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The Fastest and Simplest Way to Oriental-Charged Healthy Living Is Traditional Chinese Medicine

Long before the introduction of the western medical system most people that lived in the ancient days survived and even lived a healthier lifestyle than people in this present generation. The Chinese people are renowned for this attribute; their traditional way of healing and preserving health has in fact been studied at increasing pace in the western world.

Traditional Chinese Medicine is an ancient medical system that was used by the ancient people of China; this system involves a profound understanding of natural laws and their inter-relation with human bodies. It adopts a holistic approach that combines the personality, mind, spirit, soul, and emotions of humans in order to prescribe a totally effective solution that aims to heal the root causes of illnesses and not just the symptoms.

One of the most popular methods of these ancient traditional medicines is acupuncture. Acupuncture are the points at which human body picks its responses and senses. They are the body locations from which energies are transmitted between the body and the internal organs. These points are referred to as Shuxue in the Mandarin, or Chinese language.

Acupuncture is just one of the diverse plethora of effective Traditional Chinese Medicine techniques and remedies. There are several other methods of applying the ancient Chinese healing methods; some of these include - Qigang, herbal therapy, Acupressure, food for healing and Chinese psychology.

Purists of ancient Chinese medicines contest that they bring more benefits to the body than the more used western medicines. Apart from the fact that this type of medicine has a rich history of proven success, both amongst the ancient people, and also the surviving generations, it is also proven to have healed both the body and the mind of the sick.

Holistic Medicine - The Greatest Alternative Medicine

The utilization of homeopathy originated in Germany. This is formed on the deduction that our body uses autonomous mending capacities, and this normally aims at preventing diseases plus promoting well being. These naturopathic medical treatments are rightly called homeopathic treatments rather than drugs since they include numerous natural components derived from minerals as well as plants.

These homeopathic remedies are famous for protecting against diseases by enhancing the defense system. Though naturopathic therapies are not used for treating unexpected emergency circumstances such as most cancers, or even heart attack, they can be put to use for treating minor injuries and even persistent ailments.

What are Natural Treatments?

Well, naturopathic treatments are weakened preparations which are made making use of things obtained from minerals, herbs, animals, and countless other sources. Those materials are diluted (using pure water or alcohol) using a process termed distilization. An additional procedure, known as succussion is applied where distilled materials are repetitively hit across highly flexible surface areas. The complete procedure of creating these cures makes them really effective in treating various illnesses and ailments.

Unprocessed Elements - Completely Reliable with regard to Humans

As homeopathic solutions are prepared making use of materials gotten from natural resources, they are actually absolutely nontoxic even when consumed after a very long time. There is little chance of negative reaction, and consequently numerous household remedies are indeed recommended for pregnant females. In addition to that, these treatments can easily also be consumed with conventional treatments without the worry of any negative reaction.

The Importance of Calcium for the Human Body

The mineral calcium is the most abundantly found mineral in the human body. And we all know that along with phosphorous and Vitamin D, it is essential for strong teeth and bones. The average adult human body will contain as much as one kilogram of this mineral. Almost all of it is in the bones and teeth. Some amount of it is used by the body to ensure proper muscle contraction, blood clotting, and maintaining neurotransmission.

There exist many sources of dietary calcium and supplements can be easily purchased as over the counter medication; however, these should be taken under medical guidance. Milk and milk products are amongst the best known natural sources of calcium. However, there are many people who are lactose intolerant. Fortunately, for such people there is choice from leafy greens; nuts such as almonds, hazelnuts, etc; seaweed; blackstrap molasses; and fish whose bones can be consumed easily. It is also possible to obtain it from fortified products such as juices and breads.

The most commonly used calcium supplement is calcium carbonate. This supplement should ideally be taken with food. It is cheap and contains a good amount of elemental calcium. It may be a good idea to take vitamin D supplements to improve calcium absorption by the body. Calcium supplements can also contain calcium citrate, calcium lactate, and calcium phosphate. Calcium citrate is expensive and does not contain as much elemental calcium as calcium carbonate.

The lowest-mortality BMI: What is the role of nutrient intake from food?

In a previous post (), 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.


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 (). 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 ().

Another relevant example comes from the China Study II (see, e.g., ), 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.


Now take a look at the graph below, also based on the China Study II dataset, from a previous post (), 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.

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.


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.


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.


Industrial nutrient isolation for supplementation use has not been a very successful long-term strategy for health optimization (). 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 ().


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 (), 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.


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.


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 ().


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.

The lowest-mortality BMI: What is its relationship with fat-free mass?

Do overweight folks live longer? It is not uncommon to see graphs like the one below, from the Med Journal Watch blog (), suggesting that, at least as far as body mass index (BMI) is concerned (), overweight folks (25 < BMI < 30) seem to live longer. The graph shows BMI measured at a certain age, and risk of death within a certain time period (e.g., 20 years) following the measurement. The lowest-mortality BMI is about 26, which is in the overweight area of the BMI chart.



Note that mortality risk, relative to the mortality risk of people in the same age group, increases less steeply in response to weight variations as one becomes older. An older person increases the risk of dying to a lesser extent by weighing more or less than does a younger person. This seems to be particularly true for gains in weight.

The table below is from a widely cited 2002 article by Allison and colleagues (), where they describe a study of 10,169 males aged 25-75. Almost all of the participants, ninety-eight percent, were followed up for many years after measurement; a total of 3,722 deaths were recorded.



Take a look at the two numbers circled in red. The one on the left is the lowest-mortality BMI not adjusting for fat mass or fat-free mass: a reasonably high 27.4. The one on the right is the lowest-mortality BMI adjusting for fat mass and fat-free mass: a much lower 21.6.

I know this may sound confusing, but due to possible statistical distortions this does not mean that you should try to bring your BMI to 21.6 if you want to reduce your risk of dying. What this means is that fat mass and fat-free mass matter. Moreover, all of the participants in this study were men. The authors concluded that: “…marked leanness (as opposed to thinness) has beneficial effects.”

Then we have an interesting 2003 article by Bigaard and colleagues () reporting on a study of 27,178 men and 29,875 women born in Denmark, 50 to 64 years of age. The table below summarizes deaths in this study, grouping them by BMI and waist circumference.



These are raw numbers; no complex statistics here. Circled in green is the area with samples that appear to be large enough to avoid “funny” results. Circled in red are the lowest-mortality percentages; I left out the 0.8 percentage because it is based on a very small sample.

As you can see, they refer to men and women with BMIs in the 25-29.9 range (overweight), but with waist circumferences in the lower-middle range: 90-96 cm for men and 74-82 cm for women; or approximately 35-38 inches for men and 29-32 inches for women.

Women with BMIs in the 18.5-24.9 range (normal) and the same or lower waists also died in small numbers. Underweight men and women had the highest mortality percentages.

A relatively small waist (not a wasp waist), together with a normal or high BMI, is an indication of more fat-free mass, which is retained together with some body fat. It is also an indication of less visceral body fat accumulation.

Traumatic Brain Injury Overview

A traumatic brain injury is something we hope will never happen to us or our loved ones. Once injured, your life can be changed forever. Some forms of head injuries are treatable where others will leave you permanently disabled for the rest of your life. This could mean that you or your family member will require around the clock treatment for the rest of their life.

A traumatic brain injury (TBI) can happen a number of different ways. Car crashes, pedestrian accidents, on the job injuries, and sports accidents among others can all cause TBI. No matter how the injury happened these types of catastrophic injury accidents usually involve one of two circumstances. Either the head experiences a high impact collision with another object (or vice versa) or an object passes through the skull and enters the brain tissue. Both of these traumatic injuries can cause serious damage to the brain.

Symptoms of Brain Injuries

According to the National Institute of Neurological Disorders and Stroke, some of the symptoms of traumatic brain injury include the following:

Loss of consciousness for seconds or minutes
Headache
Lightheadedness or dizziness
Confusion
Blurred vision
Ears ringing
Fatigue
Bad taste in mouth
Change in sleep patterns
Change in mood
Difficultly remembering or concentrating

How to make white rice nutritious

One of the problems often pointed out about rice, and particularly about white rice, is that its nutrition content is fairly low. It is basically carbohydrates with some trace amounts of protein. A 100-g portion of cooked white rice will typically deliver 28 g of carbohydrates, with zero fiber, and 3 g of protein. The micronutrient content of such a portion leaves a lot to be desired when compared with fruits and vegetables, as you can see below (from Nutritiondata.com). Keep in mind that this is for 100 g of “enriched” white rice; the nutrients you see there, such as manganese, are added.

White rice is rice that has had its husk, bran, and germ removed. This prevents spoilage and thus significantly increases its shelf life. As it happens, it also significantly reduces both its nutrition and toxin content. White rice is one of the refined foods with the lowest toxin content.

Another interesting property of white rice is that it absorbs moisture to the tune of about 2.5 times its weight. That is, a 100-g portion of dry white rice will lead to a 250-g portion of edible white rice after cooking. This does not only dramatically decrease white rice’s glycemic load () compared with wheat-based products in general (with some exceptions, such as pasta), but also allows for white rice to be made into a highly nutritious dish.

If you slow cook almost anything in water, many of its nutrients will seep into the water. All you have to do is to then use that water (often called broth) to cook white rice in it, and you will end up with highly nutritious rice. Typically you will need twice as much broth as rice, cooked for about 15 minutes – e.g., 2 cups of broth for 1 cup of rice.

You can add meats to the white rice, such as pulled chicken or shrimp; add some tomato sauce to that and you’ll make it a chicken or shrimp risotto. You can also add vegetables to the rice. If you want your rice to have something like an al dente consistency, I recommend doing these after the rice is ready; i.e., after you cooked it in the broth.

For the white rice-based dish below I used a broth from about two hours of slow cooking of diced vegetables; namely red bell peppers, carrots, celery, onions, and cabbage. After cooking the rice for 15 minutes, and letting it "sit" for a while (another 15 minutes with the pan covered), I also added the vegetables to it.

As a side note, the cabbage and onion tend to completely dissolve after 1 h or so of slow cooking. The added vegetables give the dish quite a nutritional punch. For example, the cabbage alone seems to be a great source of vitamin C (which is not completely destroyed by the slow cooking), the anti-inflammatory amino acid glutamine, and the DNA repair-promoting substance known as indole-3-carbinol ().

The good folks over at the Highbrow Paleo group on Facebook () had a few other great ideas posted in response to my previous post on the low glycemic load of white rice (), such as cooking white rice in bone broth (thanks Derrick!).

The Beginnings of Emergency Medicine in the United Kingdom

The specialty of Emergency Medicine developed in the United Kingdom out of a recognition of the need for injured people to receive better care. Over the years its scope has broadened to include serious illnesses, disease, infections and other more medically related problems.

The First World War provided the catalyst needed to kick-start the process of specialism within overstretched hospitals. A pioneering surgeon named Robert Jones was appalled by the lack of provision for those suffering gunshot wounds in the First World War. This led him to establish the British Orthopedic Association in 1918 with Robert Osgood, which became one of the most important developments in the care of the injured, and led to increased cooperation among orthopedic surgeons.

An early example of specialism for fracture patients was the establishment of separate fracture clinics in Manchester by Harry Platt in 1913-14. It was he who, many years later, as the chairman of the Accident and Emergency Services Sub-Committee of the Standing Medical Advisory Committee, produced the famous Platt report in 1962. This report highlighted major concerns over the level of care provided for the seriously ill and injured patients.

Learning About Acupuncture

For those people who are not aware, acupuncture is a technique that makes use of disposable needles to be able to cure different types of pains in a person's body. The needles are expertly inserted in different locations in a person's skin known as meridians; pricking these meridians will cause the pain from the body to be eliminated immediately, allowing the person to feel more relieved.

A lot of people who are interested in the techniques used in acupuncture are probably wondering about the overall cost that they would have to endure just to be able to get the amazing benefits from it. For starters, people should not think about the acupuncture cost as only the exact amount that they would have to spend for it. Rather, people should take into consideration both the amount and the benefits that acupuncture will be able to give them in the long run.

However, to give people a better idea about the specific amounts that they need to spend, they should know that for the first visit, they will be billed with an amount that is quite expensive. The good news is that the acupuncture cost for the first session will not be as expensive as the succeeding sessions that people would have to attend to; first-timers should be ready to shell out 75 up to 100 dollars for the first session. The reason why the first session costs more than all the other succeeding ones is because this usually lasts the longest; having said this, people should expect their first session to last for at least an hour.

Acupuncture Healing Success

The first known and recorded therapeutic success of traditional Chinese medicine that soon became one of China's achievements was during the extremely effective interval of war from different states and countries. According to the history of China's, a physician named Pien Cheuh used acupuncture to treat a Governor of the State of Kuo. The acupuncture treatment with the use of natural herbs has revived the governor from a severe coma. Soon, the physician was then awarded with prestige. In ancient China and up to these days, this celebration is considered in favor of the support, acceptance and popularity of any form of treatment.

How Does Acupuncture Work According to Traditional Chinese Medicine and Modern Science?

Through scientific experiences and natural encounters, the use of such particular factors like pressure points on the epidermis of our skin were proven to be of great help and value in treating particular illnesses.

Interruption of the circulation can lead to psychological problems, emotional illnesses, and physical diseases, such as inability to conceive, allergies, heart diseases and many more. We have channels or programs called meridians that run through our bodies. They are usually in comparison to estuaries operating through the entire body system in order for the cells to be taken care and for the tissues to be nourished. The circulation of energy will then be supported and be flowing back.

Alternative Healing Therapies

The human body is a miraculous thing. It can survive serious injuries and recover from great illnesses. In the "old" days, people relied on "Alternative Healing Therapies", known to them as home remedies to heal illnesses. Today, as a culture, we have come to rely so heavily on modern medicine that we often forget how capable are our bodies are of healing all on their own with, a little help from nature.

Lately, people are becoming aware of the damage pharmaceutical overuse can have. Because of this, more people are turning to alternative healing therapies. In nearly every home there seems to be a small pharmacy worth of alternative remedies.

What Are Alternative Healing Therapies?

Alternative Healing Therapies, also called complementary healing therapies, are types of health treatment outside the realm of modern medicine. These range from basic massage to acupuncture and aromatherapy. While the methods used in alternative healing vary greatly, they all follow one basic principle. All types of alternative healing therapies seek to assist the body in healing itself in the least invasive and damaging ways possible.

There are many types of Alternative Therapies. You can easily become certified in some of them such as; massage, aromatherapy, meditation, and acupressure. Others, like acupuncture and holistic dentistry require professional training for which you earn a degree. Most types of alternative healing therapies are improved by use of one or more of the others. This is why you often see massage therapists who also practice acupressure and aromatherapy.

Rice consumption and health

Carbohydrate-rich foods lead to the formation of blood sugars after digestion (e.g., glucose, fructose), which are then used by the liver to synthesize liver glycogen. Liver glycogen is essentially liver-stored sugar, which is in turn used to meet the glucose needs of the human brain – about 5 g/h for the average person.

(Source: Wikipedia)

When one thinks of the carbohydrate content of foods, there are two measures that often come to mind: the glycemic index and the glycemic load. Of these two, the first, the glycemic index, tends to get a lot more attention. Some would argue that the glycemic load is a lot more important, and that rice, as consumed in Asia, may provide a good illustration of that importance.

A 100-g portion of cooked rice will typically deliver 28 g of carbohydrates, with zero fiber, and 3 g of protein. By comparison, a 100-g portion of white Italian bread will contain 54 g of carbohydrates, with 4 g of fiber, and 10 g of protein – the latter in the form of gluten. A 100-g portion of baked white potato will have 21 g of carbohydrates, with 2 g of fiber, and 2 g of protein.

As you can see above, the amount of carbohydrate per gram in white rice is about half that of white bread. One of the reasons is that the water content in rice, as usually consumed, is comparable to that in fruits. Not surprisingly, rice’s glycemic load is 15 (medium), which is half the glycemic load of 30 (high) of white Italian bread. These refer to 100-g portions. The glycemic load of 100 g of baked white potato is 10 (low).

The glycemic load of a portion of food allows for the estimation of how much that portion of food raises a person's blood glucose level; with one unit of glycemic load being equivalent to the blood glucose effect of consumption of one gram of glucose.

Two common denominators between hunter-gatherer groups that consume a lot of carbohydrates and Asian populations that also consume a lot of carbohydrates are that: (a) their carbohydrate consumption apparently has no negative health effects; and (b) they consume carbohydrates from relatively low glycemic load sources.

The carbohydrate-rich foods consumed by hunter-gatherers are predominantly fruits and starchy tubers. For various Asian populations, it is predominantly white rice. As noted above, the water content of white rice, as usually consumed by Asian populations, is comparable to that of fruits. It also happens to be similar to that of cooked starchy tubers.

An analysis of the China Study II dataset, previously discussed here, suggests that widespread replacement of rice with wheat flour may have been a major source of problems in China during the 1980s and beyond ().

Even though rice is an industrialized seed-based food, the difference between its glycemic load and those of most industrialized carbohydrate-rich foods is large (). This applies to rice as usually consumed – as a vehicle for moisture or sauces that would otherwise remain on the plate. White rice combines this utilitarian purpose with a very low anti-nutrient content.

It is often said that white rice’s nutrient content is very low, but this problem can be easily overcome – a topic for the next post.

4 Departments in Your Health Care Organization That Must Adhere To Universal Precautions

Infectious pathogens in the blood can be transferred from one person to another causing bloodborne diseases. Other body fluids like semen, vaginal discharges, amniotic fluid, saliva, etc. can also cause such infectious diseases. And prior to proper testing, it is impossible to know which blood samples are infected. Therefore, it is best to assume all samples as potentially infected and handle them with the utmost care. And as such, basic precautionary measures that should be taken by your health care employees while handling potentially blood and body fluids are known as universal precautions.

There are four main departments in the hospital that must strictly adhere to the universal precautions, as discussed below.

· Laboratory: It is mainly the hospital laboratory that deals with blood samples on a daily basis. This place is filled with chemicals, sharp instruments and equipment, and uncountable blood samples that are likely to be infected. There is maximum risk of employees contracting bloodborne diseases here. It is mandatory for your health workers to wear protective clothing like gloves, gowns and eye gear at all times. Other personal protective equipment like foot covers and face shields must also be worn depending on the work being done. All these equipments are important as they keep your skin protected from pathogens and also from dangerous chemical splashes.

· Housekeeping: The housekeeping department in your health care organization will not just be responsible for sweeping and swabbing but also for the decontamination of work areas and for proper disposal of fluids and sharp instruments. All housekeeping workers must be given gloves, aprons and gowns to wear while cleaning work stations, floors, walls and furniture. All necessary precautions must be properly followed to prevent spread of infection.

Understanding the Health Insurance Portability and Accountability Act

When it comes to hospital medical transcription, transcribers do not just translate audio to text; there are also some legal matters that have to be put into consideration when working. As a transcriber, weather you are working at home or at a transcription company, it is important to take note of the HIPAA. The HIPAA act or the Health Insurance Portability and Accountability Act is a law that protects all individuals when it comes to the privacy of their medical records. Information that is exchanged during the entire transcribing process is considered private and should be disclosed to anyone else. Sometimes, some information is disclosed by accident without the knowledge of the transcriber.

There are some ways and tips in order that this information is kept private and away from the public eye during a hospital medical translation process.

Keep your work station private

When you are working at home for a surgery medical transcription company or a hospital medical transcription company, a lot of information be shared between you are and your client. Make sure that your computer or workstation is locked away from the prying eyes of your house-mates. It is important that they have no access to your computer or any file or folder that contains all this information. Most home based transcriptions working on a hospital medical transcription would have a separate computer for transcribing purposes alone; if the latter is possible it is probably the best option when it comes to protecting all private date.

Evasive Action to Overbearing Healthcare Reform

With the June 28, 2012 ruling by the Supreme Court of the United States in declaring the Affordable Care Act constitutional, very important personal decisions need to be made by Joe and Jill Citizen as to how they will deal with the changes as it relates to their conscious health decisions. For instance, in order to keep the plan as cost effective as possible steps may be implemented to limit doctors in the types of treatments they can provide their patients. Is this something Joe and Jill Citizen is ready to contend with?

In the likelihood of that type of scenario affecting millions of people, there lies enormous potential for aware and free-thinking individuals to take more responsibility for their own health and well being. Preparing for the worst is, if not should be, a natural-born instinct for every human being. We prepare for bad weather by having candles or flashlights on hand should there be a power outage, going to a cellar in case of a tornado, having road salt and snow shovels on hand before the blizzard of the decade hits. Wouldn't it make sense, in the case of our health, to make preparations as well? In fact, one would say that it makes much more sense to take em>preventative measures to ensure our good health endures for as long as humanly possible.

The looming threat of being denied a critical medication or procedure to provide a work-around for a current ailment is a frightening prospect, even more so if your life depends on it. Scientific study has shown that many of today's sicknesses and diseases could easily be prevented with proper nutrition. Much of these findings were derived from the field of animal husbandry which, these days, is very big business. And as in any business if you have damaged product (diseased animals) your customers will not buy and you go out of business. One can quickly understand why reversing the sickness and disease in livestock would be very important to that particular business. The same applies to human health only with greater consequences if you, the product, is damaged due to nutritional deficiency.

The 2012 Arch Intern Med red meat-mortality study: The “protective” effect of smoking

In a previous post () I used WarpPLS () to analyze the model below, using data reported in a recent study looking at the relationship between red meat consumption and mortality. The model below shows the different paths through which smoking influences mortality, highlighted in red. The study was not about smoking, but data was collected on that variable; hence this post.

When one builds a model like the one above, and tests it with empirical data, the person does something similar to what a physicist would do. The model is a graphical representation of a complex equation, which embodies the beliefs of the modeler. WarpPLS builds the complex equation automatically for the user, who would otherwise have to write it down using mathematical symbols.

The results yielded by the complex equation, partly in the form of coefficients of association for direct relationships (the betas next to the arrows), have a meaning. Some may look odd, and require novel interpretations, much in the same way that odd results from an equation describing planetary motions may have led to the development of the theory of black holes.

Nothing is actually "proven" by the results. They are part of the long and painstaking process we call "research". To advance new knowledge, one needs a lot more than a single study. Darwin's theory of evolution is still being tested. Based on various tests and partial refutations, it has itself evolved a great deal since its original formulation.

One set of results that are generated based on the model above by WarpPLS, in addition to coefficients for direct relationships, are coefficients of association called "total effects". They aggregate all of the effects, via multiple paths, between each pair of variables. Below is a table of total effects, with the total effects of smoking on diabetes incidence and overall mortality highlighted in red.

As you can see, the total effects of smoking on diabetes incidence and overall mortality are negative, but small enough to be considered insignificant. This is interesting, because smoking is definitely not health-promoting. Among hunter-gatherers, who often smoke tobacco, it increases the incidence of various types of cancer (). And it may be at the source of many of the health problems suggested by analyses on the China Study II data ().

So what are these results telling us? They tell us that smoking has an intermediate protective effect, very likely associated with its anorexic effect. Smoking is an appetite suppressor. Its total effect on food intake is negative, and strong. As we can see from the table of total effects, just below the two numbers highlighted in red, the total effect of smoking on food intake is -0.356.

Still, it looks like smoking is nearly as bad as overeating to the point of becoming obese (), in terms of its overall effect on health. Otherwise we would see a positive total effect on overall mortality of comparable strength to the negative total effect on food intake.

Smoking may make one eat less, but it ends up hastening one’s demise through different paths.

Hunger is your best friend: It makes natural foods taste delicious and promotes optimal nutrient partitioning

One of the biggest problems with modern diets rich in industrial foods is that they promote unnatural hunger patterns. For example, hunger can be caused by hypoglycemic dips, coupled with force-storage of fat in adipocytes, after meals rich in refined carbohydrates. This is a double-edged post-meal pattern that is induced by, among other things, abnormally elevated insulin levels. The resulting hunger is a rather unnatural type of hunger.

By the way, I often read here and there, mostly in blogs, that “insulin suppresses hunger”. I frankly don’t know where this idea comes from. What actually happens is that insulin is co-secreted with a number of other hormones. One of those, like insulin also secreted by the beta-cells in the pancreas, is amylin – a powerful appetite suppressor. Amylin deficiency leads to hunger even after a large carbohydrate-rich meal, when insulin levels are elevated.

Abnormally high insulin levels – like those after a “healthy” breakfast of carbohydrate-rich cereals, pancakes etc. – lead to abnormal blood glucose dips soon after the meal. What I am talking about here is a fall in glucose levels that is considerable, and that also happens very fast – illustrated by the ratio between the lengths of the vertical and horizontal black lines on the figure below, from a previous post ().

Those hypoglycemic dips induce hunger, because the hormonal changes necessary to apply a break to the fall in glucose levels (which left unchecked would lead to death) leave us with a hormonal mix that ends up stimulating hunger, in an unnatural way. At the bottom of those dips, insulin levels are much lower than before. I am not talking about diabetics here. I am talking about normoglycemic folks, like the ones whose glucose levels are show on the figure above.

On a diet primarily of natural foods, or foods that are not heavily modified from their natural state, hunger patterns tend to be better synchronized with nutrient deficiencies. This is one of the main advantages of a natural foods diet. By nutrients, I do not mean only micronutrients such as vitamins and minerals, but also macronutrients such as amino and fatty acids.

On a natural diet, nutrient deficiencies should happen regularly. Our bodies are designed for sporadic nutrient intake, remaining most of the time in the fasted state. Human beings are unique in that they have very large brains in proportion to their overall body size, brains that run primarily on glucose – the average person’s brain consumes about 5 g/h of glucose. This latter characteristic makes it very difficult to extrapolate diet-based results based on other species to humans.

As hunger becomes better synchronized with nutrient deficiencies, it should promote optimal nutrient partitioning. This means that, among other things: (a) you should periodically feel hungry for different types of food, depending on your nutrient needs at that point in time; (b) if you do weight training, and fell hungry, some muscle gain should follow; and (c) if you let hunger drive food consumption, on a diet of predominantly natural foods, body fat levels should remain relatively low.

In this sense, hunger becomes your friend – and the best spice!

Hormonal reductionism is as myopic as biochemical reductionism

Biochemistry-based arguments can be very misleading. Yet, biochemistry can be extremely useful in the elucidation of diet and lifestyle effects that are suggested by well-designed studies of humans. If you start with a biochemistry-based argument though, and ignore actual studies of humans, you can easily convince someone that glycogen-depleting exercise (e.g., weight training) is unhealthy, because many health markers change for the worse after that type of exercise. But it is the damage caused by glycogen-depleting exercise that leads to health improvements, via short- and long-term compensatory adaptations ().

Biochemistry is very helpful in terms of providing “pieces for the puzzle”, but biochemical reductionism is a problem. Analogous to biochemical reductionism, and perhaps one example of it, is hormonal reductionism – trying to argue that all diet and lifestyle effects are mediated by a single hormone. A less extreme position, but still myopic, is to argue that all diet and lifestyle effects are mostly mediated by a single hormone.

One of my own “favorite” hormones is adiponectin, which I have been discussing for years in this blog (). Increased serum adiponectin has been found to be significantly associated with: decreased body fat (particularly decreased visceral fat), decreased risk of developing diabetes type 2, and decreased blood pressure. Adiponectin appears to also have anti-inflammatory and athero-protective properties.

As a side note, typically women have higher levels of serum adiponectin than men, particularly young women. Culturally we have a tendency to see young women as “delicate” and “vulnerable”. Guess what? Young women are the closest we get to “indestructible” in the human species. And there is an evolutionary reason for that, which is that fertile women have been in our evolutionary past, and still are, the bottleneck of any population. A population of 100 individuals, where 99 are men and 1 is a woman, will quickly disappear. If it is 99 women and 1 fertile man, the population will grow; but there will also be some problems due to inbreeding. Even if the guy is ugly the population will grow; without competition, he will look very cute.

Jung and colleagues measured various hormone levels in 78 obese people who had visited obesity clinics at five university hospitals (Ajou, Ulsan, Catholic, Hanyang and Yonsei) in Korea (). Those folks restricted their caloric intake to 500 calories less than their usual intake, and exercised, for 12 weeks. Below are the measured changes in tumor necrosis factor α (TNF-α, now called only TNF), interleukin-6 (IL-6), resistin, leptin, adiponectin, and interleukin-10 (IL-10).

We see from the table above that the hormonal changes were all significant (all at the P equal to or lower than 0.001 level except one, at the P lower than 0.05 level), and all indicative of health improvements. The serum concentrations of all hormones decreased, with two exceptions – adiponectin and interleukin-10, which increased. Interleukin-10 is an anti-inflammatory hormone produced by white blood cells. The most significant increase of the two was by far in adiponectin (P = .001, versus P = .041 for interleukin-10).

Now, should we try to find a way of producing synthetic adiponectin then? My guess is that doing that will not lead to very positive results in human trials; because, as you can see from the table, hormones vary in concert. At the moment, the only way to “supplement” adiponectin is to lose body fat, and that leads to concurrent changes in many other hormones (e.g., TNF decreases).

Trying to manipulate one single hormone, or build an entire health-improvement approach based on its effects, is myopic. But that is what often happens. Leptin is a relatively recent example.

One reason why biochemistry is so complex, with so many convoluted processes, is that evolution is a tinkerer that is “blind” to complexity. Traits appear at random in populations and spread if they increase reproductive success; even if they decrease survival success, by the way ().

Evolution is not an engineer, and is not even our “friend” (). To optimize our health, we need to “hack” evolution.

If your NEAT is low, maybe you should chill

I wrote most of this post a while ago, and then forgot about it. The recent blogosphere storm of comments regarding cold-induced thermogenesis caught me by surprise (), and provided a motivation to get this post out. Contrary to popular perception, I guess, cold-induced thermogenesis is an extensively researched topic. Some reasonably well cited references are linked here.

Let us backtrack a bit. When people say that they want to lose weight, usually what they really want is to lose is body fat. However, they frequently do things that make them lose what they do not want – muscle glycogen, water, and even some muscle protein. Physical activity in general depletes muscle glycogen; even aerobic physical activity.

Walking, for example, depletes muscle glycogen; but slowly, and proportionally to how fast one walks. Weight training and sprints deplete muscle glycogen much faster. Whatever depletes muscle glycogen also lowers the amount of water stored in myocytes (muscle cells), effectively reducing muscle mass. Depleted muscle glycogen needs to be replenished; protein and carbohydrates are the sources. If you deplete muscle glycogen through strength training, this will provide a strong stimulus for glycogen replenishment and thus muscle growth, even beyond the original level – a phenomenon called supercompensation ().

In conjunction with strength training, situations in which one burns mostly fat, and very little glycogen, should be at the top of the list for those wishing to lose weight by losing body fat and nothing else. These are not very common though. One example is nonexercise activity thermogenesis (NEAT), or heat generation from nonexercise activities such as fidgeting (). There is a great deal of variation in NEAT across individuals; for some it is high, for others it is annoyingly low.

Walking slowly is almost as good as NEAT for body fat burning, when done in conjunction with strength training. Up the pace a bit though, and you’ll be burning more muscle glycogen. But if you walk slowly you don’t burn that much body fat per unit of time. If you walk a bit faster you’ll burn more fat, but also more glycogen. C’mon, there is no way to win in this game!

This is why being physically active, in a “non-exercise way”, seems to be so important for health; together with strength training, limiting calorie intake, and all the while having a nutritious diet. These are not very common things in modern urban environments. Long term, there isn’t a lot of margin for error. It is ultimately a game of small numbers in the short term, played over long periods of time.

But there is an alternative if your NEAT is low – just chill. That is, another situation in which one can burn mostly fat, and very little glycogen, is exposure to mildly cold temperatures, but above the level that induces shivering (mild cold: 16 degrees Celsius or so; about 60 degrees Fahrenheit). Shivering in general, and particularly intense shivering, is associated with levels of muscle activity that would induce glycogen depletion () (). If muscle glycogen depletion happens while one is fasting, liver glycogen will be used to replenish muscle glycogen, and also to supply the needs of the brain – which is always hungry for glucose.

As the liver glycogen tank goes down beyond a certain point, and no protein or carbohydrates are eaten, the body will use amino acids from muscle to produce glucose. Muscle glycogen will be locked until it is needed. Interesting eh!? The body sacrifices muscle protein but doesn’t tap into muscle glycogen, which is only used to fuel violent muscle contractions. We are talking about fight-or-flight responses here. From an evolutionary perspective, sacrificing some muscle beats losing a lot of it to a predator any day.

Cold-induced thermogenesis is a very interesting phenomenon. The figure below, where open circles represent lean and closed circles obese folks, shows that it leads to different responses in lean and obese folks, and also that it presents a lot of variation across different individuals (like NEAT). This type of thermogenesis actually seems to be strongly associated with an increase in NEAT (); although it seems to also be associated with futile cycles used by the body to generate heat without any movement, as in thermogenesis during hibernation in certain animals () (). Having more brown fat as an adult, or being able to make brown fat more easily, is associated with more cold-induced thermogenesis; and also with a lower obesity risk.

In fact, cold-induced thermogenesis leads to an increase in energy expenditure that is comparable with that of another major energy sinkhole – overfeeding () (). Unlike overfeeding though, cold-induced thermogenesis does not require calories to go in. And, no, you don’t burn more than you take in with overfeeding.

How can one burn fat via cold-induced thermogenesis? Here are some ideas. Set the home thermostat to a mildly cold temperature in the winter (this will also save you some money). When it is a little cooler than normal, don’t wear heavy clothes. Take mildly cold showers, or end a warm shower with some mildly cold water.

What about more extreme cold exposure? It should be no surprise that one would feel pretty good after a dip in ice-cold water; that is, if the person does not suffer from a glycogen storage disease (e.g., McArdle's disease). At least in theory, that type of cold exposure should induce whole-body muscle glycogen depletion, just like an intense whole-body exercise session, with the resulting hormonal changes ().

Growth hormone should be up after that, perhaps for hours. Done right after weight training, or intense exercise, it may have a boosting effect on the hormonal response. But if you do that in the recovery phase (e.g., several hours after the weight training session), it should impair muscle recovery. It would be a bit like doing another strength training session, when the body is trying to recover from the previous one.

The 2012 Arch Intern Med red meat-mortality study: Eating 234 g/d of red meat could reduce mortality by 23 percent

As we have seen in an earlier post on the China Study data (), which explored relationships hinted at by Denise Minger’s previous and highly perceptive analysis (), one can use a multivariate analysis tool like WarpPLS () to explore relationships based on data reported by others. This is true even when the dataset available is fairly small.

So I entered the data reported in the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality into WarpPLS to do some exploratory analyses. I discussed the study in my previous post; it was conducted by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). The data I used is from Table 1 of the article; it reports figures on several variables along 5 quintiles, based on separate analyses of two samples, called “Health Professionals” and “Nurses Health” samples. The Health Professionals sample comprised males; the Nurses Health sample, females.

Below is an interesting exploratory model, with results. It includes a number of hypotheses, represented by arrows, which seem to make sense. This is helpful, because a model incorporating hypotheses that make sense allows for easy identification of nonsense results, and thus rejection of the model or the data. (Refutability is one of the most important characteristics of good theoretical models.) Keep in mind that the sample size here is very small (N=10), as the authors of the study reported data along 5 quintiles for the Health Professionals sample, together with 5 quintiles for the Nurses Health sample. In a sense, this is somewhat helpful, because a small sample tends to be “unstable”, leading nonsense results and other signs of problems to show up easily – one example would be multivariate coefficients of association (the beta coefficients reported near the arrows) greater than 1 due to collinearity ().

So what does the model above tell us? It tells us that smoking (Smokng) is associated with reduced physical activity (PhysAct); beta = -0.92. It tells us that smoking (Smokng) is associated with reduced food intake (FoodInt); beta = -0.36. It tells us that physical activity (PhysAct) is associated with reduced incidence of diabetes (Diabetes); beta = -0.25. It tells us that increased food intake (FoodInt) is associated with increased incidence of diabetes (Diabetes); beta = 0.93. It tells us that increased food intake (FoodInt) is associated with increased red meat intake (RedMeat); beta = 0.60. It tells us that increased incidence of diabetes (Diabetes) is associated with increased mortality (Mort); beta = 0.61. It tells us that being female (SexM1F2) is associated with reduced mortality (Mort); beta = -0.67.

Some of these betas are a bit too high (e.g., 0.93), due to the level of collinearity caused by such a small sample. Due to being quite high, they are statistically significant even in a small sample. Betas greater than 0.20 tend to become statistically significant when the sample size is 100 or greater; so all of the coefficients above would be statistically significant with a larger sample size. What is the common denominator of all of the associations above? The common denominator is that all of them make sense, qualitatively speaking; there is not a single case where the sign is the opposite of what we would expect. There is one association that is shown on the graph and that is missing from my summary of associations above; and it also makes sense, at least to me. The model also tells us that increased red meat intake (RedMeat) is associated with reduced mortality (Mort); beta = -0.25. More technically, it tells us that, when we control for biological sex (SexM1F2) and incidence of diabetes (Diabetes), increased red meat intake (RedMeat) is associated with reduced mortality (Mort).

How do we roughly estimate this effect in terms of amounts of red meat consumed? The -0.25 means that, for each standard deviation in the amount of red meat consumed, there is a corresponding 0.25 standard deviation reduction of mortality. (This interpretation is possible because I used WarpPLS’ linear analysis algorithm; a nonlinear algorithm would lead to a more complex interpretation.) The standard deviation for red meat consumption is 0.897 servings. Each serving has about 84 g. And the highest number of servings in the dataset is 3.1 servings, or 260 g/d (calculated as: 3.1*84). To stay a bit shy of this extreme, let us consider a slightly lower intake amount, which is 3.1 standard deviations, or 234 g/d (calculated as: 3.1*0.897*84). Since the standard deviation for mortality is 0.3 percentage points, we can conclude that an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent (calculated as: 3.1*0.25*0.3).

Let me repeat for emphasis: the data reported by the authors suggests that, when we control for biological sex and incidence of diabetes, an extra 234 g of red meat per day is associated with a reduction in mortality of approximately 23 percent. This is exactly the opposite, qualitatively speaking, of what was reported by the authors in the article. I should note that this is also a minute effect, like the effect reported by the authors. (The mortality rates in the article are expressed as percentages, with the lowest being around 1 percent. So this 23 percent is a percentage of a percentage.) If you were to compare a group of 100 people who ate little red meat with another group of the same size that ate 234 g more of red meat every day, over a period of more than 20 years, you would not find a single additional death in either group. If you were to compare matched groups of 1,000 individuals, you would find only 2 additional deaths among the folks who ate little red meat.

At the same time, we can also see that excessive food intake is associated with increased mortality via its effect on diabetes. The product beta coefficient for the mediated effect FoodInt --> Diabetes --> Mort is 0.57. This means that, for each standard deviation of food intake in grams, there is a corresponding 0.57 standard deviation increase in mortality, via an increase in the incidence of diabetes. This is very likely at levels of food consumption where significantly more calories are consumed than spent, ultimately leading to many people becoming obese. The standard deviation for food intake is 355 calories. The highest daily food intake quintile reported in the article is 2,396 calories, which happens to be associated with the highest mortality (and is probably an underestimation); the lowest is 1,202 (also probably underestimated).

So, in summary, the data suggests that, for the particular sample studied (made up of two subsamples): (a) red meat intake is protective in terms of overall mortality, through a direct effect; and (b) the deleterious effect of overeating on mortality is stronger than the protective effect of red meat intake. These conclusions are consistent with those of my previous post on the same study (). The difference is that the previous post suggested a possible moderating protective effect; this post suggests a possible direct protective effect. Both effects are small, as was the negative effect reported by the authors of the study. Neither is statistically significant, due to sample size limitations (secondary data from an article; N=10). And all of this is based on a study that categorized various types of processed meat as red meat, and that did not distinguish grass-fed from non-grass-fed meat.

By the way, in discussions of red meat intake’s effect on health, often iron overload is mentioned. What many people don’t seem to realize is that iron overload is caused primarily by hereditary haemochromatosis. Another cause is “blood doping” to improve athletic performance (). Hereditary haemochromatosis is a very rare genetic disorder; rare enough to be statistically “invisible” in any study that does not specifically target people with this disorder.

The 2012 red meat-mortality study (Arch Intern Med): The data suggests that red meat is protective

I am not a big fan of using arguments such as “food questionnaires are unreliable” and “observational studies are worthless” to completely dismiss a study. There are many reasons for this. One of them is that, when people misreport certain diet and lifestyle patterns, but do that consistently (i.e., everybody underreports food intake), the biasing effect on coefficients of association is minor. Measurement errors may remain for this or other reasons, but regression methods (linear and nonlinear) assume the existence of such errors, and are designed to yield robust coefficients in their presence. Besides, for me to use these types of arguments would be hypocritical, since I myself have done several analyses on the China Study data (), and built what I think are valid arguments based on those analyses.

My approach is: Let us look at the data, any data, carefully, using appropriate analysis tools, and see what it tells us; maybe we will find evidence of measurement errors distorting the results and leading to mistaken conclusions, or maybe not. With this in mind, let us take a look at the top part of Table 3 of the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality, authored by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). This is a prominent journal, with an average of over 270 citations per article according to Google Scholar. The study has received much media attention recently.

Take a look at the area highlighted in red, focusing on data from the Health Professionals sample. That is the multivariate-adjusted cardiovascular mortality rate, listed as a normalized percentage, in the highest quintile (Q5) of red meat consumption from the Health Professionals sample. The non-adjusted percentages are 1.4  percent mortality in Q5 and 1.13 in Q1 (from Table 1 of the same article); so the multivariate adjustment-normalization changed the values of the percentages somewhat, but not much. The highlighted 1.35 number suggests that for each group of 100 people who consumed a lot of red meat (Q5), when compared with a group of 100 people who consumed little red meat (Q1), there were on average 0.35  more deaths over the same period of time (more than 20 years).

The heavy red meat eaters in Q5 consumed 972.37 percent more red meat than those in Q1. This is calculated with data from Table 1 of the same article, as: (2.36-0.22)/0.22. In Q5, the 2.36 number refers to the number of servings of red meat per day, with each serving being approximately 84 g. So the heavy red meat eaters ate approximately 198 g per day (a bit less than 0.5 lb), while the light red meat eaters ate about 18 g per day. In other words, the heavy red meat eaters ate 9.7237 times more, or 972.37 percent more, red meat.

So, just to be clear, even though the folks in Q5 consumed 972.37 percent more red meat than the folks in Q1, in each matched group of 100 you would not find a single additional death over the same time period. If you looked at matched groups of 1,000 individuals, you would find 3 more deaths among the heavy red meat eaters. The same general pattern, of a minute difference, repeats itself throughout Table 3. As you can see, all of the reported mortality ratios are 1-point-something. In fact, this same pattern repeats itself in all mortality tables (all-cause, cardiovascular, cancer). This is all based on a multivariate analysis that according to the authors controlled for a large number of variables, including baseline history of diabetes.

Interestingly, looking at data from the same sample (Health Professionals), the incidence of diabetes is 75 percent higher in Q5 than in Q1. The same is true for the second sample (Nurses Health), where the Q5-Q1 difference in incidence of diabetes is even greater - 81 percent. This caught my eye, being diabetes such a prototypical “disease of affluence”. So I entered the whole data reported in the article into HCE () and WarpPLS (), and conducted some analyses. The graphs below are from HCE. The data includes both samples – Health Professionals and Nurses Health.

HCE calculates bivariate correlations, and so does WarpPLS. But WarpPLS stores numbers with a higher level of precision, so I used WarpPLS for calculating coefficients of association, including correlations. I also double-checked the numbers with other software, just in case (e.g., SPSS and MATLAB). Here are the correlations calculated by WarpPLS, which refer to the graphs above: 0.030 for red meat intake and mortality; 0.607 for diabetes and mortality; and 0.910 for food intake and diabetes. Yes, you read it right, the correlation between red meat intake and mortality is a very low and non-significant 0.030 in this dataset. Not a big surprise when you look at the related HCE graph, with the line going up and down almost at random. Note that I included the quintiles data from both the Health Professionals and Nurses Health samples in one dataset.

Those folks in Q5 had a much higher incidence of diabetes, and yet the increase in mortality for them was significantly lower, in percentage terms. A key difference between Q5 and Q1 being what? The Q5 folks ate a lot more red meat. This looks suspiciously suggestive of a finding that I came across before, based on an analysis of the China Study II data (). The finding was that animal food consumption (and red meat is an animal food) was protective, actually reducing the negative effect of wheat flour consumption on mortality. That analysis actually suggested that wheat flour consumption may not be so bad if you eat 221 g or more of animal food daily.

So, I built the model below in WarpPLS, where red meat intake (RedMeat) is hypothesized to moderate the relationship between diabetes incidence (Diabetes) and mortality (Mort). Below I am also including the graphs for the direct and moderating effects; the data is standardized, which reduces estimation error, particularly in moderating effects estimation. I used a standard linear algorithm for the calculation of the path coefficients (betas next to the arrows) and jackknifing for the calculation of the P values (confidence = 1 – P value). Jackknifing is a resampling technique that does not require multivariate normality and that tends to work well with small samples; as is the case with nonparametric techniques in general.

The direct effect of diabetes on mortality is positive (0.68) and almost statistically significant at the P < 0.05 level (confidence of 94 percent), which is noteworthy because the sample size here is so small – only 10 data points, 5 quintiles from the Health Professionals sample and 5 from the Nurses Health sample. The moderating effect is negative (-0.11), but not statistically significant (confidence of 61 percent). In the moderating effect graphs (shown side-by-side), this negative moderation is indicated by a slightly less steep inclination of the regression line for the graph on the right, which refers to high red meat intake. A less steep inclination means a less strong relationship between diabetes and mortality – among the folks who ate the most red meat.

Not too surprisingly, at least to me, the results above suggest that red meat per se may well be protective. Although we should consider a least two other possibilities. One is that red meat intake is a marker for consumption of some other things, possibly present in animal foods, that are protective - e.g., choline and vitamin K2. The other possibility is that red meat is protective in part by displacing other less healthy foods. Perhaps what we are seeing here is a combination of these.

Whatever the reason may be, red meat consumption seems to actually lessen the effect of diabetes on mortality in this sample. That is, according to this data, the more red meat is consumed, the fewer people die from diabetes. The protective effect might have been stronger if the participants had eaten more red meat, or more animal foods containing the protective factors; recall that the threshold for protection in the China Study II data was consumption of 221 g or more of animal food daily (). Having said that, it is also important to note that, if you eat excess calories to the point of becoming obese, from red meat or any other sources, your risk of developing diabetes will go up – as the earlier HCE graph relating food intake and diabetes implies.

Please keep in mind that this post is the result of a quick analysis of secondary data reported in a journal article, and its conclusions may be wrong, even though I did my best not to make any mistake (e.g., mistyping data from the article). The authors likely spent months, if not more, in their study; and have the support of one of the premier research universities in the world. Still, this post raises serious questions. I say this respectfully, as the authors did seem to try their best to control for all possible confounders.

I should also say that the moderating effect I uncovered is admittedly a fairly weak effect on this small sample and not statistically significant. But its magnitude is apparently greater than the reported effects of red meat on mortality, which are not only minute but may well be statistical artifacts. The Cox proportional hazards analysis employed in the study, which is commonly used in epidemiology, is nothing more than a sophisticated ANCOVA; it is a semi-parametric version of a special case of the broader analysis method automated by WarpPLS.

Finally, I could not control for confounders because, given the small sample, inclusion of confounders (e.g., smoking) leads to massive collinearity. WarpPLS calculates collinearity estimates automatically, and is particularly thorough at doing that (calculating them at multiple levels), so there is no way to ignore them. Collinearity can severely distort results, as pointed out in a YouTube video on WarpPLS (). Collinearity can even lead to changes in the signs of coefficients of association, in the context of multivariate analyses - e.g., a positive association appears to be negative. The authors have the original data – a much, much larger sample - which makes it much easier to deal with collinearity.

Moderating effects analyses () – we need more of that in epidemiological research eh?

Gaining muscle and losing fat at the same time: A more customized approach based on strength training and calorie intake variation

In the two last posts I discussed the idea of gaining muscle and losing fat at the same time () (). This post outlines one approach to make that happen, based on my own experience and that of several HCE () users. This approach may well be the most natural from an evolutionary perspective.

But first let us address one important question: Why would anyone want to reach a certain body weight and keep it constant, resorting to the more difficult and slow strategy of “turning fat into muscle”, so to speak? One could simply keep on losing fat, without losing or gaining muscle, until he or she reaches a very low body fat percentage (e.g., a single-digit body fat percentage, for men). Then he or she could go up from there, slowly putting on muscle.

The reason why it is advisable to reach a certain body weight and keep it constant is that, below a certain weight, one is likely to run into nutrient deficiencies. Non-exercise energy expenditure is proportional to body weight. As you keep on losing body weight, calorie intake may become too low to allow you to have a nutrient intake that is the minimum for your body structure. Unfortunately eating highly nutritious vegetables or consuming copious amounts of vitamin and mineral supplements will not work very well, because the nutritional needs of your body include both micro- and macro-nutrients that need co-factors to be properly absorbed and/or metabolized. One example is dietary fat, which is necessary for the absorption of fat-soluble vitamins.

If you place yourself into a state of nutrient deficiency, your body will compensate by mounting a multipronged defense, resorting to psychological and physiological mechanisms. Your body will do that because it is hardwired for self-preservation; as noted below, being in a state of nutrient deficiency for too long is very dangerous for one's health. Most people cannot oppose this body reaction by willpower alone. That is where binge-eating often starts. This is one of the key reasons why looking for a common denominator of most diets leads to the conclusion that all succeed at first, and eventually fail ().

If you are one of the few who can oppose the body’s reaction, and maintain a very low calorie intake even in the face of nutrient deficiencies, chances are you will become much more vulnerable to diseases caused by pathogens. Individually you will be placing yourself in a state that is similar to that of populations that have faced famine in the past. Historically speaking, famines are associated with decreases in degenerative diseases, and increases in diseases caused by pathogens. Pandemics, like the Black Death (), have historically been preceded by periods of food scarcity.

The approach to gaining muscle and losing fat at the same time, outlined here, relies mainly on the following elements: (a) regularly conducting strength training; (b) varying calorie intake based on exercise; and (c) eating protein regularly. To that, I would add becoming more active, which does not necessarily mean exercising but does mean doing things that involve physical motion of some kind (e.g., walking, climbing stairs, moving things around), to the tune of 1 hour or more every day. These increase calorie expenditure, enabling a slightly higher calorie intake while maintaining the same weight, and thus more nutrients on a diet of unprocessed foods. In fact, even things like fidgeting count (). These activities should not cause muscle damage to the point of preventing recovery from strength training.

As far as strength training goes, the main idea, as discussed in the previous post, is to regularly hit the supercompensation window, with progressive overload, and maintain your current body weight. In fact, over time, as muscle gain progresses, you will probably want to increase your calorie intake to increase your body weight, but very slowly to keep any fat gain from happening. This way your body fat percentage will go down, even as your weight goes up slowly. The first element, regularly hitting the supercompensation window, was discussed in a previous post ().

Varying calorie intake based on exercise. Here one approach that seems to work well is to eat more in the hours after a strength training session, and less in the hours preceding the next strength training session, keeping the calorie intake at maintenance over a week. Individual customization here is very important. Many people will respond quite well to a calorie surplus window of 8 – 24 h after exercise, and a calorie deficit in the following 40 – 24 h. This assumes that strength training sessions take place every other day. The weekend break in routine is a good one, as well as other random variations (e.g., random fasts), as the body tends to adapt to anything over time ().

One example would be someone following a two-day cycle where on the first day he or she would do strength training, and eat the following to satisfaction: muscle meats, fatty seafood (e.g., salmon), cheese, eggs, fruits, and starchy tubers (e.g., sweet potato). On the second day, a rest day, the person would eat the following, to near satisfaction, limiting portions a bit to offset the calorie surplus of the previous day: organ meats (e.g., heart and liver), lean seafood (e.g., shrimp and mussels), and non-starchy nutritious vegetables (e.g., spinach and cabbage). This would lead to periodic glycogen depletion, and also to unsettling water-weight variations; these can softened a bit, if they are bothering, by adding a small amount of fruit and/or starchy foods on rest days.

Organ meats, lean seafood, and non-starchy nutritious vegetables are all low-calorie foods. So restricting calories with them is relatively easy, without the need to reduce the volume of food eaten that much. If maintenance is achieved at around 2,000 calories per day, a possible calorie intake pattern would be 3,000 calories on one day, mostly after strength training, and 1,000 calories the next. This of course would depend on a number of factors including body size and nonexercise thermogenesis. A few calories could be added or removed here and there to make up for a different calorie intake during the weekend.

Some people believe that, if you vary your calorie intake in this way, the calorie deficit period will lead to muscle loss. This is the rationale behind the multiple balanced meals a day approach; which also works, and is successfully used by many bodybuilders, such as Doug Miller () and Scooby (). However, it seems that the positive nitrogen balance stimulus caused by strength training leads to a variation in nitrogen balance that is nonlinear and also different from the stimulus to muscle gain. Being in positive or neutral nitrogen balance is not the same as gaining muscle mass, although the two should be very highly correlated. While the muscle gain window may close relatively quickly after the strength training session, the window in which nitrogen balance is positive or neutral may remain open for much longer, even in the face of a calorie deficit during part of it. This difference in nonlinear response is illustrated through the schematic graph below.

Eating protein regularly. Here what seems to be the most advisable approach is to eat protein throughout, in amounts that make you feel good. (Yes, you should rely on sense of well being as a measure as well.) There is no need for overconsumption of protein, as one does not need much to be in nitrogen balance when doing strength training. For someone weighing 200 lbs (91 kg) about 109 g/d of high-quality protein would be an overestimation () because strength training itself pushes one’s nitrogen balance into positive territory (). The amount of carbohydrate needed depends on the amount of glycogen depleted through exercise and the amount of protein consumed. The two chief sources for glycogen replenishment, in muscle and liver, are protein and carbohydrate – with the latter being much more efficient if you are not insulin resistant.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder (). Still, consumption of protein stimulates muscle growth through complex processes. And protein does not usually become fat if one is in calorie deficit, particularly if consumption of carbohydrates is limited ().

The above is probably much easier to understand than to implement in practice, because it requires a lot of customization. It seems natural because our Paleolithic ancestors probably consumed more calories after hunting-gathering activities (i.e., exercise), and fewer calories before those activities. Our body seems to respond quite well to alternate day calorie restriction (). Moreover, the break in routine every other day, and the delayed but certain satisfaction provided by the higher calorie intake on exercise days, can serve as powerful motivators.

The temptation to set rigid rules, or a generic formula, always exists. But each person is unique (). For some people, adopting various windows of fasting (usually in the 8 – 24 h range) seems to be a very good strategy to achieve calorie deficits while maintaining a positive or neutral nitrogen balance.

For others, fasting has the opposite effect, perhaps due to an abnormal increase in cortisol levels. This is particularly true for fasting windows of 12 – 24 h or more. If regularly fasting within this range stresses you out, as opposed to “liberating” you (), you may be in the category that does better with more frequently meals.