Is red meat killing us?

Ironically, as I’m writing up this piece, yet another negative study is being splashed across news channels; this one revealing an association between bacon consumption and bowel cancer. There is no shortage of studies claiming that red meat, in one way or another, is killing us. But is it really? And if it isn’t, why on earth is there so much science saying that it does? With the Easter Bank Holiday approaching, I’m sure you’re wondering whether that lovely roast lamb is putting you at risk… In order to answer the question, I am going to look in detail at one of the largest studies published to date on the subject. You may remember this one: it had every media outlet on the planet decrying the mortal dangers of red meat in 2012.

Science, and particularly nutritional science, is all about controlling for biases. And before I go any further, a quick word about the most critical bias of all: your own; my own. As Richard Feynman famously said:

“The first principle is that you must not fool yourself — and you are the easiest person to fool.”

We’re all guilty of this and I am no less guilty. Biases are so incipient that you don’t even notice them. So I should admit to you that my natural bias inclines me to believe that red meat is not killing us in any significant way. Certainly, I don’t believe that moderate quantities of unprocessed, high quality red meat is a big killer. In order to write this piece objectively I therefore had to pay careful attention to my own bias. If you want to get to the truth, you need to treat every piece of evidence with the same level of scrutiny. That’s what I have tried to do here. As an aside, there are a number of other reasons why you may choose not to eat red meat, or any meat at all. If you choose to avoid red meat because of ethical or environmental reasons, or because it just doesn’t work for you, those are entirely different, and valid, considerations.

Epidemiology and the criteria of good scientific research

Before we get into the details, we should have a set of criteria by which we judge scientific research. The study in question is epidemiological (or observational), as most studies in this field are. In this kind of experiment, scientists take large cohorts of individuals and over time record two sorts of data: one related to a hypothesised risk factor and the other related to a negative outcome of interest. In this case, the researchers set out to record data on red meat consumption (the risk factor) and death (the outcome). Once researchers have a sufficient quantity of data, they then use statistical techniques to examine the correlation, or association, between the risk factor and the outcome.

So what can go wrong with a study like this? The truth is, a lot can go wrong. First of all, studies like this can never prove causality. They can only prove statistical association and suggest causality. There is a huge difference. With correlation there is always the possibility that there is another variable, or set of variables, that is the true causal factor. Here is a good example: in Alzheimer’s disease, patients build up deposits of amyloid beta and tau protein in their brains. There is a very clear association between the build up of these proteins and Alzheimer’s disease. But scientists disagree about whether these plaques are the cause of the disease or the consequence of some other more fundamental cause such as vascular pathology or insulin resistance. Association does not equal causation.

In order to take epidemiological evidence as a strong indication of causality, the observed effects need to be very large. For example, having collected 50 years worth of data on smoking, Richard Doll et al. observed in 2004 that smoking increases your risk of developing lung cancer by 14 times. It’s unlikely with data like this that smoking is not causally linked to lung cancer in some way. Vanishingly few observational studies can present data with risk multiples anywhere near this large.

With that caveat in mind, there are at least three fundamental biases that observational studies need to mitigate: selection bias; data-gathering bias and confounding variable bias.

Selection bias occurs when the population being studied fails to be representative of the general population (or a subset of the population that you are interested in). Are the study’s subjects for some reason more or less at risk than the average person would be?

Data-gathering bias happens when data being recorded are inaccurate. Outcomes can normally be measured fairly precisely, but what about data regarding the risk factor?

Confounding variable bias. With epidemiological studies, it’s impossible to control for all variables. These are not lab-based experiments and you can’t hold other factors constant. Therefore, researchers have to use statistical techniques to adjust for the effects of other risk factors that are not included in the hypothesis. How well are they able to do this?

The headlines

Ok, so, what does this study say and should we believe it? Let’s look at effect size, selection bias, data-gathering and confounding variables in turn.

The headlines: researchers found that an additional serving of red meat per day increases your risk of mortality by 12%. They also found that an extra serving of processed red meat was associated with a 20% increase in mortality risk. Looking at specific diseases: cardiovascular disease and cancer mortality increased by 16% and 10% respectively for a 1 serving per day increase in red meat.

This was a huge study, which followed around 38,000 men in the Health Professionals Follow-up Study (HFPS) cohort and 84,000 women in the Nurses’ Health Study (NHS) cohort for 26 and 20 years respectively. So on face value, the data sounds fairly significant and the population being studied a reasonable representation of the general population. However, even bearing my natural bias in mind, things start to unravel rapidly when you get into details.

Effect size

Let’s look at effect size first. Increasing your mortality risk by 12% sounds significant. But is it really? Right off the bat, we know we are not dealing with a risk factor like smoking. Red meat increases your mortality rate by a factor of 1.12. Smoking increases your rate of lung cancer by a factor of 14. Smoking increases your risk of death by 83%; red meat by 12%. Ok, but even if red meat is not as deleterious as smoking, 12% is still something right?

In order to increase your risk of mortality by 12% you need to increase your red meat consumption by 1 serving per day. In the study, 1 serving is defined as 85g. So you’d need to eat ~600g of extra red meat per week in order to increase your mortality risk by 12%. That’s a lot of meat… Especially when you start looking at absolute effect size, rather than relative risk.

The mortality rate for men in the study was 1.18% per year and for women it was 0.68%. For men, an extra serving of red meat per day increases their mortality rate to 1.35%, or by 0.17% points. For women, an extra serving of red meat per day increases their mortality rate to 0.75%, or 0.17% points. Suddenly these risks start to look almost negligible. Can you think of any other risk factors that might increase your mortality risk by less than 1/5 of a percent? I’m sure there are quite a few.

That being said, this is the least problematic aspect of the study. If I could really be convinced that red meat consumption was associated with a 12% increase in mortality risk, I would still take notice.

Data-collection bias

The first major issue is with data-collection bias. The researchers collected data about participants’ red meat consumption using Food Frequency Questionnaires. In these surveys, subjects estimate the frequency and quantity of their consumption of various food items. I know from personal experience just how large a gap there often is between what people eat and what they remember eating. That’s just anecdotal of course, but if you’ve ever done a food diary you’ve likely had the enlightening experience of realising just how much you were eating and drinking with no conscious awareness. What’s more, subjects only filled in updated FFQs at either 2 or 4 year intervals. How accurately can you expect participants to report their average food intake over the preceding 2 or 4 year period? I often ask members and clients what they’ve eaten that day and they’re hazy on the details.

The authors anticipate this objection and address it in the text. They write:

“the FFQs used in these studies were validated against multiple diet records”

Here they are referencing studies which investigate the correlation between FFQs and actual dietary intake in the populations being studied (the HPFS and NHS cohorts). Ironically, these reports do little to reinforce the case for FFQs; quite the opposite in fact. One study, which examines the HPFS population, finds that for processed meat items the correlation between intake reported in FFQs and actual intake is only 0.52. For red meat, the correlation is between 0.5 and 0.59. These are moderate correlations at best. This inaccuracy is particularly problematic in the case of processed meats. While participants, on average, reported eating 0.22-0.26 servings per day, actual dietary records showed an average of 0.53 servings. So the average subject under-reports their processed meat consumption by a factor of more than 2. And this discrepancy is found to be statistically significant.

(Data presented as “mean +/- standard deviation”)

When the input data is this imprecise, can we make any inferences at all, let alone inferences of effects as small as a factor of 1.12? The problem with looking at such a broad category as a risk factor (red meat) is that the opportunity for errors on the input side are commensurately magnified. While someone might be able to reliably report how many cigarettes they’ve been smoking, how can they be expected to remember how often they’ve been eating the huge variety of items that make up the category in question: everything from beef, pork and lamb to hot dogs, burgers, bacon and cold-cuts?

Confounding variables

Potentially even more problematic than data-bias is the presence of multiple confounding variables and a clear relationship between these variables and red meat consumption. The paper’s authors split their cohorts into quintiles based on red meat consumption. If you look at the table describing the attributes of subjects in each of the 5 quintiles you can see a striking relationship: as red meat consumption goes up practically every other health risk factor worsens. In the study, people who eat more read meat also smoke more, drink more alcohol, eat more calories, do less exercise, have higher blood pressure and have higher incidence of diabetes.


Given the general perception that red meat is bad for you, those that eat less tend to be more health conscious and those that eat more, less health conscious. We have a perfect example of the healthy user bias, which I discussed here. You could argue that the real causal variable here is the degree of health consciousness of the subject. It is your level of health consciousness that determines your level of red meat consumption, which in turn is associated with mortality risk.

But the authors claim they have controlled for all these confounding variables by including them in their regressions. Can this really be done? A quick dive into the numbers raises some doubts. Bear with me here, because this is the crux of the matter. If you compare the mortality risks generated by these statistical regressions with the raw unadjusted mortality rates across the 5 quintiles of red meat consumption, something strange appears. Check out the numbers below for the HPFS cohort and total red meat consumption.

Q1 Q2 Q3 Q4 Q5
Regression Mortality Risk 1.00 1.12 1.21 1.25 1.37
Raw Mortality Rate 1.13 1.06 1.11 1.18 1.41

As you’d expect, in the author’s statistical regression mortality risk increases in a neat linear fashion with increasing meat consumption across the quintiles (Q1-Q5). However, that relationship is not preserved in the raw data. As you can see, the raw mortality rate for the lowest quintile of red meat consumption is higher than for the second and third quintiles. It is only in the fourth and fifth quintiles that the mortality rates exceed the lowest quintile. Now, if there were conflicting confounding risk factors, some of which were positively correlated with red meat consumption and other which were negatively correlated, you wouldn’t necessarily think much of this. But remember: every risk factor included in this study got worse as red meat consumption increased. Therefore, if there really was a relationship between red meat and mortality, you’d expect the raw data to reflect this and show mortality monotonically increasing with increasing consumption. As you can see, it doesn’t.

This trend is even clearer when we consider unprocessed red meat, as you can see below. For unprocessed red meat, only the mortality rate of the fifth quintile of consumption exceeded the first quintile. The mortality rate for quintiles 2-4 were actually lower than for quintile 1. That’s pretty staggering. The only conclusion I can draw from this is that there is no real association between red meat consumption and mortality risk. The effect seems to materialise out of convoluted statistical regressions. You only need to look at the raw data to see the reality.

Q1 Q2 Q3 Q4 Q5
Regression Mortality Risk 1.00 1.11 1.14 1.20 1.29
Raw Mortality Rate 1.23 1.15 0.99 1.21 1.30

I don’t consider this paper evidence to link red meat with chronic disease and death. It is epidemiological in nature which limits its power to imply causality from the outset. The absolute effects on mortality are small and associated with large alterations in red meat consumption habits. More fundamentally, the FFQ data is a seriously flawed method for assessing consumption and calls into question any statistical inference drawn from it. The authors have not been able to adequately control for confounding variables. Red meat consumption is associated with a number of other risk factors; an effect which is possible mediated by the fact that people generally believe red meat is unhealthy. Therefore, the relationship deduced between red meat and mortality is more likely the result of a healthy user bias. More health conscious people tend to live longer. And they also tend to eat less red meat.

Which all leaves one question: why do so many studies, and this one in particular, conclude that red meat is dangerous? I think the answer comes in two parts. The first part is that there is a prevailing and powerful bias: the status quo believes that red meat is deleterious to health. As the authors of this paper state in their introduction:

Substantial evidence from epidemiological studies shows that meat intake, particularly red meat, is associated with increased risks of diabetes, cardiovascular disease (CVD), and certain cancers”

If you think the current evidence is “substantial”, you are unlikely to be conducting research with a fully open mind. Add on to this bias the money and time spent on an enormous study like this and you have a recipe for unrigorous science. How likely is it that you will accept a null result when you thoroughly believe your hypothesis and have spent 20+ years and many millions of dollars trying to prove it? I’m not trying to allege any form of malpractice or malice on the part of the these researchers. I’m sure they are driven as much by a desire to help people as I am. They genuinely believe that red meat is dangerous and that they are aiding in a public health effort to save lives. But these pressures and biases result in unconvincing data.

What do you think?

Can cold water therapy decrease strength and muscle gains?

Welcome back to the Science Series! If you missed last week, check it out here.

The Science Series is designed to help you navigate the obscure and confusing world of health research; a world in which almost anything appears to be provable and nothing is written in stone; a world in which one week eggs cause heart disease and the next they cure diabetes. I’ll be looking at topics in the field, paper by paper, to help you understand the data and the evidence so that you can figure out for yourself what to believe.

As ever, bear in mind that I’m not a medical or scientific professional. Nothing I say should be taken as advice and feel free to add a pinch of salt too.

Today I’m looking at a paper that examines the effects of post-workout cold water therapy on strength training results. Cold water and ice baths have been used for decades as post-workout recovery strategies to reduce inflammation and allow athletes to perform at their best in the next workout or game. But is it possible that cold water actually reduces strength and muscle adaptations? This paper claims that it does.

The headlines: researchers found that post-workout (PWO) cold water immersion (CWI) reduced muscle gain by 67% and strength gains by 34% over the course of a 12-week lower body strength programme. In a separate study, they found that CWI reduced the acute expression of anabolic genes by 30%.

The first study was a randomised controlled trial in which participants were initially paired up to match for strength and lean body mass. Then, one of each pair was randomly assigned to 10 minutes of CWI and the other to low intensity active recovery (ACT), post-workout. Participants then went through an identical 12-week strength programme, after which muscle mass in the quadriceps and leg press strength was re-measured.

The differences between the two groups were pretty significant. While the ACT group on average added 309g of new muscle mass to their dominant-leg quad, the CWI group only managed 103g. You definitely care if a strategy denies you 206g of muscle over 12 weeks, in just one of your quads. Assuming that both legs accrue muscle similarly, we’re talking about close to half a kilo of lean mass left on the table. And how detrimental could this potentially be over longer periods of time?

The effects were equally visible for strength. The average ACT group participant increased their leg-press 1RM by 201kg over the 12 weeks, while the CWI group saw an increase of 133kg. If you’re a CrossFitter, you don’t like the sound of this at all. Imagine if someone tried to take ⅓ of your squat gains in the next strength cycle! You should have increased your squat from 100kg to 130kg, but because of your ice baths you only got to 110kg…

A natural objection is that there’s likely high variability in how individuals respond to recovery strategies and high variability in the rate at which individuals increase strength and gain muscle mass. And this is correct. The standard deviation of strength gains in the ACT group was 65kg, or 32%, and was similarly high across all measurements. But, when researchers corrected for this, the effects were still large. They used a metric called Cohen’s effect size, which expresses effects as a multiple of ‘pooled’ standard deviation (standard deviation for the pooled data set including both ACT and CWI groups – check out the footnotes for nerdiness). Muscle mass gain was 4.1 pooled standard deviations greater for the ACT group than the CWI group, on average. The ACT group’s strength gains were 1.5 pooled standard deviations greater than they were for the CWI group. Anything with a Cohen’s effect size above 0.8 is considered a large effect and if you remember your stats 101 from school, you know that 4.1 standard deviations is enormous. In other words, you can’t explain away the average effects of cold water therapy with the variation between individuals.

In order to figure out how post-workout cold water therapy might be blunting gains, researchers conducted another clever experiment to look at anabolic signalling in muscles. In this set-up, rather than splitting subjects into two groups, each participant applied CWI to one leg and ACT to the other leg, post-training. This unilateral approach is a standard trick in the sport scientist’s tool-box and allows them to eliminate all inter-individual variations. All the differences in signalling observed between legs is due to the recovery interventions.

The authors studied an enzyme called p70S6 kinase as a marker of anabolic activity. This protein is activated by the mTOR and IGF-1 pathways, classic anabolic pathways important for muscle hypertrophy. p70S6K’s function is to stimulate cells to increase protein synthesis. Phosphorylation of p70S6K (a measure of its activity) was 90% higher 2 hours post-exercise and 60% higher 24 hours after in the ACT trial as compared with CWI. Total p70SK protein did not change significantly 48 hours after exercise in the CWI trial, but increased by an average of 30% in the ACT trial. The paper also looked at satellite cells in the muscle tissue. Satellite cells are precursors to full-blown muscle cells. They proliferate in response to trauma and exercise in order to help repair damaged muscle and are therefore an important marker of anabolism. In the study, satellite cell counts increased by 48% post-exercise in the ACT trial but remained at baseline for CWI.

So it looks like cold water therapy blunts acute anabolic responses to strength training and consequently reduce gains over time. The authors offer a few hypotheses as to why this might be the case. Cold exposure limits blood flow which has been associated with muscle protein synthesis. The reduction in blood flow might limit the delivery to muscle cells of amino acids and other compounds that serve as fuel and signalling molecules for repair processes. The researchers also suggest that low temperatures may interfere with the expression of genes important for muscle synthesis.

Are there any problems or limitations with this study? As always: yes. First of all, it’s worth noting that the subjects were young men with an average age of 21. As we all know, anabolic responses are typically highly up-regulated in young individuals. A 30 or 40 year old will naturally have decreased anabolic responses to exercise. What difference does the general anabolic context of an individual have on their responses to cold water therapy? Is it possible that those with a lower natural anabolic state are affected less? Secondly, although the study was supposedly conducted on ‘trained’ individuals, the subjects were clearly starting from a fairly low baseline. If you can add 200kg to your leg press over 12 weeks, my guess is you haven’t done much leg-pressing before that! That naturally leads me to wonder what the effects would be for truly trained individuals. The study was also conducted on men, so there is no telling whether these results necessarily translate to women.

It’s also important to keep in mind the exact context of CWI being studied. I don’t think this study implies that cold water immersion is without any benefits. if you avoid CWI immediately after training, you can likely avoid all the potentially deleterious effects. If the authors are right, then all we need to do is make sure that blood flow and temperature are normal post-exercise in order to initiate the recovery processes. In fact, the very problems with CWI outlined here also give a picture of how cold water might have other positive effects. In the context of muscle hypertrophy and strength, mTOR and IGF-1 up-regulation is critical. But there is evidence that chronic down-regulation (as opposed to acute up-regulation) of these pathways is an effective means of increasing longevity. High IGF-1 levels are associated with increased risk of all-cause mortality and inhibition of mTOR is the mechanism of one of the most researched longevity drugs, rapamycin. So if cold water therapy can globally decrease these signalling pathways, perhaps it has a role to play in long-term health. That’s pure speculation, of course. But it goes to show that what might be a benefit in one context can quickly look like a problem in another.

The takeaway for me is that I should avoid ice baths and cold-exposure post-training. This study also points to a more general rule of thumb. Given damage to muscle caused by training or injury, I think you should avoid excessive anti-inflammatory approaches. This paper found that taking ibuprofen post-workout had a similar effect to CWI in terms of reductions in training results. And another paper found that applying ice to crush injuries actually lengthened the recovery process and led to lower quality muscle tissue (in rats). Acute inflammation is a critical part of the recovery process (see this review). It has nothing to do with the dreaded chronic low-level inflammation of modern diseases. So interfere with these mechanism at your peril! What do you think? Leave comments below and feel free to disagree with me over email at

Can the sauna prevent heart disease?

The field of health, fitness, nutrition and supplements is bewildering. It can be near impossible to know what to believe with the sheer volume of research being reported in the media and the almost daily rebuttals, controversies and debunkings. Are eggs good or bad for you? Will red meat give you cancer? Is saturated fat bad? Are mushrooms more effective than chemotherapy? Unfortunately, journalists often present misleading (and sometimes plain false) versions of the science.

My idea with this new blog series is to provide an insight into what the science of health really says, paper by paper. Most of us will never have the time to survey the whole landscape of research on any particular subject. But you can learn a lot by paying careful attention to particular, high quality studies. I will only look at papers that are well regarded by people that I trust as adjudicators on these fields. I’ll be casting a critical (if non-professional) eye over the research to give you my take on what conclusions we should draw, what potential actions we should take and the degree to which we should trust the results. Almost everything will be in shades of grey and forms of ‘it depends’. That’s the nature of health science and you should get used to it.

I am by no means a medical professional or academic scientist, so you should certainly not take anything I say as advice and you should probably add a pinch of salt too. I’m just a nerd who loves to dig in to the science and has the inclination to read papers start to finish with a critical eye. If you think I’ve got anything wrong, I’d love to hear your thoughts. I’m more than happy to pointed in the right direction. Just email me at

Today’s topic will be the sauna. Saunas have become very popular and all sorts of health benefits are touted. But what does the science say? In particular, what does the science say about saunas and heart disease?

In this paper, the authors followed a group of 2,315 Finnish men for 20 years to investigate the association between sauna use and cardiovascular disease and death. The headline: they found that men who used the sauna 2-3 times per week were 27% less likely to die of cardiovascular disease and 24% less likely to die of any cause than were men who used the sauna only once a week, after controlling for other major risk factors. These numbers improved more for men that used the sauna 4-7 times per week. They had reduced risk of CVD and all-cause mortality of 50% and 40% respectively. There were also similar effects seen for sudden cardiac death and coronary heart disease (see below).

One of the things that can be misleading when it comes to research in this field is the difference between relative risk and absolute risk. The truth is, if an intervention reduces your risk of a disease from 0.0001% to 0.00005%, you don’t really care, even though that represents a 50% reduction in risk. The difference in relative risk is 50% but the difference in absolute risk is 0.00005%. It’s clearly the latter that we care about. So what are the absolute risks we are talking about in this study?

The good news is that since we are considering prevalent diseases, the absolute risk effects are fairly large. Men who used the sauna once a week had a 22% risk of dying of CVD over the 20 year period. Men who used the sauna 2-3 times per week had a 16.5% risk and men who used the sauna 4-7 times per week had an 11.9% risk. These numbers don’t take into account other risk factors so there may be some confounding involved, but the absolute risk differentials of 5.5% and 10.1% respectively tell a significant story. You definitely care if you can reduce your risk of dying of CVD from 22% to 11.9%!

That being said, there are a few important limitations to the study. It’s always difficult to tease out causality and mechanisms from associative studies. Did sauna users have better outcomes because of the sauna or because those who sauna more are typically less busy and less stressed? Is it the sauna itself that created the benefit or the relaxation time that it implies or the social effect of having a sauna with friends?

The researchers controlled for physical activity and certain other lifestyle factors, but it’s possible there is still a hidden ‘healthy user bias’. In other words, it’s possible that people who care more about their health use the sauna more and it could therefore be their generally healthier lifestyles that account for better cardiovascular outcomes rather than the sauna itself. The concept of the healthy user bias is a powerful tool to keep in mind when reading research and can radically change the way you interpret results about, for example, associations between red meat and cancer.

The other major issue is that the population being studied were not, in my opinion, particularly healthy. The mean LDL cholesterol level was 4 mmol/L (numbers shown in mg/dL below). Typically, being over 3 mmol/L is considered a risk factor for CVD. 30% of the participants were smokers, the average blood pressure was 134 / 88 and the mean VO2 max was 30. I wonder what effect the sauna would have on healthier individuals. What benefits does the sauna have over and above an active lifestyle? The authors themselves note that some of the benefits of the sauna seem to be mediated by mechanisms similar to exercise. The sauna raises your heart rate for example, sometimes to over 150 bpm, suggesting that using the sauna is similar to doing cardio. The researchers point out that the sauna seemed to confer greater benefits on those who had lower levels of cardiovascular fitness. I would love to see a study done on healthy, active individuals to tease out the sauna-specific effects.



So it might be that sauna use is less protective of heart health for active, healthy individuals than it is for the sedentary. The truth is that most interventions will benefit the unhealthy more than the healthy. It’s a bit like newbie gains. In the beginning, most things will work. But I think the case for sauna use being protective of heart health is still fairly strong, just based on the fact that the effect sizes are so large. There is a lot of margin for error in the results. Although some of the sauna’s mechanisms are similar to exercise, some are distinct. The level of heat-stress caused by saunas is much greater than with exercise, for example. For my part, I’m going to continue to use the sauna 3-4 times per week, for 15 minutes at around 75-80 degrees C.


Binary Fitness Podcast Episode #1

In the inaugural episode hosts Tom McAdam and BJ Rule discuss the story behind Binary Fitness, a next-generation gym launching in the City of London. They talk about the philosophy and mission behind Binary Fitness and how they set out to create a truly unique brand in the fitness space. They also answer some fitness questions sent in by members and users and discuss their favourite CrossFit WODs and most memorable member transformations at City Road CrossFit.

Fasting 101

Fasting and Time Restricted Eating

Fasting has become incredibly popular in the fitness and health communities. But the subject is a bit of a minefield. With so many different forms of fasting and adherents of each making bold claims of their benefits, it’s hard to know which protocol to adopt and what to expect. In this week’s post I am going to outline my experience with a particular fasting regimen that I have been experimenting with for 6 months. I’ll tell you how to do it, what benefits I have experienced personally and what other benefits the research indicates are likely.

*Please note, as ever, that this post does not constitute a recommendation to implement fasting. Please consult your doctor before undertaking any protocol of fasting.

The What

There are a number of different fasting approaches. The approach I’m going to talk about is called time-restricted eating, or TRE for short. TRE involves adhering to daily eating and fasting windows. It is distinguished from intermittent fasting (IF) by the fact that TRE’s eating and fasting windows coincide with the circadian rhythm. In TRE your eating window occurs during day-time hours and your fasting window occurs in the evening and overnight. By contrast, IF often involves fasting throughout the morning.

TRE usually involves a daily eating window of between 8-10 hours and a fasting window of 14-16 hours. A typical structure might be to eat between 8am and 6pm and fast from 6pm to 8am the next morning. These, incidentally, are the timings I use.

During fasting windows, caloric intake should be zero. And you should also be careful to limit any non-caloric drinks that may still affect the digestive system.

I follow the TRE protocol from Sunday to Thursday and then ‘cheat’ on Friday and Saturday, allowing myself to eat with an unrestricted window. Luckily, the evidence suggests that TRE with weekends off is still effective.

The Why

So that’s how it works. But why bother with it? After all, it’s much more fun to just eat all the time, isn’t it?

From an evolutionary perspective, there is an intuitive case for TRE. Our hunter-gatherer forebears were forced to habitually deal with the limited availability of food. We have evolved as a species that underwent periods of fasting. It is, therefore, part of our inherited biology. We have adapted so that we can thrive in such conditions.

The fasting biology is part of our DNA and it turns out that certain physiological processes require or are vastly improved by being in an unfed state. Broadly speaking, our bodies appear to use the fasted state as an opportunity to undertake basic maintenance or organs and tissues. During fasting, processes of flushing-out dead and damaged cells (e.g. apoptosis, autophagy) are heightened and the following re-feed causes increased stem-cell production to replace these dysfunctional cells.

Beyond the evolutionary and scientific arguments, if my personal experience is anything to go by, there are some other very compelling reasons to give TRE a whirl.


This was the first effect I noticed and it occurred almost straight away. I found that I was able to go to bed earlier and sleep much more deeply. The length and quality of my sleep increased significantly. This is no small victory, especially considering I had no real difficulty with sleep to begin with. And remember how important sleep is for recovery and performance!

This is where the circadian rhythm aspect is so crucial (by contrast with IF). The theory is that our organs have internal clocks, just like our brain does. Food in the system is interpreted by organs as a signal of activity and day-time hours, in much the same way as light is interpreted (even artificial light). This causes a feedback loop in which hormones associated with sleep are inhibited and therefore sleep is harder to achieve. Restricting food at night prevents this breakdown of normal signalling, allowing your body to more effectively wind down.


This is commonly reported by people who fast and I experienced it myself. I have found that my focus and mental acuity have improved. I feel a sense of clarity. The research in animal models has indicated powerful effects in this area. Fasting in mice has been shown to increase memory as well as problem-solving ability. Fasting appears to increase the body’s ability to synthesise new neurons (by increasing neurotrophic factors) and enhances synaptic plasticity and the repair of damaged neurons.

In studies of mice, fasting protocols have been shown to be protective against a number of neurodegenerative diseases like Alzheimer’s and Parkinson’s. During fasted states, all organs apart from the brain shrink. The brain must turn to ketone bodies as a source of energy and it may be that this is a more efficient fuel source for cognition.


Look, I don’t necessarily want to get into the nitty gritty here, but suffice it to say that I have seen some indicators of digestive health improve dramatically since fasting. Again, this is not an area where I had previously identified a problem, but there has been an improvement nonetheless. According to Dr Satchin Panda, the world’s leading researcher on TRE, his project’s human data indicates strong anecdotal evidence that TRE alleviates digestive problems and gut health. This seems to make intuitive sense. Endlessly taxing the gut with relentless caloric intake can’t be a good thing. The gut needs time to rejuvenate and repair itself. Fasting gives it the opportunity to do so just like sleep gives the brain a chance to flush toxins.

All that being said, there are many reasons I fast that have little to do with what I have observed so far on a personal level. Although studies on humans are scarce for the time being, research does indicate some exciting prospects for those who fast.


This is the big one. In most animal models, fasting protocols have been shown to increase average lifetime, though this effect is not undisputed. Researches hypothesise that the probable mechanism for longevity is that fasting decreases IGF-1, suppresses the mTOR pathway and increases autophagy (the body’s mechanism for destroying cells with damaged DNA).

Look, I’m no biochemist either and I won’t pretend I fully understand those terms. But the short story is that IGF-1 and mTOR are involved in anabolic processes and appear to be related to ageing and certain chronic diseases such as cancer and Alzheimer’s. Suppressing mTOR signalling has been shown to increase average life-span in mice. At the same time, autophagy gives your body a better chance to get rid of cancerous cells before they divide and metastasise.

You may well have heard of these things in relation to muscle growth and it is true that you need functional mTOR signalling and IGF-1 to stimulate muscle synthesis. But you can have too much of a good thing. Have you ever noticed that ‘roid bros and olympic weightlifters look a lot older that their years? Well that may be because of overactive or dysfunctional mTOR signalling and its consequent ageing effect.

As well as regulating IGF-1 and mTOR, TRE has also been shown to reduce inflammation and improve insulin sensitivity and glucose metabolism in miceThere is strong evidence to suggest that TRE decreases blood pressure and resting heart rate in both animals and humans. In this way, there are hopes that fasting may contribute to preventing chronic diseases like diabetes and heart disease.


In mouse models, TRE has been shown to have robust protective effects against obesity. In experiments where two groups of mice ate the same diet and the same number of total calories, those that were restricted to a 9-hour feeding window gained 28% less weight than those that were left to eat as they pleased. Mice that ate within a 9-hour window put on 70% less fat mass than their freer counterparts. All on the same number of calories. That’s pretty crazy! And it strongly suggests that there is a relationship between when you eat and how lean you are. Eating within a day-time time window appears to protect against fat-gain and may promote accelerated fat-loss.

In Summary

In summary, there is reason to believe that time restricted eating can:

  • Aid cognition
  • Help prevent neurodegenerative diseases
  • Improve sleep quality
  • Improve gut health and digestion
  • Improve insulin sensitivity
  • Decrease chronic inflammation
  • Lower blood pressure and resting heart rate
  • Protect against chronic diseases like cancer and diabetes
  • Prevent weight-gain

Wooo! That’s quite a list. And it is worth mentioning that a lot of these hypotheses are far from being confirmed experimentally. But, I do think there is enough evidence to warrant a serious consideration of TRE as a lifestyle. What do you think?

If you want to learn more about the theory behind fasting, take a look at the resources below. If you guys are interested in this stuff, let me know and I will do another post shortly on practical implementation or TRE.

Learn more:

My Circadian Clock – Dr Panda’s website on TRE

Found My Fitness podcast episode with Dr Panda (part 1)

Found My Fitness podcast episode with Dr Panda (part 2)

Daily Eating Patterns and Their Impact on Health and Disease 

Time restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high fat diet

Fasting, circadian rhythms, and time restricted feeding in healthy lifespan

Fasting: Molecular Mechanisms and Clinical Applications