Long COVID and Possible Solutions

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I had a talk with someone who had a question about a skin condition, something that’s been a minor issue but suddenly got much worse. I happened to mention that sometimes when you deal with the flu or COVID, your immune system is weakened and it can impact other conditions; people report their symptoms began to get worse after a bout of COVID, almost to the day after recovering. Cause and effect? No way to know for sure. But long COVID is a reality; long COVID is a smorgasbord of illnesses that have been somehow triggered by COVID.

Coincidentally, an article was published on the development of treatments for long COVID. While there isn’t complete agreement exactly why it happens to some but not all those who’ve had COVID, physicians and scientists are looking at combinations of medications and supplements for possible solutions. While the article focused on using the antiviral Paxlovid, I was interested in the nutrient approach—specifically, the nutrients that focus on helping the mitochondria. Whatever else may be going on in long COVID, fatigue seems to be the worst side effect of the long COVID symptoms. That has to include an impact on the mitochondria regardless of whether it’s neurological or hormonal or even genetic in nature.

While other supplements were discussed, my experience is with N-acetyl cysteine and the amino acid glycine. Research has shown that when taken together, they help with repair and recycling of mitochondria. The typical amounts are about 600 mg of each per day; I’ve taken it regularly for a few years and I’ve noticed a difference in energy levels. Talk with your physician if you have long COVID or just have fatigue. There are also meds being tested for long COVID. The rest you know: eat less, eat better, move more.

What are you prepared to do today?

        Dr. Chet

Reference: Paxlovid, Vitamin Supplements Show Promise With Long COVID – Medscape – July 29, 2024.

Ice Cream, the Health Food

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Paula gave me an article to read from The Atlantic written last summer, in which author David Merritt Johns tackled the issue of outlandish results in nutrition-science studies. In this case, the issue was research demonstrating that eating ice cream regularly reduced the risk of developing type 2 diabetes and cardiovascular disease. He interviewed the chairman of acclaimed nutrition research schools and departments. He interviewed the scientists who published the results of the studies—or not, in one case, because they wouldn’t talk about it. No one could explain it, it didn’t fit their model, so the results got buried.

I reviewed the studies in question, including the dissertation of the researchers who wouldn’t talk about the study. Any way you look at it, two servings per week of a half cup of high-fat ice cream reduced the risk of the aforementioned conditions between 12–54%. The researchers didn’t want to talk about it or acknowledge it, but the data is the data.

Five Reasons the Data Should Be Accepted

  • The assumption is that the Dietary Guidelines are correct for everyone. Maybe they aren’t.
  • The studies used a food frequency questionnaire. They’ve been used forever and are still no better than they were when developed, like trying to paint the Mona Lisa with a 6-inch brush. For example, how many servings of carrots did you have in July? Few people could answer with any accuracy, so why are we still depending on these tools?
  • They could have assessed the data differently. Divide the subjects by caloric intake first, then by foods or macronutrients. They used the same approach as I’ve talked about before and statistically added a percentage of calories to see how it would impact the results.
  • Maybe the results are just the results. It really confirmed prior studies. Why would you ignore data just because it doesn’t agree with your view of how things should be?
  • Maybe it’s time to stop parsing the imparsible. When the data tell a different story, quit trying to make it fit your theory of nutrition.

Maybe what they should have done is find out what is found in full-fat ice cream but not in high-fat milk or cream, which do contribute to CVD and T2D. Maybe it’s a microbiome issue. Stop saying it’s an outlier and find out why it appears to work.

The Bottom Line

Nobody asked me, but I think it’s portion control. Two half-cup servings per week is very different from two pints a day. That may be the real reason behind the positive results. If you want to have a couple of half-cup servings of ice cream a week, I don’t think it will harm you and just may help. Just pay attention to the portion size.

What are you prepared to do today?

        Dr. Chet

References:
1. http://nrs.harvard.edu/urn-3:HUL.InstRepos:37925665
2. Arch Intern Med. 2005;165:997-1003
3. JAMA. 2002;287(16):2081-2089. doi:10.1001/jama.287.16.2081
4. https://www.theatlantic.com/magazine/archive/2023/05/ice-cream-bad-for-you-health-study/673487/

Questions About Fish Oil

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While I’m spending the day making sure today’s primary election in Michigan goes smoothly in my precinct, here are some of my thoughts and questions about fish oil and omega-3 fatty acids.

  • None of the research to date has focused on complete nutrient intake, and that may have an impact on fish oil utilization. I’ve suggested that before, but vitamins, minerals, and especially phytonutrients from food may have a role to play in how the body uses fish oil and all the fatty acids within it.
  • Speaking of the fatty acids, the omega-3s that are always mentioned are EPA and DHA. In reality, they make up a small part of the fatty acid distribution in fish oil. Could that make a difference? In other words, would the emphasis on those fatty acids impact how omega-3s are used in the body, positively or negatively, compared with straight fish oil?
  • The form of the omega-3 may be important when it comes to bioavailability. There are phospholipids, re-esterified triglyceride (rTG), TG, free fatty acids (FFAs), and ethyl-ester forms of omega-3s. Does the form matter?
  • This is just my opinion, but there’s something in fish that works to improve absorption of omega-3s. Maybe it’s the other fatty acids or maybe it’s the protein in the fish when we eat it. There’s no evidence that oily fish intake increases AFib, so why would the oils alone contribute to any issue unless something is impacting the form mentioned above?

That’s the way I see it. It’s also why I think eating a good diet will prove to be beneficial when taking fish oil. We’ll just have to wait and see.

What are you prepared to do today? If you’re in Michigan, I hope you’re prepared to go vote.

        Dr. Chet

Fish Oil: Reserve Judgment

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Let’s put the fish oil study from Tuesday in perspective. The most important thing is that observational studies such as these cannot demonstrate cause and effect. That’s not just a way to weasel out of making definitive statements; it’s because while fish oil supplements are associated in some way with atrial fibrillation in people who have no diagnosed heart disease, it may be something else that people who take fish oil supplements do that’s actually the culprit. Remember the hazard ratio (HR) was only 13%. What were the remaining 87% doing that was different?

The Problems

As I see it, these were the problems with this research paper.

Just as in the multivitamin study, researchers collected a whole host of dietary data and didn’t use much of it; they adjusted for those who ate oily and un-oily fish, but that was it. Fruit intake, vegetable intake, fiber intake, and a whole lot more dietary factors that have been shown to limit the development of cardiovascular disease were not considered. That may have impacted the HR.

The major problem was that they didn’t report the rate of AFib in those who did not take fish oil supplements. How can you not? What happens to those who do not take fish oil supplements could have provided comparison groups, which seems like a better analysis to conduct. No explanation. They just chose not to do it.

The final critique is that this study was conceived and executed by statisticians and epidemiologists. There were no nutrition experts on the team reported in the paper. I don’t know how that’s possible. If you’re considering a nutritional intervention, such as taking a fish oil supplement, there has to be someone who understands nutrition to consider other factors. It can’t be all statistics without thoughtful guidance.

The Big Question

How? How would fish oil supplements cause the development of AFib?

Research has shown that eating oily fish does not appear to cause AFib. Why would fish oil? The researchers cited a couple of possibilities having to do with an impact on channels that control electrical pathways but overall, no one has given any explanation.

This was not the first study that has examined fish oil supplements in large studies and found some relationship with AFib; there are also several that show no relationship at all. In this case, we have to reserve judgment because we can’t prove things either way.

The Bottom Line

What should you do? First, eat the healthiest diet you can and exercise regularly, because lifestyle is more important than supplements. Second, if you have already been diagnosed with CVD as I have—a stent more than 20 years ago—taking fish oil may be beneficial. If you’re under 60, it seems taking fish oil isn’t an issue and there’s no reason to stop. If you’re older than 60, should you begin to take fish oil supplements? It’s a matter of choice. I have to reserve judgment for now.

Next Tuesday is our primary election day in Michigan, and I’m taking the day off—I’m an election worker—but you’ll still get a Memo that goes into questions on this fish oil issue. There’s more to consider and I’ll let you know what those issues are.

What are you prepared to do today?

        Dr. Chet

Reference: BMJMED 2024;3:e000451.doi:10.1136/ bmjmed-2022-000451

Fish Oil and Atrial Fibrillation

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Researchers recently published an observational study on over 415,000 subjects in the UK Biobank database who took a fish oil supplement. During a follow-up period of almost 12 years, they statistically demonstrated a 13% increased hazard ratio (a measure over time of how often a particular event happens in one group compared to another group) in the development of atrial fibrillation in subjects. Atrial fibrillation is a type of arrhythmia, or abnormal heartbeat, that can result in extremely fast and irregular beats from the upper chambers of the heart. In those subjcts, there was a 5% increased risk of stroke.

The resultant impact was an attack on dietary supplements for being too easily available, leading to overconsumption, and questionable because of the lack of purity in dietary supplements. The Medscape Cardiology online section put out a video by a reputable researcher explaining who should take fish oil supplements. But if they’re so bad, why would she recommend them at all?

The other part of the results showed that if someone already had cardiovascular disease (CVD), the hazard ratio of developing major cardiac events was reduced if they took fish oil supplements. That’s why the expert made the video, taking the good and trying to make sense of it. Still, it gave the appearance of being a pitch for a pharmaceutical solution.

That’s the set-up for this week’s Memos. I’ll give you at least one of the questions you might have: Yes, this study tested only supplement use (and dietary intake) upon entrance to the study and nothing the rest of the 11.9 years, just like the multivitamin study from last week. But there’s so much more that I’ll cover on Saturday about the problems with this study. Just so you know, I’m still taking my fish oil supplements.

What are you prepared to do today?

        Dr. Chet

Reference: BMJMED 2024;3:e000451.doi:10.1136/ bmjmed-2022-000451

Nothing to Fear from the Multivitamin Study

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If you’re concerned about taking your multivitamin, I think you can lower the concern. Is it still possible that there may be individuals who may have a unique set of genes and covariates that may increase the risk? Sure, it’s possible, but this study brought us no closer to finding out if that’s true. Here’s why.

The Issues with the Study

The problems lie in what the researchers didn’t do.

While the researchers used 13 different covariates, they didn’t break the data down by macronutrient or micronutrient. They used the Healthy Eating Index, but that ranks the quality of the diet from 0 to 100; that’s not the same as breaking the subjects’ diets down by intake of vegetables or antioxidants. It’s possible that someone who ate more vegetables could have higher antioxidant levels, which could contribute to getting too much of a nutrient by taking a multivitamin. The same would be true if they also were taking a complete multivitamin-multimineral and getting too much calcium or iron. That might have given valuable information to the people most at risk if there were such a relationship.

The researchers also did not give any explanation for mechanisms through which a multivitamin could increase mortality. That’s not unusual, because they didn’t examine any nutrient factors—but still, what was the point of saying there may be an increase in mortality, but nothing more than that?

The most likely explanation is that the results happened by chance because they tested multivitamin intake only twice early in the studies. Think of what you were eating 20 years ago. Has that changed? It’s reasonable to expect that some peoples’ habits changed, just as their dietary intake may have changed. We don’t know because they couldn’t go back and do the questionnaires every year or two, or even every five years. They suggest that this was a problem due to the latency of the data, and they were correct in my opinion.

The Bottom Line

This study illustrates the problem with going back to analyze data collected decades ago: you’re limited by the data you have rather than actually planning the study from the beginning. It’s an interesting observation after chunking lots of numbers, but it’s not meaningful in the real world due to the lack of ability to do an adequate analysis of the data.

What are you prepared to do today?

        Dr. Chet

Reference: JAMA Network Open. 2024;7(6):e2418729.

Will Taking a Multivitamin Increase Your Risk of Death?

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Close to a month ago, the health headlines were full of warnings about multivitamins. A long-time researcher even did a video to explain the study. This headline was based on a study that demonstrated an increase in Hazard Ratio if a person took a multivitamin every day, compared to occasionally or never. On the face of it, this seemed to be a compelling study. Data were combined from three large studies that totaled over 390,000 participants. The data were taken from health and nutrition questionnaires first given more than 20 years ago, with the mean follow up time of about 21 years. The questionnaires asked about a variety of demographic data as well as health and nutrition habits; the nutrition data were the old-style FFQ form.

After analyzing the data, researchers found a 4% increase in mortality risk in those participants who took a multivitamin every day compared to those who did not. Should you be worried? Aside from the number of covariates they considered, and you know how those combinations can add up, there were at least two problems. I’ll cover those on Saturday.

The Winners of the Challenge

Everyone who responded to the 4th of July challenge did an amazing job—no one had fewer than 20 vegetables and fruits. Where it got a little murky was in the herbs and spices; I’ll take the blame for that as I didn’t explain it as well as I should. For the overall total, I’m going to declare a tie between RE and KB; they each had close to 60 foods that qualified! For vegetables alone, VK topped the list with 23 and MW topped the fruit list with 13. Great job, everybody!

What are you prepared to do today?

        Dr. Chet

Reference: JAMA Network Open. 2024;7(6):e2418729.

Nutritional Epidemiology: Still Confusing

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Remember where we began: frustrated with the conflicting studies on nutrition and their impact on our health. The researchers used specification curve analysis to illustrate several issues. The most important point is that there are many ways to analyze large datasets in nutritional epidemiology. Reviewing 15 studies in 24 papers, they found that the number of ways to analyze the data could reach 10 quadrillion (that’s 10,000,000,000,000,000). Obviously, that’s not realistic.

Instead, the approach that could be used by researchers doing these types of studies in any field is to select a randomized sample of different analytical approaches and present the results in the way I did in Tuesday’s Memo. Using that approach showed that fewer than 4% of the studies reached statistical significance. But how much of that could be just dumb luck? Setting the probability of significance at less than .05 (which is most common) means that out of 100 statistical approaches, five could show significance just by chance alone.

This paper addresses a long-standing problem in nutritional research and other areas as well. Researchers who do these types of longitudinal studies already use different analytic techniques in a haphazard way. They just keep chunking data until they find an analytic approach that’s statistically significant, and that’s the one they publish, sort of like a thief checking car doors until he finds one unlocked. Journals won’t publish results that don’t demonstrate significance, even though that would be beneficial for others to find out what not to do. “Publish or perish” just doesn’t work that way.

The Bottom Line

In this series of Memos, I’ve tried to lay out one of the reasons that long-term nutritional studies that look at morbidity and mortality can be flawed, if not contradictory. To be sure, the statistical analyses I’ve talked about are complicated, but that wasn’t my purpose. It’s to let you know that because of the lack of hard and fast rules for outlining the statistical approaches before looking at the data (as is done in randomized controlled trials), the results and the interpretation of those results will always be suspect.

In plain words, never get too excited about longitudinal studies, whether positive or negative. In the coming weeks, I’ll examine some studies on fish oil and multivitamins to illustrate the points I’ve tried to make.

By the way, for those of you really wanting to know whether you should eat red meat based on the analytic study the researchers tested as an example, they stated that there were some holes in the NHANES data that could impact the outcomes they reported. For now, it seems that women may benefit more from eating red meat than men will, but there’s no definitive answer yet.

What are you prepared to do today?

        Dr. Chet

References:
1. https://www.sensible-med.com/p/the-definitive-analysis-of-observational
2. Journal of Clinical Epidemiology 168 (2024) 111278

Nutritional Epidemiology: Specification Curve Analysis

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Did you look up quadrillion? It’s a 1 with a whole lot of 0s—15 to be exact.

When I finished Saturday’s Memo, the researchers had chosen an area of nutritional epidemiology to focus on: the analytics used to analyze the data. They began with the premise that there are many ways to analyze any data set. They then identified published research studies that examined the consumption of red meat and mortality. They identified 15 publications reporting on 24 studies that examined the effect of red meat on all-cause mortality.

They weren’t doing a meta-analysis to see all the results of all the studies combined; they used a newer technique called specification curve analysis. They identified the type of data used in the analysis, the number of variables, the number of covariates, as well as demographic data. From that information, they then calculated the total number of ways each data set could be analyzed—the specification curve analysis. Turns out that number is 10 quadrillion! That exceeds the capacity of the computing power of a small country, and I can’t even imagine how much electricity that would consume.

They decided to take a randomized sample of the possible ways to analyze the data with specific variables and covariates in each and came up with 1,440 different approaches to analyzing the data. They ran additional tests on the data and eliminated 232 approaches because the data exceeded norms.

Then they ran the remaining analytic approaches on data from several years of the NHANES study. What did they find?

  • The median hazard ratio (HR) was 0.94 for the effect of red meat on all-cause mortality. That means the mortality risk was decreased 6% if the subject ate red meat.
  • HRs ranged from 0.51 to 1.75; 435 approaches yielded HRs more than 1.0 (increased risk) and 773 less than 1.0 (decreased risk). Most analyses showed that eating red meat reduced the HR, and thus reduced the risk of dying.
  • Of all the results, 48 (almost 4%) were statistically significant; of those, 40 indicated that red meat reduced all-cause mortality and 8 that red meat increased all-cause mortality.

Does this mean that eating red meat decreases your risk of dying early? We’re not done yet. We’ll put it all in perspective on Saturday.

What are you prepared to do today?

        Dr. Chet

References:
1. https://www.sensible-med.com/p/the-definitive-analysis-of-observational
2. Journal of Clinical Epidemiology 168 (2024) 111278

Nutritional Epidemiology: The Problem

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The health headline shouts: “Fish oil increases your risk of heart disease.” The next week, “Fish oil beneficial for reducing risk of Alzheimer’s disease.” It makes one wonder what is going on in research. I’ve felt that way for a long time, and I know you have as well. I think I’ve found part of the problem, and I’m going to talk about in the next three Memos. As I said, this involves statistics, but I’ll keep it as simple as I can; we’ll be talking about some heavy duty statistical analytics, but there’s no math in my approach.

The frustration with conflicting research outcomes is especially prevalent in nutrition studies. It also happens in other fields as well including treatments for cardiovascular disease. Dr. John Mandrola, a cardiologist, wrote about a paper that examined why nutritional epidemiology is subject to conflicting results. After reading his post, I listened to the interview he had with the principal investigator of that study; then I read the paper itself. Statistics are not my forte but putting aside the high-level math, what they did was pretty amazing. By reviewing the paper here, I think you’ll get a better understanding of why research papers yield differing results even when using the same database of subjects.

Let’s begin with what has become accepted in the nutritional literature: eating red meat increases your risk of all-cause mortality. I know that many readers have given up or severely restricted red meat because so many physician, dietetic, and other health organizations have said it’s bad for your heart, among other organs. But is it really?

I used the phrase “nutritional epidemiology.” Those are studies that use some form of diet record, usually a Food Frequency Questionnaire, to track the food intake of a large group of people. Then they are tracked for 5, 10, even 20 or more years to see the differences in mortality between groups who ate red meat and those who did not. This contrasts with randomized controlled trials; they generally have as few as six subjects up to several hundred. They’re difficult to do because of the labor intensity of collecting data and ensuring subjects adhere to the protocol, whether a diet, specific foods, or dietary supplement.

With that in mind, the researchers examined all the nutritional epidemiological studies on the relationship of eating red meat and all-cause mortality. They then calculated all the ways that the data could be analyzed given the number of outcome variables and covariates used. We’ll pick it up on Tuesday, but as a tease, look up how much a quadrillion is.

What are you prepared to do today?

        Dr. Chet

References:
1. https://www.sensible-med.com/p/the-definitive-analysis-of-observational
2. Journal of Clinical Epidemiology 168 (2024) 111278