The 2013 PLoS ONE sugar and diabetes study: Sugar from fruits is harmless

A new study linking sugar consumption with diabetes prevalence has gained significant media attention recently. The study was published in February 2013 in the journal PLoS ONE (). The authors are Sanjay Basu, Paula Yoffe, Nancy Hills and Robert H. Lustig.

Among the claims made by the media is that “… sugar consumption — independent of obesity — is a major factor behind the recent global pandemic of type 2 diabetes” (). As it turns out, the effects revealed by the study seem to be very small, which may actually be a side effect of data aggregation; I will discuss this further below.

Fruits are exonerated

Let me start by saying that this study also included in the analysis the main natural source of sugar, fruit, as a competing variable (competing with the effects of sugar itself), and found it to be unrelated to diabetes. As the authors note: “None of the other food categories — including fiber-containing foods (pulses, nuts, vegetables, roots, tubers), fruits, meats, cereals, and oils — had a significant association with diabetes prevalence rates”.

This should not surprise anyone who has actually met and talked with Dr. Lustig, the senior author of the study and a very accessible man who has been reaching out to the public in a way that few in his position do. He is a clinician and senior researcher affiliated with a major university; public outreach, in the highly visible way that he does it, is probably something that he does primarily (if not solely) to help people. Dr. Lustig was at the 2012 Ancestral Health Symposium, and he told me, and anyone who asked him, that sugar in industrialized foods was his target, not sugar in fruits.

As I noted here before, the sugar combination of fruits, in their natural package, may in fact be health-promoting (). The natural package probably promotes enough satiety to prevent overconsumption.

Both (unnatural) sugar and obesity have effects, but they are tiny in this study

The Diabetes Report Card 2012 () provides a wealth of information that can be useful as a background for our discussion here.

In the USA, general diabetes prevalence varies depending on state, with some states having higher prevalence than others. The vast majority of diabetes cases are of type 2 diabetes, which is widely believed to be strongly associated with obesity.

In 2012, the diabetes prevalence among adults (aged 20 years or older) in Texas was 9.8 percent. This rate is relatively high compared to other states, although lower than in some. So, among a random group of 1,000 adult Texans, you would find approximately 98 with diabetes.

Prevalence increases with age. Among USA adults in general, prevalence of diabetes is 2.6 percent within ages 20–44, 11.7 percent within ages 45–64, and 18.9 percent at age 64 or older. So the numbers above for Texas, and prevalence in almost any population, are also a reflection of age distribution in the population.

According to the 2013 study published in PLoS ONE, a 1 percent increase in obesity prevalence is associated with a 0.081 percent increase in diabetes prevalence. This comes directly from the table below, fifth column on the right. That is the column for the model that includes all of the variables listed on the left.



We can translate the findings above in more meaningful terms by referring to hypothetical groups of 1,000 people. Let us say we have two groups of 1,000 people. In one of them we have 200 obese people (20 percent); and no obese person in the other. We would find only between 1 and 2 people with diabetes in the group with 200 obese people.

The authors also considered overweight prevalence as a cause of diabetes prevalence. A section of the table with the corresponding results in included below. They also found a significant effect, of smaller size than for obesity – which itself is a small effect.



The study also suggests that consumption of the sugar equivalent of a 12 oz. can of regular soft drink per person per day was associated with a 1.1 percent rise in diabetes prevalence. The effect here is about the same as that of a 1 percent increase in obesity.

That is, let us say we have two groups of 1,000 people. In one of them we have 200 people (20 percent) consuming one 12 oz. can of soft drink per day; and no one consuming sugar in the other. (Sugar from fruits is not considered here.) We would find only about 2 people with diabetes in the group with 200 sugary soda drinkers.

In other words, the effects revealed by this study are very small. They are so small that their corresponding effect sizes make them borderline irrelevant for predictions at the individual level. Based on this study, obesity and sugar consumption combined would account for no more than 5 out of each 100 cases of diabetes (a generous estimate, based on the results discussed above).

Even being weak, the effects revealed by this study are not irrelevant for policy-making, because policies tend to influence the behavior of very large numbers of people. For example, if the number of people that could be influenced by policies to curb consumption of refined sugar were 100 million, the number of cases of diabetes that could be prevented would be 200 thousand, notwithstanding the weak effects revealed by this study.

Why are the effects so small?

The effects in this study are based on data aggregated by country. When data is aggregated by population, the level of variation in the data is reduced; sometimes dramatically, a problem that is proportional to the level of aggregation (e.g., the problem is greater for country aggregation than for city aggregation).

Because there can be no association without correlation, and no correlation without variation, coefficients of association tend to be reduced when data aggregation occurs. This is, in my view, the real problem behind what statisticians often refer to, in “statospeech”, as “ecological fallacy”. The effects in aggregated data are weaker than the effects one would get without aggregation.

So, I suspect that the effects in this study, which are fairly weak at the level of aggregation used (the country level), reflect much stronger effects at the individual level of analysis.

Bottom line

Should you avoid getting obese? Should you avoid consuming industrialized products with added sugar? I think so, and I would still have recommended these without this study. There seems to be no problem with natural foods containing sugar, such as fruits.

This study shows evidence that sugar in industrialized foods is associated with diabetes, independently from obesity, but it does not provide evidence that obesity doesn’t matter. It shows that both matter, independently of one another, which is an interesting finding that backs up Dr. Lustig’s calls for policies to specifically curb refined sugar consumption.

Again, what the study refers to as sugar, as availability but implying consumption, seems to refer mostly to industrialized foods where sugar was added to make them more enticing. Fruit consumption was also included in the study, and found to have no significant effect on diabetes prevalence.

Here is a more interesting question. If a group of people have a predisposition toward developing diabetes, due to any reason (genetic, epigenetic, environmental), what would be the probability that they would develop diabetes if they became obese and/or consumed unnatural sugar-added foods?

This type of question can be answered with a moderating effects analysis, but as I noted here before (), moderating effects analyses are not conducted in health research.

How to make white rice nutritious

One of the problems often pointed out about rice, and particularly about white rice, is that its nutrition content is fairly low. It is basically carbohydrates with some trace amounts of protein. A 100-g portion of cooked white rice will typically deliver 28 g of carbohydrates, with zero fiber, and 3 g of protein. The micronutrient content of such a portion leaves a lot to be desired when compared with fruits and vegetables, as you can see below (from Nutritiondata.com). Keep in mind that this is for 100 g of “enriched” white rice; the nutrients you see there, such as manganese, are added.

White rice is rice that has had its husk, bran, and germ removed. This prevents spoilage and thus significantly increases its shelf life. As it happens, it also significantly reduces both its nutrition and toxin content. White rice is one of the refined foods with the lowest toxin content.

Another interesting property of white rice is that it absorbs moisture to the tune of about 2.5 times its weight. That is, a 100-g portion of dry white rice will lead to a 250-g portion of edible white rice after cooking. This does not only dramatically decrease white rice’s glycemic load () compared with wheat-based products in general (with some exceptions, such as pasta), but also allows for white rice to be made into a highly nutritious dish.

If you slow cook almost anything in water, many of its nutrients will seep into the water. All you have to do is to then use that water (often called broth) to cook white rice in it, and you will end up with highly nutritious rice. Typically you will need twice as much broth as rice, cooked for about 15 minutes – e.g., 2 cups of broth for 1 cup of rice.

You can add meats to the white rice, such as pulled chicken or shrimp; add some tomato sauce to that and you’ll make it a chicken or shrimp risotto. You can also add vegetables to the rice. If you want your rice to have something like an al dente consistency, I recommend doing these after the rice is ready; i.e., after you cooked it in the broth.

For the white rice-based dish below I used a broth from about two hours of slow cooking of diced vegetables; namely red bell peppers, carrots, celery, onions, and cabbage. After cooking the rice for 15 minutes, and letting it "sit" for a while (another 15 minutes with the pan covered), I also added the vegetables to it.

As a side note, the cabbage and onion tend to completely dissolve after 1 h or so of slow cooking. The added vegetables give the dish quite a nutritional punch. For example, the cabbage alone seems to be a great source of vitamin C (which is not completely destroyed by the slow cooking), the anti-inflammatory amino acid glutamine, and the DNA repair-promoting substance known as indole-3-carbinol ().

The good folks over at the Highbrow Paleo group on Facebook () had a few other great ideas posted in response to my previous post on the low glycemic load of white rice (), such as cooking white rice in bone broth (thanks Derrick!).

Rice consumption and health

Carbohydrate-rich foods lead to the formation of blood sugars after digestion (e.g., glucose, fructose), which are then used by the liver to synthesize liver glycogen. Liver glycogen is essentially liver-stored sugar, which is in turn used to meet the glucose needs of the human brain – about 5 g/h for the average person.

(Source: Wikipedia)

When one thinks of the carbohydrate content of foods, there are two measures that often come to mind: the glycemic index and the glycemic load. Of these two, the first, the glycemic index, tends to get a lot more attention. Some would argue that the glycemic load is a lot more important, and that rice, as consumed in Asia, may provide a good illustration of that importance.

A 100-g portion of cooked rice will typically deliver 28 g of carbohydrates, with zero fiber, and 3 g of protein. By comparison, a 100-g portion of white Italian bread will contain 54 g of carbohydrates, with 4 g of fiber, and 10 g of protein – the latter in the form of gluten. A 100-g portion of baked white potato will have 21 g of carbohydrates, with 2 g of fiber, and 2 g of protein.

As you can see above, the amount of carbohydrate per gram in white rice is about half that of white bread. One of the reasons is that the water content in rice, as usually consumed, is comparable to that in fruits. Not surprisingly, rice’s glycemic load is 15 (medium), which is half the glycemic load of 30 (high) of white Italian bread. These refer to 100-g portions. The glycemic load of 100 g of baked white potato is 10 (low).

The glycemic load of a portion of food allows for the estimation of how much that portion of food raises a person's blood glucose level; with one unit of glycemic load being equivalent to the blood glucose effect of consumption of one gram of glucose.

Two common denominators between hunter-gatherer groups that consume a lot of carbohydrates and Asian populations that also consume a lot of carbohydrates are that: (a) their carbohydrate consumption apparently has no negative health effects; and (b) they consume carbohydrates from relatively low glycemic load sources.

The carbohydrate-rich foods consumed by hunter-gatherers are predominantly fruits and starchy tubers. For various Asian populations, it is predominantly white rice. As noted above, the water content of white rice, as usually consumed by Asian populations, is comparable to that of fruits. It also happens to be similar to that of cooked starchy tubers.

An analysis of the China Study II dataset, previously discussed here, suggests that widespread replacement of rice with wheat flour may have been a major source of problems in China during the 1980s and beyond ().

Even though rice is an industrialized seed-based food, the difference between its glycemic load and those of most industrialized carbohydrate-rich foods is large (). This applies to rice as usually consumed – as a vehicle for moisture or sauces that would otherwise remain on the plate. White rice combines this utilitarian purpose with a very low anti-nutrient content.

It is often said that white rice’s nutrient content is very low, but this problem can be easily overcome – a topic for the next post.

Hunger is your best friend: It makes natural foods taste delicious and promotes optimal nutrient partitioning

One of the biggest problems with modern diets rich in industrial foods is that they promote unnatural hunger patterns. For example, hunger can be caused by hypoglycemic dips, coupled with force-storage of fat in adipocytes, after meals rich in refined carbohydrates. This is a double-edged post-meal pattern that is induced by, among other things, abnormally elevated insulin levels. The resulting hunger is a rather unnatural type of hunger.

By the way, I often read here and there, mostly in blogs, that “insulin suppresses hunger”. I frankly don’t know where this idea comes from. What actually happens is that insulin is co-secreted with a number of other hormones. One of those, like insulin also secreted by the beta-cells in the pancreas, is amylin – a powerful appetite suppressor. Amylin deficiency leads to hunger even after a large carbohydrate-rich meal, when insulin levels are elevated.

Abnormally high insulin levels – like those after a “healthy” breakfast of carbohydrate-rich cereals, pancakes etc. – lead to abnormal blood glucose dips soon after the meal. What I am talking about here is a fall in glucose levels that is considerable, and that also happens very fast – illustrated by the ratio between the lengths of the vertical and horizontal black lines on the figure below, from a previous post ().

Those hypoglycemic dips induce hunger, because the hormonal changes necessary to apply a break to the fall in glucose levels (which left unchecked would lead to death) leave us with a hormonal mix that ends up stimulating hunger, in an unnatural way. At the bottom of those dips, insulin levels are much lower than before. I am not talking about diabetics here. I am talking about normoglycemic folks, like the ones whose glucose levels are show on the figure above.

On a diet primarily of natural foods, or foods that are not heavily modified from their natural state, hunger patterns tend to be better synchronized with nutrient deficiencies. This is one of the main advantages of a natural foods diet. By nutrients, I do not mean only micronutrients such as vitamins and minerals, but also macronutrients such as amino and fatty acids.

On a natural diet, nutrient deficiencies should happen regularly. Our bodies are designed for sporadic nutrient intake, remaining most of the time in the fasted state. Human beings are unique in that they have very large brains in proportion to their overall body size, brains that run primarily on glucose – the average person’s brain consumes about 5 g/h of glucose. This latter characteristic makes it very difficult to extrapolate diet-based results based on other species to humans.

As hunger becomes better synchronized with nutrient deficiencies, it should promote optimal nutrient partitioning. This means that, among other things: (a) you should periodically feel hungry for different types of food, depending on your nutrient needs at that point in time; (b) if you do weight training, and fell hungry, some muscle gain should follow; and (c) if you let hunger drive food consumption, on a diet of predominantly natural foods, body fat levels should remain relatively low.

In this sense, hunger becomes your friend – and the best spice!

Laser surgery for myopia early in life may create reading problems after 40

Shortsightedness, or myopia, seems to be endemic in urban populations. The National Institutes of Health suggests that myopia cannot be prevented, and that neither reading nor watching television causes myopia. I find that doubtful, as reading is a rather unnatural activity, and there is evidence that myopia is significantly associated with amount of reading at early ages.

(Source: WebMD.com)

Trying to avoid reading early in life would not be a highly recommended Paleolithic-mimicking choice, except for those who later decide to live among hunter-gatherers. (In spite of our romantic views of hunter-gatherer life, it is very rare to see an urbanite do this outside the context of anthropological studies.) Education requires a lot of reading, and without education in urban environments one is likely to end up suffering from other diseases of civilization. Diabetes, for example, is strongly and inversely associated with education level in urban environments.

Also, keeping up with friends on Facebook, without which life as we know it now could go on, requires a lot of reading and writing.

A different theory, often associated with Cordain, is that myopia is due to consumption of industrial carbohydrate-rich foods. Interestingly, according to Cordain and colleagues, myopia is typically accompanied by higher stature, a finding that is supported by empirical evidence. The idea here is that industrial carbohydrate-rich foods promote abnormal growth patterns during developmental stages, which arguably include abnormal growth of the human eye and its various structures.

Avoiding industrial carbohydrate-rich foods during developmental stages is feasible, but currently very difficult given public health policies that strongly promote the consumption of some of those foods, during development stages, as healthy choices (e.g., cereals). In part as a result of those policies, and also due to budget constraints (those foods tend to be generally cheap), industrial carbohydrate-rich foods are frequently served as meals in schools.

Okay, now to the main topic of this post. Let us say a person has myopia, should he or she fix it surgically?

As one ages, the ability to read at a short-distance (as in reading from books, or from a computer screen) goes down, because the ability to focus on short-distance objects becomes impaired. This phenomenon is called presbyopia, and is also associated with excessive reading. Therefore it could be called a disease of civilization as well. Most college professors at the level of Associate Professor and higher I know (that is, older folks, like me) have developed it, sometimes as early as in their late 30s.

In the general population, normally presbyopia sets in between 40 and 50 years of age, requiring the use of "reading glasses" afterwards … except for those with myopia. This is sometimes called the “myopia payoff of presbyopia”. People with myopia are often able to read well, without the help of glasses, after presbyopia sets in. The reason is that myopia essentially opposes presbyopia at short distances.

Someone with myopia will still have it after presbyopia sets in, and thus will have difficulty seeing at long distances, but will frequently be able to read well at short distances.

So, if you undergo eye laser surgery (the most common type) to correct myopia early in life, you may create reading problems after 40.

P.S.: A friend of mine who has been studying this tells me that eye problems in general are caused by avoidance of indirect sunlight. I am planning on looking into this more deeply in the future.

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.

My transformation: I cannot remember the last time I had a fever

The two photos below (click to enlarge) were taken 4 years apart. The one on the left was taken in 2006, when I weighed 210 lbs (95 kg). Since my height is 5 ft 8 in, at that weight I was an obese person, with over 30 percent body fat. The one on the right was taken in 2010, at a weight of 150 lbs (68 kg) and about 13 percent body fat. I think I am a bit closer to the camera on the right, so the photos are not exactly on the same scale. For a more recent transformation update, see this post.

My lipids improved from borderline bad to fairly good numbers, as one would expect, but the two main changes that I noticed were in terms of illnesses and energy levels. I have not had a fever in a long time. I simply cannot remember when it was the last time that I had to stay in bed because of an illness. I only remember that I was fat then. Also, I used to feel a lot more tired when I was fat. Now I seem to have a lot of energy, almost all the time.

In my estimation, I was obese or overweight for about 10 years, and was rather careless about it. A lot of that time I weighed in the 190s; with a peak weight of 210 lbs. Given that, I consider myself lucky not to have had major health problems by now, like diabetes or cancer. A friend of mine who is a doctor told me that I probably had some protection due to the fact that, when I was fat, I was fat everywhere. My legs, for example, were fat. So were my arms and face. In other words, I lot of the fat was subcutaneous, and reasonably distributed. In fact, most people do not believe me when I say that I weighed 210 lbs when that photo was taken in 2006; but maybe they are just trying to be nice.

If you are not obese, you should do everything you can to avoid reaching that point. Among other things, your chances of having cancer will skyrocket.

So, I lost a whopping 60 lbs (27 kg) over about 2-3 years. That is not so radical; about 1.6-2.5 lbs per month. There were plateaus with no weight loss, and even a few periods with weight gain. Perhaps because of that and the slow weight loss, I had none of the problems usually associated with body responses to severe calorie restriction, such as hypothyroidism. I remember a short period when I felt a little weak and miserable; I was doing exercise after long fasts (20 h or so), and not eating enough afterwards. I did that for a couple of weeks and decided against the idea.

There are no shortcuts with body fat loss, it seems. Push it too hard and the body will react; compensatory adaptation at work.

My weight has been stable, at around 150 lbs, for a little less than 2 years now.

What did I do to lose 60 lbs? I did a number of things at different points in time. I measured various variables (e.g., intake of macronutrients, weight, body fat, HDL cholesterol etc.) and calculated associations, using a prototype version of HealthCorrelator for Excel (HCE). Based on all that, I am pretty much convinced that the main factors were the following:

- Complete removal of foods rich in refined carbohydrates and sugars from my diet, plus almost complete removal of plant foods that I cannot eat raw. (I do cook some plant foods, but avoid the ones I cannot eat raw; with a few exceptions like sweet potato.) That excluded most seeds and grains from my diet, since they can only be eaten after cooking.

- Complete removal of vegetable oils rich in omega-6 fats from my diet. I cook primarily with butter and organic coconut oil. I occasionally use olive oil, often with water, for steam cooking.

- Consumption of plenty of animal products, with emphasis on eating the animal whole. All cooked. This includes small fish (sardines and smelts) eaten whole about twice a week, and offal (usually beef liver) about once or twice a week. I also eat eggs, about 3-5 per day.

- Practice of moderate exercise (2-3 sessions a week) with a focus on resistance training and high-intensity interval training (e.g., sprints). Also becoming more active, which does not necessarily mean exercising but  doing things that involve physical motion of some kind (e.g., walking, climbing stairs, moving things around), to the tune of 1 hour or more every day.

- Adoption of more natural eating patterns; by eating more when I am hungry, usually on days I exercise, and less (including fasting) when I am not hungry. I estimate that this leads to a caloric surplus on days that I exercise, and a caloric deficit on days that I do not (without actually controlling caloric intake).

- A few minutes (15-20 min) of direct skin exposure to sunlight almost every day, when the sun is high, to get enough of the all-important vitamin D. This is pre-sunburn exposure, usually in my backyard. When traveling I try to find a place where people jog, and walk shirtless for 15-20 min.

- Stress management, including some meditation and power napping.

- Face-to-face social interaction, in addition to online interaction. Humans are social animals, and face-to-face social interaction contributes to promoting the right hormonal balance.

When I was fat, my appetite was a bit off. I was hungry at the wrong times, it seemed. Then slowly, after a few months eating essentially whole foods, my hunger seemed to start “acting normally”. That is, my hunger slowly fell into a pattern of increasing after physical exertion, and decreasing with rest. Protein and fat are satiating, but so seem to be fruits and vegetables. Never satiating for me were foods rich in refined carbohydrates and sugars – white bread, bagels, doughnuts, pasta etc.

Looking back, it almost seems too easy. Whole foods taste very good, especially if you are hungry.

But I will never want to each a peach after I have a doughnut. The peach will be tasteless!

Insulin responses to foods rich in carbohydrates and protein

Insulin is often presented as a hormone that is at the core of the diseases of civilization, particularly because of the insulin response elicited by foods rich in refined carbohydrates and sugars. What is often not mentioned is that protein also elicits an insulin response and so do foods where carbohydrates are mixed with fat. Sometimes the insulin responses are way more than one would expect based on the macronutrient compositions of the foods.

Holt et al. (1997; full reference at the end of this post) conducted a classic study of insulin responses. This study has been widely cited, and paints an interesting picture of differences in insulin responses to various foods. But you have to be careful where you look. There has been some confusion about the results because of the way they are often reported in places like Wikipedia and on various Internet sites that refer to the study.

The key thing to bear in mind when reviewing this study is that the amounts of food used were designed to have the same calorie content: 1000 kJ or 240 kcal (i.e., 240 calories). This led to wild variations in the size of the portions that are compared and their weight in grams. Also, some of the food portions are probably not what people usually eat in one sitting.

In Holt et al.’s (1997) study the participants were 41 lean and healthy university students. They were fed 1000 kJ (240 kcal) portions of the test foods on separate mornings after a 10-hour fast overnight. Blood insulin levels were measured at different times within a 120-minute period after each meal. An insulin score was then calculated from the area under the insulin response curve for each food; white bread was used as the reference food.

Part of Table 2 on page 1267 is shown below (the full text version of the paper is linked at the end of this post), just to illustrate the types and amounts of food served, and the macronutrient breakdown for each food. I hope you can see what I meant when I said that some of the food portions are probably not what people usually eat in one sitting. I don’t think it would be hard to find someone who would eat 158 g of beef steak in one sitting, but 333 g of fish is a little more difficult. Fish has a higher proportion of protein than beef steak, and thus is more satiating. The same goes for 625 g of orange, about 6 oranges. Foods that have more fat have more calories per gram; hence the smaller portions served for high-fat foods.

Table 4 of the article is a bit long, so I am providing it in two parts below. AUC stands for “area under the curve”. As you can see, for isocaloric portions of different foods (i.e., with the same amount of calories), there is a huge variation in insulin response. The insulin AUCs are shown on the second numeric column from the left. Also note that the insulin responses (AUC) for white bread varied in different meals. This complicates things a bit, but at least provides a more realistic view of the responses since each participant served as his or her own control.

Look at the third column from the right, which shows the insulin responses per gram of each food, compared with the response to white bread, always shown at the top for each group of related foods (e.g., protein-rich foods). The gram-adjusted response for whole-meal bread is rather high, and so is the glucose response. The gram-adjusted insulin response to potatoes is less than one-third of the response to white bread, even though the non-gram-adjusted glucose response is higher. The insulin response to beef is also less than one-third of the response to white bread, gram-for-gram. Even cheese leads to a gram-adjusted response that is about half the one for white bread, and I don’t think many people will eat the same amount of cheese in one sitting as they would do with white bread.

In summary, insulin responses to protein-rich foods are often 50 to 70 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods. Also, insulin responses to unrefined carbohydrate-rich foods (e.g., potato, fruits) are often 70 to 90 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods.

Why do insulin levels go up in response to dietary protein?

One of the reasons is that insulin is needed for tissue protein synthesis. That is, increased circulating protein (as amino acids) and insulin have a net anabolic effect, promoting muscle growth and inhibiting muscle breakdown. (Muscle protein synthesis and breakdown happen all the time; the net effect defines whether muscle grows or shrinks.) In this respect, insulin acts in conjunction with other hormones, such as growth hormone and insulin-like growth factor 1.

Reference:

Holt, S.H., Miller, J.C., & Petocz, P. (1997). An insulin index of foods: The insulin demand generated by 1000-kJ portions of common foods. American Journal of Clinical Nutrition, 66, 1264-1276.

The huge gap between glycemic loads of refined and unrefined carbohydrate-rich foods

I often refer to foods rich in refined carbohydrates in this blog as among the most disease-promoting agents of modern diets. Yet, when one looks at the glycemic indices of foods rich in refined and unrefined carbohydrates, they are not all that different.

The glycemic index of a carbohydrate-rich food reflects how quickly the food is digested and generate a blood glucose response. Technically, it is measured as the area under a two-hour blood glucose response curve following the consumption of a portion of the food with a fixed amount of carbohydrates.

A measure that reflects much better the underlying difference between foods rich in refined and unrefined carbohydrates is the glycemic load, which is the product of the glycemic index of a food by the carbohydrate content in a 100 g portion of the food.

The glycemic load is also the reason for one known fact among diabetics. If a diabetic person eats a very small amount of a high glycemic index food, he or she will have a relatively small increase in blood sugar. If that person consumes a large amount of the same food, the increase in blood sugar will be dramatic.

The table below (click on it to enlarge) shows the remarkable differences between the glycemic loads of foods rich in refined and unrefined carbohydrates. It was taken from an article co-authored by Loren Cordain, Michael R. Eades, and Mary D. Eades (full reference at the end of this post).

At the time of this post’s writing, the article from which the table above was taken had a solid number of citations to it; a total of 74 citations on Google Scholar’s database. It is an excellent article, which I highly recommend reading in full (the link to the online full text is at the end of this post).

What is the reason for the differences in glycemic loads?

The answer is that foods rich in unrefined carbohydrates, even those with a high glycemic index (such as potatoes), are also packed with a number of other things – e.g., micronutrients, fiber, water, and even some protein. An Irish (white) potato is 75 percent water. By comparison, cereal, without milk added, is about 1 percent water. You have to add a lot of whole milk to it to make it a bit healthier. And even unsweetened whole milk is about 5 percent sugar.

There was nothing even remotely similar to modern foods rich in refined carbohydrates in the diet of our Paleolithic ancestors. In fact, the types of food rich in refined carbohydrates shown on the table above are very recent, typically dating back to less than a hundred years ago. That is, they are so recent that it is unlikely that any of us have genetic adaptations to those types of food.

Once one’s glucose metabolism is seriously impaired, which seems to be associated with consumption over many years of refined carbohydrates and sugars (as well as some genetic predisposition, which may have evolved among some of our ancestors), then even the foods with high glycemic index and low glycemic load (e.g., potato) will lead to highly elevated glucose levels if eaten in more than very small amounts.

Insulin resistant individuals should avoid even foods with high glycemic index and low glycemic load, as well as any food that significantly increases their blood glucose levels after a meal, because highly elevated glucose levels are toxic to various tissues in the body. The longer those highly elevated serum glucose levels are maintained, the more damage is done; e.g., 2 hours as opposed to 30 minutes at 180 mg/dl. One reason why they are toxic is because they lead to high levels of protein glycation; this is a process whereby sugar binds to protein and “warps” it, impairing its functions.

Generally speaking, the more glycation is going on in our body, the more accelerated is the aging process.

Reference:

Loren Cordain, Michael R. Eades, Mary D. Eades (2003). Hyperinsulinemic diseases of civilization: More than just Syndrome X. Comparative Biochemistry and Physiology: Part A, 136, 95–112.

PepsiCo to reduce sugar and fat in products

I guess PepsiCo is moving ahead of the competition, but in a snail pace and in a very, very politically correct way. Will this help in any way? I doubt. It is just too little, too late. See article here.

The article states that:

The company also set two goals for the next 10 years: to cut the average added sugar per serving by 25 percent and saturated fat per serving by 15 percent, in addition to adding more whole grains, fruits, vegetables and low-fat dairy into its array of products.

While it is nice to see more of a focus on sugar than on saturated fat, I would have preferred to see something like this:

The company also set two goals for the next 5 years: to cut the average added sugar per serving by 95 percent and increase saturated fat per serving by 50 percent, in addition to adding more vegetables and full-fat dairy into its array of products.

What would happen? Well, Indra Nooyi is a very smart CEO, and the company has many competent people. They know that they would probably lose enough customers to go out of business … or become the Apple of their industry.

The bottom line is that, if you want to improve your health, you should generally avoid any food or liquid that is highly industrialized.

Maybe PepsiCo should add unprocessed coconut water to their portfolio of drinks.

Want to improve your cholesterol profile? Replace refined carbs and sugars with saturated fat and cholesterol in your diet

An interesting study by Clifton and colleagues (1998; full reference and link at the end of this post) looked at whether LDL cholesterol particle size distribution at baseline (i.e., beginning of the study) for various people was a determinant of lipid profile changes in each of two diets – one low and the other high in fat. This study highlights a few interesting points made in a previous post, which are largely unrelated to the main goal or findings of the study, but that are supported by side findings:

- As one increases dietary cholesterol and fat consumption, particularly saturated fat, circulating HDL cholesterol increases significantly. This happens whether one is taking niacin or not, although niacin seems to help, possibly as an independent (not moderating) factor. Increasing serum vitamin D levels, which can be done through sunlight exposure and supplementation, are also known to increase circulating HDL cholesterol.

- As one increases dietary cholesterol and fat consumption, particularly saturated fat, triglycerides in the fasting state (i.e., measured after a 8-hour fast) decrease significantly, particularly on a low carbohydrate diet. Triglycerides in the fasting state are negatively correlated with HDL cholesterol; they go down as HDL cholesterol goes up. This happens whether one is taking niacin or supplementing omega 3 fats or not, although these seem to help, possibly as independent factors.

- If one increases dietary fat intake, without also decreasing carbohydrate intake (particularly in the form of refined grains and sugars), LDL cholesterol will increase. Even so, LDL particle sizes will shift to more benign forms, which are the larger forms. Not all LDL particles change to benign forms, and there seem to be some genetic factors that influence this. LDL particles larger than 26 nm in diameter simply cannot pass through the gaps in the endothelium, which is a thin layer of cells lining the interior surface of arteries, and thus do not induce plaque formation.

The study by Clifton and colleagues (1998) involved 54 men and 51 women with a wide range of lipid profiles. They first underwent a 2-week low fat period, after which they were given two liquid supplements in addition to their low fat diet, for a period of 3 weeks. One of the liquid supplements contained 31 to 40 g of fat, and 650 to 845 mg of cholesterol. The other was fat and cholesterol free.

Studies that adopt a particular diet at baseline have the advantage of departing from a uniform diet across conditions. They also typically have one common characteristic: the baseline diet reflects the beliefs of the authors about what an ideal diet is. That is not always the case, of course. If this was indeed the case here, we have a particularly interesting study, because in that case the side findings discussed below contradicted the authors’ beliefs.

The table below shows the following measures for the participants in the study: age, body mass index (BMI), waist-to-hip ratio (WHR), total cholesterol, triglycerides, low-density lipoprotein (LDL) cholesterol, and three subtypes of high-density lipoprotein (HDL) cholesterol. LDL cholesterol is the colloquially known as the “bad” type, and “HDL” as the good one (which is an oversimplification). In short, the participants were overweight, middle-aged men and women, with relatively poor lipid profiles.

At the bottom of the table is the note “P < 0.001”, following a small “a”. This essentially means that on the rows indicated by an “a”, like the “WHR” row, the difference in the averages (e.g., 0.81 for women, and 0.93 for men, in the WHR row) was significantly different from what one would expect it to be due to chance alone. More precisely, the likelihood that the difference was due to chance was lower than 0.001, or 0.1 percent, in the case of a P < 0.001. Usually a difference between averages (a.k.a. means) associated with a P < 0.05 will be considered statistically significant.

Since the LDL cholesterol concentrations (as well as other lipoprotein concentrations) are listed on the table in mmol/L, and many people receive those measures in mg/dL in blood lipid profile test reports, below is a conversion table for LDL cholesterol (from: Wikipedia).

The table below shows the dietary intake in the low and high fat diets. Note that in the high fat diet, not only is the fat intake higher, but so is the cholesterol intake. The latter is significantly higher, more than 4 times the intake in the low fat diet, and about 2.5 times the recommended daily value by the U.S. Food and Drug Administration. The total calorie intake is reported as slightly lower in the high fat diet than in the low fat diet.

Note that the largest increase was in saturated fat, followed by an almost equally large increase in monounsaturated fat. This, together with the increase in cholesterol, mimics a move to a diet where fatty meat and organs are consumed in higher quantities, with a corresponding reduction in the intake of refined carbohydrates (e.g., bread, pasta, sugar, potatoes) and lean meats.

Finally, the table below shows the changes in lipid profiles in the low and high fat diets. Note that all subtypes of HDL (or "good") cholesterol concentrations were significantly higher in the high fat diet, which is very telling, because HDL cholesterol concentrations are much better predictors of cardiovascular disease than LDL or total cholesterol concentrations. The higher the HDL cholesterol, the lower the risk of cardiovascular disease.

In the table above, we also see that triglycerides are significantly lower in the high fat diet, which is also good, because high fasting triglyceride concentrations are associated with cardiovascular disease and also insulin resistance (which is associated with diabetes).

However, the total and LDL cholesterol were also significantly higher in the high fat compared to the low fat diet. Is this as bad as it sounds? Not when we look at other factors that are not clear from the tables in the article.

One of those factors is the likely change in LDL particle size. LDL particle sizes almost always increase with significant increases in HDL; frequently going up in diameter beyond 26 nm, and thus passing the threshold beyond which an LDL particle can penetrate the endothelium and help form a plaque.

Another important factor to take into consideration is the somewhat strange decision by the authors to use the Friedewald equation to estimate the LDL concentrations in the low and high fat diets. Through the Friedewald equation, LDL is calculated as follows (where TC is total cholesterol):

    LDL = TC – HDL – Triglycerides / 5

Here is one of the problems with the Friedewald equation. Let us assume that an individual has the following lipid profile numbers: TC = 200, HDL = 50, and trigs. = 150. The calculated LDL will be 120. Let us assume that this same individual reduces trigs. to 50, from the previous 150, keeping all of the other measures constant. This is a major improvement. Yet, the calculated LDL will now be 140, and a doctor will tell this person to consider taking statins!

By the way, most people who do a blood test and get their lipid profile report also get their LDL calculated through the Friedewald equation. Usually this is indicated through a "CALC" note next to the description of the test or the calculated LDL number.

Finally, total cholesterol is not a very useful measure, because an elevated total cholesterol may be primarily reflecting an elevated HDL, which is healthy. Also, a slightly elevated total cholesterol seems to be protective, as it is associated with reduced overall mortality and also reduced mortality from cardiovascular disease, according to U-curve regression studies comparing mortality and total cholesterol levels in different countries.

We do not know for sure that the participants in this study were consuming a lot of refined carbohydrates and/or sugars at baseline. But it is a safe bet that they were, since they were consuming 214 g of carbohydrates per day. It is difficult, although not impossible, to eat that many carbohydrates per day by eating only vegetables and fruits, which are mostly water. Consumption of starches makes it easier to reach that level.

This is why when one goes on a paleo diet, he or she reduces significantly the amount of dietary carbohydrates; even more so on a targeted low carbohydrate diet, such as the Atkins diet. Richard K. Bernstein, who is a type 1 diabetic and has been adopting a strict low carbohydrate diet during most of his adult life, had the following lipid profile at 72 years of age: HDL = 118, LDL = 53, trigs. = 45. His fasting blood sugar was reportedly 83 mg/dl. Click here to listen to an interview with Dr. Bernstein on the The Livin' La Vida Low-Carb Show.

The lipid profile improvement observed (e.g., a 14 percent increase in HDL from baseline for men, and about half that for women, in only 3 weeks) was very likely due to an increase in dietary saturated fat and cholesterol combined with a decrease in refined carbohydrates and sugars. The improvement would probably have been even more impressive with a higher increase in saturated fat, as long as it was accompanied by the elimination of refined carbohydrates and sugars from the participants’ diets.

Reference:

Clifton, P. M., M. Noakes, and P. J. Nestel (1998). LDL particle size and LDL and HDL cholesterol changes with dietary fat and cholesterol in healthy subjects. J. Lipid. Res. 39: 1799–1804.

How much vitamin D? Vitamin D Council's recommendations

Since my recent post on problems related to vitamin D deficiency and excess I received several questions. I have also participated in several discussions in other blogs related to vitamin D in the past few days.

There is a lot of consensus about vitamin D deficiency being a problem, but not much about vitamin D in excess being a problem as well.

Some bloggers recommend a lot of supplementation, which may be dangerous because: (a) our body evolved to obtain most of its vitamin D from a combination of sunlight exposure and cholesterol, and thus body accumulation regulation mechanisms are not designed to deal with excessive oral supplementation; and (b) vitamin D, like many fat-soluble vitamins, accumulates in fat tissue over time, and is not easily eliminated by the body when in excess.

The Vitamin D Council has the following general recommendation regarding supplementation:

Take an average of 5,000 IU a day, year-round, if you have some sun exposure. If you have little, or no, sun exposure you will need to take at least 5,000 IU per day. How much more depends on your latitude of residence, skin pigmentation, and body weight. Generally speaking, the further you live away from the equator, the darker your skin, and/or the more you weigh, the more you will have to take to maintain healthy blood levels.

They also provide a specific example:

For example, Dr. Cannell lives at latitude 32 degrees, weighs 220 pounds, and has fair skin. In the late fall and winter he takes 5,000 IU per day. In the early fall and spring he takes 2,000 IU per day. In the summer he regularly sunbathes for a few minutes most days and thus takes no vitamin D on those days in the summer.

For those who have problems with supplementation, here is what Dr. Cannell, President of the Vitamin D Council, has to say:

For people who have trouble with supplements, I recommend sunbathing during the warmer months and sun tanning parlors in the colder months. Yes, sun tanning parlors make vitamin D, the most is made by the older type beds. Another possibility is a Sperti vitamin D lamp.

One thing to bear in mind is that if your diet is rich in refined carbohydrates and sugars, you need to change that before you are able to properly manage your vitamin D levels. You need to remove refined carbohydrates and sugars from your diet. No more white bread, bagels, doughnuts, table sugar, sodas sweetened with high-fructose corn syrup; just to name a few of the main culprits.

In fact, a diet rich in refined carbohydrates and sugars, in and of itself, may be one of the reasons of a person''s vitamin D deficiency in the case of appropriate sunlight exposure or dietary intake, and even of excessive levels of vitamin D accumulating in the body in the case of heavy supplementation.

The hormonal responses induced by a diet  rich in  refined carbohydrates and sugars promote fat deposition and, at the same time, prevent fat degradation. That is, you tend to put on body fat easily, and you tend to have trouble burning that fat.

This causes a "hoarding" effect which leads to an increase in vitamin D stored in the body, and at the same time reduces the levels of vitamin D in circulation. This is because vitamin D is stored in body fat tissue, and has a long half-life, which means that it accumulates (as in a battery) and then slowly gets released into the bloodstream for use, as body fat is used as a source of energy.

It should not be a big surprise that vitamin D deficiency problems correlate strongly with problems associated with heavy consumption of refined carbohydrates and sugars. Both lead to symptoms that are eerily similar; several of which are the symptoms of the metabolic syndrome.

Cancer patterns in Inuit populations: 1950-1997

Some types of cancer have traditionally been higher among the Inuit than in other populations, at least according to data from the 1950s, when a certain degree of westernization had already occurred. The incidence of the following types of cancer among the Inuit has been particularly high: nasopharynx, salivary gland, and oesophageal.

The high incidence of these “traditional” types of cancer among the Inuit is hypothesized to have a strong genetic basis. Nevertheless some also believe these cancers to be associated with practices that were arguably not common among the ancestral Inuit, such as preservation of fish and meat with salt.

Genetic markers in the present Inuit population show a shared Asian heritage, which is consistent with the higher incidence of similar types of cancer among Asians, particularly those consuming large amounts of salt-preserved foods. (The Inuit are believed to originate from East Asia, having crossed the Bering Strait about 5,000 years ago.)

The incidence of nasopharynx, salivary gland, and oesophageal cancer has been relatively stable among the Inuit from the 1950s on. More modern lifestyle-related cancers, on the other hand, have increased dramatically. Examples are cancers of the lung, colon, rectum, and female breast.

The figure below (click on it to enlarge), from Friborg & Melbye (2008), shows the incidence of more traditional and modern lifestyle-related cancers among Inuit males (top) and females (bottom).

Two main lifestyle changes are associated with this significant increase in modern lifestyle-related cancers. One is increased consumption of tobacco. The other, you guessed it, is a shift to refined carbohydrates, from animal protein and fat, as the main source of energy.

Reference:

Friborg, J.T., & Melbye, M. (2008). Cancer patterns in Inuit populations. The Lancet Oncology, 9(9), 892-900.

Applied evolutionary thinking: Darwin meets Washington

Charles Darwin, perhaps one of the greatest scholars of all time, thought about his theory of mutation, inheritance, and selection of biological traits for more than 20 years, and finally published it as a book in 1859.  At that time, many animal breeders must have said something like this: “So what? We knew this already.”

In fact George Washington, who died in 1799 (many years before Darwin’s famous book came out), had tried his hand at what today would be called “genetic engineering.” He produced at least a few notable breeds of domestic animals through selective breeding. Those include a breed of giant mules – the “Mammoth Jackstock” breed. Those mules are so big and strong that they were used to pull large boats filled with coal along artificial canals in Pennsylvania.

Washington learned the basic principles of animal breeding from others, who learned it from others, and so on. Animal breeding has a long tradition.

So, not only did animal breeders, like George Washington, had known about the principles of mutation, inheritance, and selection of biological traits; but they also had been putting that knowledge into practice for quite some time before Darwin’s famous book “The Origin of Species” was published.

Yet, Darwin’s theory has applications that extend well beyond animal breeding. There are thousands of phenomena that would look very “mysterious” today without Darwin’s theory. Many of those phenomena apply to nutrition and lifestyle, as we have been seeing lately with the paleo diet movement. Among the most amazing and counterintuitive are those in connection with the design of our brain.

Recent research, for instance, suggests that “surprise” improves cognition. Let me illustrate this with a simple example. If you were studying a subject online that required memorization of key pieces of information (say, historical facts) and a surprise stimulus was “thrown” at you (say, a video clip of an attacking rattlesnake was shown on the screen), you would remember the key pieces of information (about historical facts) much better than if the surprise stimulus was not present!

The underlying Darwinian reason for this phenomenon is that it is adaptively advantageous for our brain to enhance our memory in dangerous situations (e.g., an attack by a poisonous snake), because that would help us avoid those situations in the future (Kock et al., 2008; references listed at the end of this post). Related mental mechanisms increased our ancestors’ chances of survival over many generations, and became embedded in our brain’s design.

Animal breeders knew that they could apply selection, via selective breeding, to any population of animals, and thus make certain traits evolve in a matter of a few dozen generations or less. This is known as artificial selection. Among those traits were metabolic traits. For example, a population of lambs may be bred to grow fatter on the same amount of food as leaner breeds.

Forced natural selection may have been imposed on some of our ancestors, as I argue in this post, leading metabolic traits to evolve in as little as 396 years, or even less, depending on the circumstances.

In a sense, forced selection would be a bit like artificial selection. If a group of our ancestors became geographically isolated from others, in an environment where only certain types of food were available, physiological and metabolic adaptations to those types of food might evolve. This is also true for the adoption of cultural practices; culture can also strongly influence evolution (see, e.g., McElreath & Boyd, 2007).

This is why it is arguably a good idea for people to look at their background (i.e., learn about their ancestors), because they may have inherited genes that predispose them to function better with certain types of diets and lifestyles. That can help them better tailor their diets to their genetic makeup, and also understand why certain diets work for some people but not for others. (This is essentially what medical doctors do, on a smaller time scale, when they take a patients' parents health history into consideration when dispensing medical advice.)

By ancestors I am not talking about Homo erectus here, but ancestors that lived 3,000; 1,000; or even 500 years ago. At times when medical care and other modern amenities were not available, and thus selection pressures were stronger. For example, if your no-so-distant ancestors have consumed plenty of dairy, chances are you are better adapted to consume dairy than people whose ancestors have not.

Very recent food inventions, like refined carbohydrates, refined sugars, and hydrogenated fats are too new to have influenced the genetic makeup of anybody living today. So, chances are, they are bad for the vast majority of us. (A small percentage of the population may not develop any hint of diseases of civilization after consuming them for years, but they are not going to be as healthy as they could be.) Other, not so recent, food inventions, such as olive oil, certain types of bread, certain types of dairy, may be better for some people than for others.

References:

Kock, N., Chatelain-Jardón, R., & Carmona, J. (2008). An experimental study of simulated web-based threats and their impact on knowledge communication effectiveness. IEEE Transactions on Professional Communication, 51(2), 183-197.

McElreath, R., & Boyd, R. (2007). Mathematical models of social evolution: A guide for the perplexed. Chicago, IL: The University of Chicago Press.

Go see your doctor, often

As I blog about health issues, and talk with people about them, I often notice that there is a growing contempt for the medical profession.

This comes in part from the fact that many MDs are still providing advice based on the mainstream assumption that saturated fat is the enemy. Much recent (and even some old) research suggests that among the main real enemies of good health are: chronic stress, refined carbs, refined sugars, industrial trans-fats, and an omega-6/omega-3 imbalance caused by consumption of industrial vegetable oils rich in omega-6 fats.

Because of this disconnect, some people stop seeing their doctors regularly; others avoid doctors completely. Many rely exclusively on Internet advice, from health-related blogs (like this) and other sources. In my opinion, this is a BIG mistake.

A good MD has something that no blogger who is not an MD (like me) can have. He or she has direct access to a much larger group of people, and to confidential information that can clarify things that would look mysterious to non-MDs. They cannot share that information with others, but they know.

For example, often I hear from people that they did this and that, in terms of diet a lifestyle, and that their lab tests were such and such. Later I find out that what they told me was partially, or completely, wrong. That is, they distorted the truth, maybe subconsciously.

I have never met an MD who completely ignored hard facts, such as results of lab tests and common health-related measurements. I have never met an MD who tried to force me to do anything either; although I have to admit that some tend to be a bit pushy.

I see a doctor who does not agree with me; e.g., he wanted me to take statins. No problem; that is the way I like it. If my doctor will agree 100% with all I say, do I need to see that doctor?

My doctor does not question lab results though, and maybe I am changing a bit the way he thinks. He wanted me to take statins, but once I told him that I wanted to try a few other things first, he said: no problem. When the results came, he had that look on this face - maybe u wuz royt eh!?

Many, many patients are under the mistaken assumption that they need to please their doctors. A subconscious assumption for most, no doubt. I guess this is part of human nature, but I don’t think it is helpful to doctors or patients.

Patients actually need to work together with their doctors, see them often, do their own research, ask questions, and do those things that lead to health improvements – ideally measurable ones.

Refined carbs, sugar, and cholesterol: My own experience

A few years ago I went to the doctor for a routine appointment, and I was told that my LDL cholesterol was elevated. I was in my early 40s. My lipid profile was the following - LDL: 156, HDL: 38, triglycerides: 188. The LDL was calculated. I was weighing about 210 lbs, which was too high for my height (5 ft 8 in). My blood pressure was low, as it has always been - systolic: 109, diastolic: 68.

My doctor gave me the standard advice in these cases: exercise, lose weight, and, most importantly, reduce your intake of saturated fat. I was also told that I would probably have to take statins, as my high LDL likely had something to do with my genetic makeup. Again, this is quite standard, and we see it all over the place, particularly in commercials for statins.

I told my doctor that I would do some research on the topic, which I am going to save for other posts. Let me get to the point, by telling you what my lipid profile is today - LDL: 123, HDL: 66, triglycerides: 46. Again, the LDL value is calculated. I am weighing about 152 lbs now, with about 13 percent of body fat.

The HDL and triglycerides numbers above are shown in bold font because my research convinced me that these two numbers are the ones most people should really worry about when trying to address what is known as dyslipidemia. Here I am assuming that only standard lipid profiles are available; there are better alternatives, such as particle type analyses, which are not yet standard.

Many people who suffer from cardiovascular disease have low LDL cholesterol, but very few of those have high HDL cholesterol, which is one of the best predictors of cardiovascular disease among lipids. More specifically, if you have an HDL higher than 60, you have a very small chance of developing cardiovascular disease. (It can happen, but it is very unlikely, with a percentage chance in the single digits.)

Interestingly, low HDL cholesterol is also associated with the metabolic syndrome. This syndrome is characterized by the following:

- High fasting serum glucose (hyperglycemia), which is one of many signs of insulin resistance, a precursor to diabetes type 2;
- High blood pressure;
- Abdominal obesity (also known as pot or beer belly);
- Low HDL cholesterol; and
- Elevated triglycerides.

Now, you may ask, how did you increase your HDL? Well, I tried a number of things - diet and lifestyle changes - and had a blood test every 3 months. After a while I was able to put all of the measures in a spreadsheet table, and correlate them using a statistical software that I developed, to give me an idea of what was going on.

Weight was a big factor on LDL, and I was able to bring my weight down to 150 lbs and my LDL to below 100 at some point. For me, and many other people, body weight and LDL cholesterol are strongly and positively correlated (the higher the weight, the higher the LDL cholesterol - actually body fat seems to be the real culprit). Moreover, my LDL seemed to decrease more markedly when my weight was on the way down, and not as much when it was stable, even if low.

But the HDL would only increase if I increased my saturated fat intake. The problem is that every time I increased my saturated fat intake my LDL would go up; it reached 162 at one point, when my HDL went up to a modest but encouraging 47. That was my highest HDL until I eliminated refined carbs and sugars (e.g., bread, pasta, cereals, doughnuts, bagels, regular sodas) from my diet.

When I brought my intake of refined carbs and sugars down to zero, my intake of protein and saturated fat went up. Either that would happen, or I would starve, because you have to eat something. (I figured that I would not die by doing a low carb/high fat-protein experiment for 3 months to see what happened.) Also, I dramatically increased my dietary cholesterol - two to four eggs per day, organ meats, and seafood.

That is when my HDL shot up, to 66, and my LDL went down. Yes, my LDL levels seem to be negatively correlated with dietary saturated fat and cholesterol amounts, as long as I do not consume refined carbs and sugars. Moreover, it is very likely that my LDL particle size increased, and large LDL particles DO NOT cause atherosclerosis because they cannot penetrate the artery walls.

So, the bottom line is that, at least for me, an INCREASE in saturated fat and a DECREASE in refined carbs and sugars, happening together, seem to have taken me out of my previous path toward the metabolic syndrome.

Moreover, I feel a lot more energetic than before, my immune system seems to have gotten better at fighting disease, and even my pollen allergies are not as bad as they were before. Admittedly, these benefits may be strongly associated with the weight loss and the related reduction in body fat percentage.

I hope this post is helpful to others. The standard advice that people with high LDL cholesterol receive, which usually focuses on reducing saturated fat intake, has a big problem. When you reduce your intake of a type of food, you usually increase your intake of other types of food. Most people who try to reduce their saturated fat intake invariably increase their carb intake, usually with the wrong types of carb-rich foods (the man-made ones), simply because they go hungry.