Beef heart

I have posted here before about the nutrition value of beef liver, nature’s “super-multivitamin”. I have even speculated that grain-fed beef liver could be particularly nutritious (). What I should have done also was to post about beef liver’s equal in terms of nutrition value – beef heart. In this post I am correcting the omission.

Contrary to popular belief, not all organ meats are inherently fatty. The fat that is attached to an animal’s heart after slaughter, even if from grain-fed cattle, can be easily removed. The resulting cut will have a very low fat-to-protein ratio; often significantly less than fat-trimmed non-organ muscle cuts.

I don't say this because I consider fat to be unhealthy. In fact, dietary fat is necessary for the absorption of fat-soluble vitamins, and can thus be uniquely healthy. However, fat also is the most calorie-dense macronutrient. Even though the caloric values of macronutrients vary based on a number of factors, excess calories tend to be stored as excess body fat.

A 100 g portion of cooked beef heart, as in the photos below, will have 28 g of protein and only 5 g of fat (see this link, you may have to reset the serving size field: ). The photos below show two different beef heart dishes I have prepared. In the first the beef heart was barbecued. In the second it was simmered in a pan with vegetables for about 8 h.

Below is a simple recipe for the barbecued beef heart, which I recommend cutting into steaks. For the simmered beef heart I suggest cutting it into chunks that resemble cubes; then you can just add the dry seasoning powder mentioned below to the water, some vegetables, enough water to last about 8 h, and leave it simmering.

- Prepare some dry seasoning powder by mixing salt, garlic power, chili powder, and a small amount of cayenne pepper.
- Season the beef heart steaks at least 2 hours prior to placing them on the grill.
- Grill with the lid on, checking the meat every 10 minutes or so. (I use charcoal, one layer only to avoid burning the surface of the meat.) Turn it frequently, always putting the lid back on.
- If you like it rare, 20 minutes (or a bit less) may be enough.

Beef heart is a very good source of vitamins and minerals, and is one of the least expensive cuts of meat (in meat sections of grocery stores, not in paleo restaurants). Many people prefer beef heart over beef liver because of beef heart’s texture.

While I have restricted my comments in this post to “beef” heart, the hearts of most animals that are eaten by humans (e.g., chicken, duck, deer, turkey) are fairly nutritious, and they seem to have that uniformly meaty texture that many people like.

Here is an interesting factoid. The largest known carnivorous marsupial of modern times was the now extinct Tasmanian tiger. It was an elusive and solitary animal, and the subject of the beautiful film "The Hunter (2001)" (). The Tasmanian tiger was known to frequently eat only the hearts of prey. I hope this is not why it became extinct!

Waist-to-weight ratio vs. body max index

The optimal waist / weight ratio (WWR) theory () is one of the most compatible with evidence regarding the lowest mortality body mass index (BMI).

But why do we need the WWR when we already have the BMI? This was a question that a reader asked me in connection with a post on the John Stone transformation ().

The montage below shows photos of the John Stone transformation with the respective WWR and BMI measures.

Well, which one is the most useful measure, WWR or BMI?

Doing crossfit and looking like a bodybuilder?

Top crossfit athletes like Annie Thorisdottir and Rich Froning Jr. (pictured below; photos from Crossfitthestables.com and List09.com) look like bodybuilders even though their training practices are markedly different from those of most top natural bodybuilders. It is instructive, from a human physiology perspective, to try to understand why.

First of all we should make it clear that what makes Annie Thorisdottir and Rich Froning Jr. look the way they do is not only crossfit training. Genetics plays a key role here. Some people don’t accept this argument at all. Can you imagine someone arguing that top basketball players are generally tall because the stretching and reaching moves inherent in playing basketball make them tall? Top basketball players are not tall because they play basketball; the causality is stronger in the opposite direction: they play basketball because they are tall. The situation is not all that different with top crossfit competitors.

Often people will point at before and after photos as evidence that anyone can achieve the level of muscularity of a champion natural bodybuilder, if they do the right things. The problem with these before and after photos is that one can “go down” in terms of muscularity and definition quite a lot, but there is a clear ceiling in terms of “going up”. For example, if one goes from competitive marathon running to competitive bodybuilding, after a few years the difference will be dramatic if the person has the genetics necessary to gain a lot of muscle.

In other words, those who have the genetics to become very muscular can lose muscle and/or gain body fat to the point that they would look like they don’t have much genetic potential for muscle gain. Someone who doesn’t have the required genetics, on the other hand, will also be very effective at losing muscle and/or gaining body fat, but will be much more limited at the upper end of the scale.

The table below is from a widely cited and classic study by Fryburg and colleagues on the effects of growth hormone, insulin, and amino acid infusion on muscle accretion of protein. The article is available online as a PDF file (). The measurements shown on the table were taken basally (BAS) and at 3 h and 6 h after the start of the infusions, one of which was of a balanced amino acid mixture that raised arterial phenylalanine concentration to about twice what it was before the infusion. Phenylalanine is one of the essential amino acids present in muscle ().

There were four experimental conditions, two with only amino acid infusion, one with insulin and amino acid infusions, and one with insulin-like growth factor 1 (IGF-1) and amino acid infusions. Protein synthesis and breakdown numbers are based on phenylalanine kinetics inferences. The balance number is based on the synthesis and breakdown numbers; the former minus the latter. Note that at BAS the balance is always negative; this implies a net amino acid loss from muscle. At BAS the measurements were taken after a 12 h fast.

All infusions – of insulin, IGF-1, and amino acids – were continuously applied during the 6 h period. There was no exercise involved in this infusion study, and the amino acid mixture was balanced; as opposed to focused on certain amino acids, such as BCAAs.

The numbers in the table suggest that insulin infusion brings the balance to positive territory at the 3-h mark, with the effect wearing down at 6 h. IGF-1 infusion brings the balance to positive territory at 3 h, with the effect increasing and almost doubling at 6 h. Amino acid infusion alone brings the balance to positive territory a bit at 3 h and 6 h, and much less than when it is combined with insulin or IGF-1 infusions.

The effects of these infusions were due to both reductions in breakdown (amino acid loss) and increases in synthesis. We see that insulin exerts its effect on the balance primarily by suppressing breakdown. IGF-1 exerts its effect on the balance primarily by increasing synthesis. The effect of IGF-1 on the balance is significantly stronger than those of insulin and amino acid infusions, even when these latter two are taken together.

While this is an infusion study, one can derive conclusions about what would happen in response to different types of exercise and nutrients. Under real life conditions, insulin will increase in response to ingestion of carbohydrates and/or protein. IGF-1 will increase in response to growth hormone (GH) elevation, of which a major trigger is intense exercise.

The type of exercise that leads to the highest elevation of GH levels is intense exercise that raises heart rate significantly and rapidly. Examples are sprints, large-muscle resistance exercise, and resistance exercise involving multiple muscles at the same time. At the very high end of GH secretion are exercises that use large upper and lower body muscles at the same time, such as the deadlift. At the low end of GH secretion are localized small-muscle exercises, such as calf raises and isolated curls.

Anecdotally it seems that, at least for beginners, those exercises that lead to the highest GH secretion are the least “comfortable” for them. That is, those are the exercises that cause the most “huffing and puffing”. So next time you do an exercise like that, use this as a motivator: these are the exercises with the biggest return on investment; whether you are looking for health improvement, muscle gain, or both.

Competitive crossfit practitioners tend to favor variations of high-intensity interval training (HIIT), with an emphasis on a blend of endurance and strength exercises. Endurance and strength are both needed in crossfit competition. Competitive bodybuilders tend to focus more on strength, often exercising with more resistance or weight than competitive crossfit practitioners.

Extrapolating from the infusion study, one could argue that high GH secretion exercises are critical for amino acid accretion in muscle. Both groups mentioned above – competitive crossfit practitioners and competitive bodybuilders – exercise in ways that lead to high GH secretion. Surprising as this may sound (to some), if you do chin-ups, you’ll probably have better results in terms of biceps hypertrophy than if you do isolated bicep curls. This will happen even though the overall load on the bicep muscles will be lower with the chin-ups. The reason is that the GH secretion will be significantly higher with the chin-ups, because more muscles are involved at the same time, including large ones (e.g. the lats).

It is interesting to see competitive crossfit practitioners talking about needing to lose some weight but not being able to (). The reason is that they do not have much body fat to lose, and the types of exercise that they do create such a powerful stimulus toward positive nitrogen balance () that they end up gaining weight even as they restrict calorie intake.

Carbohydrate ingestion prior to exercise may raise insulin levels, but will blunt GH secretion; protein without carbohydrate, on the other hand, will raise insulin levels without blunting GH secretion (). Whether ingesting protein immediately before exercising is necessarily good in the long run is an open question, however, because GH secretion is likely to be greater for someone who is exercising in the fasted state, as GH secretion is in part a response to glycogen depletion (, ). And, as we have seen from the infusion study, GH secretion is disproportionately important as a positive nitrogen balance factor.

Compensatory adaptation applied to human biology () suggests that the body responds to challenges over time, in a compensatory way. Which scenario poses the bigger challenge: (a) high GH exercise with more amino acid loss during the exercise, or (b) high GH exercise with less amino acid loss during the exercise? I think it is (a), because the message being sent to the body is that “we need more muscle to do all of this and still compensate for the loss during exercise”.

Maybe this is why top crossfit practitioners end up looking like bodybuilders, and cannot lose muscle even when a slightly lighter frame would make them more competitive in crossfit games. Their bodies are just responding to the stimuli they are getting.

You can eat a lot during the Holiday Season and gain no body fat, as long as you also eat little

This post has been revised and re-published. The original comments are preserved below. Typically this is done with posts that attract many visits at the time they are published, and whose topics become particularly relevant or need to be re-addressed at a later date.

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

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



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

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

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

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

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

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

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

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

Latitude and cancer rates in US states: Aaron Blaisdell’s intuition confirmed

In the comments section of my previous post on cancer rates in the US states () my friend Aaron Blaisdell noted that: …comparing states that are roughly comparable in terms of number of seniors per 1000 individuals, latitude appears to have the largest effect on rates of cancer.

Good point, so I collected data on the latitudes of US states, built a more complex model (with several multivariate controls), and analyzed it with WarpPLS 4.0 ().

The coefficient of association for the effect of latitude on cancer rates (path coefficient) turned out to be 0.35. Its P value was lower than 0.001, meaning that the probability that this is a false positive is less than a tenth of a percent, or that we can be 99.9 percent confident that this is not a false positive.

This was calculated controlling for the: (a) proportion of seniors in the population (population age); (b) proportion of obese individuals in the population (obesity rates); and (c) the possible moderating effect of latitude on the effect of population age on cancer rates. The graph below shows this multivariate-adjusted association.



What is cool about a multivariate analysis is that you can control for certain effects. For example, since we are controlling for proportion of seniors in the population (population age), the fact that we have a state with a very low proportion of seniors (Alaska) does not tilt the effect toward that outlier as much as it would if we had not controlled for the proportion of seniors. This is a mathematical property that is difficult to grasp, but that makes multivariate adjustment such a powerful technique.

I should note that the 99.9 percent confidence mentioned above refers to the coefficient of association. That is, we are quite confident that the coefficient of association is not zero; that is it. The P value does not support the hypothesized direction of causality (latitude -> cancer) or exclude the possibility of a major confounder causing the effect.

Nonetheless, among the newest features of WarpPLS 4.0 (still a beta version) are several causality assessment coefficients: path-correlation signs, R-squared contributions, path-correlation ratios, path-correlation differences, Warp2 bivariate causal direction ratios, Warp2 bivariate causal direction differences, Warp3 bivariate causal direction ratios, and Warp3 bivariate causal direction differences. Without going into a lot of technical detail, which you can get from the User Manual () without even having to install the software, I can tell you that all of these causality assessment coefficients support the hypothesized direction of causality.

Also, while we cannot exclude the possibility of a major confounder causing the effect, we included two possible confounders in the analysis and controlled for their effects. They were the proportion of seniors in the population (population age) and the proportion of obese individuals in the population (obesity rates).

Having said all of the above, I should also say that the effect is similar in magnitude to the effect of population age on cancer rates, which I discussed in the previous post linked above. That is, it is not the type of effect that would be clearly noticeable in a person’s normal life.

Sunlight exposure? Maybe.

We do know that our 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”.

Aging and cancer: The importance of taking a hard look at the numbers

The table below is from a study by Hayat and colleagues (). It illustrates one common trend regarding cancer – it increases dramatically in incidence among those who are older. With some exceptions, such as Hodgkin's lymphoma, there is a significant increase in risk particularly after 50 years of age.



So I decided to get state data from the US Census web site (), on the percentage of seniors (age 65 or older) by state and cancer diagnoses per 1,000 people. I was able to get some recent data, for 2011.

I analyzed the data with WarpPLS (version 4.0 has been just released: ), generating the types of coefficients that would normally be reported by researchers who wanted to make an effect appear very strong.

In this case, the effect would be essentially of population aging on cancer incidence (assessed indirectly), summarized in the graph below. The graph was generated by WarpPLS. The scales are standardized, and so are the coefficients of association in the two segments shown. As you can see, the coefficients of association increase as we move along the horizontal scale, because this is a nonlinear relationship. The overall coefficient of association, which is a weighted average of the two betas shown, is 0.84. The probability that this is a false positive is less than 1 percent.



A beta coefficient of 0.84 essentially means that a 1 standard deviation variation in the percentage of seniors in a state is associated with an overall 84 percent increase in cancer diagnoses, taking the standardized unit of the number of cancer diagnoses as the baseline. This sounds very strong and would usually be presented as an enormous effect. Since the standard deviation for the percentage of seniors in various states is 1.67, one could say that for each 1.67 increment in the percentage of seniors in a state the number of cancer diagnoses goes up by 84 percent.

Effects expressed in percentages can sometimes give a very misleading picture. For example, let us consider an increase in mortality due to a disease from 1 to 2 cases for each 1 million people. This essentially is a 100 percent increase! Moreover, the closer the baseline is from zero, the more impressive the effect becomes, since the percentage increase is calculated by dividing the increment by the baseline number. As the baseline number approaches zero, the percentage increase from the baseline approaches infinity.

Now let us take a look at the graph below, also generated by WarpPLS. Here the scales are unstandardized, which means that they refer to the original measures in their respective original scales. (Standardization makes the variables dimensionless, which is sometimes useful when the original measurement scales are not comparable – e.g., dollars vs. meters.) As you can see here, the number of cancer diagnoses per 1,000 people goes from a low of 3.74 in Utah to a high of 6.64 in Maine.



One may be tempted to explain the increase in cancer diagnoses that we see on this graph based on various factors (e.g., lifestyle), but the percentage of seniors in a state seems like a very good and reasonable predictor. You may say: This is very depressing. You may be even more depressed if I tell you that controlling for state obesity rates does not change this picture at all.

But look at what these numbers really mean. What we see here is an increase in cancer diagnoses per 1,000 people of less than 3. In other words, there is a minute increase of less than 3 diagnoses for each group of 1,000 people considered. It certainly feels terrible if you are one of the 3 diagnosed, but it is still a minute increase.

Also note that one of the scales, for diagnoses, refers to increments of 1 in 1,000; while the other, for seniors, refers to increments of 1 in 100. This leads to an interesting effect. If you move from Alaska to Florida you will see a significant increase in the number of seniors around, as the difference in the percentage of seniors between these two states is about 10. However, the difference in the number of cancer diagnoses will not be even close to the difference in the presence of seniors.

The situation above is very common in medical research. An effect that is fundamentally tiny is stated in such a way that the general public has the impression that the effect is enormous. Often the reason is not to promote a drug, but to attract media attention to a research group or organization.

When you look at the actual numbers, the magnitude of the effect is such that it would go unnoticed in real life. By real life I mean: John, since we moved from Alaska to Maine I have been seeing a lot more people of my age being diagnosed with cancer. An effect of the order of 3 in 1,000 would not normally be noticed in real life by someone whose immediate circle of regular acquaintances included fewer than 333 people (about 1,000 divided by 3).

But thanks to Facebook, things are changing … to be fair, the traditional news media (particularly television) tends to increase perceived effects a lot more than social media, often in a very stressful way.