The 14-percent advantage of eating little and then a lot: Putting it in practice

In another post () I discussed evidence that the human body may react to “eating big” as it would to overfeeding, increasing energy expenditure by a certain amount. That increase seems to lead to a reduction in the caloric value of the meals during overfeeding; a reduction that seems to gravitate around 14 percent of the overfed amount.

And what is the overfed amount? Let us assume that your daily calorie intake to maintain your current body weight is 2,000 calories. However, one day you consume 1,000 calories, and the next 3,000 – adding up to 4,000 calories in 2 days. This amounts to 2,000 calories per day on average, the weight maintenance amount; but the extra 1,000 on the second day is perceived by your body as overfeeding. So 140 calories are “lost”.

The mechanisms by which this could happen are not entirely clear. Some studies contain clues; one example is the 2002 study conducted with mice by Anson and colleagues (), from which the graphs below were taken.

In the graphs above AL refers to ad libitum feeding, LDF to limited daily feeding (40 percent less than AL), IF to intermittent (alternate-day) fasting, and PF to pair-fed mice that were provided daily with a food allotment equal to the average daily intake of mice in the IF group. PF was added as a control condition; in practice, the 2-day food consumption was about the same in AL, IF and PF.

After a 20-week period, intermittent fasting was associated with the lowest blood glucose and insulin concentrations (graphs a and b), and the highest concentrations of insulin growth factor 1 and ketones (graphs c and d). These seem to be fairly positive outcomes. In humans, they would normally be associated with metabolic improvements and body fat loss.

Let us go back to the 14 percent advantage of eating little and then a lot; a pattern of eating that can be implemented though intermittent fasting, as well as other approaches.

So it seems that if you consume the same number of calories, but you do that while alternating between underfeeding and overfeeding, you actually “absorb” 14 percent fewer calories – with that percentage applied to the extra calorie intake above the amount needed for weight maintenance.

And here is a critical point: energy expenditure does not seem to be significantly reduced by underfeeding, as long as it is short-term underfeeding – e.g., about 24 h or less. So you don’t “gain back” the calories due to a possible reduction in energy expenditure in the (relatively short) underfeeding period.

What do 140 calories mean in terms of fat loss? Just divide that amount by 9 to get an estimate; about 15 g of fat lost. This is about 1 lb per month, and 12 lbs per year. Does one lose muscle due to this, in addition to body fat? A period of underfeeding of about 24 h or less should not be enough to lead to loss of muscle, as long as one doesn’t do glycogen-depleting exercise during that period ().

Also, underfeeding appears to increase the body’s receptivity to both micronutrients and macronutrients. This applies to protein, carbohydrates, vitamins etc. For example, the activity of liver and muscle glycogen synthase is significantly increased by underfeeding (the scientific term is “phosphorylation”), particularly carbohydrate underfeeding, effectively raising the insulin sensitivity of those tissues.

The same happens, in general terms, with a host of other tissues and nutrients; often mediated by enzymes. This means that after a short period of underfeeding your body is primed to absorb micronutrients and macronutrients more effectively, even as it uses up some extra calories – leading to a 14 percent increase in energy expenditure.

There are many ways in which this can be achieved. Intermittent fasting is one of them; with 16-h to 24-h fasts, for example. Intermittent calorie restriction is another; e.g., with a 1/3 and 2/3 calorie consumption pattern across two-day periods. Yet another is intermittent carbohydrate restriction, with other macronutrients kept more or less constant.

If the same amount of food is consumed, there is evidence suggesting that such practices would lead to body weight preservation with improved body composition – same body weight, but reduced fat mass. This is what the study by Anson and colleagues, mentioned earlier, suggested.

A 2005 study by Heilbronn and colleagues on alternate day fasting by humans suggested a small decrease in body weight (); although the loss was clearly mostly of fat mass. Interestingly, this study with nonobese humans suggested a massive decrease in fasting insulin, much like the mice study by Anson and colleagues.

Having said all of the above, there may be people who gain body fat by alternating between eating little and a lot. Why would that be? A possible reason is that when they eat a lot their caloric intake exceeds the increased energy expenditure.

The 14-percent advantage of eating little and then a lot: Is it real?

When you look at the literature on overfeeding, you see a number over and over again – 14 percent. That is approximately the increase in energy expenditure you get when you overfeed people; that is, when you feed people more calories that they need to maintain their current weight.

This phenomenon is related to another interesting one: the nonlinear increase in body weight and fat mass following overfeeding after a period starvation, illustrated by the top graph below from an article by Kevin Hall (). The data for the squares on the top graph is from the Minnesota Starvation Experiment (). The graph at the bottom is based mostly on the results of a simulation, and doesn’t clearly reflect the phenomenon.

Due to the significant amount of weight lost in what is called above the semistarvation stage (SS), the controlled refeeding period (CR) actually involved significant overfeeding. Nevertheless, weight was not gained right away, due to a sharp increase in energy expenditure. That is illustrated by the U-curve shape of the weight gain in response to overfeeding. Initially the gain is minimal, increasing over time, and continuing through the ad libitum refeeding stage (ALR).

Interestingly, overfeeding leads to increased energy expenditure almost immediately after it starts happening. It seems that even one single unusually big meal will significantly increase energy expenditure. Also, the 14 percent is usually associated with meals with a balanced amount of macronutrients. That percentage seems to go down if the balance is significantly shifted toward dietary fat (), probably because the metabolic “cost” of converting dietary fat into body fat is low. In other words, large meals with a lot of fat in them tend to cause a reduced increase in energy expenditure – less than 14 percent. Shifting the balance to protein appears to have the opposite effect, increasing energy expenditure even more, probably because protein is the jack-of-all-trades among macronutrients ().

The calorie surplus used in experiments where the 14 percent increase in energy expenditure is observed is normally around 1,000 calories, but the percentage seems to hold steady when people are overfed to different degrees () (). Let us assume that one is overfed 1,000 calories. What happens? About 140 calories are “lost” due to overfeeding.

What does this have to do with eating little, and then a lot, in an alternate way? It allows for some reasonable speculation, based on a simple pattern: when you alternate between underfeeding and overfeeding, you reduce food consumption for short period of time (usually less than 24 h), and then eat big, because you are hungry.

It is reasonable to assume, based on the empirical evidence on what happens during overfeeding, that the body reacts to “eating big” as it would to overfeeding, increasing energy expenditure by a certain amount. That increase leads to a reduction in the caloric value of the meals during overfeeding; a reduction of about 14 percent of the overfed amount.

But the body does not seem to significantly decrease energy expenditure if one reduces food consumption for a short period of time, such as 24 h. So you have the potential here for some steady fat loss without a reduction in caloric intake. Keeping a calorie intake up above a certain point is more important than many people think, because a calorie intake that is too low may lead to nutrient deficiencies (). This is possibly one of the reasons why carrying a bit of extra weight is associated with increased longevity in relatively sedentary populations ().

Is this 14-percent effect real, or just another mirage? If yes, what does it possibly translate into in terms of fat loss? More on these issues is coming in the next post.

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 relative age-adjusted mortality risk (i.e., 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 weight gain (as opposed to weight loss).

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.

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!).

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.

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!