Sunday, March 26, 2017

Lipotoxicity or tired pancreas? Abnormal fat metabolism as a possible precondition for type 2 diabetes

The term “diabetes” is used to describe a wide range of diseases of glucose metabolism; diseases with a wide range of causes. The diseases include type 1 and type 2 diabetes, type 2 ketosis-prone diabetes (which I know exists thanks to Michael Barker’s blog), gestational diabetes, various MODY types, and various pancreatic disorders. The possible causes include genetic defects (or adaptations to very different past environments), autoimmune responses, exposure to environmental toxins, as well as viral and bacterial infections; in addition to obesity, and various other apparently unrelated factors, such as excessive growth hormone production.

Type 2 diabetes and the “tired pancreas” theory

Type 2 diabetes is the one most commonly associated with the metabolic syndrome, which is characterized by middle-age central obesity, and the “diseases of civilization” brought up by Neolithic inventions. Evidence is mounting that a Neolithic diet and lifestyle play a key role in the development of the metabolic syndrome. In terms of diet, major suspects are engineered foods rich in refined carbohydrates and refined sugars. In this context, one widely touted idea is that the constant insulin spikes caused by consumption of those foods lead the pancreas (figure below from Wikipedia) to get “tired” over time, losing its ability to produce insulin. The onset of insulin resistance mediates this effect.



Empirical evidence against the “tired pancreas” theory

This “tired pancreas” theory, which refers primarily to the insulin-secreting beta-cells in the pancreas, conflicts with a lot of empirical evidence. It is inconsistent with the existence of isolated semi/full hunter-gatherer groups (e.g., the Kitavans) that consume large amounts of natural (i.e., unrefined) foods rich in easily digestible carbohydrates from tubers and fruits, which cause insulin spikes. These groups are nevertheless generally free from type 2 diabetes. The “tired pancreas” theory conflicts with the existence of isolated groups in China and Japan (e.g., the Okinawans) whose diets also include a large proportion of natural foods rich in easily digestible carbohydrates, which cause insulin spikes. Yet these groups are generally free from type 2 diabetes.

Humboldt (1995), in his personal narrative of his journey to the “equinoctial regions of the new continent”, states on page 121 about the natives as a group that: "… between twenty and fifty years old, age is not indicated by wrinkling skin, white hair or body decrepitude [among natives]. When you enter a hut is hard to differentiate a father from son …" A large proportion of these natives’ diets included plenty of natural foods rich in easily digestible carbohydrates from tubers and fruits, which cause insulin spikes. Still, there was no sign of any condition that would suggest a prevalence of type 2 diabetes among them.

At this point it is important to note that the insulin spikes caused by natural carbohydrate-rich foods are much less pronounced than the ones caused by refined carbohydrate-rich foods. The reason is that there is a huge gap between the glycemic loads of natural and refined carbohydrate-rich foods, even though the glycemic indices may be quite similar in some cases. Natural carbohydrate-rich foods are not made mostly of carbohydrates. Even an Irish (or white) potato is 75 percent water.

More insulin may lead to abnormal fat metabolism in sedentary people

The more pronounced spikes may lead to abnormal fat metabolism because more body fat is force-stored than it would have been with the less pronounced spikes, and stored body fat is not released just as promptly as it should be to fuel muscle contractions and other metabolic processes. Typically this effect is a minor one on a daily basis, but adds up over time, leading to fairly unnatural patterns of fat metabolism in the long run. This is particularly true for those who lead sedentary lifestyles. As for obesity, nobody gets obese in one day. So the key problem with the more pronounced spikes may not be that the pancreas is getting “tired”, but that body fat metabolism is not normal, which in turn leads to abnormally high or low levels of important body fat-derived hormones (e.g., high levels of leptin and low levels of adiponectin).

One common characteristic of the groups mentioned above is absence of obesity, even though food is abundant and often physical activity is moderate to low. Repeat for emphasis: “… even though food is abundant and often physical activity is moderate to low”. Note that having low levels of activity is not the same as spending the whole day sitting down in a comfortable chair working on a computer. Obviously caloric intake and level of activity among these groups were/are not at the levels that would lead to obesity. How could that be possible? See this post for a possible explanation.

Excessive body fat gain, lipotoxicity, and type 2 diabetes

There are a few theories that implicate the interaction of abnormal fat metabolism with other factors (e.g., genetic factors) in the development of type 2 diabetes. Empirical evidence suggests that this is a reasonable direction of causality. One of these theories is the theory of lipotoxicity.

Several articles have discussed the theory of lipotoxicity. The article by Unger & Zhou (2001) is a widely cited one. The theory seems to be widely based on the comparative study of various genotypes found in rats. Nevertheless, there is mounting evidence suggesting that the underlying mechanisms may be similar in humans. In a nutshell, this theory proposes the following steps in the development of type 2 diabetes:

    (1) Abnormal fat mass gain leads to an abnormal increase in fat-derived hormones, of which leptin is singled out by the theory. Some people seem to be more susceptible than others in this respect, with lower triggering thresholds of fat mass gain. (What leads to exaggerated fat mass gains? The theory does not go into much detail here, but empirical evidence from other studies suggests that major culprits are refined grains and seeds, as well as refined sugars; other major culprits seem to be trans fats, and vegetable oils rich in linoleic acid.)

    (2) Resistance to fat-derived hormones sets in. Again, leptin resistance is singled out as the key here. (This is a bit simplistic. Other fat-derived hormones, like adiponectin, seem to clearly interact with leptin.) Since leptin regulates fatty acid metabolism, the theory argues, leptin resistance is hypothesized to impair fatty acid metabolism.

    (3) Impaired fat metabolism causes fatty acids to “spill over” to tissues other than fat cells, and also causes an abnormal increase in a substance called ceramide in those tissues. These include tissues in the pancreas that house beta-cells, which secrete insulin. In short, body fat should be stored in fat cells (adipocytes), not outside them.

    (4) Initially fatty acid “spill over” to beta-cells enlarges them and makes them become overactive, leading to excessive insulin production in response to carbohydrate-rich foods, and also to insulin resistance. This is the pre-diabetic phase where hypoglycemic episodes happen a few hours following the consumption of carbohydrate-rich foods. Once this stage is reached, several natural carbohydrate-rich foods also become a problem (e.g., potatoes and bananas), in addition to refined carbohydrate-rich foods.

    (5) Abnormal levels of ceramide induce beta-cell apoptosis in the pancreas. This is essentially “death by suicide” of beta cells in the pancreas. What follows is full-blown type 2 diabetes. Insulin production is impaired, leading to very elevated blood glucose levels following the consumption of carbohydrate-rich foods, even if they are unprocessed.

It is widely known that type 2 diabetics have impaired glucose metabolism. What is not so widely known is that usually they also have impaired fatty acid metabolism. For example, consumption of the same fatty meal is likely to lead to significantly more elevated triglyceride levels in type 2 diabetics than non-diabetics, after several hours. This is consistent with the notion that leptin resistance precedes type 2 diabetes, and inconsistent with the “tired pancreas” theory.

Weak and strong points of the theory of lipotoxicity

A weakness of the theory of lipotoxicity is its strong lipophobic tone; at least in the articles that I have read. There is ample evidence that eating a lot of the ultra-demonized saturated fat, per se, is not what makes people obese or type 2 diabetic. Yet overconsumption of trans fats and vegetable oils rich in linoleic acid does seem to be linked with obesity and type 2 diabetes. (So does the consumption of refined grains and seeds, and refined sugars.) The theory of lipotoxicity does not seem to make these distinctions.

In defense of the theory of lipotoxicity, it does not argue that there cannot be thin diabetics. Many type 1 diabetics are thin. Type 2 diabetics can also be thin, although this is much less common. In certain individuals, the threshold of body fat gain that will precipitate lipotoxicity may be quite low. In others, the same amount of body fat gain (or more) may in fact increase their insulin sensitivity under certain circumstances – e.g., when growth hormone levels are abnormally low.

Autoimmune disorders, perhaps induced by environmental toxins, or toxins found in certain refined foods, may cause the immune system to attack the beta-cells in the pancreas. This may lead to type 1 diabetes if all beta cells are destroyed, or something that can easily be diagnosed as type 2 (or type 1.5) diabetes if only a portion of the cells are destroyed, in a way that does not involve lipotoxicity.

Nor does the theory of lipotoxicity predict that all those who become obese will develop type 2 diabetes. It only suggests that the probability will go up, particularly if other factors are present (e.g., genetic propensity). There are many people who are obese during most of their adult lives and never develop type 2 diabetes. On the other hand, some groups, like Hispanics, tend to develop type 2 diabetes more easily (often even before they reach the obese level). One only has to visit the South Texas region near the Rio Grande border to see this first hand.

What the theory proposes is a new way of understanding the development of type 2 diabetes; a way that seems to make more sense than the “tired pancreas” theory. The theory of lipitoxicity may not be entirely correct. For example, there may be other mechanisms associated with abnormal fat metabolism and consumption of Neolithic foods that cause beta-cell “suicide”, and that have nothing to do with lipotoxicity as proposed by the theory. (At least one fat-derived hormone, tumor necrosis factor-alpha, is associated with abnormal cell apoptosis when abnormally elevated. Levels of this hormone go up immediately after a meal rich in refined carbohydrates.) But the link that it proposes between obesity and type 2 diabetes seems to be right on target.

Implications and thoughts

Some implications and thoughts based on the discussion above are the following. Some are extrapolations based on the discussion in this post combined with those in other posts. At the time of this writing, there were hundreds of posts on this blog, in addition to many comments stemming from over 2.5 million page views. See under "Labels" at the bottom-right area of this blog for a summary of topics addressed. It is hard to ignore things that were brought to light in previous posts.

    - Let us start with a big one: Avoiding natural carbohydrate-rich foods in the absence of compromised glucose metabolism is unnecessary. Those foods do not “tire” the pancreas significantly more than protein-rich foods do. While carbohydrates are not essential macronutrients, protein is. In the absence of carbohydrates, protein will be used by the body to produce glucose to supply the needs of the brain and red blood cells. Protein elicits an insulin response that is comparable to that of natural carbohydrate-rich foods on a gram-adjusted basis (but significantly lower than that of refined carbohydrate-rich foods, like doughnuts and bagels). Usually protein does not lead to a measurable glucose response because glucagon is secreted together with insulin in response to ingestion of protein, preventing hypoglycemia.

    - Abnormal fat gain should be used as a general measure of one’s likelihood of being “headed south” in terms of health. The “fitness” level for men and women shown on the table in this post seem like good targets for body fat percentage. The problem here, of course, is that this is not as easy as it sounds. Attempts at getting lean can lead to poor nutrition and/or starvation. These may make matters worse in some cases, leading to hormonal imbalances and uncontrollable hunger, which will eventually lead to obesity. Poor nutrition may also depress the immune system, making one susceptible to a viral or bacterial  infection that may end up leading to beta-cell destruction and diabetes. A better approach is to place emphasis on eating a variety of natural foods, which are nutritious and satiating, and avoiding refined ones, which are often addictive “empty calories”. Generally fat loss should be slow to be healthy and sustainable.

    - Finally, if glucose metabolism is compromised, one should avoid any foods in quantities that cause an abnormally elevated glucose or insulin response. All one needs is an inexpensive glucose meter to find out what those foods are. The following are indications of abnormally elevated glucose and insulin responses, respectively: an abnormally high glucose level 1 hour after a meal (postprandial hyperglycemia); and an abnormally low glucose level 2 to 4 hours after a meal (reactive hypoglycemia). What is abnormally high or low? Take a look at the peaks and troughs shown on the graph in this post; they should give you an idea. Some insulin resistant people using glucose meters will probably realize that they can still eat several natural carbohydrate-rich foods, but in small quantities, because those foods usually have a low glycemic load (even if their glycemic index is high).

Lucy was a vegetarian and Sapiens an omnivore. We apparently have not evolved to be pure carnivores, even though we can be if the circumstances require. But we absolutely have not evolved to eat many of the refined and industrialized foods available today, not even the ones marketed as “healthy”. Those foods do not make our pancreas “tired”. Among other things, they “mess up” fat metabolism, which may lead to type 2 diabetes through a complex process involving hormones secreted by body fat.

References

Humboldt, A.V. (1995). Personal narrative of a journey to the equinoctial regions of the new continent. New York, NY: Penguin Books.

Unger, R.H., & Zhou, Y.-T. (2001). Lipotoxicity of beta-cells in obesity and in other causes of fatty acid spillover. Diabetes, 50(1), S118-S121.

Monday, February 27, 2017

Want to make coffee less acidic? Add cream to it

The table below is from a 2008 article by Ehlen and colleagues (), showing the amount of erosion caused by various types of beverages, when teeth were exposed to them for 25 h in vitro. Erosion depth is measured in microns. The third row shows the chance probabilities (i.e., P values) associated with the differences in erosion of enamel and root.


As you can see, even diet drinks may cause tooth erosion. That is not to say that if you drink a diet soda occasionally you will destroy your teeth, but regular drinking may be a problem. I discussed this study in a previous post (). After that post was published here some folks asked me about coffee, so I decided to do some research.

Unfortunately coffee by itself can also cause some erosion, primarily because of its acidity. Generally speaking, you want a liquid substance that you are interested in drinking to have a pH as close to 7 as possible, as this pH is neutral (). Tap and mineral water have a pH that is very close to 7. Black coffee seems to have a pH of about 4.8.

Also problematic are drinks containing fermentable carbohydrates, such as sucrose, fructose, glucose, and lactose. These are fermented by acid-producing bacteria. Interestingly, when fermentable carbohydrates are consumed as part of foods that require chewing, such as fruits, acidity is either neutralized or significantly reduced by large amounts of saliva being secreted as a result of the chewing process.

So what to do about coffee?

One possible solution is to add heavy cream to it. A small amount, such as a teaspoon, appears to bring the pH in a cup of coffee to a little over 6. Another advantage of heavy cream is that it has no fermentable carbohydrates; it has no carbohydrates, period. You will have to get over the habit of drinking sweet beverages, including sweet coffee, if you were unfortunate enough to develop that habit (like so many people living in cities today).

It is not easy to find reliable pH values for various foods. I guess dentistry researchers are more interested in ways of repairing damage already done, and there doesn't seem to be much funding available for preventive dentistry research. Some pH testing results from a University of Cincinnati college biology page were available at the time of this writing; they appeared to be reasonably reliable the last time I checked them ().

Monday, January 30, 2017

Blood glucose variations in normal individuals: A chaotic mess

I love statistics. But statistics is the science that will tell you that each person in a group of 20 people ate half a chicken per week over six months, until you realize that 10 died because they ate nothing while the other 10 ate a full chicken every week.

Statistics is the science that will tell you that there is an “association” between these two variables: my weight from 1 to 20 years of age, and the price of gasoline during that period. These two variables are indeed highly correlated, by neither has influenced the other in any way.

This is why I often like to see the underlying numbers when I am told that such and such health measure on average is this or that, or that this or that disease is associated with elevated consumption of whatever. Statistical results must be interpreted carefully. Lying with statistics is very easy.

A case in point is that of blood glucose variations among normal individuals. Try plotting them on graphs. What do you see? A chaotic mess, even when the individuals are pre-screened to exclude anybody with blood glucose abnormalities that would even hint at pre-diabetes. You see wild fluctuations that, while not going up to levels like 200 mg/dl, are much less predictable than many people are told they should be.

Blood glucose levels are influenced by so many factors (Elliott & Elliott, 2009) that I would be surprised if they were as smooth as those in graphs that are frequently used to show how blood glucose is supposed to vary in healthy individuals. Often we see a flat line up until the time of a meal, when the line curves up rapidly and then goes down quickly. It usually peaks at around 140 mg/dl, dropping well below 120 mg/dl after 2 hours.

Those smooth graphs are usually obtained through algorithms that have statistical methods at their core. The algorithms are designed to generate a smooth representations of scattered or disorganized data points. A little bit like the algorithms in software tools that plot best-fit regression curves passing through scattered points (e.g., warppls.com).

The picture below (click on it to enlarge) is from a 2006 symposium presentation by Prof. J.S. Christiansen, who is a widely cited diabetes researcher. The whole presentation is available from: www.diabetes-symposium.org. It shows the blood glucose variations of 21 young and normal individuals, based on data collected over a period of 2 days. Each individual is represented by a different color. The points on each curve are actually averages of two blood glucose measurements; the original measurements themselves vary even more chaotically.


As you can see from the picture above, each individual has a unique set of responses to main meals, which are represented by the three main blood glucose peaks. Overall, blood glucose levels vary from about 50 to 170 mg/dl, and in several cases remain above 120 mg/dl after 2 hours since a large meal. They vary somewhat chaotically during the night as well, often getting up to around 110 mg/dl.

And these are only 21 individuals, not 100 or 1000. Again, these individuals were all normal (i.e., normoglycemic, in medical research parlance), with an average glycated hemoglobin (HbA1c) of 5 percent, and a range of variation of HbA1c of 4.3 to 5.4 percent.

We can safely assume that these individuals were not on a low carbohydrate diet. The spikes in blood glucose after meals suggest that they were eating foods loaded with refined carbohydrates and/or sugars, particularly for breakfast. So, we can also safely assume that they were somewhat "desensitized" (in terms of glucose response) to those types of foods. Someone who had been on a low carbohydrate diet for a while, and who would thus be more sensitive, would have had even wilder blood glucose variations in response to the same meals.

Many people measure their glucose levels throughout the day with portable glucometers, and quite a few are likely to self-diagnose as pre-diabetics when they see something that they think is a “red flag”. Examples are a blood glucose level peaking at 165 mg/dl, or remaining above 120 mg/dl after 2 hours passed since a meal. Another example is a level of 110 mg/dl when they wake up very early to go to work, after several hours of fasting.

As you can see from the picture above, these “red flag” events do occur in young normoglycemic individuals.

If seeing “red flags” helps people remove refined carbohydrates and sugars from their diet, then fine.

But it may also cause them unnecessary chronic stress, and stress can kill.

Reference:

Elliott, W.H., & Elliott, D.C. (2009). Biochemistry and molecular biology. 4th Edition. New York: NY: Oxford University Press.

Wednesday, December 28, 2016

Tooth decay and silver diamine fluoride


Silver diamine fluoride (SDF) is a substance that can be applied on dental caries - where tooth mass has been destroyed by the action of bacteria that feed primarily on simple sugars. (Candy and sugary drinks are major culprits in this respect; fruits are not, in part because of the combination of a relatively low sugar content with the protective effect of the extra chewing needed.) Many studies have shown the effectiveness of SDF in the treatment of dental caries. The chart below is based on a study by Chu and colleagues ().



The chart above compares, in terms of normalized performance, “arrested” caries in a group of children using SDF against a control group not using SDF. Arrested caries are those in which there is no progression of the lesion; that is, in arrested caries the destruction of tooth mass is either stopped or reversed. The control group level can be seen as one in which a limited amount of arrest occurs (probably due to dietary changes and improved dental care), because the percentage of arrests among those treated with SDF was 100! And, yes, in spite of what most dentists will tell you, tooth decay can be reversed ().

As a side note, dentists do not necessarily tell their clients that tooth decay is irreversible because they want to keep the revenue flow coming into their offices. The sad reality is that most dental care patients will not be able to change their diet enough to reverse tooth decay. Because of that, it would arguably be professionally irresponsible to tell those patients that tooth decay progression can be stopped or reversed without treatment. As Weston Price has shown in his pioneering field studies (), reversing tooth decay requires not only elimination of refined sugars but also increased intake of fat-soluble vitamins (particularly vitamins A, D, and K2).

The chart below is from the same study by Chu and colleagues. It compares, also in terms of normalized performance, new caries formed over a period of time in a group of children using SDF against a control group not using SDF. As you can see from this and the previous chart, not only does SDF application stop or reverse tooth decay, it also prevents new dental caries from forming. From the empirical results it appears that this extends to teeth other than the teeth treated with SDF, presumably because of the action of the offending bacteria on the teeth that are next to those with caries.



SDF has been used in the past in various countries such as China, Japan, and New Zealand. Only recently the use of SDF has been approved in the USA. So, next time you go to the dentist to have dental caries treated, ask if they are able to use SDF and what the likely outcomes will be. Probably there will be no injections, drillings, or fillings. It seems that the only downside is that the brown spots characteristic of tooth decay tend to turn black after SDF is successfully used!

Thursday, October 20, 2016

Virtual Paleo Summit video: What is your ideal weight?


You may want to check out my recent video at the (Virtual Paleo Summit) explaining the waist-to-weight ratio theory for estimation of one's ideal weight. The theory is also discussed below. It may look a little complex, but its application is very simple.

***

There is a significant amount of empirical evidence suggesting that, for a given individual and under normal circumstances, the optimal weight is the one that maximizes the ratio below, where: L = lean body mass, and T = total mass.

L / T

L is difficult and often costly to measure. T can be measured easily, as one’s total weight.

Through some simple algebraic manipulations, you can see below that the ratio above can be rewritten in terms of one’s body fat mass (F).

L / T = (T – F) / T = 1 – F / T

Therefore, in order to maximize L / T, one should maximize 1 – F / T. This essentially means that one should minimize the second term, or the ratio below, which is one’s body fat mass (F) divided by one’s weight (T).

F / T

So, you may say, all I have to do is to minimize my body fat percentage. The problem with this is that body fat percentage is very difficult to measure with precision, and, perhaps more importantly, body fat percentage is associated with lean body mass (and also weight) in a nonlinear way.

In English, it becomes increasingly difficult to retain lean body mass as one's body fat percentage goes down. Mathematically, body fat percentage (F / T) is a nonlinear function of T, where this function has the shape of a J curve.

This is what complicates matters, making the issue somewhat counterintuitive. Six-pack abs may look good, but many people would have to sacrifice too much lean body mass for their own good to get there. Genetics definitely plays a role here, as well as other factors such as age.

Keep in mind that this (i.e., F / T) is a ratio, not an absolute measure. Given this, and to facilitate measurement, we can replace F with a variable that is highly correlated with it, and that captures one or more important dimensions particularly well. This new variable would be a proxy for F. One the most widely used proxies in this type of context is waist circumference. We’ll refer to it as W.

W may well be a very good proxy, because it is a measure that is particularly sensitive to visceral body fat mass, an important dimension of body fat mass. W likely captures variations in visceral body fat mass at the levels where this type of body fat accumulation seems to cause health problems.

Therefore, the ratio that most of us would probably want to minimize is the following, where W is one’s waist circumference, and T is one’s weight.

W / T = waist / weight


Based on the experience of HCE () users, variations in this ratio are likely to be small and require 4-decimals or more to be captured. If you want to avoid having so many decimals, you can multiply the ratio by 1000. This will have no effect on the use of the ratio to find your optimal weight; it is analogous to multiplying a ratio by 100 to express it as a percentage.

Also based on the experience of HCE users, there are fluctuations that make the ratio look like it is changing direction when it is not actually doing that. Many of these fluctuations may be due to measurement error.

If you are obese, as you lose weight through dieting, the waist / weight ratio should go down, because you will be losing more body fat mass than lean body mass, in proportion to your total body mass.

It would arguably be wise to stop losing weight when the waist / weight ratio starts going up, because at that point you will be losing more lean body mass than body fat mass, in proportion to your total body mass.

One’s lowest waist / weight ratio at a given point in time should vary depending on a number of factors, including: diet, exercise, general lifestyle, and age. This lowest ratio will also be dependent on one’s height and genetic makeup.

Mathematically, this lowest ratio is the ratio at which d(W / T) / dT = 0 and d(d(W / T) / dT) / dT > 0. That is, the first derivative of W / T with respect to T equals zero, and the second derivative is greater than zero.

The lowest waist / weight ratio is unique to each individual, and can go up and down over time (e.g., resistance exercise will push it down). Here I am talking about one's lowest waist / weight ratio at a given point in time, not one's waist / weight ratio at a given point in time.

This optimal waist / weight ratio theory is one of the most compatible with evidence regarding the lowest mortality body mass index (, ). Nevertheless, it is another ratio that gets a lot of attention in the health-related literature. I am talking about the waist / hip ratio (). In this literature, waist circumference is often used alone, not as part of a ratio.

Friday, September 30, 2016

PLS Applications Symposium; 5 - 7 April 2017; Laredo, Texas


PLS Applications Symposium; 5 - 7 April 2017; Laredo, Texas
(Abstract submissions accepted until 10 February 2017)

*** Health researchers ***

The research techniques discussed in this Symposium are finding growing use among health researchers. This is in part due to steady growth in the use of the software WarpPLS (visit: http://warppls.com) among those researchers. For those interested in learning more, a full-day workshop will be conducted (see below).

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

http://plsas.net

*** Workshop on PLS-SEM ***

On 5 April 2017 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock, using the software WarpPLS. Dr. Kock is the original developer of this software, which is one of the leading PLS-SEM tools today; used by thousands of researchers from a wide variety of disciplines, and from many different countries. This workshop will be hands-on and interactive, and will have two parts: (a) basic PLS-SEM issues, conducted in the morning (9 am - 12 noon); and (b) intermediate and advanced PLS-SEM issues, conducted in the afternoon (2 pm - 5 pm). Participants may attend either one, or both of the two parts.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Conducting a Multi-group Analysis with Range Restriction - Viewing Nonlinear Relationships - Conducting a Factor-Based PLS-SEM Analysis - Viewing and Changing Missing Data Imputation Settings - Isolating Mediating Effects - Identifying and Dealing with Outliers - Solving Indicator Problems - Solving Collinearity Problems.

-----------------------------------------------------------
Ned Kock
Symposium Chair
http://plsas.net


Sunday, September 25, 2016

Niacin turbocharges the growth hormone response to anaerobic exercise: A delayed effect

Niacin is also known as vitamin B3, or nicotinic acid. It is an essential vitamin whose deficiency leads to pellagra. In large doses of 1 to 3 g per day it has several effects on blood lipids, including an increase in HDL cholesterol and a marked decreased in fasting triglycerides. Niacin is also a powerful antioxidant.

Among niacin’s other effects, when taken in large doses of 1 to 3 g per day, is an acute elevation in growth hormone secretion. This is a delayed effect, frequently occurring 3 to 5 hours after taking niacin. This effect is independent of exercise.

It is important to note that large doses of 1 to 3 g of niacin are completely unnatural, and cannot be achieved by eating foods rich in niacin. For example, one would have to eat a toxic amount of beef liver (e.g., 15 lbs) to get even close to 1 g of niacin. Beef liver is one of the richest natural sources of niacin.

Unless we find out something completely unexpected about the diet of our Paleolithic ancestors in the future, we can safely assume that they never benefited from the niacin effects discussed in this post.

With that caveat, let us look at yet another study on niacin and its effect on growth hormone. Stokes and colleagues (2008) conducted a study suggesting that, in addition to the above mentioned beneficial effects of niacin, there is another exercise-induced effect: niacin “turbocharges” the growth hormone response to anaerobic exercise. The full reference to the study is at the end of this post. Figure 3, shown below, illustrates the effect and its magnitude. Click on it to enlarge.


The closed diamond symbols represent the treatment group. In it, participants ingested a total of 2 g of niacin in three doses: 1 g ingested at 0 min, 0.5 g at 120 min, and 0.5 g at 240 min. The control group ingested no niacin, and is represented by the open square symbols. (The researchers did not use a placebo in the control group; they justified this decision by noting that the niacin flush nullified the benefits of using a placebo.) The arrows indicate points at which all-out 30-second cycle ergometer sprints occurred.

Ignore the lines showing the serum growth hormone levels in between 120 and 300 min; they were not measured within that period.

As you can see, the peak growth hormone response to the first sprint was almost two times higher in the niacin group. In the second sprint, at 300 min, the rise in growth hormone is about 5 times higher in the niacin group.

We know that growth hormone secretion may rise 300 percent with exercise, without niacin. According to this study, this effect may be “turbocharged” up to a 600 percent rise with niacin within 300 min (5 h) of taking it, and possibly 1,500 percent soon after 300 min passed since taking niacin.

That is, not only does niacin boost growth hormone secretion anytime after it is taken, but one still gets the major niacin increase in growth hormone at around 300 min of taking it (which is about the same, whether you exercise or not). Its secretion level at this point is, by the way, higher than its highest level typically reached during deep sleep.

Let me emphasize that the peak growth hormone level achieved in the second sprint is about the same you would get without exercise, namely a bit more than 20 micrograms per liter, as long as you took niacin (see Quabbe's articles at the end of this post).

Still, if you time your exercise session to about 300 min after taking niacin you may have some extra benefits, because getting that peak growth hormone secretion at the time you are exercising may help boost some of the benefits of exercise.

For example, the excess growth hormone secretion may reduce muscle catabolism and increase muscle anabolism, at the same time, leading to an increase in muscle gain. However, there is evidence that growth hormone-induced muscle gain occurs only when testosterone levels are elevated. This explains why growth hormone levels are usually higher in young women than young men, and yet young women do not put on much muscle in response to exercise.

Reference:

Stokes, K.A., Tyler, C., & Gilbert, K.L. (2008). The growth hormone response to repeated bouts of sprint exercise with and without suppression of lipolysis in men. Journal of Applied Physiology, 104(3), 724-728.