Figure out Food: Eat what works!

No, that’s not the name of my new blog (although it is awfully catchy, isn’t it?), but it sure does capture the spirit of my own approach to nutrition these days.

It’s the name of what I think will be the future of nutrition–an app that helps you figure out what to eat to be healthy by connecting what you eat to how you feel.  Can I get an “It’s about damn time”?

Kenny's app

Wading through the muck of nutrition science and public health, I’ve learned just a few things that I can say with assurance:

1) We know very little about the relationship between diet and prevention of chronic disease.  Somebody tells you that they have a scientifically proven diet for preventing chronic disease?  This person may have a diet, it may even work (as far as we can tell at the moment), but it not going to be scientifically proven because we simply don’t have the science to prove it.  As they say in the biz, our methodology sucks green tomatoes.

2) The focus in public health (and private care) on weight loss is misguided.  Weight loss does not equal health and even if it did, we’re really bad at helping people do it successfully and long-term.  Does weight loss result in better health?  Sometimes.  But is that due to the weight loss per se, or due to whatever metabolic changes had to happen in order for weight loss to occur?  And, truth is, sometimes attempts at weight loss compromise health.  Loss of muscle mass, disordered eating patterns, nutritional deficiencies, restricted lifestyle, hunger, fatigue, general misery and bitchiness–all of these can accompany attempts at weight loss & may cause more problems than they solve.

BUT–and it’s a big but, like it always is–food is really important.  Some foods make us feel satisfied and full of energy and ready to leap over tall buildings with nary a second thought.  Other foods make us Sleepy and Sneezy and Dopey and a few other dwarves that Snow White didn’t meet:  Cranky, Burpy, and Farty.

And foods that make my body happy are not necessarily the ones that make yours happy.

How do we know which foods are which?  

Ta-da!  Kenny Gow to the rescue with a totally cool app that he’s been working on for a while now.

The main thing to know about this app is that it’s about having health now (not about weight loss or disease prevention–see above) and it’s about you (not an aggregate of information from datasets full of people who aren’t you).

I think it’s pretty cool & when I eventually get back to working with patients, I hope this app is there to help me help them.  But–for that to happen, he needs some support from us.

With that in mind, check out his Indiegogo campaign, which I’m about to donate to, as soon as I finish this blog post.

Fist-bump to Gingerzini who beat me to it.

 

 

 

 

TMAO? LMAO.

Move over saturated fat and cholesterol. There’s a new kid on the heart disease block: TMAO.

TMAO is not, as I first suspected, a new internet acronym that I was going to have to get my kids to decipher for me, while they snickered under their collective breaths. Rather, TMAO stands for Trimethylamine N-oxide, and it is set to become the reigning king of the “why meat is bad for you” argument. Former contenders, cholesterol and saturated fat, have apparently lost their mojo. After years of dominating the heart disease-diet debate, it turns out they were mere poseurs, only pretending to cause heart disease, the whole time distracting us from the true evils of TMAO.

The news is, the cholesterol and saturated fat in red meat can no longer be held responsible for clogging up your arteries. TMAO, which is produced by gut bacteria that digest the carnitine found in meat, is going to gum them up instead. This may be difficult to believe, especially in light of the fact that, while red meat intake has declined precipitously in the past 40 years, prevalence of heart disease has continued to climb. However, this is easily accounted for by the increase in consumption of Red Bull—which also contains carnitine—even though it is not, as some may suspect, made from real bulls (thank you, BW).

Here to explain once again why we should all be afraid of eating a food our ancestors ignorantly consumed in scandalous quantities (see what happened to them?  they are mostly dead!) is the Medical Media Circus! Ringleader for today is the New York Times’ Gina Kolata, who never met a half-baked nutrition theory she didn’t like (apparently Gary Taubes’ theory regarding carbohydrates was not half-baked enough for her).

Step right up folks and meet TMAO, the star of “a surprising new explanation of why red meat may contribute to heart disease” (because, frankly, the old explanations aren’t looking too good these days).

We know that red meat maybe almost probably for sure contributes to heart disease, because that wild bunch at Harvard just keeps cranking out studies like this one, Eat Red Meat and You Will Die Soon.

This study and others just like it definitely prove that if you are a white, well-educated, middle/upper-middle class health professional born between 1920 and 1946 and you smoke and drink, but you don’t exercise, watch your weight, or take a multivitamin, then eating red meat will maybe almost probably for sure increase your risk of heart disease. With evidence like that, who needs evidence?

Flying like the Wallenda family in the face of decades of concrete and well-proven assumptions that the reason we should avoid red meat is because of its saturated fat and cholesterol content, the daring young scientists who discovered the relationship between TMAO and heart disease “suspected that saturated fat and cholesterol made only a minor contribution to the increased amount of heart disease seen in red-meat eaters” [meaning that is, the red-meat eaters that are white, well-educated, middle/upper-middle class health professionals, who smoke and drink and don’t exercise, watch their weight, or take a multivitamin; emphasis mine].

Perhaps their suspicions were alerted by studies such as this one, that found that, in randomized, controlled trials, with over 65 thousand participants, people who reduced or changed their dietary fat intake didn’t actually live any longer than the people who just kept eating and enjoying the same artery-clogging, saturated fat- and cholesterol-laden foods that they always had. (However, this research was able to determine that a steady diet of broiled chicken breasts does in fact make the years crawl by more slowly.)

You can almost ALWAYS catch something on a fishing expedition.

Our brave scientists knew they couldn’t just throw up their hands and say “Let them eat meat!” That would undermine decades of consistent public health nutrition messaging and those poor stupid Americans might get CONFUSED—and we wouldn’t want that! So, instead the scientists went on a “scientific fishing expedition” (Ms. Kolata’s words, not mine) and hauled in a “little-studied chemical called TMAO that gets into the blood and increases the risk of heart disease.” Luckily, TMAO has something to do with meat. [As Chris Masterjohn points out, it also has something to do with fish, peas, and cauliflower, but–as I’m sure these scientists noticed immediately–those things do not contain meat.] Ta-da! Problemo solved.

Exactly how TMAO increases the risk of heart disease, nobody knows. But, good scientists that they are, the scientists have a theory. (Just to clarify, in some situations the word theory means: a coherent group of tested general propositions, commonly regarded as correct. This is not one of those situations.) The researcher’s think that TMAO enables cholesterol to “get into” artery walls and prevents the body from excreting “excess” cholesterol. At least that’s how it works in mice. Although mice don’t normally eat red meat, it should be noted that mice are exactly like people except they don’t have Twitter accounts. We know this because earlier mouse studies allowed scientists to prove beyond the shadow of a doubt that dietary cholesterol and saturated fat cause heart disease mice definitely do not have Twitter accounts.

Look, just because the scientists can’t explain how TMAO does all the bad stuff it does, doesn’t mean it’s not in there doing, you know, bad stuff. Remember, we are talking about molecules that are VERY VERY small and really small things can be hard to find–unless of course you are on a scientific fishing expedition.

What will happen to the American Heart Association’s seal of approval now that saturated fat and cholesterol are no longer to be feared?

Frankly, I’m relieved that we FINALLY know exactly what has been causing all this heart disease. Okay, so it’s not the saturated fat and cholesterol that we’ve been avoiding for 35 years. Heck, everybody makes mistakes. Even though Frank Sacks and Robert Eckel, two scientists from the American Heart Association, told us for decades that eating saturated fat and cholesterol was just greasing the rails on the fast track to death-by-clogged-arteries, they have no reason to doubt this new theory. And even though they apparently had no reason to doubt the now-doubtful old theory, at least not until just now—as a nation, we can rest assured that THIS time, they got it right.

Now that saturated fat and cholesterol are no longer Public Enemies Number One and Two, whole milk, cheese, eggs, and butter—which do not contain red meat—MUST BE OKAY! I guess there’s no more need for the AHA’s dietary limits on saturated fat, or for the USDA Guidelines restrictions on cholesterol intake, or for those new Front of Package labels identifying foods with too much saturated fat. Schools can start serving whole milk again, butter will once again be legal in California, and fat-free cheese can go back to being the substance that mouse pads are made out of. Halla-freaking- looyah! A new day has dawned.

But—amidst the rejoicing–don’t forget: Whether we blame saturated fat or cholesterol or TMAO, meat is exactly as bad for you now as it was 50 years ago.

A beautifully-written summary by Emily Contois regarding the recent Critical Nutrition Symposium held at UC-Santa Cruz. Organized by Julie Guthman, author of Weighing In, this symposium brought together food scholars from around the country (plus me) and invited us and the audience to participate in a thought-provoking and nuanced conversation about food, nutrition, culture, and ways of knowing.

Emily Contois

On March 8, 2013, I had the pleasure of attending the Critical Nutrition Symposium at UC Santa Cruz, organized by Julie Guthman, author of Weighing In. The event was spawned from a roundtable discussion at last year’s Association for the Study of Food and Society conference. The symposium brought together an interdisciplinary group of scholars to critically examine what is missing from conventional nutrition science research and practice, discuss why it matters, and brainstorm how to move forward in an informed and balanced way. What follows are a few of my favorite key ideas from the day’s discussions.

Adele Hite, a registered dietitian and public health advocate who is not afraid to ask big and delightfully confrontational questions regarding nutrition science, began the day by dissecting Michael Pollan’s now famous aphorism—Eat food. Not too much. Mostly plants. Step by step, she revealed the decades of revisionist myth…

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Quote of the day

As usual, Weight Maven has the scoop. She’ll point you to an excellent article by Modern Paleo that addresses the issue of why a one-size-fits-all approach–whether plant-based or paleo–isn’t going to work. I would probably not have seen this if it weren’t for her.

Weight Maven

Diana Hsieh has a great read over on Modern Paleo on “three major obstacles” — the value of health, individual differences, and the science of nutrition — that make it difficult to categorize essential vs optional paleo principles:

Of course, we can define a paleo diet, because it means something definite. We can also identify the general principles of a paleo approach to health … That’s crucial for doing paleo well, I think.

Yet to think of some of these principles as universally “essential” versus universally “optional” would be a mistake. Instead, they should stand in our minds as “more or less important for me.”

Do read the whole post! BTW, I’ve been in my new digs for a week and a half and hope to be back to a regular posting schedule fairly soon. Thanks for your patience.

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Why Race Doesn’t Matter in Nutrition Policy

This is the first of a series looking at what does and doesn’t matter when it comes to nutrition policy. When I started out on this adventure, I thought that science would give me the answers to the questions I had about why public health and clinical recommendations for nutrition were so limited. Silly me. The science part is easy. But policy, politics, economics, industry, media framing, the scientific bureaucracy, cultural bias—now that stuff is crazy complicated. It’s like an onion: when you start peeling back the layers, you just want to cry. I am also honored to say that this post is part of the Diversity in Science Carnival on Latino / Hispanic Health: Science and Advocacy

When we began investigating relationships between diet and chronic disease, we didn’t pay much attention to race. The longest-running study of the relationship between dietary factors and chronic disease is the Framingham Heart Study, a study made up entirely of white, middle-class participants. Since 1951, the Framingham study has generated over 2 thousand journal articles and retains a central place in the creation of public health nutrition policy recommendations for all Americans.

More recent datasets—especially the large ones—are nearly as demographically skewed.

The Nurses’ Health Study is 97% Caucasian and consists of 122,000 married registered nurses who were between the ages of 30 and 55 when the study began in 1976. An additional 116,686 nurses ages 25 – 42 were added in 1989, but the racial demographics remained unchanged.

The Health Professionals’ Follow-up Study began in 1986, as a complementary dataset to the Nurses’ Health Study. It is 97% Caucasian and consists, as the name suggests, of 51, 529 men who were health professionals, aged 40-75, when the study began.

The Physicians’ Health Study began in 1982, with 29, 071 men between the ages of 40-84. The second phase started in 1997, adding men who were then over 50. Of participants whose race is indicated, 91% are Caucasian, 4.5% are Asian/Pacific Islander, 2% are Hispanic, and less than 1% are African-American or American Indian. I have detailed information about the racial subgroups of this dataset because I had to write the folks at Harvard and ask for them. Race was of such little interest that the racial composition of the participants is never mentioned in the articles generated from this dataset.

Over the years, these three mostly-white datasets have generated more journal articles than five of the more diverse datasets all put together.* These three datasets, all administered by Harvard, have been used to generate some of the more sensationalist nutrition headlines of the past few years–red meat kills, for instance–with virtually no discussion about the fact that the findings apply to a population–mostly white, middle to upper middle class, well-educated, health professionals, most of whom who were born before the atomic bomb–to which most of us do not belong.

Shift in demographics in past 50 years;
predicted shift in next 50 years

Although we did begin to realize that race and other characteristics might actually matter with regard to health (hence the existence of datasets with more diversity), we can’t really fault those early researchers for creating such lopsided datasets. At that point, not only was the US more white than it is now, scientific advances that would reveal more about how our genetic background might affect health had not yet been developed. We had not yet mapped the human genome; epigenetics (the study of the interaction between environmental inputs and the expression of genetic traits) was in its infancy, and biochemical individuality was little more than a glimmer in Roger Williams’ eye.

Socially, culturally, and I think, scientifically, we were all inclined to want to think that everyone was created equal, and this “equality” extended to how our health would be affected by food. Stephen Jay Gould’s 1981 book, The Mismeasure of Man, critiqued the notion that “the social and economic differences between human groups—primarily races, classes, and sexes—arise from inherited, inborn distinctions and that society, in this sense, is an accurate reflection of biology.” In the aftermath of the civil rights movement, with its embarrassingly racist behavior on the part of some representatives of the majority race and the heartbreaking violence over differences in something as superficial as skin color, it was patently unhip to suggest that racial differences were anything more than just skin deep.

But does that position still serve us now?

In the past 35 years, our population has become more diverse and nutrition science has become more nuanced—but our national nutrition recommendations have stayed exactly the same. The first government-endorsed dietary recommendations to prevent chronic disease were given to the US public in 1977. These Dietary Goals for Americans told us to reduce our intake of dietary saturated fat and cholesterol and increase our intake of dietary carbohydrates, especially grains and cereals in order to prevent obesity, diabetes, heart disease, cancer, and stroke.

Since 1980, the decreases in hypertension and serum cholesterol—health biomarkers—have been linked to Guidelines-directed dietary changes in the US population [1, 2, 3, 4].

“Age-adjusted mean Heart Disease Prevention Eating Index scores increased in both sexes during the past 2 decades, particularly driven by improvements in total grain, whole grain, total fat, saturated fatty acids, trans-fatty acids, and cholesterol intake.” [1]

However, with regard to the actual chronic diseases that the Dietary Guidelines were specifically created to prevent, the Dietary Guidelines have been a resounding failure. If public health officials are going to attribute victory on some fronts to Americans adopting dietary changes in line with the Guidelines, I’m not sure how to avoid the conclusion that they also played a part in the dramatic increases in obesity, diabetes, stroke, and congestive heart failure.

If the Dietary Guidelines are a failure, why have policy makers failed to change them?

It is not as if there is an overwhelming body of scientific evidence supporting the recommendations in the Guidelines. Their weak scientific underpinnings made the 1977 Dietary Goals controversial from the start. The American Society for Clinical Nutrition issued a report in 1979 that found little conclusive evidence for linking the consumption of fat, saturated fat, and cholesterol to heart disease and found potential risks in recommending a diet high in polyunsaturated fats [5]. Other experts warned of the possibility of far-reaching and unanticipated consequences that might arise from basing a one-size-fits-all dietary prescription on such preliminary and inconclusive data: “The evidence for assuming that benefits to be derived from the adoption of such universal dietary goals . . . is not conclusive and there is potential for harmful effects from a radical long-term dietary change as would occur through adoption of the proposed national goals” [6]. Are the alarming increases in obesity and diabetes examples of the “harmful effects” that were predicted? It does look that way. But at this point, at least one thing is clear: in the face of the deteriorating health of Americans and significant scientific evidence to the contrary, the USDA and HHS have continued to doggedly pursue a course of dietary recommendations that no reasonable assessment would determine to be effective.

But what does this have to do with race?

Maintaining the myth that a one-size diet approach works for everyone is fine if that one-size works for you—socially, financially, and in terms of health outcomes. The single positive health outcome associated with the Dietary Guidelines has been a decrease in heart attacks—but only for white people.

And if that one-size diet doesn’t fit in terms of health, if you end up with one of the other numerous adverse health effects that has increased in the past 35 years, if you’re a member of the mostly-white, well-educated, middle/upper-middle class demographic—you know, the one represented in the datasets that we continue to use as the backbone for our nutrition policy—you are likely to have the financial and social resources to eat differently from the Guideline recommendations should you choose to do so, to exercise as much as you need to, and to demand excellent healthcare if you get sick anyway. Even if you accept that these foods are Guidelines-recommended “healthy” foods, you are not stuck with the commodity crop-based processed foods for which our nutrition programs have become a convenient dumping ground.

In the meantime, low-income women, children, and minorities and older adults with limited incomes—you know, the exact population not represented in those datasets—remain the primary recipients of federal nutrition programs. Black, Hispanic, and American Indian kids are more likely to qualify for free or reduced-price school lunches; non-white participants make up 68% of the Special Supplemental Nutrition Program for Women, Infants, and Children enrollment. These groups have many fewer social, financial, and dietary options. If the food they’re given doesn’t lead to good health—and there is evidence that it does not—what other choices do they have?

When it comes to health outcomes in minorities and low-income populations, the “healthier” you eat, the less likely you are to actually be healthy. Among low-income children, “healthy eaters” were more likely to be obese than “less-healthy eaters,” despite similar amounts of sedentary screen time. Among low-income adults, “healthy eaters” were more likely to have health insurance, watch less television, and to not smoke. Yet the “healthy eaters” had the same rates of obesity as the “less-healthy heaters” and increased rates of diabetes, even after adjustment for age.

These associations don’t necessarily indicate a cause-effect relationship between healthy eating and health problems. But there are other indications that being a “healthy eater” according to US Dietary Guidelines does not result in good health. Despite adherence to “healthy eating patterns” as determined by the USDA Food Pyramid, African American children remain at higher risk for development of diabetes and prediabetic conditions, and African American adults gain weight at a faster pace than their Caucasian counterparts [7,8].

Adjusted 20-year mean weight change according to low or high Diet Quality Index (DQI) scores [8]

In this landmark study by Zamora et al, “healthy eaters” (with a high DQI) were compared to “less-healthy eaters” (with a low DQI). Everyone (age 18-30 at baseline) gained weight over time; the slowest gainers—white participants who were “healthy eaters”—still gained a pound a year. More importantly however, for blacks, being a “healthy eater” according to our current high-carbohydrate, low-fat recommendations actually resulted in more weight gain over time than being a “less healthy eater,” an outcome predicted by known differences in carbohydrate metabolism between blacks and whites [9].

Clearly, we need to expand our knowledge of how food and nutrients interact with different genetic backgrounds by specifically studying particular racial and ethnic subpopulations. Social equality does not negate small but significant differences in biology. But it won’t matter how much diversity we build into our study populations if the conclusions arrived at through science are discarded in favor of maintaining public health nutrition messages created when most human beings studied were of the adult, mostly white, mostly male variety.

Right now the racial demographics of the participants in an experimental trial or an observational study dataset doesn’t matter, and the reason it doesn’t is because the science doesn’t matter. What really matters? Maintaining a consistent public health nutrition message—regardless of its affect on the health of the population—that means never having to say you’re sorry for 35 years of failed nutritional guidance.

*ARIC – Atherosclerosis Risk In Communities (1987), 73% white; MESA – Multi Ethnic Study of Atherosclerosis (2000), 38% white, 28% African American, 12% Chinese, 22% Hispanic; CARDIA – Coronary Artery Risk Development in Young Adults (1985), 50% black, 50% white; SHS – Strong Heart Study (1988), 100% Native American; BWHS – Black Women’s Health Study(1995), 100% black women.

References:

1. Lee S, Harnack L, Jacobs DR Jr, Steffen LM, Luepker RV, Arnett DK. Trends in diet quality for coronary heart disease prevention between 1980-1982 and 2000-2002: The Minnesota Heart Survey. J Am Diet Assoc. 2007 Feb;107(2):213-22.

2. Hu FB, Stampfer MJ, Manson JE, Grodstein F, Colditz GA, Speizer FE, Willett WC. Trends in the incidence of coronary heart disease and changes in diet and lifestyle in women. N Engl J Med. 2000 Aug 24;343(8):530-7.

3. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med. 2008 Apr 14;168(7):713-20. Erratum in: Arch Intern Med. 2008 Jun 23;168(12):1276.

4. Briefel RR, Johnson CL. Annu Rev Nutr. 2004;24:401-31. Secular trends in dietary intake in the United States.

5. Broad, WJ. NIH Deals Gingerly with Diet-Disease Link. Science, New Series, Vol. 204, No. 4398 (Jun. 15, 1979), pp. 1175-1178.

6. American Medical Association. Dietary goals for the United States: statement of The American Medical Association to the Select Committee on Nutrition and Human Needs, United States Senate. R I Med J. 1977 Dec;60(12):576-81.

7. Lindquist CH, Gower BA, Goran MI Role of dietary factors in ethnic differences in early risk of cardiovascular disease and type 2 diabetes. Am J Clin Nutr. 2000 Mar; 71(3):725-32.

8. Zamora D, Gordon-Larsen P, Jacobs DR Jr, Popkin BM. Diet quality and weight gain among black and white young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study (1985-2005). American Journal of Clinical Nutrition. 2010 Oct;92(4):784-93.

9. Hite AH, Berkowitz VG, Berkowitz K. Low-carbohydrate diet review: shifting the paradigm. Nutr Clin Pract. 2011 Jun;26(3):300-8. Review.

N of 1 Part 5: A Different Question

The magic formula

My friend, Andrew Abrahams, puts the current “diet wars” situation this way:

1.  the n of 1 view:  what works for you is what works, this is all that matters, end of story.

2.  the Platonic view: this is how your body/metabolism works, and so this is what you should do and if it isn’t working you probably are not doing right.

I think many of us start off being interested in nutrition because we like to know stuff, and knowing stuff about how to be healthy and fit is really cool because then you get to look better in your bathing suit than most or you can solve health problems that others can’t or any number of other minor acts of smug superiority masquerading as an objective search for knowledge. When we start out, we usually are completely immersed in perspective #2, that there is a “right” way to eat and exercise. We figure out what the “right” way is through various forms of scientific investigation/reporting brought to us by experts and/or the media; we apply that magic formula to ourselves, and we wait for the magic results to happen. If we are young and unencumbered by reality, they usually do—no matter what formula for fitness and health we’ve chosen from the ones offered by the experts—and we congratulate ourselves for our hard work and strength of character.

Enter reality. Crying babies. Crazy work hours. Demoralizing paychecks. Chronic injuries. Insane parents. Needy friends. Crying, crazy, demoralizing, chronically insane, needy life partners (No, my dear sweet rockstar hubby, I certainly couldn’t have had you in mind when I wrote this.)

A little reality can drop-kick your magic fitness formula into outer space.

For many of us, somewhere along the line, the magic formula stops working, or we stop working at the magic formula, or a little (or a lot) of both.

Some of us respond to this by looking for the next—better, easier, quicker, more doable—magic formula. Some of us respond by working even harder at the magic formula we haven’t given up on—yet. Some of us give up looking and trying because life is hard enough already.

But that doesn’t mean we’ve given up on the idea that there is a “right” way to go about being healthy. I was a low-fat vegetarian eater for 16 years because I thought it was the “right” way to eat. I’ve been a (mostly) low-carb, animal eater for 13 years, during most of which I thought I’d—finally—found the really “right” way to eat.

What I’d really found was a new and different way to be wrong.

I wasn’t wrong about the diet plan–for me. It helped me lose 60 pounds that I’ve kept off for 13 years without hunger, without a calculator, and without having to exercise more than I want to. What I was wrong about was being right. I was wrong about the magic formula—any magic formula.

[In blog posts yet to come, I’ll tell you all the story of the woman who changed my perspective on everything.]

I hate being wrong (although goodness knows I’m really good at it, from years of practice). I really want there to be a formula, magic or otherwise. I like order, routine, facts, and answers. Gray areas make me woozy. That’s why I love biochemistry. It’s a game with nothing but rules that, literally, every body has to follow.

But, to quote Andrew Abrahams again, a detailed understanding of the minutiae of biochemical mechanisms doesn’t really help us in the big messy world of real people. Although everyone is subject to the same biochemical rules, how those rules play out in any given individual is difficult—perhaps impossible—to predict.

I salute the work that Gary Taubes and Peter Attia are doing with NuSI, which will focus on providing randomized controlled experimental evidence regarding nutritional interventions. The idea is to have both highly controlled experiments and more “real world” ones. Hooray for both. These experiments may help us understand how well certain nutrition interventions work—in experimental situations with a selected group of individuals. As awesome as this might be for a scientific pursuit, this science still may not be of much help for you personally, depending on how closely matched you feel your life and your self are to the experimental conditions—and it won’t provide any easy answers for the hardest issue of all, public health policy.

One big long experiment

Is there a way to round up our messy, individual realities into comprehensible information that will eventually translate into meaningful policy? Maybe. Andrew Abrahams and others in the ancestral health community have been tossing around the idea of “n of 1” nutrition for a while. The basis for this approach is the idea that we all experiment. In fact, life is one big long experiment.

But how do we conduct these “n of 1” experiments in a manner that

  • helps the person doing the experiment learn the right lessons (rather than be distracted by coincidences or random events)?
  • helps the clinician give better nutrition guidance, not of the “one size fits all” variety?
  • helps the field of nutrition science develop more meaningful methods of investigation, especially with regard to long-term health and prevention of chronic disease?
  • helps us renegotiate the top-down, one-size-fits-all framework of current public health nutrition policy?

Andrew Abrahams had the brainchild of setting up a community for n of 1 nutritional experimentation to do exactly this.

As Andrew says, and I agree, individual characteristics, circumstances, and history are tremendously important as far as choosing food and activity that works for you. His idea is to create a way to help people with this n of 1 experimentation so they can evaluate how their body will respond to changes and find what’s right for them.

The purpose of this community would be to capture the wide variety of attributes that may contribute to the outcomes for any individual, and provide modeling tools that can help people make the right decisions about what changes to make.

From a participant’s perspective, it would:

  • provide a way for you to observe and analyze personal health in an organized and (more or less) objective fashion
  • give direction, support, and structure to your own n of 1 experimentation
  • create a community of fellow experimenters with whom you could compare/contrast results

From a health professional’s perspective, it would:

  • provide a way to assist clients/patients in find what works best for them without a superimposing “it’s supposed to work this way for everyone” bias
  • create a set of algorithms for adapting common patterns to individualized recommendations and further experimentation
    • For example: A postmenopausal female who wants to lose weight may start one way and experiment in a series of steps that is different from, say, a 30-year old marathoner who wants to have a healthy pregnancy.

From a researcher’s perspective, it would:

  • create a way to structure and conduct experiments across a variety of nutritional (and other) factors
  • allow sharing and analysis of both pooled results and case studies/series of relevant community members or subpopulations with common characteristics
  • develop tools allowing one to interpret the community results in an individual context, make predictions and suggest “next steps”
  • contribute to the development of modeling systems for complex and interrelated inputs and outputs

A different question means a different approach to public health

I see the value of n=1 as a scientific pursuit because it will teach us to ask a very different question than the one we’ve been asking.  We’ve been asking, “What way of eating will prevent chronic disease in most/all Americans?” Typically, nutrition epidemiology is recruited to try to answer that question with the idea that there is some factor or factors (like smoking and lung cancer) that can be included/eliminated to reach this goal.  We’ve been so phenomenally unsuccessful at chronic disease prevention with our current population-wide model that I think a new framework of investigation is needed. Thus, n of 1 investigation changes the question to something more like: “What way of eating will bring improved health to you now?”

As people make incremental changes toward shorter-term personal health goals, modeling tools can be used to map out “nearest neighbor” communities. These communities may be similar in terms of personal characteristics and health history, but also attributes relating to culture, region, lifestyle, ethnic and family background, education, income, etc. Over time, this information will reflect long-term health outcomes built on a background of complex human traits interacting with complex human environments.

The complexity of n of 1 nutrition seems to be the very opposite of public health nutrition. And it would be naïve to think that the concept of n of 1 will not be at least partially co-opted by the food, drug, and research industries (“Try new Methylation Carbonation –for PEMT polymorphisms!”).  But by its very nature, n of 1 nutrition resists being turned into yet another “magic formula.”  More importantly, it reframes our current approach to public health nutrition along two very important lines:

First, it weakens the current public health message that a one-size-fits-all dietary recommendation is appropriate. This is especially important because it has been assumed for 30+ years that dietary recommendations that are normed on one population are equally applicable to other populations. A landmark study published in 2010 shows that African-Americans who consumed a “healthier” diet according to Dietary Guidelines standards actually gained more weight over time than African-Americans who ate a “less healthy” diet [1].

DQI stands for Diet Quality Index. Blacks with a higher DQI had more weight gain over time than blacks with a lower DQI. From [1]



Second, n of 1 nutrition emphasizes the need to return to a focus on the provision of basic nutritional needs rather than prevention of chronic disease.  Balancing the complexity of the n of 1 concept (i.e. each human is radically different from another) with the simplicity of promoting/understanding essential nutrition (i.e. but each human shares these same basic needs provided by food) moves us away from the prevention model to the provision model. And the literature is pretty straightforward about what our basic nutritional needs are:

  • essential amino acids
  • essential fatty acids
  • vitamins and minerals
  • sufficient energy

Notice anything missing on that list of essentials? As the Institute of Medicine’s Food and Nutrition Board says: The lower limit of dietary carbohydrate compatible with life is apparently zero” (DRI, Ch. 6, 275) [2]. This doesn’t mean you can’t or shouldn’t eat carbohydrate foods, or that some carbohydrate foods aren’t beneficial for some people or even many people. Indeed, some of my best friends are carbs. But dietary carbohydrate is not an essential component of our nutritional needs and never has been (although it is a fine source of energy if energy is what is you need and you aren’t wearing a 6-month supply on your backside like I am). Rather, carbohydrate has been recommended as the source of the majority of our calories as a means of replacing the fat, saturated fat, and cholesterol that we’ve been told cause chronic disease.* This recommendation seems to have conveniently upsized the market for the industrialized and heavily marketed foods—made mostly from corn, wheat, and soy—that take up most of the space on our grocery store shelves.

But I think the most significant ramification of the history of our Dietary Guidelines is not its effect on diet so much as the acceptance of the notion that something as intimately and intricately related to our health, culture, personality, lifestyle, family, and history as food can and should be directed—in a most comprehensive manner—from a place exceedingly remote from the places where we actually get fed.

Focus on community

While the ostensible focus of n of 1 nutrition is the individual, the real focus is the community. Advances in both biological and social sciences are increasingly focused on what are now considered to be the primary determinants of health status for an individual: that person’s genetic community and that person’s present community. What health behaviors you as an individual think you “choose” have already been largely determined by social factors: culture, socioeconomic status, education, etc. Those behaviors interact with genetic and epigenetic mechanisms that you didn’t have much choice about either. Although every individual has some control over his/her health behaviors, many of the health outcomes that we think of as being a result of “individual choice” are already largely predetermined.

One of the enduring myths of healthcare in the US is that there are some folks out there who “choose” poor health. Maybe there are, but I’ve met a lot of people in poor health, and I’ve never met anyone who deliberately chose it.

As we find virtual “nearest neighbor” communities in our n of 1 nutrition database, we may be able to use this information to assist real communities to develop their own appropriate food-health systems. Despite our increasing diversity, much of America still clusters itself in communities that reflect shared characteristics which play leading roles in health and health behavior. Culturally-influenced food preferences and nutrition beliefs may be part of that community formation and/or may reinforce those communities. With scientific tools that embrace complexity and diversity, we can honor those characteristics that make one community (real or virtual) different from the next, rather than ignore them.

N of 1 nutritional approaches will give us a new way to think about public health nutrition and the individuals and communities most affected by nutrition policy. I’m proud to say that Healthy Nation Coalition will be supporting the project.

Up next:  My take on why nutrition is a feminist issue, or “I am Woman, hear my stomach growl.”

*While on a field trip to Washington, DC in January of 2010, I met Linda Meyers, one of the authors of reference #2 below. I asked her why carbohydrates were recommended as such a large part of our diet if there is no essential requirement for them. Her response was that the recommendation was based on prevention of chronic disease. I’m still not sure I get that.

References:

1. Zamora D, Gordon-Larsen P, Jacobs DR Jr, Popkin BM. Diet quality and weight gain among black and white young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study (1985-2005). American Journal of Clinical Nutrition. 2010 Oct;92(4):784-93.

2. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients) (2005)

N of 1 Nutrition Part 4: The Elephant in the Room

“Nutrition is for real people. Statistical humans are of little interest.”
Roger J. Williams, PhD

Nutritional epidemiology has many shortcomings when it comes to acting as a basis for public health nutrition policy.   But you don’t have to take Walter Willett’s word for it.  Apart from the weaknesses in the methodology, there is one great big elephant in the nutrition epidemiology room that no one really wants to talk about:  our current culture-wide “health prescription.”

(Thanks to Utopia Theory!)

You don’t have to care about or read about nutrition to know that “fat is bad” and “whole grains are good” [1,2]. Whether or not you follow the nutrition part of the current  “health prescription” is likely to depend on a host of other factors related to general “health prescription” adherence, which in turn may have a much larger impact on your health than your actual nutritional choices. This is especially true because variation in intake and/or variation in risk related to intake are frequently quite small.

For example, in a study relating French fry consumption to type 2 diabetes, the women who ate the least amount of French fries ate 0 servings per day while the women who ate the most ate 0.14 servings per day or about 5 French fries per day (i.e. not a big difference in intake) [3]. The risk of developing type 2 diabetes among 5-fries a day piggies was observed to be .21 times greater than the risk among the no-fry zone ladies (i.e. not a big variation in risk).

Okay, everyone knows that French fries are “bad for you.” But these ladies ate them anyway. Were there other factors related to general “health prescription” adherence which may have had an impact on their risk of diabetes?

The French fry eaters also “tended to have a higher dietary glycemic load and higher intakes of red meat, refined grain, and total calories. They were more likely to smoke but were less likely to take multivitamins and postmenopausal hormone therapy.” (They also exercised less.) In other words, the French fry eaters, within a context of a known “health prescription” had chosen to ignore a number of healthy lifestyle recommendations, not just the ones related to French fries.

“As a general rule, noncompliant patients will usually have worse outcomes than compliant patients. In fact, there is solid evidence that patients who fail to comply with a placebo have worse outcomes than patients who comply with a placebo [4, 5] . . . . Patients who comply poorly with a placebo probably have other poor self-care habits.”

[Also, see Gary Taubes’ characteristically exhaustive discussion of the compliance effect. Pack a lunch.]

If you think of our current default diet recommendation as the “placebo” (although its effects may not be exactly benign), it is clear that people who fail to comply with dietary prohibitions against red meat, saturated fats, and “junk” food like French fries may also be more likely to have other poor self-care habits, like smoking and not exercising. That poor health care habits are related to poor health is of no surprise to anyone.

Statistical people

In their statistical manipulation of a dataset, nutritional epidemiologists attempt to “control” for confounding variables (confounders), such as differences in health behavior. A confounder is something that may be related to both the hypothesized cause under investigation (i.e. French fry eating) and the outcome (i.e. type 2 diabetes).  As such, it muddies the water when you are trying to figure out exactly what causes what.

When statisticians “control” or “adjust” for these confounders in a data set, they essentially “pretend” (that’s the exact word my biostats professor used) that the other qualities that any given individual brings to a data set are now equalized and that the specific factor under investigation—diet—has been isolated. Well, it has and it hasn’t. The “statistical humans” created by computer programs that now have equalized risk factors are a mirage; these people do not exist. The people who contributed the data that ostensibly demonstrates that “French fries increase risk of type 2 diabetes” are the exact same people who had other behaviors that may also contribute to increased risk of diabetes. (Please note: I chose this example, rather than “red meat causes heart disease” because there are many plausible explanations for French fries causing type 2 diabetes, it is just that you aren’t going to find evidence for them using this approach.)

If nutrition epidemiologists were clinicians.

(Thanks and apologies to Baloocartoons.com.)

Most nutritional epidemiology articles contain some version the following statement in their conclusions:

“We cannot rule out the possibility of unknown or residual confounding.”

Meaning: We can not rule out the possibility that our results can be explained by factors that we failed to fully take into account. Like the elephant in the room.

That this is actually the case becomes apparent when hypotheses that seem iron-clad in observational studies are put to the test in experimental conditions.

Lack of experimental confirmation

If ever there was a field about which you could say “for every study there is an equal and opposite study,” it is nutritional epidemiology–although experimental results are generally considered “more equal” than observational data. Associations that link specific nutrients to the prevention of specific diseases can be (relatively) strong and consistent in the context of nutritional epidemiology observational data, but absent in experimental situations. Epidemiological studies suggested that beta carotene could prevent cancer; experimental evidence suggested just the opposite and in fact, smokers given beta carotene supplements had increased risk of cancer [6]. Epidemiological studies suggest that low-fat, high-carb diets are related to a healthy weight. This may be the case, but experimental evidence shows that reducing carbs and increasing fat is more effective for weight loss [7, 8]. In one study, when experiment participants added carbs back into their diet (the increase in calories from 2 months to 12 months is entirely accounted for–and then some–by carbohydrate), they regained the weight they had lost.*

Data from [7]

Kenneth Rothman, in his book Epidemiology: An Introduction, emphasizes the importance of applying Karl Popper’s philosophy of refutationism to epidemiology:

“The refutationist philosophy postulates that all scientific knowledge is tentative in that it may one day need to be refined or even discarded. Under this philosophy, what we call scientific knowledge is a body of as yet unrefuted hypotheses that appear to explain existing observations.” [9]

Rothman makes the point that there is an asymmetry when it comes to refuting hypotheses based on observations: a single contrary observation carries more weight in judging whether or not a hypothesis is false than a hundred observations that suggest that it is true.

In the case of the current “low fat, whole grain diets will prevent chronic disease” hypothesis, there is not just one contrary observation, but scores of them, including the results of applying this hypothesis in a 30-year, population-wide experiment in the US.

If the current nutrition paradigm needs to be “refined or even discarded,” how will we acquire the knowledge we need to create a better system? How can we move away from “statistical people” towards a perspective that encompasses the individual variations in genetics, culture, and lifestyle that have such a tremendous impact on health?

Tune in next time for the final episode of N of 1 nutrition when I ask the all-important question: What the heck does n of 1 nutrition have to do with public health?

*This doesn’t mean that carbs are evil–some of my best friends are carbs–but that the conditions in a population that are associated with a healthy weight and the conditions in an experiment to that lead to increased weight loss are very different.

References:

1. Eckel RH, Kris-Etherton P, Lichtenstein AH, Wylie-Rosett J, Groom A, Stitzel KF, Yin-Piazza S. Americans’ awareness, knowledge, and behaviors regarding fats: 2006-2007. J Am Diet Assoc. 2009 Feb;109(2):288-96.

2. Marquart L, Pham AT, Lautenschlager L, Croy M, Sobal J. Beliefs about whole-grain foods by food and nutrition professionals, health club members, and special supplemental nutrition program for women, infants, and children participants/State fair attendees. J Am Diet Assoc. 2006 Nov;106(11):1856-60.

3. Halton TL, Willett WC, Liu S, et al. Potato and french fry consumption and the risk of type 2 diabetes in women. Am J Clin Nutr. 2006 Feb;83(2):284-90.

4. Coronary Drug Project Research Group. Influence of adherence to treatment and response of cholesterol on mortality in the coronary drug project. N Engl J Med. 1980 Oct 30;303(18):1038-41.

5. Horwitz RI, Viscoli CM, Berkman L et al. Treatment adherence and risk of death after a myocardial infarction. Lancet. 1990 Sep 1;336(8714):542-5.

6. Willett, W. Nutrition Epidemiology, 2nd ed. New York: Oxford University Press, 1998.

7. Gardner C, Kiazand A, Alhassan, et al. Weight Loss Study: A Randomized Trial Among Overweight Premenopausal Women: The A TO Z Diets for Change in Weight and Related Risk Factors .Comparison of the Atkins, Zone, Ornish, and LEARN. Journal of the American Medical Association. 2007;297(9):969-977

8. Shai I, Schwarzfuchs D, Henkin Y, Shahar DR, et al; Dietary Intervention Randomized Controlled Trial (DIRECT) Group. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N Engl J Med. 2008 Jul 17;359(3):229-41.

9. Rothman, K. Epidemiology: An Introduction. New York: Oxford University Press, 2002.