Sheriff Attacks Deaf Cancer Patient

Sheriff Attacks Deaf Cancer Patient in Alberta, Canada.



Must-see raw video showing a sheriff’s “unjustified” and “excessive” use of force on a mute, deaf man in a Red Deer courthouse has been released.



On Dec. 9, cancer survivor Bill Berry, 52, was paying a traffic ticket when a sheriff told him he didn’t go through the proper security screening, as he had come in through an exit door.



Berry, who is deaf, mute and breathes through his neck with a tube, tried to signal that he couldn’t hear or speak, at which point the sheriff became more forceful and tried to forcibly carry him out.



Berry collapsed to the ground and his stoma tube fell out.



He was unable to breathe until other sheriffs noticed and reinserted it.



A probe conducted by the Solicitor General Office’s Law Enforcement and Oversight Professional Standards Unit, found the sheriff, identified as Thomas Bounds, was fine to advise Berry of his error but was unlawful in physically confronting him “without hesitation.” ... Read more: http://www.calgarysun.com/2012/02/15/video-shows-sheriff-manhandle-deafmute-red-deer-resident



Video source HoChiMinh30abril1975

Does Calorie Restriction Extend Lifespan in Mammals?

Until about two years ago, the story went something like this: calorie restriction extends lifespan in yeast, worms, flies, and rodents.  Lifespan extension by calorie restriction appears to be biologically universal, therefore it's probably only a matter of time until it's demonstrated in humans as well.  More than 20 years ago, independent teams of researchers set out to demonstrate the phenomenon in macaque monkeys, a primate model closer to humans than any lifespan model previously tested.

Recent findings have caused me to seriously question this narrative.  One of the first challenges was the finding that genetically wild mice (as opposed to inbred laboratory strains) do not live longer when their calorie intake is restricted, despite showing hormonal changes associated with longevity in other strains, although the restricted animals do develop less cancer (1).  One of the biggest blows came in 2009, when researchers published the results of a study that analyzed the effect of calorie restriction on lifespan in 41 different strains of mice, both male and female (2).  They found that calorie restriction extends lifespan in a subset of strains, but actually shortens lifespan in an even larger subset.  Below is a graph of the effect of calorie restriction on lifespan in the 41 strains.  Positive numbers indicate that calorie restriction extended life, while negative numbers indicate that it shortened life:

Read more »

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.

Oltipraz

Oltipraz is a drug that was originally used to treat intestinal worms. It was later found to prevent a broad variety of cancers (1). This was attributed to its ability to upregulate cellular detoxification and repair mechanisms.

Researchers eventually discovered that oltipraz acts by activating Nrf2, the same transcription factor activated by ionizing radiation and polyphenols (2, 3, 4). Nrf2 activation mounts a broad cellular protective response that appears to reduce the risk of multiple health problems.

A recent paper in Diabetologia illustrates this (5). Investigators put mice on a long-term refined high-fat diet, with or without oltipraz. These carefully crafted diets are very unhealthy indeed, and when fed to rodents they rapidly induce fat gain and something that looks similar to human metabolic syndrome (insulin resistance, abdominal adiposity, blood lipid disturbances). Adding oltipraz to the diet prevented the fat gain, insulin resistance and inflammatory changes that occurred in the refined high-fat diet group.

The difference in fasting insulin was remarkable. The mice taking oltipraz had 1/7 the fasting insulin of the refined high-fat diet comparison group, and 1/3 the fasting insulin of the low-fat comparison group! Yet their glucose tolerance was normal, indicating that they were not low on insulin due to pancreatic damage. The low-fat diet they used in this study was also refined, which is why the two control groups (high-fat and low-fat) didn't diverge more in body fatness and other parameters. If they had used a group fed unrefined rodent chow as the comparator, the differences between groups would have been larger.

This shows that in addition to preventing cancer, Nrf2 activation can attenuate the metabolic damage caused by an unhealthy diet in rodents. Oltipraz illustrates the power of the cellular hormesis response. We can exploit this pathway naturally using polyphenols and other chemicals found in whole plant foods.

Polyphenols, Hormesis and Disease: Part II

In the last post, I explained that the body treats polyphenols as potentially harmful foreign chemicals, or "xenobiotics". How can we reconcile this with the growing evidence that at least a subset of polyphenols have health benefits?

Clues from Ionizing Radiation

One of the more curious things that has been reported in the scientific literature is that although high-dose ionizing radiation (such as X-rays) is clearly harmful, leading to cancer, premature aging and other problems, under some conditions low-dose ionizing radiation can actually decrease cancer risk and increase resistance to other stressors (1, 2, 3, 4, 5). It does so by triggering a protective cellular response, increasing cellular defenses out of proportion to the minor threat posed by the radiation itself. The ability of mild stressors to increase stress resistance is called "hormesis." Exercise is a common example. I've written about this phenomenon in the past (6).

The Case of Resveratrol

Resveratrol is perhaps the most widely known polyphenol, available in supplement stores nationwide. It's seen a lot of hype, being hailed as a "calorie restriction mimetic" and the reason for the "French paradox."* But there is quite a large body of evidence suggesting that resveratrol functions in the same manner as low-dose ionizing radiation and other bioactive polyphenols: by acting as a mild toxin that triggers a hormetic response (7). Just as in the case of radiation, high doses of resveratrol are harmful rather than helpful. This has obvious implications for the supplementation of resveratrol and other polyphenols. A recent review article on polyphenols stated that while dietary polyphenols may be protective, "high-dose fortified foods or dietary supplements are of unproven efficacy and possibly harmful" (8).

The Cellular Response to Oxidants

Although it may not be obvious, radiation and polyphenols activate a cellular response that is similar in many ways. Both activate the transcription factor Nrf2, which activates genes that are involved in detoxification of chemicals and antioxidant defense**(9, 10, 11, 12). This is thought to be due to the fact that polyphenols, just like radiation, may temporarily increase the level of oxidative stress inside cells. Here's a quote from the polyphenol review article quoted above (13):

We have found that [polyphenols] are potentially far more than 'just antioxidants', but that they are probably insignificant players as 'conventional' antioxidants. They appear, under most circumstances, to be just the opposite, i.e. prooxidants, that nevertheless appear to contribute strongly to protection from oxidative stress by inducing cellular endogenous enzymic protective mechanisms. They appear to be able to regulate not only antioxidant gene transcription but also numerous aspects of intracellular signaling cascades involved in the regulation of cell growth, inflammation and many other processes.

It's worth noting that this is essentially the opposite of what you'll hear on the evening news, that polyphenols are direct antioxidants. The scientific cutting edge has largely discarded that hypothesis, but the mainstream has not yet caught on.

Nrf2 is one of the main pathways by which polyphenols increase stress resistance and antioxidant defenses, including the key cellular antioxidant glutathione (14). Nrf2 activity is correlated with longevity across species (15). Inducing Nrf2 activity via polyphenols or by other means substantially reduces the risk of common lifestyle disorders in animal models, including cardiovascular disease, diabetes and cancer (16, 17, 18), although Nrf2 isn't necessarily the only mechanism. The human evidence is broadly consistent with the studies in animals, although not as well developed.

One of the most interesting effects of hormesis is that exposure to one stressor can increase resistance to other stressors. For example, long-term consumption of high-polyphenol chocolate increases sunburn resistance in humans, implying that it induces a hormetic response in skin (19). Polyphenol-rich foods such as green tea reduce sunburn and skin cancer development in animals (20, 21).

Chris Masterjohn first introduced me to Nrf2 and the idea that polyphenols act through hormesis. Chris studies the effects of green tea on health, which seem to be mediated by polyphenols.

A Second Mechanism

There is a place in the body where polyphenols are concentrated enough to be direct antioxidants: in the digestive tract after consuming polyphenol-rich foods. Digestion is a chemically harsh process that readily oxidizes ingested substances such as polyunsaturated fats (22). Oxidized fat is neither healthy when it's formed in the deep fryer, nor when it's formed in the digestive tract (23, 24). Eating polyphenol-rich foods effectively prevents these fats from being oxidized during digestion (25). One consequence of this appears to be better absorption and assimilation of the exceptionally fragile omega-3 polyunsaturated fatty acids (26).

What does it all Mean?

I think that overall, the evidence suggests that polyphenol-rich foods are healthy in moderation, and eating them on a regular basis is generally a good idea. Certain other plant chemicals, such as suforaphane found in cruciferous vegetables, and allicin found in garlic, exhibit similar effects and may also act by hormesis (27). Some of the best-studied polyphenol-rich foods are tea (particularly green tea), blueberries, extra-virgin olive oil, red wine, citrus fruits, hibiscus tea, soy, dark chocolate, coffee, turmeric and other herbs and spices, and a number of traditional medicinal herbs. A good rule of thumb is to "eat the rainbow", choosing foods with a variety of colors.

Supplementing with polyphenols and other plant chemicals in amounts that would not be achievable by eating food is probably not a good idea.


* The "paradox" whereby the French eat a diet rich in saturated fat, yet have a low heart attack risk compared to other affluent Western nations.

** Genes containing an antioxidant response element (ARE) in the promoter region. ARE is also sometimes called the electrophile response element (EpRE).

Tropical Plant Fats: Coconut Oil, Part II

Heart Disease: Animal Studies

Although humans aren't rats, animal studies are useful because they can be tightly controlled and experiments can last for a significant portion of an animal's lifespan. It's essentially impossible to do a tightly controlled 20-year feeding study in humans.

The first paper I'd like to discuss come from the lab of Dr. Thankappan Rajamohan at the university of Kerala (1). Investigators fed three groups of rats different diets:

1. Sunflower oil plus added cholesterol
2. Copra oil, a coconut oil pressed from dried coconuts, plus added cholesterol
3. Freshly pressed virgin coconut oil, plus added cholesterol

Diets 1 and 2 resulted in similar lipids, while diet 3 resulted in lower LDL and higher HDL. A second study also showed that diet 3 resulted in lower oxidized LDL, a dominant heart disease risk factor (2). Overall, these papers showed that freshly pressed virgin coconut oil, with its full complement of "minor constituents"*, partially protects rats against the harmful effects of cholesterol overfeeding. These are the only papers I could find on the cardiovascular effects of unrefined coconut oil in animals!

Although unrefined coconut oil appears to be superior, even refined coconut oil isn't as bad as it's made out to be. For example, compared to refined olive oil, refined coconut oil protects against atherosclerosis (hardening and thickening of the arteries) in a mouse model of coronary heart disease (LDL receptor knockout). In the same paper, coconut oil caused more atherosclerosis in a different mouse model (ApoE knockout) (3). So the vascular effects of coconut oil depend in part on the animals' genetic background.

In general, I've found that the data are extremely variable from one study to the next, with no consistent trend showing refined coconut oil to be protective or harmful relative to refined monounsaturated fats (like olive oil) (4). In some cases, polyunsaturated oils cause less atherosclerosis than coconut oil in the context of an extreme high-cholesterol diet because they sometimes lead to blood lipid levels that are up to 50% lower. However, even this isn't consistent across experiments. Keep in mind that atherosclerosis is only one factor in heart attack risk.

What happens if you feed coconut oil to animals without adding cholesterol, and without giving them genetic mutations that promote atherosclerosis? Again, the data are contradictory. In rabbits, one investigator showed that serum cholesterol increases transiently, returning to baseline after about 6 months, and atherosclerosis does not ensue (5). A different investigator showed that coconut oil feeding results in lower blood lipid oxidation than sunflower oil (6). Yet a study from the 1980s showed that in the context of a terrible diet composition (40% sugar, isolated casein, fat, vitamins and minerals), refined coconut oil causes elevated blood lipids and atherosclerosis (7). This is almost certainly because overall diet quality influences the response to dietary fats in rabbits, as it does in other mammals.

Heart Disease: Human Studies

It's one of the great tragedies of modern biomedical research that most studies focus on nutrients rather than foods. This phenomenon is called "nutritionism". Consequently, most of the studies on coconut oil used a refined version, because the investigators were most interested in the effect of specific fatty acids. The vitamins, polyphenols and other minor constituents of unrefined oils are eliminated because they are known to alter the biological effects of the fats themselves. Unfortunately, any findings that result from these experiments apply only to refined fats. This is the fallacy of the "X fatty acid does this and that" type statements-- they ignore the biological complexity of whole foods. They would probably be correct if you were drinking purified fatty acids from a beaker.

Generally, the short-term feeding studies using refined coconut oil show that it increases both LDL ("bad cholesterol") and HDL ("good cholesterol"), although there is so much variability between studies that it makes firm conclusions difficult to draw (8, 9). As I've written in the past, the ability of saturated fats to elevate LDL appears to be temporary; both human and certain animal studies show that it disappears on timescales of one year or longer (10, 11). That hasn't been shown specifically for coconut oil that I'm aware of, but it could be one of the reasons why traditional cultures eating high-coconut diets don't have elevated serum cholesterol.

Another marker of cardiovascular disease risk is lipoprotein (a), abbreviated Lp(a). This lipoprotein is a carrier for oxidized lipids in the blood, and it correlates with a higher risk of heart attack. Refined coconut oil appears to lower Lp(a), while refined sunflower oil increases it (12).

Unfortunately, I haven't been able to find any particularly informative studies on unrefined coconut oil in humans. The closest I found was a study from Brazil showing that coconut oil reduced abdominal obesity better than soybean oil in conjunction with a low-calorie diet, without increasing LDL (13). It would be nice to have more evidence in humans confirming what has been shown in rats that there's a big difference between unrefined and refined coconut oil.

Coconut Oil and Body Fat

In addition to the study mentioned above, a number of experiments in animals have shown that "medium-chain triglycerides", the predominant type of fat in coconut oil, lead to a lower body fat percentage than most other fats (14). These findings have been replicated numerous times in humans, although the results have not always been consistent (15). It's interesting to me that these very same medium-chain saturated fats that are being researched as a fat loss tool are also considered by mainstream diet-heart researchers to be among the most deadly fatty acids.

Coconut Oil and Cancer

Refined coconut oil produces less cancer than seed oils in experimental animals, probably because it's much lower in omega-6 polyunsaturated fat (16, 17). I haven't seen any data in humans.

The Bottom Line

There's very little known about the effect of unrefined coconut oil on animal and human health, however what is published appears to be positive, and is broadly consistent with the health of traditional cultures eating unrefined coconut foods. The data on refined coconut oil are conflicting and frustrating to sort through. The effects of refined coconut oil seem to depend highly on dietary context and genetic background. In my opinion, virgin coconut oil can be part of a healthy diet, and may even have health benefits in some contexts.


* Substances other than the fat itself, e.g. vitamin E and polyphenols. These are removed during oil refining.

The China Study one more time: Are raw plant foods giving people cancer?

In this previous post I analyzed some data from the China Study that included counties where there were cases of schistosomiasis infection. Following one of Denise Minger’s suggestions, I removed all those counties from the data. I was left with 29 counties, a much smaller sample size. I then ran a multivariate analysis using WarpPLS (warppls.com), like in the previous post, but this time I used an algorithm that identifies nonlinear relationships between variables.

Below is the model with the results. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) As in the previous post, the arrows explore associations between variables. The variables are shown within ovals. The meaning of each variable is the following: aprotein = animal protein consumption; pprotein = plant protein consumption; cholest = total cholesterol; crcancer = colorectal cancer.

What is total cholesterol doing at the right part of the graph? It is there because I am analyzing the associations between animal protein and plant protein consumption with colorectal cancer, controlling for the possible confounding effect of total cholesterol.

I am not hypothesizing anything regarding total cholesterol, even though this variable is shown as pointing at colorectal cancer. I am just controlling for it. This is the type of thing one can do in multivariate analyzes. This is how you “control for the effect of a variable” in an analysis like this.

Since the sample is fairly small, we end up with insignificant beta coefficients that would normally be statistically significant with a larger sample. But it helps that we are using nonparametric statistics, because they are still robust in the presence of small samples, and deviations from normality. Also the nonlinear algorithm is more sensitive to relationships that do not fit a classic linear pattern. We can summarize the findings as follows:

- As animal protein consumption increases, plant protein consumption decreases significantly (beta=-0.36; P<0.01). This is to be expected and helpful in the analysis, as it differentiates somewhat animal from plant protein consumers. Those folks who got more of their protein from animal foods tended to get significantly less protein from plant foods.

- As animal protein consumption increases, colorectal cancer decreases, but not in a statistically significant way (beta=-0.31; P=0.10). The beta here is certainly high, and the likelihood that the relationship is real is 90 percent, even with such a small sample.

- As plant protein consumption increases, colorectal cancer increases significantly (beta=0.47; P<0.01). The small sample size was not enough to make this association insignificant. The reason is that the distribution pattern of the data here is very indicative of a real association, which is reflected in the low P value.

Remember, these results are not confounded by schistosomiasis infection, because we are only looking at counties where there were no cases of schistosomiasis infection. These results are not confounded by total cholesterol either, because we controlled for that possible confounding effect. Now, control variable or not, you would be correct to point out that the association between total cholesterol and colorectal cancer is high (beta=0.58; P=0.01). So let us take a look at the shape of that association:

Does this graph remind you of the one on this post; the one with several U curves? Yes. And why is that? Maybe it reflects a tendency among the folks who had low cholesterol to have more cancer because the body needs cholesterol to fight disease, and cancer is a disease. And maybe it reflects a tendency among the folks who have high total cholesterol to do so because total cholesterol (and particularly its main component, LDL cholesterol) is in part a marker of disease, and cancer is often a culmination of various metabolic disorders (e.g., the metabolic syndrome) that are nothing but one disease after another.

To believe that total cholesterol causes colorectal cancer is nonsensical because total cholesterol is generally increased by consumption of animal products, of which animal protein consumption is a proxy. (In this reduced dataset, the linear univariate correlation between animal protein consumption and total cholesterol is a significant and positive 0.36.) And animal protein consumption seems to be protective again colorectal cancer in this dataset (negative association on the model graph).

Now comes the part that I find the most ironic about this whole discussion in the blogosphere that has been going on recently about the China Study; and the answer to the question posed in the title of this post: Are raw plant foods giving people cancer? If you think that the answer is “yes”, think again. The variable that is strongly associated with colorectal cancer is plant protein consumption.

Do fruits, veggies, and other plant foods that can be consumed raw have a lot of protein?

With a few exceptions, like nuts, they do not. Most raw plant foods have trace amounts of protein, especially when compared with foods made from refined grains and seeds (e.g., wheat grains, soybean seeds). So the contribution of raw fruits and veggies in general could not have influenced much the variable plant protein consumption. To put this in perspective, the average plant protein consumption per day in this dataset was 63 g; even if they were eating 30 bananas a day, the study participants would not get half that much protein from bananas.

Refined foods made from grains and seeds are made from those plant parts that the plants absolutely do not “want” animals to eat. They are the plants’ “children” or “children’s nutritional reserves”, so to speak. This is why they are packed with nutrients, including protein and carbohydrates, but also often toxic and/or unpalatable to animals (including humans) when eaten raw.

But humans are so smart; they learned how to industrially refine grains and seeds for consumption. The resulting human-engineered products (usually engineered to sell as many units as possible, not to make you healthy) normally taste delicious, so you tend to eat a lot of them. They also tend to raise blood sugar to abnormally high levels, because industrial refining makes their high carbohydrate content easily digestible. Refined foods made from grains and seeds also tend to cause leaky gut problems, and autoimmune disorders like celiac disease. Yep, we humans are really smart.

Thanks again to Dr. Campbell and his colleagues for collecting and compiling the China Study data, and to Ms. Minger for making the data available in easily downloadable format and for doing some superb analyses herself.

The China Study again: A multivariate analysis suggesting that schistosomiasis rules!

In the comments section of Denise Minger’s post on July 16, 2010, which discusses some of the data from the China Study (as a follow up to a previous post on the same topic), Denise herself posted the data she used in her analysis. This data is from the China Study. So I decided to take a look at that data and do a couple of multivariate analyzes with it using WarpPLS (warppls.com).

First I built a model that explores relationships with the goal of testing the assumption that the consumption of animal protein causes colorectal cancer, via an intermediate effect on total cholesterol. I built the model with various hypothesized associations to explore several relationships simultaneously, including some commonsense ones. Including commonsense relationships is usually a good idea in exploratory multivariate analyses.

The model is shown on the graph below, with the results. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore causative associations between variables. The variables are shown within ovals. The meaning of each variable is the following: aprotein = animal protein consumption; pprotein = plant protein consumption; cholest = total cholesterol; crcancer = colorectal cancer.

The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable). A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to.

The P values indicate the statistical significance of the relationship; a P lower than 0.05 means a significant relationship (95 percent or higher likelihood that the relationship is real). The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. Ignore the “(R)1i” below the variable names; it simply means that each of the variables is measured through a single indicator (or a single measure; that is, the variables are not latent variables).

I should note that the P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This is good, because I checked the data, and it does not look like it is normally distributed. So what does the model above tell us? It tells us that:

- As animal protein consumption increases, colorectal cancer decreases, but not in a statistically significant way (beta=-0.13; P=0.11).

- As animal protein consumption increases, plant protein consumption decreases significantly (beta=-0.19; P<0.01). This is to be expected.

- As plant protein consumption increases, colorectal cancer increases significantly (beta=0.30; P=0.03). This is statistically significant because the P is lower than 0.05.

- As animal protein consumption increases, total cholesterol increases significantly (beta=0.20; P<0.01). No surprise here. And, by the way, the total cholesterol levels in this study are quite low; an overall increase in them would probably be healthy.

- As plant protein consumption increases, total cholesterol decreases significantly (beta=-0.23; P=0.02). No surprise here either, because plant protein consumption is negatively associated with animal protein consumption; and the latter tends to increase total cholesterol.

- As total cholesterol increases, colorectal cancer increases significantly (beta=0.45; P<0.01). Big surprise here!

Why the big surprise with the apparently strong relationship between total cholesterol and colorectal cancer? The reason is that it does not make sense, because animal protein consumption seems to increase total cholesterol (which we know it usually does), and yet animal protein consumption seems to decrease colorectal cancer.

When something like this happens in a multivariate analysis, it usually is due to the model not incorporating a variable that has important relationships with the other variables. In other words, the model is incomplete, hence the nonsensical results. As I said before in a previous post, relationships among variables that are implied by coefficients of association must also make sense.

Now, Denise pointed out that the missing variable here possibly is schistosomiasis infection. The dataset that she provided included that variable, even though there were some missing values (about 28 percent of the data for that variable was missing), so I added it to the model in a way that seems to make sense. The new model is shown on the graph below. In the model, schisto = schistosomiasis infection.

So what does this new, and more complete, model tell us? It tells us some of the things that the previous model told us, but a few new things, which make a lot more sense. Note that this model fits the data much better than the previous one, particularly regarding the overall effect on colorectal cancer, which is indicated by the high R-squared value for that variable (R-squared=0.73). Most notably, this new model tells us that:

- As schistosomiasis infection increases, colorectal cancer increases significantly (beta=0.83; P<0.01). This is a MUCH STRONGER relationship than the previous one between total cholesterol and colorectal cancer; even though some data on schistosomiasis infection for a few counties is missing (the relationship might have been even stronger with a complete dataset). And this strong relationship makes sense, because schistosomiasis infection is indeed associated with increased cancer rates. More information on schistosomiasis infections can be found here.

- Schistosomiasis infection has no significant relationship with these variables: animal protein consumption, plant protein consumption, or total cholesterol. This makes sense, as the infection is caused by a worm that is not normally present in plant or animal food, and the infection itself is not specifically associated with abnormalities that would lead one to expect major increases in total cholesterol.

- Animal protein consumption has no significant relationship with colorectal cancer. The beta here is very low, and negative (beta=-0.03).

- Plant protein consumption has no significant relationship with colorectal cancer. The beta for this association is positive and nontrivial (beta=0.15), but the P value is too high (P=0.20) for us to discard chance within the context of this dataset. A more targeted dataset, with data on specific plant foods (e.g., wheat-based foods), could yield different results – maybe more significant associations, maybe less significant.

Below is the plot showing the relationship between schistosomiasis infection and colorectal cancer. The values are standardized, which means that the zero on the horizontal axis is the mean of the schistosomiasis infection numbers in the dataset. The shape of the plot is the same as the one with the unstandardized data. As you can see, the data points are very close to a line, which suggests a very strong linear association.

So, in summary, this multivariate analysis vindicates pretty much everything that Denise said in her July 16, 2010 post. It even supports Denise’s warning about jumping to conclusions too early regarding the possible relationship between wheat consumption and colorectal cancer (previously highlighted by a univariate analysis). Not that those conclusions are wrong; they may well be correct.

This multivariate analysis also supports Dr. Campbell’s assertion about the quality of the China Study data. The data that I analyzed was already grouped by county, so the sample size (65 cases) was not so high as to cast doubt on P values. (Having said that, small samples create problems of their own, such as low statistical power and an increase in the likelihood of error-induced bias.) The results summarized in this post also make sense in light of past empirical research.

It is very good data; data that needs to be properly analyzed!

Tropical Plant Fats: Palm Oil

A Fatal Case of Nutritionism

The concept of 'nutritionism' was developed by Dr. Gyorgy Scrinis and popularized by the food writer Michael Pollan. It states that the health value of a food can be guessed by the sum of the nutrients it contains. Pollan argues, I think rightfully, that nutritionism is a reductionist philosophy that assumes we know more about food composition and the human body than we actually do. You can find varying degrees of this philosophy in most mainstream discussions of diet and health*.

One conspicuous way nutritionism manifests is in the idea that saturated fat is harmful. Any fat rich in saturated fatty acids is typically assumed to be unhealthy, regardless of its other constituents. There is also apparently no need to directly test that assumption, or even to look through the literature to see if the assumption has already been tested. In this manner, 'saturated' tropical plant fats such as palm oil and coconut oil have been labeled unhealthy, despite essentially no direct evidence that they're harmful. As we'll see, there is actually quite a bit of evidence, both indirect and direct, that their unrefined forms are not harmful and perhaps even beneficial.

Palm Oil and Heart Disease

Long-time readers may recall a post I wrote a while back titled Ischemic Heart Attacks: Disease of Civilization (1). I described a study from 1964 in which investigators looked for signs of heart attacks in thousands of consecutive autopsies in the US and Africa, among other places. They found virtually none in hearts from Nigeria and Uganda (3 non-fatal among more than 4,500 hearts), while Americans of the same age had very high rates (up to 1/3 of hearts).

What do they eat in Nigeria? Typical Nigerian food involves home-processed grains, starchy root vegetables, beans, fruit, vegetables, peanuts, red palm oil, and a bit of dairy, fish and meat**. The oil palm Elaeis guineensis originated in West Africa and remains one of the main dietary fats throughout the region.

To extract the oil, palm fruit are steamed, and the oily flesh is removed and pressed. It's similar to olive oil in that it is extracted gently from an oil-rich fruit, rather than harshly from an oil-poor seed (e.g., corn or soy oil). The oil that results is deep red and is perhaps the most nutrient-rich fat on the planet. The red color comes from carotenes, but red palm oil also contains a large amount of vitamin E (mostly tocotrienols), vitamin K1, coenzyme Q10 and assorted other fat-soluble constituents. This adds up to a very high concentration of fat-soluble antioxidants, which are needed to protect the fat from rancidity in hot and sunny West Africa. Some of these make it into the body when it's ingested, where they appear to protect the body's own fats from oxidation.

Mainstream nutrition authorities state that palm oil should be avoided due to the fact that it's approximately half saturated. This is actually one of the main reasons palm oil was replaced by hydrogenated seed oils in the processed food industry. Saturated fat raises blood cholesterol, which increases the risk of heart disease. Doesn't it? Let's see what the studies have to say.

Most of the studies were done using refined palm oil, unfortunately. Besides only being relevant to processed foods, this method also introduces a new variable because palm oil can be refined and oxidized to varying degrees. However, a few studies were done with red palm oil, and one even compared it to refined palm oil. Dr. Suzanna Scholtz and colleagues put 59 volunteers on diets predominating in sunflower oil, refined palm oil or red palm oil for 4 weeks. LDL cholesterol was not different between the sunflower oil and red palm oil groups, however the red palm oil group saw a significant increase in HDL. LDL and HDL both increased in the refined palm oil group relative to the sunflower oil group (2).

Although the evidence is conflicting, most studies have not been able to replicate the finding that refined palm oil increases LDL relative to less saturated oils (3, 4). This is consistent with studies in a variety of species showing that saturated fat generally doesn't raise LDL compared to monounsaturated fat in the long term, unless a large amount of purified cholesterol is added to the diet (5).

Investigators have also explored the ability of palm oil to promote atherosclerosis, or hardening and thickening of the arteries, in animals. Not only does palm oil not promote atherosclerosis relative to monounsaturated fats (e.g., olive oil), but in its unrefined state it actually protects against atherosclerosis (6, 7). A study in humans hinted at a possible explanation: compared to a monounsaturated oil***, palm oil greatly reduced oxidized LDL (8). As a matter of fact, I've never seen a dietary intervention reduce oxLDL to that degree (69%). oxLDL is a major risk factor for cardiovascular disease, and a much better predictor of risk than the typically measured LDL cholesterol (9). The paper didn't state whether or not the palm oil was refined. I suspect it was lightly refined, but still rich in vitamin E and CoQ10.

As I discussed in my recent interview with Jimmy Moore, atherosclerosis is only one factor in heart attack risk (10). Several other factors are also major determinants of risk: clotting tendency, plaque stability, and susceptibility to arrhythmia. Another factor that I haven't discussed is how resistant the heart muscle is to hypoxia, or loss of oxygen. If the coronary arteries are temporarily blocked-- a frequent occurrence in modern people-- the heart muscle can be damaged. Dietary factors determine the degree of damage that results. For example, in rodents, nitrites derived from green vegetables protect the heart from hypoxia damage (11). It turns out that red palm oil is also protective (12, 13). Red palm oil also protects against high blood pressure in rats, an effect attributed to its ability to reduce oxidative stress (14, 15).

Together, the evidence suggests that red palm oil does not contribute to heart disease risk, and in fact is likely to be protective. The benefits of red palm oil probably come mostly from its minor constituents, i.e. the substances besides its fatty acids. Several studies have shown that a red palm oil extract called palmvitee lowers serum lipids in humans (16, 17). The minor constituents are precisely what are removed during the refining process.

Palm Oil and the Immune System

Red palm oil also has beneficial effects on the immune system in rodents. It protects against bacterial infection when compared with soybean oil (18). It also protects against certain cancers, compared to other oils (19, 20). This may be in part due to its lower content of omega-6 linoleic acid (roughly 10%), and minor constituents.

The Verdict

Yet again, nutritionism has gotten itself into trouble by underestimating the biological complexity of a whole food. Rather than being harmful to human health, red palm oil, an ancient and delicious food, is likely to be protective. It's also one of the cheapest oils available worldwide, due to the oil palm's high productivity. It has a good shelf life and does not require refrigeration. Its strong, savory flavor goes well in stews, particularly meat stews. It isn't available in most grocery stores, but you can find it on the internet. Make sure not to confuse it with refined palm oil or palm kernel oil.


* The approach that Pollan and I favor is a simpler, more empirical one: eat foods that have successfully sustained healthy cultures.

** Some Nigerians are also pastoralists that subsist primarily on dairy.

*** High oleic sunflower oil, from a type of sunflower bred to be high in monounsaturated fat and low in linoleic acid. I think it's probably among the least harmful refined oils. I use it sometimes to make mayonnaise. It's often available in grocery stores, just check the label.

Nitrate: a Protective Factor in Leafy Greens

Cancer Link and Food Sources

Nitrate (NO3) is a molecule that has received a lot of bad press over the years. It is thought to promote digestive cancers, in part due to its ability to form carcinogens when used as a preservative for processed meat. Because of this (1), nitrate was viewed with suspicion and a number of countries imposed strict limits on its use as a food additive.

But what if I told you that by far the greatest source of nitrate in the modern diet isn't processed meat-- but vegetables, particularly leafy greens (2)? And that the evidence linking exposure to nitrate itself has largely failed to materialize? For example, one study found no difference in the incidence of gastric cancer between nitrate fertilizer plant workers and the general population (3). Most other studies in animals and humans have not supported the hypothesis that nitrate itself is carcinogenic (4, 5, 6), but rather that they are only carcinogenic in the context of processed meats due to the formation of carcinogenic nitrosamines. This, combined with recent findings on nitrate biology, has changed the way we think about this molecule in recent years.

A New Example of Human Symbiosis

In 2003, Dr. K. Cosby and colleagues showed that nitrite (NO2; not the same as nitrate) dilates blood vessels in humans when infused into the blood (7). Investigators subsequently uncovered an amazing new example of human-bacteria symbiosis: dietary nitrate (NO3) is absorbed from the gut into the bloodstream and picked up by the salivary glands. It's then secreted into saliva, where oral bacteria use it as an energy source, converting it to nitrite (NO2). After swallowing, the nitrite is reabsorbed into the bloodstream (8). Humans and oral bacteria may have co-evolved to take advantage of this process. Antibacterial mouthwash prevents it.

Nitrate Protects the Cardiovascular System

In 2008, Dr. Andrew J. Webb and colleagues showed that nitrate in the form of 1/2 liter of beet juice (equivalent in volume to about 1.5 soda cans) substantially lowers blood pressure in healthy volunteers for over 24 hours. It also preserved blood vessel performance after brief oxygen deprivation, and reduced the tendency of the blood to clot (9). These are all changes that one would expect to protect against cardiovascular disease. Another group showed that in monkeys, the ability of nitrite to lower blood pressure did not diminish after two weeks, showing that the animals did not develop a tolerance to it on this timescale (10).

Subsequent studies showed that dietary nitrite reduces blood vessel dysfunction and inflammation (CRP) in cholesterol-fed mice (11). Low doses of nitrite also dramatically reduce tissue death in the hearts of mice exposed to conditions mimicking a heart attack, as well as protecting other tissues against oxygen deprivation damage (12). The doses used in this study were the equivalent of a human eating a large serving (100 g; roughly 1/4 lb) of lettuce or spinach.

Mechanism

Nitrite is thought to protect the cardiovascular system by serving as a precursor for nitric oxide (NO), one of the most potent anti-inflammatory and blood vessel-dilating compounds in the body (13). A decrease in blood vessel nitric oxide is probably one of the mechanisms of diet-induced atherosclerosis and increased clotting tendency, and it is likely an early consequence of eating a poor diet (14).

The Long View

Leafy greens were one of the "protective foods" emphasized by the nutrition giant Sir Edward Mellanby (15), along with eggs and high-quality full-fat dairy. There are many reasons to believe greens are an excellent contribution to the human diet, and what researchers have recently learned about nitrate biology certainly reinforces that notion. Leafy greens may be particularly useful for the prevention and reversal of cardiovascular disease, but are likely to have positive effects on other organ systems both in health and disease. It's ironic that a molecule suspected to be the harmful factor in processed meats is turning out to be one of the major protective factors in vegetables.

Pastured Dairy may Prevent Heart Attacks

Not all dairy is created equal. Dairy from grain-fed and pasture-fed cows differs in a number of ways. Pastured dairy contains more fat-soluble nutrients such as vitamin K2, vitamin A, vitamin E, carotenes and omega-3 fatty acids. It also contains more conjugated linoleic acid, a fat-soluble molecule that has been under intense study due to its ability to inhibit obesity and cancer in animals. The findings in human supplementation trials have been mixed, some confirming the animal studies and others not. In feeding experiments in cows, Dr. T. R. Dhiman and colleagues found the following (1):

Cows grazing pasture and receiving no supplemental feed had 500% more conjugated linoleic acid in milk fat than cows fed typical dairy diets.

Fat from ruminants such as cows, sheep and goats is the main source of CLA in the human diet. CLA is fat-soluble. Therefore, skim milk doesn't contain any. It's also present in human body fat in proportion to dietary intake. This can come from dairy or flesh.

In a recent article from the AJCN, Dr. Liesbeth Smit and colleagues examined the level of CLA in the body fat of Costa Rican adults who had suffered a heart attack, and compared it to another group who had not (a case-control study, for the aficionados). People with the highest level of CLA in their body fat were 49% less likely to have had a heart attack, compared to those with the lowest level (2).

Since dairy was the main source of CLA in this population, the association between CLA and heart attack risk is inextricable from the other components in pastured dairy fat. In other words, CLA is simply a marker of pastured dairy fat intake in this population, and the (possible) benefit could just as easily have come from vitamin K2 or something else in the fat.

This study isn't the first one to suggest that pastured dairy fat may be uniquely protective. The Rotterdam and EPIC studies found that a higher vitamin K2 intake is associated with a lower risk of heart attack, cancer and overall mortality (3, 4, 5). In the 1940s, Dr. Weston Price estimated that pastured dairy contains up to 50 times more vitamin K2 than grain-fed dairy. He summarized his findings in the classic book Nutrition and Physical Degeneration. This finding has not been repeated in recent times, but I have a little hunch that may change soon...

Vitamin K2
Cardiovascular Disease and Vitamin K2
Can Vitamin K2 Reverse Arterial Calcification?

Body mass index and cancer deaths in various US states

Ancel Keys is often heavily criticized for allegedly originating the fat phobia that we see today in the US and other countries, perhaps with good reason. But he has also made many important contributions to the health sciences.

One of them was the index known as body mass index (BMI), calculated based on a person's weight and height. Unlike other measures, such as body fat percentage and body fat mass, BMI is very easy to calculate; divide your weight (kg) by your height (m) squared.

BMI is strongly correlated with body fat percentage, and body fat mass. Very muscular people are exceptions; they may have a high BMI and yet reduced body fat.

Excessive body fat mass leads to chronic inflammation, due in part to elevated circulating levels of pro-inflammatory hormones such as tumor necrosis factor-alpha (cute name eh?).

Chronic inflammation, in turn, leads to increased incidence of cancer.

Thus it should be no surprise that having a BMI above 30 (obesity level) is strongly correlated with cancer death rates; see graph below (click on it to enlarge), from: Florida, 2009 (full reference at the end of this post).

The correlation for the graph above is a high 0.702, calculated as the square-root of the R-squared value shown at the bottom-right. The R-squared is the percentage of explained variance for cancer deaths, meaning that nearly 50 percent of the cancer deaths are "explained", or caused, by the BMI percentages.

One more reason to bring body fat down to healthy levels.

How do you do that? A good way to start is to replace refined carbohydrates and sugars with natural sources of protein and fat in your diet; eggs included, no need to worry about dietary cholesterol.

Reference:

Florida, R. (2009). The geography of obesity. Creative Class, Nov. 25.

Adiponectin, inflammation, diabetes, and heart disease

Humans, like many animals, evolved to be episodic eaters and spend most of their time fasting. Body fat is the main store of energy in the human body. Excess dietary carbohydrates and fat are stored as body fat, in specialized cells known as adipocytes. Excess dietary protein is not normally stored as body fat.

Adipocytes can be seen as being part of a very important and distributed endocrine organ, being responsible for the release of many different hormones into the bloodstream. One of these hormones is adiponectin. Other important hormones secreted by body fat tissue are leptin and tumor necrosis factor-alpha.

Among hormones, adiponectin is particularly interesting because it is negatively correlated with body fat mass. That is, unlike other hormones such as leptin and tumor necrosis factor-alpha, a decrease in body fat mass (a well known health marker) is associated with an increase in adiponectin. This has led some researchers to speculate that adiponectin is a causative factor that promotes health, in addition to being a health marker.

Jung and colleagues (2008; full reference at the end of this post) studied 78 obese individuals (41 females) who participated in an exercise program during 12 weeks. The exercise program involved mostly low intensity aerobic activities, such as brisk walking. The individuals also took an appetite suppressant, with the goal of reducing their calorie intake by about 500 kcal per day.

The table below (click on it to enlarge) shows various measurements for the participants before and after the 12-week intervention.

From the table above we can say that there were significant reductions in weight, body mass index (BMI), waist and hip circumference, waist-to-hip ratio (WHR), total body fat, and total fasting cholesterol and triglycerides. However, the participants were still obese at the end of the intervention, with an average body fat percentage of 35.5.

The table below shows the concentrations of various hormones secreted by body fat tissue, as well as other types of tissue, before and after the 12-week intervention. These hormones are all believed to be health indicators and/or health causes.

We see from the table above that the hormonal changes were all significant (all at the P < .001 level except one, at the P < .05 level), and all indicative of health improvements. The serum concentrations of all hormones decreased, with two exceptions – adiponectin and interleukin-10, which increased. Interleukin-10 is an anti-inflammatory hormone produced by white blood cells. The most significant increase of the two was by far in adiponectin (P = .001, versus P = .041 for interleukin-10).

One of the most promising effects of adiponectin seems to be an increase in insulin sensitivity. This effect appears to be unrelated to any effects on insulin secretion. That is, adiponectin seems to act directly on various cells, including muscle cells, increasing their ability to clear glucose from the blood. This effect seems to be one of the underlying, and previously unknown, reasons why loss of body fat improves health in those who suffer from diabetes type 2.

Increased serum adiponectin has been found to be significantly associated with: decreased body fat and particularly visceral fat, decreased risk of developing diabetes type 2, decreased blood pressure, and decreased fasting triglycerides.

Adiponectin appears to also have anti-inflammatory and athero-protective properties.

On average, women have higher levels of serum adiponectin than men.

According to Giannessi and colleagues (2007) administration of adiponectin in mice has shown positive results. Since research on adiponectin is new, it will probably be some time until related drugs are developed. Giannessi and colleagues also note that fish oil and vanadium salts may increase the synthesis and release of adiponectin.

So far it seems that the most effective way of increasing adiponectin levels is weight loss, particularly through body fat loss. Even as new drugs are developed, this will likely remain the most natural and safe way of increasing adiponectin levels.

All of this helps in the identification of missing links between body fat loss and health improvement. It seems that losing body fat has an effect similar to that of supplementation; it increases the blood concentration of a health-promoting substance - adiponectin!

References:

Giannessi, D., Maltinti, M., & Del Ry, S. (2007). Adiponectin circulating levels: A new emerging biomarker of cardiovascular risk. Pharmacological Research, 56(6), 459-467.

Gil-Campos, M., Cañete, R., & Gil, A. (2004). Adiponectin, the missing link in insulin resistance and obesity. Clinical Nutrition, 23(5), 963-974.

Jung, S.H. et al. (2008). Effect of weight loss on some serum cytokines in human obesity: increase in IL-10 after weight loss. The Journal of Nutritional Biochemistry, 19(6), 371-375.