What is the probability that you are NOT diabetic if your fasting blood glucose is 110-126 mg/dl?

Often I hear from readers who have changed their diets and lifestyles toward a more evolutionarily sound direction () that their fasting blood glucose (FBG) readings have gone up. Frequently numbers in the range 110-126 mg/dl (6.1-7 mmol/l) are mentioned.

If you have a FBG reading of 110-126 mg/dl (6.1-7 mmol/l) very likely your doctor will tell you that you are either diabetic or well on your way be becoming diabetic.

Diabetes is a condition that in humans is most frequently associated with damage to the beta cells in the pancreas, significantly impairing insulin secretion. With limited insulin, glucose levels tend to go up, leading to high FBG levels and high glucose peaks after consumption of carbohydrates. The latter, high glucose peaks, appear to be particularly damaging when happening regularly over time.

What is the probability that you are NOT diabetic with this FBG reading?

I put together the table below, based on data from a widely cited meta-analysis () conducted by the research group called The Emerging Risk Factors Collaboration. It shows the distribution of FBG levels in urban settings among individuals who do not have diabetes.

The numbers in this table are fairly consistent with those from various other surveys of large numbers of individuals in urban settings.

The study mentioned above also tells us that the incidence of diabetes in urban populations is in the neighborhood of 6.8 percent. This may not sound like much, but as disease incidences goes, it is very high – approximately 1 in every randomly selected group of 15 people has diabetes.

The vast majority of those diagnosed will have diabetes mellitus type 2, which tends to develop over time and be associated with the metabolic syndrome ().

We know from Bayes' theorem, which is a fundamental element of the increasingly popular Bayesian statistics, that the probability of an event A given that an event B has occurred [denoted P(A|B)] is given by:

P(A|B)=P(B|A)*P(A)/P(B).

In the equation above, P(B|A) is the probability of event B given A, P(A) is the probability of event A, and P(B) is the probability of event B.

To answer the question posed in the title of this blog post, we need to calculate the probability that a person will have no diabetes given that he or she has a fasting blood glucose of 110-126 mg/dl.

Replacing A and B in the equation above with “NoDiabetes” (short for not having diabetes) and “FBG=110-126 mg/dl” respectively, we arrive at the formula to calculate the probability that answers the question:

P(NoDiabetes|FBG=110-126 mg/dl)=P(FBG=110-126 mg/dl|NoDiabetes)*P(NoDiabetes)/P(FBG=110-126 mg/dl).

From the table above we know that P(FBG=110-126 mg/dl|NoDiabetes)=7 percent. From our previous discussion, we know that P(NoDiabetes)=(100-6.8)/100 =93.2 percent.

Finally, the study tells us that P(FBG=110-126 mg/dl) is 9.1 percent. This includes individuals with diabetes (2.1 percent) and without diabetes (7 percent).

With these numbers, we can calculate the probability that a person will have no diabetes given that he or she has a FBG of 110-126 mg/dl:

P(NoDiabetes|FBG=110-126 mg/dl)=0.07*(1-0.068)/0.091=0.72.

That is, if your fasting blood glucose is in the 110-126 mg/dl range (6.1-7 mmol/l) then the probability that you DO NOT have diabetes is 72 percent. It would be much safer to bet that you do not have diabetes than that you do, even at that relatively high range.

Surprising eh!?

The above discussion not only highlights the lack of reliability of fasting blood glucose levels for diabetes diagnoses in the 110-126 mg/dl range (6.1-7 mmol/l), but also begs the question – what could cause high fasting blood glucose levels in healthy individuals?

Some of the folks I heard from have gone through insulin sensitivity tests (see, e.g., ), and were found to be insulin sensitive (in at least one case, highly sensitive), even though their baseline glucose levels are generally high. This goes against the possible speculation that they are prediabetics well on their way to becoming diabetic.

One possibility has been discussed in a previous post, which also mentions what could happen with HbA1c levels ().

How much protein does one need to be in nitrogen balance?

The figure below, from Brooks et al. (2005), shows a graph relating nitrogen balance and protein intake. A nitrogen balance of zero is a state in which body protein mass is stable; that is, it is neither increasing nor decreasing. It seems that the graph was taken from this classic study by Meredith et al. The participants in the study were endurance exercisers. As you can see, age is not much of a factor for nitrogen balance in this group.

Nitrogen balance is greater than zero (i.e., an anabolic state) for the vast majority of the participants at 1.2 g of protein per kg of body weight per day. To convert lbs to kg, divide by 2.2. A person weighing 100 lbs (45 kg) would need 55 g/d of protein; a person weighing 155 lbs (70 kg) would need 84 g/d; someone weighing 200 lbs (91 kg) would need 109 g/d.

The above numbers are overestimations of the amounts needed by people not doing endurance exercise, because endurance exercise tends to lead to muscle loss more than rest or moderate strength training. One way to understand this is compensatory adaptation; the body adapts to endurance exercise by shedding off muscle, as muscle is more of a hindrance than an asset for this type of exercise.

Total calorie intake has a dramatic effect on protein requirements. The above numbers assume that a person is getting just enough calories from other sources to meet daily caloric needs. If a person is in caloric deficit, protein requirements go up. If in caloric surplus, protein requirements go down. Other factors that increase protein requirements are stress and wasting diseases (e.g., cancer).

But what if you want to gain muscle?

Wilson & Wilson (2006) conducted an extensive review of the literature on protein intake and nitrogen balance. That review suggests that a protein intake beyond 25 percent of what is necessary to achieve a nitrogen balance of zero would have no effect on muscle gain. That would be 69 g/d for a person weighing 100 lbs (45 kg); 105 g/d for a person weighing 155 lbs (70 kg); and 136 g/d for someone weighing 200 lbs (91 kg). For the reasons explained above, these are also overestimations.

What if you go well beyond these numbers?

The excess protein will be used primarily as fuel; that is, it will be oxidized. In fact, a large proportion of all the protein consumed on a daily basis is used as fuel, and does not become muscle. This happens even if you are a gifted bodybuilder that can add 1 lb of protein to muscle tissue per month. So excess protein can make you gain body fat, but not by protein becoming body fat.

Dietary protein does not normally become body fat, but will typically be used in place of dietary fat as fuel. This will allow dietary fat to be stored. Dietary protein also leads to an insulin response, which causes less body fat to be released. In this sense, protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body. As long as it is available, dietary protein will be favored over dietary or body fat as a fuel source.

Having said that, if you were to overeat anything, the best choice would be protein, in the absence of any disease that would be aggravated by this. Why? Protein contributes fewer calories per gram than carbohydrates; many fewer when compared with dietary fat. Unlike carbohydrates or fat, protein almost never becomes body fat under normal circumstances. Dietary fat is very easily converted to body fat; and carbohydrates become body fat when glycogen stores are full. Finally, protein seems to be the most satiating of all macronutrients, perhaps because natural protein-rich foods are also very nutrient-dense.

It is not very easy to eat a lot of protein without getting also a lot of fat if you get your protein from natural foods; as opposed to things like refined seed/grain products or protein supplements. Exceptions are organ meats and seafood, which generally tend to be quite lean and protein-rich.

References

Brooks, G.A., Fahey, T.D., & Baldwin, K.M. (2005). Exercise physiology: Human bioenergetics and its applications. Boston, MA: McGraw-Hill.

Wilson, J., & Wilson, G.J. (2006). Contemporary issues in protein requirements and consumption for resistance trained athletes. Journal of the International Society of Sports Nutrition, 3(1), 7-27.

Can salmon be a rich source of calcium?

Removing the bones from cooked fish, before eating the flesh, is not only a waste of mineral nutrients. In some cases it can be difficult, and lead to a lot of waste of meat.

We know that many ancestral cultures employed slow-cooking techniques and tools, such as earth ovens (a.k.a. cooking pits; see ). Slow-cooking fish over a long time tends to soften the bones to the point that they can be eaten with the flesh.

The photo below shows the leftovers of a whole salmon that we cooked recently. We baked it with vegetables on a tray covered with aluminum foil. We set the oven at 300 degrees Fahrenheit, and baked the salmon for about 5 hours.

The end result is that we can eat the salmon, a rich source of omega-3 fat, with the bones. No need to remove anything. Just take a chunk, as you can see in the photo, and eat it whole.

It is a good idea to marinate the salmon for a few hours prior to baking it. This will create enough moisture to ensure that the salmon does not dry up during the baking process.

If you are a carnivore, you can make a significant contribution to sustainability by eating the whole animal, or as much of the animal as possible. This applies to fish, as I discussed here before (, , ).

Add eating less to this habit, and your health will benefit greatly.

Will the aluminum pan and foil give you Alzheimer’s?

Aluminum (or aluminium) is a silvery metal that is both ductile and light. It is abundant in nature. These characteristics make it a favorite in many industries. Food utensils, such as pans and pots, are often made of aluminum. This use is dwarfed by aluminum’s widespread use in the canning of foods and drinks (e.g., sodas and beers).

Based on a systematic literature review published in 2008, Ferreira et al. argued that there is credible evidence of an “association” between Alzheimer’s disease and aluminum intake (). This argument has been challenged by other researchers, but has nevertheless gained media attention. Positive and negative associations will always be found where there are nonzero correlations, but correlation does not guarantee causation.

A research report commissioned by the U.S. Environmental Protection Agency, authored by Krewski et al. and published in 2007, reviewed a number of studies on the health effects of aluminum (). Several interesting findings emerged from this extensive review of the literature.

For example, a targeted study published in the late 1980s and early 1990s suggested that the daily intake of aluminum of a 14-16 year old male in the U.S. was about 11.5 mg; the main sources being additives to the following refined foods: cornbread (36.6% of total intake), American processed cheese (17.2%), pancakes (9.0%), yellow cake with icing (8%), taco/tostada (3.5%), cheeseburger (2.7%), tea (2.0%); hamburger (1.8%), and fish sticks (1.5%).

The meat that goes into the manufacturing of industrial hamburgers is not a significant source of aluminum. The same goes for the fish in the fish sticks. It is the industrial refining that makes the above-mentioned foods non-negligible sources of aluminum. One could argue that processed cheese should not be called “cheese”, as it is far removed from “real” cheese in terms of nutrient composition – particularly aged raw milk cheese.

Aluminum-treated water is widely believed to be a major source of aluminum to the body, with the potential of leading to health-detrimental accumulation. It appears that this is a myth based on several of the studies reviewed by Krewski et al.

One study concluded that humans drinking aluminum-treated water over a period of 70 to 80 years would have a total accumulation of approximately 1.5 mg of aluminum in their brain (1 mg/kg, the average adult human brain weighs 1.5 kg). At the high end of normal levels, and not much compared to the 34 mg found in some of those exposed to the Camelford water pollution incident (). And here is something else to consider. The study made two unlikely assumptions for emphasis: that all the ingested aluminum was absorbed, and that those exposed suffered from a condition that entirely prevented excretion from excess ingested aluminum.

Krewski et al.’s report and virtually all empirical studies I reviewed for this post suggest that the intake of aluminum from cooking utensils is negligible.

Is aluminum intake via food additives, arguably one of the main sources for most people living in urban environments today, likely to cause neurological diseases such as Alzheimer's disease?

My review of the evidence left me with the impression that most of the studies suggesting that aluminum intake can lead to neurological diseases make causal mistakes. One representative example is Rifat et al.’s study published in 1990 in The Lancet ().

This old study is interesting because it looked at the effects of ingestion of finely ground aluminum between 1944 and 1977 by miners, where the aluminum was ingested because it was believed to be protective against silicotic lung disease (caused by inhalation of crystalline silica dust).

As a side note, I should say that the intake levels reported in Rifat et al.’s study seem lower than what one would expect to see from a modern diet of refined foods. This seems odd. The levels may have been underestimated by Rifat et al. Or, what is more worrying, they may be quite high in a modern diet of refined foods.

Having said that, Rifat et al.’s article reports “… no significant differences between exposed and non-exposed miners in reported diagnoses of neurological disorder …” However, the tables below from their article show significant differences between exposed and non-exposed miners in their performance in cognitive tests. Those exposed to aluminum performed worst.

Two major variables that one would expect Rifat et al. to have controlled for are age and lung disease. They did control for age and a few other factors, with the corresponding results indicated as “adjusted” in the tables. However, they did not control for lung disease – the very factor that motivated aluminum intake.

Lung disease is likely to limit the supply of oxygen to the brain, and thus cause cognitive problems in the short and long term. Therefore, the cognitive impairments suggested by Rifat et al.'s study may have been caused by lung disease, and not by exposure to aluminum. This type of problem is a common feature of studies of the health effects of aluminum.

Will cooking in aluminum pans and aluminum foils give you Alzheimer’s? I doubt it.

Will your wireless router give you cancer?

If you pick up a magnet and move it up and down with your hand, you will be creating electromagnetic radiation. The faster you move the magnet, the higher the frequency of the radiation you create. The higher the frequency of the radiation, the lower is its wavelength. High frequency is also associated with high radiation strength, where strength can be measured in watts (W).

We are constantly bombarded by electromagnetic radiation, which is usually classified based on its frequency (and also wavelength, since frequency and wavelength are inversely proportional). The main types of electromagnetic waves, in order of increasing frequency, are: radio waves, microwaves, infrared radiation, visible light, ultraviolet radiation, X-rays, and gamma rays.

There has been a large amount of research on the health effects of wireless equipment, including wireless routers (figure below from Bestwirelessrouterreview.com), because of the electromagnetic radiation that they emit. Wireless equipment uses electromagnetic radiation of the radio waves type.

In developing countries, wireless routers are ubiquitous. They are found everywhere – at home, in hotels and businesses, and even in public parks. They allow wireless devices to connect to the Internet, by creating one or more “WiFi hotspots”.

The strength of the radiation emitted by wireless routers, when it reaches humans, is much lower than that emitted by mobile phones. One of the reasons for this is the lower strength of the radiation emitted by wireless routers, which can go from 30 to 500 milliwatts (mW); versus 125 mW to 2 W for mobile phones.

But the main reason for the lower strength of the radiation emitted by wireless routers, when it reaches humans, is that wireless routers normally are located farther away from humans than mobile phones. Radiation strength goes down according to the inverse-square law; i.e., proportionally to 1 divided by the distance between source and destination squared.

Given this, it has been estimated () that the exposure to 1 full year of radiation from a wireless router at home is equivalent, in terms of radiation reaching the body, to 20 minutes of exposure to the radiation emitted by a mobile phone.

If the radiation from wireless routers were to cause cancer, so should the radiation from mobile phones. So, what about mobile phones? Do they cause cancer?

In spite of a large amount of research conducted on the subject, no conclusive evidence has been found that the radiation from mobile phones causes cancer. A representative example of this research is a large Danish study (), whose results have recently been replicated.

Mobile phone radiation, like wireless router radiation, is currently classified by the International Agency for Research on Cancer (IARC) in Group 2B, namely “possibly carcinogenic”. This carries a recommendation of “more research”. Caffeic acid, found in coffee, is also in this group. It is useful to note that neither mobile phone nor wireless router radiation are classified in Group 2A, which is the “probably carcinogenic” IARC group.

When one considers the accumulated evidence regarding cancer risk associated with all types of electromagnetic radiation, the biggest concern by far is sunburn from ultraviolet radiation. The evidence suggests that it causes skin cancer. Chronic non-sunburn exposure to natural ultraviolet radiation, on the other hand, seems protective against most types of cancer (skin cancer included).

Will your wireless router give you cancer? I don’t think so.

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.

What is “relative risk” (RR)? The case of alcohol frequency and its impact on mortality from stroke

This post is in response to an inquiry by Ivor (sorry for the delayed response). It refers to a recent study by Rantakömi and colleagues on the effect of alcohol consumption frequency on mortality from stroke (). The study followed men who consumed alcohol to different degrees, including no consumption at all, over a period of a little more than 20 years.

The study purportedly controlled for systolic blood pressure, smoking, body mass index, diabetes, socioeconomic status, and total amount of alcohol consumption. That is, its results are presented as holding regardless of those factors.

The main results were reported in terms of “relative risk” (RR) ratios. Here they are, quoted from the abstract:

“0.71 (95% CI, 0.30–1.68; P = 0.437) for men with alcohol consumption <0.5 times per week and 1.16 (95% CI, 0.54–2.50; P = 0.704) among men who consumed alcohol 0.5–2.5 times per week. Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 (95% CI, 1.19–7.72; P = 0.020).”

Note the P values reported within parentheses. They are the probabilities that the results are due to chance and thus “not real”, or not due to actual effects. By convention, P values equal to or lower than 0.05 are considered statistically significant. In consequence, P values greater than 0.05 are seen as referring to effects that cannot be unequivocally considered real.

This means that, of the results reported, only one seems to be due to a real effect, and that is the one that: “Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 …”

Why the authors report the statistically non-significant results as if they were noteworthy is unclear to me.

Before we go any further, let us look at what “relative risk” (RR) means. RR is given by the following ratio:

(Probability of an event when exposed) / (Probability of an event when not exposed)

In the study by Rantakömi and colleagues, the event is death from stroke. The exposure refers to alcohol consumption at a certain level, compared to no alcohol consumption (no exposure).

Now, let us go back to the result regarding consumption of alcohol more than 2.5 times per week. That result sounds ominous. It is helpful to keep in mind that the study by Rantakömi and colleagues followed a total of 2609 men with no history of stroke, of whom only 66 died from stroke.

Consider the following scenario. Let us say that 1 person in a group of 1,000 people who consumed no alcohol died from stroke. Let us also say that 3 people in a group of 1,000 people who consumed alcohol more than 2.5 times per week died from stroke. Given this, the RR would be: (3/1,000) / (1/1,000) = 3.

One could say, based on this, that: “Consuming alcohol more than 2.5 times per week increases the risk of dying from stroke by 200%”. Based on the RR, this is technically correct. It is rather misleading nevertheless.

If you think that increasing sample size may help ameliorate the problem, think again. The RR would be the same if it was 3 people versus 1 person in 1,000,000 (one million). With these numbers, the RR would be even less credible, in my view.

This makes the findings by Rantakömi and colleagues look a lot less ominous, don’t you think? This post is not really about the study by Rantakömi and colleagues. It is about the following question, which is in the title of this post: What is “relative risk” (RR)?

Quite frankly, given what one sees in RR-based studies, the answer is arguably not far from this:

RR is a ratio used in statistical analysis that makes minute effects look enormous; the effects in question would not normally be noticed by anyone in real life, and may be due to chance after all.

The reason I say that the effects “may be due to chance after all” is that when effects are such that 1 event in 1,000 would make a big difference, a researcher would have to control for practically everything in order to rule out confounders.

If one single individual with a genetic predisposition toward death from stroke falls into the group that consumes more alcohol, falling in that group entirely by chance (or due to group allocation bias), the RR-based results would be seriously distorted.

This highlights one main problem with epidemiological studies in general, where RR is a favorite ratio to be reported. The problem is that epidemiological studies in general refer to effects that are tiny.

One way to put results in context and present them more “honestly” would be to provide more information to readers, such as graphs showing data points and unstandardized scales, like the one below. This graph is from a previous post on latitude and cancer rates in the USA (), and has been generated with the software WarpPLS ().

This graph clearly shows that, while there seems to be an association between latitude and cancer rates in the USA, the total variation in cancer rates in the sample is only of around 3 in 1,000. This graph also shows outliers (e.g., Alaska), which call for additional explanations.

As for the issue of alcohol consumption frequency and mortality, I leave you with the results of a 2008 study by Breslow and Graubard, with more citations and published in a more targeted journal ():

“Average volume obscured effects of quantity alone and frequency alone, particularly for cardiovascular disease in men where quantity and frequency trended in opposite directions.”

In other words, alcohol consumption in terms of volume (quantity multiplied by frequency) appears to matter much more than quantity or frequency alone. We can state this even more simply: drinking two bottles of whiskey in one sitting, but only once every two weeks, is not going to be good for you.

In the end, providing more information to readers so that they can place the results in context is a matter of scientific honesty.