I wondered whether I might run out of things to talk about after 50 or 100 episodes. I needn’t have worried.
As we celebrate 15 years of the Nutrition Diva podcast, there’s a growing sense that nutrition science can’t be trusted.
To celebrate this anniversary, I’ll be giving away some nutrition advice in a custom audio clip. Got a nutrition argument you need settled? Want to get the definitive take on a nutrition question? Enter your question in this form and I'll be picking a winner on August 2nd: https://read.macmillan.com/promo/nd15
Nutrition Diva is hosted by Monica Reinagel. A transcript is available at Simplecast.
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Hello and welcome to the Nutrition Diva podcast. I’m your host, Monica Reinagel, and this week we are celebrating a pretty big milestone: 15 years of this show. A milestone that I certainly didn’t anticipate reaching when we launched this show back in 2008. For one thing, I wondered whether I might run out of things to talk about after 50 or 100 episodes. I needn’t have worried.
From the beginning, this show has focused on providing more clarity around some of the murkier areas of nutrition science—debunking nutrition myths, scrutinizing questionable claims, and attempting to put sensational media coverage of nutrition research into perspective. So far,
I haven’t run out of myths, questionable claims, or sensational media coverage!
It seems fitting to observe this anniversary by responding to a recent op-ed in the New York Times questioning the very legitimacy of nutrition research. I have a rule of thumb that if I receive the same question or forwarded news story from more than three listeners in a single day, that topic gets an episode. (Four of you sent me this particular article within a single hour!)
The article that got everyone’s attention was penned by two researchers from Harvard Medical School. And the points that they were making weren’t exactly new. Research into how foods and dietary patterns affect human health has always been hamstrung by the challenges inherent in conducting properly controlled experiments on living subjects.
The only way to really control a subject’s diet—not to mention other variables that might have an impact, such as sleep, movement, and a thousand other artifacts of daily life—is to confine them to a laboratory setting for the duration of the experiment. Second best, perhaps, would be to provide every single thing they eat and drink (and either put them on the honor system or somehow monitor for compliance). Obviously, this is expensive and intrusive. When this sort of research is done, it virtually never lasts longer than a few weeks.
Which is not nearly enough time for the long-term impacts of our diet and lifestyle choices to play out. That takes decades, which is obviously far longer than anyone is going to live in a lab. But more importantly, what happens under strictly-controlled laboratory conditions is obviously going to be of limited relevance to those of us who are living here in the real world.
It’s also really tough to do double-blinded research because human subjects can generally tell what they are and aren’t eating. Although we can sometimes be fooled, most of us can taste the difference, for example, between regular sugar and artificial sweeteners. And our awareness of what we’re taking in can affect how we respond.
As a result of these limitations, the lion’s share of dietary guidance and advice is based on observational data. We gather information about what people eat (and this is usually self-reported data, which raises obvious concerns about reliability) as well as look at health data and outcomes over long stretches of time. From this admittedly large but messy data set, we attempt to identify patterns.
Which eating (or exercise) patterns seem to be correlated to greater or lesser incidence of various diseases? We attempt to adjust for as many variables as we can—things like smoking, education, income, and so on. But we can never adjust for them all.
And here’s something we don’t talk about nearly enough: statistical analysis can be a bit of a black box. Those of us with degrees in health sciences all struggle through those required statistics courses. We do our best to grasp things like p values, confidence intervals, and correlation coefficients so that we can do a better job interpreting research. But I’ll let you in on a dirty little secret: statistics is not a straightforward field. There are many different and completely legitimate ways that the same data can be sliced and diced—and different methodologies can yield really different results.
But the nuances of statistical analysis are often well above the pay grade of those of us who are attempting to report on this research, not to mention those of us attempting to apply that research in clinical practice. The whole enterprise is a lot grayer and more opaque than most of us feel comfortable admitting.
And this is why I often turn to colleagues who I know have bona fide expertise in this to help me understand what the data do and do not say. You’ve heard some of those voices on this podcast.
The vagaries of statistics are somewhat beside the point here. But if you are at all interested in some of the ways in which statistics can be misused (either by accident or on purpose), there is a great (and very entertaining) book by Carl Bergstrom and Jevin West, called Calling Bullshit: The Art of Skepticism in a Data-Driven World. I highly recommend it.
Now, back to the recent op-ed. The authors, Jena and Worsham, bring up the WHO’s recent guidelines on artificial sweeteners as an example of dietary guidelines based on very weak, almost circumstantial evidence. And, as I mentioned in my recent episode on the same topic, the WHO itself admitted a low level of certainty about the data. However, in their view, with little to no evidence of benefit, even the suspicion of harm is enough to warrant caution.
If you heard my episode, you’ll remember that I took a somewhat softer stance, arguing that we should simply limit our consumption of artificial sweeteners to the same degree that we recommend restricting our added sugar consumption. We generally suggest limiting added sugars to about 25 grams per day. The equivalent of that would be about 6 teaspoons of an artificial sweetener that is formulated to measure the same as regular sugar, or about 3 of those little pink, yellow, blue, or green packets.
In those amounts, I don’t think any of the artificial sweeteners pose a big risk. The problem, in my view, comes when we view zero-calorie or “natural” sweeteners as a license for unlimited consumption of sweetened foods and beverages.
Jena and Worsham do have some constructive suggestions for how we can make nutrition research more accurate and reliable—despite the challenges. They argue that we could be making much better use of “natural experiments.” And here, they are borrowing from the field of economics, which underwent a similar credibility crisis a few decades ago.
Sometimes, events outside of our control conspire to subject people to conditions that we wouldn’t impose on purpose, for either ethical or logistical reasons. These are known as “natural experiments.” The ability to gather and process huge amounts of data make these sorts of opportunistic analyses more feasible than ever.
A great example, given in the article, is the impact of sugar rationing in Britain during the period following World War II. For about 8 years after the war ended, sugar was still strictly rationed. When rationing was finally lifted in the ‘50s, sugar became much more available. British children born just after World War II would therefore have had very little exposure to sugar during their early years. Their siblings born to the same parents just a few years later would have had the opportunity to consume much more sugar during the same period of their lives.
Interestingly, the kids born during years of strict sugar rationing continued to consume less sugar throughout their entire lives. They also had a much lower incidence of Type 2 diabetes.
It’s still a correlation and it still doesn’t prove causation. But this natural experiment provides a better-matched and -controlled dataset than typical observational studies. If I were a parent of very young kids, it would probably motivate me to work a little harder to limit my kids’ added sugar consumption.
Jena and Worsham suggest that natural experiments like this “remain under-taught and underused, particularly when it comes to diet,” and conclude that, “this important research needs a credibility revolution of its own.”
It’s not a new insight but it is a valid one—and one that I will continue to address here on this podcast in our discussions of nutrition research and dietary guidelines.
Thanks for being here with me to celebrate 15 years of the Nutrition Diva podcast. As part of that celebration, you’ll notice a bonus episode in your feed this week! I recently sat down (virtually) with Mignon Fogarty, better known as Grammar Girl, and Laura Adams, host of the Money Girl podcast. All three of our podcasts are part of the Quick and Dirty Tips podcast network, which was founded by Mignon. And Laura is also celebrating her 15th anniversary with the podcast this month. We had a great conversation about podcasting and how it (and we) have evolved over the last decade and a half, as well as how our respective fields have changed over that time.
We’re also sharing a lot of videos and other bonus content on our social media channels, including a retrospective of my favorite episodes from each of the first 15 years on the show. I’m highlighting some of my most controversial episodes, as well as episodes that ended up launching much bigger projects. You can find all of that on Facebook at @qdtnutrition and on LinkedIn (just search for my name or Quick and Dirty Tips).