tag:blogger.com,1999:blog-88594567351659968932024-03-17T04:42:54.812-07:00Health CorrelatorThis blog is about statistics, evolution, nutrition, lifestyle, and health issues. A combination of these issues. The focus is on quantitative research and how it can be applied in practice. But you may see other types of posts here (e.g., recipes, ideas, concepts, theories) from time to time.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.comBlogger269125tag:blogger.com,1999:blog-8859456735165996893.post-63530231267059947532024-02-29T14:19:00.000-08:002024-02-29T14:19:49.110-08:00The lowest-mortality BMI: What is the role of nutrient intake from food?<script type="text/javascript">var citeN=0;</script>In a previous post (<a href="http://healthcorrelator.blogspot.com/2012/06/lowest-mortality-bmi-what-is-its.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), I discussed the frequently reported lowest-mortality body mass index (BMI), which is about 26. The empirical results reviewed in that post suggest that fat-free mass plays an important role in that context. Keep in mind that this "BMI=26 phenomenon" is often reported in studies of populations from developed countries, which are likely to be relatively sedentary. This is important for the point made in this post. <br><br>
A lowest-mortality BMI of 26 is somehow at odds with the fact that many healthy and/or long-living populations have much lower BMIs. You can clearly see this in the distribution of BMIs among males in Kitava and Sweden shown in the graph below, from a study by Lindeberg and colleagues (<a href="http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2796.2001.00845.x/full" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). This distribution is shifted in such a way that would suggest a much lower BMI of lowest-mortality among the Kitavans, assuming a U-curve shape similar to that observed in studies of populations from developed countries (<a href="http://healthcorrelator.blogspot.com/2012/06/lowest-mortality-bmi-what-is-its.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br><br>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuck5qGHKiKPESyvbOipscmYVorSF7QAnTIBHj7cPA_SkMfuCFH_3Rg0I1ovIjzi9j7XaE2WuQdHGmXVhZ2XaCWMuCkuXRXmeXpSsvdLVOMUEsURYYur0SP62HBDUrYMaCzu30gk-H79_p/s1600/Lindeberg_etal_2001_F01.gif" imageanchor="1" style="margin-left:1em; margin-right:1em"><img border="0" height="221" width="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuck5qGHKiKPESyvbOipscmYVorSF7QAnTIBHj7cPA_SkMfuCFH_3Rg0I1ovIjzi9j7XaE2WuQdHGmXVhZ2XaCWMuCkuXRXmeXpSsvdLVOMUEsURYYur0SP62HBDUrYMaCzu30gk-H79_p/s320/Lindeberg_etal_2001_F01.gif" /></a></div>
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Another relevant example comes from the China Study II (see, e.g., <a href="http://healthcorrelator.blogspot.com/2012/01/china-study-ii-wheats-total-effect-on.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), which is based on data from 8000 adults. The average BMI in the China Study II dataset, with data from the 1980s, is approximately 21; for an average weight that is about 116 lbs. That BMI is relatively uniform across Chinese counties, including those with the lowest mortality rates. No county has an average BMI that is 26; not even close. This also supports the idea that Chinese people were, at least during that period, relatively thin. <br><br>
Now take a look at the graph below, also based on the China Study II dataset, from a previous post (<a href="http://healthcorrelator.blogspot.com/2010/10/china-study-ii-does-calorie-restriction.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), relating total daily calorie intake with longevity. I should note that the relationship between total daily calorie intake and longevity depicted in this graph is not really statistically significant. Still, the highest longevity seems to be in the second tercile of total daily calorie intake. <br><br>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbhLkobVrcDFAo1YDujfWD72rifq3aFNbK3HIT61gR0-R8nUH_gI2pRZBftNbkF5zEPbwX8nSKcdliuuYYZddlaB8mx_mu2yBmC4k4OHPAIdiyqHLFFDrQoCVJBi0XjB4_1KFX2DZHY9u-/s1600/Kock_2010_TKCAL_RLONGEV_F02.PNG" imageanchor="1" style="margin-left:1em; margin-right:1em"><img border="0" height="158" width="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbhLkobVrcDFAo1YDujfWD72rifq3aFNbK3HIT61gR0-R8nUH_gI2pRZBftNbkF5zEPbwX8nSKcdliuuYYZddlaB8mx_mu2yBmC4k4OHPAIdiyqHLFFDrQoCVJBi0XjB4_1KFX2DZHY9u-/s320/Kock_2010_TKCAL_RLONGEV_F02.PNG" /></a></div>
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Again, the average weight in the dataset is 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, the one with the highest longevity. <br><br>
What does this have to do with the lowest-mortality BMI of 26 from studies of samples from developed countries? Populations in these countries are likely to be relatively sedentary, at least on average, in which case a low BMI will be associated with a low total calorie intake. And a low total calorie intake will lead to a low intake of nutrients needed by the body to fight disease. <br><br>
And don’t think you can fix this problem by consuming lots of vitamin and mineral pills. When I refer here to a higher or lower nutrient intake, I am not talking only about micronutrients, but also about macronutrients (fatty and amino acids) in amounts that are needed by your body. Moreover, important micronutrients, such as fat-soluble vitamins, cannot be properly absorbed without certain macronutrients, such as fat. <br><br>
Industrial nutrient isolation for supplementation use has not been a very successful long-term strategy for health optimization (<a href="http://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.83.4.546" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). On the other hand, this type of supplementation has indeed been found to have had modest-to-significant success in short-term interventions aimed at correcting acute health problems caused by severe nutritional deficiencies (<a href="http://jnci.oxfordjournals.org/content/85/18/1483.short" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br><br>
So the "BMI=26 phenomenon" may be a reflection not of a direct effect of high muscularity on health, but of an indirect effect mediated by a high intake of needed nutrients among sedentary folks. This may be so even though the lowest mortality is for the combination of that BMI with a relatively small waist (<a href="http://healthcorrelator.blogspot.com/2012/06/lowest-mortality-bmi-what-is-its.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), which suggests some level of muscularity, but not necessarily serious bodybuilder-level muscularity. High muscularity, of the serious bodybuilder type, is not very common; at least not enough to significantly sway results based on the analysis of large samples. <br><br>
The combination of a BMI=26 with a relatively small waist is indicative of more muscle and less body fat. Having more muscle and less body fat has an advantage that is rarely discussed. It allows for a higher total calorie intake, and thus a higher nutrient intake, without an unhealthy increase in body fat. Muscle mass increases one's caloric requirement for weight maintenance, more so than body fat. Body fat also increases that caloric requirement, but it also acts like an organ, secreting a number of hormones into the bloodstream, and becoming pro-inflammatory in an unhealthy way above a certain level. <br><br>
Clearly having a low body fat percentage is associated with lower incidence of degenerative diseases, but it will likely lead to a lower intake of nutrients relative to one’s needs unless other factors are present, e.g., being fairly muscular or physically active. Chronic low nutrient intake tends to get people closer to the afterlife like nothing else (<a href="http://jnci.oxfordjournals.org/content/85/18/1483.short" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br><br>
In this sense, having a BMI=26 and being relatively sedentary (without being skinny-fat) has an effect that is similar to that of having a BMI=21 and being fairly physically active. Both would lead to consumption of more calories for weight maintenance, and thus more nutrients, as long as nutritious foods are eaten.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com16tag:blogger.com,1999:blog-8859456735165996893.post-13584505219506897202024-01-28T15:23:00.000-08:002024-01-28T15:28:44.958-08:00Looking for a good orthodontist? My recommendation is Dr. Meat<script type="text/javascript">var citeN=0;</script>
The figure below is one of many in Weston Price’s outstanding book <i>Nutrition and Physical Degeneration</i> showing evidence of teeth crowding among children whose parents moved from a traditional diet of minimally processed foods to a Westernized diet. (<a href="http://www.amazon.com/Nutrition-Physical-Degeneration-Weston-Price/dp/0916764206" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>)<br />
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Tooth crowding and other forms of malocclusion are widespread and on the rise in populations that have adopted Westernized diets (most of us). Some blame it on dental caries, particularly in early childhood; dental caries are also a hallmark of Westernized diets. Varrela, however, in a study of Finnish skulls from the 15th and 16th centuries found evidence of dental caries, but not of malocclusion, which Varrela reported as fairly high in modern Finns. (<a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1600-0722.1990.tb00968.x/abstract" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>)<br />
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Why does malocclusion occur at all in the context of Westernized diets? Lombardi (<a href="http://linkinghub.elsevier.com/retrieve/pii/000294168290286X" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) put forth an evolutionary hypothesis:<br />
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<blockquote>“In modern man there is little attrition of the teeth because of a soft, processed diet; this can result in dental crowding and impaction of the third molars. It is postulated that the tooth-jaw size discrepancy apparent in modern man as dental crowding is, in primitive man, a crucial biologic adaptation imposed by the selection pressures of a demanding diet that maintains sufficient chewing surface area for long-term survival. Selection pressures for teeth large enough to withstand a rigorous diet have been relaxed only recently in advanced populations, and the slow pace of evolutionary change has not yet brought the teeth and jaws into harmonious relationship.”</blockquote><br />
So what is one to do? Apparently getting babies to eat meat is not a bad idea (<a href="https://4cflorida.org/wp-content/uploads/2015/08/feedingbabies.pdf" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). They may well just chew on it for a while and spit it out. The likelihood of meat inducing dental caries is very low, as most low carbers can attest. (In fact, low carbers who eat mostly meat often see dental caries heal.)<br />
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Concerned about the baby choking on meat? See this Google search for “baby choked on meat”: (<a href="https://www.google.com/search?q=%22baby+choked+on+meat%22" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). Now, see this Google search for “baby choked on milk”: (<a href="https://www.google.com/search?q=%22baby+choked+on+milk%22" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>).<br />
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What if you have a child with crowded teeth as a preteen or teen? Too late? Should you get him or her to use “cute” braces? Our daughter had crowded teeth as a preteen. It overlapped with the period of my transformation (<a href="http://healthcorrelator.blogspot.com/2010/07/my-transformation-i-cannot-remember.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), which meant that she started having a lot more natural foods to eat. There were more of those around, some of which require serious chewing, and less industrialized soft foods. Those natural foods included hard-to-chew beef cuts, served multiple times a week.<br />
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We noticed improvement right away, and in a few years the crowding disappeared. Soon she had the kind of smile that could land her a job as a toothpaste model:<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeWsZ_KI-chspyjUeyBEkzkOfhfctkknjfDzaZFO-7m45BGm6BP7LMLLZ9kTmnhz3bxfWa9CiXwus99ZdP0vqloUtplr1HfKuD8UOXLYD0CBP4SBcLWvLc89l7ZoNw_PSnTDuQOkmFpQkl/s1600/Kock_2011_MonicaSmile.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeWsZ_KI-chspyjUeyBEkzkOfhfctkknjfDzaZFO-7m45BGm6BP7LMLLZ9kTmnhz3bxfWa9CiXwus99ZdP0vqloUtplr1HfKuD8UOXLYD0CBP4SBcLWvLc89l7ZoNw_PSnTDuQOkmFpQkl/s320/Kock_2011_MonicaSmile.PNG" width="265" /></a></div><br />
The key seems to be to start early, in developmental years. If you are an adult with crowded teeth, malocclusion may not be solved by either tough foods or braces. With braces, you may even end up with other problems (<a href="https://meridian.allenpress.com/angle-orthodontist/article/62/2/145/55690/Problems-associated-with-ceramic-brackets-suggest" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>).Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com22tag:blogger.com,1999:blog-8859456735165996893.post-66968068329980414042023-12-27T10:56:00.000-08:002023-12-27T10:56:10.533-08:00We share an ancestor who probably lived no more than 640 years agoWe all evolved from one single-celled organism that lived billions of years ago. I don’t see why this is so hard for some people to believe, given that all of us also developed from a single fertilized cell in just 9 months.<br />
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However, our most recent common ancestor is not that first single-celled organism, nor is it the first Homo sapiens, or even the first Cro-Magnon.<br />
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The majority of the people who read this blog probably share a common ancestor who lived no more than 640 years ago. Genealogical records often reveal interesting connections - the figure below has been cropped from <a href="http://pinterest.com/pin/242279654926126602/" target="_blank">a larger one from Pinterest</a>.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHbpLs8e48SyKViXvo_edVMDRrnln0-9xkg9POqzYSuz0OhyphenhyphenS6bYzx4k3t0dodNVNW3ivvJ518io230zm6NlibqD_n1wh7YbqJdLzYQPLc9FKYiN2biYN4SDmQX-B5VZqfuvN1PLHU01aE/s1600/Pinterest_GenealogyPublicFigures_2013.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="130" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHbpLs8e48SyKViXvo_edVMDRrnln0-9xkg9POqzYSuz0OhyphenhyphenS6bYzx4k3t0dodNVNW3ivvJ518io230zm6NlibqD_n1wh7YbqJdLzYQPLc9FKYiN2biYN4SDmQX-B5VZqfuvN1PLHU01aE/s320/Pinterest_GenealogyPublicFigures_2013.png" width="320" /></a></div>
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You and I, whoever you are, have each two parents. Each of our parents have (or had) two parents, who themselves had two parents. And so on.<br />
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If we keep going back in time, and assume that you and I do not share a common ancestor, there will be a point where the theoretical world population would have to be impossibly large.<br />
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Assuming a new generation coming up every 20 years, and going backwards in time, we get a theoretical population chart like the one below. The theoretical population grows in an exponential, or geometric, fashion.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1BJhvQSF50xdjK1AzZxFoULUD9eB2-zihJa3uMiEo6-BQyGV0s2lO3fD4DC_fkdwmTcXFJ8CYTatlEXK-H8N1qEPbdWyUQc3OyEZtakwPji6BTS2PeChPxAlxuAD2d-Prg57rp0cqX-K9/s1600/Kock_2011_TheoreticalPopulation1.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="130" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1BJhvQSF50xdjK1AzZxFoULUD9eB2-zihJa3uMiEo6-BQyGV0s2lO3fD4DC_fkdwmTcXFJ8CYTatlEXK-H8N1qEPbdWyUQc3OyEZtakwPji6BTS2PeChPxAlxuAD2d-Prg57rp0cqX-K9/s320/Kock_2011_TheoreticalPopulation1.PNG" width="320" /></a></div>
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As we move back in time the bars go up in size. Beyond a certain point their sizes go up so fast that you have to segment the chart. Otherwise the bars on the left side of the chart disappear in comparison to the ones on the right side (as several did on the chart above). Below is the section of the chart going back to the year 1371.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEivx1IiWKuebkxzHeRlxYgKRDxU3pQS2AHAR7xg6vVjtam3GZJDGPxo1-7ErTde1p35oYvKiZUOmr-C4__VuDtmE7hVHnzXN_6X5tBHKdNgP_69-DTQSnPljwgol9aRyPEHFXJjeTgqJzLN/s1600/Kock_2011_TheoreticalPopulation2.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="129" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEivx1IiWKuebkxzHeRlxYgKRDxU3pQS2AHAR7xg6vVjtam3GZJDGPxo1-7ErTde1p35oYvKiZUOmr-C4__VuDtmE7hVHnzXN_6X5tBHKdNgP_69-DTQSnPljwgol9aRyPEHFXJjeTgqJzLN/s320/Kock_2011_TheoreticalPopulation2.PNG" width="320" /></a></div>
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The year 1371 is a little more than 640 years ago. (This post is revised from another dated a few years ago, hence the number 640.) And what is the theoretical population in that year if we assume that you and I have no common ancestors? The answer is: more than 8.5 billion people. We know that is not true.<br />
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Admittedly this is a somewhat simplistic view of this phenomenon, used here primarily to make a point. For example, it is possible that a population of humans became isolated 15 thousand years ago, remained isolated to the present day, and that one of their descendants just happened to be around reading this blog today.<br />
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Perhaps the most widely cited article discussing this idea is <a href="https://www.cambridge.org/core/journals/advances-in-applied-probability/article/recent-common-ancestors-of-all-presentday-individuals/330372AB57FB1CB3839FAAA46CF81B66" target="_new">this one by Joseph T. Chang</a>, published in the journal <i>Advances in Applied Probability</i>. For a more accessible introduction to the idea, see <a href="https://itotd.com/articles/4656/most-recent-common-ancestors/" target="_new">this article by Joe Kissell</a>. <br />
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Estimates vary based on the portion of the population considered. There are also assumptions that have to be made based on migration and mating patterns, as well as the time for each generation to emerge and the stability of that number over time.<br />
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Still, most people alive today share a common ancestor who lived <i>a lot</i> more recently than they think. In most cases that common ancestor probably lived less than 640 years ago.<br />
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And who was that common ancestor? That person was probably a man who, due to a high perceived social status, had many consorts, who gave birth to many children. Someone like <a href="http://en.wikipedia.org/wiki/Gengis_Khan" target="_blank">Genghis Khan</a>.<br />
<br />Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com16tag:blogger.com,1999:blog-8859456735165996893.post-46001782836688162232023-11-26T14:02:00.000-08:002023-11-26T14:02:51.502-08:00Subcutaneous versus visceral fat: How to tell the difference?The photos below, from Wikipedia, show two patterns of abdominal fat deposition. The one on the left is predominantly of subcutaneous abdominal fat deposition. The one on the right is an example of visceral abdominal fat deposition, around internal organs, together with a significant amount of subcutaneous fat deposition as well.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGlVD4rbNxQ1iVJ2TaC4VXsyvbVZUiP5LjEm5Cql9Zz1R-lxWwXGiLLhsqLUTv66w5cr7y7U8TC_-_2TzZHJyAdTuaCbC6EDQvug6czXJHnjxCZVcRa9XLleC7yJA1CB8cDlF5MMCmelNF/s1600/Wikipedia_BodyFat.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="155" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGlVD4rbNxQ1iVJ2TaC4VXsyvbVZUiP5LjEm5Cql9Zz1R-lxWwXGiLLhsqLUTv66w5cr7y7U8TC_-_2TzZHJyAdTuaCbC6EDQvug6czXJHnjxCZVcRa9XLleC7yJA1CB8cDlF5MMCmelNF/s320/Wikipedia_BodyFat.JPG" width="320" /></a></div>
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Body fat is not an inert mass used only to store energy. <b>Body fat can be seen as a “distributed organ”, as it secretes a number of hormones into the bloodstream</b>. For example, it secretes leptin, which regulates hunger. It secretes <a href="http://healthcorrelator.blogspot.com/2010/03/adiponectin-inflammation-diabetes-and.html" target="_blank">adiponectin</a>, which has many health-promoting properties. It also secretes <a href="http://healthcorrelator.blogspot.com/2010/02/body-fat-and-disease-how-much-body-fat.html" target="_blank">tumor necrosis factor-alpha</a> (more recently referred to as simply “tumor necrosis factor” in the medical literature), which promotes inflammation. Inflammation is necessary to repair damaged tissue and deal with pathogens, but too much of it does more harm than good.<br />
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<b>How does one differentiate subcutaneous from visceral abdominal fat?</b><br />
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<b>Subcutaneous abdominal fat shifts position more easily as one’s body moves</b>. When one is standing, subcutaneous fat often tends to fold around the navel, creating a “mouth” shape. Subcutaneous fat is easier to hold in one’s hand, as shown on the left photo above. Because subcutaneous fat tends to “shift” more easily as one changes the position of the body, if you measure your waist circumference lying down and standing up, and the difference is large (a one-inch difference can be considered large), you probably have a significant amount of subcutaneous fat.<br />
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<b>Waist circumference is a variable that reflects <i>individual</i> changes in body fat percentage fairly well</b>. This is especially true as one becomes lean (e.g., around 14-17 percent or less of body fat for men, and 21-24 for women), because as that happens abdominal fat contributes to an increasingly higher proportion of total body fat. For people who are lean, a 1-inch reduction in waist circumference will frequently translate into a 2-3 percent reduction in body fat percentage. Having said that, waist circumference comparisons between individuals are often misleading. <b>Waist-to-fat ratios tend to vary a lot among different individuals</b> (like almost any trait). This means that someone with a 34-inch waist (measured at the navel) may have a lower body fat percentage than someone with a 33-inch waist.<br />
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<b>Subcutaneous abdominal fat is hard to mobilize</b>; that is, it is hard to burn through diet and exercise. This is why it is often called the “stubborn” abdominal fat. One reason for the difficulty in mobilizing subcutaneous abdominal fat is that the network of blood vessels is not as dense in the area where this type of fat occurs, as it is with visceral fat. Another reason, which is related to degree of vascularization, is that subcutaneous fat is farther away from the portal vein than visceral fat. As such, it has to travel a longer distance to reach the main “highway” that will take it to other tissues (e.g., muscle) for use as energy.<br />
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<b>In terms of health, excess subcutaneous fat is not nearly as detrimental as excess visceral fat</b>. Excess visceral fat typically happens together with excess subcutaneous fat; but not necessarily the other way around. For instance, sumo wrestlers frequently have excess subcutaneous fat, but little or no visceral fat. The more health-detrimental effect of excess visceral fat is probably related to its proximity to the portal vein, which amplifies the negative health effects of excessive pro-inflammatory hormone secretion. Those hormones reach a major transport “highway” rather quickly.<br />
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Even though excess subcutaneous body fat is more benign than excess visceral fat, <b>excess body fat of any kind is unlikely to be health-promoting</b>. From an evolutionary perspective, excess body fat impaired agile movement and decreased circulating adiponectin levels; the latter leading to a host of negative health effects. In modern humans, negative health effects may be much less pronounced with subcutaneous than visceral fat, but they will still occur.<br />
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Based on studies of isolated hunger-gatherers, it is reasonable to estimate “natural” body fat levels among our Stone Age ancestors, and thus <b>optimal body fat levels in modern humans, to be around 6-13 percent in men and 14–20 percent in women</b>.<br />
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If you think that being overweight probably protected some of our Stone Age ancestors during times of famine, here is one interesting factoid to consider. <b>It will take over a month for a man weighing 150 lbs and with 10 percent body fat to die from starvation</b>, and death will not be typically caused by too little body fat being left for use as a source of energy. In starvation, normally <b>death will be caused by heart failure, as the body slowly breaks down muscle tissue (including heart muscle)</b> to maintain blood glucose levels.<br />
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References:<br />
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Arner, P. (2005). <a href="https://link.springer.com/article/10.1007/BF01572779" target="_blank">Site differences in human subcutaneous adipose tissue metabolism in obesity</a>. <i>Aesthetic Plastic Surgery</i>, 8(1), 13-17.<br />
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Brooks, G.A., Fahey, T.D., & Baldwin, K.M. (2005). <i><a href="http://www.amazon.com/gp/product/0072556420" target="_blank">Exercise physiology: Human bioenergetics and its applications</a></i>. Boston, MA: McGraw-Hill.<br />
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Fleck, S.J., & Kraemer, W.J. (2004). <i><a href="http://www.amazon.com/Designing-Resistance-Training-Programs-3rd/dp/0736042571" target="_blank">Designing resistance training programs</a></i>. Champaign, IL: Human Kinetics.<br />
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Taubes, G. (2007). <a href="http://www.amazon.com/Good-Calories-Bad-Controversial-Science/dp/1400033462" target="_blank"><i>Good calories, bad calories: Challenging the conventional wisdom on diet, weight control, and disease</i></a>. New York, NY: Alfred A. Knopf.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com42tag:blogger.com,1999:blog-8859456735165996893.post-58539089714429260792023-10-23T10:41:00.001-07:002023-10-23T10:46:01.469-07:00The Friedewald and Iranian equations: Fasting triglycerides can seriously distort calculated LDLStandard lipid profiles provide LDL cholesterol measures based on equations that usually have the following as their inputs (or independent variables): total cholesterol, HDL cholesterol, and triglycerides.<br />
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Yes, LDL cholesterol is not measured directly in standard lipid profile tests! This is indeed surprising, since cholesterol-lowering drugs with negative side effects are usually prescribed based on estimated (or "fictitious") LDL cholesterol levels.<br />
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The most common of these equations is the Friedewald equation. Through the Friedewald equation, LDL cholesterol is calculated as follows (where TC = total cholesterol, and TG = triglycerides). The inputs and result are in mg/dl.<br />
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LDL = TC – HDL – TG / 5<br />
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Here is one of the problems with the Friedewald equation. Let us assume that an individual has the following lipid profile numbers: TC = 200, HDL = 50, and TG = 150. The calculated LDL will be 120. Let us assume that this same individual reduces triglycerides to 50, from the previous 150, keeping all of the other measures constant with except of HDL, which goes up a bit to compensate for the small loss in total cholesterol associated with the decrease in triglycerides (there is always some loss, because the main carrier of triglycerides, VLDL, also carries some cholesterol). This would normally be seen as an improvement. However, the calculated LDL will now be 140, and a doctor will tell this person to consider taking statins!<br />
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There is evidence that, for individuals with low fasting triglycerides, a more precise equation is one that has come to be known as the “Iranian equation”. The equation has been proposed by Iranian researchers in an article published in the Archives of Iranian Medicine (Ahmadi et al., 2008), hence its nickname. Through the Iranian equation, LDL is calculated as follows. Again, the inputs and result are in mg/dl.<br />
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LDL = TC / 1.19 + TG / 1.9 – HDL / 1.1 – 38<br />
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The Iranian equation is based on linear regression modeling, which is a good sign, although I would have liked it even better if it was based on nonlinear regression modeling. The reason is that relationships between variables describing health-related phenomena are often nonlinear, <a href="http://warppls.blogspot.com/2010/02/nonlinearity-and-type-i-and-ii-errors.html" target="_new">leading to biased linear estimations</a>. With a good nonlinear analysis algorithm, a linear relationship will also be captured; that is, the “curve” that describes the relationship will default to a line if the relationship is truly linear (see: <a href="https://warppls.com/" target="_new">warppls.com</a>).<br />
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The Iranian equation yields high values of LDL cholesterol when triglycerides are high; much higher than those generated by the Friedewald equation. If those are not overestimations (and there is some evidence that, if they are, it is not by much), they describe an alarming metabolic pattern, because high triglycerides <a href="http://healthcorrelator.blogspot.com/2010/04/low-fasting-triglycerides-marker-for.html" target="_new">are associated with small-dense LDL particles</a>. These particles are the most potentially atherogenic of the LDL particles, in the presence of other factors such as chronic inflammation.<br />
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In other words, the Iranian equation gives a clearer idea than the Friedewald equation about the negative health effects of high triglycerides. You need a large number of small-dense LDL particles to carry a high amount of LDL cholesterol.<br />
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An even more precise measure of LDL particle configuration is the VAP test; <a href="http://healthcorrelator.blogspot.com/2012/10/the-anatomy-of-vap-test-report.html" target="_new">this post</a> has a discussion of a sample VAP test report.<br />
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Reference:<br />
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Ahmadi SA, Boroumand MA, Gohari-Moghaddam K, Tajik P, Dibaj SM. (2008). <a href="http://www.ncbi.nlm.nih.gov/pubmed/18426324" target="_new">The impact of low serum triglyceride on LDL-cholesterol estimation</a>. <i>Archives of Iranian Medicine</i>, 11(3), 318-21.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com13tag:blogger.com,1999:blog-8859456735165996893.post-32063245875247745652023-09-23T09:42:00.000-07:002023-09-23T09:42:09.051-07:00The China Study II: Cholesterol seems to protect against cardiovascular diseaseFirst of all, many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. The data has been compliled by those researchers to disseminate the data from a study often referred to as the “China Study II”. It has already been analyzed by other bloggers. Notable analyses have been conducted by Ricardo at <a href="http://www.canibaisereis.com/">Canibais e Reis</a>, Stan at <a href="http://stan-heretic.blogspot.com/">Heretic</a>, and Denise at <a href="http://rawfoodsos.com/">Raw Food SOS</a>.<br />
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The analyses in this post differ from those other analyses in various aspects. One of them is that data for males and females were used separately for each county, instead of the totals per county. Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance (for more details, see “Notes” at the end of the post), which is desirable since the dataset is relatively small. 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 complex analysis because violation of commonsense assumption 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.<br />
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The analysis was conducted using <a href="http://warppls.com/">WarpPLS</a>. Below is the model with the main results of the analysis. (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: SexM1F2 = sex, with 1 assigned to males and 2 to females; HDLCHOL = HDL cholesterol; TOTCHOL = total cholesterol; MSCHIST = mortality from schistosomiasis infection; and MVASC = mortality from all cardiovascular diseases.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgz2pgf43_4z3wypg_oGh5c7HCmNB11h17i9feWd914UG2w6N5UV0ub-ep1RSiX4kAPZRSLVSk9-7Il_WP4Ar3A78ljnbu83NY0BjMeRSXpzzo61IGsddq3io-4DtuaooazQovxB-W6FiFx/s1600/Kock_WarpPLS_ChinaStudy1989_MortChol1.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" ox="true" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgz2pgf43_4z3wypg_oGh5c7HCmNB11h17i9feWd914UG2w6N5UV0ub-ep1RSiX4kAPZRSLVSk9-7Il_WP4Ar3A78ljnbu83NY0BjMeRSXpzzo61IGsddq3io-4DtuaooazQovxB-W6FiFx/s320/Kock_WarpPLS_ChinaStudy1989_MortChol1.JPG" /></a></div><br />
The variables to the left of MVASC are the main predictors of interest in the model – HDLCHOL and TOTCHOL. The ones to the right are control variables – SexM1F2 and MSCHIST. The path coefficients (indicated as beta coefficients) reflect the strength of the relationships. A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to. The P values indicate the statistical significance of the relationship; a P lower than 0.05 generally means a significant relationship (95 percent or higher likelihood that the relationship is “real”).<br />
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In summary, this is what the model above is telling us:<br />
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- <strong>As HDL cholesterol increases, total cholesterol increases significantly</strong> (beta=0.48; P<0.01). This is to be expected, as HDL is a main component of total cholesterol, together with VLDL and LDL cholesterol.<br />
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- <strong>As total cholesterol increases, mortality from all cardiovascular diseases decreases significantly</strong> (beta=-0.25; P<0.01). This is to be expected if we assume that total cholesterol is in part an intervening variable between HDL cholesterol and mortality from all cardiovascular diseases. This assumption can be tested through a separate model (more below). Also, there is more to this story, as noted below.<br />
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- <strong>The effect of HDL cholesterol on mortality from all cardiovascular diseases is nonsignificant when we control for the effect of total cholesterol</strong> (beta=-0.08; P=0.26). This suggests that HDL’s protective role is subsumed by the variable total cholesterol, and also that it is possible that there is something else associated with total cholesterol that makes it protective. Otherwise the effect of total cholesterol might have been nonsignificant, and the effect of HDL cholesterol significant (the reverse of what we see here).<br />
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- <strong>Being female is significantly associated with a reduction in mortality from all cardiovascular diseases </strong>(beta=-0.16; P=0.01). This is to be expected. In other words, men are women with a few design flaws. (This situation reverses itself a bit after menopause.)<br />
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- <strong>Mortality from schistosomiasis infection is significantly and inversely associated with mortality from all cardiovascular diseases</strong> (beta=-0.28; P<0.01). This is probably due to those dying from schistosomiasis infection not being entered in the dataset as dying from cardiovascular diseases, and vice-versa.<br />
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Two other main components of total cholesterol, in addition to HDL cholesterol, are VLDL and LDL cholesterol. These are carried in <a href="http://healthcorrelator.blogspot.com/2010/02/large-ldl-and-small-hdl-particles-best.html">particles, known as lipoproteins</a>. VLDL cholesterol is usually represented as a fraction of triglycerides in cholesterol equations (e.g., <a href="http://healthcorrelator.blogspot.com/2010/04/friedewald-and-iranian-equations.html">the Friedewald and Iranian equations</a>). It usually correlates inversely with HDL; that is, as HDL cholesterol increases, usually VLDL cholesterol decreases. Given this and the associations discussed above, it seems that LDL cholesterol is a good candidate for the possible “something else associated with total cholesterol that makes it protective”. <strong>But waidaminet! Is it possible that the demon particle, the LDL, serves any purpose other than giving us heart attacks?</strong><br />
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The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC). The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The curve is the best-fitting S curve obtained by the software (an S curve is a slightly more complex curve than a U curve).<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBBNKm58Vbj6g5uKpx7CrTkUCjO6bCJFykCNftKwr4zhfw1K9o3hd93BkuweigJwsu0H1ncryAwfZ0yFiTPZkDtsMO_-jLozRE3A-uMxzw2Y6-2Y4P5Avr0haxwkIPrlgb28EJwgzjOdu2/s1600/Kock_WarpPLS_ChinaStudy1989_MortChol2.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" ox="true" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiBBNKm58Vbj6g5uKpx7CrTkUCjO6bCJFykCNftKwr4zhfw1K9o3hd93BkuweigJwsu0H1ncryAwfZ0yFiTPZkDtsMO_-jLozRE3A-uMxzw2Y6-2Y4P5Avr0haxwkIPrlgb28EJwgzjOdu2/s320/Kock_WarpPLS_ChinaStudy1989_MortChol2.PNG" /></a></div><br />
The graph below shows some of the data in unstandardized format, and organized differently. The data is grouped here in ranges of total cholesterol, which are shown on the horizontal axis. The lowest and highest ranges in the dataset are shown, to highlight the magnitude of the apparently protective effect. Here the two variables used to calculate mortality from all cardiovascular diseases (MVASC; see “Notes” at the end of this post) were added. <strong>Clearly the lowest mortality from all cardiovascular diseases is in the highest total cholesterol range, 172.5 to 180; and the highest mortality in the lowest total cholesterol range, 120 to 127.5. The difference is quite large; the mortality in the lowest range is approximately 3.3 times higher than in the highest.</strong><br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1v_wLYbRHrwQOogCf9nlzsXNLUFdvnAAUN3mJtRxdM-Ux09ysHG-CyLpEBS9gkAz3mGXJS9YDMCH5_M7Jz2PWEPzAvK2qlNeVD_vsTTUFBKqqBNtwNEEkiWG3osDdEUe4y3gJSOoNCx9P/s1600/Kock_WarpPLS_ChinaStudy1989_MortChol3.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" ox="true" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1v_wLYbRHrwQOogCf9nlzsXNLUFdvnAAUN3mJtRxdM-Ux09ysHG-CyLpEBS9gkAz3mGXJS9YDMCH5_M7Jz2PWEPzAvK2qlNeVD_vsTTUFBKqqBNtwNEEkiWG3osDdEUe4y3gJSOoNCx9P/s320/Kock_WarpPLS_ChinaStudy1989_MortChol3.PNG" /></a></div><br />
The shape of the S-curve graph above suggests that there are other variables that are confounding the results a bit. Mortality from all cardiovascular diseases does seem to generally go down with increases in total cholesterol, but the smooth inflection point at the middle of the S-curve graph suggests a more complex variation pattern that may be influenced by other variables (e.g., smoking, dietary patterns, or even schistosomiasis infection; see “Notes” at the end of this post).<br />
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As mentioned before, total cholesterol is strongly influenced by HDL cholesterol, so below is the model with only HDL cholesterol (HDLCHOL) pointing at mortality from all cardiovascular diseases (MVASC), and the control variable sex (SexM1F2).<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8TRB-zAUs3I87hPbSJYmX5crbwoumOtCJrbt_ozR4fLeCPcmaceasRt1akFbbwHhd3SBTyxurpBoNGIKgJ01huUXsfRHqnzYpaXxc7_iZnljyItT8dRCrlISGcj1u-AEwG5EAkfuUcdEj/s1600/Kock_WarpPLS_ChinaStudy1989_MortHDL1.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" ox="true" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8TRB-zAUs3I87hPbSJYmX5crbwoumOtCJrbt_ozR4fLeCPcmaceasRt1akFbbwHhd3SBTyxurpBoNGIKgJ01huUXsfRHqnzYpaXxc7_iZnljyItT8dRCrlISGcj1u-AEwG5EAkfuUcdEj/s320/Kock_WarpPLS_ChinaStudy1989_MortHDL1.PNG" /></a></div><br />
The graph above confirms the assumption that HDL’s protective role is subsumed by the variable total cholesterol. When the variable total cholesterol is removed from the model, as it was done above, the protective effect of HDL cholesterol becomes significant (beta=-0.27; P<0.01). The control variable sex (SexM1F2) was retained even in this targeted HDL effect model because of the expected confounding effect of sex; females generally tend to have higher HDL cholesterol and less cardiovascular disease than males.<br />
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Below, in the “Notes” section (after the “Reference”) are several notes, some of which are quite technical. Providing them separately hopefully has made the discussion above a bit easier to follow. The notes also point at some limitations of the analysis. This data needs to be analyzed from different angles, using multiple models, so that firmer conclusions can be reached. Still, the overall picture that seems to be emerging is at odds with previous beliefs based on the same dataset.<br />
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What could be increasing the apparently protective HDL and total cholesterol in this dataset? High consumption of animal foods, particularly foods rich in saturated fat and cholesterol, are strong candidates. Low consumption of vegetable oils rich in linoleic acid, and of foods rich in refined carbohydrates, are also good candidates. Maybe it is a <a href="http://healthcorrelator.blogspot.com/2010/02/want-to-improve-your-cholesterol.html">combination of these</a>.<br />
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We need more analyses!<br />
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<strong>Notes:</strong><br />
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- 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).<br />
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- The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. In complex and multi-factorial phenomena such as health-related phenomena, many would consider an R-squared of 0.20 as acceptable. Still, such an R-squared would mean that 80 percent of the variance for a particularly variable is unexplained by the data.<br />
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- The P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This and other related techniques also tend to yield more reliable results for small samples, and samples with outliers (as long as the outliers are “good” data, and are not the result of measurement error).<br />
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- Colinearity is an important consideration in models that analyze the effect of multiple predictors on one single variable. This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the “winners”. This often backfires, as colinearity can severely distort the results. Some multiple regression techniques, such as automated stepwise regression with backward elimination, are particularly vulnerable to this problem. Colinearity is not the same as correlation, and thus is defined and measured differently. Two predictor variables may be significantly correlated and still have low colinearity. A reasonably reliable measure of colinearity is the variance inflation factor. Colinearity was tested in this model, and was found to be low.<br />
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- An effort was made here to avoid multiple data points per county (even though this was available for some variables), because this could artificially reduce the variance for each variable, and potentially bias the results. The reason for this is that multiple answers from a single county would normally be somewhat correlated; a higher degree of intra-county correlation than inter-county correlation. The resulting bias would be difficult to control for, via one or more control variables. With only two data points per county, one for males and the other for females, one can control for intra-country correlation by adding a “dummy” sex variable to the analysis, as a control variable. This was done here.<br />
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- Mortality from schistosomiasis infection (MSCHIST) is a variable that tends to affect the results in a way that makes it more difficult to make sense of them. Generally this is true for any infectious diseases that significantly affect a population under study. The problem with infection is that people with otherwise good health or habits may get the infection, and people with bad health and habits may not. Since cholesterol is used by the human body to fight disease, it may go up, giving the impression that it is going up for some other reason. Perhaps instead of controlling for its effect, as done here, it would have been better to remove from the analysis those counties with deaths from schistosomiasis infection. (See also <a href="http://healthcorrelator.blogspot.com/2010/07/china-study-one-more-time-are-raw-plant.html">this post</a>, and <a href="http://healthcorrelator.blogspot.com/2010/07/china-study-again-multivariate-analysis.html">this one</a>.)<br />
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- Different parts of the data were collected at different times. It seems that the mortality data is for the period 1986-88, and the rest of the data is for 1989. This may have biased the results somewhat, even though the time lag is not that long, especially if there were changes in certain health trends from one period to the other. For example, major migrations from one county to another could have significantly affected the results.<br />
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- The following measures were used, from the China Study II dataset, like the other measures. P002 HDLCHOL, for HDLCHOL; P001 TOTCHOL, for TOTCHOL; and M021 SCHISTOc, for MSCHIST.<br />
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- SexM1F2 is a “dummy” variable that was coded with 1 assigned to males and 2 to females. As such, it essentially measures the “degree of femaleness” of the respondents. Being female is generally protective against cardiovascular disease, a situation that reverts itself a bit after menopause.<br />
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- MVASC is a composite measure of the two following variables, provided as component measures of mortality from all cardiovascular diseases: M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). A couple of obvious problems: (a) they does not include data on people older than 69; and (b) they seem to capture a lot of diseases, including some that do not seem like typical cardiovascular diseases. A factor analysis was conducted, and the loadings and cross-loadings suggested good validity. Composite reliability was also good. So essentially MVASC is measured here as a “latent variable” with two “indicators”. Why do this? The reason is that it reduces the biasing effects of incomplete data and measurement error (e.g., exclusion of folks older than 69). By the way, there is always some measurement error in any dataset.<br />
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- This note is related to measurement error in connection with the indicators for MVASC. There is something odd about the variables M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). According to the dataset, mortality from cardiovascular diseases for ages 0-34 is typically higher than for 35-69, for many counties. Given the good validity and reliability for MVASC as a latent variable, it is possible that the values for these two indicator variables were simply swapped by mistake.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com25tag:blogger.com,1999:blog-8859456735165996893.post-30253157033892843502023-09-16T14:02:00.000-07:002023-09-16T14:02:35.062-07:00Could grain-fed beef liver be particularly nutritious?<script type="text/javascript">var citeN=0;</script> <br>
There is a pervasive belief today that grain-fed beef is unhealthy, a belief that I addressed before in this blog (<a href="http://bit.ly/aOpGS8" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) and that I think is exaggerated. This general belief seems to also apply to a related meat, one that is widely acknowledged as a major micronutrient “powerhouse”, namely grain-fed beef liver. <br><br>
Regarding grain-fed beef liver, the idea is that cattle that are grain-fed tend to develop a mild form of fatty liver disease. This I am inclined to agree with. <br><br>
However, I am not convinced that this is such a bad thing for those who eat grain-fed beef liver. <br><br>
In most animals, including <i>Homo sapiens</i>, fatty liver disease seems to be associated with extra load being put on the liver. Possible reasons for this are accelerated growth, abnormally high levels of body fat, and ingestion of toxins beyond a certain hormetic threshold (e.g., alcohol). <br><br>
In these cases, what would one expect to see as a body response? The extra load is associated with high oxidative stress and rate of metabolic work. In response, the body should shuttle more antioxidants and metabolism catalysts to the organ being overloaded. Fat-soluble vitamins can act as antioxidants and catalysts in various metabolic processes, among other important functions. They require fat to be stored, and can then be released over time, which is a major advantage over water-soluble vitamins; fat-soluble vitamins are longer-acting. <br><br>
So you would expect an overloaded liver to have more fat in it, and also a greater concentration of fat-soluble vitamins. This would include vitamin A, which would give the liver an unnatural color, toward the orange-yellow range of the spectrum. <br><br>
Grain-fed beef liver, like the muscle meat of grain-fed cattle, tends to have more fat than that of grass-fed animals. One function of this extra fat could be to store fat-soluble vitamins. This extra fat appears to have a higher omega-6 fat content as well. Still, beef liver is a fairly lean meat; with about 5 g of fat per 100 g of weight, and only 20 mg or so of omega-6 fat. Clearly consumption of beef liver in moderation is unlikely to lead to a significant increase in omega-6 fat content in one’s diet (<a href="http://bit.ly/aOpGS8" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). By consumption in moderation I mean approximately once a week. <br><br>
The photo below, from Wikipedia, is of a dish prepared with <i>foie gras</i>. That is essentially the liver of a duck or goose that has been fattened through force-feeding, until the animal develops fatty liver disease. This “diseased” liver is particularly rich in fat-soluble vitamins; e.g., it is the best known source of the all-important vitamin K2. <br><br>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZW9QEK5D8L3NF75EX0lQOIAnaoyMgWalsE9oEabTHPItZ5jKVvojPH-JjlJfN9ADzCLCmGPD_0RVuw6y0tiXOOTQOwAs4WVZMDJcXYGqm4It2gLrmbSUYzql9yFZUcwjEbtui_SYhk8Uo/s1600/Foie_gras_en_cocotte.jpg" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZW9QEK5D8L3NF75EX0lQOIAnaoyMgWalsE9oEabTHPItZ5jKVvojPH-JjlJfN9ADzCLCmGPD_0RVuw6y0tiXOOTQOwAs4WVZMDJcXYGqm4It2gLrmbSUYzql9yFZUcwjEbtui_SYhk8Uo/s320/Foie_gras_en_cocotte.jpg" /></a> <br><br>
Could the same happen, although to a lesser extent, with grain-fed beef liver? I don’t think it is unreasonable to speculate that it could. <br><br>
Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com21tag:blogger.com,1999:blog-8859456735165996893.post-59660500266219720852023-08-17T07:32:00.000-07:002023-08-17T07:32:38.240-07:00Do prominent health gurus live longer?<script type="text/javascript">var citeN=0;</script> <br />
Many years ago, when I started blogging about health issues, I noticed a couple of interesting patterns. The first pattern is that prominent health “gurus” often talk about having had serious health problems in their past, which they describe as having motivated them to do research on health issues – and thus become health gurus. Frequently these problems pop up before 45 years of age; this is a threshold beyond which there is a clearly noticeable increase in severity of health problems. <br />
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In fact, I remember being somewhat surprised by one such “guru” (I will not name him), who would regularly write posts saying something to the effect that “… finally, my health is now on the right track …” In other words, every few months or so this person had to deal with serious health problems, always coming up with reasonable knowledge-based solutions. The knowledge seemed to be of good quality, but this guy’s health was poor to say the least. <br />
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The second pattern, related to the above, is that prominent health gurus seem to have a below average life expectancy. The life expectancy for the general population is around 79 years of age in the USA at the time of this writing, according to the World Health Organization (<a href="https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). Anthony Colpo has written an interesting post about this below average life expectancy pattern among health gurus (<a href="http://anthonycolpo.com/being-a-health-expert-is-a-health-hazard/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>).<br />
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To better understand and illustrate this situation to our blog’s readers, I created a dataset with 100 records, corresponding to 100 health gurus, with various variables interacting in ways that reflect the above observations. The observations are summarized as assumptions, listed later. The following variables are on a scale from 1 to 7; in real life they would have been measured retrospectively, looking back at a guru’s entire life: <br />
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- The guru's health before age 45 (BEF45). <br />
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- The guru's knowledge about health issues (KNOWL). <br />
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- The guru's health after age 45 (AFT45). <br />
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- The guru's prominence (GPROM). <br />
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Finally, the variable below is on a continuous scale of years, with an average of 79 and a standard deviation of 10. As mentioned earlier, 79 is the life expectancy for someone living in the USA at the time of this writing. The standard deviation of 10, which approximates that figure in the USA, means that approximately 68 percent of the individuals in the simulated dataset will have a life expectancy between 69 and 89. That is 79-10 and 79+10, respectively. <br />
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- The guru's age at the time of death (GAGED).<br />
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This experimental exercise with simulated data can be seen as a simulation “game”, where various variables interact to generate results that are not obvious. A widely used process to create data is known as the Monte Carlo method (<a href="https://en.wikipedia.org/wiki/Monte_Carlo_method" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), which is what we used here. I also made the following assumptions in the data creation process: <br />
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- That the poorer is the guru's health before age 45 (BEF45), the greater is the guru's knowledge about health issues (KNOWL). The reason for this is that poor health compels the person to study about health issues. <br />
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- That the poorer is the guru's health before age 45 (BEF45), the poorer is the guru's health after age 45 (AFT45). This assumes that the person has an underlying condition that causes the poor health in the first place, and that can be exacerbated by a poor diet and lifestyle. <br />
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- That the greater is the guru's knowledge about health issues (KNOWL), the better is the guru's health after age 45 (AFT45). This counteracts the effect above, and assumes that the knowledge is put to good use and contributes to improving the person’s health. <br />
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- That the greater is the guru's knowledge about health issues (KNOWL), the greater is also the guru's prominence (GPROM). In other words, a guru’s status among followers is enhanced by the guru’s knowledge. <br />
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- That the better is the guru's health after age 45 (AFT45), the higher is the guru's age at the time of death (GAGED). <br />
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A final assumption made is that the causal relationships laid out above have a small effect size (more technically, that they are associated with <i>f</i>-squared coefficients slightly below 0.1), meaning that random influences are not only present but also play a big role in what happens in the simulation. The causality links are summarized in the graph below, created with WarpPLS (<a href="http://warppls.com/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). We also used this software to analyze the data.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjT4OE705sxa-cT87PtmxbTvPsmNrS2byyHCI6-kUSWSta2jkVecOaenjyXS6xWQfH8ssdf5fFADAioRSb-tB8fBG1Zzg04gMbqagMIIDw_XW9ujqbA-qlPJhP8MqqIY4iSRhvlbYFSfIpY/s1600/Kock_2015_Graph_HealthGurus_AllVars.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="216" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjT4OE705sxa-cT87PtmxbTvPsmNrS2byyHCI6-kUSWSta2jkVecOaenjyXS6xWQfH8ssdf5fFADAioRSb-tB8fBG1Zzg04gMbqagMIIDw_XW9ujqbA-qlPJhP8MqqIY4iSRhvlbYFSfIpY/s400/Kock_2015_Graph_HealthGurus_AllVars.png" width="400" /></a></div>
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Note that in our simulated data the guru's prominence (GPROM) does not directly influence the guru's age at the time of death (GAGED). Stated differently, there is no causality link between GPROM and GAGED, one way or the other, even though these two variables are likely to be correlated due to the network of causality links in which they exist. Nevertheless, it is by looking at the relationship between these two variables, GPROM and GAGED, that we can answer the question in the title of this post: Do prominent health gurus live longer? <br />
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And the answer appears to be “no” in our simulation. The plot below shows the relationship between a guru's prominence (GPROM), on the horizontal axis, and the guru's age at the time of death (GAGED), on the vertical axis. Each data point refers to a guru. On average, the greater a guru's prominence, the lower seems to be the guru’s life expectancy. <b>Each one-point increase in prominence is associated</b>, on average, <b>with approximately a one-year decrease in life expectancy</b>. <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh450Iqqiu9pvoxBB4CpEFd8Fm16xpMBFqzuSPHs4oRFdu9A7g0Yc14Tuhftgt5XSx_Ftukvh765mVrOJUQ8meXoi2sQMlsqTaNmhztD-3yamjWrKbIkqJgLCRUyaoFtzHAOe0FACyKkIbl/s1600/Kock_2015_Plot_Prominence_Ageatdeath.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="348" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh450Iqqiu9pvoxBB4CpEFd8Fm16xpMBFqzuSPHs4oRFdu9A7g0Yc14Tuhftgt5XSx_Ftukvh765mVrOJUQ8meXoi2sQMlsqTaNmhztD-3yamjWrKbIkqJgLCRUyaoFtzHAOe0FACyKkIbl/s640/Kock_2015_Plot_Prominence_Ageatdeath.png" width="640" /></a></div>
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Note that there is <b>one very prominent guru whose age at the time of death was around 95</b>; the data point at the top-right corner (GPROM=7, GAGED~95). <b>This happened largely by chance</b> in our data. Nevertheless, assuming that our data somewhat reflects what could happen in real life, the followers of the guru would probably point at that longevity as being caused by the guru’s knowledge about health issues. They would likely be wrong. <br />
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Our dataset also allows us to estimate <b>the probability that a fairly prominent guru</b> (GPROM greater than 4, on a 1-7 scale) <b>would have a below average life expectancy</b> (GAGED lower than 79). That conditional probability would be <b>approximately 60 percent</b>. <br />
<br />Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com11tag:blogger.com,1999:blog-8859456735165996893.post-22525767786678275902023-04-16T05:18:00.000-07:002023-04-16T05:18:16.795-07:00The amounts of water, carbohydrates, fat, and protein lost during a 30-day fastWhen 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.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcFOhbKjScJIbJkiE3_BaG7O0YftozBISDV-_o7wSwZhLJQ-gG8q3wKI70YTZZUvUWXSMAs4E_LldtLbM1Oh160a2ZgS4yfJ9UUy5rKnX4L2vfGCknSbXwhL_ICIE_qFpfvgDdHiNcFnb4/s1600/Wilmore_etal_2007_F14_8.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" nx="true" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhcFOhbKjScJIbJkiE3_BaG7O0YftozBISDV-_o7wSwZhLJQ-gG8q3wKI70YTZZUvUWXSMAs4E_LldtLbM1Oh160a2ZgS4yfJ9UUy5rKnX4L2vfGCknSbXwhL_ICIE_qFpfvgDdHiNcFnb4/s320/Wilmore_etal_2007_F14_8.png" width="199"></a></div><br />
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 <a href="http://healthcorrelator.blogspot.com/2010/06/compensatory-adaptation-as-unifying.html">compensatory adaptation</a>. Those folks also tend to store more water.<br />
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Not many people will try a 30-day fast. Still, the figure above has implications for almost everybody.<br />
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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.<br />
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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”.<br />
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Strength training practitioners often will feel fatigued, and will probably be unable to generate <a href="http://healthcorrelator.blogspot.com/2010/08/theory-of-supercompensation-strength.html">supercompensation</a>, if their “glycogen tank” is constantly depleted. Still, <a href="http://healthcorrelator.blogspot.com/2010/06/compensatory-adaptation-as-unifying.html">compensatory adaptation</a> 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.<br />
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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 <a href="http://healthcorrelator.blogspot.com/2010/09/how-to-lose-fat-and-gain-muscle-at-same.html">this post</a>, 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.<br />
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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 <a href="http://healthcorrelator.blogspot.com/2010/04/ketones-and-ketosis-physiological-and.html">ketosis</a> 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.<br />
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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 (<a href="http://healthcorrelator.blogspot.com/2010/06/fructose-in-fruits-is-good-for-you.html">particularly carbohydrate sources that combine fructose and glucose</a>), 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.<br />
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Glycogen depletion appears to be very healthy, but most of the empirical evidence seems to suggest that it is the depletion that <a href="http://healthcorrelator.blogspot.com/2010/05/growth-hormone-may-rise-300-percent.html">creates a hormonal mix</a> 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.<br />
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<strong>Reference</strong><br />
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Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). <a href="http://www.amazon.com/Physiology-Sport-Exercise-Fourth-Wilmore/dp/0736055835"><em>Physiology of sport and exercise</em></a>. Champaign, IL: Human Kinetics. [Note: the figure may be found in a different edition.]<br />
<br />Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com63tag:blogger.com,1999:blog-8859456735165996893.post-77043695449471074202023-03-25T06:47:00.000-07:002023-03-25T06:47:34.277-07:00Large LDL and small HDL particles: The best combinationHigh-density lipoprotein (HDL) is one of the five main types of lipoproteins found in circulation, together with very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and chylomicrons.<br />
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After a fatty meal, the blood is filled with chylomicrons, which carry triglycerides (TGAs). The TGAs are transferred to cells from chylomicrons via the activity of enzymes, in the form of free fatty acids (FFAs), which are used by those cells as sources of energy.<br />
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After delivering FFAs to the cells, the chylomicrons progressively lose their TGA content and “shrink”, eventually being absorbed and recycled by the liver. The liver exports part of the TGAs that it gets from chylomicrons back to cells for use as energy as well, now in the form of VLDL. As VLDL particles deliver TGAs to the cells they shrink in size, similarly to chylomicrons. As they shrink, VLDL particles first become IDL and then LDL particles.<br />
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The figure below (click on it to enlarge), from Elliott & Elliott (2009; reference at the end of this post), shows, on the same scale: (a) VLDL particles, (b) chylomicrons, (c) LDL particles, and (d) HDL particles. The dark bar at the bottom of each shot is 1000 A in length, or 100 nm (A = angstrom; nm = nanometer; 1 nm = 10 A).<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbYn3TIxyhxdOLkaXgbc7ddFAsaBJm6f2pE7-D3xnL6MjZZhKlZwvceu-TADkvIfkEPx5B7T8_twDzHQK2JSvS7CJYXO1lMpnPgyEc5wWArv3ri_2rU3EzfK2pbVvKLSbsI7_GK9xzuAMV/s1600-h/Elliott_Elliott_2009_F7_13.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbYn3TIxyhxdOLkaXgbc7ddFAsaBJm6f2pE7-D3xnL6MjZZhKlZwvceu-TADkvIfkEPx5B7T8_twDzHQK2JSvS7CJYXO1lMpnPgyEc5wWArv3ri_2rU3EzfK2pbVvKLSbsI7_GK9xzuAMV/s320/Elliott_Elliott_2009_F7_13.gif" /></a></div>
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As you can see from the figure, most of the LDL particles shown are about 1/4 of the length of the dark bar in diameter, often slightly more, or about 25-27 nm in size. They come in different sizes, with sizes in this range being the most common. The smaller and denser they are, the more likely they are to contribute to the formation of atherosclerotic plaque in the presence of other factors, such as chronic inflammation. The larger they become, which usually happens in diets high in saturated fat, the less likely they are to form plaque.<br />
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Note that the HDL particles are rather small compared to the LDL particles. Shouldn’t they cause plaque then? Not really. Apparently they have to be small, compared to LDL particles, to do their job effectively.<br />
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HDL is a completely different animal from VLDL, IDL and LDL. HDL particles are produced by the liver as dense disk-like particles, known as nascent HDL particles. These nascent HDL particles progressively pick up cholesterol from cells, as well as performing a number of other functions, and “fatten up” with cholesterol in the process.<br />
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This process also involves HDL particles picking up cholesterol from plaque in the artery walls, which is one of the reasons why HDL cholesterol is informally called “good” cholesterol. In fact, neither HDL nor LDL are really cholesterol; HDL and LDL are particles that carry cholesterol, protein and fat.<br />
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As far as particle size is concerned, LDL and HDL are opposites. Large LDL particles are the least likely to cause plaque formation, because LDL particles have to be approximately 25 nm in diameter or smaller to penetrate the artery walls. With HDL the opposite seems to be true, as HDL particles need to be small (compared with LDL particles) to easily penetrate the artery walls in order to pick up cholesterol, leave the artery walls with their cargo, and have it returned back to the liver.<br />
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Another interesting aspect of this cycle is that the return to the liver of cholesterol picked up by HDL appears to be done largely via IDL and LDL particles (Elliott & Elliott, 2009), which get the cholesterol directly from HDL particles! Life is not that simple.<br />
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Reference:<br />
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William H. Elliott & Daphne C. Elliott (2009). <a href="http://www.amazon.com/Biochemistry-Molecular-Biology-William-Elliott/dp/0199226717/">Biochemistry and Molecular Biology.</a> 4th Edition. New York: NY: Oxford University Press.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com16tag:blogger.com,1999:blog-8859456735165996893.post-12755365576998755332023-02-28T14:56:00.000-08:002023-02-28T14:56:36.917-08:00Is heavy physical activity a major trigger of death by sudden cardiac arrest? Not in OregonThe idea that heavy physical activity is a main trigger of heart attacks is widespread. Often endurance running and cardio-type activities are singled out. Some people refer to this as “death by running”.<br />
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Good cardiology textbooks, such as the <a href="http://www.amazon.com/Mayo-Clinic-Cardiology-Concise-Textbook/dp/0849390575"><i>Mayo Clinic Cardiology</i></a>, tend to give us a more complex and complete picture. So do medical research articles that report on studies of heart attacks based on comprehensive surveys.<br />
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Reddy and colleagues (2009) studied sudden cardiac arrest events followed by death from 2002 to 2005 in <a href="http://en.wikipedia.org/wiki/Multnomah_County,_Oregon">Multnomah County</a> in Oregon. This study was part of the ongoing Oregon Sudden Unexpected Death Study. Multnomah County has an area of 435 square miles, and had a population of over 677 thousand at the time of the study. The full reference to the article and a link to a full-text version are at the end of this post.<br />
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The researchers grouped deaths by sudden cardiac arrests (SCAs) according to the main type of activity being performed before the event. Below is how the authors defined the activities, quoted verbatim from the article. MET is a measure of the amount of energy spent in the activity; one MET is the amount of energy spent by a person sitting quietly.<br />
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- Sleep (MET 0.9): subjects who were sleeping when they sustained SCA.<br />
- Light activity (MET 1.0–3.4): included bathing, dressing, cooking, cleaning, feeding, household walking and driving.<br />
- Moderate activity (MET 3.5–5.9): included walking for exercise, mowing lawn, gardening, working in the yard, dancing.<br />
- Heavy activity (MET score ≥6): included sports such as tennis, running, jogging, treadmill, skiing, biking.<br />
- Sexual activity (MET score 1.3): included acts of sexual intercourse.<br />
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What did they find? Not what many people would expect.<br />
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The vast majority of the people dying of sudden cardiac arrest were doing things that fit the “light activity” group above prior to their death. This applies to both genders. The figure below (click to enlarge) shows the percentages of men and women who died from sudden cardiac arrest, grouped by activity type.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjF5VVBUEPwccHqwE1vDbXVtAVLh7kDbidnMZE3CSk0URayyEKovjMu9f7X-F8sLWXdxNEWVHcsc99jDZ3VWcJd-kkgnS9MTW1JcnRxBO-Vc8c2reyRdknauQqJzEb3ELYBvyCbOLJuP9Xi/s1600/Reddy_etal_2009_F03.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjF5VVBUEPwccHqwE1vDbXVtAVLh7kDbidnMZE3CSk0URayyEKovjMu9f7X-F8sLWXdxNEWVHcsc99jDZ3VWcJd-kkgnS9MTW1JcnRxBO-Vc8c2reyRdknauQqJzEb3ELYBvyCbOLJuP9Xi/s320/Reddy_etal_2009_F03.jpg" /></a></div><br />
Sudden cardiac arrests were also categorized as witnessed or un-witnessed. For witnessed, someone saw them happening. For un-witnessed, the person was seen alive, and within 24 hours had died. So the data for witnessed sudden cardiac arrests is a bit more reliable. The table below displays the distribution of mean age, gender and known coronary artery disease (CAD) in those with witnessed sudden cardiac arrest.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFqd46nD5ueWIEq7TbPKVQhnM9lMG5fkadqHmO90bGwP1Fqo4XA2aWPxdfxj9LumWOv7Bdo6tSSM-fpI9B1TTnhHBSfewGpLJRQxaQNNiD3Fa5edqrwlNKd12Avx3ImQ5fepbprRseUIWJ/s1600/Reddy_etal_2009_T01.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFqd46nD5ueWIEq7TbPKVQhnM9lMG5fkadqHmO90bGwP1Fqo4XA2aWPxdfxj9LumWOv7Bdo6tSSM-fpI9B1TTnhHBSfewGpLJRQxaQNNiD3Fa5edqrwlNKd12Avx3ImQ5fepbprRseUIWJ/s320/Reddy_etal_2009_T01.PNG" /></a></div><br />
Look at the bottom row, showing those with known coronary artery disease. Again, light activity is the main trigger. Sleep comes second. The numbers within parentheses refer to percentages within each activity group. Those percentages are not very helpful in the identification of the most important triggers, although they do suggest that coronary artery disease is a major risk factor. For example, among those who died from sudden cardiac arrest while having sex, 57 percent had known coronary artery disease. For light activity, 36 percent had known coronary artery disease.<br />
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As a caveat, it is worth noting that heavy activity appears to be more of a trigger in younger individuals than in older ones. This may simply reflect the patterns of activities at different ages. However, this does not seem to properly account for the large differences observed in triggers; the standard deviation for age in the heavy activity group was large enough to include plenty of seniors. Still, it would have been nice to see a multivariate analysis controlling for various effects, including age.<br />
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So what is going on here?<br />
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The authors give us a hint. The real culprit may be bottled up emotional stress and sleep disorders; the latter may be caused by stress, as well as by obesity and other related problems. They have some data that points in those directions. That makes some sense.<br />
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We humans have evolved “fight-or-flight” mechanisms that involve large hormonal discharges in response to stressors. Our ancestors needed those. For example, they needed those to either fight or run for their lives in response to animal attacks.<br />
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Modern humans experience too many stressors while sitting down, as in stressful car commutes and nasty online interactions. The stresses cause “fight-or-flight” hormonal discharges, but are followed by neither “fight” nor “flight” in most cases. This cannot be very good for us.<br />
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Death by running!? More like death by not running!<br />
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Reference:<br />
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Reddy, P.R., Reinier, K., Singh, T., Mariani, R., Gunson, K., Jui, J., & Chugh, S.S. (2009). <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2725925/">Physical activity as a trigger of sudden cardiac arrest: The Oregon Sudden Unexpected Death Study</a>. <i>International Journal of Cardiology</i>, 131(3), 345–349.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com7tag:blogger.com,1999:blog-8859456735165996893.post-40936659216533211352023-01-15T07:35:00.000-08:002023-01-15T07:35:49.360-08:00What is a good low carbohydrate diet? It is a low calorie oneWhat is a good low carbohydrate diet?<br />
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For me, and many people I know, the answer is: a low calorie one. What this means, in simple terms, is that a good low carbohydrate diet is one with plenty of seafood and organ meats in it, and also plenty of veggies. These are low carbohydrate foods that are also naturally low in calories. Conversely, a low carbohydrate diet of mostly beef and eggs would be a high calorie one.<br />
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Seafood and organ meats provide essential fatty acids and are typically packed with nutrients. Because of that, they tend to be satiating. In fact, certain organ meats, such as beef liver, are so packed with nutrients that it is a good idea to limit their consumption. I suggest eating beef liver once or twice a week only. As for seafood, it seems like a good idea to me to get half of one’s protein from them.<br />
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Does this mean that the calories-in-calories-out idea is correct? No, and there is no need to resort to complicated and somewhat questionable feedback-loop arguments to prove that calories-in-calories-out is wrong. Just consider this hypothetical scenario; a thought experiment. Take two men, one 25 years of age and the other 65, both with the same weight. Put them on the same exact diet, on the same exact weight training regime, and keep everything else the same.<br />
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What will happen? Typically the 65-year-old will put on more body fat than the 25-year-old, and the latter will put on more lean body mass. This will happen in spite of the same exact calories-in-calories-out profile. Why? Because their hormonal mixes are different. The 65-year-old will typically have lower levels of circulating growth hormone and testosterone, both of which significantly affect body composition.<br />
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As you can see, it is not all about insulin, as has been argued many times before. In fact, average and/or fasting insulin may be the same for the 65- and 25-year-old men. And, still, the 65-year-old will have trouble keeping his body fat low and gaining muscle. There are other hormones involved, such as leptin and adiponectin, and probably several that we don’t know about yet.<br />
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A low carbohydrate diet appears to be ideal for many people, whether that is due to a particular health condition (e.g., diabetes) or simply due to a genetic makeup that favors this type of diet. By adopting a low carbohydrate diet with plenty of seafood, organ meats, and veggies, you will make it a low calorie diet. If that leads to a calorie deficit that is too large, you can always add a bit more of fat to it. For example, by cooking fish with butter and adding bacon to beef liver.<br />
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One scenario where I don’t see the above working well is if you are a competitive athlete who depletes a significant amount of muscle glycogen on a daily basis – e.g., 250 g or more. In this case, it will be very difficult to replenish glycogen only with protein, so the person will need more carbohydrates. He or she would need a protein intake in excess of 500 g per day for replenishing 250 g of glycogen only with protein.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com38tag:blogger.com,1999:blog-8859456735165996893.post-5165951522588932152022-11-12T11:34:00.000-08:002022-11-12T11:34:37.516-08:00The theory of supercompensation: Strength training frequency and muscle gain<div class="separator" style="clear: both; text-align: center;">
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Moderate strength training has a number of health benefits, and is viewed by many as an important component of a natural lifestyle that approximates that of our Stone Age ancestors. It increases bone density, muscle mass, and improves a number of health markers. Done properly, it may decrease body fat percentage.<br />
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Generally one would expect some muscle gain as a result of strength training. Men seem to be keen on upper-body gains, while women appear to prefer lower-body gains. Yet, <b>many people do strength training for years, and experience little or no muscle gain</b>.<br />
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Paradoxically, those people experience <b>major strength gains, both men and women, especially in the first few months after they start a strength training program</b>. <b>However, those gains are due primarily to neural adaptations, and come without any significant gain in muscle mass</b>. This can be frustrating, especially for men. Most men are after some noticeable muscle gain as a result of strength training. (Whether that is healthy is another story, especially as one gets to extremes.)<br />
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After the initial adaptation period, of “beginner” gains, typically no strength gains occur without muscle gains.<br />
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The culprits for the lack of anabolic response are often believed to be low levels of circulating testosterone and other hormones that seem to interact with testosterone to promote muscle growth, such as growth hormone. This leads many to resort to anabolic steroids, which are drugs that mimic the effects of androgenic hormones, such as testosterone. These drugs usually increase muscle mass, but have a number of negative short-term and long-term side effects.<br />
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There seems to be a better, less harmful, solution to the lack of anabolic response. Through <a href="http://healthcorrelator.blogspot.com/2010/06/compensatory-adaptation-as-unifying.html">my research on compensatory adaptation</a> I often noticed that, <b>under the right circumstances, people would overcompensate for obstacles posed to them</b>. Strength training is a form of obstacle, which should generate overcompensation under the right circumstances. <b>From a biological perspective, one would expect a similar phenomenon; a natural solution to the lack of anabolic response</b>.<br />
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This solution is predicted by a theory that also explains a lack of anabolic response to strength training, and that unfortunately does not get enough attention outside the academic research literature. It is the theory of supercompensation, which is discussed in some detail in several high-quality college textbooks on strength training. (Unlike popular self-help books, these textbooks summarize peer-reviewed academic research, and also provide the references that are summarized.) One example is <a href="http://www.amazon.com/Science-Practice-Strength-Training-Second/dp/0736056289/">the excellent book by Zatsiorsky & Kraemer (2006) on the science and practice of strength training</a>.<br />
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The figure below, from Zatsiorsky & Kraemer (2006), shows what happens during and after a strength training session. The level of preparedness could be seen as the load in the session, which is proportional to: the number of exercise sets, the weight lifted (or resistance overcame) in each set, and the number of repetitions in each set. The restitution period is essentially the recovery period, which must include plenty of rest and proper nutrition.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQPGsbhlVKFuja1zUzp4pGyIDzCIvdkke-GOyg_hjODBC8qTaMa6CRm1eca9g870wGZ2FiNzif6tdL1Nfm01iZTrNMKblHu_ubzbhIlie298Z10adLJXIttvP85z8vVjunCjIOq9zYxYeg/s1600/Zatsiorsky_Kraemer_2006_F1_4.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="130" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQPGsbhlVKFuja1zUzp4pGyIDzCIvdkke-GOyg_hjODBC8qTaMa6CRm1eca9g870wGZ2FiNzif6tdL1Nfm01iZTrNMKblHu_ubzbhIlie298Z10adLJXIttvP85z8vVjunCjIOq9zYxYeg/s320/Zatsiorsky_Kraemer_2006_F1_4.PNG" width="320" /></a></div>
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Note that <b>toward the end there is a sideways S-like curve</b> with a first stretch above the horizontal line and another below the line. <b>The first stretch is the supercompensation stretch; a window in time (e.g., a 20-hour period)</b>. The horizontal line represents the baseline load, which can be seen as the baseline strength of the individual prior to the exercise session. This is where things get tricky. <b>If one exercises again within the supercompensation stretch, strength and muscle gains will likely happen</b>. (Usually noticeable upper-body muscle gain happens in men, because of higher levels of testosterone and of other hormones that seem to interact with testosterone.) <b>Exercising outside the supercompensation time window may lead to no gain, or even to some loss, of both strength and muscle</b>.<br />
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Timing strength training sessions correctly can over time lead to significant gains in strength and muscle (see middle graph in the figure below, also from Zatsiorsky & Kraemer, 2006). For that to happen, <b>one has not only to regularly “hit” the supercompensation time window, but also progressively increase load</b>. This must happen for each muscle group. Strength and muscle gains will occur up to a point, a point of saturation, after which no further gains are possible. Men who reach that point will invariably look muscular, in a more or less “natural” way depending on supplements and other factors. Some people seem to gain strength and muscle very easily; they are often called mesomorphs. Others are hard gainers, sometimes referred to as endomorphs (who tend to be fatter) and ectomorphs (who tend to be skinnier).<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfCRZYbt205NpAyJYxmKpvxxGDo0wMctOHtDicfl2rVET5eArbEW4OfOK86OFmzhoIIbFFfek3aoq1ngPIwAQSRAAcDNyudxYBHyRS70p6yrFTps-CCfbPAUUQVbBErGCfYBrJXy9MpHuK/s1600/Zatsiorsky_Kraemer_2006_F1_5.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfCRZYbt205NpAyJYxmKpvxxGDo0wMctOHtDicfl2rVET5eArbEW4OfOK86OFmzhoIIbFFfek3aoq1ngPIwAQSRAAcDNyudxYBHyRS70p6yrFTps-CCfbPAUUQVbBErGCfYBrJXy9MpHuK/s320/Zatsiorsky_Kraemer_2006_F1_5.PNG" width="305" /></a></div>
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<b>It is not easy to identify the ideal recovery and supercompensation periods</b>. They vary from person to person. They also vary depending on types of exercise, numbers of sets, and numbers of repetitions. Nutrition also plays a role, and so do rest and stress. From an evolutionary perspective, it would seem to make sense to work all major muscle groups on the same day, and then do the same workout after a certain recovery period. (Our Stone Age ancestors did not do isolation exercises, such as bicep curls.) But this will probably make you look more like a strong hunter-gatherer than a modern bodybuilder.<br />
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<b>To identify the supercompensation time window, one could employ a trial-and-error approach, by trying to repeat the same workout after different recovery times</b>. Based on the literature, <b>it would make sense to start at the 48-hour period (one full day of rest between sessions), and then move back and forth from there</b>. A sign that one is hitting the supercompensation time window is becoming a little stronger at each workout, by performing more repetitions with the same weight (e.g., 10, from 8 in the previous session). If that happens, the weight should be incrementally increased in successive sessions. Most studies suggest that the best range for muscle gain is that of 6 to 12 repetitions in each set, <a href="http://healthcorrelator.blogspot.com/2012/01/hce-user-experience-anabolic-range-may.html">but without enough time under tension gains will prove elusive</a>.<br />
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The discussion above is not aimed at professional bodybuilders. There are a number of factors that can influence strength and muscle gain other than supercompensation. (Still, supercompensation seems to be a “biggie”.) Things get trickier over time with trained athletes, as returns on effort get progressively smaller. Even natural bodybuilders appear to benefit from different strategies at different levels of proficiency. For example, changing the workouts on a regular basis seems to be a good idea, and there is a science to doing that properly. See the “Interesting links” area of this web site for several more focused resources of strength training.<br />
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Reference:<br />
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Zatsiorsky, V., & Kraemer, W.J. (2006). <a href="http://www.amazon.com/Science-Practice-Strength-Training-Second/dp/0736056289/"><i>Science and practice of strength training</i></a>. Champaign, IL: Human Kinetics.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com36tag:blogger.com,1999:blog-8859456735165996893.post-31309853547622358822022-10-22T07:06:00.000-07:002022-10-22T07:06:08.482-07:00Total cholesterol and cardiovascular disease: A U-curve relationshipThe hypothesis that blood cholesterol levels are positively correlated with heart disease (the lipid hypothesis) dates back to Rudolph Virchow in the mid-1800s.<br />
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One famous study that supported this hypothesis was Ancel Keys's Seven Countries Study, conducted between the 1950s and 1970s. This study eventually served as the foundation on which much of the advice that we receive today from doctors is based, even though several other studies have been published since that provide little support for the lipid hypothesis.<br />
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The graph below (from O Primitivo) shows the results of one study, involving many more countries than Key's Seven Countries Study, that actually suggests a NEGATIVE linear correlation between total cholesterol and cardiovascular disease.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxZcbk30jjQoy87tdAYyZB0SYbuOwGAOAGkDE7-qAKfDMTRW_uTT5HrbBIul6Lg8NFEBlqijlizXuEwwLB_voQK4A76Von8Jg3BbdnteVx8thYbOWTtD06PYTfatCtFM7iz7JYTQD6yA52/s1600-h/cholesterol-cardiovasc-men.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxZcbk30jjQoy87tdAYyZB0SYbuOwGAOAGkDE7-qAKfDMTRW_uTT5HrbBIul6Lg8NFEBlqijlizXuEwwLB_voQK4A76Von8Jg3BbdnteVx8thYbOWTtD06PYTfatCtFM7iz7JYTQD6yA52/s200/cholesterol-cardiovasc-men.gif" /></a></div>
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Now, most relationships in nature are nonlinear, with quite a few following a pattern that looks like a U-curve (plain or inverted); sometimes called a J-curve pattern. The graph below (also from O Primitivo) shows the U-curve relationship between total cholesterol and mortality, with cardiovascular disease mortality indicated through a dotted red line at the bottom.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhklo-4D1iK_wO_LwzJ-Kq4z28ThYj6dLnMCryI2nPRCZ4PV0ojSW4KLmyZeqwBL1bdf_cAXE6OendUnnZhqlotgnfQ6_KzJdSo5u9-o5wPoXJmt19v1Z7BQwzo5ZdzpChM4Dned-ZFl5Q_/s1600-h/cholesterol-mortality.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhklo-4D1iK_wO_LwzJ-Kq4z28ThYj6dLnMCryI2nPRCZ4PV0ojSW4KLmyZeqwBL1bdf_cAXE6OendUnnZhqlotgnfQ6_KzJdSo5u9-o5wPoXJmt19v1Z7BQwzo5ZdzpChM4Dned-ZFl5Q_/s320/cholesterol-mortality.gif" /></a></div>
This graph has been obtained through a nonlinear analysis, and I think it provides a better picture of the relationship between total cholesterol (TC) and mortality. Based on this graph, the best range of TC that one can be at is somewhere between 210, where cardiovascular disease mortality is minimized; and 220, where total mortality is minimized.<br />
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The total mortality curve is the one indicated through the full blue line at the top. In fact, it suggests that mortality increases sharply as TC decreases below 200.<br />
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Now, these graphs relate TC with disease and mortality, and say nothing about LDL cholesterol (LDL). In my own experience, and that of many people I know, a TC of about 200 will typically be associated with a slightly elevated LDL (e.g., 110 to 150), even if one has a high HDL cholesterol (i.e., greater than 60).<br />
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Yet, most people who have a LDL greater than 100 will be told by their doctors, usually with the best of the intentions, to take statins, so that they can "keep their LDL under control". (LDL levels are usually calculated, not measured directly, which itself creates a whole new set of problems.)<br />
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Alas, reducing LDL to 100 or less will typically reduce TC below 200. If we go by the graphs above, especially the one showing the U-curves, these folks' risk for cardiovascular disease and mortality will go up - exactly the opposite effect that they and their doctors expected. And that will cost them financially as well, as statin drugs are expensive, in part to pay for all those TV ads.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com10tag:blogger.com,1999:blog-8859456735165996893.post-9381715010039872722022-07-20T14:29:00.000-07:002022-07-20T14:29:47.039-07:00What is your optimal weight? Maybe it is the one that minimizes your waist-to-weight ratio<script type="text/javascript">var citeN=0;</script> <br />
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. <br />
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L / T <br />
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L is difficult and often costly to measure. T can be measured easily, as one’s total weight. <br />
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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). <br />
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L / T = (T – F) / T = 1 – F / T <br />
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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). <br />
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F / T <br />
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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, <b>body fat percentage is associated with lean body mass (and also weight) in a nonlinear way</b>. <br />
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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. <br />
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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. <br />
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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. <br />
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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. <br />
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Therefore, <b>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. <br /><br />
W / T = waist / weight </b><br />
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Based on the experience of HCE (<a href="http://healthcorrelator.com/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) 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. <br />
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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. <br />
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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. <br />
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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. <br />
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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. <br />
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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. <br />
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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 <i>lowest</i> waist / weight ratio at a given point in time, not one's waist / weight ratio at a given point in time.<br />
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This optimal waist / weight ratio theory is one of the most compatible with evidence regarding the lowest mortality body mass index (<a href="http://bit.ly/NWbeMY" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>, <a href="http://bit.ly/M0Yo40" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>).
Nevertheless, it is another ratio that gets a lot of attention in the health-related literature. I am talking about the waist / hip ratio (<a href="http://en.wikipedia.org/wiki/Waist%E2%80%93hip_ratio" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). In this literature, waist circumference is often used alone, not as part of a ratio.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com35tag:blogger.com,1999:blog-8859456735165996893.post-62044691387029834932022-05-21T05:26:00.001-07:002022-05-21T05:26:53.017-07:00Vitamin D production from UV radiation: The effects of total cholesterol and skin pigmentationOur body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high. Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.<br />
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The above refers to pre-sunburn exposure. Sunburn is not associated with increased vitamin D production; it is associated with skin damage and cancer.<br />
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Solar ultraviolet (UV) radiation is generally divided into two main types: UVB (wavelength: 280–320 nm) and UVA (320–400 nm). Vitamin D is produced primarily based on UVB radiation. Nevertheless, UVA is much more abundant, amounting to about 90 percent of the sun’s UV radiation.<br />
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UVA seems to cause the most skin damage, although there is some debate on this. If this is correct, one would expect skin pigmentation to be our body’s defense primarily against UVA radiation, not UVB radiation. If so, one’s ability to produce vitamin D based on UVB should not go down significantly as one’s skin becomes darker.<br />
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Also, vitamin D and cholesterol seem to be closely linked. Some argue that one is produced based on the other; others that they have the same precursor substance(s). Whatever the case may be, if vitamin D and cholesterol are indeed closely linked, one would expect low cholesterol levels to be associated with low vitamin D production based on sunlight.<br />
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Bogh et al. (2010) published a very interesting study; one of those studies that remain relevant as time goes by. The link to the study was provided by Ted Hutchinson in the comments sections of a <a href="http://healthcorrelator.blogspot.com/2010/12/what-is-reasonable-vitamin-d-level.html" target="_blank">another post on vitamin D</a>. The study was published in a refereed journal with a solid reputation, the <i>Journal of Investigative Dermatology</i>.<br />
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The study by Bogh et al. (2010) is particularly interesting because it investigates a few issues on which there is a lot of speculation. Among the issues investigated are the effects of total cholesterol and skin pigmentation on the production of vitamin D from UVB radiation.<br />
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The figure below depicts the relationship between total cholesterol and vitamin D production based on UVB radiation. Vitamin D production is referred to as “delta 25(OH)D”. The univariate correlation is a fairly high and significant 0.51.<br />
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25(OH)D is the abbreviation for calcidiol, a prehormone that is produced in the liver based on vitamin D3 (cholecalciferol), and then converted in the kidneys into calcitriol, which is usually abbreviated as 1,25-(OH)2D3. The latter is the active form of vitamin D.<br />
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The table below shows 9 columns; the most relevant ones are the last pair at the right. They are the delta 25(OH)D levels for individuals with dark and fair skin after exposure to the same amount of UVB radiation. The difference in vitamin D production between the two groups is statistically indistinguishable from zero.<br />
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So there you have it. According to this study, <b>low total cholesterol seems to be associated with impaired ability to produce vitamin D from UVB radiation</b>. And <b>skin pigmentation appears to have little effect on the amount of vitamin D produced</b>.<br />
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The study has a few weaknesses, as do almost all studies. For example, if you take a look at the second pair of columns from the right on the table above, you’ll notice that the baseline 25(OH)D is lower for individuals with dark skin. The difference was just short of being significant at the 0.05 level.<br />
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What is the problem with that? Well, one of the findings of the study was that lower baseline 25(OH)D levels were significantly associated with higher delta 25(OH)D levels. Still, the baseline difference does not seem to be large enough to fully explain the lack of difference in delta 25(OH)D levels for individuals with dark and fair skin.<br />
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A widely cited dermatology researcher, Antony Young, published an invited commentary on this study in the same journal issue (Young, 2010). The commentary points out some weaknesses in the study, but is generally favorable. The weaknesses include the use of small sub-samples.<br />
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<b>References</b><br />
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Bogh, M.K.B., Schmedes, A.V., Philipsen, P.A., Thieden, E., & Wulf, H.C. (2010). <a href="https://www.sciencedirect.com/science/article/pii/S0022202X15347035" target="_blank">Vitamin D production after UVB exposure depends on baseline vitamin D and total cholesterol but not on skin pigmentation.</a> <i>Journal of Investigative Dermatology</i>, 130(2), 546–553.<br />
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Young, A.R. (2010). <a href="https://www.sciencedirect.com/science/article/pii/S0022202X15346625" target="_blank">Some light on the photobiology of vitamin D.</a> <i>Journal of Investigative Dermatology</i>, 130(2), 346–348.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com25tag:blogger.com,1999:blog-8859456735165996893.post-76320339502030019562022-03-20T14:19:00.000-07:002022-03-20T14:19:16.249-07:00Heavy physical activity may significantly reduce heart disease deaths, especially after age 45<script type="text/javascript">var citeN=0;</script> <br>
The idea that heavy physical activity is a main trigger of heart attacks is widespread. Often endurance running and cardio-type activities are singled out. Some people refer to this as “death by running”. Others think that strength training has a higher lethal potential. We know based on the Oregon Sudden Unexpected Death Study that this is a myth (<a href="http://healthcorrelator.blogspot.com/2010/05/is-heavy-physical-activity-trigger-of.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>).<br />
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Here is some evidence that heavy physical activity in fact has a significant protective effect. The graph below shows the number of deaths from coronary heart disease, organized by age group, in longshoremen (dock workers). The shaded bars represent those whose level of activity at work was considered heavy. The unshaded bars represent those whose level of activity at work was considered moderate or light (essentially below the “heavy” level).<br />
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The data is based on an old and classic study of 6351 men, aged 35 to 74 years, who were followed either for 22 years, or to death, or to the age of 75. It shows a significant protective effect of heavy activity, especially after age 45 (<a href="http://www.nejm.org/doi/full/10.1056/NEJM197503132921101" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) . The numbers atop the unshaded bars reflect the relative risk of death from coronary heart disease in each age group. For example, in the age group 65-74, the risk among those not in the heavy activity group is 110 percent higher (2.1 times higher) than in the heavy activity group.<br />
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It should be noted that this is a cumulative effect, of years of heavy activity. Based on the description of the types of activities performed, and the calories spent, I estimate that the heavy activity group performed the equivalent of a few hours of strength training per week, plus a lot of walking and other light physical activities. The authors of the study concluded that “<em>… repeated bursts of high energy output established a plateau of protection against coronary mortality.</em>”<br />
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Heavy physical activity may not make you lose much weight, but has the potential to make you live longer.<br />
<br />Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com25tag:blogger.com,1999:blog-8859456735165996893.post-18983839872829541022022-01-22T13:33:00.000-08:002022-01-22T13:33:55.068-08:00Insulin responses to foods rich in carbohydrates and proteinInsulin is often presented as a hormone that is at the core of the diseases of civilization, particularly because of the insulin response elicited by foods rich in refined carbohydrates and sugars. What is often not mentioned is that protein also elicits an insulin response and so do foods where carbohydrates are mixed with fat. Sometimes the insulin responses are way more than one would expect based on the macronutrient compositions of the foods.<br />
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Holt et al. (1997; full reference at the end of this post) conducted a classic study of insulin responses. This study has been widely cited, and paints an interesting picture of differences in insulin responses to various foods. But you have to be careful where you look. There has been some confusion about the results because of the way they are often reported in places like Wikipedia and on various Internet sites that refer to the study.<br />
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The key thing to bear in mind when reviewing this study is that the amounts of food used were designed to have the same calorie content: 1000 kJ or 240 kcal (i.e., 240 calories). This led to wild variations in the size of the portions that are compared and their weight in grams. Also, some of the food portions are probably not what people usually eat in one sitting.<br />
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In Holt et al.’s (1997) study the participants were 41 lean and healthy university students. They were fed 1000 kJ (240 kcal) portions of the test foods on separate mornings after a 10-hour fast overnight. Blood insulin levels were measured at different times within a 120-minute period after each meal. An insulin score was then calculated from the area under the insulin response curve for each food; white bread was used as the reference food.<br />
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Part of Table 2 on page 1267 is shown below (the full text version of the paper is linked at the end of this post), just to illustrate the types and amounts of food served, and the macronutrient breakdown for each food. I hope you can see what I meant when I said that some of the food portions are probably not what people usually eat in one sitting. I don’t think it would be hard to find someone who would eat 158 g of beef steak in one sitting, but 333 g of fish is a little more difficult. Fish has a higher proportion of protein than beef steak, and thus is more satiating. The same goes for 625 g of orange, about 6 oranges. Foods that have more fat have more calories per gram; hence the smaller portions served for high-fat foods.<br />
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Table 4 of the article is a bit long, so I am providing it in two parts below. AUC stands for “area under the curve”. As you can see, for isocaloric portions of different foods (i.e., with the same amount of calories), there is a huge variation in insulin response. The insulin AUCs are shown on the second numeric column from the left. Also note that the insulin responses (AUC) for white bread varied in different meals. This complicates things a bit, but at least provides a more realistic view of the responses since each participant served as his or her own control.<br />
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Look at the third column from the right, which shows the insulin responses per gram of each food, compared with the response to white bread, always shown at the top for each group of related foods (e.g., protein-rich foods). The gram-adjusted response for whole-meal bread is rather high, and so is the glucose response. The gram-adjusted insulin response to potatoes is less than one-third of the response to white bread, even though the non-gram-adjusted glucose response is higher. The insulin response to beef is also less than one-third of the response to white bread, gram-for-gram. Even cheese leads to a gram-adjusted response that is about half the one for white bread, and I don’t think many people will eat the same amount of cheese in one sitting as they would do with white bread.<br />
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In summary, insulin responses to protein-rich foods are often 50 to 70 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods. Also, insulin responses to unrefined carbohydrate-rich foods (e.g., potato, fruits) are often 70 to 90 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods.<br />
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Why do insulin levels go up in response to dietary protein?<br />
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One of the reasons is that insulin is needed for tissue protein synthesis. That is, increased circulating protein (as amino acids) and insulin have a net anabolic effect, promoting muscle growth and inhibiting muscle breakdown. (Muscle protein synthesis and breakdown happen all the time; the net effect defines whether muscle grows or shrinks.) In this respect, insulin acts in conjunction with other hormones, such as <a href="http://healthcorrelator.blogspot.com/search/label/growth%20hormone">growth hormone</a> and insulin-like growth factor 1.<br />
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Reference:<br />
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Holt, S.H., Miller, J.C., & Petocz, P. (1997). <a href="http://www.ajcn.org/cgi/reprint/66/5/1264">An insulin index of foods: The insulin demand generated by 1000-kJ portions of common foods</a>. <i>American Journal of Clinical Nutrition</i>, 66, 1264-1276.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com21tag:blogger.com,1999:blog-8859456735165996893.post-4820767421442803972021-12-26T07:46:00.011-08:002021-12-26T07:53:37.033-08:00Age-related trends in health markers may indicate survival advantages: The case of platelet counts<script type="text/javascript">var citeN=0;</script> <br>
Platelets (<a href="https://en.wikipedia.org/wiki/Platelet" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) are particles that circulate in the blood of mammals. They react to blood vessel injuries by forming clots. Platelet counts are provided in standard blood panels, and are used by medical doctors to diagnose possible health problems. At the time of this writing, the refence range for platelet counts is 150,000 to 450,000 per cubic millimeter. <br><br>
The figure below has two graphs, and is based on an article by Balduini and Noris, published in 2014 in the prestigious journal Haematologica (<a href=" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4040891/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). The graph on the left shows the distribution of platelet counts by age and sex. The one on the right shows the reference ranges for platelet counts by age and sex. <br><br>
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The reference ranges within the bars include all individuals in the same age-sex group, whereas the ones outside the bars are based on groups of individuals in specific areas (i.e., geographic regions) where the age-sex reference ranges are wider. A reference range is essentially an interval, derived from statistical analyses, in which one would expect individuals who are disease-free to fall into. <br><br>
A clear pattern that emerges from these graphs is that platelet counts go down with age, in both men and women. A tendency to form clots is generally associated with health problems at more advanced ages, even though blood clotting is necessary for good health. Given this, one could interpret the graphs as indicating that older individuals have lower blood clot counts because those lower counts are associated with a survival advantage. <br><br>
This interpretation is not guaranteed to be correct, of course. Nevertheless, one of the key conclusions by the authors of the study seems quite correct: “[…] using 150–400×10^9/L as the normal range for platelet count, a number of old people of some areas could be at risk of receiving a wrong diagnosis of thrombocytopenia, while young inhabitants of other areas could be at risk of an undue diagnosis of thrombocytosis.” <br><br>
If you get a platelet count in a standard blood panel that is out of the reference range, your doctor may tell you that this could be an indication of a frightening underlying health problem. If this happens, you may want to discuss this blog post, and the article on which it is based, with your doctor. There are follow-up tests that can rule out serious underlying problems. Having said that, a low count may in fact be a good sign. <br><br>
Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com0tag:blogger.com,1999:blog-8859456735165996893.post-80599180586429172402021-11-14T13:51:00.000-08:002021-11-14T13:51:02.426-08:00The man who ate 25 eggs per day: What does this case really tell us?<script type="text/javascript">var citeN=0;</script>Many readers of this blog have probably heard about the case of the man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious <i>The New England Journal of Medicine</i>, and was in surprisingly good health (<a href="http://www.nejm.org/doi/full/10.1056/NEJM199103283241306" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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The case was authored by the late Dr. Fred Kern, Jr., a widely published lipid researcher after whom the <i>Kern Lipid Conference</i> is named (<a href="http://www.kernconference.org/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). One of Kern’s research interests was bile, a bitter-tasting fluid produced by the liver (and stored in the gallbladder) that helps with the digestion of lipids in the small intestine. He frames the man’s case in terms of a compensatory adaptation tied to bile secretion, arguing that this man was rather unique in his ability to deal with a lethal daily dose of dietary cholesterol. <br />
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Kern seemed to believe that dietary cholesterol was harmful, but that this man was somehow “immune” to it. This is ironic, because often this case is presented as evidence against the hypothesis that dietary cholesterol can be harmful. The table below shows the general nutrient content of the man’s daily diet of eggs. The numbers in this and other tables are based on data from Nutritiondata.com (<a href="http://nutritiondata.self.com/" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), in some cases triangulated with other data. The 5.3 g of cholesterol in the table (i.e., 5,300 mg) is 1,775 percent the daily value recommended by the Institute of Medicine of the U.S. National Academy of Sciences (<a href="http://en.wikipedia.org/wiki/Institute_of_Medicine" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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As you can see, the man was on a very low carbohydrate diet with a high daily intake of fat and protein. The man is described as an: “… 88-year-old man who lived in a retirement community [and] complained only of loneliness since his wife's death. He was an articulate, well-educated elderly man, healthy except for an extremely poor memory without other specific neurologic deficits … His general health had been excellent, without notable symptoms. He had mild constipation.” <br />
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The description does not suggest inherited high longevity: “His weight had been constant at 82 to 86 kg (height, 1.87 m). He had no history (according to the patient and his personal physician of 15 years) of heart disease, stroke, or kidney disease … The patient had never smoked and never drank excessively. His father died of unknown causes at the age of 40, and his mother died at 76 … He kept a careful record, egg by egg, of the number ingested each day …” <br />
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The table below shows the fat content of the man’s daily diet of eggs. With over 14 g of omega-6 fat intake every day, this man was probably close to or in “industrial seed oils territory” (<a href="http://healthcorrelator.blogspot.com/2010/09/low-omega-6-to-omega-3-ratio-grain-fed.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), as far as daily omega-6 fat intake is concerned. And the intake of omega-3 fats, at less than 1 g, was not nearly enough to balance it. However, here is a relevant fact – this man was not consuming any industrial seed oils. He liked his eggs soft-boiled, which is why the numbers in this post refer to boiled eggs. <br />
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This man weighed between 82 to 86 kg, which is about 180 to 190 lbs. His height was 1.87 m, or about 6 ft 1 in. Therefore his body mass index varied between approximately 23 and 25, which is in the normal range. In other words, this person was not even close to obese during the many years he consumed 25 eggs or so per day. In the comments section of a previous post, on the sharp increase in obesity since the 1980s (<a href="http://healthcorrelator.blogspot.com/2012/10/the-steep-obesity-increase-in-usa-in.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), several readers argued that the sharp increase in obesity was very likely caused by an increase in omega-6 fat consumption. <br />
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I am open to the idea that industrialized omega-6 fats played a role in the sharp increase in obesity observed since the 1980s. When it comes to omega-6 fat consumption in general, including that in “more natural” foods (e.g., poultry and eggs), I am more skeptical. Still, it is quite possible that a diet high in omega-6 fats in general is unhealthy primarily if it is devoid of other nutrients. This man’s overall diet might have been protective not because of what he was not eating, but because of what he was eating. <br />
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The current debates pitting one diet against another often revolve around the ability of one diet or another to eliminate or reduce the intake of a “bad thing” (e.g., cholesterol, saturated fat, carbohydrates). Perhaps the discussion should be more focused on, or at least not completely ignore, what one diet or another include as protective factors. This would help better explain “odd findings”, such as the lowest-mortality body mass index of 26 in urban populations (<a href="http://healthcorrelator.blogspot.com/2012/07/lowest-mortality-bmi-what-is-role-of.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). It would also help better explain “surprising cases”; such as this 25-eggs-a-day man’s, vegetarian-vegan “ageless woman” Annette Larkins’s (<a href="http://www.youtube.com/watch?v=O6oJA_xhTa8" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), and the decidedly carnivore De Vany couple’s (<a href="http://arthurdevany.pro.subhub.com/categories/about-us" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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The table below shows the vitamin content of the man’s daily diet of eggs. The vitamin K2 content provided by Nutritiondata.com was incorrect; I had to get what seems to be the right number by triangulating values taken from various publications. And here we see something interesting. This man was consuming approximately the equivalent in vitamin K2 that one would get by eating 4 ounces of <i>foie gras</i> (<a href="http://en.wikipedia.org/wiki/Foie_gras" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>) every day. <i>Foie gras</i>, the fatty liver of overfed geese, is the richest known animal source of vitamin K2. This man’s diet was also high in vitamin A, which is believed to act synergistically with vitamin K2 – see Chris Masterjohn’s article on Weston Price’s “activator X” (<a href="http://www.westonaprice.org/fat-soluble-activators/x-factor-is-vitamin-k2" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgqbOAe9aA6IbTc7vpZwHTQ1edry_rczQd5LCajjMpUfmkrkVTwXyZQLDtyB8KWyaoiHAvYQDS7SCSj2ooJDPgSj47sMtTXTDhiMic6f4w4CPQmZCpJ4u397NYOYHoW8I7fohvsk5m2uvd_/s1600/Kock_2012_Nutrients25EggsDay_T03.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="146" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgqbOAe9aA6IbTc7vpZwHTQ1edry_rczQd5LCajjMpUfmkrkVTwXyZQLDtyB8KWyaoiHAvYQDS7SCSj2ooJDPgSj47sMtTXTDhiMic6f4w4CPQmZCpJ4u397NYOYHoW8I7fohvsk5m2uvd_/s320/Kock_2012_Nutrients25EggsDay_T03.png" width="320" /></a></div>
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Kern argued that the very high intake of dietary cholesterol led to a sharp increase in bile secretion, as the body tried to “get rid” of cholesterol (which is used in the synthesis of bile). However, the increased bile secretion might have been also been due to the high fat content of this man’s diet, since one of the main functions of bile is digestion of fats. Whatever the case may be, increased bile secretion leads to increased absorption of fat-soluble vitamins, and vitamins K2 and A are fat-soluble vitamins that seem to be protective against cardiovascular disease, cancer and other degenerative diseases. <br />
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Finally, the table below shows the mineral content of the man’s daily diet of eggs. As you can see, this man consumed 550 percent the officially recommended daily intake of selenium. This intake was slightly lower than the 400 micrograms per day purported to cause selenosis in adults (<a href="http://ods.od.nih.gov/factsheets/Selenium-HealthProfessional/#h7" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). Similarly to vitamins K2 and A, selenium seems to be protective against cardiovascular disease, cancer and other degenerative diseases. This man’s diet was also rich in phosphorus, needed for healthy teeth and bones. <br />
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Not too many people live to be 88 years of age; many fewer reach that age in fairly good health. The country with the highest average life expectancy in the world at the time of this writing is Japan, with a life expectancy of about 82 years (79 for men, and 86 for women). Those who think that they need a high HDL cholesterol and a low LDL cholesterol to be in good health, and thus live long lives, may be surprised at this man’s lipid profile: “The patient's plasma lipid levels were normal: total cholesterol, 5.18 mmol per liter (200 mg per deciliter); LDL, 3.68 mmol per liter (142 mg per deciliter); and HDL, 1.17 mmol per liter (45 mg per deciliter). The ratio of LDL to HDL cholesterol was 3.15.” <br />
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If we assume that this man is at least somewhat representative of the human species, and not a major exception as Kern argued, this case tells us that a diet of 25 eggs per day followed by over 15 years may actually be healthy for humans. Such diet has the following features:<br />
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- It is very high in dietary cholesterol.<br />
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- It involves a high intake of omega-6 fats from animal sources, with none coming from industrial seed oils.<br />
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- It involves a high overall intake of fats, including saturated fats.<br />
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- It is fairly high in protein, all of which from animal sources.<br />
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- It is a very low carbohydrate diet, with no sugar in it.<br />
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- It is a nutritious diet, rich in vitamins K2 and A, as well as in selenium and phosphorus. <br />
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This man ate 25 eggs per day apparently due to an obsession tied to mental problems. Repeated attempts at changing his behavior were unsuccessful. He said: “Eating these eggs ruins my life, but I can't help it.”Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com33tag:blogger.com,1999:blog-8859456735165996893.post-79590000389904194752021-10-24T06:45:00.000-07:002021-10-24T06:45:45.757-07:00You can eat a lot during the Holiday Season and gain no body fat, as long as you also eat little<script type="text/javascript">var citeN=0;</script> <br />
The evolutionary pressures placed by periods of famine shaped the physiology of most animals, including humans, toward a design that favors asymmetric food consumption. That is, most animals are “designed” to alternate between eating little and then a lot. <br />
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Often when people hear this argument they point out the obvious. There is no evidence that our ancestors were constantly starving. This is correct, but what these folks seem to forget is that evolution responds to events that alter reproductive success rates (<a href="http://healthcorrelator.blogspot.com/2010/06/compensatory-adaptation-as-unifying.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), even if those events are rare. <br />
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If an event causes a significant amount of death but occurs only once every year, a population will still evolve traits in response to the event. Food scarcity is one such type of event. <br />
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Since evolution is blind to complexity, adaptations to food scarcity can take all shapes and forms, including counterintuitive ones. Complicating this picture is the fact that food does not only provide us with fuel, but also with the sources of important structural components, signaling elements (e.g., hormones), and process catalysts (e.g., enzymes). <br />
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In other words, we may have traits that are health-promoting under conditions of food scarcity, but those traits are only likely to benefit our health as long as food scarcity is relatively short-term. Not eating anything for 40 days would be lethal for most people. <br />
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By "eating little" I don’t mean necessarily fasting. Given the amounts of mucus and dead cells (from normal cell turnover) passing through the digestive tract, it is very likely that we’ll be always digesting something. So eating very little within a period of 10 hours sends the body a message that is similar to the message sent by eating nothing within the same period of 10 hours. <br />
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Most of the empirical research that I've reviewed suggests that eating very little within a period of, say, 10-20 hours and then eating to satisfaction in one single meal will elicit the following responses. Protein phosphorylation underlies many of them. <br />
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- Your body will hold on to its most important nutrient reserves when you eat little, using selective autophagy to generate energy (<a href="http://www.sciencedirect.com/science/article/pii/S1550413107003361" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>, <a href="http://online.liebertpub.com/doi/abs/10.1089/rej.2005.8.3?journalCode=rej" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). This may have powerful health-promoting properties, including the effect of triggering anti-cancer mechanisms. <br />
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- Food will taste fantastic when you feast, to such an extent that this effect will be much stronger than that associated with any spice (<a href="http://healthcorrelator.blogspot.com/2012/11/no-fat-gain-while-eating-well-during.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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- Nutrients will be allocated more effectively when you feast, leading to a lower net gain of body fat (<a href="http://healthcorrelator.blogspot.com/2012/11/no-fat-gain-while-eating-well-during.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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- The caloric value of food will be decreased, with a 14 percent decrease being commonly found in the literature (<a href="http://healthcorrelator.blogspot.com/2012/07/the-14-percent-advantage-of-eating.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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- The feast will prevent your body from down-regulating your metabolism via subclinical hypothyroidism (<a href="http://healthcorrelator.blogspot.com/2012/07/the-14-percent-advantage-of-eating.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>), which often happens when the period in which one eats little extends beyond a certain threshold (e.g., more than one week). <br />
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- Your mood will be very cheerful when you feast, potentially improving social relationships. That is, if you don’t become too grouchy during the period in which you eat little. <br />
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I recall once participating in a meeting that went from early morning to late afternoon. We had the option of taking a lunch break, or working through lunch and ending the meeting earlier. Not only was I the only person to even consider the second option, some people thought that the idea of skipping lunch was outrageous, with a few implying that they would have headaches and other problems. <br />
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When I said that I had had nothing for breakfast, a few thought that I was pushing my luck. One of my colleagues warned me that I might be damaging my health irreparably by doing those things. Well, maybe they were right on both grounds, who knows? <br />
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It is my belief that the vast majority of humans will do quite fine if they eat little or nothing for a period of 20 hours. The problem is that they need to be convinced first that they have nothing to worry about. Otherwise they may end up with a headache or worse, entirely due to psychological mechanisms (<a href="http://healthcorrelator.blogspot.com/2013/04/cabeza-de-vaca-supernaturalism-and.html" target="_new"><script type="text/javascript">citeN=citeN+1;document.write(Number(citeN))</script></a>). <br />
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There is no need to eat beyond satiety when you feast. I’d recommend that you just eat to satiety, and don’t force yourself to eat more than that. If you avoid industrialized foods when you feast, that will be even better, because satiety will be achieved faster. One of the main characteristics of industrialized foods is that they promote unnatural overeating; congrats food engineers on a job well done! <br />
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If you are relatively lean, satiety will normally be achieved with less food than if you are not. Hunger intensity and duration tends to be generally associated with body weight. Except for dedicated bodybuilders and a few other athletes, body weight gain is much more strongly influenced by body fat gain than by muscle gain. <br />
<br />Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com26tag:blogger.com,1999:blog-8859456735165996893.post-10681933661156928192021-09-19T08:07:00.000-07:002021-09-19T08:07:57.592-07:00Dietary protein does not become body fat if you are on a low carbohydrate dietBy 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 appears to contribute to a better body composition.<br />
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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.<br />
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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.<br />
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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 repair and potentially growth.<br />
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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.<br />
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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.<br />
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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.<br />
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</div>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.<br />
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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.<br />
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Recreational exercisers who are normoglycemic and who do LC intermittently tend to increase the size of their liver glycogen tank over time, via <a href="http://healthcorrelator.blogspot.com/2010/06/compensatory-adaptation-as-unifying.html">compensatory adaptation</a>, 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.<br />
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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.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com48tag:blogger.com,1999:blog-8859456735165996893.post-37810685871552034362021-08-15T08:15:00.000-07:002021-08-15T08:15:30.411-07:00The China Study one more time: Are raw plant foods giving people cancer?In <a href="http://healthcorrelator.blogspot.com/2010/07/china-study-again-multivariate-analysis.html">this previous post</a> I analyzed some data from the China Study that included counties where there were cases of schistosomiasis infection. Following one of <a href="http://rawfoodsos.com/">Denise Minger</a>’s suggestions, I removed all those counties from the data. I was left with 29 counties, a much smaller sample size. I then ran a multivariate analysis using WarpPLS (<a href="http://warppls.com/">warppls.com</a>), like in the previous post, but this time I used an algorithm that identifies nonlinear relationships between variables.<br />
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Below is the model with the results. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) As in the previous post, the arrows explore associations between variables. The variables are shown within ovals. The meaning of each variable is the following: aprotein = animal protein consumption; pprotein = plant protein consumption; cholest = total cholesterol; crcancer = colorectal cancer.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGUW09iKdnr2VJMaZ7C6rXtpOgfytXghhm3BRL75gklKEWb-4zb3GFzgC6glbBqFALXEF3n0ooYaG4sh1SzzkOpel_nhMX5G_dG-4OMARvObCNOgr0_jPBUWulqmNK_MWeTKrCf0ocRjPB/s1600/Kock_W2_WarpPLS_ChinaStudyNoSchi1.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="157" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGUW09iKdnr2VJMaZ7C6rXtpOgfytXghhm3BRL75gklKEWb-4zb3GFzgC6glbBqFALXEF3n0ooYaG4sh1SzzkOpel_nhMX5G_dG-4OMARvObCNOgr0_jPBUWulqmNK_MWeTKrCf0ocRjPB/s320/Kock_W2_WarpPLS_ChinaStudyNoSchi1.JPG" width="320" /></a></div><br />
What is total cholesterol doing at the right part of the graph? It is there because I am analyzing the associations between animal protein and plant protein consumption with colorectal cancer, controlling for the possible confounding effect of total cholesterol.<br />
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I am not hypothesizing anything regarding total cholesterol, even though this variable is shown as pointing at colorectal cancer. I am just controlling for it. This is the type of thing one can do in multivariate analyzes. This is how you “control for the effect of a variable” in an analysis like this.<br />
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Since the sample is fairly small, we end up with nonsignificant beta coefficients that would normally be statistically significant with a larger sample. But it helps that we are using nonparametric statistics, because they are still robust in the presence of small samples, and deviations from normality. Also the nonlinear algorithm is more sensitive to relationships that do not fit a classic linear pattern. We can summarize the findings as follows:<br />
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- <b>As animal protein consumption increases, plant protein consumption decreases significantly</b> (beta=-0.36; P<0.01). This is to be expected and helpful in the analysis, as it differentiates somewhat animal from plant protein consumers. Those folks who got more of their protein from animal foods tended to get significantly less protein from plant foods.<br />
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- <b>As animal protein consumption increases, colorectal cancer decreases, but not in a statistically significant way</b> (beta=-0.31; P=0.10). The beta here is certainly high, and the likelihood that the relationship is real is 90 percent, even with such a small sample.<br />
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- <b>As plant protein consumption increases, colorectal cancer increases significantly</b> (beta=0.47; P<0.01). The small sample size was not enough to make this association nonsignificant. The reason is that the distribution pattern of the data here is very indicative of a real association, which is reflected in the low P value.<br />
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Remember, these results are not confounded by schistosomiasis infection, because we are only looking at counties where there were no cases of schistosomiasis infection. These results are not confounded by total cholesterol either, because we controlled for that possible confounding effect. Now, control variable or not, you would be correct to point out that the association between total cholesterol and colorectal cancer is high (beta=0.58; P=0.01). So let us take a look at the shape of that association:<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEielgwfWaEuPMoV0RoHdw5SBEDkBFSjQIrEU2oKZRQ8PH0SGBjDCHmYhd1ew1_XkfJ7YnfB0K9SQDy-2MUPpg5LwsWEiEiDUtIZFLEBw7070u0k_SDQii3LVfAKuHN1lZsQGcyQEa9JuA8Z/s1600/Kock_W2_WarpPLS_ChinaStudyNoSchi2.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="190" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEielgwfWaEuPMoV0RoHdw5SBEDkBFSjQIrEU2oKZRQ8PH0SGBjDCHmYhd1ew1_XkfJ7YnfB0K9SQDy-2MUPpg5LwsWEiEiDUtIZFLEBw7070u0k_SDQii3LVfAKuHN1lZsQGcyQEa9JuA8Z/s320/Kock_W2_WarpPLS_ChinaStudyNoSchi2.JPG" width="320" /></a></div><br />
Does this graph remind you of the one <a href="http://healthcorrelator.blogspot.com/2009/12/total-cholesterol-and-cardiovascular.html">on this post</a>; the one with several U curves? Yes. And why is that? Maybe it reflects a tendency among the folks who had low cholesterol to have more cancer because the body needs cholesterol to fight disease, and cancer is a disease. And maybe it reflects a tendency among the folks who have high total cholesterol to do so because total cholesterol (and particularly its main component, LDL cholesterol) is in part a marker of disease, and cancer is often a culmination of various metabolic disorders (e.g., the metabolic syndrome) that are nothing but one disease after another.<br />
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To believe that total cholesterol causes colorectal cancer is nonsensical because total cholesterol is generally increased by consumption of animal products, of which animal protein consumption is a proxy. (In this reduced dataset, the linear univariate correlation between animal protein consumption and total cholesterol is a significant and positive 0.36.) And animal protein consumption seems to be protective again colorectal cancer in this dataset (negative association on the model graph).<br />
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Now comes the part that I find the most ironic about this whole discussion in the blogosphere that has been going on recently about the China Study; and the answer to the question posed in the title of this post: <b>Are raw plant foods giving people cancer?</b> If you think that the answer is “yes”, think again. The variable that is strongly associated with colorectal cancer is plant <b><i>protein</i></b> consumption.<br />
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<b>Do fruits, veggies, and other plant foods that can be consumed raw have a lot of protein?</b><br />
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With a few exceptions, like nuts, they do not. Most raw plant foods have trace amounts of protein, especially when compared with foods made from refined grains and seeds (e.g., wheat grains, soybean seeds). So the contribution of raw fruits and veggies in general could not have influenced much the variable plant protein consumption. <b>To put this in perspective, the average plant protein consumption per day in this dataset was 63 g; even if they were eating 30 bananas a day, the study participants would not get half that much protein from bananas.</b><br />
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Refined foods made from grains and seeds are made from those plant parts that the plants absolutely do not “want” animals to eat. They are the plants’ “children” or “children’s nutritional reserves”, so to speak. This is why they are packed with nutrients, including protein and carbohydrates, but also often toxic and/or unpalatable to animals (including humans) when eaten raw.<br />
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<b>But humans are so smart; they learned how to industrially refine grains and seeds for consumption.</b> The resulting human-engineered products (usually engineered to sell as many units as possible, not to make you healthy) normally taste delicious, so you tend to eat a lot of them. They also tend to raise blood sugar to abnormally high levels, because industrial refining makes their high carbohydrate content easily digestible. Refined foods made from grains and seeds also tend to cause leaky gut problems, and autoimmune disorders like celiac disease. Yep, we humans are really smart.<br />
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Thanks again to Dr. Campbell and his colleagues for collecting and compiling the China Study data, and to Ms. Minger for making the data available in easily downloadable format and for doing some superb analyses herself.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com68tag:blogger.com,1999:blog-8859456735165996893.post-35761472387781341792021-07-28T14:54:00.000-07:002021-07-28T14:54:35.601-07:00What is a reasonable vitamin D level?The figure and table below are from Vieth (1999); one of the most widely cited articles on vitamin D. The figure shows the gradual increase in blood concentrations of 25-Hydroxyvitamin, or 25(OH)D, following the start of daily vitamin D3 supplementation of 10,000 IU/day. The table shows the average levels for people living and/or working in sun-rich environments; vitamin D3 is produced by the skin based on sun exposure.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzFPXsrB872wJk8Vhrx__jqvLcVMWV5LDvJ7PRAUSJFZ0TyKbiK6wflFhZDxF6XncQWQeXTq6qpwrFsR3ZqWsw9mN_5Ep8_wWYctCsHc_7INqhbvr9NmL2EsFYxxUQ5GBBby9CCi-MDQiV/s1600/Vieth_1999_F1_T1.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="193" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzFPXsrB872wJk8Vhrx__jqvLcVMWV5LDvJ7PRAUSJFZ0TyKbiK6wflFhZDxF6XncQWQeXTq6qpwrFsR3ZqWsw9mN_5Ep8_wWYctCsHc_7INqhbvr9NmL2EsFYxxUQ5GBBby9CCi-MDQiV/s320/Vieth_1999_F1_T1.PNG" width="320" /></a></div><br />
25(OH)D is also referred to as calcidiol. It is a pre-hormone that is produced by the liver based on vitamin D3. To convert from nmol/L to ng/mL, divide by 2.496. The figure suggests that levels start to plateau at around 1 month after the beginning of supplementation, reaching a point of saturation after 2-3 months. Without supplementation or sunlight exposure, levels should go down at a comparable rate. The maximum average level shown on the table is 163 nmol/L (65 ng/mL), and refers to a sample of lifeguards.<br />
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From the figure we can infer that people on average will plateau at approximately 130 nmol/L, after months of 10,000 IU/d supplementation. That is 52 ng/mL. Assuming a normal distribution with a standard deviation of about 20 percent of the range of average levels, we can expect about 68 percent of those taking that level of supplementation to be in the <b>42 to 63 ng/mL range</b>.<br />
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This might be the range most of us should expect to be in at an intake of 10,000 IU/d. This is the equivalent to the body’s own natural production through sun exposure.<br />
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Approximately 32 percent of the population can be expected to be outside this range. A person who is two standard deviations (SDs) above the mean (i.e., average) would be at around 73 ng/mL. Three SDs above the mean would be 83 ng/mL. Two SDs below the mean would be 31 ng/mL.<br />
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There are other factors that may affect levels. For example, being overweight tends to reduce them. Excess cortisol production, from stress, may also reduce them.<br />
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Supplementing beyond 10,000 IU/d to reach levels much higher than those in the range of 42 to 63 ng/mL may not be optimal. Interestingly, one cannot overdose through sun exposure, and the idea that people do not produce vitamin D3 after 40 years of age is a myth.<br />
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One would be taking in about 14,000 IU/d of vitamin D3 by combining sun exposure with a supplemental dose of 4,000 IU/d. Clear signs of toxicity may not occur until one reaches 50,000 IU/d. Still, one may develop other complications, such as kidney stones, at levels significantly above 10,000 IU/d.<br />
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In an earlier post by Chris Masterjohn (the link no longer works), which made a different argument, somewhat similar conclusions were reached. Chris pointed out that there is a point of saturation above which the liver is unable to properly hydroxylate vitamin D3 to produce 25(OH)D.<br />
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How likely it is that a person will develop complications like kidney stones at levels above 10,000 IU/d, and what the danger threshold level could be, are hard to guess. Kidney stone incidence is a sensitive measure of possible problems; but it is, by itself, an unreliable measure. The reason is that it is caused by factors that are correlated with high levels of vitamin D, where those levels may not be the problem.<br />
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There is some evidence that kidney stones are associated with living in sunny regions. This is not, in my view, due to high levels of vitamin D3 production from sunlight. Kidney stones <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1464-410X.1990.tb14954.x/abstract">are also associated with chronic dehydration</a>, and populations living in sunny regions may be at a higher than average risk of chronic dehydration. This is particularly true for sunny regions that are also very hot and/or dry.<br />
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<b>Reference</b><br />
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Vieth, R. (1999). <a href="http://www.ajcn.org/cgi/content/full/69/5/842?ijkey=efc3e2049cf96fe05dbb34cb2fd4b7ef4875406a&keytype2=tf_ipsecsha">Vitamin D supplementation, 25-hydroxyvitamin D concentrations, and safety.</a> <i>American Journal of Clinical Nutrition</i>, 69(5), 842-856.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com72tag:blogger.com,1999:blog-8859456735165996893.post-8854326041892830692021-06-22T17:38:00.000-07:002021-06-22T17:38:42.900-07:00Blood glucose control before age 55 may increase your chances of living beyond 90I have recently read an interesting study by Yashin and colleagues (2009) at Duke University’s Center for Population Health and Aging. (The full reference to the article, and a link, are at the end of this post.) This study is a gem with some rough edges, and some interesting implications.<br />
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The study uses data from the Framingham Heart Study (FHS). The FHS, which started in the late 1940s, recruited 5209 healthy participants (2336 males and 2873 females), aged 28 to 62, in the town of Framingham, Massachusetts. At the time of Yashin and colleagues’ article publication, there were 993 surviving participants.<br />
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I rearranged figure 2 from the Yashin and colleagues article so that the two graphs (for females and males) appeared one beside the other. The result is shown below (click on it to enlarge); the caption at the bottom-right corner refers to both graphs. The figure shows the age-related trajectory of blood glucose levels, grouped by lifespan (LS), starting at age 40.<br />
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As you can see from the figure above, blood glucose levels increase with age, even for long-lived individuals (LS > 90). The increases follow a <a href="http://warppls.blogspot.com/2010/02/nonlinearity-and-type-i-and-ii-errors.html">U-curve (a.k.a. J-curve)</a> pattern; the beginning of the right side of a U curve, to be more precise. The main difference in the trajectories of the blood glucose levels is that as lifespan increases, so does the width of the U curve. In other words, in long-lived people, blood glucose increases slowly with age; particularly up to 55 years of age, when it starts increasing more rapidly.<br />
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Now, here is one of the rough edges of this study. The authors do not provide <a href="http://healthcorrelator.blogspot.com/2010/03/standard-deviation-is-not-same-as-range.html">standard deviations</a>. You can ignore the error bars around the points on the graph; they are not standard deviations. They are standard errors, which are much lower than the corresponding standard deviations. Standard errors are calculated by dividing the standard deviations by the square root of the sample sizes for each trajectory point (which the authors do not provide either), so they go up with age since progressively smaller numbers of individuals reach advanced ages.<br />
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So, no need to worry if your blood glucose levels are higher than those shown on the vertical axes of the graphs. (I will comment more on those numbers below.) Not everybody who lived beyond 90 had a blood glucose of around 80 mg/dl at age 40. I wouldn't be surprised if about 2/3 of the long-lived participants had blood glucose levels in the range of 65 to 95 at that age.<br />
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Here is another rough edge. It is pretty clear that the authors’ main independent variable (i.e., health predictor) in this study is average blood glucose, which they refer to simply as “blood glucose”. However, the measure of blood glucose in the FHS is a very rough estimation of average blood glucose, because they measured blood glucose levels at random times during the day. These measurements, when averaged, are closer to fasting blood glucose levels than to average blood glucose levels.<br />
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A more reliable measure of average blood glucose levels is that of <a href="http://en.wikipedia.org/wiki/Glycated_hemoglobin">glycated hemoglobin</a> (HbA1c). Blood glucose glycates (i.e., sticks to, like most sugary substances) hemoglobin, a protein found in red blood cells. Since red blood cells are relatively long-lived, with a turnover of about 3 months, HbA1c (given in percentages) is a good indicator of average blood glucose levels (if you don’t suffer from anemia or a few other blood abnormalities). Based on HbA1c, one can then estimate his or her average blood glucose level for the previous 3 months before the test, using one of the following equations, depending on whether the measurement is in mg/dl or mmol/l.<br />
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Average blood glucose (mg/dl) = 28.7 × HbA1c − 46.7<br />
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Average blood glucose (mmol/l) = 1.59 × HbA1c − 2.59<br />
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The table below, from Wikipedia, shows average blood glucose levels corresponding to various HbA1c values. As you can see, they are generally higher than the corresponding fasting blood glucose levels would normally be (the latter is what the values on the vertical axes of the graphs above from Yashin and colleagues’ study roughly measure). This is to be expected, because blood glucose levels vary a lot during the day, and are often transitorily high in response to food intake and fluctuations in various hormones. Growth hormone, cortisol and noradrenaline are examples of hormones that increase blood glucose. Only one hormone effectively decreases blood glucose levels, insulin, by stimulating glucose uptake and storage as glycogen and fat.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeiViCIFX85-KtazGjHLY-BDTCOd2-qIX8f02-r3h1TQm4BUXQZ7i-aWTLmz4IBe2JYRn_4pUNFg3lx7f5Woc6NMrSmZ9jPAnFwbdFXQeKBb0ksecKJ4eTpFp5G18dOthFJ0aOcA1hUE6U/s1600/Wikipedia_BloodGlucoseFromHbA1c.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeiViCIFX85-KtazGjHLY-BDTCOd2-qIX8f02-r3h1TQm4BUXQZ7i-aWTLmz4IBe2JYRn_4pUNFg3lx7f5Woc6NMrSmZ9jPAnFwbdFXQeKBb0ksecKJ4eTpFp5G18dOthFJ0aOcA1hUE6U/s320/Wikipedia_BloodGlucoseFromHbA1c.PNG"></a></div><br />
Nevertheless, one can reasonably expect fasting blood glucose levels to have been highly correlated with average blood glucose levels in the sample. So, in my opinion, the graphs above showing age-related blood glucose trajectories are still valid, in terms of their overall shape, but the values on the vertical axes should have been measured differently, perhaps using the formulas above.<br />
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Ironically, those who achieve low average blood glucose levels (measured based on HbA1c) by adopting a low carbohydrate diet (one of the most effective ways) frequently have somewhat high fasting blood glucose levels because of physiological (or benign) insulin resistance. Their body is primed to burn fat for energy, not glucose. Thus when <a href="http://healthcorrelator.blogspot.com/2009/12/growth-hormone-fountain-of-youth.html">growth hormone levels spike in the morning</a>, so do blood glucose levels, as muscle cells are in glucose rejection mode. This is a benign version of the <a href="http://en.wikipedia.org/wiki/Dawn_effect">dawn effect</a> (a.k.a. dawn phenomenon), which happens with quite a few low carbohydrate dieters, particularly with those who are deep in ketosis at dawn.<br />
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Yashin and colleagues also modeled relative risk of death based on blood glucose levels, using a fairly sophisticated mathematical model that takes into consideration U-curve relationships. What they found is intuitively appealing, and is illustrated by the two graphs at the bottom of the figure below. The graphs show how the relative risks (e.g., 1.05, on the topmost dashed line on the both graphs) associated with various ranges of blood glucose levels vary with age, for both females and males.<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEixZ7UCSAAjIp_y9fVV8v9W-7piILO7tkReY2tISrlY30ItjLDQp-esTpuf3nNhq012bLJOW9Nk5CEYnP4Y1Pm7-SBfgLQFHDLsj6XUVzYfb3I65lYwnXtpMdYwG1LXWBhi1CpNX7eeYTAm/s1600/Yashin_etal_2009_F05.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEixZ7UCSAAjIp_y9fVV8v9W-7piILO7tkReY2tISrlY30ItjLDQp-esTpuf3nNhq012bLJOW9Nk5CEYnP4Y1Pm7-SBfgLQFHDLsj6XUVzYfb3I65lYwnXtpMdYwG1LXWBhi1CpNX7eeYTAm/s320/Yashin_etal_2009_F05.PNG"></a></div><br />
What the graphs above are telling us is that once you reach old age, controlling for blood sugar levels is not as effective as doing it earlier, because you are more likely to die from what the authors refer to as “other causes”. For example, at the age of 90, having a blood glucose of 150 mg/dl (corrected for the measurement problem noted earlier, this would be perhaps 165 mg/dl, from HbA1c values) is likely to increase your risk of death by only 5 percent. The graphs account for the facts that: (a) blood glucose levels naturally increase with age, and (b) fewer people survive as age progresses. So having that level of blood glucose at age 60 would significantly increase relative risk of death at that age; this is not shown on the graph, but can be inferred.<br />
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Here is a final rough edge of this study. From what I could gather from the underlying equations, the relative risks shown above do not account for the effect of high blood glucose levels earlier in life on relative risk of death later in life. This is a problem, even though it does not completely invalidate the conclusion above. As noted by several people (including Gary Taubes in his book <i><a href="http://www.amazon.com/Good-Calories-Bad-Controversial-Science/dp/1400033462">Good Calories, Bad Calories</a></i>), many of the diseases associated with high blood sugar levels (e.g., cancer) often take as much as 20 years of high blood sugar levels to develop. So the relative risks shown above underestimate the effect of high blood glucose levels earlier in life.<br />
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Do the long-lived participants have some natural protection against accelerated increases in blood sugar levels, or was it their diet and lifestyle that protected them? This question cannot be answered based on the study.<br />
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Assuming that their diet and lifestyle protected them, it is reasonable to argue that: (a) if you start controlling your average blood sugar levels well before you reach the age of 55, you may significantly increase your chances of living beyond the age of 90; (b) it is likely that your blood glucose levels will go up with age, but if you can manage to slow down that progression, you will increase your chances of living a longer and healthier life; (c) you should focus your control on reliable measures of average blood glucose levels, such as HbA1c, not fasting blood glucose levels (postprandial glucose levels are also a good option, because they contribute a lot to HbA1c increases); and (d) it is never too late to start controlling your blood glucose levels, but the more you wait, the bigger is the risk.<br />
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References:<br />
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Taubes, G. (2007). <i><a href="http://www.amazon.com/Good-Calories-Bad-Controversial-Science/dp/1400033462">Good calories, bad calories: Challenging the conventional wisdom on diet, weight control, and disease</a></i>. New York, NY: Alfred A. Knopf.<br />
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Yashin, A.I., Ukraintseva, S.V., Arbeev, K.G., Akushevich, I., Arbeeva, L.S., & Kulminski, A.M. (2009). <a href="https://www.sciencedirect.com/science/article/pii/S0047637409001055">Maintaining physiological state for exceptional survival: What is the normal level of blood glucose and does it change with age?</a> <i>Mechanisms of Ageing and Development</i>, 130(9), 611-618.Ned Kockhttp://www.blogger.com/profile/02755560885749335053noreply@blogger.com15