I found this study to be fascinating, but first and foremost, it’s a rat study, so it may not extend at all to people.
That said, the study looked at three successive generations of rats following the original parents’ generation’s switch to a purposefully obesogenic diet where the diet/environment was kept constant for each successive generation. The thinking was, as per prior research, altering the original parents’ environment would lead to offspring more likely to have obesity by way of something called “epigenetic inheritance“. The question the researchers had was whether or not that “adipogenic” trait was carried forward in their successive offspring if the environment remained constant (and obesogenic).
According to the authors, keeping the rats’ successive generations’ environments constant was important because it,
“bears more relevance to the human scenario in which populations worldwide are transitioning to and have subsequently continued to experience obesogenic environments“
And epigenetic inheritance is certainly a hot topic in obesity these days, with some researchers suggesting it’s responsible for much of why we’ve been struggling with weight as a society.
Well the good news here, at least if you’re a rat, is that with successive generations, each facing the same obesogenic environment, body fat decreased, caloric intake decreased, male rats’ ability to convert carbohydrates to body fat decreased, lean mass increased, as did the rats’ ability to convert protein to lean mass.
Until I saw this study, everything I’d read regarding epigenetics and obesity had been doom and gloom. Here’s hoping that this is one rat study that does point to some hope for humans, as we, like these rats, over the course of our past few generations, have seen our diets change from grain based unrefined ones, to ones rich in refined-ingredients, sugar, and fats.
(and these great graphics – they all come from Nestlé who sees epigenetic research as something worthy of investment)
The effects of obesity on mortality—what can we learn from weight histories?
The New Year in 2013 began with release of a major study on the health risks of obesity. For a nation grappling with rising levels of obesity, the news was comforting. The data suggested that people with overweight could expect to live longer. The results also indicated that having slight obesity conferred no excess risk of death. The results were picked up by many major news outlets, was discussed in op-eds, and on social media and sparked livid reactions from critics. The influence of the study was tremendous, which was not surprising. It was the largest study ever carried out on the subject—summarizing data on close to 3 million individuals from 97 studies. Unfortunately, the debate quickly grew acrimonious. In a rare step, Nature issued a reprimand and urged the scientific community to accept the fact that obesity’s relationship with health might be less clear cut than imagined.
But is it? This is the question I set out to answer. It seemed a futile pursuit in the face of such a large and impressive study. But a part of me wondered whether the meta-analysis just reflected systematic bias in the underlying studies.
The very first step I took was to try to replicate an earlier study by Katherine Flegal and colleagues that came to much the same conclusions as the meta-analysis. The earlier paper was based on data from the National Health and Nutrition Examination Survey (NHANES), considered the gold standard survey for monitoring the health of the American population.
The most common critique of that study, which emerged again after the 2013 meta-analysis, was that the results—a protective effect of overweight, no increased risk of mild obesity—reflected confounding by illness or reverse causality. The idea is that being slim may appear risky if some people in the sample are slim because of an illness that caused them to lose weight.
A lot of studies have tried to address this bias, but none of the approaches used have proven satisfactory. One common strategy has been to try to isolate a healthy subset of the sample by excluding people with known or suspected illnesses, people who lost weight or people in poor self-rated health. However, this approach can introduce its own biases and has been criticized for excluding large fractions of people in the sample, calling into question the generalizability of the results.
It was clear to me that to improve upon the work by Flegal and colleagues I would need to get to the heart of the reverse causality matter and find a way to address it. So what could I do differently?
The solution turned out to be really simple. Studies of obesity and mortality were almost all based on weight at a single point in time. But many people gain or lose weight during their lifetimes, especially in times of illness. I needed to find a way to incorporate weight histories.
Incorporating histories is common practice in studies on smoking. In that literature, the non-smoking population is almost always separated into never-smokers and those who smoked in the past and quit. If you didn’t separate out these two groups, you’d likely reach the conclusion that smoking is not all that harmful—it might even appear protective. But that’s only because the low risks of the never-smokers are being masked by the much higher risk of the former smokers, many of whom smoked throughout their lives.
Surprisingly, this distinction, which is clearly essential for obtaining accurate estimates of the effects of smoking on mortality, is rarely made in the obesity literature. Studies almost always lump together individuals who have never had obesity, with individuals who no longer had obesity (but once did), despite the fact that these two groups may have very different mortality risks. If it were only possible to disentangle these two groups, we might be able to obtain better estimates of the effects of obesity on mortality that are not affected by reverse causality.
So after replicating the basic approach used by Flegal and colleagues, I took the next step of incorporating weight histories to determine what people weighed over the course of their lifetime. This was enabled by a question in the survey asking subjects to recall their lifetime maximum weight.
I used this information to separate the normal weight category into two groups: those who always maintained normal weight versus those who formerly had overweight or obesity and then lost weight. I found that mortality risks in the latter group were much higher than the risks in the former group. I demonstrated that combining these two groups raises the mortality rate in the normal weight category and obscures the low risks of those who maintained normal weight throughout life. Finally, I showed that when the normal weight category is redefined to only include the always normal weight individuals, the association between excess weight and mortality strengthens dramatically. These results were published last year in Population Health Metrics.
My findings thus suggest that weight histories are an essential piece of the puzzle for understanding obesity’s effects, just as former smoking status is important in the study of the effects of smoking on mortality. Failure to take weight histories into account has likely caused widespread bias in the literature, with the effect of obscuring obesity’s true toll.
Andrew Stokes is an Assistant Professor in the Department of Global Health at Boston University. His research is focused on the causes and consequences of the global obesity epidemic and developing novel approaches to combating obesity at the population level through interventions that target aspects of the social and physical environment. You can also follow him on Twitter.
Food Policy Is Not About Sexy Results
“Well … there’s good news and bad news.”
It’s not the sexiest opening line when I’m discussing my research. Nobody who wants a simple solution to childhood obesity likes those words. I don’t like them, either, and yet I use them all the time. Food policy is a sexy topic, but food policy research tends to give complicated, unsexy answers.
Food policies are more like young professional athletes – they come with a lot of hype, and thus everyone is eager to label them a “success” or “failure,” but the truth is usually in the middle. My latest study on school nutrition standards provided a perfect example. Furthermore, the study illustrates how any food policy can have the maximum benefit – by knowing what the policy is good at, acknowledging what the policy is not good at, and realizing where complementary policies/sandbags are needed.
In this recent study, I collaborated with researchers from Bridging the Gap, the National Cancer Institute, and the University of North Carolina-Chapel Hill, to dig into the details of how schools adhered to nutrition standards for foods and beverages sold in schools outside of federal school meal programs (a.k.a. ‘competitive foods’). We also dug into whether adherence differed by local area income. Encouragingly, we found that middle schools tended to sell fewer unhealthy items if states had healthier competitive food standards, particularly among schools in low-income areas.
Good news, right?
Sort of. The encouraging results came with a catch, as there was no evidence that schools were offering more healthy foods and beverages instead. Sugar-sweetened beverages were sold less, but they were not necessarily being swapped for clean water.
Why not? Because that is not what competitive food laws do. Competitive food laws are commonly designed to limit sugar, fat, or calorie content of school foods and beverages, but they do not require healthy alternatives.
That’s not a criticism of competitive food laws; it’s simply a limitation. That said, it’s a limitation that can ultimately affect disadvantaged populations. Our study found that low-income schools were substantially less likely to sell healthy alternatives.
If unhealthy items are banned, but healthy alternatives are not provided, there’s a tsunami of unhealthy options waiting for populations that are already at a higher risk for obesity. Low-income areas tend to have more unhealthy options in the community, and as recently reported by the Rudd Center, food/beverage marketing actively targets racial and ethnic minority children.
When disadvantaged communities face the tsunami of unhealthy options outside of school, they are less likely to benefit from any positive changes within school. Last May, a team of researchers in the Bay Area reported that California’s competitive food laws were associated with a decline in obesity, but predominantly in high-income areas. Obesity trends in low-income areas didn’t budge an inch.
As I wrote for Beyond Chron at the time, these results are inevitable if you expect a magical cure to obesity. Any single policy or program can only do what it is designed to do.
Competitive food laws are good at what they do by limiting unhealthy options at school. This may improve weight status in the general population, as I also found in a longitudinal study in 2012. We need to acknowledge, however, that competitive food laws may be less effective in low-income areas.
That is when other policies and programs become more important. The National School Lunch Program in the U.S., for example, is designed to do the exact opposite of competitive food laws – i.e., require healthy items for school meals, particularly to benefit students from low-income households. As a complementary pair, competitive food standards and school meal standards can benefit all children.
Food systems, like any kind of system, work best when different parts enhance each other in this way. Systems do not change when we think that any single part is going to drive change on its own.
My latest study adds to the mountain of evidence that competitive food policies are good at what they do. But, like any policy or program, they can achieve more if we understand their limitations.
Understanding a policy’s strengths and limitations isn’t about declaring the policy to be a “success” or “failure,” the sexy answers we all want. It’s about giving us a roadmap to understand where further action is needed.
Dr. Taber is an Assistant Professor at the University of Texas School of Public Health, where he specializes in childhood obesity policy research and systems science. For years, he has evalauated the impact of policies that are designed to improve diet and physical activity behaviors in children. Multiple studies led by Dr. Taber were cited in the United States Department of Agriculture’s landmark “Smart Snacks” rule for competitive foods. His research has been featured in the New York Times, NPR, CNN, Wall Street Journal, and several other media outlets, and he writes about obesity policy news on his personal blog. You can also find him on Twitter or his website. He also teaches “Systems Thinking in Public Health” at UTSPH. This article represents his personal views; any views or opinions expressed do not represent the University of Texas.
For those who don’t know Kevin, he’s a researcher with the NIH and is undeniably one of the world’s foremost experts in the area of metabolism. Last week he published a study that I tweeted out, “would be sure to rock the dogmatic”, and it certainly did. There’s been lots of angry comments and criticisms, and I thought it’d be great to hear from Kevin himself and I invited him to weigh in. And just as a reminder to readers, I have no horse in this race. As far as success with weight management goes, adherence is king and consequently I’m for any diet that a person enjoys enough to sustain. I also don’t think low-carb diets are risky, I have patients in my office on low-carb diets, and I have been highly critical of studies that purported low-carb diets were dangerous when in fact it was more that those studies methodologies were poor. I put this proviso out there because when it comes to discussions about the tenets of low-carb dieting, the volume, and the nonsense, tends to rise rapidly.
Is the carbohydrate-insulin theory dead? Maybe not, but it’s at least wounded.
Thanks to Yoni for the invitation to describe our recent study in Cell Metabolism entitled Calorie for calorie, dietary fat restriction results in more body fat loss than carbohydrate restriction in people with obesity. But first, a bit of recent history:
In a 2010 blog post, journalist Gary Taubes berated nutrition scientists for not understanding the seemingly simple concept of controlling diet variables. He chastised the field for altering multiple diet components at once and said that controlling variables is something that even
“school children are supposed to understand”
The failure of nutrition scientists to understand this basic concept
“has led to what may be another of the great misconceptions in modern nutrition research”
Mr. Taubes then exposes the horrendous misconception:
“carbohydrate-restricted diets are ‘valuable tools’ in the arsenal against overweight and obesity, but they’re just one of the dietary tools.”
Why was such a seemingly reasonable statement proclaimed to be a “great misconception”? Because, in Mr. Taubes’ view, the carbohydrate-insulin theory implies
“that the only meaningful way to lose fat … is by reducing the amount of carbohydrates consumed.” [bold mine KH.]
Doubling down on this claim in his most recent book Why We Get Fat, Mr. Taubes states that
“any diet that succeeds does so because the dieter restricts fattening carbohydrates…Those who lose fat on a diet do so because of what they are not eating – the fattening carbohydrates.”
At the time, I read these proclamations with great interest. I had just begun collecting data from a carefully controlled metabolic ward study which is the first to avoid the confounding nature of changing multiple macronutrients at once. Thankful to have an understanding of clinical trial design equal to an average school child, I also realized that our study would directly test Mr. Taubes’ version of the carbohydrate-insulin theory which has become greatly influential.
We confined 19 consenting adults with obesity to a metabolic ward at the NIH Clinical Center for a pair of 2 week visits where all food intake and physical activities were monitored and controlled. For the first 5 days we fed people a standard baseline diet whose calories matched their energy expenditure and was composed of 35% fat, 15% protein and 50% carbohydrate with about 20% of total calories coming from sugar which is believed to represent a typical habitual diet. We then the cut diet calories by 30% for the next 6 days, either entirely through restricting carbohydrates (RC), keeping protein and fat at baseline, or selectively restricting fat (RF) keeping protein, carbs and sugar at baseline. On their first visit, the participants were randomly assigned the RC or RF diet. After a 2-4 week washout period, they returned for a second 2 week visit when the alternate diet was delivered. Therefore, both diets were studied in the same people.
The diet changes resulted in an average ~800 Calorie reduction from baseline and the composition of the RC diet was 21% protein, 50% fat, and 29% carbohydrate with ~8% sugar. The RF diet had the same calories and was 21% protein, 8% fat, and 71% carbohydrate with ~35% sugar. According to the carbohydrate-insulin theory, the RF diet should not lead to body fat loss because insulin secretion won’t decrease since total carbohydrates, sugar, and protein were unchanged from baseline. According to Mr. Taubes, if insulin doesn’t decrease, then fat is effectively trapped in fat cells. In contrast, because the RC diet decreased total carbohydrates and sugars, insulin secretion should decrease thereby mobilizing fat from fat tissue and increasing net fat oxidation resulting in body fat loss.
All but one of these predictions of Mr. Taubes’ version of the carbohydrate-insulin theory held true in our study. Unfortunately, a theory is disproven when any of its major predictions fail, and in this case the failure was a doozy!
Despite no significant change in insulin secretion, the RF diet resulted in body fat loss by all measures. This finding alone was enough to disprove the claim that body fat loss requires decreased carbohydrates and insulin secretion. Furthermore, the RF diet led to a significantly greater rate of fat loss than the RC diet using the most sensitive method for measuring body fat loss: metabolic balance. The other measurement of fat mass change (DXA) showed no statistically significant difference between the RF and RC diets, but this method is known to lack the precision required to detect such a small difference in body fat.
As you may have guessed, low-carb advocates have complained vehemently about many aspects of the study. One criticism is that the diets don’t emulate “real” low-carb or low-fat diets. However, we wanted to isolate the metabolic effects of restricting dietary carbohydrates versus fat. This made it arithmetically impossible to investigate a very low carbohydrate diet since dietary fat would have to be added to control for calories. This would commit the sin of not controlling as many diet variables as possible, and might lead to wearing the dunce cap in Mr. Taubes’ classroom. Nevertheless, the metabolic response to very low carbohydrate diets is an interesting question and one that we have invested some effort in studying, so stay tuned! [Whoa! Yoni here – you should click that link – describes the details concerning the now completed (but not yet published or discussed) 8 week! metabolic ward study done by Kevin.]
Another complaint is that the study only lasted for 6 days and therefore was not long enough for subjects to become “fat adapted”. However, it actually takes less than a week to reach a plateau in mobilizing fat from adipose tissue to provide the fuel required to support the increased fat oxidation which also reaches a plateau within 1 week. Many previous studies have observed this rapid transition to increase fat metabolism and it was also observed in our study with the RC diet. There is no evidence that fat oxidation increases after the first several days of cutting carbohydrates. However, this does not negate the fact that longer time periods, perhaps weeks, may be required to optimize exercise performance or improve general feelings of well-being on low carbohydrate diets. This is what most people mean when they say “fat adapted”, but exercise performance and cognitive function were not important for our study results.
Many of my critics in the low-carb camp have ignored the caveats that this basic human physiology study does not imply that low-carb diets don’t work. They may even be preferable for many people. I have repeatedly acknowledged that prescribing low-carb diets appears to be more effective in outpatient randomized controlled trials, at least for several months when diet adherence is likely to be highest. The question is why? Our small contribution is that Mr. Taubes’ version of the carbohydrate-insulin theory likely isn’t the explanation.
As a parting thought, imagine that the study results had been different. What if instead we found no changes in body fat with the RF diet? Would there still have been passionate objections from the low-carb community, or would this study have been touted as a major victory for the carbohydrate insulin theory?
Dr. Kevin Hall is a tenured Senior Investigator at the National Institute of Diabetes & Digestive & Kidney Diseases, one of the National Institutes of Health in Bethesda MD, where his main research interests are the regulation of food intake, macronutrient metabolism, energy balance, and body weight. Dr. Hall’s laboratory performs experiments in humans and rodents and develops mathematical models and computer simulations to help design, predict, and interpret the experimental data. Dr. Hall is the recipient of the NIH Director’s Award, the NIDDK Director’s Award, the Lilly Scientific Achievement Award from The Obesity Society, the Guyton Award for Excellence in Integrative Physiology from the American Society of Physiology, and his award-winning Body Weight Planner (Yoni note: Indirect blog post on this forthcoming) has been used by more than a million people to help predict how diet and physical activity dynamically interact to affect human body weight.
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