Dried mussels: A little plate with 160 g of protein (plus some comments on high-protein low-carbohydrate dieting)

Many hunter-gatherer groups employed various methods of drying to preserve meats. Drying also increases significantly the protein content of meats; this is the case with dried mussels. I discussed this effect of drying before here with respect to small fish (). The photo below is of a plate with about 240 g of dried mussels that I prepared using the simple recipe below.



To prepare your mussels as in the photo above, you will have to steam and then dry them. You can season the mussels after you steam them, but I rarely season mine. Almost none of the food I eat requires much seasoning anyway, because I use nature’s super-spice, which makes everything that has a high nutrient content taste delicious: hunger ().

- Steam the mussels for about 10 minutes, or until all are open.
- Remove the mussels from the shells; carefully, to avoid small shell pieces from coming off into the mussels (they are not kind to your teeth).
- Preheat the oven to about 200 degrees Fahrenheit, and place the mussels in it (on a tray) for about 1 hour.
- Leave the mussels in the oven until they are cold, this will dry them further.

About 240 g of mussels, after drying, will yield a meal with a bit more than 160 g of protein – i.e., the proportion of protein will go from about 20 percent up to about 67 percent. In this case, most of the calories in the meal will come from the protein, if you had nothing else with it, adding up to less than 800 calories.

This comes in handy if you need to have lunch out, as the dried mussels can be carried in a plastic bag or container and eaten cold or after a light re-heating in a microwave. To me, they taste very good either way; but then again anything that is nutritious tends to taste very good when you are hungry, and I rarely have breakfast. I often eat them with pre-cooked sweet potato, which I eat with the skin (it tastes like candy).

You may want to think of dried mussels prepared in this way as a protein supplement, but a very nutritious one. You will be getting a large dose of omega-3 fats (3.11 g) with less omega-6 fats than you usually get through fish oil softgels (where n-6s are added for stability), about 1,224 percent of the recommended daily value (RDV) of magnesium, 461 percent of the RDV of selenium, 1,440 of the RDV of vitamin B12, a large dose of zinc, and (interestingly) almost 100 percent of the RDV of vitamin C.

Since mussels are very low in the food chain, accumulation of compounds that can be toxic to humans is not amplified by biomagnification (). But, still, mussels can be significantly affected by contaminants (e.g., petroleum hydrocarbons), so sourcing is important. The supermarket chain I use here in Texas, HEB, claims to do very careful sourcing. Telltale signs of contamination are developmental problems such as thin shells that shatter easily and stunted growth ().

For those readers who are on a low-carbohydrate diet, please pay attention to this: there is NO WAY your body will turn protein into fat if you are on a low-carbohydrate diet, unless you have a serious metabolic disorder (see this post: , and this podcast: ). And I mean SERIOUS; probably way beyond prediabetes. Do not believe the nonsense that has been circulating in some areas of the blogosphere lately.

A high-protein low-carbohydrate diet is one of the most effective diets at reducing body fat, particularly if you do resistance exercise (and you do not have to do it like a bodybuilder). That is not to say that a high-fat low-protein diet (like the "optimal diet") is a bad idea; in fact, the optimal diet is a good option if you do not do resistance exercise, but that is a topic for a different post.

Dried meat: Homemade beef jerky

You can dry many types of meat, including beef, pork, goat, deer, and even some types of seafood, such as mussels. Drying meat tends to significantly increase the meat’s protein content per gram, often more than doubling it. It also helps preserve the meat, as bacteria need an aqueous environment to grow; adding salt helps further prevent bacterial growth.

Dried meat preparation and consumption was common among the Plains Indians (e.g., of the Cheyenne, Comanche, and Lakota tribes), and also a valuable trade item for them. They often ground the dried meat into a powder, mixing fat and berries with them; the result of which was pemmican. Many other hunter-gatherer cultures around the world have incorporated dried meat into their diets.

Below is a recipe for homemade beef jerky, which is very close in terms of nutrition content to the dried meat of the Plains Indians's time; that is, the time when the Plains Indians subsisted mostly on bison. Commercial beef jerky typically has a lower nutrient-to-calorie ratio, in part because sugar is added to it. The recipe is for beef jerky, but can be used to make jerky with bison meat as well.

- Cut about 3 lbs of beef muscle into thin strips (see photo below). Ideally you should buy it partially cut already, with most of the fat trimmed. Cutting with or against the grain doesn’t seem to make much difference, at least to me.

- Prepare some dry seasoning powder by mixing salt and cayenne pepper.

- Season the strips and place them on a tray with a grid on top, so that the fat that will come off the meat is captured by the tray and doesn’t drip into the oven.

- Preheat the oven to about 180 degrees Fahrenheit, and place the strips in it until you can easily pull a piece of the meat off with your fingers (see photos below, for an idea of how they would look). This should take about 1 hour or so. You will not technically be “baking” or "cooking" the meat at this temperature, although the digestibility of the final product will be comparable to that of cooked meat – i.e., greater digestibility than raw meat.

- Leave the strips in the oven until they are cold, this will dry them further.







Homemade beef jerky, prepared as above, is supposed to be eaten cold. In this sense, it could be thought of as a bit like salami, but with a higher protein-to-fat ratio. If your kids eat this on a regular basis, I suspect that their future orthodontist needs will be significantly reduced. Homemade beef jerky, like the commercial one, requires some serious chewing.

The dried strips of meat can be kept outside the fridge for a long time, but if you intend to keep them for more than a few weeks, I would suggest that you keep them in the fridge. Interestingly, adding sugar apparently increases the non-refrigerated shelf life of beef jerky even further. It doesn’t improve the flavor though, in my opinion.

This is a zero-carbohydrate food item, which may be a good choice for those who are insulin resistant or diabetic, and also for those on low-carbohydrate or just-enough-carbohydrate diets. Often I hear bodybuilders who eat multiple meals per day to say that it is hard for them to prepare high-protein snacks that they can easily carry with them. Well, beef jerky is one option.

Gaining muscle and losing fat at the same time: A more customized approach based on strength training and calorie intake variation

In the two last posts I discussed the idea of gaining muscle and losing fat at the same time () (). This post outlines one approach to make that happen, based on my own experience and that of several HCE () users. This approach may well be the most natural from an evolutionary perspective.

But first let us address one important question: Why would anyone want to reach a certain body weight and keep it constant, resorting to the more difficult and slow strategy of “turning fat into muscle”, so to speak? One could simply keep on losing fat, without losing or gaining muscle, until he or she reaches a very low body fat percentage (e.g., a single-digit body fat percentage, for men). Then he or she could go up from there, slowly putting on muscle.

The reason why it is advisable to reach a certain body weight and keep it constant is that, below a certain weight, one is likely to run into nutrient deficiencies. Non-exercise energy expenditure is proportional to body weight. As you keep on losing body weight, calorie intake may become too low to allow you to have a nutrient intake that is the minimum for your body structure. Unfortunately eating highly nutritious vegetables or consuming copious amounts of vitamin and mineral supplements will not work very well, because the nutritional needs of your body include both micro- and macro-nutrients that need co-factors to be properly absorbed and/or metabolized. One example is dietary fat, which is necessary for the absorption of fat-soluble vitamins.

If you place yourself into a state of nutrient deficiency, your body will compensate by mounting a multipronged defense, resorting to psychological and physiological mechanisms. Your body will do that because it is hardwired for self-preservation; as noted below, being in a state of nutrient deficiency for too long is very dangerous for one's health. Most people cannot oppose this body reaction by willpower alone. That is where binge-eating often starts. This is one of the key reasons why looking for a common denominator of most diets leads to the conclusion that all succeed at first, and eventually fail ().

If you are one of the few who can oppose the body’s reaction, and maintain a very low calorie intake even in the face of nutrient deficiencies, chances are you will become much more vulnerable to diseases caused by pathogens. Individually you will be placing yourself in a state that is similar to that of populations that have faced famine in the past. Historically speaking, famines are associated with decreases in degenerative diseases, and increases in diseases caused by pathogens. Pandemics, like the Black Death (), have historically been preceded by periods of food scarcity.

The approach to gaining muscle and losing fat at the same time, outlined here, relies mainly on the following elements: (a) regularly conducting strength training; (b) varying calorie intake based on exercise; and (c) eating protein regularly. To that, I would add becoming more active, which does not necessarily mean exercising but does mean 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. These increase calorie expenditure, enabling a slightly higher calorie intake while maintaining the same weight, and thus more nutrients on a diet of unprocessed foods. In fact, even things like fidgeting count (). These activities should not cause muscle damage to the point of preventing recovery from strength training.

As far as strength training goes, the main idea, as discussed in the previous post, is to regularly hit the supercompensation window, with progressive overload, and maintain your current body weight. In fact, over time, as muscle gain progresses, you will probably want to increase your calorie intake to increase your body weight, but very slowly to keep any fat gain from happening. This way your body fat percentage will go down, even as your weight goes up slowly. The first element, regularly hitting the supercompensation window, was discussed in a previous post ().

Varying calorie intake based on exercise. Here one approach that seems to work well is to eat more in the hours after a strength training session, and less in the hours preceding the next strength training session, keeping the calorie intake at maintenance over a week. Individual customization here is very important. Many people will respond quite well to a calorie surplus window of 8 – 24 h after exercise, and a calorie deficit in the following 40 – 24 h. This assumes that strength training sessions take place every other day. The weekend break in routine is a good one, as well as other random variations (e.g., random fasts), as the body tends to adapt to anything over time ().

One example would be someone following a two-day cycle where on the first day he or she would do strength training, and eat the following to satisfaction: muscle meats, fatty seafood (e.g., salmon), cheese, eggs, fruits, and starchy tubers (e.g., sweet potato). On the second day, a rest day, the person would eat the following, to near satisfaction, limiting portions a bit to offset the calorie surplus of the previous day: organ meats (e.g., heart and liver), lean seafood (e.g., shrimp and mussels), and non-starchy nutritious vegetables (e.g., spinach and cabbage). This would lead to periodic glycogen depletion, and also to unsettling water-weight variations; these can softened a bit, if they are bothering, by adding a small amount of fruit and/or starchy foods on rest days.

Organ meats, lean seafood, and non-starchy nutritious vegetables are all low-calorie foods. So restricting calories with them is relatively easy, without the need to reduce the volume of food eaten that much. If maintenance is achieved at around 2,000 calories per day, a possible calorie intake pattern would be 3,000 calories on one day, mostly after strength training, and 1,000 calories the next. This of course would depend on a number of factors including body size and nonexercise thermogenesis. A few calories could be added or removed here and there to make up for a different calorie intake during the weekend.

Some people believe that, if you vary your calorie intake in this way, the calorie deficit period will lead to muscle loss. This is the rationale behind the multiple balanced meals a day approach; which also works, and is successfully used by many bodybuilders, such as Doug Miller () and Scooby (). However, it seems that the positive nitrogen balance stimulus caused by strength training leads to a variation in nitrogen balance that is nonlinear and also different from the stimulus to muscle gain. Being in positive or neutral nitrogen balance is not the same as gaining muscle mass, although the two should be very highly correlated. While the muscle gain window may close relatively quickly after the strength training session, the window in which nitrogen balance is positive or neutral may remain open for much longer, even in the face of a calorie deficit during part of it. This difference in nonlinear response is illustrated through the schematic graph below.

Eating protein regularly. Here what seems to be the most advisable approach is to eat protein throughout, in amounts that make you feel good. (Yes, you should rely on sense of well being as a measure as well.) There is no need for overconsumption of protein, as one does not need much to be in nitrogen balance when doing strength training. For someone weighing 200 lbs (91 kg) about 109 g/d of high-quality protein would be an overestimation () because strength training itself pushes one’s nitrogen balance into positive territory (). The amount of carbohydrate needed depends on the amount of glycogen depleted through exercise and the amount of protein consumed. The two chief sources for glycogen replenishment, in muscle and liver, are protein and carbohydrate – with the latter being much more efficient if you are not insulin resistant.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder (). Still, consumption of protein stimulates muscle growth through complex processes. And protein does not usually become fat if one is in calorie deficit, particularly if consumption of carbohydrates is limited ().

The above is probably much easier to understand than to implement in practice, because it requires a lot of customization. It seems natural because our Paleolithic ancestors probably consumed more calories after hunting-gathering activities (i.e., exercise), and fewer calories before those activities. Our body seems to respond quite well to alternate day calorie restriction (). Moreover, the break in routine every other day, and the delayed but certain satisfaction provided by the higher calorie intake on exercise days, can serve as powerful motivators.

The temptation to set rigid rules, or a generic formula, always exists. But each person is unique (). For some people, adopting various windows of fasting (usually in the 8 – 24 h range) seems to be a very good strategy to achieve calorie deficits while maintaining a positive or neutral nitrogen balance.

For others, fasting has the opposite effect, perhaps due to an abnormal increase in cortisol levels. This is particularly true for fasting windows of 12 – 24 h or more. If regularly fasting within this range stresses you out, as opposed to “liberating” you (), you may be in the category that does better with more frequently meals.

Gaining muscle and losing fat at the same time: Various issues and two key requirements

In my previous post (), I mentioned that the idea of gaining muscle and losing fat at the same time seems impossible to most people because of three widely held misconceptions: (a) to gain muscle you need a calorie surplus; (b) to lose fat you need a calorie deficit; and (c) you cannot achieve a calorie surplus and deficit at the same time.

The scenario used to illustrate what I see as a non-traumatic move from obese or seriously overweight to lean is one in which weight loss and fat loss go hand in hand until a relatively lean level is reached, beyond which weight is maintained constant (as illustrated in the schematic graph below). If you are departing from an obese or seriously overweight level, it may be advisable to lose weight until you reach a body fat level of around 21-24 percent for women or 14-17 percent for men. Once you reach that level, it may be best to stop losing weight, and instead slowly gain muscle and lose fat, in equal amounts. I will discuss the rationale for this in more detail in my next post; this post will focus on addressing the misconceptions above.

Before I address the misconceptions, let me first clarify that, when I say “gaining muscle” I do not mean only increasing the amount of protein stored in muscle tissue. Muscle tissue is mostly water, by far. An important component of muscle tissue is muscle glycogen, which increases dramatically with strength training, and also tends to increase the amount of water stored in muscle. So, when you gain muscle, you gain a significant amount of water.

Now let us take a look at the misconceptions. The first misconception, that to gain muscle you need a calorie surplus, was dispelled in a previous post featuring a study by Ballor and colleagues (). In that study, obese subjects combined strength training with a mild calorie deficit, and gained muscle. They also lost fat, but ended up a bit heavier than at the beginning of the intervention. Another study along the same lines was linked by Clint (thanks) in the comments section under the last post ().

The second misconception, that to lose fat you need a calorie deficit; is related to the third, that you cannot achieve a calorie surplus and deficit at the same time. In part these misconceptions are about semantics, as most people understand “calorie deficit” to mean “constant calorie deficit”. One can easily vary calorie intake every other day, generating various calorie deficits and surpluses over a week, but with no overall calorie deficit or surplus for the entire week. This is why I say that one can achieve a calorie surplus and deficit “at the same time”. But let us make a point very clear, most of the evidence that I have seen so far suggests that you do not need a calorie deficit to lose fat, but you do need a calorie deficit to lose structural weight (i.e., non-water weight). With a few exceptions, not many people will want to lose structural weight by shedding anything other than body fat. One exception would be professional athletes who are already very lean and yet are very big for the weight class in which they compete, being unable to "make weight" through dehydration.

Perhaps the most surprising to some people is that, based on my own experience and that of several HCE () users, you don’t even need to vary your calorie intake that much to gain muscle and lose fat at the same time. You can achieve that by eating enough to maintain your body weight. In fact, you can even slowly increase your calorie intake over time, as muscle growth progresses beyond the body fat lost. And here I mean increasing your calorie intake very slowly, proportionally to the amount of muscle you gain; which also means that the incremental increase in calorie intake will vary from person to person. If you are already relatively lean, at around 21-24 percent of body fat for women and 14-17 percent for men, gaining muscle and losing fat in equal amounts will lead to a visible change in body composition over time () ().

Two key requirements seem to be common denominators for most people. You must eat protein regularly; not because muscle tissue is mostly protein, but because protein seems to act as a hormone, signaling to muscle tissue that it should repair itself. (Many hormones are proteins, actually peptides, and also bind to receptor proteins.) And you also must conduct strength training to the point that you are regularly hitting the supercompensation window (). This takes a lot of individual customization (). You can achieve that with body weight exercises, although free weights and machines seem to be generally more effective. Keep in mind that individual customization will allow you to reach your "sweet spots", but that still results will vary across individuals, in some cases dramatically.

If you regularly hit the supercompensation window, you will be progressively spending slightly more energy in each exercise session, chiefly in the form of muscle glycogen, as you progress with your strength training program. You will also be creating a hormonal mix that will increase the body’s reliance on fat as a source of energy during recovery. As a compensatory adaptation (), your body will gradually increase the size of its glycogen stores, raising insulin sensitivity and making it progressively more difficult for glucose to become body fat.

Since you will be progressively spending slightly more energy over time due to regularly hitting the supercompensation window, that is another reason why you will need to increase your calorie intake. Again, very slowly, proportionally to your muscle gain. If you do not do that, you will provide a strong stimulus for autophagy () to occur, which I think is healthy and would even recommend from time to time. In fact, one of the most powerful stimuli to autophagy is doing strength training and fasting afterwards. If you do that only occasionally (e.g., once every few months), you will probably not experience muscle loss or gain, but you may experience health improvements as a result of autophagy.

The human body is very adaptable, so there are many variations of the general strategy above. In my next post, I will talk a bit more about a variation that seems to work well for many people. It involves a combination of strength training and calorie intake variation that may well be the most natural from an evolutionary perspective.

The impressive nutrition value of whole dried small fish

When I visited Japan a few years ago I noticed a variety of dried small fish for sale in grocery stores and supermarkets. They came in what seemed to be vacuum-packed flat plastic bags, often dried. The packing was a bit like that of beef jerky in the USA. Since I could not read the labels, I could not tell if preservatives or things like sugar were added. Beef jerky often has sugar added to it; at least the popular brands.

I have since incorporated dried or almost dried small fish, eaten whole, into my diet. My family eats it, but they don’t seem to like it as much as I do. The easiest small fish to find for sale where I live are smelts. A previous post has a recipe (). I can easily eat 200 g of smelts, about twice as much as on the plate below; not quite dried, but almost so. The veggies are a mix of lettuce and cabbage.

As you can see from the macronutrient composition below (from Nutritiondata.com, for a 100 g portion), 200 g of smelts have about 112 g of protein, and 36 g of fat. No carbohydrates; or a very small amount of them.

Unless you misguidedly think that they will “give you cholesterol”, the macronutrient to calorie ratio of a plate with 200 g of dried (or almost dried) smelts is very good. Let us take a look at the fat content, below (from Nutritiondata.com as well), which is for 100 g of dried smelts.

The “net” omega-3 content of 200 g of dried smelts, after subtracting the omega-6 content, is approximately 4.4 g. The concept of “net” omega-3 content was discussed in a previous post ().

So, the net omega-3 content of 200 g of dried smelts is the equivalent to the net omega-3 content of about 20 fish oil softgels. (Yes, you read it right!) And you would get a lot more omega-6 from the softgels.

Not to mention the fact that isolated omega-3 and omega-6 fats tend to become oxidized much more easily than when they come in “nature’s package”.

Below is the mineral content (also from Nutritiondata.com) of a 100 g portion. Dried smelts are clearly a very good source of selenium. The significant amount of calcium comes mostly from the bones, as with many varieties of small fish that are eaten whole. Combined with the above, we could say that, overall, the nutrient content is high up there next to beef liver as a super food; a natural multivitamin, if you will.

Smelts, like many small non-predatory fish, are not a significant source of toxic metals. Many people avoid seafood because of concerns about toxic metal contamination, particularly mercury. The infamous incident that led to a major scare in that respect – in Minamata, Japan – did involve consumption of small marine animals. But it also involved years of direct and indirect exposure to very high levels of methylmercury from untreated industrial waste.

Other cases have been reported among populations consuming large amounts of whale, shark, dogfish and other relatively large marine animals with tissues compromised via biomagnification. Generally speaking, large predatory fish and predatory aquatic mammals are best avoided as food. If they are consumed, they should be consumed very sporadically.

Many people would say that a plate like the one above, with smelts and veggies, is not very appetizing. But I can really devour it quickly and go for seconds. How come? I use a special spice that enhances the natural flavor or almost any combination of “natural” foods – foods that are not engineered by humans – making them taste delicious.

This special spice is “hunger”. This spice can be your best friend, or your worst enemy.

The China Study II: Wheat’s total effect on mortality is significant, complex, and highlights the negative effects of low animal fat diets

The graph below shows the results of a multivariate nonlinear WarpPLS () analysis including the variables listed below. Each row in the dataset refers to a county in China, from the publicly available China Study II dataset (). As always, I thank Dr. Campbell and his collaborators for making the data publicly available. Other analyses based on the same dataset are also available ().
    - Wheat: wheat flour consumption in g/d.
    - Aprot: animal protein consumption in g/d.
    - PProt: plant protein consumption in g/d.
    - %FatCal: percentage of calories coming from fat.
    - Mor35_69: number of deaths per 1,000 people in the 35-69 age range.
    - Mor70_79: number of deaths per 1,000 people in the 70-79 age range.

Below are the total effects of wheat flour consumption, along with the number of paths used to calculate them, and the respective P values (i.e., probabilities that the effects are due to chance). Total effects are calculated by considering all of the paths connecting two variables. Identifying each path is a bit like solving a maze puzzle; you have to follow the arrows connecting the two variables. Version 3.0 of WarpPLS (soon to be released) does that automatically, and also calculates the corresponding P values.

To the best of my knowledge, this is the first time that total effects are calculated for this dataset. As you can see, the total effects of wheat flour consumption on mortality in the 35-69 and 70-79 age ranges are both significant, and fairly complex in this model, each relying on 7 paths. The P value for mortality in the 35-69 age range is 0.038; in other words, the probability that the effect is “real”, and thus not due to chance, is 96.2 percent (100-3.8=96.2). The P value for mortality in the 70-79 age range is 0.024; a 97.6 percent probability that the effect is “real”.

Note that in the model the effects of wheat flour consumption on mortality in both age ranges are hypothesized to be mediated by animal protein consumption, plant protein consumption, and fat consumption. These mediating effects have been suggested by previous analyses discussed on this blog (). The strongest individual paths are between wheat flour consumption and plant protein consumption, plant protein consumption and animal protein consumption, as well as animal protein consumption and fat consumption.

So wheat flour consumption contributes to plant protein consumption, probably by being a main source of plant protein (through gluten). Plant protein consumption in turn decreases animal protein consumption, which significantly decreases fat consumption. From this latter connection we can tell that most of the fat consumed likely came from animal sources.

How much fat and protein are we talking about? The graphs below tell us how much, and these graphs are quite interesting. They suggest that, in this dataset, daily protein consumption tended to be on average 60 g, whatever the source. If more protein came from plant foods, the proportion from animal foods went down, and vice-versa.

The more animal protein consumed, the more fat is also consumed in this dataset. And that is animal fat, which comes mostly in the form of saturated and monounsaturated fats, in roughly equal amounts. How do I know that it is animal fat? Because of the strong association with animal protein. By the way, with a few exceptions (e.g., some species of fatty fish) animal foods in general provide only small amounts of polyunsaturated fats – omega-3 and omega-6.

Individually, animal protein and wheat flour consumption have the strongest direct effects on mortality in both age ranges. Animal protein consumption is protective, and wheat flour consumption detrimental.

Does the connection between animal protein, animal fat, and longevity mean that a diet high in saturated and monounsaturated fats is healthy for most people? Not necessarily, at least without extrapolation, although the results do not suggest otherwise. Look at the amounts of fat consumed per day. They range from a little less than 20 g/d to a little over 90 g/d. By comparison, one steak of top sirloin (about 380 g of meat, cooked) trimmed to almost no visible fat gives you about 37 g of fat.

These results do suggest that consumption of animal fats, primarily saturated and monounsaturated fats, is likely to be particularly healthy in the context of a low fat diet. Or, said in a different way, these results suggest that longevity is decreased by diets that are low in animal fats.

How much fat should one eat? In this dataset, the more fat was consumed together with animal protein (i.e., the more animal fat was consumed), the better in terms of longevity. In other words, in this dataset the lowest levels of mortality were associated with the highest levels of animal fat consumption. The highest level of fat consumption in the dataset was a little over 90 g/d.

What about higher fat intake contexts? Well, we know that men on a high fat diet such as a variation of the Optimal Diet can consume on average a little over 170 g/d of animal fat (130 g/d for women), and their health markers remain generally good ().

One of the critical limiting factors, in terms of health, seems to be the amount of animal fat that one can eat and still remain relatively lean. Dietary saturated and monounsaturated fats are healthy. But when accumulated as excess body fat, beyond a certain level, they become pro-inflammatory.

Protein powders before fasted weight training? Here is a more natural and cheaper alternative

The idea that protein powders should be consumed prior to weight training has been around for a while, and is very popular among bodybuilders. Something like 10 grams or so of branched-chain amino acids (BCAAs) is frequently recommended. More recently, with the increase in popularity of intermittent fasting, it has been strongly recommended prior to “fasted weight training”. The quotation marks here are because, obviously, if you are consuming anything that contains calories prior to weight training, the weight training is NOT being done in a fasted state.

(Source: Ecopaper.com)

Most of the evidence available suggests that intermittent fasting is generally healthy. In fact, being able to fast for 16 hours or more, particularly without craving sweet foods, is actually a sign of a healthy glucose metabolism; which may complicate a cause-and-effect analysis between intermittent fasting and general health. The opposite, craving sweet foods every few hours, is generally a bad sign.

One key aspect of intermittent fasting that needs to be highlighted is that it is also arguably a form of liberation ().

Now, doing weight training in the fasted state may or may not lead to muscle loss. It probably doesn’t, even after a 24-hour fast, for those who fast and replenish their glycogen stores on a regular basis ().

However, weight training in a fasted state frequently induces an exaggerated epinephrine-norepinephrine (i.e., adrenaline-noradrenaline) response, likely due to depletion of liver glycogen beyond a certain threshold (the threshold varies for different people). The same is true for prolonged or particularly intense weight training sessions, even if they are not done in the fasted state. The body wants to crank up consumption of fat and ketones, so that liver glycogen is spared to ensure that it can provide the brain with its glucose needs.

Exaggerated epinephrine-norepinephrine responses tend to cause a few sensations that are not very pleasant. One of the first noticeable ones is orthostatic hypotension; i.e., feeling dizzy when going from a sitting to a standing position. Other related feelings are light-headedness, and a “pins and needles” sensation in the limbs (typically the arms and hands). Many believe that they are having a heart attack whey they have this “pins and needles” sensation, which can progress to a stage that makes it impossible to continue exercising.

Breaking the fast prior to weight training with dietary fat or carbohydrates is problematic, because those nutrients tend to blunt the dramatic rise in growth hormone that is typically experienced in response to weight training (). This is not good because the growth hormone response is probably one of the main reasons why weight training can be so healthy ().

Dietary protein, however, does not seem to significantly blunt the growth hormone response to weight training; even though it doesn't seem to increase it either (). Dietary protein seems to also suppress the exaggerated epinephrine-norepinephrine response to fasted weight training. And, on top of all that, it appears to suppress muscle loss, which may well be due to a moderate increase in circulating insulin ().

So everything points at the possibility that the ingestion of some protein, without carbohydrates or fat, is a good idea prior to fasted weight training. Not too much protein though, because insulin beyond a certain threshold is also likely to suppress the growth hormone response.

Does the protein have to be in the form of a protein powder? No.

Supplements are made from food, and this is true of protein powders as well. If you hard-boil a couple of large eggs, and eat only the whites prior to weight training, you will be getting about 8-10 grams of one of the highest quality protein "supplements" you can possibly get. Included are BCAAs. You will get a few extra nutrients with that too, but virtually no fat or carbohydrates.

The China Study II: How gender takes us to the elusive and deadly factor X

The graph below shows the mortality in the 35-69 and 70-79 age ranges for men and women for the China Study II dataset. I discussed other results in my two previous posts () (), all taking us to this post. The full data for the China Study II study is publicly available (). The mortality numbers are actually averages of male and female deaths by 1,000 people in each of several counties, in each of the two age ranges.

Men do tend to die earlier than women, but the difference above is too large.

Generally speaking, when you look at a set time period that is long enough for a good number of deaths (not to be confused with “a number of good deaths”) to be observed, you tend to see around 5-10 percent more deaths among men than among women. This is when other variables are controlled for, or when men and women do not adopt dramatically different diets and lifestyles. One of many examples is a study in Finland (); you have to go beyond the abstract on this one.

As you can see from the graph above, in the China Study II dataset this difference in deaths is around 50 percent!

This huge difference could be caused by there being significantly more men than women per county included the dataset. But if you take a careful look at the description of the data collection methods employed (), this does not seem to be the case. In fact, the methodology descriptions suggest that the researchers tried to have approximately the same number of women and men studied in each county. The numbers reported also support this assumption.

As I said before, this is a well executed research project, for which Dr. Campbell and his collaborators should be commended. I may not agree with all of their conclusions, but this does not detract even a bit from the quality of the data they have compiled and made available to us all.

So there must be another factor X causing this enormous difference in mortality (and thus longevity) among men and women in the China Study II dataset.

What could be this factor X?

This situation helps me illustrate a point that I have made here before, mostly in the comments under other posts. Sometimes a variable, and its effects on other variables, are mostly a reflection of another unmeasured variable. Gender is a variable that is often involved in this type of situation. Frequently men and women do things very differently in a given population due to cultural reasons (as opposed to biological reasons), and those things can have a major effect on their health.

So, the search for our factor X is essentially a search for a health-relevant variable that is reflected by gender but that is not strictly due to the biological aspects that make men and women different (these can explain only a 5-10 percent difference in mortality). That is, we are looking for a variable that shows a lot of variation between men and women, that is behavioral, and that has a clear impact on health. Moreover, as it should be clear from my last post, we are looking for a variable that is unrelated to wheat flour and animal protein consumption.

As it turns out, the best candidate for the factor X is smoking, particularly cigarette smoking.

The second best candidate for factor X is alcohol abuse. Alcohol abuse can be just as bad for one’s health as smoking is, if not worse, but it may not be as good a candidate for factor X because the difference in prevalence between men and women does not appear to be just as large in China (). But it is still large enough for us to consider it a close second as a candidate for factor X, or a component of a more complex factor X – a composite of smoking, alcohol abuse and a few other coexisting factors that may be reflected by gender.

I have had some discussions about this with a few colleagues and doctoral students who are Chinese (thanks William and Wei), and they mentioned stress to me, based on anecdotal evidence. Moreover, they pointed out that stressful lifestyles, smoking, and alcohol abuse tend to happen together - with a much higher prevalence among men than women.

What an anti-climax for this series of posts eh?

With all the talk on the Internetz about safe and unsafe starches, animal protein, wheat bellies, and whatnot! C’mon Ned, give me a break! What about insulin!? What about leucine deficiency … or iron overload!? What about choline!? What about something truly mysterious, related to an obscure or emerging biochemistry topic; a hormone du jour like leptin perhaps? Whatever, something cool!

Smoking and alcohol abuse!? These are way too obvious. This is NOT cool at all!

Well, reality is often less mysterious than we want to believe it is.

Let me focus on smoking from here on, since it is the top candidate for factor X, although much of the following applies to alcohol abuse and a combination of the two as well.

One gets different statistics on cigarette smoking in China depending on the time period studied, but one thing seems to be a common denominator in these statistics. Men tend to smoke in much, much higher numbers than women in China. And this is not a recent phenomenon.

For example, a study conducted in 1996 () states that “smoking continues to be prevalent among more men (63%) than women (3.8%)”, and notes that these results are very similar to those in 1984, around the time when the China Study II data was collected.

A 1995 study () reports similar percentages: “A total of 2279 males (67%) but only 72 females (2%) smoke”. Another study () notes that in 1976 “56% of the men and 12% of the women were ever-smokers”, which together with other results suggest that the gap increased significantly in the 1980s, with many more men than women smoking. And, most importantly, smoking industrial cigarettes.

So we are possibly talking about a gigantic difference here; the prevalence of industrial cigarette smoking among men may have been over 30 times the prevalence among women in the China Study II dataset.

Given the above, it is reasonable to conclude that the variable “SexM1F2” reflects very strongly the variable “Smoking”, related to industrial cigarette smoking, and in an inverse way. I did something that, grossly speaking, made the mysterious factor X explicit in the WarpPLS model discussed in my previous post. I replaced the variable “SexM1F2” in the model with the variable “Smoking” by using a reverse scale (i.e., 1 and 2, but reversing the codes used for “SexM1F2”). The results of the new WarpPLS analysis are shown on the graph below. This is of course far from ideal, but gives a better picture to readers of what is going on than sticking with the variable “SexM1F2”.

With this revised model, the associations of smoking with mortality in the 35-69 and 70-79 age ranges are a lot stronger than those of animal protein and wheat flour consumption. The R-squared coefficients for mortality in both ranges are higher than 20 percent, which is a sign that this model has decent explanatory power. Animal protein and wheat flour consumption are still significantly associated with mortality, even after we control for smoking; animal protein seems protective and wheat flour detrimental. And smoking’s association with the amount of animal protein and wheat flour consumed is practically zero.

Replacing “SexM1F2” with “Smoking” would be particularly far from ideal if we were analyzing this data at the individual level. It could lead to some outlier-induced errors; for example, due to the possible existence of a minority of female chain smokers. But this variable replacement is not as harmful when we look at county-level data, as we are doing here.

In fact, this is as good and parsimonious model of mortality based on the China Study II data as I’ve ever seen based on county level data.

Now, here is an interesting thing. Does the original China Study II analysis of univariate correlations show smoking as a major problem in terms of mortality? Not really.

The table below, from the China Study II report (), shows ALL of the statistically significant (P<0.05) univariate correlations with mortality in 70-79 age range. I highlighted the only measure that is directly related to smoking; that is “dSMOKAGEm”, listed as “questionnaire AGE MALE SMOKERS STARTED SMOKING (years)”.

The high positive correlation with “dSMOKAGEm” does not even make a lot of sense, as one would expect a negative correlation here – i.e., the earlier in life folks start smoking, the higher should be the mortality. But this reverse-signed correlation may be due to smokers who get an early start dying in disproportionally high numbers before they reach age 70, and thus being captured by another age range mortality variable. The fact that other smoking-related variables are not showing up on the table above is likely due to distortions caused by inter-correlations, as well as measurement problems like the one just mentioned.

As one looks at these univariate correlations, most of them make sense, although several can be and probably are distorted by correlations with other variables, even unmeasured variables. And some unmeasured variables may turn out to be critical. Remember what I said in my previous post – the variable “SexM1F2” was introduced by me; it was not in the original dataset. “Smoking” is this variable, but reversed, to account for the fact that men are heavy smokers and women are not.

Univariate correlations are calculated without adjustments or control. To correct this problem one can adjust a variable based on other variables; as in “adjusting for age”. This is not such a good technique, in my opinion; it tends to be time-consuming to implement, and prone to errors. One can alternatively control for the effects of other variables; a better technique, employed in multivariate statistical analyses. This latter technique is the one employed in WarpPLS analyses ().

Why don’t more smoking-related variables show up on the univariate correlations table above? The reason is that the table summarizes associations calculated based on data for both sexes. Since the women in the dataset smoked very little, including them in the analysis together with men lowers the strength of smoking-related associations, which would probably be much stronger if only men were included. It lowers the strength of the associations to the point that their P values become higher than 0.05, leading to their exclusion from tables like the one above. This is where the aggregation process that may lead to ecological fallacy shows its ugly head.

No one can blame Dr. Campbell for not issuing warnings about smoking, even as they came mixed with warnings about animal food consumption (). The former warnings, about smoking, make a lot of sense based on the results of the analyses in this and the last two posts.

The latter warnings, about animal food consumption, seem increasingly ill-advised. Animal food consumption may actually be protective in regards to the factor X, as it seems to be protective in terms of wheat flour consumption ().

The China Study II: Gender, mortality, and the mysterious factor X

WarpPLS and HealthCorrelator for Excel were used to do the analyses below. For other China Study analyses, many using WarpPLS as well as HealthCorrelator for Excel, click here. For the dataset used, visit the HealthCorrelator for Excel site and check under the sample datasets area. As always, I thank Dr. T. Colin Campbell and his collaborators for making the data publicly available for independent analyses.

In my previous post I mentioned some odd results that led me to additional analyses. Below is a screen snapshot summarizing one such analysis, of the ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. As I said before, this is a subset of the China Study II dataset, which does not include all of the variables for which data was collected. The associations shown below were generated by HealthCorrelator for Excel.

The top associations are positive and with mortality in the other range (the “M006 …” and “M005 …” variables). This is to be expected if ecological fallacy is not a big problem in terms of conclusions drawn from this dataset. In other words, the same things cause mortality to go up in the two age ranges, uniformly across counties. This is reassuring from a quantitative analysis perspective.

The second highest association in both age ranges is with the variable “SexM1F2”. This variable is a “dummy” variable coded as 1 for male sex and 2 for female, which I added to the dataset myself – it did not exist in the original dataset. The association in both age ranges is negative, meaning that being female is protective. They reflect in part the role of gender on mortality, more specifically the biological aspects of being female, since we have seen before in previous analyses that being female is generally health-protective.

I was able to add a gender-related variable to the model because the data was originally provided for each county separately for males and females, as well as through “totals” that were calculated by aggregating data from both males and females. So I essentially de-aggregated the data by using data from males and females separately, in which case the totals were not used (otherwise I would have artificially reduced the variance in all variables, also possibly adding uniformity where it did not belong). Using data from males and females separately is the reverse of the aggregation process that can lead to ecological fallacy problems.

Anyway, the associations with the variable “SexM1F2” got me thinking about a possibility. What if females consumed significantly less wheat flour and more animal protein in this dataset? This could be one of the reasons behind these strong associations between being female and living longer. So I built a more complex WarpPLS model than the one in my previous post, and ran a linear multivariate analysis on it. The results are shown below.

What do these results suggest? They suggest no strong associations between gender and wheat flour or animal protein consumption. That is, when you look at county averages, men and women consumed about the same amounts of wheat flour and animal protein. Also, the results suggest that animal protein is protective and wheat flour is detrimental, in terms of longevity, regardless of gender. The associations between animal protein and wheat flour are essentially the same as the ones in my previous post. The beta coefficients are a bit lower, but some P values improved (i.e., decreased); the latter most likely due to better resample set stability after including the gender-related variable.

Most importantly, there is a very strong protective effect associated with being female, and this effect is independent of what the participants ate.

Now, if you are a man, don’t rush to take hormones to become a woman with the goal of living longer just yet. This advice is not only due to the likely health problems related to becoming a transgender person; it is also due to a little problem with these associations. The problem is that the protective effect suggested by the coefficients of association between gender and mortality seems too strong to be due to men "being women with a few design flaws".

There is a mysterious factor X somewhere in there, and it is not gender per se. We need to find a better candidate.

One interesting thing to point out here is that the above model has good explanatory power in regards to mortality. I'd say unusually good explanatory power given that people die for a variety of reasons, and here we have a model explaining a lot of that variation. The model  explains 45 percent of the variance in mortality in the 35-69 age range, and 28 percent of the variance in the 70-79 age range.

In other words, the model above explains nearly half of the variance in mortality in the 35-69 age range. It could form the basis of a doctoral dissertation in nutrition or epidemiology with important  implications for public health policy in China. But first the factor X must be identified, and it must be somehow related to gender.

Next post coming up soon ...

The China Study II: Animal protein, wheat, and mortality … there is something odd here!

WarpPLS and HealthCorrelator for Excel were used in the analyses below. For other China Study analyses, many using WarpPLS and HealthCorrelator for Excel, click here. For the dataset used, visit the HealthCorrelator for Excel site and check under the sample datasets area. I thank Dr. T. Colin Campbell and his collaborators at the University of Oxford for making the data publicly available for independent analyses.

The graph below shows the results of a multivariate linear WarpPLS analysis including the following variables: Wheat (wheat flour consumption in g/d), Aprot (animal protein consumption in g/d), Mor35_69 (number of deaths per 1,000 people in the 35-69 age range), and Mor70_79 (number of deaths per 1,000 people in the 70-79 age range).

Just a technical comment here, regarding the possibility of ecological fallacy. I am not going to get into this in any depth now, but let me say that the patterns in the data suggest that, with the possible exception of some variables (e.g., blood glucose, gender; the latter will get us going in the next few posts), ecological fallacy due to county aggregation is not a big problem. The threat of ecological fallacy exists, here and in many other datasets, but it is generally overstated (often by those whose previous findings are contradicted by aggregated results).

I have not included plant protein consumption in the analysis because plant protein consumption is very strongly and positively associated with wheat flour consumption. The reason is simple. Almost all of the plant protein consumed by the participants in this study was probably gluten, from wheat products. Fruits and vegetables have very small amounts of protein. Keeping that in mind, what the graph above tells us is that:

- Wheat flour consumption is significantly and negatively associated with animal protein consumption. This is probably due to those eating more wheat products tending to consume less animal protein.

- Wheat flour consumption is positively associated with mortality in the 35-69 age range. The P value (P=0.06) is just shy of the 5 percent (i.e., P=0.05) that most researchers would consider to be the threshold for statistical significance. More consumption of wheat in a county, more deaths in this age range.

- Wheat flour consumption is significantly and positively associated with mortality in the 70-79 age range. More consumption of wheat in a county, more deaths in this age range.

- Animal protein consumption is not significantly associated with mortality in the 35-69 age range.

- Animal protein consumption is significantly and negatively associated with mortality in the 70-79 age range. More consumption of animal protein in a county, fewer deaths in this age range.

Let me tell you, from my past experience analyzing health data (as well as other types of data, from different fields), that these coefficients of association do not suggest super-strong associations. Actually this is also indicated by the R-squared coefficients, which vary from 3 to 7 percent. These are the variances explained by the model on the variables above the R-squared coefficients. They are low, which means that the model has weak explanatory power.

R-squared coefficients of 20 percent and above would be more promising. I hate to disappoint hardcore carnivores and the fans of the “wheat is murder” theory, but these coefficients of association and variance explained are probably way less than what we would expect to see if animal protein was humanity's salvation and wheat its demise.

Moreover, the lack of association between animal protein consumption and mortality in the 35-69 age range is a bit strange, given that there is an association suggestive of a protective effect in the 70-79 age range.

Of course death happens for all kinds of reasons, not only what we eat. Still, let us take a look at some other graphs involving these foodstuffs to see if we can form a better picture of what is going on here. Below is a graph showing mortality at the two age ranges for different levels of animal protein consumption. The results are organized in quintiles.

As you can see, the participants in this study consumed relatively little animal protein. The lowest mortality in the 70-79 age range, arguably the range of higher vulnerability, was for the 28 to 35 g/d quintile of consumption. That was the highest consumption quintile. About a quarter to a third of 1 lb/d of beef, and less of seafood (in general), would give you that much animal protein.

Keep in mind that the unit of analysis here is the county, and that these results are based on county averages. I wish I had access to data on individual participants! Still I stand by my comment earlier on ecological fallacy. Don't worry too much about it just yet.

Clearly the above results and graphs contradict claims that animal protein consumption makes people die earlier, and go somewhat against the notion that animal protein consumption causes things that make people die earlier, such as cancer. But they do so in a messy way - that spike in mortality in the 70-79 age range for 21-28 g/d of animal protein is a bit strange.

Below is a graph showing mortality at the two age ranges (i.e., 35-69 and 70-79) for different levels of wheat flour consumption. Again, the results are shown in quintiles.

Without a doubt the participants in this study consumed a lot of wheat flour. The lowest mortality in the 70-79 age range, which is the range of higher vulnerability, was for the 300 to 450 g/d quintile of wheat flour consumption. The high end of this range is about 1 lb/d of wheat flour! How many slices of bread would this be equivalent to? I don’t know, but my guess is that it would be many.

Well, this is not exactly the smoking gun linking wheat with early death, a connection that has been reaching near mythical proportions on the Internetz lately. Overall, the linear trend seems to be one of decreased longevity associated with wheat flour consumption, as suggested by the WarpPLS results, but the relationship between these two variables is messy and somewhat weak. It is not even clearly nonlinear, at least in terms of the ubiquitous J-curve relationship.

Frankly, there is something odd about these results.

This oddity led to me to explore, using HealthCorrelator for Excel, all ordered associations between mortality in the 35-69 and 70-79 age ranges and all of the other variables in the dataset. That in turn led me to a more complex WarpPLS analysis, which I’ll talk about in my next post, which is still being written.

I can tell you right now that there will be more oddities there, which will eventually take us to what I refer to as the mysterious factor X. Ah, by the way, that factor X is not gender - but gender leads us to it.

Book review: Perfect Health Diet

Perfect Health Diet is a book that one should own. It is not the type of book that you can get from your local library and just do a quick read over (and, maybe, write a review about it). If you do that, you will probably miss several important ideas that form the foundation of this book, which is a deep foundation.

The book is titled “Perfect Health Diet”, not “The Perfect Health Diet”. If you think that this is a mistake, consider that the most successful social networking web site of all time started as “The Facebook”, and then changed to simply “Facebook”; which was perceived later as a major improvement.

Moreover, “Perfect Health Diet” makes for a cool and not at all inappropriate acronym – “PHD”.

What people eat has an enormous influence on their lives, and also on the lives of those around them. Nutrition is clearly one of the most important topics in the modern world - it is the source of much happiness and suffering for entire populations. If Albert Einstein and Marie Curie were alive today, they would probably be interested in nutrition, as they were about important topics of their time that were outside their main disciplines and research areas (e.g., the consequences of war, and future war deterrence).

Nutrition attracts the interest of many bright people today. Those who are not professional nutrition researchers often fund their own research, spending hours and hours of their own time studying the literature and even experimenting on themselves. Several of them decide to think deeply and carefully about it. A few, like Paul Jaminet and Shou-Ching Jaminet, decide to write about it, and all of us benefit from their effort.

The Jaminets have PhDs (not copies of their books, degrees). Their main PhD disciplines are somewhat similar to Einstein’s and Curie’s; which is an interesting coincidence. What the Jaminets have written about nutrition is probably analogous, in broad terms, to what Einstein and Curie would have written about nutrition if they were alive today. They would have written about a “unified field theory” of nutrition, informed by chemistry.

To put it simply, the main idea behind this book is to find the “sweet spot” for each major macronutrient (e.g., protein and fat) and micronutrient (e.g., vitamins and minerals) that is important for humans. The sweet spot is the area indicated on the graph below. This is my own simplified interpretation of the authors' more complex graphs on marginal benefits from nutrients.

The book provides detailed information about each of the major nutrients that are important to humans, what their “sweet spot” levels are, and how to obtain them. In this respect the book is very thorough, and also very clear, including plenty of good arguments and empirical research results to back up the recommendations. But this book is much more than that.

Why do I refer to this book as proposing a “unified field theory” of nutrition? The reason is that this book clearly aims at unifying all of the current state of the art knowledge about nutrition, departing from a few fundamental ideas.

One of those fundamental ideas is that a good diet would provide nutrients in the same ratio as those provided by our own tissues when we “cannibalize” them – i.e., when we fast. Another is that human breast milk is a good basis for the estimation of the ratios of macronutrients a human adult would need for optimal health.

And here is where the depth and brilliance with which the authors address these issues can lead to misunderstandings.

For example, when our body “cannibalizes” itself (e.g., at the 16-h mark of a water fast), there is no digestion going on. And, as the authors point out, what you eat, in terms of nutrients, is often not what you get after digestion. It may surprise many to know that a diet rich in vegetables is actually a high fat diet (if you are surprised, you should read the book). One needs to keep these things in mind to understand that not all dietary macronutrient ratios will lead to the same ratios of nutrients after digestion, and that the dietary equivalent of “cannibalizing” oneself is not a beef-only diet.

Another example relates to the issue of human breast milk. Many seem to have misunderstood the authors as implying that the macronutrient ratios in human breast milk are optimal for adult humans. The authors say nothing of the kind. What they do is to use human breast milk as a basis for their estimation of what an adult human should get, based on a few reasonable assumptions. One of the assumptions is that a human adult’s brain consumes proportionally much less sugar than an infant’s.

Yet another example is the idea of “safe starches”, which many seem to have taken as a recommendation that diabetics should eat lots of white rice and potato. The authors have never said such a thing in the book; not even close. "Safe starches", like white rice and sweet potatoes (as well as white potatoes), are presented in the book as good sources of carbohydrates that are also generally free from harmful plant toxins. And they are, if consumed after cooking.

By the way, I have a colleague who has type 2 diabetes and can eat meat with white potatoes without experiencing hyperglycemia, as long as the amount of potato is very small and is eaten after a few bites of meat.

Do I disagree with some of the things that the authors say? Sure I do, but not in a way that would lead to significantly different dietary recommendations. And, who knows, maybe I am wrong.

For example, the authors seem to think that dietary advanced glycation end-products (AGEs) can be a problem for humans, and therefore recommend that you avoid cooking meat at high temperatures (no barbecuing, for example). I have not found any convincing evidence that this is true in healthy people, but following the authors’ advice will not hurt you at all. And if your digestive tract is compromised to the point that undigested food particles are entering your bloodstream, then maybe you should avoid dietary sources of AGEs.

Also, I think that humans tend to adapt to different macronutrient ratios in more fundamental ways than the authors seem to believe they can. These adaptations are long-term ones, and are better understood based on the notion of compensatory adaptation. For instance, a very low carbohydrate diet may bring about some problems in the short term, but long-term adaptations may reverse those problems, without a change in the diet.

The authors should be careful about small errors that may give a bad impression to some experts, and open them up to undue criticism; as experts tend to be very picky and frequently generalize based on small errors. Here is one. The authors seem to imply that eating coconut oil will help feed colon cells, which indeed seem to feed on short-chain fats; not exactly the medium-chain fats abundantly found in coconut oil, but okay. (This may be the main reason why indigestible fiber contributes to colon health, by being converted by bacteria to short-chain fats.) The main problem with the authors' implied claim is that coconut oil, as a fat, will be absorbed in the small intestine, and thus will not reach colon cells in any significant amounts.

Finally, I don’t think that increased animal protein consumption causes decreased longevity; an idea that the authors seem to lean toward. One reason is that seafood consumption is almost universally associated with increased longevity, even when it is heavily consumed, and seafood in general has a very high protein-to-fat ratio (much higher than beef). The connection between high animal protein consumption and decreased longevity suggested by many studies, some of which are cited in the book, is unlikely to be due to the protein itself, in my opinion. That connection is more likely to be due to some patterns that may be associated in certain populations with animal protein consumption (e.g., refined wheat and industrial seed oils consumption).

Thankfully, controversial issues and small errors can be easily addressed online. The authors maintain a popular blog, and they do so in such a way that the blog is truly an extension of the book. This blog is one of my favorites. Perhaps we will see some of the above issues addressed in the blog.

All in all, this seems like a bargain to me. For about 25 bucks (less than that, if you trade in quid; and more, if you do in Yuan), and with some self-determination, you may save thousands of dollars in medical bills. More importantly, you may change your life, and those of the ones around you, for the better.

Potassium deficiency in low carbohydrate dieting: High protein and fat alternatives that do not involve supplementation

It is often pointed out, at least anecdotally, that potassium deficiency is common among low carbohydrate dieters. Potassium deficiency can lead to a number of unpleasant symptoms and health problems. This micronutrient is present in small quantities in meat and seafood; main sources are plant foods.

A while ago this has gotten me thinking and asking myself: what about isolated hunter-gatherers that seem to have thrived consuming mostly carnivorous diets with little potassium, such as various Native American tribes?

Another thought came to mind, which is that animal protein seems to be associated with increased bone mineralization, even when calcium intake is low. That seems to be due to animal protein being associated with increased absorption of calcium and other minerals that make up bone tissue.

Maybe animal protein intake is also associated with increased potassium absorption. If this is true, what could be the possible mechanism?

As it turns out, there is one possible and somewhat surprising connection, insulin seems to promote cell uptake of potassium. This is an argument made many years ago by Clausen and Kohn, and further discussed more recently by Benziane and Chibalin. See also this recent commentary by Clausen.

Protein is the only macronutrient that normally causes transient insulin elevation without any glucose response. And the insulin response to protein is nowhere near that associated with refined carbohydrate-rich foods. It is much lower, analogous to the response to natural carbohydrate-rich foods.

A very low carbohydrate diet with more animal protein, and less fat, would induce insulin responses after meals, possibly helping with the absorption of potassium, even if potassium intake were rather limited. Primarily carnivorous diets, like those of some traditional Native American groups, would fit the bill.

Also, a low carbohydrate diet with emphasis on fat, but that was not so low in carbohydrates from certain sources, would probably achieve the same effect. This latter sounds like Kwaśniewski’s Optimal Diet, where people are encouraged to eat a lot more fat than protein, but also a small amount of carbohydrates (e.g., 50-100 g/d) from things like potatoes.

Kwaśniewski’s suggestions may sound counterintuitive sometimes. But, as it turns out, potatoes are good sources of potassium. One potato may not be a lot, but that potato will also increase insulin levels, bringing potassium intake up at the cell level.

Dietary protein does not become body fat if you are on a low carbohydrate diet

By definition LC is about dietary carbohydrate restriction. If you are reducing carbohydrates, your proportional intake of protein or fat, or both, will go up. While I don’t think there is anything wrong with a high fat diet, it seems to me that the true advantage of LC may be in how protein is allocated, which seems to contribute to a better body composition.

LC with more animal protein and less fat makes particularly good sense to me. Eating a variety of unprocessed animal foods, as opposed to only muscle meat from grain-fed cattle, will get you that. In simple terms, LC with more protein, achieved in a natural way with unprocessed foods, means more of the following in one's diet: lean meats, seafood and vegetables. Possibly with lean meats and seafood making up more than half of one’s protein intake. Generally speaking, large predatory fish species (e.g., various shark species, including dogfish) are better avoided to reduce exposure to toxic metals.

Organ meats such as beef liver are also high in protein and low in fat, but should be consumed in moderation due to the risk of hypervitaminosis; particularly hypervitaminosis A. Our ancestors ate the animal whole, and organ mass makes up about 10-20 percent of total mass in ruminants. Eating organ meats once a week places you approximately within that range.

In LC liver glycogen is regularly depleted, so the amino acids resulting from the digestion of protein will be primarily used to replenish liver glycogen, to replenish the albumin pool, for oxidation, and various other processes (e.g., tissue repair, hormone production). If you do some moderate weight training, some of those amino acids will be used for muscle growth.

In this sense, the true “metabolic advantage” of LC, so to speak, comes from protein and not fat. “Calories in” still counts, but you get better allocation of nutrients. Moreover, in LC, the calorie value of protein goes down a bit, because your body is using it as a “jack of all trades”, and thus in a less efficient way. This renders protein the least calorie-dense macronutrient, yielding fewer calories per gram than carbohydrates; and significantly fewer calories per gram when compared with dietary fat and alcohol.

Dietary fat is easily stored as body fat after digestion. In LC, it is difficult for the body to store amino acids as body fat. The only path would be conversion to glucose and uptake by body fat cells, but in LC the liver will typically be starving and want all the extra glucose for itself, so that it can feed its ultimate master – the brain. The liver glycogen depletion induced by LC creates a hormonal mix that places the body in fat release mode, making it difficult for fat cells to take up glucose via the GLUT4 transporter protein.

Excess amino acids are oxidized for energy. This may be why many people feel a slight surge of energy after a high-protein meal. (A related effect is associated with alcohol consumption, which is often masked by the relaxing effect also associated with alcohol consumption.) Amino acid oxidation is not associated with cancer. Neither is fat oxidation. But glucose oxidation is; this is known as the Warburg effect.

A high-protein LC approach will not work very well for athletes who deplete major amounts of muscle glycogen as part of their daily training regimens. These folks will invariably need more carbohydrates to keep their performance levels up. Ultimately this is a numbers game. The protein-to-glucose conversion rate is about 2-to-1. If an athlete depletes 300 g of muscle glycogen per day, he or she will need about 600 g of protein to replenish that based only on protein. This is too high an intake of protein by any standard.

A recreational exerciser who depletes 60 g of glycogen 3 times per week can easily replenish that muscle glycogen with dietary protein. Someone who exercises with weights for 40 minutes 3 times per week will deplete about that much glycogen each time. Contrary to popular belief, muscle glycogen is only minimally replenished postprandially (i.e., after meals) based on dietary sources. Liver glycogen replenishment is prioritized postprandially. Muscle glycogen is replenished over several days, primarily based on liver glycogen. It is one fast-filling tank replenishing another slow-filling one.

Recreational exercisers who are normoglycemic and who do LC intermittently tend to increase the size of their liver glycogen tank over time, via compensatory adaptation, and also use more fat (and ketones, which are byproducts of fat metabolism) as sources of energy. Somewhat paradoxically, these folks benefit from regular high carbohydrate intake days (e.g., once a week, or on exercise days), since their liver glycogen tanks will typically store more glycogen. If they keep their liver and muscle glycogen tanks half empty all the time, compensatory adaptation suggests that both their liver and muscle glycogen tanks will over time become smaller, and that their muscles will store more fat.

One way or another, with the exception of those with major liver insulin resistance, dietary protein does not become body fat if you are on a LC diet.