A viral cure for cancer only a few years away?

Adopting an evolutionarily sound lifestyle may reduce the probability that one will develop cancer, but there will be those who will nevertheless have cancer. As we live longer lives, cancer diagnoses are likely to become more and more common.

There are viruses that cause the formation and growth of cancer tumors: oncoviruses. However, and quite interestingly, there are also viruses that seek and kill cancer cells: oncolytic viruses. The video below discusses emerging treatments based on oncolytic viruses.



This Penn Medicine YouTube video is about 6 minutes in length. (A previous HBO video was about 40 minutes in length, and it was worth watching in full. However it became unavailable soon after I linked it here. Its title on YouTube was "Vice Special Report: Killing Cancer".)

Cancer treatment via oncolytic viruses had a promising start in the mid-1990s. However, due to technical complications it has been sidelined for years. Interest has been picking up dramatically in recent years. Could it be foundation for the long promised cure for cancer, as the video implies?

Only time and research will tell …

Will your wireless router give you cancer?

If you pick up a magnet and move it up and down with your hand, you will be creating electromagnetic radiation. The faster you move the magnet, the higher the frequency of the radiation you create. The higher the frequency of the radiation, the lower is its wavelength. High frequency is also associated with high radiation strength, where strength can be measured in watts (W).

We are constantly bombarded by electromagnetic radiation, which is usually classified based on its frequency (and also wavelength, since frequency and wavelength are inversely proportional). The main types of electromagnetic waves, in order of increasing frequency, are: radio waves, microwaves, infrared radiation, visible light, ultraviolet radiation, X-rays, and gamma rays.

There has been a large amount of research on the health effects of wireless equipment, including wireless routers (figure below from Bestwirelessrouterreview.com), because of the electromagnetic radiation that they emit. Wireless equipment uses electromagnetic radiation of the radio waves type.

In developing countries, wireless routers are ubiquitous. They are found everywhere – at home, in hotels and businesses, and even in public parks. They allow wireless devices to connect to the Internet, by creating one or more “WiFi hotspots”.

The strength of the radiation emitted by wireless routers, when it reaches humans, is much lower than that emitted by mobile phones. One of the reasons for this is the lower strength of the radiation emitted by wireless routers, which can go from 30 to 500 milliwatts (mW); versus 125 mW to 2 W for mobile phones.

But the main reason for the lower strength of the radiation emitted by wireless routers, when it reaches humans, is that wireless routers normally are located farther away from humans than mobile phones. Radiation strength goes down according to the inverse-square law; i.e., proportionally to 1 divided by the distance between source and destination squared.

Given this, it has been estimated () that the exposure to 1 full year of radiation from a wireless router at home is equivalent, in terms of radiation reaching the body, to 20 minutes of exposure to the radiation emitted by a mobile phone.

If the radiation from wireless routers were to cause cancer, so should the radiation from mobile phones. So, what about mobile phones? Do they cause cancer?

In spite of a large amount of research conducted on the subject, no conclusive evidence has been found that the radiation from mobile phones causes cancer. A representative example of this research is a large Danish study (), whose results have recently been replicated.

Mobile phone radiation, like wireless router radiation, is currently classified by the International Agency for Research on Cancer (IARC) in Group 2B, namely “possibly carcinogenic”. This carries a recommendation of “more research”. Caffeic acid, found in coffee, is also in this group. It is useful to note that neither mobile phone nor wireless router radiation are classified in Group 2A, which is the “probably carcinogenic” IARC group.

When one considers the accumulated evidence regarding cancer risk associated with all types of electromagnetic radiation, the biggest concern by far is sunburn from ultraviolet radiation. The evidence suggests that it causes skin cancer. Chronic non-sunburn exposure to natural ultraviolet radiation, on the other hand, seems protective against most types of cancer (skin cancer included).

Will your wireless router give you cancer? I don’t think so.

Sensible sun exposure

Sun exposure leads to the production in the human body of a number of compounds that are believed to be health-promoting. One of these is known as “vitamin D” – an important hormone precursor ().

About 10,000 IU is considered to be a healthy level of vitamin D production per day. This is usually the maximum recommended daily supplementation dose, for those who have low vitamin D levels.

How much sun exposure, when the sun is at its peak (around noon), does it take to reach this level? Approximately 10 minutes.

We produce about 1,000 IU per minute of sun exposure, but seem to be limited to 10,000 IU per day. This assumes a level of skin exposure comparable to that of someone wearing a bathing suit.

Contrary to popular belief, this does not significantly decrease with aging. Among those aged 65 and older, pre-sunburn full-body exposure to sunlight leads to 87 percent of the peak vitamin D production seen in young subjects ().

Evolution seems to have led to a design that favors chronic (every day or so) but relatively brief sun exposure. Most of the sun rays are of the UVA type. However it is the UVB rays, which peak when the sun is high, that stimulate vitamin D production the most. The UVA rays in fact deplete vitamin D. Therefore, after 10 minutes of sun exposure per day when the sun is high, we would be mostly depleting vitamin D by sunbathing when the sun is low.

There is a lot of research that suggests that extended sun exposure also causes skin damage, even exposure below skin cancer levels. Also, anecdotally there are many reports of odd things happening with people who sunbathe for extended periods of time at the pool. Examples are moles appearing in odd places like the bottom of the feet, cases of actinic keratosis, and even temporary partial blindness.

Source: Lifecasting.org

There is something inherently unnatural about sunbathing at the pool, and exponentially more so in tan booths. Hunter-gatherers enjoy much sun exposure by generally avoiding the sun; particularly from the front, as this impairs the vision.

Pools often have reflective surfaces around them, so that people will not burn their feet. They cause glare, and over time likely contribute to the development of cataracts.

When you go to the pool, put your hands perpendicular to your face below you nose so that much of the light coming from those reflective surfaces does not hit your eyes directly. If you do this, you’ll probably notice that the main source of glare is what is coming from below, not from above.

In the African savannas, where our species emerged, this type of reflective surface has no commonly found analog. You don't have to go to the pool to find all kinds of sources of unnatural glare in urban environments.

Snow is comparable. Hunter-gatherers who live in areas permanently or semi-permanently covered with snow, such as the traditional Inuit, have a much higher incidence of cataracts than those who don’t.

So, what would be some of the characteristics of sensible sun exposure during the summer, particular at pools? Considering all that is said above, I’d argue that these should be in the list:

- Standing and moving while sunbathing, as opposed to sitting or lying down.

- Sunbathing for about 10 minutes, when the sun is high, staying mostly in the shade after 10 minutes or so of exposure.

- Wearing eye protection, such as polarized sunglasses.

- Avoiding the sun hitting you directly in the face, even with eye protection, as the facial skin is unlikely to have the same level of resistance to sun damage as other parts that have been more regularly exposed in our evolutionary past (e.g., shoulders).

- Covering those areas that get sunlight perpendicularly while sunbathing when the sun is high, such as the top part of the shoulders if standing in the sun.

Doing these things could potentially maximize the benefits of sun exposure, while at the same time minimizing its possible negative consequences.

Latitude and cancer rates in US states: Aaron Blaisdell’s intuition confirmed

In the comments section of my previous post on cancer rates in the US states () my friend Aaron Blaisdell noted that: …comparing states that are roughly comparable in terms of number of seniors per 1000 individuals, latitude appears to have the largest effect on rates of cancer.

Good point, so I collected data on the latitudes of US states, built a more complex model (with several multivariate controls), and analyzed it with WarpPLS 4.0 ().

The coefficient of association for the effect of latitude on cancer rates (path coefficient) turned out to be 0.35. Its P value was lower than 0.001, meaning that the probability that this is a false positive is less than a tenth of a percent, or that we can be 99.9 percent confident that this is not a false positive.

This was calculated controlling for the: (a) proportion of seniors in the population (population age); (b) proportion of obese individuals in the population (obesity rates); and (c) the possible moderating effect of latitude on the effect of population age on cancer rates. The graph below shows this multivariate-adjusted association.



What is cool about a multivariate analysis is that you can control for certain effects. For example, since we are controlling for proportion of seniors in the population (population age), the fact that we have a state with a very low proportion of seniors (Alaska) does not tilt the effect toward that outlier as much as it would if we had not controlled for the proportion of seniors. This is a mathematical property that is difficult to grasp, but that makes multivariate adjustment such a powerful technique.

I should note that the 99.9 percent confidence mentioned above refers to the coefficient of association. That is, we are quite confident that the coefficient of association is not zero; that is it. The P value does not support the hypothesized direction of causality (latitude -> cancer) or exclude the possibility of a major confounder causing the effect.

Nonetheless, among the newest features of WarpPLS 4.0 (still a beta version) are several causality assessment coefficients: path-correlation signs, R-squared contributions, path-correlation ratios, path-correlation differences, Warp2 bivariate causal direction ratios, Warp2 bivariate causal direction differences, Warp3 bivariate causal direction ratios, and Warp3 bivariate causal direction differences. Without going into a lot of technical detail, which you can get from the User Manual () without even having to install the software, I can tell you that all of these causality assessment coefficients support the hypothesized direction of causality.

Also, while we cannot exclude the possibility of a major confounder causing the effect, we included two possible confounders in the analysis and controlled for their effects. They were the proportion of seniors in the population (population age) and the proportion of obese individuals in the population (obesity rates).

Having said all of the above, I should also say that the effect is similar in magnitude to the effect of population age on cancer rates, which I discussed in the previous post linked above. That is, it is not the type of effect that would be clearly noticeable in a person’s normal life.

Sunlight exposure? Maybe.

We do know that our body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high (). Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.

Aging and cancer: The importance of taking a hard look at the numbers

The table below is from a study by Hayat and colleagues (). It illustrates one common trend regarding cancer – it increases dramatically in incidence among those who are older. With some exceptions, such as Hodgkin's lymphoma, there is a significant increase in risk particularly after 50 years of age.



So I decided to get state data from the US Census web site (), on the percentage of seniors (age 65 or older) by state and cancer diagnoses per 1,000 people. I was able to get some recent data, for 2011.

I analyzed the data with WarpPLS (version 4.0 has been just released: ), generating the types of coefficients that would normally be reported by researchers who wanted to make an effect appear very strong.

In this case, the effect would be essentially of population aging on cancer incidence (assessed indirectly), summarized in the graph below. The graph was generated by WarpPLS. The scales are standardized, and so are the coefficients of association in the two segments shown. As you can see, the coefficients of association increase as we move along the horizontal scale, because this is a nonlinear relationship. The overall coefficient of association, which is a weighted average of the two betas shown, is 0.84. The probability that this is a false positive is less than 1 percent.



A beta coefficient of 0.84 essentially means that a 1 standard deviation variation in the percentage of seniors in a state is associated with an overall 84 percent increase in cancer diagnoses, taking the standardized unit of the number of cancer diagnoses as the baseline. This sounds very strong and would usually be presented as an enormous effect. Since the standard deviation for the percentage of seniors in various states is 1.67, one could say that for each 1.67 increment in the percentage of seniors in a state the number of cancer diagnoses goes up by 84 percent.

Effects expressed in percentages can sometimes give a very misleading picture. For example, let us consider an increase in mortality due to a disease from 1 to 2 cases for each 1 million people. This essentially is a 100 percent increase! Moreover, the closer the baseline is from zero, the more impressive the effect becomes, since the percentage increase is calculated by dividing the increment by the baseline number. As the baseline number approaches zero, the percentage increase from the baseline approaches infinity.

Now let us take a look at the graph below, also generated by WarpPLS. Here the scales are unstandardized, which means that they refer to the original measures in their respective original scales. (Standardization makes the variables dimensionless, which is sometimes useful when the original measurement scales are not comparable – e.g., dollars vs. meters.) As you can see here, the number of cancer diagnoses per 1,000 people goes from a low of 3.74 in Utah to a high of 6.64 in Maine.



One may be tempted to explain the increase in cancer diagnoses that we see on this graph based on various factors (e.g., lifestyle), but the percentage of seniors in a state seems like a very good and reasonable predictor. You may say: This is very depressing. You may be even more depressed if I tell you that controlling for state obesity rates does not change this picture at all.

But look at what these numbers really mean. What we see here is an increase in cancer diagnoses per 1,000 people of less than 3. In other words, there is a minute increase of less than 3 diagnoses for each group of 1,000 people considered. It certainly feels terrible if you are one of the 3 diagnosed, but it is still a minute increase.

Also note that one of the scales, for diagnoses, refers to increments of 1 in 1,000; while the other, for seniors, refers to increments of 1 in 100. This leads to an interesting effect. If you move from Alaska to Florida you will see a significant increase in the number of seniors around, as the difference in the percentage of seniors between these two states is about 10. However, the difference in the number of cancer diagnoses will not be even close to the difference in the presence of seniors.

The situation above is very common in medical research. An effect that is fundamentally tiny is stated in such a way that the general public has the impression that the effect is enormous. Often the reason is not to promote a drug, but to attract media attention to a research group or organization.

When you look at the actual numbers, the magnitude of the effect is such that it would go unnoticed in real life. By real life I mean: John, since we moved from Alaska to Maine I have been seeing a lot more people of my age being diagnosed with cancer. An effect of the order of 3 in 1,000 would not normally be noticed in real life by someone whose immediate circle of regular acquaintances included fewer than 333 people (about 1,000 divided by 3).

But thanks to Facebook, things are changing … to be fair, the traditional news media (particularly television) tends to increase perceived effects a lot more than social media, often in a very stressful way.

The 2012 Arch Intern Med red meat-mortality study: The “protective” effect of smoking

In a previous post () I used WarpPLS () to analyze the model below, using data reported in a recent study looking at the relationship between red meat consumption and mortality. The model below shows the different paths through which smoking influences mortality, highlighted in red. The study was not about smoking, but data was collected on that variable; hence this post.

When one builds a model like the one above, and tests it with empirical data, the person does something similar to what a physicist would do. The model is a graphical representation of a complex equation, which embodies the beliefs of the modeler. WarpPLS builds the complex equation automatically for the user, who would otherwise have to write it down using mathematical symbols.

The results yielded by the complex equation, partly in the form of coefficients of association for direct relationships (the betas next to the arrows), have a meaning. Some may look odd, and require novel interpretations, much in the same way that odd results from an equation describing planetary motions may have led to the development of the theory of black holes.

Nothing is actually "proven" by the results. They are part of the long and painstaking process we call "research". To advance new knowledge, one needs a lot more than a single study. Darwin's theory of evolution is still being tested. Based on various tests and partial refutations, it has itself evolved a great deal since its original formulation.

One set of results that are generated based on the model above by WarpPLS, in addition to coefficients for direct relationships, are coefficients of association called "total effects". They aggregate all of the effects, via multiple paths, between each pair of variables. Below is a table of total effects, with the total effects of smoking on diabetes incidence and overall mortality highlighted in red.

As you can see, the total effects of smoking on diabetes incidence and overall mortality are negative, but small enough to be considered insignificant. This is interesting, because smoking is definitely not health-promoting. Among hunter-gatherers, who often smoke tobacco, it increases the incidence of various types of cancer (). And it may be at the source of many of the health problems suggested by analyses on the China Study II data ().

So what are these results telling us? They tell us that smoking has an intermediate protective effect, very likely associated with its anorexic effect. Smoking is an appetite suppressor. Its total effect on food intake is negative, and strong. As we can see from the table of total effects, just below the two numbers highlighted in red, the total effect of smoking on food intake is -0.356.

Still, it looks like smoking is nearly as bad as overeating to the point of becoming obese (), in terms of its overall effect on health. Otherwise we would see a positive total effect on overall mortality of comparable strength to the negative total effect on food intake.

Smoking may make one eat less, but it ends up hastening one’s demise through different paths.

Does pork consumption cause cirrhosis? Perhaps, if people become obese from eating pork

The idea that pork consumption may cause cirrhosis has been around for a while. A fairly widely cited 1985 study by Nanji and French () provides one of the strongest indictments of pork: “In countries with low alcohol consumption, no correlation was obtained between alcohol consumption and cirrhosis. However, a significant correlation was obtained between cirrhosis and pork.”

Recently Paul Jaminet wrote a blog post on the possible link between pork consumption and cirrhosis (). Paul should be commended for bringing this topic to the fore, as the implications are far-reaching and very serious. One of the key studies mentioned in Paul’s post is a 2009 article by Bridges (), from which the graphs below were taken.

The graphs above show a correlation between cirrhosis and alcohol consumption of 0.71, and a correlation between cirrhosis and pork consumption of 0.83. That is, the correlation between cirrhosis and pork consumption is the stronger of the two! Combining this with the Nanji and French study, we have evidence that: (a) in countries with low alcohol consumption we can find a significant correlation between cirrhosis and pork consumption; and (b) in countries where both alcohol and pork are consumed, pork consumption has the strongest correlation with cirrhosis.

Do we need anything else to ban pork from our diets? Yes, we do, as there is more to this story.

Clearly alcohol and pork consumption are correlated as well, as we can see from the graphs above. That is, countries where alcohol is consumed more heavily also tend to have higher levels of pork consumption. If alcohol and pork consumption are correlated, then a multivariate analysis of their effects should be conducted, as one of the hypothesized effects (of alcohol or pork) on cirrhosis may even disappear after controlling for the other effect.

I created a dataset, as best as I could, based on the graphs from the Bridges article. (I could not get the data online.) I then entered it into WarpPLS (). I wanted to run a moderating effect analysis, which is a form of nonlinear multivariate analysis. This is important, because the association between alcohol consumption and disease in general is well known to be nonlinear.

In fact, the relationship between alcohol consumption and disease is often used as a classic example of hormesis (), and its characteristic J-curve shape. Since correlation is a measure of linear association, the lower correlation between alcohol consumption and cirrhosis, when compared with pork consumption, may be just a “mirage of linearity”. In multivariate analyses, this mirage of linearity may lead to what are known as type I and II errors, at the same time ().

I should note that the Bridges study did something akin to a moderating effect analysis; through an analysis of the interaction between alcohol and pork consumption. However, in that analysis the values of the variables that were multiplied to create a “dummy” interaction variable were on their original scales, which can be a major source of bias. A more advisable way to conduct an interaction effect analysis is to first make the variables dimensionless, by standardizing them, and then creating a dummy interaction variable as a product of the two variables. That is what WarpPLS does for moderating effects’ estimation.

One more detour, leading to an important implication, and then we will get to the results. In a 1988 article, Jeanneret and colleagues show evidence of a strong and possibly causal association between alcohol consumption and protein-rich diets (). One possible implication of this is that in countries where pork is a dietary staple, like Denmark and Germany, alcohol consumption should be strongly and causally associated with pork consumption. (I guess Anthony Bordain would agree with this eh?)

Below are the results of a multivariate analysis on a model that incorporates the above implication, by including a link between alcohol and pork consumption. The model also explores the role of pork consumption as a moderator of the relationship between alcohol and cirrhosis, as well as the direct effect of pork consumption on cirrhosis. Finally, the total effects of alcohol and pork consumption on cirrhosis are also investigated; they are shown on the left.

The total effects are both statistically significant, with the total effect of alcohol consumption being 94 percent stronger than the total effect of pork consumption on cirrhosis. Looking at the model, alcohol consumption is strongly associated with pork consumption (which is consistent with Jeanneret and colleagues’s study). Alcohol consumption is also strongly associated with cirrhosis, through a direct effect; much more so than pork. Finally, pork consumption seems to strengthen the relationship between alcohol consumption and cirrhosis (the moderating effect).

As we can see the relationship between pork consumption and cirrhosis is still there, in moderating and direct effects, even though it seems to be a lot weaker than that between alcohol consumption and cirrhosis. Why does pork seem to influence cirrhosis at all in this dataset?

Well, there is another factor that is strongly associated with cirrhosis, and that is obesity (). In fact, obesity is associated with just about any major disease, including various types of cancer ().

And in countries where pork is a dietary staple, isn’t it reasonable to assume that pork consumption will play a role in obesity? Often folks who consume a lot of addictive industrial foods (e.g., bread, candy, regular sodas) also eat plenty of foods with saturated fat; and the latter end up showing up in disease statistics, misleadingly supporting the lipid hypothesis. The phenomenon involving pork and cirrhosis may well be similar.

But you may find the above results and argument not convincing enough. Maybe you want to see some evidence that pork is actually good for one’s health. The results above suggest that it may not be bad at all, if you buy into the obesity angle, but not that it can be good.

So I downloaded the most recent data from Nationmaster.com () on the following variables: pork consumption, alcohol consumption, and life expectancy. The list of countries was a bit larger than and different from that in the Bridges study; the following countries were included: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. Below are the results of a simple multivariate analysis with WarpPLS.

As with the Bridges dataset, there is a strong multivariate association between alcohol and pork consumption (0.43). The multivariate association between alcohol consumption and life expectancy is negative (-0.14). The multivariate association between pork consumption and life expectancy is positive (0.36). Neither association is statistically significant, although the association involving pork consumption gets close to significance with a P=0.11 (a confidence level of 89 percent; calculated through jackknifing, a nonparametric technique). The graphs show the plots for the associations and the best-fitting lines; the blue dashed arrows indicate the multivariate associations to which the graphs refer. So, in this second dataset from Nationmaster.com, the more pork is consumed in a country, the longer is the life expectancy in that country.

In other words, for each 1 standard deviation variation in pork consumption, there is a 0.36 standard deviation variation in life expectancy, after we control for alcohol consumption. The standard deviation for pork consumption is 36.281 lbs/person/year, or 45.087 g/person/day; for life expectancy, it is 4.677 years. Working the numbers a bit more, the results above suggest that each extra gram of pork consumed per person per day is associated with approximately 13 additional days of overall life expectancy in a country! This is calculated as: 4.677/45.087*0.36*365 = 13.630.

Does this prove that eating pork will make you live longer? No single study will “prove” something like that. Pork consumption is also likely a marker for wealth in a country; and wealth is strongly and positively associated with life expectancy at the country level. Moreover, when you aggregate dietary and disease incidence data by country, often the statistical effects are caused by those people in the dietary extremes (e.g., alcohol abuse, not moderate consumption). Finally, if people avoid death from certain diseases, they will die in higher quantities from other diseases, which may bias statistical results toward what may look like a higher incidence of those other diseases.

What the results summarized in this post do suggest is that pork consumption may not be a problem at all, unless you become obese from eating it. How do you get obese from eating pork? Eating it together with industrial foods that are addictive would probably help.

All diets succeed at first, and eventually fail

It is not very hard to find studies supporting one diet or another. Gardner and colleagues, for example, conducted a study in which the Atkins diet came out on top when compared with the Zone, Ornish, and LEARN diets (). In Dansinger and colleagues’ study (), on the other hand, following the Atkins diet led to relatively poor results compared with the Ornish, Weight Watchers, and Zone diets.

Often the diets compared have different macronutrient ratios, which end up becoming the focus of the comparison. Many consider Sacks and colleagues’ conclusion, based on yet another diet comparison study (), to be the most consistent with the body of evidence as a whole: “Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasize”.

I think there is a different conclusion that is even more consistent with the body of evidence out there. This conclusion is highlighted by the findings of almost all diet studies where participants were followed for more than 1 year. But the relevant findings are typically buried in the papers that summarize the studies, and are almost never mentioned in the abstracts. Take for example the study by Toubro and Astrup (); Figure 3 below is used by the authors to highlight the study’s main reported finding: “Ad lib, low fat, high carbohydrate diet was superior to fixed energy intake for maintaining weight after a major weight loss”.

But what does the figure above really tell us? It tells us, quite simply, that both diets succeeded at first, and then eventually failed. One failed slightly less miserably than the other, in this study. The percentage of subjects that maintained a weight loss above 25 kg (about 55 lbs) approached zero after 12 months, in both diets. This leads us to the conclusion below, which is always missing in diet studies even when the evidence is staring back at us. This is arguably the conclusion that is the most consistent with the body of evidence out there.

All diets succeed at first, and eventually fail.

In using the terms “succeed” and “fail” I am referring to the diets’ effects on the majority of the participants. This is in fact better demonstrated by the figure below, from the same study by Toubro and Astrup; it is labeled as Figure 2 there. Most of the participants at a certain weight, lose a lot of weight within a period of 1 year or so, and after 2 years (see the two points at the far right) are at the same original weight again. What is the average time to regain back the weight? From what I’ve seen in the literature, all the weight and some tends to be regained after 2-3 years.

The regained weight is not at all lean body mass. It is primarily, if not entirely, body fat. In fact, many studies suggest that those who diet tend to have a higher percentage of body fat when they regain their original weight; proportionally to how fast they regain the weight lost. Since the extra body fat tends to cause additional problems, which are compounded by the dieting process’ toll on the body, those dieters would have been slightly better off not having dieted in the first place.

Guyenet and Schwartz have recently authored an article that summarizes quite nicely what tends to happen with both obese and lean dieters (). Take a look at Figure 2 of the article below. The obese need to lose body fat to improve health markers, and avoid a number of downstream complications, such as type 2 diabetes and cancer (). Yet, with very few exceptions, the obese (and even the overweight) remain obese (or overweight) after dieting; regardless of the diet.

So what about those exceptions, what do they do to lose significant amounts of body fat and keep it off? Well, I rarely use myself as an example for anything in this blog, but this is something with which I unfortunately/fortunately have personal experience. I was obese, lost about 60 lbs of weight, and kept it off for quite a while already (). Like most of the formerly obese, I can very easily gain body fat back.

But I don’t seem to be gaining back the formerly lost body fat, and the reason is consistent with some of the studies based on data from the National Weight Control Registry, which stores information about adults who lost 30 lbs or more of weight and kept it off for at least 1 year (). I systematically measure my weight, body fat percentage, and a number of other variables; probably even more than the average National Weight Control Registry member. Based on those measurements, I try to understand how my body responds in the short and long term to stimuli such as different exercise, types of food, calorie restriction, sleep patterns etc.

And I act accordingly to keep any body fat gain from happening; by, for example, varying calorie intake, increasing exercise intensity, varying the types of food I eat etc. With a few exceptions (e.g., avoiding industrial seed oils), there is no generic formula. Customization based on individual responses and cyclical patterns seems to be a must.

Looking back, it was relatively easy for me to lose all that fat. This is consistent with the studies summarized in this post; all diets that rely on caloric reduction work marvelously at first for most people. The really difficult part is to keep the body fat off. I believe that this is especially true as the initial years go by, and becomes easier after that. This has something to do with initial inertia, which I will discuss soon in a post on metabolic rates and their relationship with overall body mass.

For people living in the wild, I can see one thing working in their favor. And that is not regular starvation; sapiens is too smart for that. It is laziness. Hunger has to reach a certain threshold for people to want to do some work to get their food; this acts as a natural body composition regulator, something that I intend to discuss in one of my next posts. It seems that people almost never become obese in the wild, without access to industrial foods.

As for living in the wild, in spite of the romantic portrayals of it, the experience is not as appealing after you really try it. The book Yanomamo: The Fierce People () is a solid, if not somewhat shocking, reminder of that. I had the opportunity to meet and talk at length with its author, the great anthropologist Nap Chagnon, at one of the Human Behavior and Evolution Society conferences. The man is a real-life Indiana Jones ().

In the formerly obese, the body seems to resort to “guerrilla warfare”, employing all kinds of physiological and psychological mechanisms, some more subtle than others, to make sure that the lost fat is recovered. Why? I have some ideas, which I have discussed indirectly in posts throughout this blog, but I still need to understand the whole process a bit better. My ideas build on the notion of compensatory adaptation ().

You might have heard some very smart people say that you do not need to measure anything to lose body fat and keep it off. Many of those people have never been obese. Those who have been obese often had not cleared the 2-3 year “danger zone” by the time they made those statements.

There are many obese or overweight public figures (TV show hosts, actors, even health bloggers) who embark on a diet and lose a dramatic amount of body fat. They talk and/or write for a year or so about their success, and then either “disappear” or start complaining about health issues. Those health issues are often part of the “guerrilla warfare” I mentioned above.

A few persistent public figures will gain the fat back, in part or fully, and do the process all over again. It makes for interesting drama, and at least keeps those folks in the limelight.