#22: Health Headlines: Helpful? Harmful? Or Just Plain Confusing?

Come join me on this very important journey and I hope that you feel empowered along the way . I'm a physician , ironman , triathlete and have published several hundred scientific studies . I'm honored to be your guide . Welcome back everyone . This is episode 22 , health Headlines Helpful , harmful or just plain confusing . Almost every day , there are new health headlines .

They might be ones recently like apple cider vinegar leads to 12 pounds weight loss . Or red light therapy reduces pain , inflammation and aging skin . Or intermittent fasting causes heart attacks . How about hypothermia ? Kills cancer cells . And finally , wearing socks to bed improves your sleep . Well , should we believe these or not ? How do you tell ?

Today we're going to explore some issues that might help you decide whether to believe a headline in the article that relates to it or not .

Now , this is just the beginning of our journey on this topic , because this type of knowledge and exploration will take some time , over the next few months , to teach you how to do it and some tricks and some questions that will guide you along the way . Well , last episode we talked about health type and many of you took the quiz . Thank you .

And if you haven't , you might want to , because your health type helps you to understand how you approach health . And it may help you understand how you approach a headline and an article that you may read .

For example , a holistic health hacker who focuses on really all aspects of health might read a headline and say great , a new thing that I might dive into and add to my regimen .

Now that same headline for a purposeful path planner might get them overwhelmed oh my gosh , there's this new thing I should do and I don't know what to do and I don't know whether to believe it . And they might be confused . Well , a contentment creator , again , may read the same headline and say well , you know , it doesn't really fit with my lifestyle .

So if you're interested and you haven't done the quiz , just go to my website , drbobbylivelongandwellcom . Well , as you know , I like to begin each episode with a personal story . Now , the most important reason that I want to talk about this topic is I used to have a full head of hair and I've pulled most of it out .

When my family and friends and colleagues told me oh my gosh , there's this headline . I just read this article and now it's my new understanding of health and life . Don't eat seed oils Well , we talked about that in episode 19 . Oh , you got to take this new supplement packet that I read about .

Or , oh my gosh , there was a wonderful article on hydrogen water and it's now my new secret weapon for health . Well , all kidding aside , yes , I did lose my hair , but I probably can't fully blame it on this .

When I hear my friends and colleagues and family talk about this and say why they're so excited about trying this new thing they read about , I ask them okay , so what compelled you to believe this is the way to go ? And they have a couple of different answers .

One is well , you know it was published in the New York Times or CNN , and you know must be right , because otherwise it wouldn't be in one of those magazines or newspapers that are like . Or they might say well , my friend's friend's friend tried it and it worked . Well , my friend's friend's friend tried it and it worked .

Another challenge with headlines that people bring to me say well , dr Bobby , you know I read a headline and then again sometime later I read the opposite . You know dueling headlines . One headline might be don't use hormone replacement therapy in menopause , even when you have significant menopausal symptoms , but then you might run across kind of the opposite article .

You should consider hormone replacement therapy for menopausal symptoms . And which do you follow ? Also , keep in mind , explaining why I pulled my hair out is that today's headline may be the opposite tomorrow . Remember when eggs are bad , whoops , now eggs are good .

Headlines not that long ago were well , don't expose babies and infants to peanuts because that will affect their development of peanut allergies . Oops , that was wrong . Development of peanut allergies . Oops , that was wrong .

As folks may know , my career was spent looking at evidence , published articles and figuring out what does and doesn't work in healthcare and for whom , and I've had an opportunity to publish about 180 peer-reviewed articles of my own on this and related topics .

Now , whether it's back surgery and who should get it , an expensive new medication , who would benefit , who doesn't ? The latest fad . So in my day-to-day life , it truly pains me when folks get swept up by the latest headline . Well , our plan for today's episode three parts .

First , how to read a headline in the news article what to think about , and I'm going to give you nine questions to ask yourself as you go through the article , and , of course , I'll put all nine of these in the show notes .

Second part there are various types of scientific studies and what's called a hierarchy of evidence , from the most believable type of study to perhaps the least believable , and I'll walk you through that and then I'll run through in part three some examples to bring out a few of these issues .

Again , this is just the beginning of a journey that we will travel together and for you , please suggest some headlines you might want me to talk about and , if you like the podcast , please leave a review on Apple or Spotify or wherever else . All right , part one reading a headline and what to think about . Nine questions .

Well , there was a recent headline that was what you eat for breakfast influences weight loss and , as I'll get to in a moment , what you're supposed to eat differs if you're a man or a woman . So question number one is this headline and this article in a reputable publication was it written by a science writer ?

Well , in , this breakfast food that you're supposed to eat to lose weight it came out in Newsweek . So if something comes out in Newsweek or the New York Times Wall Street Journal , you might think it's more credible than if it comes out in Newsweek or the New York Times Wall Street Journal .

You might think it's more credible than if it comes out in a fitness magazine or in Reddit . So that's the first thing . Get a sense of where you found this article and it may ratchet up or down on the believability depending upon that . Depending upon that .

Two is the headline and the article based upon an actual scientific study that was done in people or just the opinion of scientists ? So in this article about what you should eat to lose weight , for breakfast , men , they recommend that you eat carbohydrates like oatmeal , and women , their breakfast should have fats like omelets .

And they then wrote an article in a journal and then this headline came out of it . Basically , it was called the best breakfast foods for men and women revealed . So when I say , was this based on an actual study in people ? The answer for this one was not exactly this article . The underlying study was really a mathematical equation .

So they did take 12 people and they put them on a seven-day water-only fast and they measured all sorts of proteins that were being produced by the body , and they then took this information and built a mathematical model . So they didn't actually test in people different foods for breakfast and then see whether they lost weight or not .

It was basically a biochemical analysis that they extrapolated with a model , and so when I read a headline that's based upon something that isn't actually a study of testing different breakfast foods and looking at weight loss , all of a sudden I'm now less excited and less likely to believe it .

Okay , number three Is the underlying study published yet or was it just presented at a meeting and hasn't yet undergone a rigorous peer review ? So we're going to talk in a little bit about another study , which basically the headline was fasting , intermittent fasting increases heart-related deaths , and , as we'll see for that article , it wasn't yet published .

It was just based upon what was presented at a meeting . So if something was presented at a meeting and this is an article about it , it's much less rigorously reviewed than one that was published . Going back to our breakfast example , it was published , okay . So what we've now done are three questions that relate to the article you just read and the headline .

Now the next set of questions will need to be based upon finding what that scientific study was . So there may be a hyperlink in your news article . You might be able to just look it up , because they often say , well , this came out in Lancet and Jones was the author and you could try to look it up For these next questions .

You will actually see need to get the article in hand . Okay , question next . Question Number four where was it published ? Which journal was it published in ? Now , was it a peer-reviewed journal and there's something called an impact factor , so every journal could be JAMA , new England Journal , journal of Nutrition . They have what's called an impact factor .

The higher the number related to the journal , the better the journal is likely to be , and it's based upon how often articles in the journal get cited by other scientific articles and if it's above the numbers above 30 , by other scientific articles . And if it's above the numbers above 30 , like the New England Journal or the JAMA , that's a high number .

Cardiology journals some of the top ones have numbers in the sort of 15 to 40 . And nutrition ones tend to be much , much lower sort of in numbers of one to three and that's starting to get down to a level of maybe the journal isn't as prestigious and rigorous as it could be .

Okay , so this article , the one that was the mathematical model about what you should eat for breakfast to lose weight , did have an impact factor of 20 . So that's a good thing . Next question who did the work ? Was this done in the US ? Was this done outside the work ? Was this done in the US ? Was this done outside the US ?

Was it done in a rigorous and known academic institution ? Well , the breakfast study was done in London and in Netherlands at a couple of different academic institutions . Critical one how big was the study ? So the study I just talked about , the breakfast choices was based on 12 people .

A study that you read an article about that's about 12 people , or 15 people , or 20 people doesn't excite me . Was it about 500 people ? 5,000 people ? Now I'm more interested , so be wary when the studies are very , very small . Seven what type of study was it ? Was it a randomized controlled trial ?

We're going to talk about that in a bit or was it an observational study ? Or , like I talked about with the breakfast one , is it a model ? Okay , here's something . If you're looking at the actual article , was there an editorial about the article ? That's a great way to learn about what the limitations are about what the study was .

So if you can read about other experts as they've talked about this study , that's a great window into whether this is something to believe or not . And then there's the sort of grandmother test , question number nine Does it seem to be too good to be true ? That's a good one , because so much of these headlines seem too good to be true .

And if that's the case , again I'm going to wonder . Now , none of these nine questions by themselves will tell you whether to absolutely believe the study or not . But you know , you add up the numbers of these nine that answered in a proper way or a positive way , or suggested it might be a good study . Now , all of a sudden , you might want to believe it .

If there are very , very few that are supportive in these nine areas , then maybe I'll really wonder about the study . Okay , part two . Let's talk about what's called the hierarchy of evidence . So there are six different tiers or types of studies and they're either more likely to be something that's believable and valid or perhaps less likely .

The best of the best is what's called a meta-analysis . So let's say there were three or six or 20 different clinical studies , clinical trials , where they actually compared two different groups of people and randomized them . But there isn't just one of these .

And now there's got a bunch of these studies where you can statistically bring all of those clinical trials together in what's called a meta-analysis , and in general , a meta-analysis is sort of the highest level of evidence and the most likely to be believable likely to be believable .

The next is the actual randomized control trial itself , and it is the gold standard for so much of what we do . So any drug or most drugs that are approved on the market by the FDA have gone through a randomized control trial . What does that mean ? It means you've got two different groups . You start from the beginning . You randomize them .

So you say , okay , this 50 people will get this drug , this 50 people will get placebo , and we'll see what happens . Well , this is where we learned in randomized control trials that statins like Lipitor reduce your risk of heart attack , or recently , the Ozempic and other GLP-1s and how much weight loss they do . And again , these were randomized trials .

They took people , divided them into two groups at random , gave them a drug , gave them a placebo , and then observed what happened after that . So underneath meta-analysis , you now have randomized control trials .

Underneath that you have observational studies . This is typically where you get two different groups of people and you follow them forward in time . For example , oh , there's this population and some eat fish and some don't eat fish . Or some eat a lot of red wine , some drink a very little red wine , and what happens ? You know which people do better .

Now the challenge with observational studies is you have to adjust for other things . So people who eat fish may not be the same as people who don't . People who eat fish may be eating fewer calories during the day . They may be exercising more . They may be eating their fish while drinking some red wine or white wine .

So you have to adjust for differences because this wasn't randomized . You didn't say well , this group of people , I'm going to give you a fish diet and this group of people won't , and I'm going to follow forward in time .

That's a randomized control trial , when you just grab people and see who did something or who didn't and try to make some inferences about it . That's an observational study and you definitely have to adjust for what are called confounders . Okay , lower on the hierarchy , a case series .

Case series is when a doctor or a academic institution says okay , I had 10 patients with knee pain and I gave them injections of platelets in their knee and eight of them got better . And I'm going to tell you about those eight and 10 people . So that's a case series . There is no randomization , there's no placebo . That's just explaining what happened .

So this is a lower level of evidence . Below that are guidelines . So guidelines are really experts coming together and saying this is our understanding of what works or what doesn't . This is how you get recommendations about mammograms or recommendations not to do whole body MRI screening , and guidelines seem like gosh .

Wouldn't that be the best thing , because these are experts looking at the evidence . The problem is that if we went back into history in 1491 and we got a bunch of experts together and asked , well , is the world flat ? They would have said yes , columbus obviously proved that wasn't the case a year or so later .

So guidelines are based on the current evidence and that doesn't necessarily mean that they are correct . And then the last in the hierarchy is just a single person's expert opinion . Now if you're higher on the hierarchy , like a meta-analysis , it's more likely to be believable than if it's just a case series or expert opinion .

But there are poorly done randomized control trials and they're really well done observational studies . So just because it's an RCT doesn't necessarily mean it's going to be more valid than an observational study . But in general , the higher up on the hierarchy you are , the more likely to believe it .

So again , when you are reading your headline and reading the article and asking what type of study is that article based upon , again the hierarchy can be helpful to say should I believe the headline or not ? Okay , so I've laid out a variety of sort of frameworks and conceptual things .

So now I think what's important is let's talk about actual studies and we're going to talk about one observational study and one randomized control trial . Now , the observational studies is , I would say , the biggest problem today . And if you look at the headlines , I would say a huge percentage of them are based on an observational study .

And again , in observational studies you follow folks over time and you compare different groups and you see what happens . So not long ago there was a very widely seen headline in lots and lots of news media and it went something like this Intermittent fasting , which is a weight loss approach , has a 91% higher risk of death from cardiovascular disease .

Well , that's worrisome . So you read this headline oh gosh , I've been doing intermittent fasting . I think this is something that you know . When I've been reading about it , boy , it seems like it's worth trying . But this article , this headline , says there's a 91% higher risk of death . So what did they do in this study ? Again , this was an observational study .

There were two different groups . At the beginning of this data collection , folks were asked a food diary and basically asked hmm , what did you eat yesterday and when did you eat it ?

And then they divided these groups of people into two One who ate all their food in a fairly narrow time window so maybe they didn't eat breakfast , and so it was all between the hours of noon and 7 or 8 pm Versus a group of people that ate throughout the day , just like sort of more normal people .

So they found these two groups and then they looked forward in time over the course of eight years and then found oh my gosh , there's 91% more deaths in that intermittent fasting group , in the group that had all their food in a narrow time window . Oh no , this is a problem , okay .

So before you throw out any idea about doing intermittent fasting , let's walk behind the headlines and begin to understand the study and whether to believe it . Okay . So there were a number of concerns with this study . The first is that the study hasn't been published .

It was a poster at a news conference or at a medical convention by the American Heart Association , so it hasn't yet undergone peer review . Hasn't been come out in a publication , so it has not undergone rigorous peer review .

So when you see a headline and it's based on something that hasn't even been published yet , okay , now all of a sudden , I'm not very convinced that it's worth worrying about . All right . Well , the next concern I have is are the data reliable or believable ? So how do they do this study ?

At the beginning of this eight-year observation , they said to people well , what did you eat yesterday and what time did you eat it ? And they did this on two different days yesterday and what time did you eat it ? And they did this on two different days . Well , we know from lots of other studies that food diaries , food recall , is really , really bad .

You know , if I said to you now , and you can think about this what did you eat yesterday and how much of each food did you eat yesterday ? Was it a , you know , a quarter a cup of cottage cheese . Was a full cup of cottage cheese ? Did you eat it at eight in the morning or did you eat yesterday ? Was it a quarter a cup of cottage cheese ?

Was a full cup of cottage cheese ? Did you eat it at eight in the morning or did you eat it at 10 am ? When did you eat it ? So food diaries are notoriously problematic , and so what this study was built around was a food diary and projecting forward eight years .

Now , what is the likelihood that what you ate eight years ago was what you continued to eat every day for the next eight years ?

So just because at this moment in time , at the beginning of when they collected data , maybe you didn't eat throughout the day , you just ate during a very narrow period of time , what's the likelihood that was true the next week , the next year and eight years later ? So projecting forward one day of food diary for eight years seems really problematic to me .

Next , were the comparisons groups similar ? Okay , so we're trying to say that people who fasted or didn't eat throughout the day had a higher rate of heart attacks than people who ate throughout the day , and what the implication is that the higher heart attack rate was directly attributed to the fasting group .

So now I scratch my head and I say , well , wait , a second sort of who tended to skip meals . Now I should point out that this data was collected 15 to 20 years ago , long before anybody talked about intermittent fasting as a way to lose weight . So 20 years ago , why did people perhaps eat only during a narrow portion of the day ?

Well , that may be because they're working at two or three jobs and they don't have a chance to sit down throughout the day and eat . Maybe they're just under a lot of stress and they missed meals . Or maybe they had issues with finances and money . So fasting may not have been oh , I'm doing this to be healthy .

It may have just been an artifact of other things going on in their life . And these people who maybe were at high stress , maybe back then they were smokers and they didn't have time to exercise because they didn't have time to eat . So they probably didn't have time to exercise . So that also then says wait a second . Does this really really make sense ?

So when I step back and say , does intermittent fasting cause an increase in heart attacks , my take is that this is a really flawed study and that what we observe was probably due to something else and not the fasting . So rolling this back up again to the headline easy to see the headline , easy to get to scared .

But if we drill in a bit further we learn that this was so flawed on so many levels that I'm not going to pay attention to it . Okay , so this was what's called a database study . Database study is you have these existing databases ? The kind of the granddaddy of all of them is called the Framingham Heart Study , which was started in the 1940s .

They collected information on a whole group of people . Basically they collected heart risk factors , cardiovascular risk factors , and then they followed these people in time to figure out who ended up developing heart disease . And from this wonderful database we learned that blood pressure and cholesterol are really critical risk factors for heart disease .

There's also the NHANES database , which you may hear about . This was begun in the 60s . 5,000 people basically . They survey them every year or every couple of years and they're monitoring 17 different conditions like diabetes and kidney disease , et cetera , et cetera , and they're collecting risk factors and they're trying to put these two together .

And then , most recently , there's something called the UK Biobank done in the UK and there are 500,000 people . It's been going on for almost 20 years and they're measuring things like your physical activity , your cognitive function , biochemistry , genetics lab tests , and so on and so forth function , biochemistry , genetics lab tests , and so on and so forth .

The problem with these databases is that they are sitting there waiting to be analyzed . It's cheap to analyze them , it's quick and it's almost irresistible to play with the data . So somebody might say , oh , I've got this interesting database and I have access

to it . I wonder if I tested whether eating apricots is related to breast cancer and you do an analysis and it's like nah , no relationship . Well , okay , maybe it wasn't apricots , maybe it's peaches . Nah , that didn't show anything . Well , how about highly processed foods ? Nah , that didn't show anything . Well , how about highly processed foods ?

Or certain genetic types gene types that we have in our bodies raises your heart disease risk ? Well , maybe I didn't find that . But how about your dementia risk ? Oh , didn't find that . What about pancreatic cancer ?

This is called data drudging or p-hacking , where you basically play with the data and try to see if you can find something that explains or predicts something else . The problem is that if you do enough of this testing , you're going to come up with something that's positive .

Just statistically we talked about this in episode 12 , to test or not to test , 5% of a blood test will likely be abnormal . Well , if you do 10 or 15 or 20 investigations of a database , you're going to find something abnormal and something positive . So we don't know .

The problem is when people write up oh , we found that apricots cause cancer or peaches cause heart disease . Well , you don't know how many times they played with the data before finding something that's published and that's called publication bias , meaning we do lots of things but we don't necessarily write it all up and submit it for publication .

So if it doesn't show anything , we probably won't write it up for publication . If it looks exciting , like intermittent fasting increases your risk of heart disease death , well then you write it up and it gets a lot of attention . So unfortunately , we don't know how much the data was played with before they came up with something positive .

And that's a really big problem . And because these databases are so common now , lots of people are playing with them and coming up with things that make great . Lots of people are playing with them and coming up with things that make great headlines .

And one of the other critical things about database analyses or observational studies is it gives you a correlation meaning ? Oh , you know peaches and cancer , but that doesn't mean that peaches cause cancer , and we'll sort of talk about this in future episodes . Okay , randomized control trials , that's been felt to be the gold standard .

That's how drugs are approved and they've given us huge insights . Medications are as powerful as surgery for heart disease . But even a randomized control trial doesn't always give us the right answer . Well , there was a headline recently meal replacement shakes lead to greater weight loss , and this was a randomized trial with 60 obese individuals .

One group got meal replacement this was like a shake for dinner and that group got about 1,300 calories in their diet . The second group had a reduced calorie lunch to bring their total down to around 1,300 calories , and the third group had normal eating . Now what they found was that both of the groups that had fewer calories on a daily basis lost weight .

Now , that's no huge surprise . What was a surprise is that the ones that had the meal replacement for dinner lost 15 pounds versus the others that had the smaller lunch , and that was their way of reducing calories lost six pounds . So a huge difference and huge differences in fat loss . This sounds really , really , really impressive .

The problem is , when you read the discussion in the limitation section in the actual article , probably the group that had the low-calorie lunch may not have had lower calories , or at least not nearly as low , as the other group .

So the group that had the shake for dinner , they probably did get close to 1300 calories in their diet total , but the one that had the reduced calorie lunch , well , maybe they really didn't actually eat a whole lot less and that's why the shake group did better , not because shakes are better than lower calories , but maybe it was a way that there was actually

lower calories and in the other group there wasn't . Now this was a study done in China and folks there have their own unique diet , their own unique amounts of kind of how much they walk and how much they exercise . So even though this was a randomized , controlled trial , there can be problems .

So reading the discussion section brought out some of the limitations by reading the actual article and if there had been an editorial , that could be helpful . This is a really important topic and you are likely to run across headlines daily or even weekly .

I hope I've given you some tools or questions about how to read the headline in the article and how to look at the underlying study and , as I mentioned , your health type may influence how you approach these headlines , so please send me examples and I look forward to talking about this with you , getting feedback from you and continuing on our journey to live long

and well . Thanks so much for listening to Live Long and Well with Dr Bobby . If you liked this episode , please provide a review on Apple or Spotify or wherever you listen . If you want to continue this journey or want to receive my newsletter on practical and scientific ways to improve your health and longevity , please visit me at drbobbilivelongandwellcom .

That's , doctor , as in D-R Bobby . Live long and wellcom .