The Dr. Oz Show is an archetype of pseudoscience and quackery, giving a voice to those who spread bad health advice over the years. It also has a structured website that makes it easy to grab info from.

So, I wrote a quick script to scrape the details of all 910 show episodes through this week from the site, as I couldn’t find them elsewhere. This database is available here. Please feel free to republish in other formats to make them more searchable. They include air date, episode titles, and guests who appear in each episode. Here is a word cloud base on the episode titles. Top words make for a perfect Oz-ian title “Dr. Oz’s new secret health food that you need to know“.

1,846 different guests have appeared on his show. Who appears most often?

Note: some appearances may be from show replays.

Most of these people have notable records of pseudoscientific ideas. Of the names I recognize, Mark Hyman is known for his promotion of “functional medicine” and anti-vaccine tendencies, never mind his books about detox, revving up the metabolism, eating fat to lose weight, or his many blog absurd posts that do things such as recommend eating garlic instead of getting the flu shot. Michael Roizen is Dr. Oz’s chief medical correspondent, which is really all that needs to be said. But here’s an excerpt on his thinking:

I think it’s likely that future tests and studies will continue to show the relationship between health and prayer. That said, this kind of unexplained energy we feel is the next big frontier in medicine. That is, we define life at the level of the cell. As long as the membrane maintains an energy gradient between the inside and outside world, our cells are alive. When you aggregate cells into organs and then put these in the right spot to make a human, you have life. That’s why we’re interested in adjusting energy in the body through such vehicles as acupuncture, homeopathy, and hard-to-explain methods like reiki and prayer. After all, everything that matters in life – like love – can’t always be measured with blood, machines, and complex calculations. They’re measured in the way you live.

It’s a nice feel-good sentiment but acupuncture, homeopathy, reiki, and prayer all have been shown to be no better than placebo treatments. There is no mystical “energy” that we can adjust for health. Ashley Koff sells detox products and promotes unscientific ideas about GMOs. Andrew Weil is a well known long time “integrative medicine” practitioner whose pseudoscientific record is too extensive to summarize. And this is just in the top 10 of guests.

I also scraped 560 articles from doctoroz.com for titles and authors. The database is available here for anyone to explore. Since it is naturopathic week, let’s look at a few articles by naturopaths:

• “The Best Teas for Teatoxing That Won’t Cost a Fortune”, which makes claims that tea can reduce bloating, cravings, constipation, boost metabolism, and detoxify you.
• “Best Natural Antidepressants”, which is frightening as hell because it promotes unproven “natural” products for depression instead of well-studied drugs.
• “Natural Prescription Alternatives”, similarly promoting unproven herbs for serious conditions.
• “Colonics: The Good, the Bad and the Ugly”, which flirts with the old claim that we are full of toxins that need to be flushed out.
• “Eat Right for Your Type”, which promotes a naturopath’s book claiming we need to eat depending on our blood type.
• “Anti-Aging Supplements”, which claims you can reverse wrinkles, varicose veins, hair loss, arthritis, etc with unproven natural products.

To these authors I would suggest some additional colonics, because they’re so full of shit.

The post Data Mining The Dr. Oz Show #NATMEDWEEK2016 appeared first on nutsci.org.

I currently live in the state of Indiana, which does not license naturopathic doctors*. However, a resolution was introduced in February to explore the potential for licensure. The lobbying efforts of state naturopaths at the time prompted me to mail letters making a case against licensure to a number of my state representatives. Below is that letter. Suggestions welcome for future letters.

*On the bright side, licensure is being rejected in many other states, and hopefully will go nowhere in Indiana as well. See here for a summary.

The post A letter to my state representatives against naturopathic licensure #NATMEDWEEK2016 appeared first on nutsci.org.

Pardon the dust here, I know it’s been awhile…

This week (October 10-16) marks the 4th annual “US Naturopathic Medicine Week”. This week was actually officially designated by the US Senate by a resolution in 2014 to recognize “the value of naturopathic medicine in providing safe, effective, and affordable health care”.

That this description reflects the reality of naturopathic treatment could not be further from the truth. It is not a science-based profession. This week as time allows I’ll be posting about the fallacies of naturopathic treatment, alternative medicine, or the troubling trend of integrative medicine. If others choose to write about these topics this week, I’ll also link them in this post. “Happy” naturopathic week!

The post A Week of Harm #NATMEDWEEK2016 appeared first on nutsci.org.

Perlmutter has a long history with pseudoscience- detailed in this great article by Dr. Alan Levinovitz. One of the most enlightening parts is that Perlmutter claims a treatment for Parkinson’s works when a study in which he is the last author shows that it doesn’t. Read the article, then Alan’s great book – the perspective of nutrition from a scholar of religion is unique and I found it refreshing. If you want a taste, listen to an interview of him here.

It takes enormous resources to thoroughly fact check such claims, so it is rarely done. In the end, it usually doesn’t inflict any consequence to the person making the claims anyway. But blogs are a nice medium to do so, and I think going forward we need to be more proactive in countering nutritional misinformation. I plan to do more of this here.

Recently, both Levinovitz and Perlmutter appeared on a radio show on the topic of gluten. It isn’t long- start at 15 minutes in and listen to about 35 minutes. Perlmutter drops a few references so we can see if they back up what he is claiming without spending days going through his books.

The episode is supposed to be about gluten, but Perlmutter pretty quickly steers the conversation to carbohydrate in general and the brain. Of note, at the start, he mentions Dr. Alessio Fasano to support his generalized connection between gluten and leaky gut, but Fasano has stated that Perlmutter’s book is “full of exaggeration and generalization“.

Reference 1.

28:30 minutes: “study of more than 27,000 individuals for 58 weeks in 40 countries and demonstrated the powerful role of the diet in determining a person’s risk in becoming demented”

This paper is here: “Healthy eating and reduced risk of cognitive decline: A cohort from 40 countries.”

This study used the “Alternative Healthy Eating Index” to assess diet quality and correlate it with cognitive decline in a population at high cardiovascular risk. It is notable that this index rates whole grains as a positive factor in the index. A higher score on this index, the lower association with cognitive impairment. This study says nothing about carbs or gluten specifically, although it would benefit Perlmutter to read the discussion, which notes a meta-analysis suggesting that adherence to a Mediterranean diet is associated with a reduced risk of cognitive impairment. This diet includes plenty of carbohydrates and gluten.

Reference 2.

29:30 minutes: “Dr. Dale Bredesen at UCLA has actually for the first time in history published results where he’s been able to reverse Alzheimer’s … but he did so by using a 36 point approach, not a pill … including exercising, repleting vitamin D, lowering carbohydrate, gluten free, welcoming fat back to the table, adjusting sleep patterns, the point is, we’re now starting to understand food matters… The study demonstrated that those individuals who ate less carbohydrates less of a refined diet has less chance of becoming demented.”

By the very design of this study we can’t know for sure if any of these dietary changes actually did matter. Here are the lifestyle changes for one participant:

This everything-but-the-kitchen-sink approach may be clinically interesting (although it can easily be argued that many of these treatments are not based on strong evidence or plausibility) but it certainly can’t lead us to say that any one change like reducing carbohydrates or gluten was a reason it was effective. For one, the improvements were almost all subjective/anecdotal- this was not a clinical study measuring some objective brain measure before and after treatment in a bunch of people and performing statistical analysis. It also noted that adherence was a problem, but didn’t measure how closely the patients adhered to each goal. We don’t know if they actually reduced carbohydrates or gluten. Not all of the patients were on the same program, either.

In other words, not a strong study to cite when suggesting to put the entire population on a low carbohydrate diet.

Reference 3.

30:10 minutes: “a study published in the Journal Alzheimer’s Disease [sic] from 2013 where Mayo Clinic researchers demonstrated over about a 6.5 year period of time that those individuals who at the most carbohydrate had an increased risk for dementia of 88%, whereas those individuals who derived most of their calories from fat had a 44% reduced risk for dementia”

This study used a food frequency questionnaire to assess the diets of 937 people in a county in Minnesota aged 70-89 years. Cognitive status was assessed by an examination and cognitive testing. Indeed, there was an association between high carbohydrate intake (>58%) and cognitive impairment, and a negative association with high fat. However, there was a trend between increased percentage of sugar and impairment that may suggest that carbohydrate/diet quality is the issue, which the authors note in the discussion. Such studies have weaknesses in what they can tell us, and the authors note that we can’t rule out reverse causation:

“The dietary patterns observed may be causal or alternately, may be a marker for preclinical disease and risk of cognitive impairment or dementia in elderly persons. These associations need to be examined in other longitudinal studies.”

It would be extremely premature to conclude that all carbohydrate is bad from such research, and there is again nothing here about gluten.

Conclusion

Anyone trained in science can easily see that these studies are weak support for a blanket case against carbohydrate and gluten and the brain (and parts of them directly contradict it). Perlmutter should be able to cite strong, well-designed clinical trials and longitudinal studies if there was convincing research.

He goes on to say (even though, again, the interview was supposed to be about gluten) that we need to eat fermented foods to nurture our microbiome to prevent dementia, discussing a list of indicators that you may have “trauma” in your microbiome, but I am not aware of any strong evidence to support these claims. The science of the microbiome is in its infancy, and it is inappropriate to invoke it to make definitive disease connections to an impressionable public. No doubt we have a lot to learn about the microbiome and health, and there are likely meaningful connections, but that doesn’t mean we can fill the gaps with the certitudes that Perlmutter does.

The post Fact Checking a Perlmutter Interview appeared first on nutsci.org.

This year, there was an increase in the total number of tweets to the hashtags over the official conference start and end times of 8:00am EST, Saturday March 28 to 4:30pm, Wednesday April 1. The hashtags that were collected were: #xbio, #eb2015, #expbio, #asnatexpbio (this one was added at 10:36pm EST on Saturday, March 28 after I noticed that people were using it). Note: there were small differences in collection methodologies and times between these years but they still reflect a growing interest in twitter use for EB.

Here is the tweet frequency per hour over the official conference time:

Total Tweets Per Year During Experimental Biology

There was also an increase in the number of people who tweeted to the hashtag(s):

Number of Tweeters

Top 10 Tweeters

Top 10 Tweets 2015 (+ Retweeted Count)

Hashtag Confusion?

Like last year, there was a change in the official hashtag for EB- previous years have used the #xbio format or #eb2013. Except this year there was additional confusion because the official account promoted #expbio as the hashtag, yet several people reported that within the conference it was advertised on signs as #EB2015.

Just a reminder: the official hashtag this year is #expbio

— Experimental Biology (@expbio) March 30, 2015

@expbio of course then I need to ask why the signs all say #eb2015. I know book got misprinted.

— Brian Cohen (@profbdcohen) March 30, 2015

In addition, orgs like ASN had separate hashtags:

Download the Experimental Biology 2015 app and use it to plan out your sessions! t.co/go2hCN58Zv #ASNatExpBio

— ASN (@nutritionorg) March 27, 2015

An analysis of the data suggested that people were indeed confused on what to use. Of the 6,720 tweets:

• 52% (3,492) only contained #expbio
• 18% (1,198) only contained #eb2015
• 11% (745) used both #expbio and #eb2015 tags in the tweet
• 0.3% (22) only contained #xbio (last year’s hashtag)
• 5.3% (359) used only #ASNatExpBio in the tweet

This confusion makes the use of these datasets for accurate analysis a little more difficult, and I likely missed some additional official and unofficial tags from separate societies. Of course if each society promotes its own hashtag this could lead to richer insights within each as well.

Network Graph

Here is a network graph showing the relationship of the most common words (several are twitter handles) in tweets and retweets that contained the hashtags. The connections between words are weighted in line size and color by how common they appear together in tweets.

*Tweets were collected using the Twitter Streaming API using a node.js app on Heroku. Analyses were done with Python and R, and the network graph in Gephi.

The post Experimental Biology 2015 Tweet Analysis #expbio appeared first on nutsci.org.

Let’s take a class of compounds widely present in foods – pesticides. It may be surprising to most that we consume many fold more natural pesticides than synthetic – regardless of whether you eat organic food. Ames, Profet, and Gold estimated in 1990 that the average synthetic pesticide intake was 0.09 mg per day, whereas that of natural pesticides was about 1.5 grams per day. In other words, 99.99% of pesticide consumption is from “natural” sources – or 10,000 times that of synthetic pesticide residues. This is because there are literally thousands of chemicals in plants that are produced in an effort to ward off pests. In addition, Gold, Ames, and Slone (2008) showed that in research through the 1990’s, the proportion of these natural chemicals found in plants that cause cancer in rodents at high doses is not much different than commercial pesticides.

Table reproduced from: Gold, Ames, and Slone. 2008. Animal Cancer Tests and Human Cancer Risk: A Broad Perspective. toxnet.nlm.nih.gov/cpdb/MOE.html

These researchers also produced a large table of how the average consumption of many natural food chemicals, food additives, and pesticides compares to the dose known to cause tumors in rodents. The accompanying graphic is a powerful visualization of how much closer to the cancer-causing dose most natural food chemical exposure is than food additives or pesticide residues are.

Of course, fruits and vegetables do not cause cancer (at least the vast majority of them). In fact, many probably reduce the risk of various cancers, and confer other health benefits. But plants can contain some other nasty chemicals that are harmful to us in other ways. In a recent paper on this topic, Gribble (2013) reminds us how toxic some fruits and vegetables can be. For example, aflatoxins, or mold metabolites found on peanuts and corn, are known to cause liver cancer. Other types of mycotoxins found on corn and cereals have led to esophageal cancer in humans, probable fatal birth defects, and pet deaths from contamination. Potatoes contain chaconine and solanine and there are documented cases of toxicity and death. Dioxins and furans, which are well studied in their potential for toxicity, can both be found in nature as well, and the latter are present in coffee, bread, and olives. Gribble reminds us that even though many obsess about whether food safety and security is accomplished in a natural or artificial manner, this debate is rather irrelevant when considering that the chemical risk of foods is generally of low concern in a diversified diet. However, pathogens, bacteria, and fungi are natural risks that exceed the threshold for concern. In fact, the CDC estimated that in 2011, about 48 million illnesses were caused by foodborne pathogens in the US alone, causing over 3,000 deaths.

As another matter of perspective, it is helpful to consider what we know is risky. The International Agency for Research on Cancer (IARC) of the WHO classified alcoholic beverages in the highest category: “carcinogenic to humans” – back in 1988. Alcohol is widely consumed yet this fact does not dominate public discourse. Arsenic and aflatoxins also make this group. “Emissions from high-temperature frying” and nitrate or nitrite (in conditions that promote nitrosation) make the “probably carcinogenic to humans” group, and coffee makes the “possibly carcinogenic to humans” list with an association with urinary bladder cancer. Meanwhile, it is sometimes incorrectly claimed around the internet that artificial sweeteners or GMOs cause cancer. It’s also quite easy to find single studies associating with cancer for almost any food ingredient, but most of these are found not to be consistent when multiple studies are analyzed. It becomes easy to see why novice research readers can make erroneous conclusions about ingredient safety.

Everything we consume is a complex mixture of chemicals. The chemicals added on and to foods undergo risk assessment, and we try to study the acute and long-term risks of both natural and non-natural chemicals as best we can. Any system cannot reduce risk to zero, and there may be good reasons to conclude that certain pesticides or food additives – synthetic or natural – are not 100% safe. However, any argument using naturality as a safety measure is not good science. The dose determines toxicity for all chemicals, and it would be rather silly to be concerned with the small amount of formaldehyde in your apple.

The post Chemicals and Food: A Little Perspective appeared first on nutsci.org.

I used NHANES ’09-’10 data to do a similar analysis, because I wanted to see if anyone met the guidelines for all food groups, and also to see if we are anywhere close. Since this is the most recent data available, we need to use the MyPyramid recommendations, which was replaced by MyPlate in 2011. The following recommendations (which were designed to meet nutrient needs within calorie limits) were used based on calorie level, except for discretionary calories which I omitted for simplicity.

Calorie levels were estimated for each person based on age and activity. A section of NHANES asks participants about activity, so I designated active as being physically active for at least 60 minutes a day for at least 5 days per week. 

Results

The proportion of Americans achieving recommendations for fruits, vegetables, grains, meat & beans, milk, and oils: 0.098%. Yes, you read that right: it is 1/10th of 1%.

If we add in another recommendation – to make half of all grains whole – 0% of the population meets recommendations. There are additional recommendations for vegetable subgroups that aren’t even worth exploring because nobody is left.

Looking at only the proportion who meet recommendations for both fruits and vegetables, it is 5.5%. The milk group alone is 18.3%. Only 5.9% made half of their grains whole.

Are We Even Close?

To see if we are even on the right track to meet recommendations, I cut them all in half. So at 50% of the recommendations for fruits, vegetables, grains, meat & beans, milk, and oils, 3.3% of the population now reaches them. If we cut the whole grain recommendation to 25% of total grains and include this, we are back below 1%: 0.81%.

Conclusions

The state of the American diet is grim. I previously estimated the proportion of the U.S. population achieving recommendations for 21 micronutrients: 0.5%. The proportion of the population meeting food group recommendations is even lower, and in fact on average, it is 0%. Even being generous and cutting recommendations in half, we are still at less than 1% who meet them.

When someone says our guidelines make us sick, they simply don’t understand the science. Many studies use the Healthy Eating Index, which directly measures how closely each individual adheres to recommendations. For example, Reedy and colleagues published a paper earlier this year associating a higher Healthy Eating Index score with a reduced risk of all-cause, cardiovascular disease, and cancer mortality in the NIH-AARP Diet and Health cohort of 424,662 people. We can also infer from similar healthy consumption patterns (e.g. high fruits and vegetables, whole grains etc) that shifting toward the guidelines would improve population health. An exhaustive review is outside of the scope of this post, but for some additional references, see this nice slide set by Reijo Laatikainen.

Notes & Caveats

This analysis uses one weighted 24-hour recall from NHANES. It is known that self report food intake is not great for some things and some foods may be underestimated while others are overestimated. It would be ideal to use the NCI method to improve estimations of usual food intakes, but for simplicity I have not yet done this. I also did not explore outliers. There is no direct match for “active” as defined for the MyPyramid calorie levels in NHANES, so I used my best judgement of which to use.

The post No, Dietary Guidelines Are Not Making Us Sick appeared first on nutsci.org.

Tweet Frequency Over the Conference

This year reached a higher peak tweet frequency compared to last year:

Tweets Per Person and Number of Tweeters

There was a lower average number of tweets per person this year, but more people tweeted to the hashtags.

The median for both years was 1 tweet.

Top 10 Tweeters

Top 10 Tweets 2014 (+ Retweeted Count)

Network Graph

Here is a network graph showing the relationship of the most common words (several are twitter handles) in tweets and retweets that contained the hashtags. The connections between words are weighted by how common they appear together in tweets.

Locations

These are maps of the home locations of each person who tweeted to the hashtags and who included their home locations in their profile. It is a similar pattern to last year: Most are from the US, but some are all over the world (click to enlarge):

Hashtag Confusion?

This year, the hashtag was switched to #xbio instead of of #eb2014 (last year was #eb2013). I looked to see if people adopted the new one. Most appeared to get the message:

• 83% of tweets contained only the #xbio hashtag
• 12% contained only the #eb2014 hashtag
• 5% contained both hashtags

Have any additional analysis requests? Post them in the comments!

*Tweets were collected using the Twitter Streaming API using a node.js app on Heroku. Locations were geocoded with Google Maps API using Python, analyses were done in R, and the network graph in Gephi.

The post Experimental Biology 2014 Tweet Analysis #xbio appeared first on nutsci.org.

EDIT: See videos of this session here.

The session began with Dr. David Allison giving a touching tribute to the late Dr. John Milner, who was originally a chair of the session. It was noted that Milner was a promoter of public-private relationships, and “in the spirit of John Milner” was a frequent phrase. His impact on nutrition science and many of the speakers was clearly boundless. Read more about his prolific career here.

The first speaker was Dr. Janet King, who discussed where fortification has been successful and where there are limitations. She first emphasized the distinction between mandatory fortification/enrichment, where specific nutrients are added for a specific health program (by the US definition), versus discretionary fortification which is done at the choice of a food manufacturer. Iodine to salt, vitamin D to dairy products, thiamin, niacin, riboflavin, and iron to flour, fluoride to water, and folic acid to grains are a few that have reduced incidence of disease and the proportion of the population that consumes less than the estimated average requirements, as Fulgoni and colleagues (2011) have shown. Dr. King highlighted some limitations in fortification: programs used to be based on insufficient diet intakes rather than health/clinical problems; in the US, staple foods vary; many people are increasingly avoiding milk and dairy, and physiological bioavailability is often unknown in tissues. For example, increasing zinc by supplementation increases plasma zinc concentration but increasing it by food does not, according to one study.

In the 1970’s and 80’s, there was a shift in thinking as fortification as more of a “balancing act,” according to Dr. Christine Taylor. The National Academy of Sciences recommended iron fortification, but there were strong objections because of toxicity concerns, and thereafter the FDA conclude that fortification must be based on clinical measures rather than dietary intake. Determining whether to fortify grains with folate in the 1990s exemplifies the complex issues that must be considered. For instance, animal data suggested that folate can exacerbate vitamin B12 deficiency, and fortification would increase folate intake to a greater extent in those who already had a high-intake in a simulation study. A key theme throughout the program was that the reliance on dietary intake can be problematic. This message was really driven home by Dr. Taylor, who emphasized that intake data over-estimates inadequacy, and that we need more valid biochemical measures. Fortification should be a 2 prong approach- modest fortification, followed by targeted fortification where needed. Taylor speculated on the next US fortification experience – perhaps vitamin D. She highlighted a paper just published by their group that suggests we are underestimating vitamin D intake by about 15-30% because we currently don’t include 25(OH)D from animal products in food composition tables, a perfect example of why biomarkers are needed.

Dr. Omar Dary was next with a more global perspective on fortification. He stressed that fortification reach and coverage depends on the consumption pattern of the fortification vehicle. For example, iron is added to wheat flour in Jordan which improves status in children, but not women. Vitamin A is also added but that pattern is the opposite. In Chile, folic acid is added to wheat flour which reduces neural tube defects, but also increases the risk of excessive intake for much of the population. Multiple food vehicles would therefore be necessary in these countries to target different segments of the population. However, sugar is fortified with vitamin A in Guatemala, and because all segments of the population consume sugar, it doesn’t need to be added to anything else. Where detailed national diet intake data are not available, Dary and others propose a model based on “fortifiable food energy” – an optimal combination of food vehicles to achieve an ideal balance of increasing nutrients without unnecessary excess risk.

Dr. Regan Bailey discussed some of her NHANES research on children, looking at how fortification alters the proportion below micronutrient EARs and above ULs. She noted that not all nutrients have a large gap between the RDA and the UL, so fortification can push some above the UL. For example, 0% of children were above the ULs for folate and niacin without enrichment/fortification, but including these raised them both to 4%. Dietary supplements further increased these to 15% and 16% of the children, respectively. Bailey emphasized that we need need better analytically derived data for food and supplements and ideally we need to incorporate diet, supplements, and biomarkers to gain an accurate picture of the contributions of fortification and supplementation on nutrient status in the population.

Dr. Valerie Tarasuk covered discretionary fortified foods, for which the definition varies somewhat in different countries. In Canada, this means fortification that is not mandatory, and in the US it is fortification outside a planned health program. She showed how discretionary fortification is being used in marketing (certain energy drinks for instance) – in some products up to “many hundreds of times” the DRI. On some cereals, the % daily value is being used to market the product on the front of the box. Tarasuk’s lab has found that discretionary fortified foods do increase some nutrient intakes above the UL in some subpopulations. She emphasized that there is no evidence that going above usual intakes confers health benefits, and cautioned that research on utilizing nutrient supplements to prevent chronic disease keeps coming back negative. We need to monitor discretionary fortification by including it in food composition tables, and design surveys to enable subgroup analyses, she said. Along with these, tracking systems for adverse effects are needed and we need to study potential health consequences of chronic high intakes for fortificants and supplements.

Dr. Martin Philbert gave a talk that departed from the other topics: exploring fortification by nano-delivery. Nano-packages can encapsulate nutrients and altering their properties can target specific tissues if desired. However, absorption, excretion, metabolism, and toxicity can be altered when changing particle size, so much research needs to be done to explore these areas. For a technical overview of nanotechnology applications in nutrition, see this review.

Finally, Dr. Carl Keen took the audience through a hypothetical scenario in considering a new avenue of fortification: flavanols. There is a large body of evidence that suggests flavonoid intake is associated with a reduced coronary heart disease mortality. Intervention trials are relatively consistent, but epidemiological studies are not as clean, said Dr. Keen. This may be because interventions are traditionally done at the upper 10% of intake, so maybe fortification is necessary. Reliable physiological markers must be established. Biomarkers are not possible because metabolites can be gone by the evening if they are consumed in the morning. There is antioxidant activity in vitro but possibly not in vivo. Their effect on blood pressure is inconsistent. Platelet markers and vascular function as measured by flow mediated dilation may work, as there are consistent short-term changes in these. There are potential negative effects that would need to be considered: anti-nutrition effects, thyroid toxicity, genotoxicity, etc. Research would need to be done to define the amount of flavanols needed to achieve “optimal” status, to examine whether acute effects persist, explore mechanisms of action, and determine safety thresholds.

How we think about fortification has changed from nearly a century ago when the US first fortified salt with iodine. There still remain many challenges, especially outside of the US, in targeting specific populations with low nutrient intakes. Accurate monitoring of nutrient intakes and health outcomes is needed now more than ever as discretionary fortification is being used for food marketing. The broad perspectives that were covered in this session left a lot to think about, and no doubt fostered conversation as John Milner would have intended.

The post Food Fortification in a Changing Environment appeared first on nutsci.org.

Dr. Allison was the first presenter, taking a hard position on self-reported energy intake in nutritional research: it just isn’t good enough. Not only that, it often flat-out misleads obesity research. Allison highlighted a recent paper by Archer and colleagues that looked at energy intake of respondents in NHANES from 1971-2012, finding that 67.3% of women and 58.7% of men were not physiologically plausible – i.e. the number of calories is “incompatible with life”. Correlations with the IOM’s gold standard equation for estimating total energy expenditure were 0.163 for women and 0.225 for men, effectively yielding no meaningful relationship. This “doesn’t seem like science anymore,” Allison stated. This problem has been known for a long time: in 1991, Goldberg and others looked at 37 studies across 10 countries and found that over 65% of the mean ratio between reported energy intake and basal metabolic rate measures were implausible. Forrestal also published a review in 2010 of 28 papers looking specifically at children and adolescents, finding that about half misreport energy intake.

It is time to abandon self-reported energy intakes in favor of less misleading paths in obesity research, Allison said. It is not worthy of scientific use because the measurement errors are not random and modest, estimates are often not in the correct direction, and errors will not lead to the detection of false effects under plausible circumstances. He told a story of how originally, self-report intake data suggested that the overweight consumed less energy than they expended, but using more rigorous methods proved exactly the opposite to be true (here is a 1990 review by Schoeller). Allison said that we currently have no economic and social incentive to make a complete transition to incorporating doubly labeled water, as the cost has been flat since the 1980s. It will be painful initially, but clearly we need to make the transition.

Dr. Amy Subar argued that energy intake is not the only important aspect of diet data, and improvements are being made to collection methods, and therefore we shouldn’t throw the baby out with the bathwater. Even if total energy intake isn’t accurate, we still can track food patterns, diet quality, nutrient intakes, and social and physical environments. Subar emphasized the utility of self-reported data- it can yield more comprehensive data with much less of an investigator burden compared to biomarkers or observation, but there is the issue with error. Within-person variation and memory are 2 potential errors, but adjustments are possible. New technologies, such as keeping food records with mobile phones or wearable sensors to reduce reactivity to monitoring and burden, are being developed to improve self-report data. In addition, Subar has been involved in the development of self-administered 24-hour recalls – ASA24 – to be able to gather a lot more data from participants without investigator burden. They have validated the accuracy of this method against interviewer-administered recalls. Dr. Subar noted that food frequency questionnaires have more bias than short-term methods but combining multiple recalls with food frequency questionnaires could reduce this.

Dr. Elizabeth Yetley expanded on how self-reported diet data is currently relied on in national policy. For example, fortification strategies would not be possible without such data. Many considerations go into fortification, and modeling specific foods and evaluating the outcomes of such programs are important. The IOM uses diet data to track added sugars and salt disappearance. Nutrient safety can also be tracked. For example, data from the Total Diet Study in 1981 was able to quickly identify unexpected iodine sources in the food supply that were resulting in extremely high intakes. Diet data is also used for food additive/GRAS reviewing, to examine what has been added vs naturally occurring. Yetley states that there would be a significant adverse effect on policy if intake data wasn’t available. However, intake data can fail to accurately predict nutrient status, as Pfeiffer et al. (2012) have demonstrated. In 1988, Lewis and colleagues showed that cola intake could be underestimated by about 50%, though Yetley notes that surveys have been improved since then. Iron fortified cereals also virtually always underestimate the actual intake when using the amount listed on the label. Self-reported intake using label data is therefore not accurate. Infrequently consumed foods such as alcohol beverages also cause problems in nutritional epidemiology. However, Dr. Yetley reiterated that it is still crucial for many uses and we can work to improve precision while using caution when interpreting.

Finally, Dr. Laurence Freedman discussed some studies that are being done to improve self-reported intake measurement. Freedman began by emphasizing that we can do validation for some nutrients by comparing to recovery of biological products; for example, using doubly-labeled water for energy expenditure, nitrogen for protein, potassium and sodium for themselves. The error is indicative of true intake. However, for many we don’t have accurate recovery products. Freedman described a project he is involved in – the Validation Studies Pooling Project – that aims to better understand measurement errors of food frequency questionnaires and 24-hour recalls using recovery biomarkers. For example, in the AMPM study, energy intake is underreported on 24-hour recalls by about 10%, but underreporting of intake differs by nutrient. Measurement error effects diet-health outcomes by attenuating relative risks and statistical power. This attenuation is expressed as an “attenuation factor” – the ratio of attenuation to the actual value. Preliminary data shows that attenuation factors are more extreme for energy intake compared to protein, and protein density is less so than both. Adjusting datasets from energy intake alleviates attenuation factors somewhat but does not solve it, and increasing samples size does not itself solve it because of unknown confounding. Freedman went into more detail about the ASA24 (multiple 24-hour recalls) – emphasizing that they have a high response and low attrition. With 3 or more recalls, the attenuation factor for protein improves. Relative risks increase with additional recalls compared to 1 food frequency questionnaire, and combining both methods yields even better data according to Carroll and colleagues (2012). Combining biomarkers with self-report data improves statistical power because measurement error is reduced, as Freedman and others (2011) have shown. Dr. Freedman reiterated that self-report data is extremely useful for surveillance, education, dietary guidance apart from the difficulties of using it to measure energy intake.

It is clear that self-reported diet data has many important uses, but caution must be accepted when interpreting it. Hopefully improvements that are currently being validated will be adopted quickly, and for some measures such as energy intake, it seems necessary that current methods be abandoned because we know they are unacceptable.

The post Is Self-Reported Diet Data Good Enough for Nutrition Science? appeared first on nutsci.org.