The 2013 PLoS ONE sugar and diabetes study: Sugar from fruits is harmless

A new study linking sugar consumption with diabetes prevalence has gained significant media attention recently. The study was published in February 2013 in the journal PLoS ONE (). The authors are Sanjay Basu, Paula Yoffe, Nancy Hills and Robert H. Lustig.

Among the claims made by the media is that “… sugar consumption — independent of obesity — is a major factor behind the recent global pandemic of type 2 diabetes” (). As it turns out, the effects revealed by the study seem to be very small, which may actually be a side effect of data aggregation; I will discuss this further below.

Fruits are exonerated

Let me start by saying that this study also included in the analysis the main natural source of sugar, fruit, as a competing variable (competing with the effects of sugar itself), and found it to be unrelated to diabetes. As the authors note: “None of the other food categories — including fiber-containing foods (pulses, nuts, vegetables, roots, tubers), fruits, meats, cereals, and oils — had a significant association with diabetes prevalence rates”.

This should not surprise anyone who has actually met and talked with Dr. Lustig, the senior author of the study and a very accessible man who has been reaching out to the public in a way that few in his position do. He is a clinician and senior researcher affiliated with a major university; public outreach, in the highly visible way that he does it, is probably something that he does primarily (if not solely) to help people. Dr. Lustig was at the 2012 Ancestral Health Symposium, and he told me, and anyone who asked him, that sugar in industrialized foods was his target, not sugar in fruits.

As I noted here before, the sugar combination of fruits, in their natural package, may in fact be health-promoting (). The natural package probably promotes enough satiety to prevent overconsumption.

Both (unnatural) sugar and obesity have effects, but they are tiny in this study

The Diabetes Report Card 2012 () provides a wealth of information that can be useful as a background for our discussion here.

In the USA, general diabetes prevalence varies depending on state, with some states having higher prevalence than others. The vast majority of diabetes cases are of type 2 diabetes, which is widely believed to be strongly associated with obesity.

In 2012, the diabetes prevalence among adults (aged 20 years or older) in Texas was 9.8 percent. This rate is relatively high compared to other states, although lower than in some. So, among a random group of 1,000 adult Texans, you would find approximately 98 with diabetes.

Prevalence increases with age. Among USA adults in general, prevalence of diabetes is 2.6 percent within ages 20–44, 11.7 percent within ages 45–64, and 18.9 percent at age 64 or older. So the numbers above for Texas, and prevalence in almost any population, are also a reflection of age distribution in the population.

According to the 2013 study published in PLoS ONE, a 1 percent increase in obesity prevalence is associated with a 0.081 percent increase in diabetes prevalence. This comes directly from the table below, fifth column on the right. That is the column for the model that includes all of the variables listed on the left.



We can translate the findings above in more meaningful terms by referring to hypothetical groups of 1,000 people. Let us say we have two groups of 1,000 people. In one of them we have 200 obese people (20 percent); and no obese person in the other. We would find only between 1 and 2 people with diabetes in the group with 200 obese people.

The authors also considered overweight prevalence as a cause of diabetes prevalence. A section of the table with the corresponding results in included below. They also found a significant effect, of smaller size than for obesity – which itself is a small effect.



The study also suggests that consumption of the sugar equivalent of a 12 oz. can of regular soft drink per person per day was associated with a 1.1 percent rise in diabetes prevalence. The effect here is about the same as that of a 1 percent increase in obesity.

That is, let us say we have two groups of 1,000 people. In one of them we have 200 people (20 percent) consuming one 12 oz. can of soft drink per day; and no one consuming sugar in the other. (Sugar from fruits is not considered here.) We would find only about 2 people with diabetes in the group with 200 sugary soda drinkers.

In other words, the effects revealed by this study are very small. They are so small that their corresponding effect sizes make them borderline irrelevant for predictions at the individual level. Based on this study, obesity and sugar consumption combined would account for no more than 5 out of each 100 cases of diabetes (a generous estimate, based on the results discussed above).

Even being weak, the effects revealed by this study are not irrelevant for policy-making, because policies tend to influence the behavior of very large numbers of people. For example, if the number of people that could be influenced by policies to curb consumption of refined sugar were 100 million, the number of cases of diabetes that could be prevented would be 200 thousand, notwithstanding the weak effects revealed by this study.

Why are the effects so small?

The effects in this study are based on data aggregated by country. When data is aggregated by population, the level of variation in the data is reduced; sometimes dramatically, a problem that is proportional to the level of aggregation (e.g., the problem is greater for country aggregation than for city aggregation).

Because there can be no association without correlation, and no correlation without variation, coefficients of association tend to be reduced when data aggregation occurs. This is, in my view, the real problem behind what statisticians often refer to, in “statospeech”, as “ecological fallacy”. The effects in aggregated data are weaker than the effects one would get without aggregation.

So, I suspect that the effects in this study, which are fairly weak at the level of aggregation used (the country level), reflect much stronger effects at the individual level of analysis.

Bottom line

Should you avoid getting obese? Should you avoid consuming industrialized products with added sugar? I think so, and I would still have recommended these without this study. There seems to be no problem with natural foods containing sugar, such as fruits.

This study shows evidence that sugar in industrialized foods is associated with diabetes, independently from obesity, but it does not provide evidence that obesity doesn’t matter. It shows that both matter, independently of one another, which is an interesting finding that backs up Dr. Lustig’s calls for policies to specifically curb refined sugar consumption.

Again, what the study refers to as sugar, as availability but implying consumption, seems to refer mostly to industrialized foods where sugar was added to make them more enticing. Fruit consumption was also included in the study, and found to have no significant effect on diabetes prevalence.

Here is a more interesting question. If a group of people have a predisposition toward developing diabetes, due to any reason (genetic, epigenetic, environmental), what would be the probability that they would develop diabetes if they became obese and/or consumed unnatural sugar-added foods?

This type of question can be answered with a moderating effects analysis, but as I noted here before (), moderating effects analyses are not conducted in health research.

The man who ate 25 eggs per day: What does this case really tell us?

Many readers of this blog have probably heard about the case of the man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious The New England Journal of Medicine, and was in surprisingly good health ().

The case was authored by the late Dr. Fred Kern, Jr., a widely published lipid researcher after whom the Kern Lipid Conference is named (). One of Kern’s research interests was bile, a bitter-tasting fluid produced by the liver (and stored in the gallbladder) that helps with the digestion of lipids in the small intestine. He frames the man’s case in terms of a compensatory adaptation tied to bile secretion, arguing that this man was rather unique in his ability to deal with a lethal daily dose of dietary cholesterol.

Kern seemed to believe that dietary cholesterol was harmful, but that this man was somehow “immune” to it. This is ironic, because often this case is presented as evidence against the hypothesis that dietary cholesterol can be harmful. The table below shows the general nutrient content of the man’s daily diet of eggs. The numbers in this and other tables are based on data from Nutritiondata.com (), in some cases triangulated with other data. The 5.3 g of cholesterol in the table (i.e., 5,300 mg) is 1,775 percent the daily value recommended by the Institute of Medicine of the U.S. National Academy of Sciences ().

As you can see, the man was on a very low carbohydrate diet with a high daily intake of fat and protein. The man is described as an: “… 88-year-old man who lived in a retirement community [and] complained only of loneliness since his wife's death. He was an articulate, well-educated elderly man, healthy except for an extremely poor memory without other specific neurologic deficits … His general health had been excellent, without notable symptoms. He had mild constipation.”

The description does not suggest inherited high longevity: “His weight had been constant at 82 to 86 kg (height, 1.87 m). He had no history (according to the patient and his personal physician of 15 years) of heart disease, stroke, or kidney disease … The patient had never smoked and never drank excessively. His father died of unknown causes at the age of 40, and his mother died at 76 … He kept a careful record, egg by egg, of the number ingested each day …”

The table below shows the fat content of the man’s daily diet of eggs. With over 14 g of omega-6 fat intake every day, this man was probably close to or in “industrial seed oils territory” (), as far as daily omega-6 fat intake is concerned. And the intake of omega-3 fats, at less than 1 g, was not nearly enough to balance it. However, here is a relevant fact – this man was not consuming any industrial seed oils. He liked his eggs soft-boiled, which is why the numbers in this post refer to boiled eggs.

This man weighed between 82 to 86 kg, which is about 180 to 190 lbs. His height was 1.87 m, or about 6 ft 1 in. Therefore his body mass index varied between approximately 23 and 25, which is in the normal range. In other words, this person was not even close to obese during the many years he consumed 25 eggs or so per day. In the comments section of a previous post, on the sharp increase in obesity since the 1980s (), several readers argued that the sharp increase in obesity was very likely caused by an increase in omega-6 fat consumption.

I am open to the idea that industrialized omega-6 fats played a role in the sharp increase in obesity observed since the 1980s. When it comes to omega-6 fat consumption in general, including that in “more natural” foods (e.g., poultry and eggs), I am more skeptical. Still, it is quite possible that a diet high in omega-6 fats in general is unhealthy primarily if it is devoid of other nutrients. This man’s overall diet might have been protective not because of what he was not eating, but because of what he was eating.

The current debates pitting one diet against another often revolve around the ability of one diet or another to eliminate or reduce the intake of a “bad thing” (e.g., cholesterol, saturated fat, carbohydrates). Perhaps the discussion should be more focused on, or at least not completely ignore, what one diet or another include as protective factors. This would help better explain “odd findings”, such as the lowest-mortality body mass index of 26 in urban populations (). It would also help better explain “surprising cases”; such as this 25-eggs-a-day man’s, vegetarian-vegan “ageless woman” Annette Larkins’s (), and the decidedly carnivore De Vany couple’s ().

The table below shows the vitamin content of the man’s daily diet of eggs. The vitamin K2 content provided by Nutritiondata.com was incorrect; I had to get what seems to be the right number by triangulating values taken from various publications. And here we see something interesting. This man was consuming approximately the equivalent in vitamin K2 that one would get by eating 4 ounces of foie gras () every day. Foie gras, the fatty liver of overfed geese, is the richest known animal source of vitamin K2. This man’s diet was also high in vitamin A, which is believed to act synergistically with vitamin K2 – see Chris Masterjohn’s article on Weston Price’s “activator X” ().

Kern argued that the very high intake of dietary cholesterol led to a sharp increase in bile secretion, as the body tried to “get rid” of cholesterol (which is used in the synthesis of bile). However, the increased bile secretion might have been also been due to the high fat content of this man’s diet, since one of the main functions of bile is digestion of fats. Whatever the case may be, increased bile secretion leads to increased absorption of fat-soluble vitamins, and vitamins K2 and A are fat-soluble vitamins that seem to be protective against cardiovascular disease, cancer and other degenerative diseases.

Finally, the table below shows the mineral content of the man’s daily diet of eggs. As you can see, this man consumed 550 percent the officially recommended daily intake of selenium. This intake was slightly lower than the 400 micrograms per day purported to cause selenosis in adults (). Similarly to vitamins K2 and A, selenium seems to be protective against cardiovascular disease, cancer and other degenerative diseases. This man’s diet was also rich in phosphorus, needed for healthy teeth and bones.

Not too many people live to be 88 years of age; many fewer reach that age in fairly good health. The country with the highest average life expectancy in the world at the time of this writing is Japan, with a life expectancy of about 82 years (79 for men, and 86 for women). Those who think that they need a high HDL cholesterol and a low LDL cholesterol to be in good health, and thus live long lives, may be surprised at this man’s lipid profile: “The patient's plasma lipid levels were normal: total cholesterol, 5.18 mmol per liter (200 mg per deciliter); LDL, 3.68 mmol per liter (142 mg per deciliter); and HDL, 1.17 mmol per liter (45 mg per deciliter). The ratio of LDL to HDL cholesterol was 3.15.”

If we assume that this man is at least somewhat representative of the human species, and not a major exception as Kern argued, this case tells us that a diet of 25 eggs per day followed by over 15 years may actually be healthy for humans. Such diet has the following features:

- It is very high in dietary cholesterol.

- It involves a high intake of omega-6 fats from animal sources, with none coming from industrial seed oils.

- It involves a high overall intake of fats, including saturated fats.

- It is fairly high in protein, all of which from animal sources.

- It is a very low carbohydrate diet, with no sugar in it.

- It is a nutritious diet, rich in vitamins K2 and A, as well as in selenium and phosphorus.

This man ate 25 eggs per day apparently due to an obsession tied to mental problems. Repeated attempts at changing his behavior were unsuccessful. He said: “Eating these eggs ruins my life, but I can't help it.”

The steep obesity increase in the USA in the 1980s: In a sense, it reflects a major success story

Obesity rates have increased in the USA over the years, but the steep increase starting around the 1980s is unusual. Wang and Beydoun do a good job at discussing this puzzling phenomenon (), and a blog post by Discover Magazine provides a graph (see below) that clear illustrates it ().

What is the reason for this?

You may be tempted to point at increases in calorie intake and/or changes in macronutrient composition, but neither can explain this sharp increase in obesity in the 1980s. The differences in calorie intake and macronutrient composition are simply not large enough to fully account for such a steep increase. And the data is actually full of oddities.

For example, an article by Austin and colleagues (which ironically blames calorie consumption for the obesity epidemic) suggests that obese men in a NHANES (2005–2006) sample consumed only 2.2 percent more calories per day on average than normal weight men in a NHANES I (1971–1975) sample ().

So, what could be the main reason for the steep increase in obesity prevalence since the 1980s?

The first clue comes from an interesting observation. If you age-adjust obesity trends (by controlling for age), you end up with a much less steep increase. The steep increase in the graph above is based on raw, unadjusted numbers. There is a higher prevalence of obesity among older people (no surprise here). And older people are people that have survived longer than younger people. (Don’t be too quick to say “duh” just yet.)

This age-obesity connection also reflects an interesting difference between humans living “in the wild” and those who do not, which becomes more striking when we compare hunter-gatherers with modern urbanites. Adult hunter-gatherers, unlike modern urbanites, do not gain weight as they age; they actually lose weight (, ).

Modern urbanites gain a significant amount of weight, usually as body fat, particularly after age 40. The table below, from an article by Flegal and colleagues, illustrates this pattern quite clearly (). Obesity prevalence tends to be highest between ages 40-59 in men; and this has been happening since the 1960s, with the exception of the most recent period listed (1999-2000).

In the 1999-2000 period obesity prevalence in men peaked in the 60-74 age range. Why? With progress in medicine, it is likely that more obese people in that age range survived (however miserably) in the 1999-2000 period. Obesity prevalence overall tends to be highest between ages 40-74 in women, which is a wider range than in men. Keep in mind that women tend to also live longer than men.

Because age seems to be associated with obesity prevalence among urbanites, it would be reasonable to look for a factor that significantly increased survival rates as one of the main reasons for the steep increase in the prevalence of obesity in the USA in the 1980s. If significantly more people were surviving beyond age 40 in the 1980s and beyond, this would help explain the steep increase in obesity prevalence. People don’t die immediately after they become obese; obesity is a “disease” that first and foremost impairs quality of life for many years before it kills.

Now look at the graph below, from an article by Armstrong and colleagues (). It shows a significant decrease in mortality from infectious diseases in the USA since 1900, reaching a minimum point between 1950 and 1960 (possibly 1955), and remaining low afterwards. (The spike in 1918 is due to the influenza pandemic.) At the same time, mortality from non-infectious diseases remains relatively stable over the same period, leading to a similar decrease in overall mortality.

When proper treatment options are not available, infectious diseases kill disproportionately at ages 15 and under (). Someone who was 15 years old in the USA in 1955 would have been 40 years old in 1980, if he or she survived. Had this person been obese, this would have been just in time to contribute to the steep increase in obesity trends in the USA. This increase would be cumulative; if this person were to live to the age of 70, he or she would be contributing to the obesity statistics up to 2010.

Americans are clearly eating more, particularly highly palatable industrialized foods whose calorie-to-nutrient ratio is high. Americans are also less physically active. But one of the fundamental reasons for the sharp increase in obesity rates in the USA since the early 1980s is that Americans have been surviving beyond age 40 in significantly greater numbers.

This is due to the success of modern medicine and public health initiatives in dealing with infectious diseases.

PS: It is important to point out that this post is not about the increase in American obesity in general over the years, but rather about the sharp increase in obesity since the early 1980s. A few alternative hypotheses have been proposed in the comments section, of which one seems to have been favored by various readers: a significant increase in consumption of linoleic acid (not to be confused with linolenic acid) since the early 1980s.

Good insulin, bad insulin: Its role in obesity?

Gary Taubes makes insulin out to be a bad guy. In his latest article in Newsweek Magazine commenting on HBO's Weight of the Nation documentary, he once again challenges energy balance (energy intake versus energy expended) as a paradigm for understanding obesity. The author of Good Calories, Bad Calories offers an alternative theory: refined sugars and grains trigger insulin, which leads to fat accumulation. He also doesn't think much of physical activity as playing a "meaningful role in keeping off the pounds."

Is Taubes right? Not according to Jim Hill, Ph.D., a professor of pediatrics and medicine at the University of Colorado School of Medicine, Denver. Hill is the cofounder of the National Weight Control Registry, a registry of individuals who've succeeded in maintaining weight loss over time. He is also the co-founder of America on the Move, a national weight-gain prevention initiative.

At a session at Experimental Biology, Hill said that the the "energy-in energy-out" framework continues to dominate as correct in current scientific literature on obesity. When asked whether or not the rise of obesity epidemic is related to diet or physical activity, Hill simply responds, "Yes." That is because studies have shown that either restriction of calories or greater physical activity can lead to weight loss.

Then, what's wrong with Taubes's insulin hypothesis? First, it's important to point out that insulin is also a good guy. As kinesiologist John Ivy, Ph.D., of the University of Texas at Austin, pointed out to me a few years ago, insulin is too often misunderstood. The unfortunate consequence can be a detriment of muscle and strength. Ivy's own research is on muscle insulin resistance and how it is reduced with exercise.

Insulin's role is more clearly explained in Ivy's book The Future of Sports Nutrition: Nutrient Timing. He writes that, yes, insulin is a promoter of fat synthesis. But it is also a crucial hormone for promoting protein synthesis, reducing protein degradation (including suppressing cortisol, which can be catabolic in nature), and promoting glucose uptake and glycogen storage in muscle. Insulin, notably, also suppresses appetite.

According to Ivy, the most important factor involved in whether or not insulin promotes fat storage, carbohydrate storage, or protein synthesis is the "individual's body state." For example, under conditions where insulin sensitivity heightened in fat cells (a sedentary lifestyle), there will be more promotion of fat storage. On the other hand, after physical activity, when muscle cells are more insulin sensitive, insulin will promote glycogen and protein synthesis.

Perhaps where Taubes goes wrong is in failing to realize the role of muscle in body metabolism. It wouldn't be the first time. As I've discussed before in a post about the work of another kinesiologist, Stuart Phillips, Ph.D., of McMaster University, skeletal muscle is often forgotten in discussions of obesity. However, as Phillips affirms, skeletal muscle is a highly metabolically active tissue, consuming a great deal of energy as a primary site for glycogen storage and the largest site for fat burning. Skeletal muscle mass also helps determine metabolic rate.

Taubes, in this latest article, also fails to mention that carbohydrate is not the only macronutrient that stimulates insulin. Protein stimulates insulin too; in fact, it's the branched-chain amino acids (leucine, isoleucine, valine) that trigger the insulin release -- these same amino acids are also the key players in triggering protein synthesis, which is explained in part by their effects on insulin.

Ivy explains that insulin has earned the title "anabolic regulator of muscle," meaning it's the most important hormone to increase muscle and strength. Yet, by Taubes's judgment, insulin release should be avoided as much as possible. Taken to its logical conclusion, Taubes's mindset means that one should eat less carbohydrate and protein per day, and eat plenty more fat -- along with the dismissal of exercise as being important, that's the perfect recipe for gradual muscle degradation and (what?) insulin resistance, hyperglycemia, and hyperinsulinemia!

Is there something really wrong with "eat less, move more"? After all, this "tired advice" as Taubes calls it has largely failed in producing results in the United States. There still exists an obesity epidemic and it's getting worse. Is there another alternative theory to energy balance? Hill says energy balance still stands, although he acknowledges "eat less, move more" is too simplistic as advice. He offers his own new paradigm, which largely represents what other nutrition scientists have concluded including the American Society for Nutrition (see my report here). It's that "diet and physical activity interact." And how they interact may explain how the body regulates -- with a sort of "settling point," according to Hill -- balance of energy, energy stores, glucose, and temperature.

Looking at the problem from historical standpoint, Hill reminds, we no longer have to hunt or travel long distances to gather food anymore. We no longer have to farm to produce our food. Now, it's all about heading to the supermarket, filling our carts, and sitting in some form or another for the rest of the day. Our environment has changed. What's the solution to an obesity epidemic? Hill suggests in taking "small steps" for changing our environment back; this means continuing with "eat less, move more," and finding any opportunity to bring reduced-calorie eating, walking, and other physical activities back into lifestyles.

Another recommendation comes from Ivy and Phillips, which is to make greater use of the "anabolic regulator of muscle" and focus on muscle maintenance and growth through regular physical activity. They also encourage balanced eating with healthy portions of quality protein, carbohydrates, and fats. Yes, carbohydrate is important for endurance and maximal recovery of glycogen stores.

Resistance training is primary for muscle building; aerobic exercise also helps in depletion of glycogen stores. Both forms of physical activity make muscles more insulin sensitive, cause greater uptake of glucose into muscles, and they also help keep extra calories from heading toward fat stores. Far from Taubes's advice that physical activity is meaningless, these kinesiologists suggest some form of exercise should happen every single day.

To greater understand the role of "nutrient timing" and how carbohydrate and protein relate to exercise, read Ivy's book and see this 2008 position statement from the International Society of Sports Nutrition where Ivy serves as part of the editorial board.

Update: Those of you who've read Good Calories, Bad Calories or Why We Get Fat may also be interested in Yoni Freedhoff's review of the latter over on his "Weighty Matters" blog. I have only read the first book.

BMI puts young Asian-American women at risk of being "skinny fat"

As if we needed any more reason to pick on Body Mass Index (BMI): new research finds that this most accepted approach for assessing overweight and obesity based on height and weight could lead to misclassification of young Asian-American women as healthy when they're really "skinny fat," which could put them at greater risk for type 2 diabetes and cardiovascular disease.

In my last post, I discussed the scary, growing problem of sarcopenic obesity (aka "skinny fat") in older adults, described as age-related muscle loss in combination with the accumulation of body fat. One common result of sarcopenic obesity is a misclassification using BMI as "normal-weight" in these aged individuals. Misclassification in older adults with sarcopenic obesity is just one reason why BMI is loathed by those interested in public health.

Sadly, I learned at Experimental Biology, misclassification also occurs frequently across all ages depending on ethnicity. Asian Americans with normal weight sarcopenic obesity, for example, are often misclassified even in a college-aged population, according to nutritional epidemiologist Catherine Carpenter, an associate professor from University of California, Los Angeles.

In a symposium organized by the American Society for Nutrition to discuss the topic of obesity and BMI classification, Carpenter presented findings of a yet-to-be-published cross-sectional study that evaluated BMI and percent body fat measured by biolectrical impedance analysis among on the multi-ethnic college students on campus. The study's findings were that college-aged Asian American women were most likely to be misclassified by BMI.

Kindly, Carpenter provided me with further details about the study after the event. The study included 940 college students recruited over four consecutive years. The average participant was 21.4 years old with an average normal weight BMI of 22.9 kilogram over meter squared (kg/m2). The average percent body fat was 24.8 percent. The ethnic samples consisted of four ethnic groups: 49 percent Asian, 23 percent Caucasian, 7 percent Hispanic, and 21 percent as Other.

Among the college-age students, Asian-American women had the lowest BMI (21.5 kg/m2), but the second highest percent body fat (27.8 percent). In comparison, Hispanic women had the highest percent body fat (29.9 percent) and the highest BMI (23.5 kg/m2).

These findings suggest Asian-American women are most likely to be misclassified by BMI, which could mean greater likelihood risk of normal-weight obesity gone unnoticed. Ultimately, greater risk of obesity-related chronic disease such as type 2 diabetes and cardiovascular disease is the result.

No question, studies like these will continue to reveal the limitations of BMI. Once again, I'll repeat that it may be better to focus physical activity and amount of skeletal muscle mass (along with or maybe more than dietary calories or macronutrients like sugar) for prevention of obesity and chronic disease.

Skeletal muscle is critical as a large site for fat burning, glucose disposal, and metabolic rate. Sarcopenia in college-age adults should be most disturbing! It's much easier to hold on to muscle than it is to gain it back. Prevention, as usual, is key; and, prevention can be achieved with daily physical activity and a balanced diet -- with sufficient daily intake of quality protein, especially after exercise and throughout the day.

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How to fight "job-esity"

Workplace programs are an effective and worthwhile way for employers to help improve the health of their employees and reduce medical costs, scientists said Tuesday at Experimental Biology 2012 in San Diego at a session organized by the American Society for Nutrition. 

The medical expenses for employees who are obese are estimated at about 42 percent higher than for those with a healthy weight, according to the Centers for Disease Control and Prevention. Yet for the approximately 60 percent of Americans who are employed, it may be the workplace itself that is at the root of weight gain in the first place.

Shirley Beresford, Ph.D., professor of epidemiology of University of Washington, expressed optimism that workplace obesity programs could help reduce obesity based on a history of research. She explained that programs offered a way for employers to partner in intervention; however, she noted that the enthusiasm of employees for behavioral change may fall short.

The trouble with the workplace of Americans is that too often the mix of job insecurity, stress, sedentary behavior, and social eating present an "obesogenic environment," said Sai Krupa Das, Ph.D., assistant professor at the Friedman School of Nutrition Science and Policy, Tufts University.

Preliminary findings of the Tufts Healthy Weight Study offered a more promising picture for workplace intervention, Das said. The study resulted in substantial weight loss at six months that was sustained for at least a year.

Among the reasons why the intervention worked, Das said, are multiple components: encouraging physical activity combined with diet instead of either alone, behavioral counseling versus educational approaches, a high intensity versus a moderate intensity support group, and a structured maintenance protocol versus an unstructured self-directed program.

"These promising practices provide a broad framework for going forward," Das said. "We had a very robust intervention."

A workplace intervention program is likely to be cost effective for companies, according to economist Chad Meyerhoefer, Ph.D., an assistant professor of Lehigh University. In his presentation on cost-benefit projections for healthcare savings for employers, he gave the estimate that for every 5 pounds of weight loss the savings could range from $30 to $80 per person annually.  

William Dietz, Ph.D., director of the division of nutrition, physical, and obesity at the CDC, discussed a toolkit named LEAN Works! that was developed to promote worksite interventions. To set the example "at home," Dietz said, The toolkit was adopted on CDC campus. 

"We focused on changes on the environment," he said. For example, the campus featured music in stairwells to encourage their use, conducted a walkability audit, redesigned the campus to be more walkable, and installed lactation rooms. In addition, the CDC introduced fruit-and-vegetable vendors on campus so that employees could buy fresh produce once a week, made campuses smoke-free, and installed "lifestyle centers" (gyms).

Beyond calories in, calories out -- look to the Amish

What is wrong with "eat less, move more"? Most of us are familiar with this mantra as weight-loss advice. However, a new consensus statement from the American Society for Nutrition (ASN) and the International Life Sciences Institute (ILSI) contends that this energy-in-energy-out framework isn't really so simple.

The problem lies in that consuming fewer calories and burning more through physical activity doesn’t always translate well to weight management. That is not to say that the framework of energy balance—negative energy balance for weight loss; positive energy balance for weight—is wrong. At some level, it’s right; however, several factors come into the equation.

During a Saturday morning session of Experimental Biology (#EB2012) in San Diego, Calif., researchers discussed the topic of this complexity and promoting a new paradigm on energy balance.

Energy balance is not just about addition of diet and exercise; each affects the other, so that changing one changes the others, explained Jim Hill, professor of pediatrics and medicine at the University of Colorado School of Medicine, Denver, and new recipient of the W.O. Atwater Lectureship awarded by the American Society for Nutrition.

What must be appreciated is the body's system of active regulation, he said.

"It's not just a little man in your head that," he said, adding that the body may rely on a 'set point' or 'settling point' type of system that attempts to balance energy, energy stores, glucose, glycogen, fat stores, and temperature.

"You might argue that up to the 1970s, the system worked pretty well," Hill said. "This whole system is based on inherited factors. If you look at what’s really changed since the 70s, it's really the environmental factors. We don't have to hunt and kill our food anymore. We go to the supermarket and fill our carts. We also sit in some form of fashion every day."

Obesity, a problem of overwhelmed "active regulation"

Most of these environmental influences are designed to increase energy intake. It becomes more difficult to avoid overeating and underactivity. These influences, summed up, overwhelm active regulation and the body’s energy balance system. Then the body's physiology adjusts.

Are there things in the food supply that cause to eat more? Are they influences that affect our brains and nervous system? Hill said that however they may affect us, it is still through energy balance. He said he "laughs every time" he sees studies questioning whether the rise of obesity epidemic is related to energy intake or expenditure.

"Diet and physical activity interact," he said. "Please don't ask if it's diet or physical activity. The answer is 'yes'."

What wrong with diets? No matter which diet, Hill said, the results are always the same: Body weight may increase, may not change, or may decrease. The inconsistent outcomes may be because of poor compliance, physical activity, metabolic rate differences, or food intake adjustments. Eventually, what goes wrong is that environment affects behavior so strongly that over time that people are gaining a little weight. They don’t gain a lot, but they gain about a pound or two a year with some push back from their active regulation systems.

One way to combat the obesity epidemic is by encouraging an environment more like that of a half a century ago, Hill said. How the old order Amish live today is a good example.

"The Amish walk 18 thousand steps a day. They don’t have spandex or a gym membership. That explains about 400 to 600 calories a day. A typical person in the U.S. walks only five to six thousand steps a day," Hill said.

Hill suggests preventing weight gain with small steps—or, more specifically, 2 thousand steps daily.

"We have a poor ability to maintain weight loss, but we have a better ability to prevent weight loss,” he said. “This is why we started a movement to move. Two-thousand steps a day is not going to create weight loss, but might prevent weight gain."

What role can small changes play in an environment where we need bigger systemic changes? Hill argues that it will "set the table" for bigger change. Another factor may be that it may help to improve performance or "reset" active regulation.

"Our biology works best at high levels of physical activity. Obesity is in the 'unregulated zone.' When physical activity increases, you enter 'regulated zone'. Physical activity may help the 'regulatory system' work better."

Stealth health

Until bigger changes can be created in the environment, Hill is a fan of "stealth health" strategies for reducing obesity. That is, finding ways of encouraging better energy in, energy out balance without the public noticing.

For example, Hill explains, at Disney parks and at Starbucks, drinks are now made with low-fat milk. Does anyone notice the difference? What about portion sizes? Would anyone notice a 5 percent reduction at restaurants?

Another idea Hill proposed was that of regulation of school drop offs—so that they would be 500 steps away from the school. Would it be so far that the children would notice a difference?

The implementation of these "stealth health" strategies would increase physical activity and reduce overeating. Combined with teaching children energy balance skills, it could reduce childhood and overall obesity.

What also may be important is to combine both diet and physical activity guidelines for society? But how can this be achieved when studies show that energy balance is such a dynamic versus static process?

Learn more about the questions raised about energy balance at Experimental Biology from the American Society for Nutrition’s cover page story in Nutrition Notes Daily. 

The “pork paradox”? National pork consumption and obesity

In my previous post () I discussed some country data linking pork consumption and health, analyzed with WarpPLS (). One of the datasets used, the most complete, contained data from Nationmaster.com () for the following countries: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. That previous post also addressed a study by Bridges (), based on country-level data, suggesting that pork consumption may cause liver disease.

In this post we continue that analysis, but with a much more complex model containing the following country variables: wealth (PPP-adjusted GNP/person), pork consumption (lbs/person/year), alcohol consumption (liters/person/year), obesity (% of population), and life expectancy (years). The model and results, generated by WarpPLS, are shown on the figure below. (See notes at the end of this post.) These results are only for direct effects.

WarpPLS also calculates total effects, which are the effects of each variable on any other variable to which it is linked directly and/or indirectly. Two variables may be linked indirectly, through various paths, even if they are not linked directly (i.e., have an arrow directly connecting them). Another set of outputs generated by the software are effect sizes, which are calculated as Cohen’s f-squared coefficients. The figure below shows the total effects table. The values underlined in red are for total effects that are both statistically significant and also above the effect size threshold recommended by Cohen to be considered relevant (f-squared > 0.02).

As I predicted in my previous post, wealth is positively associated with pork consumption. So is alcohol consumption, and more strongly than wealth; which is consistent with a study by Jeanneret and colleagues showing a strong association between alcohol consumption and protein rich diets (). The inclusion of wealth in the model, compared with the model without wealth in the previous post, renders the direct and total effects of alcohol and pork consumption on life expectancy statistically indistinguishable from zero. (This often happens when a confounder is added to a model.)

Pork consumption is negatively associated with obesity, which is interesting. So is alcohol consumption, but much less strongly than pork consumption. This does not mean that if you eat 20 doughnuts every day, together with 1 lb of pork, you are not going to become obese. What this does suggest is that maybe countries where pork is consumed more heavily are somewhat more resistant to obesity. Here it should be noted that pork is very popular in Asian countries, which are becoming increasingly wealthy, but without the widespread obesity that we see in the USA.

But it is not the inclusion of Asian countries in the dataset that paints such a positive picture for pork consumption vis-à-vis obesity, and even weakens the association between wealth and obesity so much as to make it statistically non-significant. Denmark is a wealthy country that has very low levels of obesity. And it happens to have the highest level of pork consumption in the whole dataset: 142.6 lbs/person/year. So we are not talking about an “Asian paradox” here.

More like a “pork paradox”.

Finally, as far as life expectancy is concerned, the key factors seem to be wealth and obesity. Wealth has a major positive effect on life expectancy, while obesity has a much weaker negative effect. Well, access to sanitation, medical services, and other amenities of civilization, still trumps obesity in terms of prolonging life; however miserable life may turn out to be. The competing effects of these two variables (i.e., wealth and obesity) were taken into consideration, or controlled for, in the calculation of total effects and effect sizes.

The fact that pork consumption is negatively associated with obesity goes somewhat against the idea that pork is inherently unhealthy; even though pork certainly can cause disease if not properly prepared and/or cooked, which is true for many other plant and animal foods. The possible connection with liver problems, alluded to in the previous post, is particularly suspicious in light of these results. Liver diseases often impair that organ’s ability to make glycogen based on carbohydrates and protein; that is, liver diseases frequently lead to liver insulin resistance. And obesity frequently follows from liver insulin resistance.

Given that pork consumption appears to be negatively associated with obesity, it would be surprising if it was causing widespread liver disease, unless its relationship with liver disease was found to be nonlinear. (Alcohol consumption seems to be nonlinearly associated with liver disease.) Still, most studies that suggest the existence of a causal link between pork consumption and liver disease, like Bridges’s (), hint at a linear and dose-dependent relationship.

Notes

- Country-level data is inherently problematic, particularly when simple models are used (e.g., a model with only two variables). There are just too many possible confounders that may lead to the appearance of causal associations.

- More complex models ameliorate the above situation somewhat, but bump into another problem associated with country-level data – small sample sizes. We used data from 18 countries in this analysis, which is more than in the Bridges study. Still, the effective sample size here (N=18) is awfully small.

- There were some missing values in this dataset, which were handled by WarpPLS employing the most widely used approach in these cases – i.e., by replacing the missing values with the mean of each column. The percentages of missing values per variable (i.e., column) were: alcohol consumption: 27.78%; life expectancy: 5.56%; and obesity: 33.33%.

Does pork consumption cause cirrhosis? Perhaps, if people become obese from eating pork

The idea that pork consumption may cause cirrhosis has been around for a while. A fairly widely cited 1985 study by Nanji and French () provides one of the strongest indictments of pork: “In countries with low alcohol consumption, no correlation was obtained between alcohol consumption and cirrhosis. However, a significant correlation was obtained between cirrhosis and pork.”

Recently Paul Jaminet wrote a blog post on the possible link between pork consumption and cirrhosis (). Paul should be commended for bringing this topic to the fore, as the implications are far-reaching and very serious. One of the key studies mentioned in Paul’s post is a 2009 article by Bridges (), from which the graphs below were taken.

The graphs above show a correlation between cirrhosis and alcohol consumption of 0.71, and a correlation between cirrhosis and pork consumption of 0.83. That is, the correlation between cirrhosis and pork consumption is the stronger of the two! Combining this with the Nanji and French study, we have evidence that: (a) in countries with low alcohol consumption we can find a significant correlation between cirrhosis and pork consumption; and (b) in countries where both alcohol and pork are consumed, pork consumption has the strongest correlation with cirrhosis.

Do we need anything else to ban pork from our diets? Yes, we do, as there is more to this story.

Clearly alcohol and pork consumption are correlated as well, as we can see from the graphs above. That is, countries where alcohol is consumed more heavily also tend to have higher levels of pork consumption. If alcohol and pork consumption are correlated, then a multivariate analysis of their effects should be conducted, as one of the hypothesized effects (of alcohol or pork) on cirrhosis may even disappear after controlling for the other effect.

I created a dataset, as best as I could, based on the graphs from the Bridges article. (I could not get the data online.) I then entered it into WarpPLS (). I wanted to run a moderating effect analysis, which is a form of nonlinear multivariate analysis. This is important, because the association between alcohol consumption and disease in general is well known to be nonlinear.

In fact, the relationship between alcohol consumption and disease is often used as a classic example of hormesis (), and its characteristic J-curve shape. Since correlation is a measure of linear association, the lower correlation between alcohol consumption and cirrhosis, when compared with pork consumption, may be just a “mirage of linearity”. In multivariate analyses, this mirage of linearity may lead to what are known as type I and II errors, at the same time ().

I should note that the Bridges study did something akin to a moderating effect analysis; through an analysis of the interaction between alcohol and pork consumption. However, in that analysis the values of the variables that were multiplied to create a “dummy” interaction variable were on their original scales, which can be a major source of bias. A more advisable way to conduct an interaction effect analysis is to first make the variables dimensionless, by standardizing them, and then creating a dummy interaction variable as a product of the two variables. That is what WarpPLS does for moderating effects’ estimation.

One more detour, leading to an important implication, and then we will get to the results. In a 1988 article, Jeanneret and colleagues show evidence of a strong and possibly causal association between alcohol consumption and protein-rich diets (). One possible implication of this is that in countries where pork is a dietary staple, like Denmark and Germany, alcohol consumption should be strongly and causally associated with pork consumption. (I guess Anthony Bordain would agree with this eh?)

Below are the results of a multivariate analysis on a model that incorporates the above implication, by including a link between alcohol and pork consumption. The model also explores the role of pork consumption as a moderator of the relationship between alcohol and cirrhosis, as well as the direct effect of pork consumption on cirrhosis. Finally, the total effects of alcohol and pork consumption on cirrhosis are also investigated; they are shown on the left.

The total effects are both statistically significant, with the total effect of alcohol consumption being 94 percent stronger than the total effect of pork consumption on cirrhosis. Looking at the model, alcohol consumption is strongly associated with pork consumption (which is consistent with Jeanneret and colleagues’s study). Alcohol consumption is also strongly associated with cirrhosis, through a direct effect; much more so than pork. Finally, pork consumption seems to strengthen the relationship between alcohol consumption and cirrhosis (the moderating effect).

As we can see the relationship between pork consumption and cirrhosis is still there, in moderating and direct effects, even though it seems to be a lot weaker than that between alcohol consumption and cirrhosis. Why does pork seem to influence cirrhosis at all in this dataset?

Well, there is another factor that is strongly associated with cirrhosis, and that is obesity (). In fact, obesity is associated with just about any major disease, including various types of cancer ().

And in countries where pork is a dietary staple, isn’t it reasonable to assume that pork consumption will play a role in obesity? Often folks who consume a lot of addictive industrial foods (e.g., bread, candy, regular sodas) also eat plenty of foods with saturated fat; and the latter end up showing up in disease statistics, misleadingly supporting the lipid hypothesis. The phenomenon involving pork and cirrhosis may well be similar.

But you may find the above results and argument not convincing enough. Maybe you want to see some evidence that pork is actually good for one’s health. The results above suggest that it may not be bad at all, if you buy into the obesity angle, but not that it can be good.

So I downloaded the most recent data from Nationmaster.com () on the following variables: pork consumption, alcohol consumption, and life expectancy. The list of countries was a bit larger than and different from that in the Bridges study; the following countries were included: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. Below are the results of a simple multivariate analysis with WarpPLS.

As with the Bridges dataset, there is a strong multivariate association between alcohol and pork consumption (0.43). The multivariate association between alcohol consumption and life expectancy is negative (-0.14). The multivariate association between pork consumption and life expectancy is positive (0.36). Neither association is statistically significant, although the association involving pork consumption gets close to significance with a P=0.11 (a confidence level of 89 percent; calculated through jackknifing, a nonparametric technique). The graphs show the plots for the associations and the best-fitting lines; the blue dashed arrows indicate the multivariate associations to which the graphs refer. So, in this second dataset from Nationmaster.com, the more pork is consumed in a country, the longer is the life expectancy in that country.

In other words, for each 1 standard deviation variation in pork consumption, there is a 0.36 standard deviation variation in life expectancy, after we control for alcohol consumption. The standard deviation for pork consumption is 36.281 lbs/person/year, or 45.087 g/person/day; for life expectancy, it is 4.677 years. Working the numbers a bit more, the results above suggest that each extra gram of pork consumed per person per day is associated with approximately 13 additional days of overall life expectancy in a country! This is calculated as: 4.677/45.087*0.36*365 = 13.630.

Does this prove that eating pork will make you live longer? No single study will “prove” something like that. Pork consumption is also likely a marker for wealth in a country; and wealth is strongly and positively associated with life expectancy at the country level. Moreover, when you aggregate dietary and disease incidence data by country, often the statistical effects are caused by those people in the dietary extremes (e.g., alcohol abuse, not moderate consumption). Finally, if people avoid death from certain diseases, they will die in higher quantities from other diseases, which may bias statistical results toward what may look like a higher incidence of those other diseases.

What the results summarized in this post do suggest is that pork consumption may not be a problem at all, unless you become obese from eating it. How do you get obese from eating pork? Eating it together with industrial foods that are addictive would probably help.

Kleiber's law and its possible implications for obesity

Kleiber's law () is one of those “laws” of nature that is both derived from, and seems to fit quite well with, empirical data. It applies to most animals, including humans. The law is roughly summarized through the equation below, where E = energy expenditure at rest per day, and M = body weight in kilograms.

Because of various assumptions made in the original formulation of the law, the values of E do not translate very well to calories as measured today. What is important is the exponent, and what it means in terms of relative increases in weight. Since the exponent in the equation is 3/4, which is lower than 1, the law essentially states that as body weight increases animals become more efficient from an energy expenditure perspective. For example, the energy expenditure at rest of an elephant, per unit of body weight, is significantly lower than that of a mouse.

The difference in weight does not have to be as large as that between an elephant and a mouse for a clear difference in energy expenditure to be noticed. Moreover, the increase in energy efficiency predicted by the law is independent of what makes up the weight; whether it is more or less lean body mass, for example. And the law is very generic, also applying to different animals of the same species, and even the same animal at different developmental stages.

Extrapolating the law to humans is quite interesting. Let us consider a person weighing 68 kg (about 150 lbs). According to Kleiber's law, and using a constant multiplied to M to make it consistent with current calorie measurement assumptions (see Notes at the end of this post), this person’s energy expenditure at rest per day would be about 1,847 calories.

A person weighing 95 kg (about 210 lbs) would spend 2,374 calories at rest per day according to Kleiber's law. However, if we were to assume a linear increase based on the daily calorie expenditure at a weight of 68 kg, this person weighing 95 kg would spend 2,508 calories per day at rest. The difference of approximately 206 calories per day is a reflection of Kleiber's law.

This difference of 206 calories per day would translate into about 23 g of extra body fat being stored per day. Per month this would be about 688 g, a little more than 1.5 lbs. Not a negligible amount. So, as you become obese, your body becomes even more efficient on a weight-adjusted basis, from an energy expenditure perspective.

One more roadblock to go from obese to lean.

Now, here is the interesting part. It is unreasonable to assume that the extra mass itself has a significantly lower metabolic rate, with this fully accounting for the relative increase in efficiency. It makes more sense to think that the extra mass leads to systemic adaptations, which in turn lead to whole-body economies of scale (). In existing bodies, these adaptations should happen over time, as long-term compensatory adaptations ().

The implications are fascinating. One implication is that, if the compensatory adaptations that lead to a lower metabolic rate are long term, they should also take some time to undo. This is what some call having a “broken metabolism”; which may turn out not to be “broken”, but having some inertia to overcome before it comes back to a former state. Thus, lower metabolic rates should generally be observed in the formerly obese, with reductions compatible with Kleiber's law. Those reductions themselves should be positively correlated with the ratio of time spent in the obese and lean states.

Someone who was obese at 95 kg should have a metabolic rate approximately 5.6 percent lower than a never obese person, soon after reaching a weight of 68 kg (5.6 percent = [2,508 – 2,374] / 2,374). If the compensatory adaptation can be reversed, as I believe it can, we should see slightly lower percentage reductions in studies including formerly obese participants who had been lean for a while. This expectation is consistent with empirical evidence. For example, a study by Astrup and colleagues () concluded that: “Formerly obese subjects had a 3–5% lower mean relative RMR than control subjects”.

Another implication, which is related to the one above, is that someone who becomes obese and goes right back to lean should not see that kind of inertia. That is, that person should go right back to his or her lean resting metabolic rate. Perhaps Drew Manning’s Fit-2-Fat-2-Fit experiment () will shed some light on this possible implication.

A person becoming obese and going right back to lean is not a very common occurrence. Sometimes this is done on purpose, for professional reasons, such as before and after photos for diet products. Believed it or not, there is a market for this!

Notes

- Calorie expenditure estimation varies a lot depending on the equation used. The multiplier used here was 78,  based on Cunningham’s equation, and assuming 10 percent body fat. The calorie expenditure for the same 68 kg person using Katch-McArdle’s equation, also assuming 10 percent body fat, would be about 1,692 calories. That would lead to a different multiplier.

- The really important thing to keep in mind, for the purposes of the discussion presented here, is the relative decrease in energy expenditure at rest, per unit of weight, as weight goes up. So we stuck with the 78 multiplier for illustration purposes.

- There is a lot of variation across individuals in energy expenditure at rest due to other factors such as nonexercise activity thermogenesis ().

Men who are skinny-fat: There are quite a few of them

The graph below (from Wikipedia) plots body fat percentage (BF) against body mass index (BMI) for men. The data is a bit old: 1994. The top-left quadrant refers to men with BF greater than 25 percent and BMI lower than 25. A man with a BF greater than 25 has crossed into obese territory, even though a BMI lower than 25 would suggest that he is not even overweight. These folks are what we could call skinny-fat men.

The data is from the National Health and Nutrition Examination Survey (NHANES), so it is from the USA only. Interesting that even though this data is from 1994, we already could find quite a few men with more than 25 percent BF and a BMI of around 20. One example of this would be a man who is 5’11’’, weighing 145 lbs, and who would be technically obese!

About 8 percent of the entire sample of men used as a basis for the plot fell into the area defined by the top-left quadrant – the skinny-fat men. (That quadrant is one in which the BMI measure is quite deceiving; another is the bottom-right quadrant.) Most of us would be tempted to conclude that all of these men were sick or on the path to becoming so. But we do not know this for sure. On the standard American diet, I think it is a reasonably good guess that these skinny-fat men would not fare very well.

What is most interesting for me regarding this data, which definitely has some measurement error built in (e.g., zero BF), is that it suggests that the percentage of skinny-fat men in the general population is surprisingly high. (And this seems to be the case for women as well.) Almost too high to characterize being skinny-fat as a disease per se, much less a genetic disease. Genetic diseases tend to be rarer.

In populations under significant natural selection pressure, which does not include modern humans living in developed countries, genetic diseases tend to be wiped out by evolution. (The unfortunate reality is that modern medicine helps these diseases spread, although quite slowly.)  Moreover, the prevalence of diabetes in the population was not as high as 8 percent in 1994, and is not that high today either; although it tends to be concentrated in some areas and cluster with obesity as defined based on both BF and BMI.

And again, who knows, maybe these folks (the skinny-fat men) were not even the least healthy in the whole sample, as one may be tempted to conclude.

Maybe being skinny-fat is a trait, passed on across generations, not a disease. Maybe such a trait was useful at some point in the not so distant past to some of our ancestors, but leads to degenerative diseases in the context of a typical Western diet. Long-living Asians with low BMI tend to gravitate more toward the skinny-fat quadrant than many of their non-Asian counterparts. That is, long-living Asians generally tend have higher BF percentage at the same BMI (see a discussion about the Okinawans on this post).

Evolution is a deceptively simple process, which can lead to very odd results.

This “trait-not-disease” idea may sound like semantics, but it has major implications. It would mean that many of the folks who are currently seen as diseased or disease-prone, are in fact simply “different”. At a point in time in our past, under a unique set of circumstances, they might have been the ones who would have survived. The ones who would have been perceived as healthier than average.

Safe weight loss for seniors through diet and exercise

In the United States, the number of obese older adults has reached disturbing heights—now affecting approximately 20 percent of those ages 65 and older—and is only expected to rise as more Baby Boomers become senior citizens.

Weight loss through calories reduction or exercise are generally good for most people as an intervention in obesity, although the appropriateness of these methods has historically been a matter of controversy in older, obese adults.

A major concern with weight loss is the accompanying loss of lean tissue, which can accelerate existing sarcopenia (age-related loss of muscle and strength), and result in reduction of bone mineral density that could worsen frailty. This could lead to greater risk of bone fractures and broken hips. Studies have yet to provide sufficient evidence, one way or another, as to whether or not weight loss provides a true enhancement to quality of life.

In a one-year, randomized, controlled trial, researchers from Washington University School of Medicine in St. Louis evaluated independent and combined effects of weight loss and exercise in nearly 100 obese older adults with an average age of 70.

The study, published their findings in the March issue of New England Journal of Medicine, randomized subjects into one of four groups:

1. Control group – participants of which did not receive any advice to change diet or activity.

2. Diet group – prescribed a diet with a deficit of 500 to 750 Calories per day and containing 1 gram of high-quality protein per kilogram of body weight per day

3. Exercise group – prescribed a diet to maintain weight while participating in three group exercise trainings weekly, which included 90 minutes of aerobic exercises, resistance trainings, and flexibility and balance exercises.

4. Diet-exercise group – prescribed a combination of the weight management instructions and exercise trainings as described in 1 and 2.

To "even the playing field" and reduce confounding variables of vitamin D and calcium status, the researchers gave all participants supplements: approximately 1500 milligrams of calcium and 1000 IU of vitamin D per day.


Results from this carefully designed study show the "diet-exercise group" preserved more lean muscle and bone density when compared to the other groups. They gained significantly better physical function and were less frail than other groups and outperformed other groups in all measured parameters: Physical Performance Test (PPT), peak oxygen consumption (VO2pseak), and Functional Status Questionnaire (FSQ) (see graphs).

"Weight loss combined with regular exercise may be beneficial in helping obese older adults maintain their functional independence," the authors concluded.

Generally, most older, obese adults are able to safely engage in regular physical activity; however, a medical professional can determine which exercises are appropriate for an individual's specific needs. Because fitness levels vary, it's important to consult a physician prior to beginning any exercise program. Certain medical conditions, as well as medications, can also affect a person’s tolerance for exercise.

Engaging in a variety of exercises, such as aerobic exercises, resistance training, and flexibility exercises, can lead to optimal health benefits. Each is essential for healthy aging.

Aerobic Exercise

Aerobic exercise of moderate intensity, 30 minutes a day, five times per week is currently recommended for adults ages 65 and older, according to the guidelines presented by the American College Sports of Medicine (ACSM). Those who are not used to exercising can start out with a shorter duration at a lower intensity and work their way up to the recommendations.

Aerobic exercise can lead to improved cardiovascular function, better quality of sleep, improved mental health, weight loss and enhanced immune function. Suggested aerobic activities for older adults include low-impact exercises such as walking, biking, low-impact aerobics, and water activities such as swimming or water aerobics.

Resistance Training

Resistance training is essential to preserve lean muscle and bone density or even reverse previous losses. In addition to improving physical function, resistance-based exercises can also reduce risk of some medical conditions like diabetes and hypertension.

Older, obese adults should perform resistance-training exercises two times weekly. The trainings should consist of 8 to 10 different strength exercises with 8 to 12 repetitions each. Again, it's best to start out slow, with lighter weights and fewer repetitions.

There are many different types of strength training exercises and a variety of equipment that can be used, including: weight-training machines, dumbbells, resistance bands, medicine balls, weighted bars, resistance of water or even one’s own body weight.

For optimal benefits, it is best to work muscles to the point of fatigue, without overstraining, while taking enough time between workouts to allow the muscles to rest and recover.

Flexibility Exercises

Flexibility and balance are also factors important to health that decrease with age. Leading a sedentary lifestyle can cause connective tissues to weaken and joints to stiffen. Ultimately, the lack of activity affects a person's range of motion, balance and posture.

Performing stretching exercises regularly can help improve flexibility and increase freedom of movement. Every workout should begin and end with proper stretching exercises to help warm up and soothe the muscles. Stretching, along with strength exercises, can also improve balance, which can help reduce the risk of falling – particularly important for elderly individuals.

Final Word

It's never too late to begin a weight-control and exercise program. Along with a healthy diet, engaging in individually-appropriate physical activity—aerobics, resistance training, and flexibility exercises—can provide older adults with improved physical function and a variety of health benefits.

Reference

Villareal DT, Chode S, Parimi N et al. Weight loss, exercise, or both and physical function in obese older adults. N Engl J Med 2011;364:1218-29.

The China Study II: Carbohydrates, fat, calories, insulin, and obesity

The “great blogosphere debate” rages on regarding the effects of carbohydrates and insulin on health. A lot of action has been happening recently on Peter’s blog, with knowledgeable folks chiming in, such as Peter himself, Dr. Harris, Dr. B.G. (my sista from anotha mista), John, Nigel, CarbSane, Gunther G., Ed, and many others.

I like to see open debate among people who hold different views consistently, are willing to back them up with at least some evidence, and keep on challenging each other’s views. It is very unlikely that any one person holds the whole truth regarding health matters. Unfortunately this type of debate also confuses a lot of people, particularly those blog lurkers who want to get all of their health information from one single source.

Part of that “great blogosphere debate” debate hinges on the effect of low or high carbohydrate dieting on total calorie consumption. Well, let us see what the China Study II data can tell us about that, and about a few other things.

WarpPLS was used to do the analyses below. For other China Study analyses, many using WarpPLS as well as HealthCorrelator for Excel, click here. For the dataset used here, visit the HealthCorrelator for Excel site and check under the sample datasets area.

The two graphs below show the relationships between various foods, carbohydrates as a percentage of total calories, and total calorie consumption. A basic linear analysis was employed here. As carbohydrates as a percentage of total calories go up, the diet generally becomes a high carbohydrate diet. As it goes down, we see a move to the low carbohydrate end of the scale.

The left parts of the two graphs above are very similar. They tell us that wheat flour consumption is very strongly and negatively associated with rice consumption; i.e., wheat flour displaces rice. They tell us that fruit consumption is positively associated with rice consumption. They also tell us that high wheat flour consumption is strongly and positively associated with being on a high carbohydrate diet.

Neither rice nor fruit consumption has a statistically significant influence on whether the diet is high or low in carbohydrates, with rice having some effect and fruit practically none. But wheat flour consumption does. Increases in wheat flour consumption lead to a clear move toward the high carbohydrate diet end of the scale.

People may find the above results odd, but they should realize that white glutinous rice is only 20 percent carbohydrate, whereas wheat flour products are usually 50 percent carbohydrate or more. Someone consuming 400 g of white rice per day, and no other carbohydrates, will be consuming only 80 g of carbohydrates per day. Someone consuming 400 g of wheat flour products will be consuming 200 g of carbohydrates per day or more.

Fruits generally have much less carbohydrate than white rice, even very sweet fruits. For example, an apple is about 12 percent carbohydrate.

There is a measure that reflects the above differences somewhat. That measure is the glycemic load of a food; not to be confused with the glycemic index.

The right parts of the graphs above tell us something else. They tell us that the percentage of carbohydrates in one’s diet is strongly associated with total calorie consumption, and that this is not the case with percentage of fat in one’s diet.

Given the above, one may be interested in looking at the contribution of individual foods to total calorie consumption. The graph below focuses on that. The results take nonlinearity into consideration; they were generated using the Warp3 algorithm option of WarpPLS.

As you can see, wheat flour consumption is more strongly associated with total calories than rice; both associations being positive. Animal food consumption is negatively associated, somewhat weakly but statistically significantly, with total calories. Let me repeat for emphasis: negatively associated. This means that, as animal food consumption goes up, total calories consumed go down.

These results may seem paradoxical, but keep in mind that animal foods displace wheat flour in this dataset. Note that I am not saying that wheat flour consumption is a confounder; it is controlled for in the model above.

What does this all mean?

Increases in both wheat flour and rice consumption lead to increases in total caloric intake in this dataset. Wheat has a stronger effect. One plausible mechanism for this is abnormally high blood glucose elevations promoting abnormally high insulin responses. Refined carbohydrate-rich foods are particularly good at raising blood glucose fast and keeping it elevated, because they usually contain a lot of easily digestible carbohydrates. The amounts here are significantly higher than anything our body is “designed” to handle.

In normoglycemic folks, that could lead to a “lite” version of reactive hypoglycemia, leading to hunger again after a few hours following food consumption. Insulin drives calories, as fat, into adipocytes. It also keeps those calories there. If insulin is abnormally elevated for longer than it should be, one becomes hungry while storing fat; the fat that should have been released to meet the energy needs of the body. Over time, more calories are consumed; and they add up.

The above interpretation is consistent with the result that the percentage of fat in one’s diet has a statistically non-significant effect on total calorie consumption. That association, although non-significant, is negative. Again, this looks paradoxical, but in this sample animal fat displaces wheat flour.

Moreover, fat leads to no insulin response. If it comes from animals foods, fat is satiating not only because so much in our body is made of fat and/or requires fat to run properly; but also because animal fat contains micronutrients, and helps with the absorption of those micronutrients.

Fats from oils, even the healthy ones like coconut oil, just do not have the latter properties to the same extent as unprocessed fats from animal foods. Think slow-cooking meat with some water, making it release its fat, and then consuming all that fat as a sauce together with the meat.

In the absence of industrialized foods, typically we feel hungry for those foods that contain nutrients that our body needs at a particular point in time. This is a subconscious mechanism, which I believe relies in part on past experience; the reason why we have “acquired tastes”.

Incidentally, fructose leads to no insulin response either. Fructose is naturally found mostly in fruits, in relatively small amounts when compared with industrial foods rich in refined sugars.

And no, the pancreas does not get “tired” from secreting insulin.

The more refined a carbohydrate-rich food is, the more carbohydrates it tends to pack per unit of weight. Carbohydrates also contribute calories; about 4 calories per g. Thus more carbohydrates should translate into more calories.

If someone consumes 50 g of carbohydrates per day in excess of caloric needs, that will translate into about 22.2 g of body fat being stored. Over a month, that will be approximately 666.7 g. Over a year, that will be 8 kg, or 17.6 lbs. Over 5 years, that will be 40 kg, or 88 lbs. This is only from carbohydrates; it does not consider other macronutrients.

There is no need to resort to the “tired pancreas” theory of late-onset insulin resistance to explain obesity in this context. Insulin resistance is, more often than not, a direct result of obesity. Type 2 diabetes is by far the most common type of diabetes; and most type 2 diabetics become obese or overweight before they become diabetic. There is clearly a genetic effect here as well, which seems to moderate the relationship between body fat gain and liver as well as pancreas dysfunction.

It is not that hard to become obese consuming refined carbohydrate-rich foods. It seems to be much harder to become obese consuming animal foods, or fruits.