[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.

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[Next Time: SUSPECT NUMBER ONE]

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