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Monday, October 25, 2010

The amounts of water, carbohydrates, fat, and protein lost during a 30-day fast

When it comes to losing fat and maintaining muscle, at the same time, there are no shortcuts. The process generally has to be slow to be healthy. When one loses a lot of weight in a few days, most of what is being lost is water, followed by carbohydrates. (Carbohydrates are stored as liver and muscle glycogen.) Smaller amounts of fat and protein are also lost. The figure below, from Wilmore et al. (2007), shows the weights in grams of stored water, carbohydrates (glycogen), fat, and protein lost during a 30-day water fast.


On the first few days of the fast a massive amount of water is lost, even though drinking water is allowed in this type of fast. A significant amount of glycogen is lost as well. This is no surprise. About 2.6 g of water are lost for each 1 g of glycogen lost. That is, water is stored by the body proportionally to the amount of glycogen stored. People who do strength training on a regular basis tend to store more glycogen, particular in muscle tissue; this is a compensatory adaptation. Those folks also tend to store more water.

Not many people will try a 30-day fast. Still, the figure above has implications for almost everybody.

One implication is that if you use a bioimpedance scale to measure your body fat, you can bet that it will give you fairly misleading results if your glycogen stores are depleted. Your body fat percentage will be overestimated, because water and glycogen are lean body mass. This will happen with low carbohydrate dieters who regularly engage in intense physical exercise, aerobic or anaerobic. The physical exercise will deplete glycogen stores, which will typically not be fully replenished due to the low intake of carbohydrates.

Light endurance exercise (e.g., walking) is normally easier to maintain with a depleted “glycogen tank” than strength training, because light endurance exercise relies heavily on fat oxidation. It uses glycogen, but more slowly. Strength training, on the other hand, relies much more heavily on glycogen while it is being conducted (significant fat oxidation occurs after the exercise session), and is difficult to do effectively with a depleted “glycogen tank”.

Strength training practitioners often will feel fatigued, and will probably be unable to generate supercompensation, if their “glycogen tank” is constantly depleted. Still, compensatory adaptation can work its “magic” if one persists, and lead to long term adaptations that make athletes rely much more heavily on fat than the average person as a fuel for strength training and other types of anaerobic exercise. Some people seem to be naturally more likely to achieve this type of compensatory adaptation; others may never do so, no matter how hard they try.

Another implication is that you should not worry about short-term weight variations if your focus is on losing body fat. Losing stored water and glycogen may give you an illusion of body fat loss, but it will be only that – an illusion. You may recall this post, where body fat loss coupled with muscle gain led to some weight gain and yet to a much improved body composition. That is, the participants ended up leaner, even though they also weighed more.

The figure above also gives us some hints as to what happens with very low carbohydrate dieting (i.e., daily consumption of less than 20 grams of carbohydrates); at least at the beginning, before long term compensatory adaptation. This type of dieting mimics fasting as far as glycogen depletion is concerned, especially if protein intake is low, and has many positive short term health benefits. The depletion is not as quick as in a fast because a high fat and/or protein diet promotes higher rates of fat/protein oxidation and ketosis than fasting, which spare glycogen. (Yes, dietary fat spares glycogen. It also spares muscle tissue.) Still, the related loss of stored water is analogous to that of fasting, over a slightly longer period. The result is a marked weight loss at the beginning of the diet. This is an illusion as far as body fat loss is concerned.

Dietary protein cannot be used directly for glycogenesis; i.e., for replenishing glycogen stores. Dietary protein must first be used to generate glucose, through a process called gluconeogenesis. The glucose is then used for liver and muscle glycogenesis, among other things. This process is less efficient than glycogenesis based on carbohydrate sources (particularly carbohydrate sources that combine fructose and glucose), which is why for quite a few people (but not all) it is difficult to replenish glycogen stores and stimulate muscle growth on very low carbohydrate diets.

Glycogen depletion appears to be very healthy, but most of the empirical evidence seems to suggest that it is the depletion that creates a hormonal mix that is particularly health-promoting, not being permanently in the depleted state. In this sense, the extent of the glycogen depletion that is happening should be positively associated with the health benefits. And significant glycogen depletion can only happen if glycogen stores are at least half full to start with.

Reference

Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics.

Tuesday, October 19, 2010

Slow-cooked meat: Round steak, not grilled, but slow-cooked in a frying pan

I am yet to be convinced that grilled meat is truly unhealthy in the absence of leaky gut problems. I am referring here to high heat cooking-induced Maillard reactions and the resulting advanced glycation endproducts (AGEs). If you are interested, see this post and the comments under it, where I looked into some references provided by an anonymous commenter. In short, I am more concerned about endogenous (i.e., inside the body) formation of AGEs than with exogenous (e.g., dietary) intake.

Still, the other day I had to improvise when cooking meat, and used a cooking method that is considered by many to be fairly healthy – slow-cooking at a low temperature. I seasoned a few pieces of beef tenderloin (filet mignon) for the grill, but it started raining, so I decided to slow-cook them in a frying pan with water and some olive oil. After about 1 hour of slow-cooking, and somewhat to my surprise, they tasted more delicious than grilled!

I have since been using this method more and more, with all types of cuts of meat. It is great for round steak and top sirloin, for example, as well as cuts that come with bone. The pieces of meat come off the bone very easily, are soft, and taste great. So does much of the marrow. You also end up with a delicious sauce. Almost any cut of beef end up very soft when slow-cooked, even cuts that would normally come out from a grill a bit hard. Below is a simple recipe, for round steak (a.k.a. eye round).

- Prepare some dry seasoning powder by mixing sea salt, black pepper, dried garlic bits, chili powder, and a small amount of cayenne pepper.
- Season the round steak pieces at least 2 hours prior to placing them in the pan.
- Add a bit of water and olive oil to one or more frying pans. Two frying pans may be needed, depending on their size and the amount of meat.
- Place the round steak pieces in the frying pan, and add more water, almost to the point of covering them.
- Cook on low fire covered for 2-3 hours.

Since you will be cooking with low fire, the water will probably not evaporate completely even after 3 h. Nevertheless it is a good idea to check it every 15-30 min to make sure that this is the case, because in dry weather the water may evaporate rather fast. The water around the cuts should slowly turn into a fatty and delicious sauce, which you can pour on the meat when serving, to add flavor. The photos below show seasoned round steak pieces in a frying pan before cooking, and some cooked pieces served with sweet potatoes, orange pieces and a nectarine.



A 100 g portion will have about 34 g of protein. (A 100 g portion is a bit less than 4 oz, cooked.) The amount of fat will depend on how trimmed the cuts are. Like most beef cuts, the fat will be primarily saturated and monounsatured (both very healthy), with approximately equal amounts of each. It will provide good amounts of the following vitamins and minerals: iron, niacin, phosphorus, potassium, zinc, selenium, vitamin B6, and vitamin B12.

Monday, October 11, 2010

Blood glucose levels in birds are high yet HbA1c levels are low: Can vitamin C have anything to do with this?

Blood glucose levels in birds are often 2-4 times higher than those in mammals of comparable size. Yet birds often live 3 times longer than mammals of comparable size. This is paradoxical. High glucose levels are generally associated with accelerated senescence, but birds seem to age much slower than mammals. Several explanations have been proposed for this, one of which is related to the formation of advanced glycation endproducts (AGEs).

Glycation is a process whereby sugar molecules “stick” to protein or fat molecules, impairing their function. Glycation leads to the formation of AGEs, which seem to be associated with a host of diseases, including diabetes, and to be implicated in accelerated aging (or “ageing”, with British spelling).

The graphs below, from Beuchat & Chong (1998), show the glucose levels (at rest and prior to feeding) and HbA1c levels (percentage of glycated hemoglobin) in birds and mammals. HbA1c is a measure of the degree of glycation of hemoglobin, a protein found in red blood cells. As such HbA1c (given in percentages) is a good indicator of the rate of AGE formation within an animal’s body.


The glucose levels are measured in mmol/l; they should be multiplied by 18 to obtain the respective measures in mg/dl. For example, the 18 mmol/l glucose level for the Anna’s (a hummingbird species) is equivalent to 324 mg/dl. Even at that high level, well above the level of a diabetic human, the Anna’s hummingbird species has an HbA1c of less than 5, which is lower than that for most insulin sensitive humans.

How can that be?

There are a few possible reasons. Birds seem to have evolved better mechanisms to control cell permeability to glucose, allowing glucose to enter cells very selectively. Birds also seem to have a higher turnover of cells where glycation and thus AGE formation results. The lifespan of red blood cells in birds, for example, is only 50 to 70 percent that of mammals.

But one of the most interesting mechanisms is vitamin C synthesis. Not only is vitamin C a powerful antioxidant, but it also has the ability to reversibly bind to proteins at the sites where glycation would occur. That is, vitamin C has the potential to significantly reduce glycation. The vast majority of birds and mammals can synthesize vitamin C. Humans are an exception. They have to get it from their diet.

This may be one of the many reasons why isolated human groups with traditional diets high in fruits and starchy tubers, which lead to temporary blood glucose elevations, tend to have good health. Fruits and starchy tubers in general are good sources of vitamin C.

Grains and seeds are not.

References

Beuchat, C.A., & Chong, C.R. (1998). Hyperglycemia in hummingbirds and its consequences for hemoglobin glycation. Comparative Biochemistry and Physiology Part A, 120(3), 409–416.

Holmes D.J., Flückiger, R., & Austad, S.N. (2001). Comparative biology of aging in birds: An update. Experimental Gerontology, 36(4), 869-883.

Tuesday, October 5, 2010

The China Study II: Does calorie restriction increase longevity?

The idea that calorie restriction extends human life comes largely from studies of other species. The most relevant of those studies have been conducted with primates, where it has been shown that primates that eat a restricted calorie diet live longer and healthier lives than those that are allowed to eat as much as they want.

There are two main problems with many of the animal studies of calorie restriction. One is that, as natural lifespan decreases, it becomes progressively easier to experimentally obtain major relative lifespan extensions. (That is, it seems much easier to double the lifespan of an organism whose natural lifespan is one day than an organism whose natural lifespan is 80 years.) The second, and main problem in my mind, is that the studies often compare obese with lean animals.

Obesity clearly reduces lifespan in humans, but that is a different claim than the one that calorie restriction increases lifespan. It has often been claimed that Asian countries and regions where calorie intake is reduced display increased lifespan. And this may well be true, but the question remains as to whether this is due to calorie restriction increasing lifespan, or because the rates of obesity are much lower in countries and regions where calorie intake is reduced.

So, what can the China Study II data tell us about the hypothesis that calorie restriction increases longevity?

As it turns out, we can conduct a preliminary test of this hypothesis based on a key assumption. Let us say we compared two populations (e.g., counties in China), based on the following ratio: number of deaths at or after age 70 divided by number deaths before age 70. Let us call this the “ratio of longevity” of a population, or RLONGEV. The assumption is that the population with the highest RLONGEV would be the population with the highest longevity of the two. The reason is that, as longevity goes up, one would expect to see a shift in death patterns, with progressively more people dying old and fewer people dying young.

The 1989 China Study II dataset has two variables that we can use to estimate RLONGEV. They are coded as M005 and M006, and refer to the mortality rates from 35 to 69 and 70 to 79 years of age, respectively. Unfortunately there is no variable for mortality after 79 years of age, which limits the scope of our results somewhat. (This does not totally invalidate the results because we are using a ratio as our measure of longevity, not the absolute number of deaths from 70 to 79 years of age.) Take a look at these two previous China Study II posts (here, and here) for other notes, most of which apply here as well. The notes are at the end of the posts.

All of the results reported here are from analyses conducted using WarpPLS. Below is a model with coefficients of association; it is a simple model, since the hypothesis that we are testing is also simple. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore associations between variables, which are shown within ovals. The meaning of each variable is the following: TKCAL = total calorie intake per day; RLONGEV = ratio of longevity; SexM1F2 = sex, with 1 assigned to males and 2 to females.



As one would expect, being female is associated with increased longevity, but the association is just shy of being statistically significant in this dataset (beta=0.14; P=0.07). The association between total calorie intake and longevity is trivial, and statistically indistinguishable from zero (beta=-0.04; P=0.39). Moreover, even though this very weak association is overall negative (or inverse), the sign of the association here does not fully reflect the shape of the association. The shape is that of an inverted J-curve; a.k.a. U-curve. When we split the data into total calorie intake terciles we get a better picture:


The second tercile, which refers to a total daily calorie intake of 2193 to 2844 calories, is the one associated with the highest longevity. The first tercile (with the lowest range of calories) is associated with a higher longevity than the third tercile (with the highest range of calories). These results need to be viewed in context. The average weight in this dataset was about 116 lbs. A conservative estimate of the number of calories needed to maintain this weight without any physical activity would be about 1740. Add about 700 calories to that, for a reasonable and healthy level of physical activity, and you get 2440 calories needed daily for weight maintenance. That is right in the middle of the second tercile.

In simple terms, the China Study II data seems to suggest that those who eat well, but not too much, live the longest. Those who eat little have slightly lower longevity. Those who eat too much seem to have the lowest longevity, perhaps because of the negative effects of excessive body fat.

Because these trends are all very weak from a statistical standpoint, we have to take them with caution. What we can say with more confidence is that the China Study II data does not seem to support the hypothesis that calorie restriction increases longevity.

Reference

Kock, N. (2010). WarpPLS 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.

Notes

- The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable). Whenever nonlinear relationships were modeled, the path coefficients were automatically corrected by the software to account for nonlinearity.

- Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance, which is desirable since the dataset is relatively small (each county, not individual, is a separate data point is this dataset). This also allowed for the test of commonsense assumptions (e.g., the protective effects of being female), which is always a good idea in a multivariate analyses because violation of commonsense assumptions may suggest data collection or analysis error. On the other hand, it required the inclusion of a sex variable as a control variable in the analysis, which is no big deal.

- Mortality from schistosomiasis infection (MSCHIST) does not confound the results presented here. Only counties where no deaths from schistosomiasis infection were reported have been included in this analysis. The reason for this is that mortality from schistosomiasis infection can severely distort the results in the age ranges considered here. On the other hand, removal of counties with deaths from schistosomiasis infection reduced the sample size, and thus decreased the statistical power of the analysis.