Slow versus slow-brisk walking: Effects on type 2 diabetics

I am not a big fan of reviewing new studies published in refereed journals, particularly those that make it to the news. I prefer studies that have been published for a while, so that I can look at citations to them – both positive and negative.

But I am making an exception here to a study by Kristian Karstoft and colleagues (the senior author is diabetes researcher Thomas Solomon: ), accepted for publication on 30 June 2014 in the fairly targeted and selective journal Diabetologia (full text freely available in a .zip file at the time of this writing: ).

This is a small study. Individuals diagnosed with type 2 diabetes, and who were not being treated for the condition, were allocated to three groups: a control group (CON), an “interval” walking group (IWT), and a slow walking group (CWT).

The groups had 8, 12, and 12 people in them, respectively. Those people in the IWT group alternated between walking briskly and slowly for 1 hour five times a week. Those in the CWT group only walked slowly. Those in the CON group supposedly did not do any targeted exercise.

One of the interesting findings of this study was that there was no difference in terms of health effects between the CWT and the CON groups. The only group that benefited was the IWT group. That is, those who alternated between walking briskly and slowly benefited in a way that was observable from the exercise, but those who walked slowly did not.

This study highlights two facts that I have mentioned here before, but that are often overlooked by those who suffer from type 2 diabetes or are on their way to developing the condition. They refer to visceral fat and are listed below. Visceral fat accumulates around the abdominal organs ().

- Type 2 diabetes is strongly associated with visceral fat accumulation, and is somewhat unrelated to subcutaneous fat accumulation (see the case of sumo wrestlers: ).

- Visceral fat is very easy to burn via glycolytic exercise, but does not seem to respond well to non-glycolytic exercise.

Glycolytic exercise burns sugar stored in muscle, in the form of glycogen, while it is being performed. This form of exercise raises growth hormone levels acutely. Weight training and sprints are types of glycolytic exercise, which also takes other names, such as glycogen-depleting and anaerobic exercise.

Often one sees prediabetics and type 2 diabetics avoiding this type of exercise because it pushes their blood glucose levels through the roof. That happens, however, only during the exercise. After, the benefits are tremendous and appear to clearly outweigh the possible problems associated with the temporary exercise-induced hyperglycemia.

Take a look at the last line of this cropped version of Table 1 from the study, shown below. The relevant line for the point made above is the one that refers to visceral fat volume. As you can see, those in the IWT group had the greatest reduction in visceral fat. This was also the only statistically significant reduction among the three groups; according to an analysis of variance (ANOVA) test, the probability that it was due to chance was lower than one tenth of one percent.

The ANOVA test is "parametric", in the sense that it assumes that the data is normally distributed. However, the authors did not report conducting a test of normality. Also, the sample is very small. Given these, "non-parametric" tests, such as multiple one-group-two-conditions tests run with WarpPLS (link to specific page of the .pdf file of a relevant academic paper: ) would not only be more advisable but also provide more much more information to readers.

If you compare the line showing visceral fat with the other two above it, within the body composition section of the table, you will notice another interesting pattern. In the IWT group the changes in average total body mass and total fat mass were also the greatest, but the largest change in percentage terms was the one in average visceral fat mass. Visceral fat mass is often correlated with total fat mass, with this correlation being a function of how sedentary individuals are, and it does not take a lot of it to cause serious problems.

Sumo wrestlers tend to have large ratios of total to visceral fat mass. Virtually all of their body fat is subcutaneous. They also carry a lot of muscle mass. They achieve these through intense glycolytic exercise alternated with periods of rest and consumption of large amounts of calorie-dense food. To these they add another ingredient - exercise in the fasted state, usually in the morning prior to a large breakfast. Exercise in the fasted state seems particularly conducive to visceral fat mobilization.

By the way, sumo wrestlers consume enormous amounts of carbohydrates, but as noted by Karam () have "low visceral fat, absent hyperglycemia and absent dyslipidemia despite massive subcutaneous obesity".

In my opinion the folks in the study by Karstoft and colleagues would have benefited even more, possibly a lot more, if they had alternated between sprinting and regular walking.

Waist-to-weight ratio vs. body max index

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

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

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

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

Waist-to-weight ratios in pictures: The John Stone transformation

John Stone is a bodybuilder and founder of a bodybuilding and fitness web site (). There he has provided pictures and stats of his remarkable transformation, which were used to prepare the montage below.



John’s height is reported as 5' 11.5". Below the photos are the months in which they were taken, the waist circumferences in inches, the weights in lbs, and the waist-to-weight ratios (WWRs). Abhi was kind enough to provide a more detailed plot of John Stone’s WWRs ().

Assuming that minimizing one’s WWR is healthy, an idea whose rationale was explained here before (), we could say that John was at his most unhealthy in the photo on the left.

The second photo from the left shows a slightly more healthy state, at a reported 8 percent body fat (his lowest). The two photos on the right represent states in which John’s WWR is at its lowest, namely 0.1544. That is, in these two photos John minimized his WWR; at a reported 14 and 13.8 percent body fat, respectively.

When we look at the WWRs in these photos, it seems that he is only marginally healthier in the second photo from the left than in the leftmost photo. In the two photos on the right, the WWRs are much lower (they are the same), suggesting that he was significantly healthier in those photos.

Interestingly, in both photos on the right John reported to have been at the end of bulking periods. Whenever he entered a cutting period his WWR started going up. This suggests that his ratio of lean body mass to total mass started decreasing just as soon as he started cutting. I suspect the same would happen if he continued gaining weight.

Which of the two photos on the right represents the best state? Assuming that both states are sustainable, over the long run I would argue that the best state is the one where the WWR was minimized with the lowest weight. There whole-day joint stress is lower. This corresponds to the photo at the far right.

By sustainable states I mean states that are not reached through approaches that are unhealthy in the long term; e.g., approaches that place organs under such an abnormal stress that they are damaged over time. This kind of damage is essentially what happens when we become obese – i.e., too fat. One can also become too muscular for his or her own good.

An illustration of the waist-to-weight ratio theory: The fit2fat2fit experiment

In my previous blog post, I argued that one’s optimal weight may be the one that minimizes one’s waist-to-weight ratio. I built this argument based on the fact that body fat percentage is associated with lean body mass (and also weight) in a nonlinear way.

The fit2fat2fit experiment (), provides what seems to be an interestingly way to put this optimal waist-to-weight ratio theory to test. This is due to a fortuitous event, as I explain in this post.

In this experiment, Drew Manning, a personal trainer, decided to undergo a transformation where he went from what he argued was his fittest level, all the way to obese, and then back to fit again. He said that he wanted to do that so that he could better understand his clients’ struggles. This may be true, but it looks like he planned very well his experiment from a marketing perspective.

His fittest level was at the start, with a weight of 193 lbs, at a height of 6 ft 2 in. That was his fittest level according to his own opinion. At that point, he had a waist of 34.5 in, and looked indeed very fit (). At his fattest level, he reached the weight of 264.8 pounds, with a 47.5 waist.

As he moved back to fit, one interesting thing happened. Toward the end of this journey back to fit, he moved past the level that he felt was his optimal. He dropped down to 190.1 lbs, and a 34 in waist; which he perceived as too skinny. He talks about this in a video ().

As a self-defined “fanatic” personal trainer, I figured that he knew when he had gone too far. That is, he is probably as qualified as one can get to identify the point at which he moved past his optimal. So I thought that this would be an interesting way of putting my optimal waist-to-weight ratio theory to the test.

Below is a bar chart showing variations in waist-to-weight ratio against weight for Drew Manning during his fit2fat2fit experiment. I included only three data points in this chart because I would have to view all of his video clips to get all of the data points.



As you can see, at the point at which he felt he was too thin, his waist-to-weight ratio clearly started going up from what seems to have been its optimal at 34.5 in / 193 lbs. This is exactly what you would expect based on my optimal waist-to-weight ratio theory. You probably can’t tell that something was not right at that point, because he looked very fit.

But apparently he felt that something was not entirely right. And that is consistent with the idea that he had passed his optimal waist-to-weight ratio, and became too lean for his own good. Note that his waist decreased, and probably could go down even further, even though that was no longer optimal.

What is your optimal weight? Maybe it is the one that minimizes your waist-to-weight ratio

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

L / T

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

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

L / T = (T – F) / T = 1 – F / T

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

F / T

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

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

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

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

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

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

W / T = waist / weight

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

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

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

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

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

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

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

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

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.

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!

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.