[PART I – MYSTERIES]
[PART II – CURRENT THEORIES OF OBESITY ARE INADEQUATE]
[PART III – ENVIRONMENTAL CONTAMINANTS]
[INTERLUDE A – CICO KILLER, QU’EST-CE QUE C’EST?]
[PART IV – CRITERIA]
[PART V – LIVESTOCK ANTIBIOTICS]
[INTERLUDE B – THE NUTRIENT SLUDGE DIET]
[PART VI – PFAS]

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

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

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

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

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

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

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

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

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

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

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

Dosage

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

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

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

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

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

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

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

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

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

Trace Exposure

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Common Uses of Lithium

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

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

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

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

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

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

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

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

Bioaccumulation

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

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

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

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

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

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

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

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

---

[Next Time: HIGHLIGHTS FROM THE COMMENTS]

---

Follow @mold_time

[PART I – MYSTERIES]
[PART II – CURRENT THEORIES OF OBESITY ARE INADEQUATE]
[PART III – ENVIRONMENTAL CONTAMINANTS]
[INTERLUDE A – CICO KILLER, QU’EST-CE QUE C’EST?]
[PART IV – CRITERIA]

Obesity in the United States – Dysbiosis from Exposure to Low-Dose Antibiotics? suggests that the obesity epidemic is driven by population-wide exposure to residual antibiotics from livestock cultivation, and the resulting impact on gut microbiota.

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?”

They make a pretty compelling case. A large percentage of antibiotics are excreted in animal waste and end up in the water supply, where they affect natural microorganisms. Relevant to our interests, antibiotics are more and more prevalent in rivers as they make their way towards the ocean: “The only site at which no antibiotics were detected,” they write, “was the pristine site in the mountains before the river had encountered urban or agricultural landscapes. By the time the river had exited the urban area, 6 of the 11 antibiotic compounds that were monitored were found in the samples. At Site 5, which had both urban and agricultural influences all five of the TCs monitored were detected.”

Exposure has increased in the US over time, closely matched with the increasing prevalence of obesity — “practically overlapping with the counties with the highest obesity prevalence in the US.” Similar trends can be observed in other countries. For example, in Great Britain, by 1958, around 50% of British pigs were fed antibiotics, and nearly all piglets were given food containing tetracyclines. In West Germany in the 1960s, an estimated 80% of feeds for pigs, calves, and poultry contained antibiotics.

The Effects on Human Health of Subtherapeutic Use of Antimicrobials in Animal Feeds opens by saying, “The food-producing animal and poultry industries have undergone a dramatic change that began around 1950.” That’s a little earlier than we would expect, but depending on how you measure things, it took until the 70’s or 80’s before things really got rolling.

In meat animals, antibiotics often lead to weight gain, sometimes as high as 40% weight gain compared to control, and there’s reason to suspect that this might be linked to the microbiome. Gut microbiota influence energy intake and body weight in mammals, and even short courses of antibiotics can reduce gut microbiota and increase BMI in humans (though the BMI effect was only seen in some antibiotics).

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.

Consider:

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.

---

[Next Time: THE NUTRIENT SLUDGE DIET]

---

Follow @mold_time

[PART I – MYSTERIES]
[PART II – CURRENT THEORIES OF OBESITY ARE INADEQUATE]
[PART III – ENVIRONMENTAL CONTAMINANTS]
[INTERLUDE A – CICO KILLER, QU’EST-CE QUE C’EST?]

Evidence strongly suggests that the obesity epidemic is the result of environmental contaminants.

However, it’s not entirely clear exactly which contaminants are responsible.

If we’re lucky, a few compounds are entirely responsible for the increase in obesity over the past forty years, and we can ban those chemicals.

If we’re not lucky, the obesity epidemic is the result of dozens or even hundreds of different contaminants, each with a small effect, which when combined lead to extreme obesity. In this case we can try to ban or regulate them all, but it will be much more difficult to find ways to get all of them out of our food, water, and homes.

While more work will be needed to pin down exactly what contaminants are responsible, we can make some very educated guesses, because we already know what kind of contaminants we’re looking for.

4.1    Invention and Introduction

The big inflection point for the obesity epidemic was around 1980, so we should be looking for compounds that entered the environment slightly before then. Either they were discovered around 1960-1970 and were immediately introduced, or they were discovered some time before and went into widespread use just before 1980.

These contaminants may be synthetic, but they don’t have to be. These could also be naturally occurring compounds that are introduced into the environment through human activity.

We’re aware that correlation doesn’t imply causation. By itself, showing that the time course of some contaminant is related to the upward trend in obesity rates isn’t enough to show that the contaminant is responsible for the obesity epidemic. But it is suspicious. We should keep in mind that the link between smoking and lung cancer was largely established through correlational data.

If we don’t see a relationship between a contaminant and the obesity epidemic, it’s harder to make the case for that particular contaminant. For example, some people have proposed that certain milk proteins might be behind the obesity epidemic. But if you look at dairy consumption by country, you immediately see that some of the leanest countries are near the top and many of the most obese countries are near the bottom. As a result, we don’t think this is a serious candidate and we don’t discuss it in this paper. A relationship is one of the first signs we should look for in a proposed explanation, even though, by itself, it’s not enough to be convincing.

4.2    Between-Group Differences

There are large differences in rates of obesity between counties, states, countries, and even professions.

Some of this is due to differences in factors like altitude or genetics. For example, in a group of about 43,500 patients from the San Francisco bay area, the rate of obesity in European-Americans was about 26%, but the rate of obesity among Asian-Americans was much lower, at 12% obese. This also differed quite a bit by country of origin. Among Asian-Americans, Filipino-Americans (24%) and Indian-Americans (17%) were the most obese, and Chinese-Americans (7%) and Vietnamese-Americans (6%) were the least obese.

This suggests that even if these environmental contaminants were just as widespread in China as they are in the United States, the rate of obesity would only be about 7%. In this light, it’s not surprising that the rate of obesity in China is currently around 6%. That isn’t a mystery that we need to explain, it’s about what we would expect based on what we know about the genetics of obesity.

Despite this, some of the difference in obesity rates between countries is probably due to differences in exposure to contaminants. This is something we should keep an eye out for. In contrast to China, Indian-Americans are about 17% obese, less obese than European-Americans but much more obese than people living in India, who are only about 4% obese.

4.3    Dose Dependence

An obvious smoking gun would be if the amount of exposure to a contaminant were related to obesity. If people who are exposed to a high dose of the contaminant are fatter than those who are exposed to a low dose, that would be a strong indication that the contaminant is responsible.

Right? 

Well, maybe.

We are all living in a fattening environment. A 2012 meta-analysis of 115 studies concluded that around 75% of individual difference in BMI is genetic. This is entirely compatible with the idea that contaminants are responsible for the difference between obesity rates in 1970 and 2020. It just means that, now that everyone has been exposed to more or less the same contaminants, 75% of the variation in the population is genetic.

That only leaves 25% of the variance to potentially be explained. If we assume that all of that variance is due to differences in dose, then some simple math tells us that the correlation with dose would be r = 0.50. This is a reasonably large correlation, but we also shouldn’t expect things to be so simple. If we expect that there are ten contaminants, the correlation between dose and BMI for each would be about r = 0.16. If dose explains only 15% of the remaining variance rather than 25% (leaving a reasonable 10% as the result of noise), the correlation for each of the contaminants would be r = .12.

With a large enough sample size, this would certainly be detectable. But dose alone almost certainly can’t explain all the remaining variance. We should be aware that even if there is a strong dose-dependent effect, the effect size might appear statistically to be quite small.

At some point in the past, these contaminants weren’t in the environment at all. Now, they’re so widespread that almost everyone in the industrialized world is getting a dose. This means we’re working with a restricted range. No one, or almost no one, has a dose near zero. Most of us are probably getting similar doses — that’s part of what it means when we see that 75% of the variation in the current population is genetic.

When the range of a variable is restricted, the correlation always ends up looking smaller than it really is. That link leads to a paper where they show that, for a dataset with a true correlation of r = .82, with different range restrictions the apparent correlation can be .51, .47, or even as small as .18! In some cases, a restricted range can even make a positive correlation appear to be negative.

We know that in most samples, everyone will have similar levels of exposure to the contaminants. We’ll be working with a restricted range, and any correlation we see will be smaller than the true correlation. It’s not clear how restricted our range is, but the correlation we see may be much smaller than the true relationship. It might disappear altogether, or even appear slightly negative. And remember, if genetics is 75% of the variance, then the largest dose-dependent correlation we can expect to see is only .50, which is not all that large to begin with.

(This is a known issue in studying public health. E.g. Lindeberg: “Another difficulty is that the variation of dietary habits in the population being studied may be too small to allow for demonstration of a possible relationship with health. Salt consumption among a particular ethnic group may not show much variation, and the majority has an intake that is much higher than what was practically feasible during evolution. This problem can be compared to studying the importance of smoking for myocardial infarction without having access to non-smokers. (In the case of smoking, often no relation is apparent in epidemiological studies.)” )

One pattern you might expect to find is that dose-dependent effects only become apparent in samples with a wide range of dosages — that is, in cases where the range isn’t restricted. They might be especially pronounced in groups that have extremely high levels of exposure, such as people who work with these contaminants directly, because that increases the range of dosages.

There might also be diminishing returns. Let’s imagine that today, people on average get a dose of 100 units. Back in 1970, everyone got a dose of 0 units. Now, the first 20 units might be very fattening indeed. But in general, the human body can only get so fat. The first 20 units might make you gain 20lbs on average. But the next 20 units only make you gain 10lbs. And the next 20 units make you gain only 5lbs. Now that everyone is at 100 units, every additional unit of exposure leads to a nearly undetectable change in body mass.

We don’t know that there is diminishing weight gain from greater doses of these contaminants, but diminishing returns are pretty common in pharmacology (see for example here). If there are diminishing returns, we may be near or past the ceiling effect, in which case we might not be able to detect a dose-dependent effect. If this were the case, however, we might expect to see dose-dependent effects in samples with lower average doses; perhaps samples from the 1980s or 1990s, or samples from developing countries which don’t yet have doses in the same range as industrialized countries.

A further complication is that being exposed to contaminants doesn’t make you gain weight that very same day. Even on Olanzapine, which makes people gain an average of 13.7 kg after 48 weeks, you generally don’t see your first kilogram of weight gain until after 8 weeks. The dose in your system today will be less correlated with your weight than the dose you were on 6 months ago. The dose will be a lagging indicator, and this will also reduce any correlation.

This is further complicated by the fact that these compounds might have paradoxical reactions, which is what we call it when a drug sometimes has the opposite of its normal effect. This would cause some portion of people to actually lose weight, and would further reduce the apparent correlation between the contaminant and obesity.

Finally, there are a priori reasons for us not to expect there to be strong correlations in the existing literature. If there were a compound or contaminant that was correlated with BMI, even a relatively small correlation like r = 0.20, someone probably would have noticed. This means that either 1) the contaminants are compounds that we don’t usually measure, so no dataset exists where we can compare them to measures of obesity, or 2) the relevant contaminants are commonly measured, but for statistical reasons like the ones above, there isn’t an obvious correlation with obesity.

Evidence of a dose-dependent relationship would be a smoking gun in favor of a contaminant being one of the causes of the obesity epidemic. But the lack of a dose-dependent relationship isn’t evidence against the contaminant being involved.

4.4    Environmental Interactions

In the following sections, we do our best to identify which of the contaminants we’re putting into the environment could be the cause of the obesity epidemic, and we believe that we have found some likely candidates. Unfortunately, this search is complicated by the fact that chemistry and biology allow for bewildering interactions, and sometimes seem to be working against us.

There are two general ways this can happen. The first is that when chemical contaminants end up in the environment, they can be transformed into different compounds. This can occur as a result of interactions with minerals in the groundwater, from exposure to sunlight, from exposure to radioactivity, or from chemical interactions with other contaminants. Since contaminants can sit in soil and groundwater for decades, there’s a lot of time for these transformations to happen.

In her book Silent Spring, Rachel Carson describes how contaminants “pass mysteriously by underground streams until they emerge and, through the alchemy of air and sunlight, combine into new forms that kill vegetation, sicken cattle, and work unknown harm on those who drink from once pure wells.”

She provides a few illustrative examples. One case involved a manufacturing plant in Colorado. In 1943, the Rocky Mountain Arsenal of the Army Chemical Corps began to use the plant, located near Denver, to manufacture war materials. After eight years, the same manufacturing plant used to make these war materials was leased to a private oil company for the production of insecticides. Even before this point, however, there were already reports from miles away of mysterious sickness in livestock, crops dying and turning yellow, and even human illness, possibly related. A thorough investigation eventually revealed that the groundwater between the arsenal and the farms had become contaminated, but it had propagated so slowly that it took several years for the contamination to reach the farmland.

Analysis of the farms’ shallow wells revealed contamination with arsenic, chlorides, and other dangerous substances. This was enough to explain the majority of the reports of illness and crop damage. But most mysterious was the discovery of the weed killer 2,4-D in some of the wells:

Certainly its presence was enough to account for the damage to crops irrigated with this water. But the mystery lay in the fact that no 2,4-D had been manufactured at the arsenal at any stage of its operations. After long and careful study, the chemists at the plant concluded that the 2,4-D had been formed spontaneously in the open basins. It had been formed there from other substances discharged from the arsenal; in the presence of air, water, and sunlight, and quite without the intervention of human chemists, the holding ponds had become chemical laboratories for the production of a new chemical—a chemical fatally damaging to much of the plant life it touched. And so the story of the Colorado farms and their damaged crops assumes a significance that transcends its local importance. What other parallels may there be, not only in Colorado but wherever chemical pollution finds its way into public waters? In lakes and streams everywhere, in the presence of catalyzing air and sunlight, what dangerous substances may be born of parent chemicals labeled ‘harmless’?

While we can try to identify the contaminants that cause obesity, the disturbing fact is that the contaminants responsible may be compounds which we are unfamiliar with, because they weren’t created in a lab and have never been examined for safety. Again, Rachel Carson puts it better than we could:

Indeed one of the most alarming aspects of the chemical pollution of water is the fact that here—in river or lake or reservoir, or for that matter in the glass of water served at your dinner table—are mingled chemicals that no responsible chemist would think of combining in his laboratory. The possible interactions between these freely mixed chemicals are deeply disturbing to officials of the United States Public Health Service, who have expressed the fear that the production of harmful substances from comparatively innocuous chemicals may be taking place on quite a wide scale. The reactions may be between two or more chemicals, or between chemicals and the radioactive wastes that are being discharged into our rivers in ever-increasing volume. Under the impact of ionizing radiation some rearrangement of atoms could easily occur, changing the nature of the chemicals in a way that is not only unpredictable but beyond control.

To make matters worse, something quite similar can happen inside our bodies. As surprising and chaotic as the interactions between contaminants can be, their interactions with human biochemistry can be even more complicated.

“A human being,” writes Carson, “unlike a laboratory animal living under rigidly controlled conditions, is never exposed to one chemical alone. Between the major groups of insecticides, and between them and other chemicals, there are interactions that have serious potentials. Whether released into soil or water or a man’s blood, these unrelated chemicals do not remain segregated; there are mysterious and unseen changes by which one alters the power of another for harm.” She goes on to describe several such interactions in gory detail.

The organic phosphates, “those poisoners of the nerve-protective enzyme cholinesterase,” become much more dangerous if a person has previously been exposed to chlorinated hydrocarbons that injure the liver. Pairs of different organic phosphates themselves can also interact with each other, “in such a way as to increase their toxicity a hundredfold.” Organic phosphates also have the potential to interact with all sorts of other things in the environment, including prescription drugs, synthetic materials, and food additives.

Similarly, a person exposed to DDT is much worse off if they have already been exposed to another hydrocarbon that causes liver damage — “so widely used as solvents, paint removers, de-greasing agents, dry-cleaning fluids, and anesthetics.” As a result, a dose of DDT that is survivable for one person may be devastating to someone else. “The effect of a chemical of supposedly innocuous nature can be drastically changed by the action of another,” Carson tells us. “One of the best examples is a close relative of DDT called methoxychlor”:

Because it doesn’t accumulate in the body to any great extent when given alone, we are told that methoxychlor is a safe chemical. But this is not necessarily true. If the liver has been damaged by another agent, methoxychlor is stored in the body at 100 times its normal rate, and will then imitate the effects of DDT with long-lasting effects on the nervous system. Yet the liver damage that brings this about might be so slight as to pass unnoticed. It might have been the result of any of a number of commonplace situations—using another insecticide, using a cleaning fluid containing carbon tetrachloride, or taking one of the so-called tranquilizing drugs, a number (but not all) of which are chlorinated hydrocarbons and possess power to damage the liver.

Another good example is malathion, an insecticide that at the time was commonly used by gardeners. The name of this product sounds so evil that we’re surprised it passed the review of the corporate public relations people, but apparently the name comes from the smell and means “bad sulphur”, so there you go. Malathion is extremely deadly to insects but is “safe” for mammals, including humans. But malathion is only “safe” because the mammalian liver detoxifies it with an enzyme, rendering it harmless. “If, however, something destroys this enzyme or interferes with its action,” we are warned, “the person exposed to malathion receives the full force of the poison. Unfortunately for all of us, opportunities for this sort of thing to happen are legion.”

In particular, Carson relates the story of how a team from the FDA found that when malathion was administered at the same time as some of the other organic phosphates, “a massive poisoning results—up to 50 times as severe as would be predicted on the basis of adding together the toxicities of the two.” This led them to test the combination of many different organic phosphates, and found that many pairs of these compounds are exceedingly dangerous in combination.

The reason for this appears to be “potentiation” of their combined action — when one of the compounds destroys the liver enzyme responsible for detoxifying the other. “The two need not be given simultaneously,” we are warned. “The hazard exists not only for the man who may spray this week with one insecticide and next week with another; it exists also for the consumer of sprayed products. The common salad bowl may easily present a combination of organic phosphate insecticides. Residues well within the legally permissible limits may interact. The full scope of the dangerous interaction of chemicals is as yet little known, but disturbing findings now come regularly from scientific laboratories.”

Carson relates several more examples in a similar vein. Malathion also appears to become much more dangerous when a person is exposed to certain plasticizing agents. Just like its combination with other organic phosphates, “this is because it inhibits the liver enzyme that normally would ‘draw the teeth’ of the poisonous insecticide.” Similarly, exposure to malathion seems to increase the effect of certain prescription drugs, including muscle relaxants and barbiturates.

In addition, we found that malathion can, under some conditions, transform into malaoxon, which is 61x more deadly. One of these conditions is when malathion is exposed to chlorine, as it might be in some drinking water.

Our concern in this paper isn’t the toxicity of different insecticides, of course. The point is that the contaminants that cause obesity may not have a straightforward profile. It’s possible that two (or more) well-known and relatively safe contaminants combine in groundwater to form an unknown new contaminant that causes weight gain, and a host of other problems. It’s possible that a single contaminant becomes something else entirely when it is exposed to sunlight in fields, ponds, and rivers. It’s possible that there are two contaminants, neither of which cause obesity in isolation, but which in combination overwhelm the body.

4.5        Three Possible Contaminants

In the next parts, we propose some contaminants that we think might be responsible for the obesity epidemic. But we should make it clear upfront that the theory itself doesn’t hinge on these compounds. Even if it turns out that none of these compounds could possibly be responsible for the modern rise in obesity, we still think that the evidence is very strong that environmental contaminants are responsible.

We take a close look at three contaminants, and examine the evidence for each.

---

[Next Time: SUSPECT NUMBER ONE]

---

Follow @mold_time

[PART I – MYSTERIES]
[PART II – CURRENT THEORIES OF OBESITY ARE INADEQUATE]

Only one theory can account for all of the available evidence: the obesity epidemic is caused by one or more environmental contaminants, compounds in our water, food, air, at our jobs and in our homes, that change how our bodies regulate weight.

These contaminants are the only cause of the obesity epidemic, and the worldwide increase in obesity rates since 1980 is entirely attributable to their effects. For any two people in a group, the difference between their weights is largely genetic, because everyone is exposed to similar levels of contamination. But the difference between the average weight in 1980 and the average weight today is the result of environmental contaminants.

3.1    Weight Gain in Response to Medication

We know that this is biologically plausible because there are many compounds that reliably cause people to gain weight, sometimes a lot of weight.

Weight gain is a common side effect of the psychiatric drug Clozapine (Clozaril). On average, people taking Clozaril gain about 10 to 15 pounds. This is dose-dependent — If you take a larger dose, you gain more weight — and on high doses people sometimes gain fifty or even a hundred pounds.

Weight gain is a common side effect of the psychiatric drug Olanzapine (Zyprexa). According to some sources, more than 50% of people taking this drug gain weight as a side effect. In certain populations, the rate of extreme weight gain can be as high as 90%! In one study, a group of 442 people taking Olanzapine gained an average of 30 lbs (13.7kg) after 48 weeks on a normal dose. Other studies find similar numbers — about 20 lbs after a couple weeks and about 30 lbs after a year. There’s some evidence that the weight gain is dose-dependent.

Weight gain is a common side effect of the psychiatric drug Haloperidol (Haldol). In one study, average weight gain on Haloperidol was 8.4 lbs (3.8 kg) at 3 months and 21.3 lbs (9.7 kg) at 1 year. There was a lot of variation between individuals though. If we assume that weight gain was approximately normally distributed, some back-of-the-envelope statistics suggest that about 2% of patients would gain 60 lbs or more after 1 year.

Weight gain is a common side effect of the psychiatric drug Risperidone (Risperidal). In the same study as above, average weight gain on Risperidone was 13 lbs (5.9 kg) at 3 months and 19.6 lbs (8.9 kg) at 1 year. Again there was a lot of variation, and while it’s hard to tell without the raw data, again a reasonable guess would be that some people gain as much as 60lbs. This also appears to be dose-dependent.

Weight gain is a common side effect of the element lithium (lithium), which is often used as a psychiatric medication. Almost all patients seem to gain some weight on lithium, and about half of them report serious weight gain, on average 22 lbs (10 kg). Weight gained is correlated (r = .44, p < .001) with the dosage. Some reports suggest that 20% of patients gain more than 10 kg on lithium.

Unsurprisingly from the lipostat perspective, you’ll notice that all of these are psychiatric medications. Presumably, they affect the brain. Weight gain is a side effect of many drugs, but it’s especially famous in the antipsychotics. Further, weight gain in many of these drugs is associated with enhanced leptin levels.

3.2    Mysteries

We need a theory that can account for all of the mysteries we reviewed earlier. Another way to put this is to say that, based on the evidence, we’re looking for a factor that:

1. Changed over the last hundred years 
2. With a major shift around 1980 
3. And whatever it is, there is more of it every year
4. It doesn’t affect people living nonindustrialized lives, regardless of diet
5. But it does affect lab animals, wild animals, and animals living in zoos
6. It has something to do with palatable human snackfoods, unrelated to nutritional value
7. It differs in its intensity by altitude for some reason
8. And it appears to have nothing to do with our diets

Environmental contamination by artificial, human-synthesized compounds fits this picture very well, and no other account does.

Mystery 1: The Obesity Epidemic 

People were skinny before the modern era because these contaminants didn’t exist back then.

People’s diets were “worse” in the past — full of lard and bread — because diet doesn’t cause obesity. The ~1% of people who were obese in the past were people with one of the various medical conditions known to cause obesity, such as Prader-Willi Syndrome, hypothyroidism, or hypothalamic lesions.

Mystery 2: An Abrupt Shift 

People rapidly started getting more and more obese starting around 1980 because the contaminants are the product or byproduct of some industrial process. We’re looking for compounds that were invented around 1960 or 1970, because it would probably take a few years for enough to get into the environment to start affecting us.

Alternatively, these might be compounds that had been invented much earlier, but only began to see widespread deployment around 1980. Either way, we’re looking for that abrupt shift.

Mystery 3: The Ongoing Crisis 

The obesity epidemic keeps getting worse because these contaminants continue to be produced and continue to build up in the environment. Every year they accumulate and each of us gets a larger dose. This suggests that we are looking for compounds that don’t break down easily, or at least are being introduced into the environment faster than they break down.

Mystery 4: Hunter-Gatherers 

Different groups of hunter-gatherers remain lean while eating very different diets because the human body can thrive on many kinds of food. Some of the diets are extremely high-fat. Some of them are extremely high-starch. Some of them are extremely high-sugar. Some eat an extremely varied diet, while others get almost half of their calories from a single food source. But they don’t become obese, because they’re eating fat right off the gemsbok or yams straight out of the ground, and living in grass huts.

When hunter-gatherers adopt an industrialized lifestyle, however, they become obese just like anyone else. You’ll recall that, in 1990, a team led by Staffan Lindeberg traveled to the island of Kitava and found that none of the Kitavans were obese, despite the fact that they had plenty of foods and ate a very starchy diet.

It’s true that none of the Kitavans living on the island were at all overweight. But there were actually two overweight Kitavans — both men who had grown up in Kitava, had since moved away for many years, and who happened to be visiting at the time of the study. Lindeberg managed to examine one of them, a 44-year-old businessman named Yutala, who had left the island fifteen years earlier to become a businessman in Papua New Guinea. At the time of the study, Yutala was almost fifty pounds heavier than the average Kitavan man of his height, twelve pounds heavier than the next heaviest man, and had the highest blood pressure of any Kitavan Lindeberg examined.

When he moved away from the island, Yutala was exposed to a modern way of living. More importantly, he was exposed to the contaminants of an industrialized society. As a result, he became overweight.

Yutala isn’t an isolated case. In fact, this happens with some regularity. Lindeberg notes, “an epidemic of obesity and weight gain has occurred in former traditional populations that transitioned to a Western lifestyle,” and cites a total of 17 sources to support this claim, including examples from Sudanese communities, Native Americans, Pacific Islanders, South Australian Aborigines, and the people of Vanuatu. “When humans switch from an ancient to a Western lifestyle,” he says, “they experience increased waistlines, reduced insulin sensitivity, higher blood pressure and a host of related disorders and diseases.“

Mystery 5: Lab Animals and Wild Animals 

Lab animals and wild animals are becoming more obese because they are exposed to the same environmental contaminants that we are. If they are living around humans, in or around our buildings, eating industrially-prepared foods, or the scraps of such foods, they are exposed to contaminants in the same way as the rest of us. Even if they’re not living in close proximity to humans, these compounds are probably in groundwater and drinking water.

Mystery 6: Palatable Human Food 

Lab rats gain more weight from human foods than they do from rat chow with similar nutritional properties because obesity doesn’t come from fat or carbohydrate content, but from contaminants in the food, and human food has more contaminants than the rat chow does, likely from packaging and processing.

Processed foods end up with more contaminants in them — for example, there are 4x more phthalates in Kraft mac and cheese powders than in block cheese, string cheese, and cottage cheese. Another example are these results from the FDA, as reported by the AP. In terms of explaining why the cafeteria diet is so fattening, it’s especially illustrative that grocery store chocolate cake was an extreme outlier, with concentrations of PFPeA more than 100 times higher than chocolate milk.

Doesn’t this mean that avoiding packaged and processed food should reverse obesity? We think the answer is “maybe”. There’s not a lot of research on “whole foods” diets, but the evidence that we do have is quite promising. People seem to lose a reasonable amount of weight and keep it off for up to 12 months when they’re eating largely unprocessed plant-based foods. But we should be skeptical of these results until there are more studies. While this diet lowered almost everyone’s BMI, when we look at individual results, most people remained obese or overweight after 12 months on this diet. Similar findings are characteristic of Paleolithic diets. Even when the studies are conducted by advocates of these diets, they produce very moderate benefits, in one case causing only 6.6 lbs (3 kg) more weight loss than a comparison diet across a 3-month period.

There do happen to be a couple compelling anecdotes. In 2010, Chris Voigt, the Executive Director of the Washington State Potatoes Commission, vowed to eat nothing but 20 plain potatoes (and a small amount of cooking oil) for 60 days straight to demonstrate that potatoes are perfectly healthy, and in fact nutritious enough to sustain a person for quite a while.

This wasn’t intended as a weight loss diet — in fact, 20 potatoes a day was the amount he calculated he would need to maintain his weight (2,200 calories). Despite this, Voigt lost 21 pounds over his sixty-day diet. He even had trouble eating enough — he just wasn’t very hungry. Why would this happen? Well, unprocessed potatoes are about as raw a food as you can find, and won’t pick up contaminants from industrial cooking and packaging. If Voigt was being exposed to contaminants through his everyday diet, then switching to potatoes largely cooked at home would lead him to getting a much lower dose of contaminants.

Most people are likely exposed to some of these contaminants in their diet, so eating a diet with fewer contaminants helps. But most people are also likely exposed at home, at work, and through their drinking water, so diet alone can only help so much.

Mystery 7: Altitude 

Obesity is less common at high altitudes because of the watershed. Environmental contaminants build up as water flows downhill and are in much higher concentrations as you approach sea level.

For example, take a look at this map of by-state obesity levels from the CDC:

The Mississippi watershed is America’s largest drainage basin, covering 41% of the country. If you compare this map of state-level obesity to a map of the Mississippi watershed (below), you’ll see that every single state with obesity rates of >35% borders on a river from this watershed system. Also informative is that the three states at the mouth of the river, Mississippi, Arkansas, and Louisiana, are #1, #3, and #4 in the nation in terms of obesity rate (39.5%, 37.1%, and 36.8%, respectively).

Obesity rates are high everywhere in America, but we can see that they are higher in states where the groundwater has covered more distance, and had more time to accumulate contaminants (see continental watershed map below). States where groundwater comes from shorter river systems have a clear tendency towards lower (though still in the range of 25%-30%) rates of obesity.

If this is the case, we should also see similar patterns in other countries.

It’s hard to find good province-level maps of obesity for China, but most of them look something like this:

China has two major rivers, the Yangzi and the Yellow river. Comparing our map of obesity to a map of China’s rivers, we see that Shandong Province, with the highest rate of obesity, is at the mouth of the Yellow River. Shanghai, at the mouth of the Yangzi, is not quite as obese, but still more obese than the neighboring provinces. And in general we see that provinces at lower elevations are more obese.

There are always a few confusing outliers, of course. Why are Maine, North Dakota, and Alabama so obese? In China, why are Xinjiang and Heilongjian provinces so obese? The answer is that watersheds play a role in the distribution of contaminants, but are not the whole story.

In some cases, though, the answer may come back to watersheds after all. For example, Xinjiang province’s main watershed is the Tarim Basin, an endorheic basin that captures water and has no outlet. Rain that falls in the Tarim Basin flows to Lop Nur and stays there. The water might evaporate, but any contaminants it carried will stay in the basin.

We see similar trends in data from Iran. In the map of Iran shown below, you can see that many of the most obese provinces are near the Caspian Sea, another endorheic basin. We weren’t able to find similar maps for Russia or for Kazakhstan, two other large countries bordering on the Caspian, but we would expect them to look similar.

There are obvious and often extreme differences in obesity between people at 0 ft of altitude and 500 ft of altitude, both in the US and in other countries. The changes in CO₂ aren’t enough to make any difference, but water runoff could.

It’s important to note that altitude itself doesn’t affect obesity directly. Instead, altitude is a proxy for how high an area is in the watershed, which is itself a proxy for how badly the local water supply is contaminated. This is why Mississippi is more obese than low-lying areas of California. In California the water supply hasn’t traveled nearly as far in its path to the ocean, and has traveled past fewer farms, highways, cities, and factories.

Mystery 8: Diets Don’t Work 

Finally, no diet will reliably help because obesity isn’t caused by a bad diet and can’t be cured by a good one. Hypothetically speaking, if there was a person who was only exposed to these contaminants in their food, cutting out the contaminated food for long enough would theoretically cure them. This may be what happened with Chris Voight when he cut out everything but potatoes.

Ultimately, the fact that diets don’t work very well for most people suggests that we pick up these contaminants from other sources than just our food. Probably they are also, to varying degrees, in our water, our workplaces, and our homes.

3.3    Contamination

We aren’t the first researchers who are concerned about environmental contaminants and the role they might play in rising obesity rates. But the scope of this inquiry has traditionally been limited to how contaminants might contribute to the obesity epidemic.

One review focuses on food additives, both intentional (e.g. artificial sweeteners) and unintentional (pesticides), suggesting that “environmental contaminants are contributing to the global epidemic of obesity”, and suggests that the review will be “helpful in elucidating their role in the obesity epidemic”. Another review focused on endocrine disrupting chemicals, but closes by saying, “public health officials should think of the obesity epidemic as a function of a multifactorial complex of events, including environmental-endocrine disruptors.” Yet another mentions “nutrient quality, stress, fetal environment and pharmaceutical or chemical exposure as relevant contributing influences.”

Canaries in the Coal Mine documents the rise in obesity in wild and captive animals and suggests that “the aetiology of increasing body weight may involve several as-of-yet unidentified and/or poorly understood factors.” What factors could these be? They list a couple: viral pathogens, epigenetic factors, and at the very end of the paper, “the collection of endocrine-disrupting chemicals (endocrine-disruptors), widely present in the environment.”

A statement from the 2nd International Workshop on Obesity and Environmental Contaminants in Uppsala, Sweden concluded that “the findings from numerous animal and epidemiological studies are consistent with the hypothesis that environmental contaminants could contribute to the global obesity epidemic.”

A National Toxicology Program workshop from 2012 suggests that “exposures to environmental chemicals may be contributing factors to the epidemics of diabetes and obesity.” They suggest that there is a link between some forms of contamination and type 2 diabetes, but overall, they say that there is still not enough research to draw firm conclusions.

Despite this interest, all the claims have been quite mild, identifying environmental contaminants as possibly being one of many factors contributing in some small way to the obesity epidemic. In contrast, we propose that the obesity epidemic is entirely driven by environmental contaminants. The entire difference in obesity between 1980 and today is attributable to one or more contaminants that we are exposed to in our food, water, and living spaces.

Still, not everyone today is obese. There are two reasons for this. First of all, even though everyone is exposed, some people are exposed to more than other people. If you live in an environment with less exposure, for example at a higher altitude, on average you will be less obese.

Some people are also less affected by these contaminants than other people, even at the same dose, and this difference is largely genetic. But even these people probably still, on average, have much more body fat than their ancestors did. Hunter-gatherers have BMIs of around 22 or 23. Civil War veterans in the 1890s had average BMIs of about 23 as well. If your BMI is higher than 23, you’re probably fatter than you would be without the action of these contaminants.

Sometimes it is these factors in combination. If you have a genetic resistance and you’re exposed to low levels of these contaminants, you’ll be much less obese than average.

3.4    Further Evidence in Favor of Contaminants

The difference in obesity rates between countries, as well as the differences between states or provinces within a country, is also the result of differences in contamination. Some of it will be genetic, but some of it is because some places are more contaminated than others.

Some of the strongest evidence for this comes from immigration. When they arrive in a new country, immigrants usually have lower obesity rates than their native counterparts do, but over time they become about as obese as the natives are. Looking at the trends, it appears that much of the effect of the contaminants occurs in the first year of exposure, though it takes 10-15 years before immigrants have obesity rates similar to the rates in the host country.

During the Cuban economic crisis known as the “Special Period”, obesity rates plummeted, from 14% obese to 7% obese. Normally this is attributed to the decrease in calorie consumption and the increase in exercise, as oil shortages led people to drive less and walk or bike more. But we know already that reducing consumption and increasing exercise have very modest effects on weight loss.

The average Cuban lost about 20 pounds in just a couple of years — even though in 1993, two years into the crisis, the average Cuban was still eating a very reasonable 2,099 calories per day.

Food was restricted, but very few people were starving. While obesity dropped from 14% to 7%, the number of people listed as “underweight” only went from 8% to 10.3% (see above). And it’s not like they were eating all that healthy — “the primary sources of energy during the crisis were sugar cane and rice.”

Rarely mentioned but particularly notable is that food imports virtually ceased during this period. If we assume that Cuban obesity was partially a result of contaminants in their food imports, this explains the data perfectly. Alternately, we can note that fertilizer and pesticide use sharply declined in this period as well, as both were normally derived from oil, which the island was now seriously lacking. These are also potential contaminants.

One surprising fact is that the most obese countries in the world by BMI are all tiny island nations in the south or central Pacific — Nauru, Tonga, Samoa, Tuvalu, Palau, the Cook Islands, and others. Depending on the year and the source, the 10 most obese nations in the world are usually small Pacific islands. Obesity rates in these countries are not merely high, they are clear outliers. The most obese mainland nations are around 35-40% obese, but these small Pacific islands have obesity rates in the range of 45-60%. Certainly this requires some sort of explanation.

To begin with, there are some reasons to suspect that this is largely an artefact. These islands all have very small populations and are genetically homogeneous, so it’s possible that much of the difference is genetic. Polynesians also appear to be slightly stouter and more muscular than other groups, which may mean that BMI is an especially bad measure for this group and leads us to slightly overestimate how obese they are. With a better measure of obesity, their obesity rates might be more similar to the rates of other very obese countries, like Kuwait and the United States.

In addition, Polynesian countries import most of their food and eat a lot of highly processed, canned meat (famously spam), which may be more contaminated than average. It’s also notable that Nauru, the most obese (61%) country in the world, has been heavily strip-mined for phosphate. This is interesting because mining is a major source of environmental contamination. For comparison, West Virginia is an obesity outlier in the United States, and it too has a long history of strip mining. In any case, this is why Nauru imports so much food — with about 80% of the island strip-mined, they can’t grow anything there. Most of these islands are not so heavily mined, of course, but this might explain why Nauru is 61% obese and Samoa is “only” 47% obese.

---

[Next Time: RETURN TO HOT CICO]

---

Follow @mold_time

[PART I – MYSTERIES]

Current theories of the obesity epidemic are inadequate. None of them hold up to closer scrutiny, and none can explain all of the mysteries mentioned in Part I. But these mysteries are real, puzzling data about the obesity epidemic.

You’re probably familiar with several theories of the obesity epidemic, but there is strong evidence against all of them. In this section, we focus on the case against a couple of the most popular theories. 

2.1    Calories In, Calories Out

A popular theory of obesity is that it’s simply a question of calories in versus calories out (CICO). You eat a certain number of calories every day, and you expend some number of calories based on your metabolic needs and physical activity. If you eat more calories than you expend, you store the excess as fat and gain weight, and if you expend more than you eat, you burn fat and lose weight.

This perspective assumes that the body stores every extra calorie you eat as body fat, and that it doesn’t have any tools for using more or less energy as the need arises. But this isn’t the case. Your body has the ability to regulate things like its temperature, and it has similar tools to regulate body fatness. When we look closely, it turns out that “calories in, calories out” doesn’t match the actual facts of consumption and weight gain.

“This model seems to exist mostly to make lean people feel smug,” writes Stephen Guyenet, “since it attributes their leanness entirely to wise voluntary decisions and a strong character. I think at this point, few people in the research world believe the CICO model.”

It’s not that calories don’t matter at all. People who are on a starvation diet of 400 calories per day will lose weight, and as we will see in this section, people who eat hundreds of calories more than they need will usually gain weight. The problem is that this ignores how the body accounts for the calories coming in and going out. If you don’t eat enough, your body finds ways to burn fewer calories. If you eat too much, your body doesn’t store all of the excess as fat, and compensates by making you less hungry later on. Calories are involved in the math but it’s not as simple as “weight gain = calories in – calories out”.

[Edit: We’ve added an interlude clarifying this section on CICO in response to reader questions and objections. If you have objections to anything below, read the interlude here because we probably address it!]

2.1.1    Common Sense

First, we want to present some common-sense arguments for why diet and exercise alone don’t explain modern levels of obesity.

Everyone “knows” that diet and exercise are the solution to obesity. Despite this, rates of obesity continue to increase, even with all the medical advice pointing to diet and lifestyle interventions, and a $200 billion global industry devoted to helping people implement these interventions. It’s not that no one is listening. People are exercising more today than they were 10 or even 20 years ago. Contrary to stereotypes, more than 50% of Americans meet the HHS guidelines for aerobic exercise. But obesity is still on the rise.

It’s true that people eat more calories today than they did in the 1960s and 70s, but the difference is quite small. Sources have a surprisingly hard time agreeing on just how much more we eat than our grandparents did, but all of them agree that it’s not much. Pew says calorie intake in the US increased from 2,025 calories per day in 1970 to about 2,481 calories per day in 2010. The USDA Economic Research Service estimates that calorie intake in the US increased from 2,016 calories per day in 1970 to about 2,390 calories per day in 2014. Neither of these are jaw-dropping increases.

If we go back further, the story actually becomes even more interesting. Based on estimates from nutrient availability data, Americans actually ate more calories in 1909 than they did in 1960.

Finally, there are many medical conditions that cause obesity. For example, Prader-Willi Syndrome, a genetic disorder characterized by intense hunger and resulting obesity, hypothyroidism, an endocrine disorder where people experience loss of appetite yet still gain 5-10 pounds, and lesions to the hypothalamus, which often lead to intense weight gain, sometimes accompanied by great hunger but many times not.

2.1.2    Scientific Evidence

In addition to these common-sense objections, decades of research suggests that diet and exercise are not to blame for rising rates of obesity.

Studies of controlled overfeeding — you take a group of people and get them to eat way more than they normally would — reliably find two things. First, a person at a healthy weight has to eat huge amounts of calories to gain even a couple pounds. Second, after the overfeeding stops, people go right back to the weight they were before the experiment.

The great-grandaddy of these studies is the Vermont prison experiment, published in 1971. Researchers recruited inmates from the Vermont State Prison, all at a healthy weight, and assigned some of them to eat enormous amounts of food every day for a little over three months. How big were these meals? The original paper doesn’t say, but later reports state that some of the prisoners were eating 10,000 calories per day.

On this olympian diet, the prisoners did gain considerable weight, on average 35.7 lbs (16.2 kg). But following the overfeeding section of the study, the prisoners all rapidly lost weight without any additional effort, and after 10 weeks, all of them returned to within a couple pounds of their original weight. One prisoner actually ended up about 5 lbs (2.3 kg) lighter than before the experiment began!

Inspired by this, in 1972, George Bray decided to conduct a similar experiment on himself. He was interested in conducting overfeeding studies, and reasoned that if he was going to inflict this on others, he should be willing to undergo the procedure himself. First he tried to double each of his meals, but found that he wasn’t able to gain any weight — he simply couldn’t fit two sandwiches in his stomach at every sitting.

He switched to energy-dense foods, especially milkshakes and ice cream, and started eating an estimated 10,000 calories per day. Soon he began to put on weight, and gained about 22 lbs (10 kg) over 10 weeks. He decided this was enough and returned to his normal diet. Six weeks later, he was back at his original weight, without any particular effort.

In both cases, you’ll notice that even when eating truly stupendous amounts of food, it actually takes more time to gain weight than it does to lose it. Many similar studies have been conducted and all of them find basically the same thing — check out this recent review article of 25 studies for more detail.

Overfeeding in controlled environments does make people gain weight. But they don’t gain enough weight to explain the obesity epidemic. If you eat 10,000 calories per day, you might be able to gain 20 or 30 pounds, but most Americans aren’t eating 10,000 calories per day.

We can compare these numbers to the increases in average calories per day we reviewed earlier. Sure, consumption in the US went from 2,025 calories per day in 1970 to 2,481 calories per day in 2010, a difference of 456 calories. But consider Poehlman et al. (1986), where researchers fed a group of 12 men 1,000 extra calories a day for 22 days. On average the men gained about 5 lbs (2.2 kg), but some of them actually lost weight instead.

And it’s not as though these participants are eating 1,000 extra calories of celery and carrots. In one study, the extra calories came from “sherbet, fruit juices, margarine, corn oil, and cookies”. But the content doesn’t seem to matter very much. Another study compared overfeeding with carbohydrates (mostly starch and sugar) and overfeeding with fat (mostly dairy fat like cream and butter). The two groups got their extra calories from different sources, but they were overfed by the same amount. After two weeks, both groups gained the same amount of fat, 3.3 lbs on average. A similar study overfed volunteers by 1,194 calories on either a high-carb or a high-fat diet for 21 days. Both groups gained only about 2 lbs of fat.

The fact that many of these are twin studies provides even more evidence against CICO. In groups of twins that are all overfed by the same amount, there is substantial variation between the different participants in general. Some people gain a lot of weight, others gain almost none. But each person gains (or loses!) about the same amount of weight as their twin. In some cases these correlations can be substantial, as high as r = 0.90. This strongly suggests that genetics plays a large role in determining how the body responds to overfeeding.

The story with exercise is the same as with overeating — it makes a difference, but not much. One randomized controlled trial assigned overweight men and women to different amounts of exercise. More exercise did lead to more body fat loss, but even in the group exercising the most — equivalent to 20 miles (32.0 km) of jogging every week for eight months — people only lost about 7 lbs.

You might think that hunter-gatherers have a more active lifestyle than we do, but this isn’t always true. The Kitavans examined in 1990 by Staffan Lindeberg were only slightly more active than westerners, had more food than they knew what to do with, and yet were never obese. “Many Westerners have a level of physical activity that is well within the range of the Kitava population,” he wrote. “Hence, physical activity does not seem to explain most of the differences in disease pattern between Kitava and the Western world.”

A recent meta-analysis of 36 studies compared the effects of interval training exercise with more traditional moderate-intensity continuous training. The authors call interval training “the magic bullet for fat loss” (this is literally in the title) and trumpet that it provides 28.5% greater reductions in total absolute fat mass than moderate exercise. But what they don’t tell you is that this is a difference between a loss of about 3 lbs and about 4 lbs, for an exercise program running 12 weeks long. Needless to say, this difference isn’t very impressive. Other meta-analyses find similar results: “neither short-term HIIT/SIT nor MICT produced clinically meaningful reductions in body fat.”

Maybe diet and exercise together are worth more than the sum of their parts? Sadly this doesn’t seem to be the case either. If anything, when combined they are worth less than the sum of their parts. One meta-analysis comparing interventions based on diet, exercise, and diet plus exercise found that people lost about 23.5 lbs (10.7 kg) on diets, 6.4 lbs (2.9 kg) on an exercise regime, and 24.2 lbs (11.0 kg) on diet plus exercise. After a year, diet plus exercise was down to 18.9 lbs (8.6 kg). Other meta-analyses are more tempered, for example, finding a loss of about 3.6 lbs (1.6 kg) after two years of diet plus exercise interventions. Again this is more weight loss than zero, but it clearly rules out diet plus exercise as an explanation for the obesity epidemic. People in 1950 were a lot leaner than they are now, but it’s not because they ate less and exercised more.

2.2    Good Calories and Bad Calories

Ok, calories themselves may not be the villain here. But maybe it’s not that we’re eating more than we used to — maybe it’s that we’re eating differently. Maybe one particular macronutrient or source of calories is to blame.

2.2.1    Dietary Fat

Dietary fat seems like a possible culprit. After all, fat makes you fat, right? Turns out it’s not so simple.

To begin with, fat consumption has actually fallen over the past few decades, while obesity has skyrocketed. This isn’t consistent with an explanation where dietary fat leads to obesity.

Plenty of cultures eat extremely high-fat diets and remain very lean indeed. You’ll remember that the Maasai diet is about 3000 calories per day, and 66% of that is from fat. But the Maasai don’t suffer from obesity. In fact, Kalahari Bushmen love fat and apparently wax poetic about it.

Clinical results agree: dietary fat doesn’t have much of an impact on long-term weight. Putting people on a low-fat diet reduces their weight in the short term, but in trials lasting for longer than one year, they tend to return to normal. When they are directly compared, low-fat and high-fat diets have about the same impact on weight loss.

This is a little difficult to square with animal studies that find that a high-fat diet reliably leads to obesity in monkeys, dogs, pigs, hamsters, squirrels, rats, and mice. It could just be that humans are not monkeys, dogs, pigs, hamsters, squirrels, rats, or mice, and that while dietary fat has an adverse effect on these species, it doesn’t do much to us. Some of the hamster studies, for example, induced obesity simply by giving the hamsters extra sunflower seeds, a phenomenon not observed in humans. Pigs, in particular, will become obese even on low-fat diets when given the opportunity.

We even see differences within a specific kind of animal. The same high-fat diet will make one species of hamster (Syrian hamsters) obese and leave another species of hamster (golden hamsters) merely chubby. If the findings can’t generalize between different species of hamsters, we shouldn’t expect them to generalize to humans.

It could also be that dietary fat leads to obesity in mammals held in captivity, possibly due to factors like stress. Metabolic ward studies restrict your movement, but it’s not exactly like living your whole life in a laboratory cage. And it’s worth noting that about 10–15% of macaque and rhesus monkeys in captivity become obese when they reach middle age, despite the fact that they are fed a relatively low-fat (10% of energy) diet.

In any case, it’s hard to square a fat-based explanation for the obesity epidemic with the fact that fat consumption hasn’t increased in step with the rise of obesity and the fact that low-fat diets don’t lead to much weight loss.

2.2.2    Carbohydrates

Ok, maybe fat doesn’t make you fat. How about carbohydrates? All this bread can’t be good for us.

This theory is dead on the starting line, though, because as obesity has gone up, consumption of carbohydrates has gone down (see figure).

This is enough to make it clear that carbohydrate consumption isn’t driving the obesity epidemic, but we can take a slightly closer look anyways, just to be sure.

Eating lots of carbs can actually make you lose weight. High-carbohydrate diets cause weight loss, even when not restricting calories. A study from 2003 examined low-fat diets in 16 overweight people. Naturally, this low-fat diet was high in carbohydrates. When patients started the low-fat diet and were told to eat as much as they wanted, they actually ate 291 calories less per day.

But their carbohydrate intake increased, from 253 grams per day to 318 grams per day. On this diet they lost 8 lbs (3.8 kg) on average over a 12-week period. In the DIETFITS randomized controlled trial, 609 people fed a whole-food, high-carbohydrate diet lost 12 pounds (5.3 kg) over one year, not significantly different from the 13 pounds (6.0 kg) of weight lost on a whole-food low-carbohydrate diet. The high-carbohydrate diet also supplied about 1.5 times as much sugar as the low-carbohydrate diet.

The residents of Kitava, mentioned earlier, have a diet of starchy roots and tubers. Almost 70% of their calories come from carbohydrates, but they don’t suffer from obesity, diabetes, or heart disease. 

(Lindeberg also says: “The long primate history of fruit eating, the high activity of human salivary amylase for effcient starch digestion, and some other features of human mouth physiology … suggest that humans are well prepared for a high carbohydrate intake from non-grain food sources. … in contrast to most other animals including non-human primates, humans have an exceptional capacity to produce salivary amylase in order to begin hydrolysis of starch in the mouth.”)

In general, cultures with very high intakes of carbohydrate tend to be lean. Most agricultural societies around the world have a diet that is high in carbohydrates and low in fat. Agricultural societies are different from industrialized ones in many ways, of course. But even in those agricultural cultures with abundant food, people are typically lean, with low rates of diabetes and cardiovascular disease.

This is true even if the carbohydrate is white rice. In Japan, white rice is a primary staple food (p. 338), and has been for a long time. About 62% of the Japanese diet is carbohydrates, and most of this is white rice. Despite this, Japanese rates of obesity have been, and continue to be, the lowest of any industrialized nation.

In fact, people who move from Japan to the US and begin eating less white rice become much heavier. This suggests that the difference isn’t simply genetic. These immigrants do end up eating a diet much higher in fat — but of course, from the previous section, we’ve seen that fat can’t be responsible for this change.

Nor is it likely to be some other carbohydrate staple. Wheat consumption, for example, has been falling for a century. People in the US ate almost twice as much wheat (primarily in the form of bread) in the 1880’s than they do today. If wheat were responsible, people would have been massively obese during reconstruction and entirely lean today. Obviously that is not what we observe.

If the historical data isn’t enough for you, there are entire reviews devoted to the health impacts of wheat, pretty conclusively showing that it isn’t a cause of obesity.

2.2.3    Sugar

Everyone knows that added sugar is the real villain, right? Wrong again.

Sugar consumption has been declining for 20 years in the US, while obesity and diabetes rates have increased. The sugar data in the figure below includes all added sugars such as honey, table sugar, and high-fructose corn syrup, but doesn’t include sugars naturally occurring in fruits and vegetables.

We see something similar in what has been called The Australian Paradox, where obesity in Australia nearly tripled between 1980-2003, while sugar consumption dropped 23%.

Multiple lines of evidence confirm that sugar consumption is falling worldwide. In the US, consumption of sugary beverages dropped between 1999 and 2010. We see the same trend in longitudinal studies of a particular cohort tracked from 1991 to 2008. It’s not that consumers can’t find the sugar they crave, of course — there have been no major changes in the availability of sugary foods.

We see that public health efforts to reduce sugar consumption have worked. In fact, they’ve worked very well. But they don’t seem to have made any difference to the obesity epidemic.

Tightly-controlled metabolic ward studies also show that the sugar content of a diet doesn’t matter much. One study of 17 men compared a 25 percent sugar, high-carbohydrate diet to a 2 percent sugar, very-low-carbohydrate (ketogenic) diet of equal calories. After four weeks, they found that the high-carbohydrate diet caused slightly more body fat loss than the very-low-carbohydrate (ketogenic) diet, despite the fact that the two diets differed more than tenfold in sugar content. We see similar results in mice and in rats: “Animals fed a low-fat, high-sucrose (LH) diet were actually leaner than animals fed a high-complex-carbohydrate diet.”

We can further cite the fact that many cultures, such as the Hadza of Tanzania, the Mbuti of the Congo, and the Kuna of Panama all eat diets relatively high in sugar (sometimes as high at 80%), and yet none of these cultures have noticeable rates of obesity, diabetes, cardiovascular disease, etc.

2.3    Diet in General

Over the past 40 years, there hasn’t been much of a change in where people get their calories from. Americans get about 50% of their calories from carbohydrates, 30% from fat, and 20% from protein, and they have for years. At the same time obesity continues to go up and up. Comparing these two trends, it’s hard to imagine that macronutrients have anything to do with the obesity epidemic.

You’ll recall that Mystery 8 is that all diets work about equally well. It doesn’t matter which diet you choose — you lose about the same number of pounds regardless.

All diets work. The problem is that none of them work very well. Stick to just about any diet for a couple weeks and you will probably lose about 10 pounds. This is ok, but it isn’t much comfort for someone who is 40 lbs overweight. And it isn’t commensurate with the size of the obesity epidemic.

Systematic comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates finds that across many different reduced-calorie diets, people lose about 13.2 lbs (6 kg) over six months, and that in all cases people began to gain weight back after 12 months. It’s not just weight loss, either. Satiety, hunger, satisfaction with the diet, and adherence to the protocol is similar for all diets.

There are too many diets to review in full, of course, but we see the same pattern in every diet that has been extensively studied. Let’s look at just a few.

2.3.1    Ketogenic Diet

We’ve already mentioned a few ketogenic diets, and as we’ve seen, they don’t work much better than other diets do.

There is one meta-analysis of ketogenic diet studies, comparing very-low-carbohydrate ketogenic diets to low fat diets in overweight and obese adults. Across thirteen randomized controlled trials, ketogenic diets only caused 2 pounds (0.9 kg) more weight loss than the traditional low-fat diets after 12 months.

2.3.2    Low-Glycemic Diet

Study after study finds that low-glycemic diets don’t work for weight loss.

One study from 2007 randomly assigned 203 women to either a high-glycemic or low-glycemic diet. The difference in glycemic index was considerable, with the high-glycemic diet having an index twice as high as the low-glycemic diet. The groups consumed the same amount of calories and reported similar levels of hunger.

Despite this, there was no difference between the groups. After two months the LGI group had lost 1.6 lbs (0.72 kg) and the HGI group had lost 0.7 lbs (0.31 kg), but this difference wasn’t sustained. After 18 months on the diet, the LGI group had lost 0.9 lbs (0.41) kg and the HGI group had lost 0.6 lbs (0.26 kg), and this difference was statistically indistinguishable (p = .93). Large differences in glycemic index have no meaningful long-term (or even short-term) effect on calorie consumption or body weight.

Another 18-month randomized trial compared a low-glycemic load (40% carbohydrate and 35% fat) vs low-fat (55% carbohydrate and 20% fat) diet in 73 obese young adults in the Boston, Massachussets area. In both diets, participants were largely eating whole foods; vegetables, beans, and fruit were major components of both diets. In both diets, people were allowed to eat as much as they wanted.

Both groups reported similar levels of hunger and consumed similar amounts of calories. The two diets were rated equally easy to stick to and equally tasty. Both groups lost about 4-5 lbs after 6 months. But both groups started to gain weight back soon after. In fact, the trajectory of weight loss is so identical, we simply have to show you the graph:

Note the p-value of 0.99, which indicates that the two trajectories are about as statistically indistinguishable as is mathematically possible.

We find this in study after study. Meta-analysis also finds that low-glycemic diets don’t do any better than other diets when it comes to weight loss. When the reviewers pick out the studies that show the best performance for low-glycemic diets, they still find a difference of only 4 lbs (1.8 kg). If that’s a success, we have to wonder what failure would look like.

2.3.3    Future Dietary Explanations

Eating fewer calories will lead most people to lose a couple pounds, and it doesn’t really matter what calories they restrict. Cutting back on fat works about as well as cutting back on carbs. In both cases, a couple pounds isn’t enough to explain the obesity epidemic.

Over the past 50 years, medical science has looked at diet from practically every angle. But none of these diet-based explanations have gone anywhere. People are still getting fatter. They got fatter over the last decade. And they got fatter over the decade before that. And the one before that. Every country in the world is growing more obese. And the trend has never once been reversed.

You could certainly cook up another diet-based explanation. But there’s no reason to expect that this explanation would do any better than any of the others.

It’s time to start looking for explanations outside the world of calories, macronutrients, and exercise. At this point, we should assume that the obesity epidemic isn’t caused by our diet.

Could it be a lifestyle difference? Possibly, but signs point against it. Smoking is more prevalent in Japan than among Japanese-Americans, yet Japanese-Americans have much higher rates of hypertension. Similarly, many hunter-gatherers are heavy smokers, including the Kitavans (76% of men and 80% of women) and the Bushmen of South Africa, but these societies have no sign of heart disease.

2.5    Lipostat

There is one theory of obesity which is almost entirely satisfying, based around the body’s ability to regulate its adiposity.

A house has a thermostat. The owner of the house sets the temperature to 72 degrees F. The thermostat detects the temperature of the house and takes action to drive the temperature to the set point of 72°F. If the house is too cold, the thermostat will turn on the furnace. If the house is too warm, the thermostat will turn on the air conditioning.

The human body has a lipostat (from the Greek lipos, meaning fat). Evolution and environmental factors set body fatness to some range — perhaps a BMI of around 23. The lipostat detects how much fat is stored and takes action to drive body fatness to the set point of a BMI of 23. If your body is too thin, the lipostat will drive you to eat more, exercise less, sleep more, and store more of what you eat as fat. If your body is too fat, the lipostat will turn on the air conditioning. Just kidding, the lipostat will drive you to eat less, move and fidget more, and store less of the food you eat as fat.

According to this theory, people become obese because something has gone wrong with the lipostat. If the owner of a house sets the thermostat to 120°F, the house will quickly become too hot, and it will stay that way until the set point is changed or the furnace explodes. Something similar is happening in obesity. The set point has been moved from a healthy and natural level of adiposity (BMI of about 23) to an unusually high level (BMI 30+), and all the regulatory systems of the body are working in concert to push adiposity to that level and keep it there.

The lipostat model is supported by more than a hundred years of evidence. By the 1970s, Dr. Michel Cabanac and collaborators were publishing papers in the journal Nature on what they called the “ponderostat” (pondero = weight). This was later revised to the adipostat (adipo = fat), and eventually, as we call it here, the lipostat.

Modern neuroscience and medical review articles (those are three separate links) overwhelmingly support this homeostatic explanation. In animals and humans, brain damage to the implicated areas leads to overeating and eventual obesity. These systems are well-understood enough that by targeting certain neurons you can cure or cause obesity in mice. While we don’t approve of destroying neurons in human brains with hyperspecific chemical techniques, the few weight-loss drugs approved by the FDA largely act on the brain (hopefully without destroying any neurons).

The lipostat explains why diet and exercise work a little, why they don’t work well enough to reverse obesity, and why even people who lose weight on diets generally end up gaining that weight right back.

In a house where the thermostat has been set to 120°F, there are a lot of things we can do to lower the temperature. We can open all the doors and windows. We can open the icebox. We can order mountains of dry ice off of the internet. All of these things will lower the temperature of the house a little, but even with these measures, the house will still be hotter than the healthy temperature of 72ºF. The furnace will work double-time to push the temperature back up to 120ºF, if it’s not redlining already. And as soon as you relax any of your heat-dissipation measures, the temperature will go right back up to where it was before.

(We can also go down into the basement and hit the furnace with crowbars until it doesn’t work very well anymore. This is a pretty extreme solution and also, incidentally, why gastric bypass surgery works so great.)

When people intentionally overeat, as in the overfeeding studies we reviewed, they temporarily gain a little weight, but when they stop overeating, they quickly return to their original weight. When people intentionally undereat, as they do on a diet, they temporarily lose a little weight, but when they stop undereating they quickly return to their original weight. In fact, they usually return to near their original weight even if they keep undereating. The lipostat has a target weight and, when not actively opposed, it will push your body weight to that weight and do its best to keep it there.

There are many signals that the brain uses to measure how much fat the body is carrying. One of the most important is the hormone leptin, which is naturally produced by fat cells. Part of the action of the lipostat is making sure that leptin levels are kept within a desired range, which helps keep us at a desired weight.

Very rarely, people are born with a genetic mutation that makes it so their fat cells no longer produce leptin. The lipostat notices that it isn’t detecting any leptin, and assumes that the body has no fat stores at all, with predictable results. Usually these children are of normal birth weight, but from the first weeks of their lives, they are insatiably hungry. By age two, they weigh more than 50 pounds, and may be as high as 60% fat by weight. They have a truly incredible drive to eat:

. . . leptin-deficient children are nearly always hungry, and they almost always want to eat, even shortly after meals. Their appetite is so exaggerated that it’s almost impossible to put them on a diet: if their food is restricted, they find some way to eat, including retrieving stale morsels from the trash can and gnawing on fish sticks directly from the freezer. This is the desperation of starvation [. . . ] they become distressed if they’re out of sight of food, even briefly. If they don’t get food, they become combative, crying and demanding something to eat.

The lipostat account is extremely convincing. The only weakness in the theory is that it’s not clear what could cause the lipostat to be set to the wrong point. In leptin-deficient children, their body simply can’t detect that they are obese. But most people produce leptin just fine. What is it that throws this system so totally out of balance?

While the lipostat perspective does in a sense explain why people become obese (their lipostat is out of alignment), it’s not really a theory of the obesity epidemic, since it doesn’t explain why our lipostats began getting more and more out of balance around 1980.

Even advocates of the theory are perfectly willing to admit this. In The Hungry Brain, Stephen Guyenet writes:

Many researchers have tried to narrow down the mechanisms by which [diet] causes changes in the hypothalamus and obesity, and they have come up with a number of hypotheses with varying amounts of evidence to support them. Some researchers believe the low fiber content of the diet precipitates inflammation and obesity by its adverse effects on bacterial populations in the gut (the gut microbiota). Others propose that saturated fat is behind the effect, and unsaturated fats like olive oil are less fattening. Still others believe the harmful effects of overeating itself, including the inflammation caused by excess fat and sugar in the bloodstream and in cells, may affect the hypothalamus and gradually increase the set point. In the end, these mechanisms could all be working together to promote obesity. We don’t know all the details yet…

Guyenet favors a “food reward” explanation, where eating “highly rewarding food” causes a mild form of brain damage that turns up the set point of the lipostat. He’s even gone so far as to propose (as an April Fools joke) a collection of boring recipes called The Bland Food Cookbook.

You’ll notice that in all these theories, the factors that damage the lipostat are related to diet. But as we’ve just argued above, the persistent failure to find a solution in our diets strongly suggests that we should start looking elsewhere for the explanation.

2.6    What, Then?

We should start seriously considering other paradigms. If diet and exercise are out as explanations for the epidemic, what could possibly explain it? And what could possibly explain all of the other bizarre trends that we have observed?

---

[Next Time: A CHEMICAL HUNGER]

---

Follow @mold_time