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 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.
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:
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:
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:
In fact, here’s a hasty photoshop with extended percentile categories:
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 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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.”
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.
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…
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.)
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:
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:
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.
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.
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.
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.
In addition, seed oils face the same problems faced by every food-based explanation for the obesity epidemic.
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.
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:
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 .
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 .
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 .
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 .
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.
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”.
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.
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.
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.
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.
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:
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.)
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:
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.
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!
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.
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.
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.
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.
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.
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.
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:
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.
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:
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.
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.
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.
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 Virginia — other states are facing similar contamination.
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.
(“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%.
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.
(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.
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.
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.”
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.
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.
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.
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 IncreasingCalorie 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.
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.
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:
Cookware and food packaging
Paints and varnishes
Automotive applications, including components in the engine, fuel systems, and brake systems, as well as automotive interiors like stain-resistant carpets and seats
This suggests that if PFAS are linked to obesity, we should expect to see disproportionate levels of obesity in:
Food workers (especially cooks)
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:
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).
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.
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.
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.
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.
In his book The Hungry Brain, neuroscientist Stephan Guyenet references a 1965 study in which volunteers received all their food from a “feeding machine” that pumped a “liquid formula diet” through a “dispensing syringe-type pump which delivers a predetermined volume of formula through the mouthpiece.” He devotes about three pages to the study, describing it like so:
What happens to food intake and adiposity when researchers dramatically restrict food reward? In 1965, the Annals of the New York Academy of Sciences published a very unusual study that unintentionally addressed this question. …
The “system” in question was a machine that dispensed liquid food through a straw at the press of a button—7.4 milliliters per press, to be exact (see figure 15). Volunteers were given access to the machine and allowed to consume as much of the liquid diet as they wanted, but no other food. Since they were in a hospital setting, the researchers could be confident that the volunteers ate nothing else. The liquid food supplied adequate levels of all nutrients, yet it was bland, completely lacking in variety, and almost totally devoid of all normal food cues.
The researchers first fed two lean people using the machine—one for sixteen days and the other for nine. Without requiring any guidance, both lean volunteers consumed their typical calorie intake and maintained a stable weight during this period.
Next, the researchers did the same experiment with two “grossly obese” volunteers weighing approximately four hundred pounds. Again, they were asked to “obtain food from the machine whenever hungry.” Over the course of the first eighteen days, the first (male) volunteer consumed a meager 275 calories per day—less than 10 percent of his usual calorie intake. The second (female) volunteer consumed a ridiculously low 144 calories per day over the course of twelve days, losing twenty-three pounds. The investigators remarked that an additional three volunteers with obesity “showed a similar inhibition of calorie intake when fed by machine.”
The first volunteer continued eating bland food from the machine for a total of seventy days, losing approximately seventy pounds. After that, he was sent home with the formula and instructed to drink 400 calories of it per day, which he did for an additional 185 days, after which he had lost two hundred pounds —precisely half his body weight. The researchers remarked that “during all this time weight was steadily lost and the patient never complained of hunger.” This is truly a starvation-level calorie intake, and to eat it continuously for 255 days without hunger suggests that something rather interesting was happening in this man’s body. Further studies from the same group and others supported the idea that a bland liquid diet leads people to eat fewer calories and lose excess fat.
This machine-feeding regimen was just about as close as one can get to a diet with zero reward value and zero variety. Although the food contained sugar, fat, and protein, it contained little odor or texture with which to associate them. In people with obesity, this diet caused an impressive spontaneous reduction of calorie intake and rapid fat loss, without hunger. Yet, strangely, lean people maintained weight on this regimen rather than becoming underweight. This suggests that people with obesity may be more sensitive to the impact of food reward on calorie intake.
In 1965, some scientists locked people in a room where they could only eat nutrient sludge dispensed from a machine. Even though the volunteers had no idea how many calories the nutrient sludge was, they ate exactly enough to maintain their normal weight, proving the existence of a “sixth sense” for food caloric content. Next, they locked morbidly obese people in the same room. They ended up eating only tiny amounts of the nutrient sludge, one or two hundred calories a day, without feeling any hunger. This proved that their bodies “wanted” to lose the excess weight and preferred to simply live off stored fat once removed from the overly-rewarding food environment. After six months on the sludge, a man who weighed 400 lbs at the start of the experiment was down to 200, without consciously trying to reduce his weight.
This study is especially meaningful for Guyenet because he favors a “food reward” explanation of the obesity epidemic, where obesity is at least partially the result of really delicious foods that make us want to eat a lot of them. He says that foods like “ice cream, brownies, french fries, chocolate, and bacon” have the ability to “powerfully drive cravings, overeating, and eventually, deeply ingrained unhealthy eating habits.” On the other hand, foods like “fruit, vegetables, potatoes, beans, oatmeal, eggs, plain yogurt, fresh meat, and seafood“ are “still enjoyable but they don’t have that intensely rewarding edge.”
First of all, how dare he say that about potatoes. But second, while this study is not exactly the cornerstone of Guyenet’s argument, it does seem especially important evidence for the food reward perspective.
We wanted to review this study when we were writing A Chemical Hunger, but we couldn’t find the original paper, and since we couldn’t confirm the results for ourselves, we decided not to include it in the piece.
But now, reader Sam Marks (thank you Sam!) has found us a copy of the study! Finally able to review it, we offer this special interlude for your reading pleasure. (If you want to read the study for yourself, email us and we would be happy to send you a copy.)
Before we review, however, we want to offer our initial impressions. This study was performed in 1965, which means that it was decidedly pre-obesity-epidemic. In Part I, we review the evidence that obesity rates were stable until about 1980, when they suddenly started increasing. We think that this is evidence that modern obesity occurs for different reasons than historical obesity did. The people in this study probably were not obese for the same reason(s) people are obese today, so the same rules may not apply.
In addition, studies from 1965 are not known for being super reliable. Back in 1965, sample sizes were small, teams had limited resources, and statistical analyses were more on the casual side.
STUDIES IN NORMAL AND OBESE SUBJECTS WITH A MONITORED FOOD DISPENSING DEVICE
Let’s take a look and see what we can learn about a diet of “homogeneous nutritionally adequate formula emulsion” (henceforth “nutrient sludge”). To give you the full experience, we will begin by covering the paper blow-by-blow.
The authors begin by describing how they are jealous of experimental psychologists, who back in those days were still putting rats in Skinner boxes. These animal researchers could get “detailed and accurate information concerning rate of food ingestion, size of meals and intervals between feedings” by giving rats a lever which would dispense one food pellet when pushed, and using electronic monitoring equipment to record each time the lever is pressed.
To this end, the authors developed a device of their own, which dispenses food to human subjects at the press of a button, and secretly records the date and time of each “delivery” on a device “in a room remote from the subject who is kept unaware of its existence.”
The feeding machine … consists of a reservoir containing a liquid formula diet. The formula mixture is constantly mixed by a magnetic stirrer.
Whenever the button is pressed, 7.4 ml. of formula are delivered directly into the mouth of the subject by the punp. [sic]
This is hilarious.
They don’t say much about the nutrient sludge, only that it was “provided as ‘Nutrament’ through the courtesy of Warren M. Cox, Mead Johnson Research Laboratories, Evansville, Ind.” and that “carbohydrate contributed 50 per cent of the calories, protein 20 per cent and fat 30 per cent.“
Let’s meet the participants. In this study, they tested the feeding device (yikes) on both “normal-weight” and obese people.
The first normal-weight subject studied was well suited to the feeding machine because a severe deformity of his mouth made ingestion of normal food difficult. … His daily calorie intake did not vary appreciably, averaging 3075 ± 438 (S.D.) calories per day.
A healthy 20 year-old volunteer subject also readily maintained his body weight during a nine-day period on the machine consuming an average of 4430 calories per day.
The results obtained in one individual, a 27-year-old man, are shown in FIGURE 4. Initially, he weighed 400 pounds.
The response to feeding by machine of another obese subject, a woman aged 36, is shown in FIGURE 5. [Figure indicates that she was just under 400lbs at start]
So, first of all, these subjects do not seem like typical patients. One has “a severe deformity of his mouth” and ate 3075 calories of nutrient sludge every day for sixteen days. One is a 20-year-old who ate 4430 calories of nutrient sludge every day for nine days. And the two obese patients were both around 400lbs at the start — not typical cases by any measure!
If both these obese people were six feet tall (unlikely), their BMI at the start of the study would have been 54.2!!! Recall that obese is a BMI of 30, and extreme obesity / morbidly obese is a BMI of 40 or greater. This BMI chart we found from the NIH only goes up to 54. These people were literally off the charts, and again that’s assuming they are both six feet tall. If they were shorter, then their BMI would be even higher.
The sample size here is FOUR. If we only count the obese patients, the sample size is merely two. Except maybe not, because the discussion says, “The data show that the five obese subjects [emphasis added] ate only a small fraction of their daily calorie requirements by machine” (the end of p669 also suggests five obese subjects). We aren’t given any specifics about these “three additional obese subjects” except that they “showed a similar inhibition of calorie intake when fed by machine.” In any case, we’re given very little case information about any of the seven subjects, and at the end of the day this study has two control subjects and five obese subjects (though it’s not even an experiment).
We understand the benefits of case studies, but this one doesn’t seem particularly likely to generalize.
Making a long story short, both “normal-weight” participants maintained their healthy weight effortlessly on a diet of nutrient sludge. The obese participants ate only a couple hundred calories per day and effortlessly lost huge amounts of weight. Somewhat strangely, the treatment periods were very different. The obese male participant lost 200 lbs after 252 days on various forms of the nutrient sludge diet, while the obese female participant was there for only 24 days and lost 23 lbs. Both patients were very obese to start with, but this rate of weight loss still seems really extreme.
This study is from 1965, and realistically, the data are from a few years earlier. As mentioned above, we think that the abrupt increase in obesity rates starting in 1980 is evidence that modern obesity occurs for different reasons than historical obesity did. The aetiology of these cases of obesity is almost certainly different from the obesity we see today, and even today, very few people are 400lbs.
Probably these people were obese for a different reason than your local bus driver with a BMI of 31. For example, they might have had a brain tumor that left their hunger response largely intact, but led them to compulsively overeat a particular food. This would explain why “the patient never complained of hunger or gastrointestinal discomfort” despite spending 26 days eating nothing but ~275 calories of nutrient sludge a day.
Even if the aetiology were the same as modern obesity, there are a few huge problems, the biggest of which is THE CUPS!!!
About the obese male participant, they say:
To determine whether the bizarre feeding situation was by itself inhibiting his food intake, he was asked (after 18 days on the machine) to feed himself the same formula ad libitum using a pitcher and cup. In the third section of FIGURE 4 it can be seen that his calorie intake increased on this program to about 500 per day [from 275 ± 57 calories per day]. He was returned to machine feeding after another 26 days and, again, spontaneous food intake dropped to a lower level.
For the obese female participant:
Her spontaneous food intake over a 12-day period of observation also was extraordinarily low, 144 ± 91 calories per day. During this time she lost 23 pounds. When she took the same formula by cup, calorie intake increased to 442 ± 190.
(Also weird: on days 11 and 17, this participant appears to have eaten about zero calories?)
For one participant, calorie intake nearly doubled when he went from drinking from the syringe-pump to using a pitcher and cups. For the other, calorie intake tripled. This makes it pretty clear that the nutrient sludge itself was only driving part of the effect (or that the measurements are hopelessly imprecise). Contra Guyenet, the palatability of the sludge doesn’t seem to be the main force at play here. In addition, this is super weird.
It’s also very strange that the healthy 20 year-old volunteer subject consumed 4430 calories of nutrient sludge per day (this was on average — one day, he consumed almost 5000 calories of the sludge). Their only explanation for this was, “the subject remained physically active throughout this period,” but this is still a LOT of calories! The FDA recommendation for an “Active” 20-year-old man is a mere 3,000 calories (same for “Very Active” from the NIH), and this guy was slurping down almost 48% more calories per day than the recommended amount, for nine days! The other “normal-weight” participant also consumed a lot of sludge, 3075 calories per day on average, and there’s no indication he was especially active.
This seems to argue against the idea that the sludge was all that unappetizing! The authors describe it as “bland”, but never suggest that it was intended to be unpalatable. The detail they give is, “carbohydrate contributed 50 per cent of the calories, protein 20 per cent and fat 30 per cent. The formula contained vitamins and minerals in amounts adequate for daily maintenance.” Maybe it was delicious, and the results from the two lean participants certainly seem to suggest that this is a possibility. Ask yourself this: would YOU eat 4430 calories of nutrient sludge per day if it were “bland”?
If the sludge truly was bland, this appears to be reasonably strong evidence against the food reward hypothesis! Taking this argument at face value, it seems like feeding healthy young men nothing but nutrient sludge is an extremely reliable way to make them overeat by 1000-2000 calories per day.
Alternately, the measurements could be way off for some reason. What seems more likely, that two normal-weight men decided to eat 3075 and 4430 calories of nutrient sludge every day for more than a week, and that five morbidly obese patients lost about 1 pound every day for up to 200 days, or that someone made a mistake and wrote down some of these numbers wrong? Even if the research team were 100% reliable, how good was the technology in 1965? How reliable were the pump and the printing timer? Small differences in the pump delivery doses could easily be responsible for the weird results we see. Not to sound paranoid, but was this guy really exactly 400 lbs to start, and did he really lose exactly 200 lbs over the course of the diet? Does eating 400 calories per day for 252 days pass a basic sanity check?
Also for comparison, Figure 4 (reproduced below) appears to show that the 400-lbs-obese man was eating only 2000 kcal/day of a “Regular Hospital Diet” for the eight days before going on a nutrient sludge diet. Was this how much he normally consumed? It seems weird. Guenet says that 275 calories was “less than 10 percent of his usual calorie intake”, suggesting the man’s normal diet was at least 2750 calories per day, but we don’t see where he’s getting that number from. If either of these numbers are right, that means that the 400 lb man had a normal calorie intake that was less than both of the lean subjects.
In any case, another serious oddity is that he started losing weight as soon as he entered the hospital, at a rate of about one pound per day — eight days before he was put on the nutrient sludge diet! That kind of makes it seem like something else is causing the rapid weight loss.
Of course, this is just one study. In fact, it’s merely the first of many! In the section we quoted at the beginning of this piece, Guyenet says, “further studies from the same group and others supported the idea that a bland liquid diet leads people to eat fewer calories and lose excess fat.” It would clearly be a big mistake for us to dismiss this early result without seeing the further studies, so let’s take a look.
Guyenet cites two further studies in The Hungry Brain, and we found a third with a little searching. This may not be the full literature on the subject, but it’s everything Guyenet cites plus one, so it seems like a good place to start.
As far as we can tell, this is simply the first test of the “automatically monitored food dispensing apparatus.” We can’t access the full article for some reason (if you can, please send it to us), but the abstract seems to specify that there was only one participant, and there’s no mention of weight loss or obesity at all.
In this study, “dispensed liquid diet was studied in five lean and four obese young adults and two obese juvenile subjects.” The twist is that the researchers varied the “nutritive density” of the nutrient sludge over time without telling the research subjects. As before, subjects were (ideally) unaware that their food intake was being recorded. Also relevant is that the study was conducted in a metabolic research ward, which allows for a certain amount of control, and that participants were “maintained on light activity”, with the research team attempting to “prevent significant day-to-day variations in energy output”.
We see the same issues here that we highlighted in the original study. This is also before 1980, and so may not be informative about the current situation. The sample size is pretty small, but it’s bigger than before, and the subjects seem less idiosyncratic.
The lean participants were “five healthy male students 20 to 25 years of age”. The “grossly obese patients” came in two groups — four women between the ages of 25 and 30, and two adolescent boys ages 13 and 15. This isn’t an experiment, but it’s still kind of worrying how different the demographics are for the lean and the obese participants.
The results for the lean participants match the previous findings. “All the lean subjects,” they report, “were able to maintain weight within fairly narrow limits (0.6 to 2.3 per cent of initial body weight) by making appropriate adjustments in the calorie intake whenever the nutritive density was varied.” Thankfully, unlike the lean participants in the previous study, none of these fellows was consuming an insane amount of the sludge.
The obese adult female participants ingested only a few hundred calories of the sludge per day, and lost weight, though the weight loss doesn’t appear to be as extreme as in the first study. Of interest, however, “there was no increase of volume intake in response to formula dilution and no decrease in volume intake after formula concentration.” In fact, in two of the four obese women (that is, half), “there were paradoxical drops in volume intake when the nutritive density of the formula was decreased.”
This is very weird. It suggests that these women were controlling for the amount of nutrient studge they drank, rather than for calorie intake. Together with the results of the first study (and THE CUPS), the conclusion ends up looking less like “if obese people eat a bland diet, they return to a healthy weight” and more like “if you give obese people a diet through a food pump nozzle, they will suck the exact same tiny amount every day for some reason.”
The story is further complicated by the fact that we get VERY different results for adolescent male participants. The 15-year-old was 101 kg and the 13-year-old was 135 kg at time of admission, and both of them maintained these weights by drinking thousands of calories of nutrient sludge. “During the periods in which caloric density was 1.0 kcal per milliliter,” they tell us, “energy intake was in excess of 3900 kcal per day.”
These participants really did seem to be controlling for calorie intake, because diluting the formula didn’t fool them. “When the formula was covertly diluted subject A.V. increased volume intake slightly, but not enough to maintain a caloric intake comparable to that achieved during intake of the more concentrated formula. In contrast, W.D. compensated for formula dilution with a striking increase in volume intake, thereby maintaining a near constant energy input.”
An examination of Table 2 reveals that at one point, one of the adolescents broke 4,000 calories of nutrient sludge per day, which is honestly impressive across a number of dimensions. In addition, “These two obese juvenile subjects differed from the obese adult subjects in that they either maintained or gained weight while receiving the machine-dispensed formula.” The magic bullet against obesity, this is not.
In fact, once again this seems like evidence against palatability as an explanation for obesity. If palatability were the driving force, then these teens wouldn’t be slurping down almost 4k calories of nutrient sludge to maintain their extreme weights. Indeed, it seems like palatability makes no difference at all.
It’s especially concerning that this diet causes weight loss in only 2/3 of the participants. These six people may be obese for different reasons (i.e. obesity is a shared symptom but the result of a different underlying condition), but none of those reasons seem to be related to palatability.
The other paper cited by Guyenet in his book was a 1976 piece titled Influence of a Monotonous Food on Body Weight Regulation in Humans. This study is not worth reviewing in depth because of its major departures from the original design. This is a two-author paper, and neither author was involved in any of the previous studies. Rather than the nutrient sludge being automatically recorded by a dispensing pump in the monitored environment of a hospital ward, subjects were sent home with “an ample stock” of Renutril®, a moderately sweet, vanilla flavored complete liquid diet that comes in 375 ml cans. For experimental control, “they were told to avoid as much as possible the odor, the sight, and even the thought of any other foods.”
Even if this design were above criticism, the results are unimpressive. The study lasted 3 weeks, and on the all-liquid bland diet, people’s weight decreased by only 3.13 kg. This is a far cry from the one pound per day reported in the 1965 study.
In addition, this study suffers from the same problems as all the studies above: the study was conducted before 1980, so it may not generalize, and the sample size was four.
Taken together, these studies do not provide much evidence in favor of palatability as a cause for obesity, or for the use of a bland diet in reversing it. The studies are all more than 40 years old, so the data predates the modern obesity epidemic. There are a number of bizarre observations and discrepancies (THE CUPS!!!) that don’t seem consistent with the palatability hypothesis. The total sample size across all four studies is 23.
In fact, these studies provide moderate evidence against the palatability hypothesis. Most participants lost weight on the nutrient sludge diet, but two patients not only ate heroic amounts, they actually gained weight. In the 1965 study, the nutrient sludge diet appears to have prompted two lean participants to overeat by something like 1000-2000 calories per day.
Finally, there is an external sanity check that makes us doubt the whole premise. If the nutrient sludge diet works, why hasn’t anyone done a real experiment on it? Why isn’t it being used to make 400 lbs men lose 200 lbs today? Either this is a huge missed opportunity, or these results are simply wrong.
If this works, why hasn’t someone replicated it by now? It would be pretty easy to run a RCT where you fed more than five obese people nutrient sludge ad libitum for a couple weeks, so this means either it doesn’t work as described, or it does work and for some reason no one has tried it. Given how huge the rewards for this finding would be, we’re going to go with the “it doesn’t work” explanation.
If you think palatable food is the relevant issue, an even better approach would be an experimental design where you develop two (or more) nutrient sludges, nutritionally identical but one more palatable than the other, and randomly assign a group of obese participants to eat either the palatable sludge or the unpalatable sludge. But we haven’t seen a design like this either.
If Guyenet — or anyone — believes this result is real, they should rush to do a metabolic ward study on a sample size of more than five people, and collect all the fame and fortune that comes with finding a diet that not only reliably works, but leads to weight loss of about one pound per day with no hunger or gastrointestinal discomfort.
This is one of the most similar proposals to the theory presented in A Chemical Hunger, though they don’t go quite as far as we do, still attributing some of the influence to diet and exercise: “Most reports attribute the obesity epidemic to factors such as excess food energy intake, changes in diet and eating behavior, and increasing sedentary life style. Undoubtedly, these factors contribute, but can they all account for the rapid increase in this problem that occurred over the last two decades?”
There’s even a study where they put fecal matter from human twins into germfree mice. (This is one of the more creative study designs we’ve seen.) They started by finding pairs of twins where one twin was fat and the other twin was lean. This is pretty uncommon — normally, twins weigh the same amount. They transplanted fecal matter from the twins into mice and found that mice that got fecal matter from the obese twin gained weight — unless it was housed with one of the mice who got fecal matter from the lean twin.
However, there is also evidence against this picture. For one thing, Germany, Spain, Italy, and Japan all use a lot of antibiotics in their meat, and none of these countries is particularly obese. Australia and South Africa are both pretty obese, but both of these countries use less antibiotics than usual. This could maybe be reconciled if these countries use different kinds of antibiotics, but we would need to see that case made to evaluate it.
There’s also some evidence in favor of this theory that this paper didn’t review.
For one thing, people who eat fewer animal products have lower BMIs, and the effect seems to be dose-dependent. In a sample from 2002-2006, average BMI was lowest in vegans (23.6) and incrementally higher in ovo-lacto vegetarians (25.7), pescitarians (26.3), semi-vegetarians (27.3), and nonvegetarians (28.8). We can note that the BMI for vegans is about the same as that found in hunter-gatherers and in Civil War veterans in the 1890s. That said, everyone in this sample was a Seventh-Day Adventist, so they may not be all that representative.
India and Japan are the least obese of the developed countries. Both have obesity rates below 5%. India is the most vegetarian country on the planet and Japan, while not especially vegetarian, mostly consumes seafood in place of meat products.
This would mean that vegan diets would work really well for weight loss, right? Well, maybe. As we previously reviewed, all diets seem to work a little, and no diet seems to work all that well. We see something similar in vegetarian and vegan diets. A 2015 meta-analysis found that people assigned to vegetarian diets lost more weight than those assigned to nonvegetarian diets. People on vegan diets lost a little more weight than people on vegetarian diets, about 5.5 pounds (2.5 kg) to 3.3 pounds (1.5 kg). The studies differed quite a bit in the size of the effect, but all of them had similar conclusions. The other meta-analysis from 2015 found the same general pattern, and individual studies comparing different types of vegetarian and vegan diets seem to confirm this dose-dependent trend.
This looks a lot like other studies, where the differences between diets are technically reliable but so small as to be basically meaningless, but the possible dose-dependent effect is interesting.
The most interesting study might be this one, that compared a vegan diet to a conventional low-fat diet. So far so standard, but unlike most diet studies, which end after 12 or 18 months, this one followed up two years later. The vegan group not only lost more weight (4.9 kg versus 1.8 kg), they kept it off better at the two-year followup (3.1 kg versus 0.8 kg). On most diets people lose a little weight but then gain it right back, so the fact that people kept most of the weight off for two years is interesting. Even so, the amount of weight lost in an absolute sense is still quite small. It could take more than two years on a vegan diet for you to see all the effects — but if this were the case, you’d think people would have lost even more weight by year two, but that’s not what we see.
None of these are smoking guns. At best, they are consistent with the idea that some of these contaminants are more prevalent in animal-based foods. And we know that this can’t be about the animal products themselves, because hunter-gatherers and our ancestors in 1890 ate lots of meat and didn’t experience modern levels of obesity.
Environmental contaminants tend to build up in animals through the plants they eat, so any contaminants in the environment will bioaccumulate, and concentrations will be higher in animals than in groundwater or in plants. Compounds in a farmer’s fields will end up in the corn or alfalfa fed to their cows, and the cows will end up getting an even larger dose, which will be passed on to the person who eats the resulting cheeseburger. So the fact that meat consumption is linked to obesity doesn’t necessarily implicate antibiotics. It could be something else in the meat.