While I was doing research for the previous article in this series on IF, I had a conversation with one of our coaches, Elle. Elle mentioned that she had come across some material online discussing the potentially negative impact IF can have on women’s hormones. Did I know anything about this, she asked? I had to admit that, no, I didn’t really know anything about it. I had heard mutterings of this sort before but to my discredit I’ve never really looked into it. Honestly, my intuition was always that 14-16 hours of fasting is unlikely to cause any deleterious effects in women since it’s a fairly moderate protocol. Intuition is overrated, however, and we’re about science and data here. So I decided to get stuck into the literature to see what I could find on women and IF. What does the evidence say?
Before I proceed, I want you to make me one promise: please read this article in full; because if you read just half of it, you won’t understand the full picture and I don’t want you coming away with an unbalanced view on this important topic. If you read just half of this article, you may leave with the impression that women should avoid IF at any cost. This isn’t the case at all. I absolutely think that IF is a viable and potentially effective strategy for women. But there may be some additional considerations you should be aware of and there may be some adjustments to optimise your approach.
The biology of scarcity
Let’s start with some theory. What is the potential biological basis for women responding differently to intermittent fasting? Animals, like businesses, are confronted by the fundamental problem of resource scarcity. Life involves a number of resource-intensive processes and there is not always enough energy available to perform all of them at the same time. We’re thinking of the basic stuff here: survival, reproduction, growth, maintenance etc. This is why, for example, animals normally grow and only then reach reproductive maturity. There simply isn’t enough energy to grow and reproduce at the same time. An Animal’s biology has to make sophisticated decisions concerning where best to expend its available energy.
Figure: relationship between reproductive rate and longevity of animals. Higher reproductive effort yields lower survival rates; lower reproductive effort yields higher survival rates. The relationship is not linear.
In contemporary times, we don’t face a huge amount of scarcity. We all know there’s plenty of energy around, in the form of junk food and fizzy drinks. But, when we undergo fasting we impose an artificial energy constraint on ourselves. Fasting is a mild stressor, and that is part of the reason why it may promote health and longevity: stressors induce stress responses which can render you more resilient in the future.
But men and women may respond differently to the same scarcity of energy. As Martin et al. point out in a paper from 2007 published in Endocrinology:
“Females and males typically play different roles in survival of the species and would be expected to respond differently to food scarcity or excess’
Evolutionarily, the role of the female in reproduction is more energy-intense. While males often simply supply sperm, females must carry the foetus to term. Females are also generally responsible for the care of their young and for producing milk (in mammals at any rate!). All of these things are incredibly resource-intensive. For that reason, when faced with food scarcity and a decision to allocate limited energy to either survival or reproduction, we might expect females to shift further away from reproductive functions and more towards survival strategies. Reproduction is so demanding of energy for females that even a moderate scarcity could mean that it’s off the cards.
The stress-response and reproductive cycle in rodent models
So that’s the theoretical basis for a difference between men and women when it comes to IF. But to what extent is this borne out in the research? Do we really see the predicted sex-difference? And do normal IF protocols impose sufficient scarcity to induce these responses?
One thing is for sure: we certainly do see these differential effects in studies carried out in rats. We will discuss the extent to which this is, or is not, relevant shortly. But at the very least, the rat data gives us a sense of the mechanisms involved.
Martin et al. found that when rats were maintained on an alternate-day fasting protocol for 6 months, males and females responded significantly differently. Corticosterone (the rodent equivalent of cortisol) levels rose significantly in female rats while it actually dropped slightly in males, indicating that the fasting protocol caused a greater stress-response in females than in males.
Figure: corticosterone levels in control (C) and intermittent fasted (IF) rodents. Males on the left panel, females on the right panel.
Adding further weight to this observation, female rats responded to IF with increased measures of cognitive performance and physical activity. Females kept on IF were more than twice as active as control rats in the day-time. Plasma levels of brain-derived neurotrophic factor (BDNF) increased in females while levels in males fell. BDNF stimulates the growth of new neurons and synapses and so rising levels mean higher potential levels of cognition. These two effects imply that female rats had activated survival mechanisms to a greater extent than male rats in response to food restriction. In times of potentially life-threatening food-scarcity, animals adapt by increasing mental capacity in order to more effectively find food and by upping movement in order to increase the area over which they can hunt or gather.
Figure: plasma brain-derived neurotrophic factor (BDNF) in male (left) and female (right) rats in control (C) and alternate day fasting (IF)
Figure: ambulatory activity levels of rats
This shift in physiology, towards survival mechanisms, becomes even clearer when you consider what happens to the rats’ reproductive functions. Martin et al. found that the oestrous cycle was disrupted in 42% of female rats on IF (the estrous cycle is the rodent equivalent of the menstrual cycle). A second paper, published by Kumar et al., specifically studied the relationship between IF and reproductive function in female rats. Their findings corroborate Martin et al.’s data and add more detail. How is the reproductive cycle disrupted by IF?
IF and the HPG axis in female rats
The hormonal system that regulates the reproductive cycle is the hypothalamic-pituitary-gonadal axis (HPG axis for short). The hypothalamus releases gonadotropin-releasing hormone (GnRH) which travels to the anterior pituitary gland to stimulate the release of luteinising hormone (LH) and follicle stimulating hormone (FSH). These hormones act on the ovaries to coordinate the development of the follicle and ovulation.
Figure: the hypothalamic-pituitary-gonadal (HPG) axis in females
Kumar et al. also compared rats maintained on alternate day fasting against ad libitum fed rats. Their findings confirm that this fasting protocol disrupts (and in many cases halts) the estrous cycle. But how does this happen?
The first observation is that serum luteinising hormone (LH) levels were much lower in IF rats than in control rats. It is a surge in LH levels in the estrous (or menstrual) cycle that brings about ovulation, the release of the egg from the follicle. Therefore, chronically low levels of LH prevent this crucial step in the reproductive cycle.
Figure: serum luteinising hormone (LH) levels in ad-libitum (AL) and alternate-day fasted (DR) rats
Looking up-stream of this effect, Kumar et al. measured that female oestradiol (one of the forms of oestrogen) levels were markedly higher in female IF rats than in the control group. In the complex cascade of the estrous cycle, oestradiol inhibits the release of GnRH from the hypothalamus, thereby reducing LH release from the pituitary gland (see the figure above).
Figure: LH in rats fed ad-libitum (AL) and on alternate-day fasting (DR)
Figure: estradiol (a form of oestrogen) levels in ad-lib (AL) and alternate-day fasted (DR) female rats
Leptin levels may be one of the mechanisms through which energy-scarcity is communicated to the HPG axis. Low leptin levels, as observed in IF female rats, are signals of low energy status. Leptin can inhibit the release of GnRH from the hypothalamus by reducing the expression of an important neuropeptide, kisspeptin. All together then, hormone levels in female IF rats do suggest that fasting is having a negative effect on reproductive health.
Figure: leptin levels in ad-lib (AL) and alternate-day fasted (DR) rats. Also compares pro-estrous (PRO) and diestrous (DIE) female rats.
The problems with rodent data
Ok, sorry. I realise that’s a lot of nerdiness and probably too much detail. But what should we make of this data?
There are some significant limitations to the relevance of these findings for women undergoing standard IF protocols. First of all, rats are a potentially misleading surrogate for human data in this field. Rats have a metabolic rate 7 times faster than humans. Coupling this with the fact that both studies used alternate day fasting means that these observations are valid only for extreme fasting protocols. If you are fasting for just 16 hours a day, what is observed in rats that fast every other day may not be relevant at all. We are comparing a very moderate with a pretty brutal fasting system. It’s worth noting that the rats in the Kumar et al. study were just 3-4 months old while most studies equate 6 month-old rats to 18 year-old humans. In other words, the rats being studied were in their late adolescence and for this reason may be less relevant for older adults.
That being said, there is limited human data on women and IF which suggest that some of the effects observed in rodents may be applicable. Heilbronn et al. placed women on an alternate-day fasting schedule for 22 days and observed that their glucose response to a test-meal was impaired, while the glucose response in men was unchanged. Research shows that elevated levels of cortisol can disrupt glucose metabolism. This paper did not measure the hormone but it’s possible that this glucose dysregulation is suggestive of an upstream stress-response mediated by cortisol, as was observed in the rats.
Figure: glucose (A) ad insulin (B) response to a test meal before and after 22 days of alternate day fasting in men and women. Statistically significant differences are highlighted with: *
I have some reservations about this study since did not have a control group and, again, used the alternate-day approach to IF. The study was also short, had few participants and did not supervise food intake. I’d like to see a better controlled study, which measures cortisol, to confirm these results but there may be something there.
Practical advice for women doing IF
I think the rat studies provide a useful framework for anyone undertaking, or thinking of undertaking IF. It can’t hurt to be mindful of the risks, even if they might be very minimal. A woman doing any kind of IF should pay attention to:
The menstrual cycle – Any disruption to the regularity of your cycle (in the absence of other obvious stressors) could indicate the IF is not right for your physiology.
Sex hormones – Measure these every 3-6 months depending on how demanding an IF protocol you are adhering to. Pay particular attention to LH and oestradiol. If LH is too low and oestradiol too high (for the relevant point in your cycle), consider switching to a less extreme protocol.
Cortisol – Again measure this every 3-6 months. If your fasting cortisol is very elevated, confirm the result with a 24 hour urine test to make sure the observation is real. Then consider switching your IF plan.
Women might also want to start with less extreme version of IF. Perhaps start with a 12 hour fast. See how you feel. If you feel ok after 3-4 weeks, progress to 13 and 14 hour fasts. I think most of the benefits of IF can be obtained with 14 hour fasts in any case, so there is no real need to push much past that.
In conclusion, there is some evidence to suggest that the more demanding IF schedules may be deleterious to women’s cortisol levels, glucose metabolism and reproductive systems. Extreme caloric restriction can lead to disruption of reproductive cycles through a cascade of effects: lower leptin and higher oestradiol leading to inhibited GnRH release and decreased LH levels. Fasting may also lead to hypercotisolaemia through a more pronounced stress response. However, the evidence is mostly based on rodent models and alternate-day fasting systems. Human data is limited and flawed. More controlled trials would certainly be needed to confirm a real risk in women undergoing normal IF protocols. It’s always sensible to be mindful of risks though. So I recommend that women consider starting with more moderate approaches and keep an eye on levels of key hormones: LH, oestradiol and cortisol; much the way men may want to keep an eye on testosterone and IGF-1. I don’t think there is anything in the literature to suggest that women should avoid IF and I believe it will still be a great approach in most cases. But, as with everything, n=1. We are all individuals and it is up to you to figure out the best approach for yourself.
The science of intermittent fasting
We’re back with the Science Series and today I’ll be discussing what the research says about intermittent fasting for health and performance. This post will be split over a number of parts as there’s quite a lot to cover and the last thing I’d want to do is bore you…
Intermittent fasting (IF) has emerged as a popular strategy in the fitness world, and beyond. In my How to eat for fat-loss nutrition guide, I recommend a 14 hour daily fast. The principle reason for using IF for fat-loss is that it serves as an effective heuristic for reducing overall caloric intake, helping to create the deficit you need in order to lose weight. But there are other benefits to IF that go beyond controlling your intake and it may be a good idea to fast daily even if your primary goal is not fat-loss, but health or performance (I fast and have no intention of losing weight).
Before we go any further. What is intermittent fasting? Intermittent fasting is any protocol that schedules precise periods of fasting and eating. Fasting periods can last anywhere from 12 hours to a day or two. IF includes popular nutritional strategies like 16:8, 5:2 and the more recent TRE (time restricted eating). In this post, I will mostly consider the more common IF protocols that implement daily fasting periods of 14-16 hours, including both those that adhere to circadian rhythms (e.g. TRE) and those that do not. Note that this article will not discuss the many potential benefits of prolonged fasting. I think of prolonged fasts as those that last 3 days or more and the physiological mechanisms and consequent benefits may be quite different.
Insulin, glucose and metabolic disease
In today’s post, I’ll discuss two important recent papers on intermittent fasting: one published by Sutton et al. in Cell Metabolism in 2018 and the other published my Moro et al. in the Journal of Translational Medicine in 2016. I’m looking at these papers in particular for two reasons:
- They are both randomised controlled trials (RCT) in humans => excepting systematic review of RCTs, these provide the best and most unbiased data.
- They both consider IF in the context of stable-weight subjects => in other words, these studies isolate the benefits of IF from the benefits of caloric restriction.
Both papers primarily concern themselves with investigating the potential metabolic benefits of IF, especially as far as glucose and insulin regulation are concerned. But they also look at some markers of cardiovascular health, endocrinology and inflammation.
If you’ve been in the fitness scene for more than 5 minutes, you’ve heard of insulin and you’re probably at least vaguely aware of the concept of insulin resistance. To put it very simply, chronically elevated levels of insulin (hyperinsulinaemia) and blood glucose (hyperglycaemia) are markers of poor metabolic health and are associated with nasty chronic diseases: obesity, type 2 diabetes, heart disease, kidney disease, stroke etc. Keeping insulin and glucose within fairly narrow ranges is therefore critical for optimal health and longevity as well as for weight management. If IF can help us achieve this goal, we should take note.
So what do these two studies say and how did they reach their conclusions? I’ll consider the papers in turn, looking at Sutton et al. first.
Early time-restricted eating: the Sutton et al. paper
The Sutton et al. trial used a classic randomised controlled ‘crossover’ set-up. Participants were first randomised to either eTRE (early TRE) or control groups. The eTRE group ate within a 6-hour window, with dinner finishing before 3pm, while the control group ate within a 12-hour window. All meals were provided and supervised by the researchers and were identical between groups (so-called ‘eucaloric’). The only difference was meal timing. Intake was set so that subjects were eating the same number of calories as before the trial and consequently maintained their weight (a so-called ‘isocaloric’ experiment). They followed this paradigm for 5 weeks with measurements being taken at baseline and at the end of the intervention. Then, following a 7 week ‘wash-out’ period, the participants swapped groups and undertook the opposite protocol. Researchers then compared intra-individual changes observed over 5 weeks of eTRE against the changes observed in 5 weeks in the control group; that is, they looked at the difference between how an individual responded to eTRE as compared with how that same individual responded to the control diet.
Although the study was very small, with just 8 participants, the fact that researchers controlled food intake and timing so rigorously and measured intra-individual differences, rather than inter-individual differences, makes the data compelling.
What the trial found is fascinating. The primary end-points under investigation were measurements of “glucose tolerance, postprandial insulin, and insulin sensitivity as measured using a 3-hr oral glucose tolerance test (OGTT)”. An OGTT involves giving a patient a 75g dose of glucose solution and then measuring blood glucose and insulin levels at 30, 60, 90, 120 and 180 minutes afterward. The peak and average values of these biomarkers over the 3 hours gives a good indication of your metabolic health. All else being equal, lower average and peak values for glucose and insulin across the test is preferable, as discussed above.
Interestingly, the experiment revealed no difference between groups in terms of fasting glucose or glucose readings during the OGTT. But insulin dynamics did change significantly. eTRE decreased fasting insulin levels as well as reducing insulin measurements at 60 and 90 minutes following the glucose test. Over the 3-hour window, eTRE lowered both average and peak insulin levels. This indicates an increase in insulin sensitivity. As most of you will know, one of the main functions of insulin is to provide a signal to cells to import glucose from the blood. So if eTRE and control participants showed similar glucose dynamics but the eTRE group achieved these dynamics at lower average insulin levels, then eTRE cells must be responding more sensitively to insulin signalling. Improving insulin sensitivity could have a substantial effect on metabolic health, even if glucose dynamics are left unchanged. There is good evidence to suggest that hyperinsulinaemia is the earliest predictor of future metabolic disease and is a risk factor independently of poor glucose regulation.
What about the size of the effect on insulin? eTRE was observed to bring fasting insulin down by 3.4 mU/L or ~14.5%, down from an average of 23.4 mU/L to 20 mU/L. That takes the average subject from moderate hyperinsulinaemia back into the normal range. So, all in all, you’d have to say that’s a pretty important effect, especially given that the intervention lasted only 5 weeks. Similarly, average and peak insulin during the OGTT both improved by around 20% each.
Figure: glucose and insulin regulation in eTRE and control
Secondary measurements revealed that eTRE may decrease blood pressure. eTRE lead to a drop in both systolic and diastolic blood pressure of 11 and 10 mm HG respectively. This is interesting. Since participants were, on average, just north of prehypertensive (120+/80+), this drop took them back into a fairly optimal range below 120/80. Sutton et al. found that IF improved a marker of oxidative stress, isoprostane-8, but did not improve markers of chronic inflammation (high sensitivity c-reactive protein [CRP], IL-6). Cortisol was also unaffected by IF.
Figure: blood pressure, inflammation and oxidative stress in eTRE and control
Given the highly controlled nature of this experiment, I think the results are worth paying attention to. That being said, the trial was very small and a larger replication would be needed to really corroborate the effects. In addition, the trial participants were pre-diabetic middle-aged men. As with other papers I’ve discussed, the effects observed in this population may not translate to normal, healthy subjects (and perhaps not women either). Luckily for us, the Moro et al. paper looks at IF in a healthier cohort.
IF in weight-trained individuals: the Moro et al. paper
Moro et al. investigated the effects of IF in a cohort of 34 weight-trained young men over the course of an 8-week RCT. These were men with a minimum of 5 years of experience lifting weights, so the bar was fairly high. Subjects were instructed to maintain their previous intake of calories throughout the experiment. Half the participants were randomised to eat all their meals between 1pm and 8pm, while the control group ate between 8am and 8pm. During the experiment, subjects took part in identical training programmes to control for any differences in exercise volume and intensity. Again, the motivation for the trial was to see what effects IF might have beyond effects mediated by reduced food intake and weight-loss.
The headline result was that intermittent fasting led to reductions in body fat, while the control group did not lose fat. IF subjects lost an average of 1.62kg of fat mass over the 8 weeks. That’s not a huge amount but you should bear in mind that these were relatively lean, trained individuals. Subjects began the experiment with an average of ~10kg of fat mass so the fat-loss seen by the IF cohort represented a loss of over 15% of their body fat. And this without a reduction in calories… IF subjects maintained their lean mass too.
You might wonder what mechanism could mediate this kind of fat-loss. The authors point out that adiponectin was higher in IF subjects. Adiponectin is a hormone produced by fat-cells (adipocytes) that controls metabolism. Chronically low levels are often observed in obese people. Higher levels of the hormone are associated with an increase in energy expenditure and this may account for the fat-loss seen in the trial. Adiponectin causes increased glucose uptake by muscle cells, increased fat oxidation in the liver and in fat cells and decreased production of glucose in the liver. These effects are mediated through the PPAR Alpha, AMPK and p38 pathways as illustrated below. Whatever the mechanism, I think we would all agree that losing fat without decreasing food intake is pretty much the dream. So if this effect is real, we should take note.
Figure: the mechanisms of adiponectin
In agreement with Sutton et al., this trial found that IF lead to decreased fasting insulin. Moreover, they found that IF had a positive effect on fasting glucose (in contrast to Sutton et al.). Given that these trained individuals started with healthy measurements of fasting glucose and insulin, these results are surprising and suggest that we might be able to use IF to optimise already healthy lifestyles. It might also be the case that blood glucose is more responsive in currently healthy individuals, which would explain why we see a change in glucose dynamics in this trial but not in the trial conducted on pre-diabetic men.
Moro et al. observed even larger effects on fasting insulin than Sutton et al. did. The average IF subject decreased their fasting insulin by 10.1 mU/L, or 36%, from 27.8 to 17.7 mU/L! I do wonder why these young weight-trained men had worse baseline fasting insulin than the pre-diabetic middle-aged men, however… Too much pasta in Padova perhaps? Fasting glucose dropped by 10.7 mg/dL, or 11%, from 96.64 to 85.92 mg/dL.
Figure: fasting glucose and insulin in weight-trained IF and control subjects
Moro et al. measured a number of other biomarkers. They found, in contrast with Sutton et al. that IF improved markers of inflammation, reducing IL-6 and TNF-alpha (tumour necrosis factor alpha).
Figure: inflammatory markers in weight-trained IF and control subjects
IF appeared to reduce both testosterone and insulin-like growth factor 1 (IGF-1). This is not necessarily surprising since insulin, being an anabolic hormone, is known to be correlated with testosterone but you might wonder if lower testosterone and IGF-1 is desirable. You should note, first of all, that the IF cohort were able to maintain muscle mass and strength so the decreased levels of testosterone does not seem to have had a negative effect in that regard. However, the study was only 8 weeks long and so the long-term effects of this drop in testosterone is unknown. The average level of testosterone after IF was still well within normal ranges so I don’t think it’s a significant cause for concern. But if your primary goals are strength and athletic performance, this might be a consideration. Just be aware that, as with anything, higher levels of testosterone are not necessarily better: just consult your local steroid-user… Anecdotally, I’ve seen no change in my testosterone levels since starting TRE; in fact, they’ve increased a little bit.
Figure: testosterone and IGF-1 in weight-trained IF and control subjects
The story with IGF-1 is a little more nuanced. IGF-1, which is a downstream product of growth hormone, is an important anabolic agent and so critical for muscle protein synthesis and neurogenesis. But, we know from the data that when it comes to longevity, both high and low IGF-1 levels are associated with increased mortality risk. So a balance is critical. Thinking of IGF-1 and testosterone as GOOD, because of their role in building muscle and fueling performance, is a dangerous game. If health is something you prioritise, balance is always key. I would recommend that if you try IF, you just keep an eye on testosterone (and IGF-1 if possible). If it drops too low, you may need to stop fasting, reduce the fasting periods, or simply increase your food (and particularly carbohydrate) intake.
Figure. Predicted HR for the association between IGF-I and all-cause mortality. [Burgers et al., 2011]
You might be wondering why I didn’t discuss the Moro et al. paper first, given the more positive findings and the more relevant population under investigation. The reason is that while I found the Sutton et al. experimental set-up to be highly rigorous, there were some limitations to the Moro et al. methodology.
The most important of these is the inherent risk of what is known as performance bias. Performance bias occurs where subjects in an experiment know what is expected of them and understand the outcomes researchers are hoping for. As a result of this, they change their behaviour, or the way in which they report their behaviour, to match with these expectations. In the current study, participants were told to keep their total caloric intake constant while adopting the fasting protocol. Now, food intake was measured using a weekly questionnaire and, unsurprisingly, the results of these questionnaires showed that subjects achieved caloric constancy. But there is a definite risk here that participants reported what they knew they should have eaten rather than what they did in fact eat. What’s more, they may have done so without knowing it. Food Frequency Questionnaires, or FFQs, are one of the major problems with the papers linking red meat with mortality and cancer (see my post here). The questionnaires used in this study were better in that they were weekly rather than yearly (how crazy is that?!) but there is still room for error and bias.
And if calories were misreported, in this case being over-reported, there could be a very different explanation for the results seen in the experiment. If the IF group were eating fewer calories than previously, then we would be forced to concede that it was most likely this caloric deficit which mediated the fat-loss and improvements in fasting glucose and fasting insulin. Now, I’m not saying that this is definitely what happened; just that there is some non-negligible risk that it did. And so I think a replication of the study would be needed to confirm the results. In the meantime, we should consider the evidence as preliminary, if very interesting.
Primary outcome bias
The other slight issue I have with this paper is related to the concept of ‘bait and switch’ and primary outcomes (which you can read more about in Ben Goldacre’s excellent book Bad Pharma). In a proper medical clinical trial it is incredibly important to define the primary outcome of the experiment before it is conducted and you should not switch that outcome during the experiment. Why? This is a little subtle but it’s related to the concept of statistical significance… stick with me, please! Most studies choose a 5% level of significance, which means that there is a 5% chance that the experimental results are purely a chance effect; a 5% chance that the effects measured in the trial don’t really exist. Now, let’s suppose you choose blood pressure as your primary outcome. But then half-way through the trial, when the blood pressure results don’t look promising, you switch the primary outcome to LDL-cholesterol. No big deal right? Wrong! Because what is the probability that the results of your trial represent pure chance now? It’s not 5% any longer because you already had a 5% shot with your blood pressure test and now you’ve got an additional 5% shot with LDL-c too (assuming these are independent variables). So, in actual fact, the chances of your finding a positive result, either with blood pressure or cholesterol, purely by chance, is 10%. And you can see what happens when you add further primary outcomes and more switching… In this way, you can simply add or remove outcomes until you get a positive result, purely by chance.
Now, with the Sutton et al. paper, the authors very clearly set out the primary outcomes of their trial: glucose and insulin dynamics as defined over a 3-hour OGTT. They measured other data-points too, but they were very precise about their primary focus. With the Moro et al. paper, the primary outcome is never defined and the number of biomarkers measured was enormous. If you measure enough data points, you’ll eventually find a positive one! That being said, this is only a marginal issue and certainly doesn’t invalidate the study because Moro et al. did not just find one positive result; they found many. So the results are unlikely to represent pure chance. I mention it only as one more thing to have on your radar when it comes to reading studies!
Putting it all together
Putting this all together, I think these two trials provide strong evidence that intermittent fasting improves insulin regulation since this effect was observed in both. Decreased fasting insulin, as well as lower mean and peak insulin during an OGTT, suggests improved insulin sensitivity and metabolic health. There is good reason to believe that hyperinsulinaemia lies at the heart of many chronic diseases and so tools to help prevent and reduce this condition may be crucially important for long-term health. As for the regulation of glucose, the evidence is mixed but it appears that healthier subjects may respond more favourably to IF in this regard.
The Moro et al. paper indicates that IF may lead to fat-loss without a reduction in overall calories, perhaps mediated by increased adiponectin. But further studies in a more highly controlled experimental set up would be needed to confirm this finding, I think.
Both studies also indicate that IF may have other important roles to play in a healthy lifestyle. IF may decrease blood pressure, oxidative stress and inflammation. Other studies, considering these data points as their primary outcomes would be needed to really tease out these benefits. On the potentially negative side, IF may lead to reduced IGF-1 and testosterone. This is probably not as bad as it sounds since both these hormones should be kept balanced as both high and low levels can be harmful. But if you are using IF, it may be a good idea to get regular bloods taken to make sure nothing untoward is happening. See below for a graphical representation of the findings of these two papers:
Before I get into this, a quick preface. This is an outline for nutrition for fat-loss. However, it also serves as the foundation of a good diet, whatever your goal. I believe that for most people, this way of eating will support health, longevity and performance to a large degree. The rest is just detail. Whether you are after performance, health or just weight-maintenance, you can use this structure as the base for your nutritional strategy. Slight adjustments can then be made to accommodate your specific situation.
Are abs made in the kitchen?
You should also know that nutrition is not the only variable involved in fat-loss. We are continually told that ‘abs are made in the kitchen’. That’s nonsense. Nutrition is critical but so are other factors, particularly movement, sleep and stress-management.
By movement, I don’t just mean training; I mean activity outside training too: walking, sports, recreation. You should train 3-5 times per week at high intensity and you should move EVERY day at lower intensities. Low intensity activity like walking will have a surprisingly substantial effect on your overall energy balance, metabolism and regulation of blood glucose etc. 10,000 steps a day is possible for everyone and will help you more than you think. Take every opportunity to be active: try new sports, do a yoga class, take a ball to the park with friends and mess around. Sitting is killing you and it’s keeping you fat too.
If you’re regularly sleeping less than 7 hours per night, you’ll find it exceptionally hard to lose weight. Why? The science is clear on this: sleep deprivation, even moderate deprivation, causes you to eat somewhere between 300-600 extra calories per day (see this study)! Not just that: these extra calories will likely come from poor food choices as studies show that tired people eat more processed food than the well rested. Lack of sleep causes insulin resistance (references: 1, 2), making you essentially pre-diabetic and unable to regulate blood glucose effectively, shunting more of your fuel into fat and making you ever hungrier.
For a lot of people this is the missing link. Sleep at least 7 hours a night, absolute MINIMUM. Ideally, I’d prefer you slept 8 hours. And make sure the quality is high: sleep in a cool, dark room and limit caffeine past midday (the average half-life of caffeine is 5-7 hours. So that coffee you have at 3pm? Half of it may still be coursing through your system when you go to bed at 10pm, keeping you alert and awake!)
Stress is also a significant factor in weight-loss. Too much stress chronically raises your cortisol levels. This will lower your immune function and cause you to get ill more frequently. Getting sick is not optimal for weight-loss, by the way. Cortisol inhibits protein synthesis and may increase proteolysis in which the body breaks down muscle protein to use as fuel. Together these two effects may lead to poor recovery, lower muscle mass and, as a result, lower metabolic rate, making fat-loss much more challenging. An overly stressed body is a body that will hang on to energy reserves. It’s super important that you counteract this with meditation, yoga or other stress-management activities.
With all that being said, let’s move on to look at nutrition. The below is the way I eat and is based on about a decade of experience, fine-tuning and experimenting with different approaches. I’ve seen what works for me and I’ve seen what works for clients and members and this is my best attempt at consolidating all of that data.
The 3 Ps
In order to know how to eat, you need to understand nutrition on 3 separate levels, which I call the “3 Ps”
In order to stick with a nutrition plan, you need to understand the theory and thinking behind it. If I just tell you to eat x and say nothing about why, you’ll struggle to find the motivation to stay compliant. We always need “the why”. The Principles lay out the why of this way of eating.
While the Principles lay things out in an abstract way, the Prescription tells you exactly what you can and can’t eat and how you can combine foods together. The Prescription is your basic “what”.
But that can’t be where we leave things. We all, on some level, know how to eat. It’s the pragmatics of how exactly to execute and stick with a nutrition plan that prevents most people from getting the results they desire. This side of things is covered by the Practicalities which discuss strategies and hacks for eating according to The Prescription.
3 Principles guide my approach to nutrition:
Consistency is king
Keep it simple, stupid (KISS)
Eliminate will power
Consistency is king
The first and most fundamental principle of nutrition is that consistency is king for fat-loss. Always remember that the good diet you can maintain is infinitely better than the perfect diet you can’t.
Results come from stringing together long periods of good quality nutrition and training. It doesn’t have to be perfect, but it does have to be constant. Sure, you can allow yourself some indulgences here and there but at least 80-90% of the time you need to be on point.
You need to keep yourself accountable and you need to track your adherence. If you’re taking part in the 12 week City Road Summer Health Challenge, then you already have the means for keeping yourself honest. If not, use a tracking spreadsheet (feel free to borrow our template here) or simply track good and bad days in a diary.
Nutritional science is complex and nuanced and there’s a lot of controversy and disagreement about the finer details. But eating for fat-loss is not, and need to be, complex at all. That’s not to say it is easy, because it isn’t; but conceptually, in terms of what you need to do, it’s straightforward. My philosophy is that you should keep it as simple as possible and you do this in 2 ways:
Limit the number of rules you need to follow
Make sure the rules are binary and unambiguous: it should be easy to say whether you stuck to your plan or not.
Overall, when it comes to nutrition for fat-loss, the goal is to create a calorie deficit. I hate to be that guy, but it’s true. It’s not the only thing that matters, of course. It’s critical to design a sustainable nutritional approach so that you can achieve long-term fat-loss and we want to emphasise food quality in order to support health, longevity and performance. But, all that notwithstanding, you can’t lose weight without a deficit; so it needs to be a priority.
But how you create this deficit is the crucial thing. You’ll hear many angry people on Instagram railing against the paleo diet or the ketogenic diet, saying, “all that matters is a calorie deficit”. That’s like saying, “Hey loser. In order to lose weight, all you have to do is create the metabolic conditions for weight-loss. Moron”. Yes, indeed. A = A, but how constructive is that?
In other words, talking about deficits without introducing a clear and effective means of creating that deficit is tautological and a waste of time. Fortunately for us, we can actually create this deficit without paying any attention at all to calories and instead focusing on food quality and meal timing. No MyFitnessPal, no macros. Just simple rules. After all, it should come as no surprise that a diet which supports health naturally encourages a low level of body fat.
Eliminate will power:
A lot of people think losing weight is all about having the discipline of a monk. If your strategy is to rely on will-power, you will lose. We aren’t wired to eat just 1 cube of chocolate and we aren’t wired to eat broccoli when cookies are readily available. We’re wired for times of scarcity and uncertainty; of not knowing when the next meal will come around. Gorging on available calories was a survival mechanism. Unfortunately, it’s a mechanism ill-suited for contemporary times of abundance which have made us fat and unhappy. So instead, I suggest you stack the deck in your favour through planning and organisation in order to steer you clear of unnecessary temptation.
When to eat:
I’ll start with food timing because I think a fasting protocol serves as a useful calorie-reducing heuristic, besides having other powerful effects. I believe fasting has positive effects on digestive and gut health and may up-regulate stress-response pathways that can be protective for health and longevity. Limiting the times at which you can eat is also one of the simplest and most effective mechanisms for creating a caloric deficit.
I tend to use a 10 hour eating window and a 14 hour fasting window and that is where I would suggest you start.
There is some debate over whether fasting during the day or in the evening is better. I think the most important thing is that you fast; not when you fast. Design an eat / fast protocol that works with your lifestyle, work and social / family commitments.
I generally aim to fast between ~8pm and ~10am. If I end up eating dinner a little later, I adjust my fasting window because I find it easier to control when I eat breakfast than when I eat dinner (this may not be the case for you!). So if I end up finishing dinner at 9pm, I’ll fast until 11am the next day. I will also sometimes do longer fasts if I decide to train in the morning or if I have meetings. Once you’ve learned how to fast, a few extra hours without food is no big deal. This will sometimes mean that my eating window is shorter or longer than 10 hours, but my fasting window is always at least 14 hours.
You may find that it’s easier for you to time your eating window, rather than your fasting window. Some people start a clock at their first meal and set an alarm for 10 hours later. All your eating must be completed by this time.
It’s important to bear in mind that during the fasting window NOTHING with any caloric content should be consumed. All you can have is water and black tea or coffee (no milk!). That means no lattes, no protein shakes, no Noccos etc.
Will being fasted affect my training?
Everyone is different and you will need to find what works best for you. BUT, in general, you should be able to train fasted although It may take some getting used to. Look, I realise we aren’t hunter-gatherers any more but do you think our ancestors needed pre-workout carbs before trying to escape a marauding tiger or going on a long hunt? Probably not and you don’t need to be fully fuelled before working out either. That being said, I wouldn’t recommend fasting for long periods of time after training since this may affect recovery. After training, 2-3 hours of additional fasting may work. If it’s much more than this, you may struggle.
That means if you’re training in the evening, you may need to fast in the morning / early afternoon to avoid fasting overnight after you train. If you train early in the morning, you may want to push your fasting window at least partly into the evening hours.
If you are doing double classes or higher volume strength workouts or longer sessions, you may start to see your performance decline and I would recommend being more fully fuelled for these types of sessions. For myself, I find that I can perform normally during 1-1.5 hours of class-like strength and conditioning programming and aerobic work after as much as 14-15 hours of fasting. But higher rep strength stuff or longer sessions start to get hard. Again, results may vary and you may be different. n=1, always.
It’s also worth mentioning that if performance is your number 1 goal and body composition is not a concern, you should probably aim to be well fuelled for all workouts.
How many meals should I have?
Within the eating window, you can eat however you like. You can have as many or as few meals as feels natural to you. You need only eat when you’re hungry and I certainly wouldn’t advise eating just because you think a certain number of meals is optimal.
For most people, 2-3 meals within a 10 hour window will work well. For me, on weekends and rest days I tend to have just 2 large meals, perhaps with some snacking in between. On training days, I’ll normally have 3. But you should give yourself free reign to find what works best for you. Listen to your body. You may find yourself eating fewer but larger meals or you may find that you can tolerate the same number of meals with a shorter break in between. Either one is totally fine.
How much should I eat?
Eat until you’re no longer hungry. Eat again when you feel hungry. Simple! One of the advantages of IF is that you should be able to control your caloric intake simply by restricting your eating periods. Sure, it is possible to eat the same number of calories on IF as you normally would but it’s unlikely to happen without deliberate effort.
What to eat:
Let’s begin with what not to eat… My recommendation is that you avoid processed foods, refined carbohydrates and all grains. The last item is somewhat controversial and may get a few ‘if it fits your macros’ bros into a frenzy. Just know that I’m not saying that grains are the devil. What I am saying is that there are better things you could be eating instead. I’m saying that although some grains have value in some contexts, most grains most of the time are of poor quality and contain relatively little micronutrient content. You can do better. I realise that we’ve been eating bread for 10,000 years. But we sure as hell haven’t been eating highly-refined and factory-produced bread like Hovis for 10,000 years…
This is also an example of a simple and effective heuristic for limiting your food intake (and increasing your overall nutritional quality). Most people rely far too heavily on grains and refined carbohydrate and often don’t even realise. Equally, processed carbs frequently go hand in hand with added sugar or fat, which makes for very palatable and incredibly energy-dense food choices. Think pasta with olive oil and cheese, butter on toast, sugary cereals and granolas etc. If you can eat these foods in moderation, you are a far better person than me!
Just to be clear, all of the below foods are, for the time being, out:
Cereal, muesli or granola of any kind
Oats / porridge
Bread and other wheat products
Sugary drinks and sodas
How do you know if a food is processed or not? If it has an ingredients label, you probably shouldn’t be eating it.
Which leads me perfectly on to what you should be eating. CrossFit’s “fitness in 100 words” does a good job at summarising:
“Eat meat and vegetables, nuts and seeds, some fruit, little starch and no sugar. Keep intake to levels that will support exercise but not body fat.”
Put another way: eat real food; eat whole foods. Eat meat, fish and plants. Or, if you’re vegan, just eat plants!
How to eat:
We’ve covered what you can and can’t include in your diet. But how do you put that together into an actual diet?
I use a template to create all of my meals. It’s a step-by-step meal-creation algorithm. For more detail on the food categories (starch vegetables, high quality protein etc.), see below.
- Every meal starts with a high-quality protein source
- You then add micronutrient rich food: either with non-starchy vegetables or fruit. Ideally you would choose fibrous vegetables but fruit is fine otherwise.
- If you’re limited to fruit, you then add some healthy fats to go with the protein. Then you’re done.
- If you added non-starchy vegetables you have two options:
- Add some starchy vegetables. This option works well if you’re eating a post-workout meal. In this case, you should be moderate with any dressing you add to the vegetables. Then you’re done.
- If you’re foregoing starchy veg, I would add a generous healthy-fat dressing to your veg first. You can then add a little fruit as a dessert too! Then you’re done.
So you have a few options to play with:
- Protein + non-starchy vegetables + starchy vegetables => PVS meal
- Protein + non-starchy vegetables + 2 of: health fat dressing; healthy fats; fruit ⇒ PVX meal
- Protein + fruit + healthy fats ⇒ PFH meal
Here are some examples:
Staying on track: contingency planning and preparation
One of the advantages of this approach is that it’s flexible. It allows you to cook incredible food but it also gives you options when time is tight.
There are two keys to keeping yourself on track when you’re busy: meal hacks and food prep.
There are certain items which you should keep in your kitchen at all times so that you can whip up nutritious food at the drop of a hat and without even cooking: super nutrient dense options that require little or no preparation. These are also items you can buy in any supermarket and use to build a healthy meal on the go. Here is a starter list:
With these simple ingredients you have almost limitless options to create delicious, nutritious and easy meals. Here are just a few of the options:
Eggs: God’s own food
Ok, so you have to cook eggs. But let’s be real for a second: few protein options pack the same nutritional bang for the 5 minute buck it takes you to scramble a few eggs. Eggs are not only a great source of protein, they also provide good fats, lots of vitamin D3, B2 and B12, folate, choline and selenium. Please remember that eggs are also not JUST for breakfast! Oh, and don’t let anyone tell you to worry about the cholesterol in eggs…
High-quality organic ready meals:
There are a few companies out there that provide super high quality ready meals made with organic meats and only whole food ingredients. My current favourite is an outfit called Pegoty Hedge, which I order from Abel & Cole (https://www.abelandcole.co.uk/). I generally have their cottage pie once a week with some sauteed greens. Having one or two of these in the fridge or freezer can be a real life-saver when the pantry is empty or you just don’t fancy cooking. See the cottage pie below:
A quick note too on what NOT to rely on in your kitchen. First of all, I would avoid buying ready-cooked meats like ham or packaged chicken. The meat in these will tend to be of really poor quality and low welfare standards and will be packed with preservatives and artificial flavourings (you should also be careful with the tinned fish and make sure it’s fished sustainably and wild wherever possible).
5-minute mouth-watering veg:
You all know you need to eat vegetables. But what if you don’t like vegetables? In my mind, it is one of the greatest travesties of contemporary times that so few people have been exposed to delicious vegetables! Coming from a Greek background, I’ve grown up revering vegetables and understanding just how delicious they can be. But you may not have been so lucky.
The truth is that preparing delicious vegetables is both quick and easy. You just need to keep a few principles in mind. I’m stealing this from Samin Nosrat’s Netflix series because it is such an effective structure for thinking about vegetables. Whenever cooking veg, you should always think about including and balancing the following elements:
If you include something from every category here, you will have something mouth-watering (and nutritious). If you boil broccoli and slap it on your plate undressed and unseasoned, don’t be surprised if you find it completely unappetising! Imagine baking a chicken breast with no oil, no salt, no pepper and no sauce. Would you eat that?
You may recognise straight away that these categories line up quite neatly with the “healthy-fat dressings” referenced above and that’s no coincidence…
I think salt is one of the least-utilised elements in general vegetable cooking. Salt can bring out the flavours of vegetables like nothing else. Always make sure you have a high-quality sea salt to hand. I use Maldon, which is great (and here’s another option). Table salt WILL NOT DO! Oh, and don’t worry so much about your salt intake. If you’re eating whole foods and training a lot (i.e. sweating and losing salts), you need not be worried about excessive salt intake. In fact, you need to add some salt to restore electrolyte balance. Of course, you should measure your blood sodium to make sure it’s not crazy high but otherwise, be liberal with the salt! You can also use other salty ingredients instead of salt: soy sauce & miso paste are good examples.
When it comes to fat, I’m generally thinking organic extra virgin olive oil or grass fed butter. But there’s nothing to stop you getting more creative here. You could experiment with coconut oil or high quality organic animal fats like streaky bacon (brussel sprouts with streaky bacon is an absolute winner!). The only classes of fats I would avoid are heat-treated nut and seed oils and vegetable oils.
Again, acid is incredibly important and there are a number of great ways to include it:
Apple cider vinegar (ideally with “the mother”)
White wine / red wine vinegar
Heat is the last category here and quite important. People so often boil vegetables to the point of soggy sadness. This may not be the best way to extract flavour! Personally, especially for leafy greens, I prefer to sautee in a little olive oil and salt. You’ll get more flavour that way and it’s super quick: you can fry up some kale or spinach in less than 5 minutes. You should also consider roasting your vegetables.
Meal-prep has some fairly negative connotations in my mind. I immediately imagine marathon sessions of baking chicken breast and piles of plastic tupperware boxes so high I can’t see over them. That doesn’t really appeal to me. Of course, if you are the kind of person who loves to do that sort of thing, please, by all means go ahead and do it.
I prefer another, simpler strategy. You see, in years gone by, before meal-prep was a thing, our grandmothers already had the perfect solution to the problem of eating well throughout the day. It was known as “leftovers”. The idea is pretty outrageous: at dinner time, cook twice as much as you need and eat the rest for lunch the next day. If you do that every evening, you’ll do just fine and you’ll never need to eat dry 5-day-old chicken breast again! I realise some of you may not love the idea of cooking every night. But unless you’re working late and given the masses of information above on how to prepare nutritious food rapidly, I have to say I don’t really love that excuse. If you don’t cook, you’ll never be lean. Suck it up, butter cup.
Again, I understand that this won’t work for everyone. If you work in the evenings you may need to do some prep on the weekends. If you prefer to ‘batch’ your tasks and knock them out in one session, be my guest. But I’ve found this to be a really enjoyable and light-work approach to meal prep.
A week of food and how to do your grocery shopping:
Some of you may already know that I am an enthusiastic promoter of online food shopping, and Abel and Cole in particular! Ordering all your food for the week ahead of time forces you to plan a little and prevents you from making poor decisions when you go shopping hungry. I know that if I go to the supermarket when I’m already peckish, I am coming home with thousands of calories of unnecessary and unhealthy foods. And once they are in my house, you better believe I am eating every last crumb! This is bad for my wallet and my waistline…
Avoid this pitfall by doing a weekly shop online. With companies like Abel & Cole, you can also set up a recurring basket with certain items delivered every week, fortnight, month, quarter or any other time period you like. Most weeks, I need do very little editing to my basket and I simply wait for a boat-load of organic, nutritious food to show up at my door on Wednesday. It’s the dream!
Specifics can be really useful so here is an example of a week’s grocery shopping for me and how I cook and eat throughout the week. Everything below is organic and sourced from high-quality farms. Please feel free to use this as a template of sorts (and you can even click on the links and add them to your trolley), if you think that would be beneficial. Although, note that I eat bread nowadays… Mostly because I’m not trying to lose fat and I have been weight stable for a long time.
2 heads of broccoli
Chicken thighs (8 thighs – 1kg)
Dry-cured smoked back bacon (180g)
Cherry tomatoes (250g)
Feta cheese (400g)
Spring greens (2 bags)
Long fermentation bread (800g)
Mini avocados (4)
Ox livers (400g)
Red lettuce (one head)
Red onions (500g)
Salmon fillets (4)
Mixed nuts (1kg)
For me, the nutritional week begins on Wednesday, since that is when the Abel and Cole arrives:
- It’s not perfect but it’s pretty good.
- It’s consistent: almost every breakfast is the same. This allows me to save time on decisions and also helps control intake since these meals will always be fairly similar in terms of calories and macros.
- I leverage leftovers relentlessly!
- Some days I eat twice, other days I eat 3 times.
- I cook regularly and do very little ordering in, especially during the week.
- I eat out on the weekend, but I try to stay fairly compliant.
- You won’t notice this, but I am forced to eat seasonally (since Abel and Cole does not sell food out of season). This basket would have been very heavy on the kale, spinach and chard a few weeks ago. But we’re moving into summer now and those greens are not available. Eating like this allows me to get a varied diet throughout the year without having to think about things.
Everyone is different and I’m certainly not saying that you should eat exactly like me. But I hope this overview gives you an idea of what a week’s groceries and cooking / eating could look like for you. Feel free to use this as a template and adjust things so that they work for you. Or, if you prefer, start from scratch.
But please do consider doing weekly shops. You’ll save time and money. You’ll also be forced to plan ahead and sketch out your week of eating. This will help immeasurably in terms of keeping you on track!
A note to performance athletes and others:
I said at the beginning that there were certain people who may want to adapt this general nutritional framework to better suit their purposes. This may be you if:
- You’re focused on performance rather than fat-loss or train at a higher volume or higher intensity (i.e. if you train 5+ times per week and 90 minutes+ per session)
- You’re fairly lean already / happy with where you’re at AND you’ve been weight stable for a while (6 months+)
- You’re not necessarily particularly lean or focused on performance but you’re happy where you are weight-wise and interested in balancing health with a more flexible and enjoyable dietary approach
If any of the above describes you, you may want to include a few other items in your diet, particularly a wider choice of carbohydrates. I would start with the following:
Good quality bread (long-fermentation sourdough is my fave)
If you’re focusing on performance, you may want to limit the fats a little and increase your carbs more. If you want to get specific, there are lots of services out there which can help you figure out macronutrient targets.
Equally, if you’re weight stable or happy where you are, adding some grains to your diet should not be a problem. Just be mindful of how much you are relying on grains. I still prefer to only include grains once a day, in general. This helps me focus on the vegetables and fruits that should serve as the base of everyone’s nutrition. You should also pay attention to how particular choices of grains make you feel. For me, oats cause uncomfortable bloating and I know they just don’t work. If you have a similar response to any of these grains, just avoid them.
So, that’s been a fairly comprehensive journey through my take on nutrition! I hope you find it helpful in achieving your own goals.
Remember that this is just one perspective on nutrition. It is not the be-all and end-all. I have found this to be very effective, both for myself and for many others. But if you have an approach that you like and understand, you shouldn’t feel compelled to suddenly jump ship. There is no ultimate diet. There are many, many good diets. Part of the problem we face in the fitness industry is that people are very tribal about their nutrition or training. I think we need to be more open-minded.
I started by highlighting the fact that fat-loss does NOT just come down to nutrition. Abs are not only made in the kitchen, but elsewhere too. Make sure to stay generally active, outside of training. Pay attention to the quality and duration of your sleep and manage your stress as best you can.
I then outlined the 3 levels of nutritional knowledge: Principles, Prescription and Practicalities.
My Principles are:
Consistency is king
Keep it simple
Eliminate will power
With these things always guiding my thinking, my Prescription is the following:
- Fast 14-16 hours per day, according to your lifestyle and commitments
- Eat as many times within your eating window as feels natural
- Eat meat and fish, vegetables, nuts and seeds, some fruit, little starch and no sugar
- Avoid processed foods and grains. No rice, oats, bread etc.
- Make sure to always include a high-quality source of protein in every meal
- Make up the rest of your plate with non-starchy and starchy vegetables, fruit and healthy fats and dressings
Moving on to Practicalities, we covered a number of topics:
- Food hacks. Make sure to keep your larder stocked with the list of foods above so that you always have options for fast and nutritious meals
- Meal-prep / left-overs. When you cook dinner, just go ahead and cook double portions. The left-overs are your lunch for tomorrow
- To make sure your vegetables are always delicious, consider Salt, Fat, Acid and Heat
- Order your weekly food online and plan your meals ahead of time
Low-carb vs. low-fat diets: surely one of the most vicious rivalries of the contemporary fitness world, full of zealotry and quasi-religious belief. Add to that the ‘calories are all that matters’ crew and you have the makings of an epic Hollywood screenplay… or perhaps not.
In any case, this aspect of nutrition has to be one of the more difficult areas in which to tease out the truth from the nonsense and the science from the quackery. Today, we’re going to compare low-carb and low-fat diets in one very specific context: fat-loss. Is there any difference in the effectiveness of low-carb and low-fat diets when it comes to losing weight?
The DIETFITS study sought to answer exactly this question in a randomised controlled trial (RCT). 600 overweight participants were randomised to either low-fat or low-carb diets at the beginning of the study. Over a 12-month period each cohort was educated about the principles of a healthy low-carb (LC) or healthy low-fat (LF) diet and how to implement the appropriate nutritional strategy. At 12 months, the researchers re-measured participants’ weight to see if there were any differences between the groups.
The headline: there was no statistically significant difference in weight-loss between the LC and LF subjects. On average the LC participants lost 6kg while the LF group lost 5.3kg. In addition, both groups showed a similarly large range of individual weight-changes. In both groups, individuals ranged from a 30kg weight-loss all the way up to a 10kg weight-gain.
There were two particularly fascinating aspects to the study. First, the trial was designed to emphasise food quality in both groups:
“Both diet groups were instructed to (1) maximize vegetable intake; (2) minimize intake of added sugars, refined flours, and trans fats; and (3) focus on whole foods that were minimally processed, nutrient dense, and prepared at home whenever possible.”
After all, food quality could surely be a significant confounding variable if it differed between groups…
Secondly, even though no explicit instruction was given to reduce caloric intake, participants in both groups ended up eating significantly less:
“Despite not being instructed to follow a specific energy (kilocalorie) intake restriction, the mean reported energy intake reduction relative to baseline was approximately 500 to 600 kcal/d for both groups at each time point after randomization.”
This tells me two things: first, that food quality and nutrient density is the primary driver when it comes to weight-loss, over and above macronutrient designs; and second, that we don’t need to necessarily measure caloric intake in order to significantly decrease it and consequently create an energy deficit. If you focus on micronutrient dense foods, you will likely eat less food overall and lose weight as a result.
So that’s it then? Is there really no difference between low-carb and low-fat diets? I would suggest that although the study found no statistically significant differences, all fat-loss metrics subtly favoured the low-carb group. In the first instance, weight-loss was greater (-6kg vs. -5.3kg). Additionally, the low-carb group’s body-fat % dropped further as did their waist circumference (see below).
It’s also important to point out that there is always a limitation to studies which find no observable effect. It’s related to the concept of statistical power (stick with me here, it’s not that technical!). Most of you are probably familiar with the idea of statistical significance, which is generally set at 5%. A significance level of 5% means that there is a 5% chance of a false-positive; in other words, a 5% chance that an observed effect doesn’t really exist but randomly materialised out of the data-set.
Power relates to the other side of the coin: false-negatives. A power of 95% would mean that there is a 5% chance of a false-negative; in other words, a 5% chance that despite no effect being observed, there really is an effect that randomly did not show up in the data. You can limit the chances of a false-negative if the effect sizes are large or if your sample size is large. Now, the researchers didn’t publish the power level but it could range from around 68% for smaller effect sizes up to 99% for larger effect sizes, given the sample size. In other words, the study was not powered to rule out smaller differences between low-carb and low-fat weight-loss strategies so could well have missed a subtle difference (and a subtle difference is suggested in the data that consistently favours low-carb).
Beyond this, the study did highlight some differences between low-carb and low-fat groups, particularly with regard to blood lipids. Over the course of the 12 months, HDL cholesterol increased significantly more and triglycerides decreased significantly more in the low-carb group compared to the low-fat group. That sounds positive. But conversely, the low-carb group saw a roughly equivalent increase in LDL cholesterol while the low-fat group saw a small decrease. What to make of that? Cholesterol and blood lipids are complex and nuanced. I’ll need to return to the subject in more detail but I should point out that the changes in cholesterol were too small to be terribly significant (LDL-C increased by about 0.09 mmol/L in the LC group, where total LDL-C should sit below 3 mmol/L) while the changes in triglycerides was larger (triglycerides decreased by around 0.3 mmol/L where total triglycerides are normally below 2.3 mmol/L). Again, this looks to be slightly favourable to the LC approach.
Interestingly, both groups saw a marked and statistically significant improvement in important health biomarkers: blood pressure reduced and fasting glucose and insulin decreased. All of which suggests that the best thing you can do for your health if you’re overweight is to lose weight, by whichever method works best for you.
We should also ask the question of what constituted low-fat or low-carb in the study. For the low-carb group, daily carbohydrate ranged from 97g at the beginning to 132g at the end (subjects were instructed to slowly titrate their carbs up during the 12 months). For the low-fat group, daily carbs ranged from 205g to 212g. Both approaches are fairly moderate in my eyes. You might see that as a strength or a weakness of the study, depending on your viewpoint.
But what about individual differences? Surely certain people will respond better to low-carb and others to low-fat approaches? That’s probably the case on some level but the study did in fact looks at two potential hypotheses related to inter-individual differences. Certain gene polymorphisms have previously been associated with better responses to low-carb or low-fat. But when the researchers analysed the data, they found that these genes had no effect on how well subjects responded to respective diets. Another theory proposes that those with a greater insulin response to carbohydrate, indicating potential insulin resistance, would do better with low-carb approaches. Yet the study concluded that insulin response did not predict outcomes.
This is just one study in the large and complex space of weight-loss. And there are certainly limitations to the experimental design and statistical power. As with all nutritional studies, I wonder about the accuracy of self-reported dietary intake. Intake was recorded using surprise visits and multiple-pass interviews to determine average intake. But participants were well aware of what was expected of them through educational seminars and so there is an inherent risk of performance bias (in which subjects tell researchers what they want to hear). I wonder how likely it is that the standard deviation of carbohydrate intake for the LC groups was only around 4g per day!
Certainly, I don’t think we can conclude from this study alone that there are no differences between low-carb and low-fat approaches to weight-loss. But we can say that the differences between moderate low-carb and moderate low-fat approaches are probably fairly small, if they do exist. Moreover, delineating nutritional approaches strictly in terms of macronutrients may be missing the point. Both groups, on average, achieved a meaningful weight-loss and my conclusion is that this happened mostly as a result of an increase in food quality and a resulting reduction in caloric intake. What we really need to get to the bottom of is why, with the same level of education and guidance, some people lose 30kg while others gain 10kg.
The paper is strong ammunition against zealots on both sides of the fence: if anyone claims that low-carb or low-fat is the only way to lose weight, you know they haven’t considered the evidence. Nutrient density is primary and educating people on this piece is what matters most. Equally, although the study found no evidence for a mechanism underlying individual responses to diets, that doesn’t mean there is no such mechanism. I believe there most likely is, even if it’s as simple as the fact that some people prefer to eat carbs and others prefer to eat fat (there may well be genomic features that define this). I’m generally in favour of a moderate low-carb approach. I think the data in this paper indicates a subtle but potentially real advantage of limiting carbohydrate to a moderately low level and that has always been my preferred method for fat-loss. What do you think?
Losing weight over the long-term is difficult. Many people can lose weight during brief periods of concerted effort but most find they rapidly relapse to previous levels as soon as they take their eye off the ball. This rebound is such a common experience that it’s almost universal. It feels like there’s a powerful force intent on maintaining our body fat. But does the science corroborate this intuition? And if so, what are the physiological processes keeping us overweight? Lastly, and perhaps most importantly, how can we use this knowledge to create effective and lasting weight-loss?
A classic paper on this subject, published in 1995 by Leibel et al., looked at the metabolic effects of weight-loss (and gain). They took a group of 41 individuals, some of whom were obese and others who had never been, and studied them in a highly controlled environment. Their nutritional intake was limited to a precisely titrated liquid formula for the duration of the experiment so that caloric intake and macronutrient splits could be tightly regulated. The set-up allowed researchers to accurately calculate metabolic rates by recording the number of calories required to maintain a certain weight.
Once baseline measurements of total energy expenditure (TEE) had been taken, subjects were limited to 800 kcal/day until they’d lost 10% of their initial bodyweight. Their nutritional intake was then adjusted back to maintenance, until bodyweight was stable within 10g/day for 14 days. At this point, the researchers re-measured energy expenditure. Then the process was repeated until participants had maintained a 20% drop in bodyweight and final measurements were taken.
The results were pretty staggering. You would predict that a 10% loss in bodyweight would result in a 10% reduction in total energy expenditure. You might even think that TEE would fall by a bit less, given that you would expect most of the weight-loss to come from less metabolically active fat. By contrast, Leibel et al. found that a 10% weight-loss actually resulted in a 15% decrease in metabolic rate. In other words, your total energy expenditure falls by 50% more than you would expect under conditions of moderate weight-loss.
These effects were corrected for body composition: non-obese subjects saw their total energy expenditure fall from 45 kcal/kg of fat-free mass (FFM) to 39 kcal/kg FFM. Obese subjects saw theirs fall from 50 kcal/kg FFM to 42 kcal/kg FFM. In calorie terms, these effects are significant. The extra reduction in metabolic rate, over and above what you would predict, equates to 218 kcal/day for the non-obese and 244 kcal/day for the obese. On average, non-obese subjects’ maintenance calories decreased from 2380 kcal/day to 1952 kcal/day and obese subjects’ from 3100 kcal/day to 2549 kcal/day.
Looking at these numbers, it becomes immediately obvious why losing weight, particularly for more overweight individuals, is so difficult. Simply maintaining a reduced body-weight entails a hugely reduced caloric intake. This is counterintuitive and psychologically challenging. After losing weight, you expect the maintenance period to be something of a reward but in fact you have to work almost as hard just to retain your new state. Indeed, the study quantified the comparison between the weight-loss and maintenance periods. They found that daily energy expenditure is only 10-15% less during rapid weight-loss, eating 800 kcal/day, than it is while subjects maintained their lowered weights, eating 2,000-2,500 kcal/day. Losing weight and maintaining a lower weight are therefore similarly difficult.
The data also elucidates another intuition about weight-loss: that’s it’s much harder for some people than others. If you look at the figures above, you’ll see that the standard deviation of the metabolic compensation is 123 kcal/day for the lean subjects and 198 kcal/day for the obese subjects. In percentage terms, these standard deviations equate to 56% and 81% of the mean effect for lean and obese participants, respectively. That’s telling us that there is a huge variation in the degree to which people’s metabolisms compensate during weight-loss. So it is not surprising that some people seem to lose weight much more easily than others. That being said, in all of the studies I’ve seen, ALL subjects saw at least some degree of extra energy expenditure loss after losing weight.
The same applies on the other side of the spectrum. A 10% gain in body-weight resulted in an equivalent compensatory metabolic response: participants’ total energy expenditure increased by 16%, showing that it is just as hard to hang on to ‘gains’ as it is to hang on to abs.
Unfortunately, these effects may not be temporary. In a study comparing weight-stable subjects with those who had recently lost weight and those who had maintained a weight-loss for more than a year, researchers found that the metabolic compensations after sustained weight-loss were similar to those after recent weight-loss. To be fair, this study was conducted exclusively on obese subjects but the lesson is simple: you should not necessarily expect your metabolic rate to speed up, even after extended periods at your new weight.
Things get even more fascinating when you consider what happened when (obese) subjects further reduced their weight to a total loss of 20%. While a 10% drop in body-weight was associated with a 16% decline in total energy expenditure, a 20% drop in body-weight only results in a 23.5% decline in TEE. So at a 10% body-weight reduction, the metabolic response is 60% greater than expected while at a 20% reduction it’s only 35% greater. Again, you might have expected the opposite. As you perturb further away from the equilibrium point, you would anticipate a greater and greater physiological ‘pull’ back. You might have imagined a kind of spring-loaded system. In reality, you see the opposite. So what’s happening here?
In a 2001 paper with a similar experimental set-up, Rosenbaum et al. set out to answer this question. One hypothesis was that there is a threshold mechanism operating in metabolic adaptation. The idea is that the body wants to protect energy stores above a certain minimum level (the threshold). If energy stores dip below this threshold, physiological processes are activated to maximally reduce energy expenditure. Beyond this point, energy expenditure simply falls linearly with loss of weight since the body has already deployed its most drastic homeostatic tools (see graph below). If the average threshold level occurs before a loss of 10% of body-weight, that would explain why we see much stronger metabolic compensations at this level than at the 20% level.
Change in energy stores from initial
You can see this mechanism in action if you look at the way the components of energy expenditure vary at different levels of weight loss. After a 10% body-weight loss, half the drop in metabolic rate is accounted for by resting energy expenditure (REE) and half by non-resting energy expenditure (NREE). By contrast, after a 20% loss, only 30% of the drop in metabolic rate is due to REE, while 70% is down to NREE. You might explain this by saying that at the threshold, the body makes adjustments to its resting metabolism. But these are one-time, step-wise adaptations that cannot be further tuned to decrease energy output.
If this is what’s happening, the next natural question is: how is this happening? A lot of research has been done to characterise this weight-reduced phenotype. There are hormonal, nervous system and muscular aspects to the picture. Hormonally, weight-reduced people appear to have lowered levels of leptin, thyroxine and triiodothryonine (two of the thyroid hormones) which coordinate to favour weight regain by lowering metabolic rate and increasing appetite. After weight-loss, the sympathetic nervous system (the “fight-or-flight” branch of the NS) is less active. Finally, muscles become more efficient meaning that they consume fewer calories to perform the same amount of work. This last effect is fascinating and, in case you’re wondering, this increased efficiency is not just a result of carrying less weight; there are actually biochemical adaptations that occur at the level of the muscle cell.
Leptin appears to be the protagonist in this story. Leptin, as you have probably heard, is a hormone that regulates appetite. It is secreted predominantly by fat cells and is primarily interpreted by the brain (although also by other cells, e.g. muscle cells) as a signal of the body’s energy storage status. Low levels of the hormone are read as a warning of decreasing reserves and potential starvation. This is translated into a neurological drive to eat. In a study in 2005, Rosenbaum et al. investigated what happens to weight-reduced subjects when they are given exogenous leptin to bring the hormone back up to its pre-weight-loss level. As you can see in the diagrams below, increased levels of leptin reversed many of the detrimental changes in thyroid hormone levels, muscular work efficiency and sympathetic nervous system tone.
This resulted in an overall increase in total energy expenditure, closing the metabolic gap left by weight-loss. So then leptin is a wonder drug that can help us stay buff? Well, sadly, no. Leptin has been a dismal failure in obesity trials; extended periods of exogenous hormone use are rarely a good idea. Just ask the 22 year-old bodybuilder at your gym who looks 40. The problem is that, just as with insulin, the body can build up resistance to leptin signalling. Just as exogenous insulin is not your best bet to improve glucose metabolism, so leptin is not the answer to fixing your weight. This experiment merely shows us how the compensations of weight-loss are mediated in the body. In fact, when you think about it, many of these adaptations to weight-loss could be positive in the long-run. The overweight state of lowered muscular efficiency and heightened sympathetic nervous system tone is not one we necessarily want to protect.
This is all well and good, but if we can’t use leptin to improve our odds of holding on to weight-loss, how can we use this knowledge in our efforts to get, and stay, lean? In the first instance, it’s useful to be aware that you can’t slack off following weight-loss. You now know that the period after weight-loss is just as crucial as the preceding period. Armed with this information, you can prepare yourself for the extra effort; which will be well worth it if you can avoid the dreaded weight ‘yo-yo’. More than that, this data is the best argument I can think of in support of a gradualist approach to weight loss. If you rapidly lose 10% of your body weight, you face a situation where you have to quickly reduce your daily food intake by ~15%. That’s a huge and very difficult adjustment to make. But if you instead lose weight gradually over time, perhaps aiming for a 10% loss over a number of months, you can gradually lower your food intake making the adjustment that much easier. Reducing calories by 100 kcal per month over 6 months is much easier than reducing by 600 kcal in 1 month. The science shows us clearly why crash diets are destined to fail: they simply require too much discipline to maintain the weight lost. The great advantage of the gradualist approach is that you may also stay above your body’s threshold fat-storage levels, avoiding the most potent metabolic counter-punches. Finally, since we now know that to lose weight we necessarily have to find a way to eat significantly less, finding a nutritional approach which promotes satiety at lower caloric levels is hugely important. This is why whole food approaches are much more effective than eating processed foods and it may also be why low-carb (and even keto) diets are powerful for many.
If you’re trying to lose weight, I hope this helps you on your journey. If you’re trying to gain weight, the same lessons apply. Knowledge is power! As ever, if you have any questions or comments, please feel free to drop a comment below or email me at firstname.lastname@example.org
Sleep is a hot topic in fitness and wellness at the moment, and rightly so. Thanks to the efforts of scientists like Dr Matthew Walker, the importance of sleep is becoming much more widely appreciated. Sleep is nature’s performance enhancing drug and it’s also a powerful tool for disease-prevention. If you haven’t read Dr Walker’s book yet, I strongly recommend that you do.
My intention with this post, along with a number of future posts, is to look in a bit more detail at the evidence supporting the absolutely critical role of sleep. Today, to start the campaign with some shock and awe, I’ll be addressing the links between poor sleep and Alzheimer’s disease. My hope is that I may be able to scare some of you into taking this seriously!
Normally, I would now warn you of my lack of credentials and discourage you from taking anything I say here as advice since I’m not a doctor or scientific professional. Notwithstanding that, in this case I’m happy to offer one piece of advice: before you think of any other aspect of your lifestyle, fix your sleep. Ok, if you smoke, take care of that first. But otherwise, sleep is primary.
Alzheimer’s disease (AD) affected some 30 million people in 2015 and it’s predicted that by 2040, 81 million patients will suffer from it. Roughly 6% of people over 65 have the disease. If there is any chance sleep can help avoid this terrible disease, I know I would take notice.
A paper by Lim et al. in Sleep in 2013 examined the association between sleep fragmentation and Alzhemeir’s disease in 737 older individuals (average age of 82) in a care home. They measured sleep fragmentation over a 10-day period using wrist-watch actigraphy (movement trackers) and then followed up over a 6 year period to record diagnoses of AD.
The team found that those in the 90th percentile for sleep fragmentation were 50% more likely to develop AD than those in the 10th percentile. In addition, general cognitive decline was 22% more rapid in the 90th percentile group than in the 10th percentile group. A 1 standard deviation increase in sleep fragmentation was associated with a 22% increase in AD risk.
I can already hear some of you chomping at the bit to call me out for hypocrisy… Just last week I outlined all the limitations of observational studies in order to challenge the hypothesis that red meat is killing us. And here I am 1 week later using observational data to make a case for sleep.
There are a few important differences between this study and the red meat study I looked at last week, however. One strength of this study is that the measurement of sleep is objective. Food frequency questionnaires are notoriously susceptible to bias and subjectivity. Wrist-watch measures of movement clearly are not. They may not measure sleep as precisely as polysomnography but nobody could claim that the measurements produced by these devices hide some bias.
Another strength is that the effect sizes under consideration are fairly large. In this study, the average risk of developing AD was 3.94% per year. For a subject with a sleep fragmentation score 1 standard deviation above the mean, they are looking at a risk of 4.73%, an increase of 0.79%. That’s not huge by any means, but not a number I would ignore given the objectivity of the data.
That being said, there are limitations with this study. The chain of causality is always at issue with epidemiology and this paper is no different. As you can see from the chart below, those in the top 90th percentile for sleep fragmentation had lower baseline composite global cognition scores than those in the 10th percentile. In other words, those who sleep more poorly already have greater levels of cognitive impairment so it’s possible that the cognitive decline comes first, the poor sleep second. You’re probably also wondering how relevant 80 year old subjects are to you. If poor sleep in your 80s is associated with AD risk, does it necessarily follow that poor sleep when you’re younger is a risk factor?
Had this been the only form of evidence supporting the link between poor sleep and AD, I probably wouldn’t have written this post. Luckily (or unluckily depending on your current sleep philosophy) there is also good data from a randomised controlled trial (RCT) supporting the hypothesis.
If you want to prove causality, you need a theory for the mechanism by which a risk factor leads to a disease. In other words, what physiological process mediates between poor sleep and AD? What is it about sleep deprivation that increases AD risk?
One theory of AD, known as the Amyloid cascade hypothesis, conjectures that Alzheimer’s disease is caused by a build of protein plaques in the brain. Amyloid-beta and Tau proteins aggregate and bring about the onset of dementia. Some scientists theorise different primary causal factors (and indeed some autopsies show brains with significant plaque build-up but no AD), but most agree that a build-up of amyloid-beta is a significant risk factor and, at any rate, is associated with AD.
Amyloid-beta is a metabolite created in the normal operation of neurones. It has also been discovered that one of the primary functions of sleep is to clear away potentially toxic metabolites through the glymphatic system. During sleep, glial cells, which fill the space between neurones, shrink by up to 60% in size, allowing cerebrospinal fluid (CSF) to enter the interstitial space and clear away metabolic waste-products, including amyloid-beta. Could this be our mechanism? If it could be shown that reduced sleep duration or quality causes a build-up of amyloid-beta, we could make a strong case that poor sleep causes AD.
And in fact this experiment has been done, in a randomised controlled trial published in 2014 by Ooms et al. Researchers took 26 healthy middle-aged men and randomly assigned them to either one night of normal sleep or one night of total sleep deprivation. They measured cerebro-spinal fluid (CSF) levels of amyloid-beta 42 at multiple points in the evening and again in the morning. They found that in the well-slept group, CSF levels of amyloid-beta 42 decreased by 6% over night while in the sleep-deprived group, CSF levels of amyloid-beta 42 were unchanged.
The study also found a correlation between hours slept and reductions of CSF amyloid-beta 42 overnight. The more participants slept, the greater was their clearance of amyloid-beta.
6% may sound like a small difference, but this study demonstrates that sleep is required for the clearance of amyloid-beta. Without it, the body cannot shuttle away this toxic compound. If one night of sleep deprivation prevents amyloid-beta clearance, what does this look like over months and years of poor and reduced sleep? Note also that these subjects were 50 years old on average, so we can’t make the claim that poor sleep is a risk factor for older individuals only. If sleep disrupts the glymphatic system in 50 year-olds, presumably it has the same effect in 40 and 30 year-olds too. How confident are you now that poor sleep is not putting you at risk?
Another observational study looked at the link between self-reported sleep duration and brain scans of amyloid-beta deposits in a group of elderly people. Although fairly small (76 subjects), the results of this study serve as another piece of supporting evidence showing that, not only does poor sleep cause acute build-up of amyloid-beta in CSF, it is also associated with a chronic build-up of the protein in the brain itself. Researchers found a negative (albeit small: -0.36-0.38) correlation between sleep duration and amyloid-beta deposits. The images of brain-scans of representative subjects from different sleep cohorts shows a visible relationship which is hard to ignore (see below).
You could argue that the case is not closed here. Since there is controversy over whether amyloid-beta plaques are a causal factor in the development of AD, evidence of a causal relationship between poor sleep and decreased amyloid-beta clearance cannot be put forward as conclusive evidence that poor sleep causes AD. But with strong observational data as well as a possible mechanism supported by randomised controlled trial research, why take the risk? It looks very likely that there is a strong link between poor sleep and AD. If you are in the 90th percentile of sleep fragmentation, you may be increasing your risk of AD by as much as 50% and every time you have a bad night’s sleep, your clearance of amyloid-beta is compromised. As far as I’m concerned, that’s enough to take the matter of sleep exceptionally seriously. Of course, sleep is likely protective against many diseases, not just AD and it can increase performance in a number of ways. So this is just one angle on the importance of sleep; but hopefully an angle that may scare you into re-thinking a ‘sleep when I die’ philosophy.
As ever, if you have an questions or comments, or if you think I’ve got anything wrong, I’d be more than happy to hear from you. Just comment below or email me at email@example.com
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?
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.
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.
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?
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.
|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.
|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?
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 firstname.lastname@example.org
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 email@example.com
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.