Tuesday, May 26, 2020

Ketones and Ketosis: Physiological and pathological forms

Ketones are compounds that have a specific chemical structure. The figure below (from: Wikipedia) shows the chemical structure of various types of ketones. As you can see, all ketones share a carbonyl group; that is the “O=” part of their chemical structure. A carbonyl group is an oxygen atom double-bonded to a carbon atom.


Technically speaking, many substances can be classified as ketones. Not all of these are involved in the same metabolic processes in humans. For example, fructose is technically a ketone, but it is not one of the three main ketones produced by humans from dietary macronutrients (discussed below), and is not metabolized in the same way as are those three main ketones.

Humans, as well as most other vertebrates, produce three main ketones (also known as ketone bodies) from dietary macronutrients. These are acetone, acetoacetate and beta-hydroxybutyrate. Low carbohydrate diets tend to promote glycogen depletion, which in turn leads to increased production of these ketones. Glycogen is stored in the liver and muscles. Liver glycogen is used by the body to maintain blood glucose levels within a narrow range in the fasted state. Examples of diets that tend to promote glycogen depletion are the Atkins Diet and Kwaśniewski’s Optimal Diet.

A search for articles on ketosis in scientific databases usually returns a large number of articles dealing with ketosis in cows. Why? The reason is that ketosis reduces milk production, by both reducing the amount of fat and glucose available for milk synthesis. In fact, ketosis is referred to as a “disease” in cows.

In humans, most articles on ketosis refer to pathological ketosis (a.k.a. ketoacidosis), especially in the context of uncontrolled diabetes. One notable exception is an article by Williamson (2005), from which the table below was taken. The table shows ketone concentrations in the blood under various circumstances, in mmol/l.


As you can see, relatively high concentrations of ketones occur in newborn babies (neonate), in adults post-exercise, and in adults fed a high fat diet. Generally speaking, a high fat diet is a low carbohydrate diet, and a high carbohydrate diet is a low fat diet. (One occasionally sees diets that are high in both carbohydrates and fat; which seem excellent at increasing body fat and thus reducing life span. This diet is apparently popular among sumo wrestlers, where genetics and vigorous exercise usually counter the negative diet effects.)

Situations in which ketosis occurs in newborn babies (neonate), in adults post-exercise, and in adults fed a high fat diet are all examples of physiological, or benign, ketosis. Ketones are also found in low concentrations in adults fed a standard American diet.

Ketones are found in very high concentrations in adults with untreated diabetes. This is an example of pathological ketosis, even though ketones are produced as part of a protective compensatory mechanism to spare glucose for the brain and red blood cells (which need glucose to function properly). Pathological ketosis leads to serum ketone levels that can be as much as 80 times (or more) those found in physiological ketosis.

Serum ketone concentrations increase proportionally to decreases in stored glycogen and, when glycogen is low or absent, correlate strongly (and inversely) with blood glucose levels. In some individuals glycogen is practically absent due to a genetic condition that leads to hepatic glycogen synthase deficiency. This is a deficiency of the enzyme that promotes glycogen synthesis by the liver. The figure below (also from Williamson, 2005) shows the variations in glucose and ketone levels in a child with glycogen synthase deficiency.


What happened with this child? Williamson answers this question: “It is of interest that this particular child suffered no ill effects from the daily exposure to high concentrations of ketone bodies, underlining their role as normal substrates for the brain when available.”

Unlike glucose and lipoprotein-bound fats (in VLDL, for example), unused ketones cannot be converted back to substances that can be stored by the body. Thus excess ketones are eliminated in the urine; leading to their detection by various tests, e.g., Ketostix tests. This elimination of unused ketones in the urine is one of the reasons why low carbohydrate diets are believed to lead to enhanced body fat loss.

In summary, ketones are present in the blood most of the time, in most people, whether they are on a ketogenic diet or not. If they do not show up in the urine, it does not mean that they are not present in the blood; although it usually means that their concentration in the blood is not that high. Like glucose, ketones are soluble in water, and thus circulate in the blood without the need for carriers (e.g., albumin, which is needed for the transport of free fatty acids; and VLDL, needed for the transport of triglycerides). Like glucose, they are used as sources of energy by the brain and by muscle tissues.

It has been speculated that ketosis leads to accelerated aging, through the formation of advanced glycation endproducts (AGEs), a speculation that seems to be largely unfounded (see this post). It is difficult to believe that a metabolic process that is universally found in babies and adults post-exercise would have been favored by evolution if it led to accelerated aging.

References:

Williamson, D.H. (2005). Ketosis. Encyclopedia of Human Nutrition, 91-98.

Wednesday, April 15, 2020

Herd immunity


The figure below is adapted from an article published in 2011 by Fine and colleagues (). The article discusses the concept of “herd immunity”: individuals with immunity against a disease act as a “shield” for the community, slowing or stopping the spread of the disease.



At the top of the figure, the number of infected individuals grows exponentially, until a certain number of individuals with immunity is achieved. At the bottom of the figure, those with immunity “absorb and kill” the infectious agent, without passing it forward – significantly limiting the progression of the disease.

This illustrates the likely impact of vaccination in cases where immunity being acquired through full infection is problematic, such as with COVID-19. In these cases, vaccination would slow or stop the spread of the disease, even if only a proportion of the community is vaccinated.

That is, until the infectious agent mutates!

Wednesday, April 1, 2020

China’s relaxing of COVID-19 social distancing policy after containment appears to have worked


The graphs below summarize key results from a study published in early 2020 by Ainslie and colleagues (). Dr. Ainslie is in the Faculty of Medicine, School of Public Health, Imperial College London. The study looked at within-city movement, as a proxy for economic activity, and how that movement has influenced the numbers of new cases of COVID-19 in various areas, after initial containment.



As you can see, after initial containment is achieved, within-city movement (measured through a “Movement Index”) seems to be uncorrelated with new COVID-19 cases; or somewhat negatively correlated, as the authors note.

This surprising and counterintuitive outcome may be due to people becoming much more cautious about social interactions.

Sunday, March 15, 2020

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 (see reference at the end of post) 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. [Note: the figure may be found in a different edition.]

Tuesday, January 28, 2020

The steep obesity increase in the USA in the 1980s: In a sense, it reflects a major success story

Obesity rates have increased in the USA over the years, but the steep increase starting around the 1980s is unusual. Wang and Beydoun do a good job at discussing this puzzling phenomenon (), and a blog post by Discover Magazine provides a graph (see below) that clear illustrates it ().



What is the reason for this?

You may be tempted to point at increases in calorie intake and/or changes in macronutrient composition, but neither can explain this sharp increase in obesity in the 1980s. The differences in calorie intake and macronutrient composition are simply not large enough to fully account for such a steep increase. And the data is actually full of oddities.

For example, an article by Austin and colleagues (which ironically blames calorie consumption for the obesity epidemic) suggests that obese men in a NHANES (2005–2006) sample consumed only 2.2 percent more calories per day on average than normal weight men in a NHANES I (1971–1975) sample ().

So, what could be the main reason for the steep increase in obesity prevalence since the 1980s?

The first clue comes from an interesting observation. If you age-adjust obesity trends (by controlling for age), you end up with a much less steep increase. The steep increase in the graph above is based on raw, unadjusted numbers. There is a higher prevalence of obesity among older people (no surprise here). And older people are people that have survived longer than younger people. (Don’t be too quick to say “duh” just yet.)

This age-obesity connection also reflects an interesting difference between humans living “in the wild” and those who do not, which becomes more striking when we compare hunter-gatherers with modern urbanites. Adult hunter-gatherers, unlike modern urbanites, do not gain weight as they age; they actually lose weight (, ).

Modern urbanites gain a significant amount of weight, usually as body fat, particularly after age 40. The table below, from an article by Flegal and colleagues, illustrates this pattern quite clearly (). Obesity prevalence tends to be highest between ages 40-59 in men; and this has been happening since the 1960s, with the exception of the most recent period listed (1999-2000).



In the 1999-2000 period obesity prevalence in men peaked in the 60-74 age range. Why? With progress in medicine, it is likely that more obese people in that age range survived (however miserably) in the 1999-2000 period. Obesity prevalence overall tends to be highest between ages 40-74 in women, which is a wider range than in men. Keep in mind that women tend to also live longer than men.

Because age seems to be associated with obesity prevalence among urbanites, it would be reasonable to look for a factor that significantly increased survival rates as one of the main reasons for the steep increase in the prevalence of obesity in the USA in the 1980s. If significantly more people were surviving beyond age 40 in the 1980s and beyond, this would help explain the steep increase in obesity prevalence. People don’t die immediately after they become obese; obesity is a “disease” that first and foremost impairs quality of life for many years before it kills.

Now look at the graph below, from an article by Armstrong and colleagues (). It shows a significant decrease in mortality from infectious diseases in the USA since 1900, reaching a minimum point between 1950 and 1960 (possibly 1955), and remaining low afterwards. (The spike in 1918 is due to the influenza pandemic.) At the same time, mortality from non-infectious diseases remains relatively stable over the same period, leading to a similar decrease in overall mortality.



When proper treatment options are not available, infectious diseases kill disproportionately at ages 15 and under (). Someone who was 15 years old in the USA in 1955 would have been 40 years old in 1980, if he or she survived. Had this person been obese, this would have been just in time to contribute to the steep increase in obesity trends in the USA. This increase would be cumulative; if this person were to live to the age of 70, he or she would be contributing to the obesity statistics up to 2010.

Americans are clearly eating more, particularly highly palatable industrialized foods whose calorie-to-nutrient ratio is high. Americans are also less physically active. But one of the fundamental reasons for the sharp increase in obesity rates in the USA since the early 1980s is that Americans have been surviving beyond age 40 in significantly greater numbers.

This is due to the success of modern medicine and public health initiatives in dealing with infectious diseases.

PS: It is important to point out that this post is not about the increase in American obesity in general over the years, but rather about the sharp increase in obesity since the early 1980s. A few alternative hypotheses have been proposed in the comments section, of which one seems to have been favored by various readers: a significant increase in consumption of linoleic acid (not to be confused with linolenic acid) since the early 1980s.

Monday, December 16, 2019

Nuts by numbers: Should you eat them, and how much?

Nuts are generally seen as good sources of protein and magnesium. The latter plays a number of roles in the human body, and is considered critical for bone health. Nuts are also believed to be good sources of vitamin E. While there is a lot of debate about vitamin E’s role in health, it is considered by many to be a powerful antioxidant. Other than in nuts, vitamin E is not easily found in foods other than seeds and seed oils.

Some of the foods that we call nuts are actually seeds; others are legumes. For simplification, in this post I am calling nuts those foods that are generally protected by shells (some harder than others). This protective layer is what makes most people call them nuts.

Let us see how different nuts stack up against each other in terms of key nutrients. The quantities listed below are per 1 oz (28 g), and are based on data from Nutritiondata.com. All are raw. Roasting tends to reduce the vitamin content of nuts, often by half, and has little effect on the mineral content. Protein and fat content are also reduced, but not as much as the vitamin content.

These two figures show the protein, fat, and carbohydrate content of nuts (on the left); and the omega-6 and omega-3 fat content (on the right).


When we talk about nuts, walnuts are frequently presented in a very positive light. The reason normally given is that walnuts have a high omega-3 content; the plant form of omega-3, alpha-linolenic acid (ALA). That is true. But look at the large amount of omega-6 in walnuts. The difference between the omega-6 and omega-3 content in walnuts is about 8 g! And this is in only 1 oz of walnuts. That is 8 g of possibly pro-inflammatory omega-6 fats to be “neutralized”. It would take many fish oil softgels to achieve that.

Walnuts should be eaten in moderation. Most studies looking at the health effects of nuts, including walnuts, show positive results in short-term interventions. But they usually involve moderate consumption, often of 1 oz per day. Eat several ounces of walnuts every day, and you are entering industrial see oil territory in terms of omega-6 fats consumption. Maybe other nutrients in walnuts have protective effects, but still, this looks like dangerous territory; “diseases of civilization” territory.

A side note. Focusing too much on the omega-6 to omega-3 ratio of individual foods can be quite misleading. The reason is that a food with a very small amount of omega-6 (e.g., 50 mg) but close to zero omega-3 will have a very high ratio. (Any number divided by zero yields infinity.) Yet, that food will contribute little omega-6 to a person’s diet. It is the ratio at the end of the day that matters, when all foods that have been eaten are considered.

The figures below show the magnesium content of nuts (on the left); and the vitamin E content (on the right).


Let us say that you are looking for the best combination of protein, magnesium, and vitamin E. And you also want to limit your intake of omega-6 fats, which is a very wise thing to do. Then what is the best choice? It looks like it is almonds. And even they should be eaten in small amounts, as 1 oz has more than 3 g of omega-6 fats.

Macadamia nuts don’t have much omega-6; their fats are mostly monounsaturated, which are very good. Their protein to fat ratio is very low, and they don’t have much magnesium or vitamin E. Coconuts (i.e., their meat) have mostly medium-chain saturated fats, which are also very good. Coconuts have little protein, magnesium, and vitamin E. If you want to increase your intake of healthy fats, both macadamia nuts and coconuts are good choices, with macadamia nuts providing about 3 times more fat.

There are many other dietary sources of magnesium around. In fact, magnesium is found in many foods. Examples are, in approximate descending order of content: salmon, spinach, sardine, cod, halibut, banana, white potato, sweet potato, beef, chicken, pork, liver, and cabbage. This is by no means a comprehensive list.

As for vitamin E, it likes to hide in seeds. While it may be a powerful antioxidant, I wonder whether Mother Nature really had it “in mind” as she tinkered with our DNA for the last few million years.

Sunday, November 24, 2019

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. (2019). WarpPLS User Manual: Version 6.0. 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.