there would be a clear result. It’s hard to look at this literature and say anything more than “wow that looks complicated”.

This is just one of those very large literatures where you have to be careful about drawing a conclusion from only a few studies. “Some studies where seed oils lead to more weight gain than other fats, some studies where they lead to less, and a couple studies where there’s no difference” is what we should expect to see if there is a small effect in either direction, or no effect at all.

There certainly doesn’t seem to be a strong effect here. If the effect were strong, it would be detected more reliably. So if seed oils have an effect on weight gain (in mice, rats, and vervets), it’s a weak effect at most, and it’s not clear from the evidence whether seed oil causes more or less weight than other fats.

Another problem with the weight gain results in rodents and monkeys is, why do none of the human studies find the same thing? Nobbs cites a number of studies on people, but none of the ones we looked into show any evidence that seed oils cause weight gain in humans. 

Let’s take a look. The Los Angeles Veterans Administration Study specifically mentions, “the unrestricted consumption of the two diets had no significant effect on average body-weight.” The Minnesota Coronary Experiment reports BMI in both conditions and finds them to be nearly identical, 24.6 versus 24.5. 

The MARGARIN Study reports BMI for all conditions and finds no difference in people who ate diets with margarine made with more fat from vegetable oil versus margarine made with less fat from vegetable oil. 

The Sydney Diet-Heart Study had two groups start out with similar BMIs (25.4 vs. 25.1) and BMI in both groups went down by a similar, small amount (ending up at 24.5 vs 24.3) after 12 months, regardless of whether they were eating mostly safflower oil and margarine, or mostly olive oil and butter.

These studies generally run for several years and have much higher sample sizes than the studies run on animals, and of course they’re looking at humans. If there’s no evidence in these studies that people gain weight when eating seed oils, then any effect of seed oils on weight would have to be very small or very subtle. Or, there might be no effect at all.

In fact, this is pretty strong evidence against seed oils causing obesity. If seed oils cause weight gain when people eat them, why didn’t seed oils cause weight gain when people ate them? Sure we didn’t review every study out there, but these are four rather large studies (a couple hundred to a couple thousand people each) and all of them show an effect of seed oil on weight that is absolutely zilch, bupkis, nothing. This is more than “no smoking gun”, this is “suspect was in another country giving a speech in front of 10,000 people on live TV.”


And also… ok, we do want to pick on the rigor here, just a little.

Nobbs says, “In the Los Angeles Veterans Administration Study, the group of participants who increased fat from vegetable oil–while keeping total fat the same–were 82% more likely to die from cancer compared to the control group that didn’t increase fat from vegetable oil.” 

This sounds very impressive — but as far as we can tell, it is referring to this result: “31 of 174 deaths in the experimental group were due to cancer, as opposed to 17 of 178 deaths in the control group.” This is about 82% more in the experimental group, but as you can see, the absolute numbers are quite small. 

This difference is not even statistically significant, which the authors note, giving p = .06. In addition, they begin the discussion by noting, “The experience of other investigators using similar diets has not been the same.” They mention a couple other studies, including one from London which tested a diet high in “soya-bean oil”, where there were 6 cancer deaths in the control group and only one cancer death in the group eating a diet high in “soya-bean oil”.

We see something similar in the other studies. Nobbs mentions that “the group consuming more vegetable oil had a 62% higher rate of death during the seven-year study compared to the group eating less vegetable oil” in the Sydney Diet-Heart Study. Again, this sounds like a lot. We’re not entirely certain which result he’s referring to, but as far as we can tell this is also not significant, p = .051. In fact, every p-value reported in this paper seems to be between .02 and .13, which does not inspire confidence and is probably an indicator of p-hacking. Either way, the majority of these results are not statistically significant, and if we were to apply a simple correction for multiple comparisons, none of them would be.

In the MARGARIN Study, the “seven times higher” figure refers to one death in the less-vegetable-oil group and seven deaths in the more-vegetable-oil group. The authors note that this is “not significant because of the small numbers.” They don’t report a p-value here, so we double-checked their analysis just to be sure and found the same thing, p = .096.

The evidence in the Minnesota Coronary Experiment is a little more mixed, but the authors themselves do not seem to have access to the raw data, and in reference to the apparent increased mortality in adults over 65, say, “in the absence of the raw data, however, we cannot determine the statistical significance of this finding.”

Nobbs did a good job reviewing the literature and providing direct references to the studies he draws his conclusions from. But the literature on weight gain in animals is far larger and more mixed than many people realize, and doesn’t clearly point to seed oils causing obesity. The literature on seed oil consumption in humans consistently shows that seed oils cause no more weight gain than other fats. When we took a closer look at some of these studies, we found serious problems with several of the analyses. The evidence here is weak at best. 

This doesn’t mean that seed oils, or vegetable oils, or whatever you want to call them, are good for you. They may still be very bad for you, and the case for other health effects (including a connection with cancer) seems stronger. But it doesn’t look like they could be a major cause of the obesity epidemic, and probably, they play no role at all.

A Chemical Hunger – Interlude D: Glyphosate (AKA the active ingredient in Roundup)


Glyphosate is a herbicide and all-around general weed killer that you probably know as the active ingredient in Roundup *whip cracks*

We were put onto the trail of this here varmint by Justin Mares, who pointed out that there are several reasons to be suspicious of glyphosate. We’ll start with the fact that this stuff is pretty much everywhere. “Because of its widespread use, glyphosate is in water, food and dust, so it’s likely almost everyone has been exposed,” says PBS.

Most sources say that glyphosate has a relatively short half-life in both soil and water, though apparently this “can vary widely based on environmental factors” and “values between 2 and 197 days have been reported in the literature.” Many sources downplay this contamination, emphasizing how quickly it degrades, but it does seem to end up in groundwater, as there is a whole water treatment literature about how to remove it (see also this other review paper). 

Relevant to our interpretation of the altitude mystery, one study that examined glyphosate contamination in two rivers in Mexico found that the rivers were generally (though not always) more and more contaminated as they flowed downstream.

Glyphosate was patented in 1971 and first sold in 1974, but the FDA didn’t test for glyphosate in food until 2016, which seems pretty weird. The results of these tests are publicly available — in the report from 2016, we see that they tested 274 samples of corn, 267 samples of soybeans, 113 samples of milk, and 106 samples of egg for glyphosate. No glyphosate at all was detected in the milk or eggs, but 63.1% of corn samples and 67.0% of soybean samples showed some glyphosate contamination, though none contained glyphosate levels above the limit set by the EPA. It’s clearly in some foods, though apparently less in animal products and not at especially high levels overall.

Glyphosate can affect the growth of microorganisms that rely on a specific pathway for growth, but this paper finds that most gut bacteria don’t have that pathway, and so glyphosate probably doesn’t affect their growth. On the other hand, this paper says that “54% of species in the core human gut microbiome are sensitive to glyphosate.”

There’s also some evidence that glyphosate interferes with various enzymes, at least in rats. Some of these enzymes are related to the metabolism and clearance of many drugs, and interference with these enzymes does seem to sometimes lead to negative drug interactions. This suggests that even if glyphosate doesn’t cause obesity by itself, it could potentially make people more susceptible to other compounds that do.

In addition there is at least one speculative paper arguing explicitly that glyphosate’s suppression of cytochrome P450 enzymes leads to many modern diseases, including obesity. However, we should mention that the journal that published this paper found it so weird that they added a note about “potential bias in opinions and bias in the choice of citation sources used in this article” and issued “an Expression of Concern [emphasis in the original] to make readers aware that the approach to collating literature citations for this article was likely not systematic.” Also see this pushback, and this pushback from a group funded by the American Chemistry Council, American Petroleum Institute, etc. etc.

When we look at a map, the distribution of glyphosate use in the United States matches county-level obesity data pretty darn well: 

County-Level Estimates of Obesity among Adults aged 20 and over, 2009. Map from the CDC.

Glyphosate Use in 2009, USGS. This and other maps are from 2009 to match the map above, but click through to the USGS to see use in other years.

It’s not a perfect match, but it’s pretty striking. Granted, this also looks a lot like the distribution of other pesticides, for example our old friend 2,4-D:

And Atrazine, another common pesticide:

Maps of pesticide use mostly highlight agricultural regions, regardless of the pesticide you look at. The big difference is that unlike these other pesticides, glyphosate is used more and more every year, matching the rise in obesity. Again from the USGS:

Glyphosate (interestingly, the increase seems to have stalled around 2012?):



Glyphosate was developed to fight weeds in the early 1970s and was first brought to market in 1974 as Roundup. Since then, glyphosate use has increased pretty much every year, both in the US and worldwide.

This is actually a little on the late side, especially since glyphosate saw relatively limited use before the 1990s. Things really kicked in with the introduction of genetically engineered Roundup Ready crops. Roundup kills most plants, so it kills most crops too, and in the beginning the only way to use it was to spray selectively. But in 1996 Monsanto introduced Roundup Ready soybeans, which are resistant to glyphosate. Now farmers could dump Roundup on the whole field and kill everything but the soybeans. This was followed by Roundup Ready corn, canola, sugar beets, cotton, and alfalfa. These new resistant varieties led to a huge increase in glyphosate use during the 1990s and onward.

(In addition, we should note that Kuwait was already 18% obese by 1975, and it’s hard to see how glyphosate could be responsible for that.)

Round Down

There’s some circumstantial evidence that if contaminants are responsible for obesity, at least one of those contaminants is related to agriculture. As we see from the maps above, the most obese parts of America are largely farm country. If we were to suspect an agricultural chemical, glyphosate would be at the top of our list.

But there are many more signs that the main contaminants are not agricultural. If the most widely used herbicide in the United States were the cause of obesity, then we would expect agricultural workers to be especially obese, as a result of their high level of exposure. Instead, we see that the rate of obesity among agricultural workers is pretty average — sometimes slightly higher than average, sometimes slightly lower.

Agricultural workers are clearly exposed to glyphosate. One study of 48 farmers, their spouses, and their 79 children found that 60% of farmers had detectable levels of glyphosate in their urine. Farmers who wore rubber gloves had less glyphosate in their urine, and farmers who made skin contact with the glyphosate had more. A small percent of spouses and children also had low levels of glyphosate in their urine, but this mostly seemed to happen when the spouses or children helped prepare the glyphosate formulation or apply it to the fields. Despite this direct exposure, farmers are not especially obese.

Maybe glyphosate (or some other pesticide) degrades over time, or combines with something else in the environment, and ends up forming some byproduct that causes obesity? Again this seems unlikely. If some byproduct of a chemical used at farms caused obesity, you would think that the people who spend all their time at the farm would get the most exposure. There are some exceptions — farm workers probably don’t get much exposure to the antibiotics given to livestock, for example — but it seems unavoidable in the case of contaminants that are sprayed directly on fields.

Maybe eating glyphosate-laced food is different from inhaling glyphosate or absorbing it through your skin. This seems possible, given that there are some signs glyphosate might disrupt the microbiome, and this could potentially explain why farmers are not all that obese, since they’re not eating the stuff. But once again this seems unlikely, because there are major differences between obesity rates for other professions.

Motor vehicle operators, healthcare workers, and law enforcement workers are some of the professions with the highest levels of obesity. Teachers, design workers, and legal workers are some of the professions with the lowest levels of obesity. Some of these differences are very robust

This seems hard to reconcile with an account where glyphosate, or a glyphosate byproduct, causes obesity. Granted, glyphosate is used other places than just on farms. But why would truck drivers and police officers be exposed to so much more glyphosate than everyone else? How could they be exposed to more than farm workers? It’s possible that there is something about driving a truck that unlocks the harmful potential of glyphosate (or something about glyphosate that unlocks the harmful potential of being a truck driver) but at the moment this seems unlikely.

Like farmers, forestry workers also work with glyphosate. But despite this, forestry workers involved in spraying don’t show any glyphosate in their urine, despite clearly getting it on their clothes and even on their skin. It’s hard to see how law enforcement workers or truck drivers could be getting an appreciable dose of glyphosate when forestry workers who handle the stuff directly don’t seem to get any in their body. And, we can note, forestry worker is a pretty lean profession as well.

While the geographic match is pretty good in the US, the international match is mixed. We haven’t been able to find the clearest sources, but here’s a map from one paper: 

Map of estimated glyphosate intake, source.

Compared to:

If glyphosate were a major driver of obesity, we would expect Central and South America and the Middle East to be much less obese. We would expect Spain, Germany, Poland, and much of Africa to be much more obese. Food imports and differences in water treatment techniques may be able explain some of this, but it’s not an immediate hit for glyphosate. 

Direct Evidence

If you look into a potential glyphosate-obesity connection you will eventually find a 2014 paper titled Genetically Engineered Crops, Glyphosate and the Deterioration of Health in the United States of America, or sources based on it, so we need to address it here. 

This paper reports an absolutely comically huge correlation of 0.962 between glyphosate application and obesity, and similarly enormous correlations with other diseases. This correlation is implausibly large, and it is not accurate — it is the result of a common error people make when working with time series data.

A good explanation of this error can be found in Avoiding Common Mistakes with Time Series Analysis by Tom Fawcett, where he shows that adding a slight trend to two totally random, unrelated time series ends up making them appear extremely correlated despite the total lack of a relationship. In his example, the apparent correlation ends up being .96, despite the true correlation being zero. What this paper reports is not evidence that glyphosate causes obesity, it is only evidence that glyphosate use has increased since 1996 (we already knew that) and that obesity has also increased since 1996 (we already knew that too).

There is some evidence of weight loss, or at least “decreased body weight gain”, in animal studies. All these studies are in rats and mice, however, and this seems to happen only at the highest doses. When we say “highest doses”, we mean really high — in one study the highest dose was 1183 mg/kg/day, in another it was ​​3500 mg/kg/day, in a third it was 4945-6069 mg/kg/day. 

In comparison, the U.S. Environmental Protection Agency reference dose for glyphosate in humans, “or estimate of daily exposure that would not cause adverse effects throughout a lifetime”, is 2 mg/kg/day. In that study from before, the highest estimated systemic dose in farmers who were working directly with glyphosate was only 0.004 mg/kg.

This is a tiny bit of evidence for glyphosate causing weight change, but it’s 1) in animals, 2) weight loss rather than weight gain, and 3) only at doses about 1000 times higher than recommended and about 250,000 times higher than the doses found in people who work with glyphosate directly.

The best evidence for glyphosate causing weight gain that we could find was from a 2019 study in rats. In this study, they exposed female rats (the original generation, F0) to 25 mg/kg body weight glyphosate daily, during days 8 to 14 of gestation. There was essentially no effect of glyphosate exposure on these rats, or in their children (F1), but there was a significant increase in the rates of obesity in their grandchildren (F2) and great-grandchildren (F3). There are some multiple comparison issues, but the differences are relatively robust, and are present in both male and female descendants, so we’re inclined to think that there’s something here.

There are a few problems with extending these results to humans, however, and we don’t just mean that the study subjects are all rats. The dose they give is pretty high, 25 mg/kg/day, in comparison to (again) farmers working directly with the stuff getting a dose closer to 0.004 mg/kg.

The timeline also doesn’t seem to line up. If we take this finding and apply it to humans at face value, glyphosate would only make you obese if your grandmother or great-grandmother was exposed during gestation. But glyphosate wasn’t brought to market until 1974 and didn’t see much use until the 1990s. There are some grandparents today who could have been exposed when they were pregnant, but obesity began rising in the 1980s. If glyphosate had been invented in the 1920s, this would be much more concerning, but it wasn’t.


It doesn’t look like glyphosate can be a major contributor to the obesity epidemic. It was introduced slightly before obesity began to skyrocket, but it didn’t see much use until decades later. It seems to have arrived too late to be responsible. There may be a slim chance that glyphosate contributes in some small way to obesity, and might be responsible for some of the increase in obesity since the mid-90s, but it doesn’t look like it could have started the epidemic.

Glyphosate exposure doesn’t seem to match international patterns of obesity, and glyphosate doesn’t seem to be able to explain the variation in obesity rates by profession. If glyphosate caused obesity, we would expect farm and forestry workers to have high levels of obesity, but in fact their obesity rates are pretty average, or even below average. Finally, there’s very limited evidence of glyphosate exposure causing weight change in animals, and no evidence in humans, at least none that we can find.

To us, it doesn’t seem likely that glyphosate plays any serious role in the obesity epidemic, and it probably doesn’t play any role at all. If you disagree or have any evidence to the contrary, however, please let us know!

A Chemical Hunger – Interlude C: Highlights from the Reddit Comments


There are a number of interesting comments on the A Chemical Hunger Discussion Thread recently posted on r/slatestarcodex, so we wanted to feature and discuss a few of them here. Thanks to everyone who commented and asked questions, we appreciate all the feedback.

Some of these questions are meaty enough to deserve a post of their own, so we won’t discuss any of those comments here. But trust us, it’s (probably) coming.

When did Obesity Spike?

The OP of the Reddit thread, u/HoldMyGin/, asks:

My biggest criticism is the assertion that obesity rates started spiking around 1980. If you look at a graph of rates, sure, they did, but isn’t that what one would expect to see if you’re measuring the percent of a normal distribution above a certain threshold, and the mean of that distribution is slowly but consistently inching upward?

This is an interesting point, but the trends don’t look that way to us. 

To test this, we ran a simple simulation where a population of 1000 people with normally distributed BMI (mean 23, sd 4) all see an increase of 0.1 points of BMI per year for 200 years. They start at about 3% obese, which is around the premodern rate of obesity, and show a pretty gradual increase over time:

In reality, the US went from about 1-3% obese in 1890 to about 10% obese in 1980, then from about 10% obese in 1980 to about 36% obese today. This is an increase of 7-9 points over 90 years versus an increase of 26 points over 40 years. In the simulation it takes about 20 years to reach 10% obesity and then about 36 more years to reach 36% obesity. The time scale seems way off for a constant mean increase.

In any case, the spike isn’t just caused by an increase in the mean because the SD of BMI has increased as well. Analysis of data from the Framingham Heart Study found that the standard deviation of BMI increased “from 4.18 kg/m(2) to 6.15 for women and 3.31 kg/m(2) to 4.73 for men” between 1971 and 2008.

u/KnotGodel also chimed in with:

Just to add to this. u/slimemoldtimemold‘s chart of obesity over time is from the CDC. I took this chart, hand-transcribed it, and then assumed BMI followed a normal distribution. From this, I computed the implied mean and standard deviation for each sex over time. … You can see from the chart that (in this model) mean BMI didn’t really change until 1978. After this point it increased by ~4 points.

Here are KnotGodel’s lovely figures based on the CDC data. Thanks Knot! 

Those SD seem very different from the numbers in the Framingham Heart Study over a very similar period, but maybe this is because the variance in Framingham, Massachusetts is going to be lower than the variance in the country as a whole.

Diseases of Deficiency

u/leerylizard raised an interesting alternative theory:

Another idea I’ve considered is that the modern diet is simply missing some key nutrient(s) we haven’t learned about yet. Like how beriberi and scurvy had unknown etiologies until they were eventually identified as thiamine and vitamin C deficiencies, respectively. Modern agribusiness tends to use different methods than were common before 1900, what with the Haber process for soil fertilization, mono-cropping, etc. Maybe some particular nutrient is lost when we switch to a modern diet.

This would be very weird because, if obesity were anything like beriberi and scurvy, it would be easily reversed when people got access to the required nutrient. It would probably be more common in people who depended on unusual or limited food supplies, e.g. arctic explorers, astronauts, people during sieges, etc. But we don’t see any of that.

As usual, this would be hard to square with the altitude data, and the fact that there is a lot of variation in obesity between different professions. Barring strong evidence for a particular nutrient, it seems pretty unlikely.

Psychiatric Doses

u/ScottAlexander, who is a practicing psychiatrist, says:

If lithium is involved, it’s got to be pretty weird and inconsistent. Lithium given in psychiatric doses – about 1000x the background dose – sometimes makes patients gain weight, in the same way that any drug can potentially make you gain weight. But you can still find plenty of psychiatric lithium patients with normal BMI. I’m not sure really sure how to square “use water filters to decrease your lithium by 0.001 mg” with “but also some people take 1000 mg lithium and are fine”. I don’t want to say it’s impossible, because there are a lot of effects like this (some people exposed to secondhand smoke get lung cancer, but some smokers do not get lung cancer). I still feel like I would need a better model of what’s happening before I cared too much about microinterventions to decrease environmental lithium.

We agree that there is something weird going on here, so let’s

As far as we can tell, the weight gain is more consistent than “sometimes patients gain weight”. Most people who take lithium at psychiatric doses gain some weight, 45/70 in one example. The weight gain tends to be quite a bit, on average 22 lbs (10kg), and about 20% of patients gain more than that. But it’s true that some people gain no weight.

Of course, 75% of the variance in modern obesity is still genetic. People who are “immune” or resistant to lithium’s effects on weight gain won’t gain much weight on psychiatric doses for the same reason they don’t gain weight on trace doses — they’re immune or resistant. And in fact we see evidence that matches this; one study says, “the patients who increased in weight during the treatment were overweight already before the start.” From this perspective, it’s not surprising that some people don’t gain weight on psychiatric doses of lithium. If someone from Mississippi has a BMI of 23, they are lean despite (presumably) high levels of exposure and will probably remain lean no matter how much you throw at them (until dead).

This is also pretty much what we would expect with the classic dose-response curve. If the inflection (EC50 or something) is at trace-level doses, then psychiatric doses blow past the inflection point and just max people out, and make them as obese as their genetics allows. For some people this is very obese, and for others it’s not obese at all.

Maybe the Silent Spring angle is correct, and lithium itself doesn’t cause obesity. Lithium is reactive, and it’s possible it forms some other compound that causes obesity, and that compound is created at higher levels in groundwater than when lithium carbonate reacts with stomach acid. 

Or it could just be that different lithium compounds lead to different levels of weight gain. Different lithium salts are used clinically, usually lithium carbonate or lithium citrate, but there are many other lithium salts, and many avenues of exposure. Has anyone ever checked to see if different salts lead to different levels of weight gain? 

As just one example, lithium bromide was used as a sedative back in the day, and is currently used as a desiccant in air conditioning and air purification systems. In a test of the oral toxicity of lithium bromide the European Chemicals Agency found that on a dose of 500 mg/kg no rats died, but “all rats gained weight by day 14 of the study.” On a dose of 2000 mg/kg, one rat died on the first day, but the rest survived, and “all surviving rats gained weight by day 14 of the study.” These doses are higher than psychiatric doses of lithium chloride, and we don’t know how much weight gain lithium bromide would cause in humans, but maybe psychiatric results aren’t a good indicator of how much weight gain can be caused by other lithium compounds.


In another comment, u/evocomp raises a number of points, the most interesting being:

The famous Pima Indians of Arizona had a tenfold increase in diabetes from 1937 to the 1950s, and then became the most obese population of the world at that time, long before 1980s. Mexican Pimas followed the trend when they modernized too. 

The Pima people, sometimes called Pima Indians, are a group of Native Americans from the area that is now southern Arizona and northwestern Mexico. In the United States, they are particularly associated with the Gila River Valley.

What evocamp describes is well-documented. The Pima seem to have had normal rates of diabetes and obesity in 1937, but both increased enormously by 1950, and by 1965 the Arizona Pima Indians had “the highest prevalence of diabetes ever recorded.” By 1970 the diabetes rate was around 40%, and by 2016, around 50%. The numbers on obesity are less specific, but it was also increasing and also very high by the 1970s.

The timeline here is very surprising — before 1970, obesity rates worldwide were almost always 10% or less. This is clearly a mystery that needs to be accounted for, so we really appreciate evocamp pointing us to this example.

If the contamination theory is right, we should be able to find evidence that the Pima were exposed to some contaminants, maybe from mining, as early as 1937. If the contamination theory is REALLY right, then we should be able to find evidence that the Pima were exposed to livestock antibiotics, PFAS, or lithium. We know that PFAS hadn’t been invented back then and antibiotics weren’t rolled out until a bit later, so that kind of leaves lithium.

Lithium was first mined in the United States in 1889. As far as we can tell, none of the early mines were in Arizona, but records from that period are spotty. There are lithium deposits in Arizona, so maybe there was a way for it to somehow get into the water supply. Around 1924, the government built a dam on the Gila river which halted the river waters, which caused the Pima to seek new water sources. Maybe it led to them switching to a water source that contained more lithium.

Or lithium could have been introduced some other way. For example, this report says, “In the Gila River Valley, deep petroleum exploration boreholes were drilled during the early 1900’s through the thick layers of gypsum and salty clay found throughout the valley. Although oil was not found, salt brines are now discharging to the land surface through improperly sealed abandoned boreholes, and the local water quality has been degraded.” Hm, that’s interesting. Did you know lithium is common in clays and oil-field brines?

The report also notes that “lithium is found in the groundwater of the Gila Valley near Safford.” There’s also this USGS report which says a Wolfberry plant (genus Lycium) “was sampled on lands inhabited by the Pima Indians in Arizona; it contained 1,120 ppm lithium in the dry weight of the plant.” To give that number some context, “an average of 150 ppm lithium in the ash and 25.8 ppm in the dry weight of all plants that were collected in both closed and open arid basins is considerably higher than the average of 1.3 ppm in dry weight reported for plants growing in a nonarid climate.” There was serious lithium contamination in this valley as early as 1974!

Also regarding the 1974 source, another USGS report says, “Sievers and Cannon (1974) expressed concern for the health problem of Pima Indians living on the Gila River Indian Reservation in central Arizona because of the anomalously high lithium content in water and in certain of their homegrown foods.”

We couldn’t have cherry-picked this example, because u/evocomp proposed it. The early and extreme incidence of obesity in the Pima is clearly a mystery that needs explaining, and sure enough, we found strong evidence for lithium contamination that fits the timeline of diabetes and obesity in the Pima. 

This seems like additional strong evidence that lithium causes obesity even at nonclinical levels. In fact, it is especially strong evidence that trace lithium alone can cause extremely high rates of obesity! There were many other groups of Native Americans living in largely similar conditions all over the country, but none of these groups were around 40% obese by 1970. It can’t be food or shelter or oppression by the US government because these things were more or less common to all groups — the difference between the Pima and other Native Americans is that the Pima were being exposed to huge doses of lithium in their food and water and other groups weren’t.

A Chemical Hunger – Part VII: Lithium


Lithium is the third element on the periodic table, the lightest metal, a hit Nirvana single, and a mood-stabilizing drug often used to treat bipolar disorder.

Lithium isn’t synthetic, of course, but it can still be an environmental contaminant. While it occurs naturally in small concentrations in groundwater, human activity might have led to serious increases over the past few decades.

Unlike the other contaminants we’ve reviewed, we don’t need to spend any time convincing you that lithium makes people gain weight: it does. Almost everyone who takes lithium at therapeutic levels gains some weight. About half of them report serious weight gain, on average 22 lbs (10kg), and about 20% of patients gain more than that. Weight gained is correlated (r = .44, p < .001) with dosage. Unsurprisingly, weight gain on lithium is related to an increase in leptin levels.

We’d love to tell you whether lithium concentrations in groundwater have increased over time. But while lithium is easy to detect, assessing lithium levels is not a part of the standard analysis of drinking water, so we don’t have reliable historical data to work with. There aren’t even EPA standards for lithium levels in drinking water.

We’d also like to tell you about how much lithium people are exposed to, and whether that has increased over the past several decades. Measuring serum lithium is relatively easy, and people who are starting lithium treatment get checked frequently to make sure that their blood levels aren’t too high. Despite this, there doesn’t seem to be any data on serum lithium levels in the general population. The NHANES data has records of how much uranium there was in your urine every year from 2000-2016, but not a single measure related to lithium. Great job, guys.

We can’t talk about trends in the groundwater or in people’s bodies directly. But what we can do is look at other trends that we would expect to be related. For example, this figure shows a graph of USGS-reported records for global lithium production since 1900:

Graph showing world lithium production from 1900 to 2007, by deposit type and year. The layers of the graph are placed one above the other, forming a cumulative total. Reproduced from USGS.

This graph is pretty telling. Almost no lithium was produced before 1950, so human activity couldn’t have been adding a meaningful amount to the groundwater back then. Serious lithium production started around 1950, which could help explain why obesity went from about 3% in 1890 to about 10% in 1980, but we see that lithium production truly spikes around 1980. While there have been a few ups and downs, production has as a rule continued to rise ever since. This graph only goes up to 2007, but USGS and other sources confirm that production has continued to increase, at about 11% per year from 2007 to 2017.

Lithium definitely ends up in the groundwater. It’s there in small concentrations naturally, but human activity adds more. In Seoul, South Korea, lithium concentrations sextuple as the Han river passes through the city’s densest districts. This is reflected not only in the river, but also in the tap water in the city — tap water from sites further along the river’s course have similarly elevated lithium levels.

This suggests that rivers pick up lithium along their course and generally have higher lithium levels as they flow downhill. This is supported by data from Austria, which shows that lithium levels in drinking water vary systematically with altitude, with higher concentrations of lithium found in districts at lower altitudes:

Lithium levels (mg/L) and altitude (meters) in Austria

We should note that a paper looking at groundwater in the United States from 1992-2003 found the opposite effect: higher levels of lithium at higher altitudes. “However,” they say, “these findings should be interpreted with caution.” We agree. There are 3,141 counties in the United States, and they only looked at data from 518. They only examined data from 15 states, most of them states at relatively low elevation. These weren’t randomly selected, either; they were the sites with the highest number of lithium samples in the years 1992-2003.

We’ve already discussed the issues that come up when you conduct analysis on a restricted range of data. Further, the Seoul data shows that lithium levels spike around urban areas. If some of the high-altitude measurements were near or immediately downstream from cities or manufacturing areas, that might make it look like higher-altitude locations have higher levels of lithium on average.


The therapeutic dose of lithium in blood serum is usually considered to be in the range of 0.8 – 1.2 mmol/L, though some sources suggest that lower doses are more effective, with the “minimum efficacious serum lithium level” being possibly as low as 0.4 mmol/L. To translate these to more familiar terms, 0.8 mmol/L is about 5600 ng/mL, and 0.4 mmol/L is about 2800 ng/mL.

That’s quite a lot. In comparison, lithium levels in groundwater rarely exceed 200 ng/mL. But perhaps surprisingly, even very low levels can have an influence on our health and mental states. One study examining data from 27 Texas counties between 1978-1987 found that rates of suicide and homicide (as well as other forms of violent and impulsive behavior) were negatively correlated with lithium in drinking water, over water lithium levels ranging from 70 to 170 ng/mL. Another study looking at various cities in Lithiuania (no relation to lithium) found a negative relationship between lithium exposure and suicide. The lithium levels in the public drinking water systems they examined ranged from 0.5 to 35.5 ng/mL, with a median level of 3.6 ng/mL. In general, reviews of this literature find that trace levels of lithium have a meaningful impact on behavior.

There’s only one randomized controlled trial examining the effects of trace amounts of lithium, but it finds the same thing. A group of former drug users (heroin, crystal meth, PCP, and cocaine), most of them with a history of violent crime or domestic violence, were given either 400 µg per day of lithium orally, or a placebo. For comparison, a normal clinical dose is 300,000 – 600,000 µg, taken two to three times per day. Even on this comparatively tiny dose, everyone in the lithium group reported feeling happier, more friendly, more kind, less grouchy, etc. over a four week period, “without exception”. 

(These former drug users didn’t have normal moods to begin with — one said “I am always extremely moody and fight with my girlfriend frequently” before he was treated — so it’s not clear if trace amounts of lithium would improve mood in everyone else. Similarly, therapeutic doses are only given to patients with bipolar disorder, and it’s not clear what the effects would be on someone without this diagnosis.)

In the placebo group, people were just about as grouchy as before. When they switched the placebo group over to lithium, these people responded in exactly the same way.

This is pretty strong evidence that even very small doses of lithium can have meaningful effects. So should we be surprised that they don’t mention any weight gain? There isn’t much data on the time course of weight gain in lithium treatment, but it seems to come on pretty fast. In one study with normal therapeutic doses, 15 bipolar inpatients gained an average of 13 lbs (5.9 kg) over six weeks. While the sample size is quite small, this tells us that sometimes a lot of weight gain can happen fast.

We don’t think this is a huge problem. The randomized controlled trial on trace exposure only lasted a couple of weeks. Even if patients were gaining weight at the same rate as patients on a therapeutic dose, they might not have noticed. The researchers didn’t intend to examine weight gain, and probably didn’t measure it. They don’t report any other side effects. Heroin, crystal meth, PCP, and cocaine all make people lose weight, so it’s possible that weight gain in the former drug users would be seen as a sign of health. It’s also worth noting that while these trace amounts do appear to have real, consequential effects, the dosage was about 1,000 times smaller than a therapeutic dose. In this situation, it’s not crazy to think that weight gain might take a few weeks or even months to manifest. We see a version of the main effect — improved mood — in this tiny dose. It seems reasonable that we might see a version of this side effect as well.

This study can give us a lower limit not only of the dosage, but by extension, of the minimum effective serum level. As a ballpark estimate, therapeutic dosages are in the range of 1,000,000 µg per day and they lead to serum levels of around 5000 ng/mL. This means the 400 µg per day dose from the randomized controlled trial would lead to a serum level of around 2 ng/mL. Other sources suggest that blood levels may end up slightly higher on low doses. For example, doses in the range of 385 to 1540 µg per day lead to serum levels around 7 to 28 ng/mL. One study testing serum lithium as a compliance marker for food and supplement intake found that giving people lithium-tagged yogurt with a dose of about 1000 µg per day over a period of six weeks led to an increase in serum levels from 6 ng/mL to 46 ng/mL.

These papers disagree on the specifics but they agree on the general picture. A serum level in the range of 10 ng/mL is enough to influence mood, and a dose of about 400 µg per day is enough to get you there. Whether lower doses have an effect is unclear, but we should certainly be interested in numbers in these ranges and up.

Trace Exposure

There aren’t any other randomized controlled trials, but if trace amounts are enough to cause obesity, then we should see relationships between trace lithium levels and obesity rates.

In Texas, a survey of mean lithium levels in public wells across 226 counties (Texas has 254 in total) found lithium levels ranging from 2.8 to 219.0 ng/mL. Now Texas is not one of the most obese states — but it tends to be more obese along its border with Lousiana, which is also where the highest levels of lithium were reported.

West Virginia and Alabama, on the other hand, are two of the most obese states in the nation. We haven’t been able to find any reliable groundwater concentration data for these states, but we can certainly note that both of them have problems with lithium contamination coming from local mining operations. And it’s not just Alabama and West Virginiaother states are facing similar contamination.

In Greece, lithium levels in drinking water range from 0.1 ng/mL in Chios island to 121 ng/mL on the island of Samos, with an average of 11.1 ng/mL. Unfortunately there’s not much data on the prevalence of obesity in Greece, but we can conduct some due diligence by checking a few of these endpoints. Samos, with the highest levels, is the obvious place to start. On Samos, 10.7% of children aged 3-12 are overweight, compared to 6.5% on the island of Corfu. A full 27% of high schoolers on Samos island were overweight in 2010, and 12.4% were obese. In comparison, about 12.5% of American high schoolers were obese in the same period.

In the Caspian Sea, lithium concentrations are 280 ng/mL. As we’ve already reviewed, some of the most obese provinces in Iran border the Caspian. The Dead Sea has concentrations even higher, at 14,000 ng/mL, but for the obvious reasons, we don’t think people are getting a lot of their drinking water from the Dead Sea. On the other hand, obesity in the West Bank is pretty high — as high as 50% in men in 2003!

Very high concentrations of lithium have also been reported in Austria. For the most part, Austria has normal amounts of lithium in its drinking water, around 13 ng/mL. But in the east, the concentrations are much higher. In the Mistelbach district, the average level of lithium in the drinking water was 82 ng/mL, and the highest single measurement was near Graz, at 1300 ng/mL. Both of these are in eastern Austria, where obesity levels are highest. Mistelbach in particular is one of the most obese districts in the country.

Chile and Argentina are the most obese countries in South America (28% each) and are two of the biggest exporters of lithium in the world. Unsurprisingly, this is reflected in their groundwater.

In northern Chile, lithium levels in the groundwater can reach levels 10,000 times higher than normal. The rivers running through many small valleys see lithium concentrations of 600 – 1600 ng/mL. The drinking water in many towns has levels up to 700 ng/mL. And in the headwaters of the Rio Camerones, lithium hits concentrations of 24,880 ng/mL. In 1980, Zaldivar reported similar levels of lithium in the groundwater, stating that they were “the highest reported in the world”. He also measured serum levels in people at these different sites, and found values ranging from 22.3 ng/mL to 85.8 ng/mL.

(“We tried to find Zaldivar to learn more about his work,” says a later paper, “but to no avail. He left Chile when Pinochet came to power and effectively disappeared.”)

In Argentina, lithium can reach up to 1000 ng/mL in drinking water, and the locals end up with a lot of exposure. At the site with the highest levels, lithium concentrations reached an average of 4,550 ng/mL in people’s urine. The highest level in someone’s urine was actually a whopping 14,300 ng/mL. The locals seem to be getting much of their lithium exposure from their tap water, as the amount of lithium in urine was correlated with the number of glasses of water consumed per day (r = 0.173, p = 0.029).

These are all freshwater levels. In seawater, lithium concentrations are reliably quite high, ranging from 100 ng/mL to over 1000 ng/mL. Now, most people are not actually drinking meaningful amounts of seawater, but if you live near the ocean, you might still be exposed indirectly.

One mystery we haven’t mentioned yet is that the Middle East is extremely obese (see map below), one of the most obese regions on earth. Jordan, Qatar, Libya, Egypt, Lebanon, and Saudi Arabia all barely trail the United States in terms of obesity, and Kuwait is actually slightly more obese than the United States, about 38% obese compared to 36% in America. These countries are very dry, and so all of them get a lot of drinking water from desalinated seawater. Saudi Arabia gets about half of its drinking water from desalination and is one of the most obese nations on earth. Kuwait built its first desalination plant in 1951, and has actually been one of the most obese countries in the world for a long time. Back in 1975, when the rate of obesity in the United States was around 10%, the rate of obesity in Kuwait was about 18%.

Share of Adults that are Obese, 2016. Reproduced from ourworldindata.org/obesity under the CC BY 4.0 license.

Desalination removes all trace elements from seawater, but because distilled water corrodes metal pipes and trace elements are important to health, the desalinated water is remineralized by blending it with 5-10% brackish water. This means that desalinated water could easily have lithium concentrations of up to 100 ng/mL. Unlike contamination in some forms of drinking water, which might vary with factors like rainfall and industrial activity, we would expect lithium levels to be reliably high in desalinated water, because they are inherent to the source.

We even have some data about lithium levels in the waters of the Persian Gulf. Near Qeshm island, at least, seawater concentrations vary somewhat by season but are usually around 300 ng/mL. This is reasonably high for seawater and much higher than the levels usually observed in groundwater. Not all of that makes it back into the water after desalination, of course, but even if only 10% got back in, 30 ng/mL is still a pretty high dose to be receiving regularly.

(If you are a Kuwaiti or Saudi desalination engineer, please contact us! This blog has gotten 15 views from Kuwait and 26 from Saudi Arabia, we know you’re out there!)

In any case, if lithium from desalinated seawater doesn’t explain why the Middle East has such incredibly high rates of obesity, then some other explanation will have to be found for this extremely striking observation.

On average, people drink about 3 liters of water per day. If they’re drinking from a normal freshwater source with 1-10 ng/mL, they’ll get a dose of about 3-30 µg / day. If they’re drinking from desalinated seawater, with concentrations of about 30-100 ng/mL, they’ll get a dose of about 90-300 µg / day. If they’re drinking from sources like those found in Texas, Greece, and Mistelbach, with concentrations of about 100-200 ng/mL, they’ll get a dose of about 300-600 µg / day. If they’re drinking from sources like those found in Graz, Chile, and Argentina, with 1000 ng/mL, they’ll easily get a dose of 3000 µg / day or more:

In comparison, therapeutic doses are in the range of 1,000,000 µg per day, but remember the randomized controlled trial showed effects at only 400 µg per day. Many people are getting doses of similar amounts from their drinking water alone. And this is assuming that they’re not also exposed to lithium in other ways.

Common Uses of Lithium

Which they probably are, because lithium has a wide variety of applications. In 2017, the USGS estimated that 48% of the global market for lithium was batteries, 26% was ceramics and glass, 7% was lubricating greases, the remainder being industrial uses like polymer production and air treatment. They also mention a couple uses like “agrochemicals”, airbag ignition, aluminum alloys, cement and concrete additives, and dyes and pigments.

You’ll remember from our review in the PFAS section that some of the most obese professions include firefighters, cooks, food workers, cleaning workers, motor vehicle operators, vehicle mechanics, transportation and material moving, healthcare support, health technicians, and some construction occupations (“Helpers, construction trades” and “Other construction and related workers”).

If you go and see your local auto mechanic, the black smears covering his hands and forearms might be engine oil. But they might also be lithium grease. This grease is ubiquitous in auto engineering, routinely applied to hinges, joints, and pivot points. It’s used in aviation and on many kinds of heavy machinery, including logging and construction equipment, trains, and tractors. It also has a number of household applications. You might put it on your garage door, or the hinges on the gate of your fence. About 7% of the global supply of lithium goes into lubricating greases of one kind or another. That’s a lot of grease.

In addition to any lithium they’re exposed to in their food and water, vehicle mechanics, truck drivers, and transportation workers are also constantly exposed to lithium grease at work. They may literally be rubbing the grease all over their hands. Like any grease, this is hard to get off your skin, in most cases requiring a special soap. Hopefully they keep it out of their eyes and mouth, but even so, it doesn’t seem like it would be great for you.

Construction workers also use lithium grease to lubricate their tools and equipment. They may be exposed through lithium added to concrete and cement. Lithium is used in agrochemicals like pesticides, though information on exactly what agrochemicals this includes is spotty. It’s possible that this explains the higher levels of obesity in cooks and food workers.

Lithium grease isn’t considered food-safe, and in theory it shouldn’t be used on food-handling equipment. In practice, however, manufacturers and restaurants don’t always follow regulations. A quick Google search reveals incidents like lithium grease being stored with food equipment, lithium grease being stored next to garlic bulbs, and lithium grease being stored above mustard.

It’s not clear if there’s a connection with healthcare professionals, firefighters, or cleaning workers. Maybe there’s a hidden lithium connection out there. But there doesn’t have to be. Lithium may just be part of the story — the rest could be explained by other contaminants, like PFAS.

And speaking of PFAS, there’s actually a connection. Lithium greases often include other substances to improve their performance, including Teflon, aka PTFE.


One strike against lithium as an explanation for the obesity epidemic is that it only stays in the body for a few days, with a half-life of 18-36 hours. The medical consensus seems to be that it probably doesn’t bioaccumulate.

But this may not be a problem, for a few reasons. First, lithium may be able to affect weight without accumulating in the body. One possible mechanism by which environmental contaminants could cause obesity is by interfering with the microbiome. If exposure to lithium changes the composition of your gut microbiota, then exposure to lithium could have serious impacts on your weight without any bioaccumulation. Even brief exposure to lithium could have long-lasting effects. And in fact there is evidence that dietary lithium affects the microbiome, at least in rats.

Second, the medical consensus might simply be wrong. While lithium is traditionally measured in the serum, this may not be the best way to evaluate bioaccumulation. “In contrast to other psychotherapeutic drugs,” says one paper on the pharmacokinetics of lithium, “Li+ is fairly evenly distributed in the body, but in tissues such as the white matter of the brain, the bones, and in the thyroid gland the concentrations per kg wet weight are about twice those in the serum.” 

A particularly interesting example is a case study of a patient who died after lithium poisoning. Researchers found that most tissue samples (“liver, spleen, kidney, lung, muscle, cardiac muscle, pancreas”) contained about the same concentration of lithium as found in the serum, in the range of 0.4-0.6 mmol/kg. But in the thyroid gland and in brain tissue (especially white matter), lithium concentrations were nearly twice as high as in serum, in the range of 0.7-0.8 mmol/kg. They suggest that this is to be expected, saying, “lithium has an increased affinity to thyroid tissue,” and, “high concentrations of lithium in brain tissue – especially in white substance – agrees with investigations that reveal the lithium elimination from brain tissue to be slow.”

This is particularly interesting not only because it seems to show evidence of bioaccumulation, but because of the particular tissues in which concentrations were found to be highest. The thyroid gland is very important to weight regulation, and so to find that lithium concentrations in that organ were second-highest in the body is very neatly in line with our expectations. Maybe it shouldn’t be a surprise, because lithium therapy is associated with thyroid disease.

For a while this was the best evidence of lithium accumulation in the brain, but a few weeks ago our friend Dr. Grace Rosen sent us this Nature paper published in March 2021. In this paper, the authors took 139 brain samples from three deceased individuals and took them to a nuclear reactor in Bavaria, where they were placed in a chamber and shot with a “well focused beam of cold neutrons with a neutron flux of ɸ = 1.2 × 1010 cm−2 s−1”. The lithium-neutron interaction gives a unique “associated coincident energy pattern”, which allowed them to measure the amount of lithium at different sites in the brains. Unsurprisingly, the patient receiving lithium therapy at the time of death had the highest concentration, but the brains of the other two individuals did as well, presumably from trace exposure.

Just like the case study above, they found that lithium was more concentrated in white than in gray matter. Additionally, the authors note that lithium concentrations were especially high in the thalamus and Brodmann Area 25. This is interesting for our purposes because Brodmann Area 25 “influences changes in appetite and sleep” and the thalamus governs “sensory relay in visual, auditory, somatosensory, and gustatory systems.” Both of these brain regions are related to eating behavior.

Sadly the authors did not examine any samples from the hypothalamus, which governs eating more directly, but this is still evidence that trace lithium bioaccumulates in brain regions that are important to the regulation of food intake and body weight.


Links for July 2021

From Wikipedia’s list of gestures:

  • “Loser, made by extending the thumb and forefinger to resemble the shape of an L on the forehead is an insulting gesture.”
  • “High five is a celebratory ritual in which two people simultaneously raise one hand and then slap these hands together.”
  • Somehow it has jazz hands (“used in dance or other performances by displaying the palms of both hands with fingers splayed”) but not spirit fingers, what gives?

The economy is endlessly fascinating, if occasionally terrifying, and a good story at the intersection of the two is this tale about the startup “Fronk”, now available for your reading pleasure thanks to an expired NDA.

You don’t often see project management fiction, but this one is amazing: Instruments of Destruction, a Star Wars fanfic 

Speaking of the fascinating details of management, consider this overview of the artillery practices of the major powers in WWII. Briefly, the Germans risked the lives of specially trained Forward Observers in a system that still came down to “guess and check”, the British assumed the earth was a perfectly flat, infinite plane and instead of worrying about accuracy, “just accepted the errors and tended to fire every available battery at the target”, and the Americans brute-forced the calculations for “a HUGE number of variations of wind/temperature, barrel wear, elevation differentials” beforehand, and then pulled out their reference materials to deliver extremely precise fire in only a couple of minutes.

The late, great Satoshi Kon passed away in 2010, at the age of 46. An interview from him in 2007 that had previously never fully seen the light of day has just been published on the substack Something Good

Before there was the replication crisis, there was the 0.1 second crisis, which rocked science and philosophy for most of the 19th century. Then, everyone stopped caring and most of us forgot, but this crisis may have created modernity. Also, for better or worse, it helped give us statistics.

Eighteen-Year-Old Tunisian Swimmer Ahmed Hafnaoui Claims Gold in the 400m Freestyle at Tokyo Olympics, Commentators Struggle to Keep Up

Very fond of Visa’s charming twitter thread on Samantha Smith, “a 10 year old American girl from Maine, wrote a letter to the new leader of the USSR, Yuri Andropov, asking him why he wanted to conquer the world, and could we please have peace instead.”

Here at SLIME MOLD TIME MOLD we are very interested in alternative approaches to education, so we were very interested to see this piece on teaching the Iliad to Chinese teenagers. To complete the cycle, we would like to see a Chinese author write a piece on teaching Romance of the Three Kingdoms to American teenagers.

Also in unusual educational approaches, see welcome to class. We have no idea if this would work out well but it gets lots of points for being original. If anyone tries this or has tried it, please let us know. (h/t commenter Noah)

Our ongoing series A Chemical Hunger (Part I here) inspired reddit user pondgrass to create /r/spudbud/, advocating “The World’s most Legible Diet” based on a simple premise: “Eat Nothing but Potato”

In case you missed it, one of the authors of this blog won third place in the Astral Codex Ten Book Review Contest for a book review of On The Natural Faculties by Galen of Pergamon. Thanks Scott for hosting, and thank you to everyone who voted in the contest! Also, congratulations to the first and second place winners, Lars Doucet and Whimsi!

A Chemical Hunger – Part VI: PFAS


Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals that are used to make a wide variety of everyday products, including food packaging, carpets, rugs, upholstered furniture, nonstick cookware, water-repellant outdoor gear like tents and jackets, firefighting foams, ski wax, clothing, and cleaning products. Many are also used in industrial, aerospace, construction, automotive, and electronic applications.

The PFAS family is enormous, containing over 5,000 different compounds. But only a couple of these compounds are well-studied. The rest remain rather mysterious. Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are two of the original PFAS, are especially widespread in the environment, and we tend to have the most information on them.

PFAS are practically indestructible. They repel oil and water and are heat-resistant, which is part of why they have so many applications, but these features also ensure that they degrade very slowly in the environment, if they degrade at all. Short-chain PFAS have half-lives of 1-2 years, but longer-chain equivalents like PFOS are stable enough that we haven’t been able to determine their half-life. As a result, they stick around in the environment for a very long time, and soon make their way into soil and groundwater. The full picture is complicated, but there’s evidence that they accumulate in rivers as they flow towards the ocean.

They not only stick around for a long time in the environment, they stick around for a long time in your body. If you’re reading this, there’s probably PFAS in your blood. A CDC report from 2015 found PFAS in the blood of 97% of Americans, and a 2019 NRDC report found that the half-life of PFAS in the human body is on the order of years. They estimate 2.3 – 3.8 years for PFOA, 5.4 years for PFOS, 8.5 years for PFHxS, and 2.5 – 4.3 years for PFNA. “PFOS, PFNA, PFHxS, and related PFAS,” they write, “are known to bioaccumulate in the bodies of people of all ages, even before birth.”

How do these chemicals get into our bodies? Every route imaginable. “People are concurrently exposed to dozens of PFAS chemicals daily,” the NRDC report explains, “through their drinking water, food, air, indoor dust, carpets, furniture, personal care products, and clothing. As a result, PFAS are now present throughout our environment and in the bodies of virtually all Americans.” Looking at one map of PFAS measurements, we see that PFAS has been detected at military sites in 49 states (no measurements given for Hawaii) and in drinking water in Utqiagvik, Alaska, the northernmost incorporated place in the United States. Unfortunately, only a few states have done comprehensive testing.

This exposure isn’t just limited to humans. There’s bioaccumulation in the remote lichen-caribou-wolf food chain in northern Canada, and in part of the Arctic Ocean, with animals higher in the food chain showing higher concentrations of PFAS in their bodies. 

If we look at the history of PFAS (below), we see that the timeline for PFAS introduction lines up pretty well with the timeline for the obesity epidemic. PFAS were invented in the 1930s, 40s, and 50s, and were rolled out over the next couple decades. This gave them some time to build up in people’s bodies and in the environment. By the 1980’s many types, including some new compounds, were in circulation. In the 2000s, some of them began to be banned, but many of them are still widely used. After all, there are more than 5,000 of them, so it’s hard to keep track.

Discovery and manufacturing history of select PFAS.

A study from the Red Cross worked with blood donor data and measured serum levels in samples from 2000-2001 and plasma levels in samples from 2006, 2010, and 2015. In general, they found serious declines in serum levels of the PFAS they examined. For example, the average PFOS concentrations went from 35.1 ng/mL to 4.3 ng/mL, a decline of 88%, and PFNA concentrations went from 0.6 ng/mL to 0.4 ng/mL, a decline of 33%. The National Health and Nutrition Examination Survey (NHANES) data from the same period matches these trends pretty closely.

These studies show that levels of PFAS in American blood are declining, but they’re only looking at the PFAS that we already know are declining. Many of these PFAS are no longer in production. PFOS and PFOA, among other compounds, were phased out in the US between 2006 and 2015. But new compounds with similar structures were brought in to replace them. The companies that make these compounds say that the new PFAS are safer, but unsurprisingly this is very controversial.

Notably absent from both the Red Cross and the NHANES data is PTFE. This is somewhat surprising given that it is the original PFAS, and it is still in production. Granted, many sources claim that PTFE is extremely inert — including the paper Polytetrafluoroethylene Ingestion as a Way to Increase Food Volume and Hence Satiety Without Increasing Calorie Content, which goes on to argue that we should replace 25% of our food with Teflon (PTFE) powder so that we feel more full while eating fewer calories, which they say will help us make “the leap into the realm of zero calorie foods.” Personally, we’d stick to celery.

Maybe PTFE really is that inert. (We find it a little hard to believe. “Word was that the compounds were inert,” said one scientist of his choice not to study PFOA and PFOS in 2000.) Either way, the safety research on these substances is pretty ridiculous. Usually the exposure period is very short and the dose is extremely high. This may be relevant to exposure for industrial workers, but it doesn’t tell us much about the long-term effects of relatively low doses on the rest of us.

Advertisement for the Happy Pan, a Teflon-coated pan from the 1960s.

In one study they gave monkeys various amounts of PFOS for 182 days, and found “significant adverse effects” only in the 0.75 mg/kg/day dose group. Effects in this group included “mortality in 2 of 6 male monkeys, decreased body weights, increased liver weights, lowered serum total cholesterol, lowered triiodothyronine concentrations (without evidence of hypothyroidism), and lowered estradiol levels.”

This is interesting, but there are some problems. First of all, 0.75 mg/kg/day is an insanely high dose. Serum concentrations in the 0.15 mg/kg/day dosage group were 82,600 ng/mL for males and 66,800 ng/mL for females. The comparable rate in human blood samples is about 20-30 ng/mL. Second, 182 days is not a very long or realistic exposure period for most humans.

At these extremely high, short-term doses, weight loss is actually a relatively common side effect. This is the opposite of obesity, of course, but it does suggest that PFAS can affect body weight.

The type of exposure might make the difference. Mice have very different developmental trajectories than we do, but mice exposed to low doses of PFOA in-utero had higher body weights at low exposures, while mice exposed to high doses as adults lost weight. ”Exposure during adulthood was not associated with later-life body weight effects,” they write, “whereas low-dose developmental exposure led to greater weight in adulthood and increased serum leptin and insulin levels. Animals exposed to higher doses of PFOA, on the other hand, had decreased weight.” Note also that while half-life of PFOA in humans is about 3.8 years, in mice it is around 18 days.

A study of 665 pregnant Danish women, recruited in 1988–1989 with the researchers following up with the children 20 years later, found that in-utero PFOA exposure was related to greater BMI and waist circumference in female but not in male children. There are some issues with multiple comparisons — they measured more than one PFAS and they subdivided by gender, both of which are degrees of freedom — but the effects are strong enough to survive reasonable corrections for multiple comparisons, and are consistent with the results from mice, so let’s mark this one down as “suggestive”.

Other studies have found small but reliable effects where male babies, but not female babies, were a few grams lighter at birth when their mothers had higher serum PFOS levels. Again this study suffers from multiple comparison issues, but again it is relatively consistent with animal research.

It doesn’t seem likely that the effect in humans can be exclusively prenatal, however, because we know that people often gain weight when they move to a more obese country. There’s pretty good evidence that different environments are exposing you to different levels of contamination, and that it makes a difference.

Your drinking water is not the only way to be exposed. Many foods are contaminated with PFAS. PFAS are also found in clothes, carpets, and upholstered furniture, so you could be exposed even if there’s no PFAS in your diet. If your favorite beer or pasta sauce is bottled at a factory where the water source is high in PFAS, you’ll be exposed even if your own drinking water is uncontaminated. And since most major brands are bottled in more than one location, there wouldn’t even be a reliable by-brand effect—you’d need to track it by factory.

A better way to do this comparison might be between countries. In fact, we see what appears to be a pattern: There’s more PFAS in tapwater in the United States than there is in tapwater in China, and there’s more PFAS in tapwater in China than there is in tapwater in Japan. The pattern isn’t perfect, however: There’s even more PFAS in tapwater in France than in the United States, and more in Japan than in Thailand.

Occupational Hazard

One place you might get a lot of reliable exposure, though, is at your job. Looking at the uses of PFAS, we see that they’re common in:

  • Firefighting foams
  • Cookware and food packaging
  • Paints and varnishes
  • Cleaning products
  • Automotive applications, including components in the engine, fuel systems, and brake systems, as well as automotive interiors like stain-resistant carpets and seats
  • Healthcare applications, both in medical devices like pacemakers and in medical garments, drapes, and curtains

This suggests that if PFAS are linked to obesity, we should expect to see disproportionate levels of obesity in:

  • Firefighters
  • Food workers (especially cooks)
  • Construction workers
  • Professional cleaners
  • Auto mechanics and others who work closely with vehicles
  • Medical professionals who work closely with medical devices and garments / drapes / curtains, though probably not medical desk jobs.

In the 2000’s, the Washington State Department of Labor and Industries surveyed more than 37,000 workers. They found that on average 24.6% of their sample was obese, which we can use as our baseline. The rate of obesity in “protective services”, which includes police, firefighters and emergency responders, was 33.3%. Among cleaning and building services workers, 29.5% were obese. Truck drivers were the most obese group of all, at 38.6%, and mechanics were #5 at 28.9% obese. Health service workers (excluding doctors and nurses) were 28.8% obese. On the other hand, only 20.1% of food preparation workers were obese, and only 19.9% of construction workers:

Table 1: Washington State Department of Labor and Industries Data, 2003-2009

We can also look at national data from US workers in general. Looking at data between 2004 and 2011, we see that the average rate of obesity went from 23.5% in 2004 to 27.6% in 2011, and was 26.2% on average in that range. Unfortunately they break these numbers down by race, so we have to look at each race separately. 

When we look at the occupations of interest for non-hispanic white adults, we see that 30.4% of firefighters, 32.0% of cooks, 35.1% of food processing workers, 29.7% of building cleaning workers (and for some reason a whopping 37.3% of cleaning supervisors), 39.2% of motor vehicle operators, 27.7% of vehicle mechanics, 36.3% of people working in healthcare support, and 29.8% of health technicians were obese (see Table 2 below). Some construction occupations were slightly less obese than average (“Construction trades workers” at 25.0%), and some were much more obese than average (“Helpers, construction trades” and “Other construction and related workers” at 31.2% and 38.6%, respectively).

 Table 2: National Health Interview Survey Data, Non-Hispanic White Adults, 2004-2011

For non-hispanic white adults, individuals with the highest age-adjusted prevalence of obesity were motor vehicle operators, “other construction and related workers”, law enforcement workers, and nursing, psychiatric, and home health aides. It’s not clear why law enforcement workers are in there, but it’s pretty remarkable that the PFAS explanation can predict the other three.

Patterns are largely similar for the other racial groups. Among black female workers, the occupations with the highest age-adjusted prevalence of obesity were health care support (49.2%), transportation and material moving (46.6%), protective service (45.8%), personal care and service (45.9%), community and social services (44.7%), food preparation and serving (44.1%), and health care practitioners and technicians (40.2%). Some of these don’t seem to fit — why is “transportation and material moving” in there? — until you realize that “transportation and material moving” includes air traffic controllers, pilots, and other transportation workers, and you remember that PFAS-based firefighting foams are still widely used at airports.

Overall when we look at professions we would expect to have high exposure to PFAS, we see that workers in those professions are more obese than average. When you look at the professions with the highest rates of obesity, we see that most of them are related to mechanical work, healthcare, cleaning, or firefighting, all professions that have disproportionate exposure to PFAS on the job.

If on-the-job PFAS exposure really does lead to obesity, we should also see higher levels of obesity in people who work with PFAS directly. This is exactly what we find.

In 1993 and 1995, 3M conducted an internal study of PFOA exposure in a group of production workers. In the mid 90s, about 20-25% of the population was obese. About 40% of these workers were obese in 1993, and about 48% were obese in 1995.

Looking closer, they found that the group with the highest amount of PFOA contamination also had the highest BMI. The authors even take a moment to draw attention to this point. “It should be noted,” they say, “that all five employees in 1995 with serum PFOA levels [30,000 ng/mL] had BMIs 28.” BMI was slightly correlated with PFOA contamination (r = .11), though with only 111 people, the correlation was not significant. The authors seem unaware of the implications of this, however, and treat BMI as a confounder for other analyses.

Of course, this was not a normal group. They had insanely high serum PFOA levels, up to 115,000 ng/mL, though a few people had no PFAS in their blood.

A later 3M paper published in 2003 looked at serum levels of both PFOA and PFOS. In these data, there is a very clear relationship between PFOS levels and BMI. Men in the lowest quartile of PFOS exposure (mean 270 ng/mL) have an average BMI of 25.8, while men in the highest quartile of PFOS exposure (mean 2,690 ng/mL) have an average BMI of 27.2. The effect is even more pronounced for female employees. Women in the lowest quartile of PFOS exposure (mean 70 ng/mL) have an average BMI of 22.8, while women in the highest quartile of PFOS exposure (mean 1,510 ng/mL) have an average BMI of 28.7. They don’t report a correlation, but they do say, “the fourth quartile had significantly higher mean values than the first quartile for … BMI.”

Dose-Dependent Relationships in the Population

This is somewhat confusing, however, because PFAS serum levels aren’t all that correlated with BMI in the general population. This paper on 2003-2004 NHANES data (a large sample intended to be nationally representative) looked at PFAS concentrations in a final sample of 640 (down from 2,368) people and found only weak evidence of PFAS having an influence on body weight. The strongest relationship they report is for PFOS levels among male participants over 60. Some analyses even report significant negative relationships between PFAS levels and BMI.

Both of these approaches, however, are looking at coefficients in regression equations where they have included many covariates. While in principle this technique can be used to adjust for confounders, in practice the resulting estimates are difficult to interpret. Without a strong model of the causal structure involved, it’s hard to know what the relationship between two variables means when it is adjusted by 20 other variables. Including covariates in an unprincipled way can even cause estimates of an effect to reverse direction. It’s not a panacea, and in fact it can be misleading.

The NHANES data is publicly available, so we decided to check for ourselves. Sure enough, PFOS levels aren’t correlated with BMI — though they are correlated with both weight and height individually.

There’s an issue with looking at simple correlations of PFAS levels, of course, because they are highly correlated with one another. If you have high serum levels of one PFAS, you probably also have high serum levels of another. This means that they may interact or mask one another’s effects in potentially complicated ways.

For example, let’s look at PFHS. A quick correlation shows that serum PFHS levels are negatively correlated with BMI. As far as we can tell, no one has ever reported this, but it’s right there in the NHANES data. In the 2003-2004 data, the correlation is r = -0.090, p < .000045. This effect is small but extremely robust — people exposed to more PFHS are slightly skinnier.

PFHS levels are also correlated with PFOS (r = .29). When we look at the relationship between PFOS and BMI controlling for PFHS, the relationship between PFOS and BMI becomes significant, p = .035, showing that people with higher PFOS exposure are more obese.

“Just wait a minute,” you say, “that’s barely significant at all! How many relationships did you look at before you found that, anyways? This sounds a lot like p-hacking.” We had the same concern, which is why it’s great that we have NHANES data from many different years that we can use to validate this result.

We can go backwards to the 1999-2000 data (we can’t use the 2001-2002 data because the PFAS data for that year are missing ID numbers) where we find a significant relationship between PFOS and BMI controlling for PFHS, p = .008. We can also go forwards to the 2005-2006 data, where we also find a significant relationship between PFOS and BMI controlling for PFHS, p = .007. It seems to be pretty reliable. Now, it’s not a huge effect — the influence of PFOS is only about a half a point of BMI for the average person. But that’s a lot more than nothing.

This isn’t the place for doing a full analysis of the relationships between the different PFAS and how they interact. The NHANES doesn’t even measure every kind of PFAS, so we wouldn’t be able to find every relationship. The point is simply that the influence on BMI may be more complicated than a simple association, and this is proof that at least one of these surprises is hiding in publicly available data.

Why is the association so apparent in the 3M workers but harder to detect in the general population? It has to do with the issues with dose-dependence that we identified earlier. The 3M studies are the sort of samples where we should be able to detect a dose-dependent effect, if one exists. The NHANES data, however, is the sort of sample where it should be hard to detect a dose-dependent effect, even if a strong one exists.

The NHANES data is intended to be nationally representative, while the 3M data is looking at a few hundred people at a couple factories. As a result, the 3M sample is much less diverse than the NHANES sample, which means that it will also be less genetically diverse. Since there’s less genetic diversity, genetics will have less influence on people’s body weight. With less variation coming from people’s genetics, there’s less noise for the dose-dependent signal to be lost in, and it will be easier to detect. Looking at other populations that are not so diverse — like pregnant Danish women between 1996 and 2002 or newborn deliveries at the Johns Hopkins Hospital in Baltimore, MD — we also find that PFAS levels are related to BMI. Similarly, a study from 2021 found a dose-dependent relationship between PFOA — but not PFOS — and obesity in children living in the United States.

The 3M studies are also looking at a much wider range of dosages than are observed in the general population. In the 2003-2004 NHANES data, the range of serum PFOA levels was 0.1 to 77 ng/mL, and the range of serum PFOS levels was 0.3 to 435 ng/mL. In comparison, the range of serum PFOA levels in the 1993 and 1995 3M study was 0 to 115,000 ng/mL. In the 2003 3M study, the range of serum PFOA levels was 10 to 12,700 ng/mL and the range of serum PFOS levels was 40 to 10,060 ng/mL. Analyzing a less restricted range makes the correlation more accurate, which is what we see in the 3M data.

In the 3M sample, some employees participated in both 1993 and in 1995, and PFOA serum levels were highly correlated among the 68 employees who appeared in both samples (r = .91, p = 0.0001). This means that levels of exposure were extremely consistent across the two years between the measurements, possibly because people’s level of exposure was related to the role they had in the production process. Normally, it takes a while for someone’s weight to catch up to the dose of a compound that influences their weight — this is clear from studies of weight gain in people taking antipsychotics. But the 3M employees had serum levels that had been stable for many years. We should expect this to reduce noise and make the correlation between serum levels and BMI more accurate, and it appears to have done just that.

Other Considerations

Dose-dependence is strong evidence that PFAS are a contributor to the obesity epidemic. Is there any other lingering evidence?

One paper looking at a dieting study from 2003 found that PFAS concentration wasn’t related to body weight or weight lost during dieting. However, it was associated with greater weight regain over the months following the diet. People with the highest plasma concentrations of PFAS gained back about 8.8 lbs (4 kg), while people with the lowest plasma concentrations of PFAS gained back only about 4.4 lbs (2 kg). This is a relatively minor but statistically significant difference, and it is consistent with an account where these compounds don’t simply cause weight gain, but damage the lipostat and lead people to defend a higher body weight.

West Virginia is usually an obesity outlier. It’s the #1 or #2 most obese state (depending on your source), and it’s been one of the most obese states for as long as we’ve been keeping statewide records for this sort of thing. But it’s also high in elevation (19th highest after Washington state and Texas) and pretty far upriver. Most of the neighboring states — Ohio (#11), Pennsylvania (#24), Maryland (#26) and Virginia (#28) — are not nearly so obese.

DuPont built the very first Teflon (PTFE) plant in Parkersburg, West Virginia. By 1948, the plant was manufacturing 2 million pounds of PTFE per year. Some sources claim that they were using PFOA as part of the manufacturing process by 1951. Unfortunately we don’t have obesity data for West Virginia in 1948 or 1951, or the years immediately following. But we can note that many years later, West Virginia was at the center of the first legal action surrounding PFAS.

This kind of legal action has come about because PFAS have been linked to a variety of harmful health effects, including cancer, thyroid hormone disorder, and immune system effects. As a result, governments have begun to regulate and sometimes ban these compounds. New York, Maine, and Washington state have all banned or restricted PFAS in various ways, and in 2021, Vermont and Connecticut both passed legislation to remove PFAS from firefighting foams, food packaging, and other consumer products. There’s even some international regulation — PFOS have been regulated under the international Stockholm Convention since 2009, which was expanded to include PFOA and PFOA-related compounds in 2019. There was also a movie about PFAS bans, starring Mark Ruffalo.

Some people are hopeful that these bans will form a sort of natural experiment that can allow us to see what happens when PFAS are removed from the environment. Unfortunately, we’re less optimistic. First off, these compounds are very durable, so even if we ban them, huge doses will still be in the environment. Second, statewide bans won’t keep these substances from entering the state in food or goods produced elsewhere. 

Finally, these bans restrict only a tiny percentage of all PFAS. As a recent report from the European Commission notes, “The ban of widely used long-chain PFAS has led to their substitution with a large number of shorter chain PFAS. Several of these alternatives are now under regulatory scrutiny in the REACH Regulation because of the concern they pose for the environment and for human health.” Efforts to limit exposure to PFAS are a great idea, but the continued use of short-chain PFAS limits the usefulness of bans as natural experiments to determine the role of PFAS in obesity.