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.

 

 

 

 

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.

View original post

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 realties 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 for 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 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 any thing 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 health care 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 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

Nutrition 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, nutrition 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 nutrition 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 nutrition 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 nutrition 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 in 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.


N of 1 Nutrition Part 3: The Love Song of Walter C. Willett

I didn’t want you all to have to wait all weekend for the truth:  Walter Willet didn’t really say, “I’ve never met a statistical person I didn’t like,” but he is sort of the Will Rogers of nutrition.

The Will Rogers of nutrition?

Everybody likes him, me included. Like Will Rogers was about politics, Willett is a staunch nutrition middle-of-the-roader who thinks fat it not so bad after all, but hey now, let’s not go any kind of crazy here, because saturated fat will still kill you in a New York minute probably maybe. 

I spent a lot of time with him earlier this year—okay, really just his book, but his book is so sweet and personal that I felt just like I was sitting at the master’s feet—which were clad in my imagination in the most sensible of shoes—as he unfolded for me the saga of nutrition epidemiology.

What I’m about to say is said with all due respect to the man himself (he’s basically created a whole freekin’ discipline for goodness sake). This is simply my reading of a particular text located within a particular context, i.e. this is what happens when they let English majors into science programs.

There are many reasons why nutrition epidemiology may not be up to the task of giving us a sound basis nutrition policy. But why take my word for it? If you want to understand the heart of nutrition epidemiology—the driving force behind our bold 40-year march in the misguided direction of one-size-fits-all dietary recommendations—you must read Walter Willett’s Nutrition Epidemiology. It is a book I love more every time I read it, and I say this in all sincerity.

The exciting cover graphics merely hint at the fabulousness that awaits inside!

While I suppose it was written as a sort of textbook, and it is certainly used as one, it doesn’t really read like a textbook. It is part apology and part defense, and is much more about “why” than “how.” And the “why?” that it tries to answer to is “Why apply the techniques of epidemiology to nutrition and chronic disease?”

In this regard, it is a touching masterpiece. Walter Willett, MD, DrPH is a professor at the Harvard School of Public Health and at Harvard Medical School. He is considered by many to be the father of nutrition epidemiology. To stretch the analogy, you can think of nutrition epidemiology as his child. Reading the book this way, it almost moves me to tears (again, not joking*), for I find this book to be a father’s sweet and sad paean to a beautiful prince full of promise, who has grown into a spoiled, churlish, and lazy adult, unfit to rule the kingdom, but with too much of the dreams of many poured into him to banish altogether. And the dreams of the father are the most poignant of all.

Apparently, to Willett’s eternal dismay, the whole field got started off on the wrong foot by focusing on dietary cholesterol (as a cause) and serum cholesterol (as an outcome), associations—as we now know—that turned out to be weak, inconsistent, nonexistent, or even the inverse of what was expected (pp. 5-6, 417-418) . We now know that sub-fractions of serum cholesterol affect heart disease risk differently (LDL-C vs HDL-C, for instance) and that different foods affect different aspects of serum cholesterol differently, making the relationship to overall heart disease risk even more obscure, which seems to be par for the course in this field, as Willett readily admits.

Here, according to Willett, is what we don’t know and can’t do in nutrition epidemiology:

  • We don’t know any given individual’s true intake. It can only be estimated with greater or lesser degrees of error. (p. 65)
  • We don’t know any given individual’s true status for a nutrient. Ditto above. (p. 174)
  • We don’t know the true nutrient content of any given food that a person might eat. Double ditto. (pp. 23-24)
  • We don’t know what factors/nutrients in a food may operate together to prevent/cause disease. Similarly, we don’t how foods commonly found together in dietary patterns may operate together to prevent/cause disease. (pp. 15, 21-22, 327-328)
  • We have a really hard time separating calorie intake from nutrient intake (Ch. 11). Ditto nutrients and food patterns, food patterns and lifestyle patterns, etc. (pp. 10, 15, 22)
  • We can’t separate metabolic consequences of food intake patterns from the food itself, i.e. what we are looking at in any given data set is really metabolism of food, not food. (p. 15)
  • We don’t know what really causes the chronic diseases we study in nutrition epidemiology (p. 12); age, genetics, education, income, and lifestyle factors may influence, modify, or be more important than any dietary factor in the origins of these diseases (pp. 10, 15).
  • We can’t distinguish between causal and coincidental associations. Furthermore, weak associations could be causal; strong associations can be coincidental (p. 12).
  • Associations we do find are likely to be weak; we will often find no associations at all. Even if we do find statistically significant associations between nutrients and disease, they may be clinically or practically irrelevant and should not necessarily be used to make public health recommendations. (pp. 12-14, 21).

But wait! Willett cries. Don’t give up! This book is also a defense of those shortcomings—although one blinkered by what I must assume is Willett’s love for the field. I am always a little touched and frustrated by the section on why we find so many instances of lack of association between an ostensible nutritional cause and a disease outcome in nutrition epidemiology. Willett meticulously lists the possible reasons one by one as to why we may not be able to “observe a statistically significant association when such an association truly exists” (pp. 12-14). At no time does he venture to offer up the possibility that perhaps—and how would we know one way or the other?—no such association does truly exist.

A new edition of the book is coming out; this should make the old edition cheap in comparison. I won’t read the new edition because I’m afraid it would ruin my romance with the old edition, which is the one I recommend to you.

If you think Gary Taubes is “a poisonous pea in an ideological pod” (as I’ve heard him called), read this book (especially Ch 17 on “Diet and Coronary Heart Disease”). On the other hand, if you think population studies investigating nutrition and chronic disease are basically a gigantic undifferentiated crock of malarkey, read this book. Why? Because there are no clear answers and no real heroes. If you want to know the strengths and weakness of nutrition epidemiology, best to hear them outlined in excruciating and loving detail by Willett himself.

You don’t have to read it cover to cover. Skip around. You’ll learn in passing some of the methodology behind the folly of trying to forge links between specific nutrients in food to long-term chronic diseases that have multiple and complex origins (just the sections on how we collect information about what we think people are eating are eye-opening in that regard—Ch. 4-8). But I think (I hope) you’ll also hear the voice of a father wise enough to know that children are—must be—brought into this world on grand faith, one that hopes that they will make the world a better place than before, and that his child—nutrition epidemiology—is no different. Willett believes in this child and the book is a statement of that faith.

Please draw your own conclusions, here’s mine: Faith is not science.

Any parent out there knows this: you seem at first to have a child of your own, but you end up sending an adult out into the world who is no longer yours and never really was. The mistakes, limitations, failures, shortcomings belong only to that grown child, not to the parent. But still. It may be hard to acknowledge the fact that your precious one is no better than the other kids and probably won’t save the world. Sometimes, when I’m reading this book—when I’m supposedly studying for an exam—I am caught unawares by the sighs of disappointment, the rally of excuses, and finally the prickly justifications: The prince must be allowed to rule; the king knows he’s a weak little louse, but he’s all we’ve got.

I know—and any of us who are students of literature know—that this is the king’s tragic flaw. The prince can’t save the kingdom; the empire must crumble. But here is the king, holding brick and mortar together through sheer force of will, somehow acknowledging and somehow—at the same time—unaware, that this particular castle was built on sand in the first place. In this book, I hear Willett’s love for a hopelessly flawed field, a touching declaration of blind optimism, and I love this book, and I deeply respect the man himself, for showing that to me.

Note: I don’t expect anybody but dweeby English majors to get the title of this post, but for dweeby wanna-bees, see T. S. Eliot’s “The Love Song of J. Alfred Prufock.”   It just makes my heart sing with joy that Willett refers to his diet of preference as the “prudent” diet.

Stay tuned for N of 1 Nutriton: Part 4, when you’ll hear Dr. Roger J. Williams say:

“Nutrition is for real people. Statistical humans are of little interest.”

*Admittedly, it could be eye strain.  I am OLD.

References:

Page numbers and chapters refer to the following edition:

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

N of 1 Nutrition Part 2: Biochemistry and Nutrition Policy – The Great Divorce

Full disclosure: I happen to love biochemistry. I have a favorite transcription factor (ChREBP) and a favorite neurotrophic factor (BDNF). I think proteins are beautiful. If I were a biochemist who had discovered a novel protein, I would carry a picture of it around with me in my wallet.

An absolutely fabulous (looking) protein.

The animal and cells models used in biochemistry are great for looking at genetics, epigenetics, at biological mechanisms, and how these things interact. We can manipulate these models in ways that we can’t with humans, and this has given us some crucial insights into mechanisms, especially neural and epigenetic ones—critical to understanding the effects of nutrition—that would be virtually impossible to study in humans.

Nutritional biochemistry can also wear the mantle of “objective-er than thou” when it comes to science. As one of the biochem profs at UNC noted: If you have to use statistics to discuss the results of your experiment, you need to redesign your experiment. Sure, the questions asked, the interpretation of results, and what gets published in biochem are influenced by funding sources, social/scientific contexts and dominant paradigms. But unless you are a truly bad scientist, you can’t make the experimental results come out in a way that supports your hypothesis.

(This is in marked contrast to observational studies in nutrition epidemiology where the whole point of the data analysis “experiment” is to find results that support your hypothesis. Sometimes you don’t find them, and those findings should be reported, although they may not be because who’s to know?  Just you and your SAS files. My point is that you are actively seeking results that confirm a particular idea, and this just might influence what “results” are found. More on this in another post.)

But beyond the utility and elegance of nutritional biochemistry, the problems with regard to health policy are two-fold.

The first problem: In many ways, nutrition policy has become almost completely divorced from the basic science investigations done in biochemistry. The Dietary Guidelines Advisory Committee (DGAC)—the committee of scientists that, at least theoretically, reviews the science upon which the US Dietary Guidelines are based—started in 1985 as mostly MDs and biochemistry professors. As time went on, the DGAC became more heavily populated with epidemiologists. This would be fine if epidemiology was meant to generate conclusive (or even semi-conclusive) results. It isn’t. Epidemiology gives us associations and relationships that are meant to be understood through a reasonably plausible, preferably known, biological mechanism. Note these interesting conclusions from the 2010 DGAC Report and the 2010 Dietary Guidelines policy document with regard to dietary cholesterol:

Here’s our mechanism: Exogenous, or dietary, cholesterol down-regulates cholesterol synthesis in the liver to maintain cholesterol balance.”
[D3-1, Reference 1, emphasis mine]

Here’s our epidemiology: Traditionally, because dietary cholesterol has been shown to raise LDL cholesterol and high intakes induce atherosclerosis in observational studies, the prevailing recommendation has been to restrict dietary cholesterol intake, including otherwise healthy foods such as eggs.”
[D3-2, Reference 1, emphasis mine, “induce”? really? how does one “observe” that cholesterol “induces” atherosclerosis? I’m assuming committee fatigue had set in at this point because that word should have been “are associated with”]

Here’s our policy recommendation: Consume less than 300 mg per day of dietary cholesterol.”
[Ch. 3, p. 21, Reference 2]

See, wasn’t that easy?

This brings me to the second problem, which is sort of the flip-side of the first: Biochemical processes that are understood primarily through mouse or cell models only work as the basis for dietary recommendations for chronic disease if you’re making them for cells or mice.

As one of my favorite professors in the Nutrition department likes to quip, “We know how to cure obesity—in mice. We know how to cure diabetes—in mice. We have all the knowledge we need to keep our rodent population quite healthy.” Obviously this knowledge has not been translatable to humans. In some ways, basic nutrition biochemistry should be divorced from public health policy.

The reason for this is that the equivalency of animal models to humans is limited in ways that go beyond simple biological comparisons—although the biological differences are significant.

Mouse large intestinal tract, courtesy of Comparative Anatomy and Histology: A Mouse and Human Atlas, edited by Piper M. Treuting, Suzanne M. Dintzis

My knowledge of comparative physiology is limited at best, but my understanding is that most rodents used in nutrition biochemistry work (rats included) have a cecum (an intestinal pouch that facilitates the breakdown of cellulose), an adaptation that would be necessary in a diet composed of hard-to-digest plant material such as seeds and grains. Because this process is not terribly efficient, many rodents also recycle nutrients by eating their feces. Humans don’t have a functional cecum for fermentation; we don’t tend to reingest our own poops (or anyone else’s poop, unless you’re starring in a John Waters film) in order to extract further nutrition from them as our bodies are already very efficient at this during the first go-round.

Furthermore, due to inherent difference in physiology, animals may not accurately model the physiological conditions that produce disease in humans. For example, in some species of rodents, a high fat diet will induce insulin resistance, but there is no definitive evidence that higher fat intake per se impairs insulin sensitivity in humans [3]. Why this is so is not entirely clear, but likely has something to do with the diet each species has consumed throughout its evolution. In a natural setting, rodents may do well on a diet of mostly grains. On the other hand, humans in a natural setting would do okay on a diet of mostly rodents.

What is more critical is that animal and cell life can’t imitate the complex environmental inputs that humans encounter throughout their lives and during each day. Animals and cells only get to consume what they are given. If you’ve ever been at a conference where the breakfast is low-fat muffins, whole grain bagels, fat-free yogurt, orange juice, and fruit, you know what that feels like. But typically our food choices are influenced by a multitude of factors. Mice, unlike humans, cannot be adversely affected by labeling information on a box of Lucky Charms.

Mice don’t know that whole grains are supposed to be good for you.
Bad on them.

Does that matter? You bet it does.

Where do most Americans get their nutrition information these days? From media sources including the internet, from their grocery stores, from the packages holding the food they buy. People who have never read a nutrition book, much less the actual Dietary Guidelines, still “know” fat is bad and whole grain is good [4, 5]. These environmental exposures affect food choices. Whether or not the person still decides to consume food with a high fat content depends on another set of cultural factors that might include socioeconomic status, education, race or ethnicity, age, gender—in other words, things we can’t even begin to replicate in animal or cell models.

Human biochemistry is unique and complex, as are our social and cultural conditions, making it very difficult to study how these primary contributors to health and food choices are related to each other.

Can we do a better job with nutritional epidemiology? I know you’re on the edge of your seat waiting for the next episode in the unfolding drama, N of 1 Nutrition, when we get to hear Walter Willett say:

“I never met a statistical man I didn’t like.”

Stay tuned.

References:

1. U.S. Department of Agriculture. Report of the Dietary Guidelines Advisory Committee on the Dietary Guidelines for Americans 2010. Accessed July 15, 2010. http://www.cnpp.usda.gov/DGAs2010-DGACReport.htm

2. U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2010. http://www.cnpp.usda.gov/DGAs2010-PolicyDocument.htm Accessed January 31, 2010

3. Report of the Panel on Macronutrients, Subcommittees on Upper Reference Levels of Nutrients and Interpretation and Uses of Dietary Reference Intakes, and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. Dietary Reference 4. Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: The National Academies Press; 2005.

4. 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.

5. 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.