For many years, there’s been an ongoing conversation about the challenges women with Polycystic Ovary Syndrome (PCOS) face in managing their weight, since women with PCOS have higher rates of overweight and obesity than women without PCOS. A significant part of this discussion centers on the claim that women with PCOS have a lower Basal Metabolic Rate (BMR). This claim is mainly supported by a single study. Because of these BMR claims, many women feel a sense of discouragement when they believe that their biology is working against them, no matter how diligently they stick to diet and training. Should they? What does the collective research say? How valid is that single study people always cite?

This article explores these claims and the research to determine if women with PCOS have a lower BMR.

Let’s dig in.

A brief primer on PCOS

PCOS stands for polycystic ovary syndrome. There are actually two terms: PCO and PCOS. PCO denotes the presence of extra follicles, whereas PCOS is a collection of the various metabolic alterations from “normal” (specifically elevated androgen and insulin hormones). The “S” adds the syndrome portion to the equation. In reality, polycystic ovaries can be an anatomical condition. The presence of polycystic ovaries alone doesn’t automatically mean a person has the syndrome. As with most pathologies, the collection of symptoms and many heterogeneous factors lead to the actual “syndrome” part. 

PCOS does not have a single standard diagnostic criterion. Different doctors, hospitals, research universities, and gynecologists may use varying criteria to determine if your collection of symptoms qualifies as PCOS. Each criterion has different symptoms that must be present.

In short, there are multiple diagnostic criteria for PCOS. But, all diagnoses require the presence of either clinical or biochemical hyperandrogenism, or chronic oligo-anovulation. In other words, there must be evidence that your body either produces different levels of hormones than women without PCOS, that it responds differently to those hormones, or both. Concerns about the metabolic effects of PCOS largely stem from these hormonal effects of the condition. 

So, why do so many people think that women with PCOS have a low BMR?

At this time, if you Google “PCOS and BMR,” you’ll immediately be hit with multiple links to a 2009 study by Georgopoulos et al titled, “Basal metabolic rate is decreased in women with polycystic ovary syndrome and biochemical hyperandrogenemia and is associated with insulin resistance.” It’s cited by over 100 studies via Google Scholar metrics, making it the most frequently cited study comparing BMRs in women with and without PCOS. It’s cited in all of the blog posts and lay articles that show up near the top of Google search results discussing the impact of PCOS on BMR (one, two, three, four, five). It’s discussed in online communities for women with PCOS. The findings of the study have even shown up in educational materials distributed by the British National Health Service. It’s not an exaggeration to state that the perception that women with PCOS have decreased BMRs is driven almost exclusively by this one study.

The study purports to show that women with PCOS but no insulin resistance have BMRs about 13% lower than women without PCOS. Additionally, it purports to show that women with both PCOS and insulin resistance have BMRs nearly 40% lower than women without PCOS.

The study states, “In women with PCOS in the present study, adjusted BMR was decreased compared with control subjects, independently of obesity and insulin resistance (IR). Adjusted BMR was significantly decreased both in women with PCOS with or without IR and particularly in women with PCOS and IR.”

That paints a pretty gloomy picture for women with PCOS, and particularly women with insulin resistance. 

So, is the 2009 study an outlier?

To ensure we did a thorough accounting of the research, we performed a systematic search of the scientific literature using variations of “RMR,” “REE,” “BMR,” “resting energy,” “basal energy expenditure,” and other terms alongside “PCOS,” “polycystic,” and similar variations to find all of the studies assessing metabolic rates in women with PCOS. Some studies were found that weren’t focusing on BMR directly, but which still assessed and reported BMR during the course of the research. It’s always possible that we missed a study or two, but we’re confident that our search turned up all – or virtually all – of the research on the topic. We also examined the methods of BMR measurement and ensured that only studies using indirect calorimetry were included, since multiple studies on the topic measured BMR using other methods that aren’t valid or reliable.

From there, we performed a meta-analysis – a statistical “study of studies” – on all of the research comparing BMRs in matched groups of women with and without PCOS. The meta-analysis was performed in JASP, using a restricted maximum likelihood model with inverse variance weighting.

We found 18 studies that assessed BMR in women with PCOS. Of these, four didn’t directly assess BMR using indirect calorimetry. Three reported predicted BMRs from body composition assessments (in other words, they didn’t actually measure BMR in the first place), and one reported predicted BMRs from wearable armbands (again, not an actual measurement of BMR). These four studies were excluded from all further analyses. Of the remaining 14 studies, 7 directly assessed BMR in women with PCOS, without any comparison to a control group of women without PCOS. These seven studies therefore couldn’t be used in our primary meta-analysis, but they’ll be discussed in secondary analyses to characterize the research on PCOS and BMR more broadly. So, seven studies ultimately met our inclusion criteria for the meta-analysis.

Results of the Studies that have Assessed Basal Metabolic Rate (BMR) in Women With and Without PCOS

As a note, two of these studies assessed BMR in three groups of women. Segal and colleagues assessed BMR in obese women with PCOS, obese women without PCOS, and non-obese women without PCOS. The comparison between the two groups of women with obesity was used for this meta-analysis, to provide an apples-to-apples comparison. Similarly, Doh and colleagues assessed BMR in obese women with PCOS, non-obese women with PCOS, and non-obese women without PCOS. The comparison between the two groups of non-obese women was used for this meta-analysis. The other five studies only included one group of women with PCOS, and one group of women without PCOS. In all five of these studies, basic demographic and anthropometric characteristics were similar between groups. So, in total, this meta-analysis pools the data from 444 subjects in 14 groups across 7 studies.

The results of the meta-analysis can be seen in the forest plot below. Across these seven studies, there was virtually no difference between the BMRs in women with and without PCOS (g = -0.01, p = 0.925).

Visually, the Georgopoulos study appears to be a bit of an outlier. It found a negative effect that was more than twice as large as any other study. And, statistically, it was identified as an “influential” study, with a covariance ratio of <1, and a standardized residual, DFFITS value, and Cook’s distance that were ~2.5-4 times larger than any other study.

Re-running the meta-analysis with the Georgopoulos study excluded improved model fit (all residual heterogeneity statistics decreased), but the pooled effect was still non-significant (g = 0.12, p = 0.274).

In effect, even with the Georgopoulos study included in the analysis, the research suggests that women with and without PCOS have pretty similar BMRs. When the Georgopoulos study is excluded as an outlier, the other six studies suggest that women with PCOS may actually have slightly higher BMRs than women without PCOS, but the difference is still trivial in magnitude and not statistically significant. Additional analyses were performed to confirm the robustness of this result (testing a fixed-effect model, excluding the studies by Doh and Robinson that didn’t report means and SDs or SEs, and adjusting weighting parameters), and none of those analyses materially changed the result.

Moving on, let’s pull back in the other seven studies that only assessed BMR in women with PCOS, without a comparison to a non-PCOS control group. This leaves us with a total pool of 14 studies that characterize the BMRs of women with PCOS. From these studies, we can draw comparisons to research that has assessed BMRs of women in the general population.

These 14 studies assessed BMR in a total of 642 women with PCOS. Below, you can see the sample size, and average height, weight, age, and BMR in all of these studies. 

Characteristics of the studies reporting BMRs in 642 women with PCOS

Given the average height, weight, and age of these subjects, we can calculate their expected average BMR, using equations developed from large samples of women in the general population. Based on a thorough analysis of the research on the topic, we tend to think that the Oxford/Henry and Mifflin-St Jeor equations are the two best “off the shelf” equations for calculating BMR from height, weight, and age. Both of these equations would calculate an estimated BMR just north of 1500 Calories (1501 for Mifflin-St Jeor, and 1512 for Oxford/Henry) for women matching the characteristics observed in the PCOS research. So, on average, the women with PCOS in these studies had BMRs that were about 50 Calories or 3% lower than would be predicted. This is a pretty trivial difference, bolstering the findings of our meta-analysis.

Finally, five of these studies also assessed both BMR and fat-free mass in women with PCOS. These 79 subjects had an average of 52.9kg of fat-free mass, and an average BMR of 1616.1 Calories. Using the 1991 version of the Cunningham equation, which estimates BMR from fat-free mass, these subjects would be predicted to have a BMR of 1512 Calories. So, on average, the women with PCOS in these studies had BMRs that were about 100 Calories or 6% higher than would be predicted. This is also a pretty trivial difference.

Studies Reporting Both Fat-Free Mass and BMR in Women with PCOS

All of these analyses paint a consistent picture: it doesn’t appear that PCOS has a notable impact on BMR. The idea that PCOS decreases BMR is driven by a single study that’s been widely cited, discussed, and shared, but the findings of that study are radically out of step with the rest of the research on the topic. A thorough analysis of this body of literature suggests that PCOS has no meaningful impact on BMR. To be clear, some women with PCOS will have low BMRs (and some will have high BMRs), because BMRs are much more variable than most people realize. But PCOS doesn’t appear to systematically and independently affect BMR.

What were some of the issues with the 2009 study?

We could leave it there, but the 2009 Georgopoulos study has been so influential that we think it’s worth pointing out a few other obvious issues with it. It departs from the rest of the literature to a much greater degree than can be explained by random chance. But, when you dig a bit deeper, an explanation for its eye-popping findings becomes much clearer.

Primarily, there’s a very good chance that the researchers ran into an equipment issue. The machine they used to measure BMR probably wasn’t very good.

This study assessed BMR using an indirect calorimeter called the PulmoLab EX-505. That may not mean much to most readers, but it was our first clue. We read a lot of metabolism research, and we see which devices and manufacturers are commonly used and trusted by researchers. The dominant manufacturers are Parvo Medics and Cosmed for stationary metabolic carts, and Breezing for portable units. DeltaTrac also pops up from time to time, especially in research conducted in hospitals. There are plenty of other players in the consumer market, but devices from those four manufacturers have the most research validating their accuracy. So, when a new name pops up, it’s good practice to try to find independent research validating the device.

We weren’t able to find any validation research on the PulmoLab EX-505. And, we were unable to find any research labs using this device other than the lab that conducted the outlier 2009 Georgopoulos study. We can’t claim that no such validation research exists, and that no other labs use the device, but we were unable to find them after a considerable amount of searching.

But, we did find a validation study on the higher-end sibling of the PulmoLab EX-505 – the PulmoLab EX-670. Unfortunately, the EX-670 performed quite poorly. It showed considerably more variability and less reliability in capturing accurate respiratory measurements than other systems. A low coefficient of variation is an indicator of high reliability, but this study reports that, “the coefficients of variation for … Douglas bags, Oxycon Pro and Oxycon Alpha were 3.3–5.1%, 4.7–7.0% and 4.5–6.3%, respectively, whilst that for the Pulmolab was highly variable (26.8–45.8%).” In other words, it was about 5-10 times less reliable than the other devices tested in that study.

To be clear, it’s possible that the EX-505 is a better device than the higher-end EX-670 from the same manufacturer. But, since we couldn’t find validation research for the EX-505, and since a (presumably better) device from the same manufacturer appears to be remarkably unreliable, we find it likely that the outlier findings of the 2009 Georgopoulos study may have been the result of simple measurement error, due to using an unreliable device to assess BMR. 

Digging deeper into the results 

Even if we couldn’t pinpoint a reason (like measurement error from using an unreliable device to assess BMR) for the eye-popping findings of the 2009 Georgopoulos study, a close examination of the data itself is enough to suggest that something went wrong when collecting the data.

To explain why, we need to discuss statistics a little bit.

When reading research, most people pay attention to the means (the averages). But, studies also report measures of variability, typically in the form of standard deviations or standard errors. The larger the standard deviation is in relation to the mean, the more variable the data is. If you see an average of 10 ± 1 (mean ± standard deviation), that means about two-thirds of values are between 9 and 11, 95% of values are between 8 and 12, and 99.9% of values are between 7 and 13. But, if an average is 10 ± 5, that means values between 5 and 15 are pretty common, values between 0 and 20 aren’t particularly rare, and values below 0 or above 20 should crop up about 5% of the time. The average is the same in both instances, but a larger standard deviation means the values are much more spread out.

I won’t bore you with the technical reasons someone might want to calculate a standard error instead of a standard deviation. All you need to know is that you calculate a standard error by dividing the standard deviation by the square root of the sample size. So, if your standard deviation is 100, and your sample size is 25, your standard error is: 100 25=20.

For certain types of data, there’s a certain amount of variability you expect to see. For example, if female subjects in a study are reported to be 165 ± 5 cm tall (mean ± standard deviation), you know you’re dealing with pretty typical data. The average woman in the study is 165cm (perfectly typical), and about 95% of the women in the study should be between 155cm and 175cm tall (also perfectly typical).

But, if female subjects are reported to be 165 ± 30 cm tall, you might start asking questions. Are one-third of your subjects really shorter than 135cm or taller than 195cm? Do you have a handful of enormous outliers dramatically increasing the variability in your data? Did you potentially make some errors when taking the measurement or transcribing your data into a spreadsheet? If you just paid attention to the averages, nothing about an average height of 165cm would seem strange. But, when you see a dramatically larger (or smaller) standard deviation than you’d expect from a particular type of data, that warrants further investigation.

How much variability do we tend to see in BMR data?

In all of the studies discussed above that didn’t come from the lab publishing the 2009 Georgopoulos study (and that didn’t use the PulmoLab EX-505 device to assess BMR), the average standard deviation for BMR values was 233 Calories. So, about two-thirds of subjects had BMRs within 233 calories of the group mean, and about 95% of subjects had BMRs within 466 calories of the group mean. In other words, if the average was 1500 Calories, most values should be between 1034 calories and 1966 calories.

Looking beyond this body of research, and using other large studies as a point of reference, the female subjects in the Mifflin-St Jeor study had an average BMR of 1349 Calories, with a standard deviation of 214. In a recent large study by Pavlidou and colleagues, the female subjects had an average BMR of 1533 Calories, with a standard deviation of 308. We won’t bore you with a dozen other examples, but most reasonably large studies (>50 subjects) that report female BMRs have standard deviations of about 200-350 calories.

So, turning our attention to the 2009 Georgopoulos study, there were 91 women with PCOS, who were reported to have an average BMR of 1445.57 Calories, with a standard error of 76 Calories. With a sample size of 91 subjects, a standard error of 76 means the standard deviation was 724 Calories. That’s way more variability than we tend to see in BMR research. Taken at face value, that would mean you’d expect about one-third of the subjects to have BMRs below 725 Calories, or above 2175 Calories. Furthermore, you should expect a decent number of subjects to have BMRs below 500 Calories or approaching 3000 Calories. For comparison, the lowest female BMR observed in Mifflin-St Jeor study was 927 Calories, and the highest was 2216 (in a sample of 247 women). In the Pavlidou study (with a sample of 549 subjects), the lowest female BMR was 908, and the highest was 2492.

Thankfully, we don’t just have to extrapolate and make assumptions about the extreme values implied by such large standard deviations. We can see the spread of reported BMR values when we turn to other research from the same lab, published in the same year, using the same device (and probably the same sample of women – This research group published four fairly large studies on BMR in women with PCOS in the span of four years. They almost certainly recruited a single sample and analyzed it multiple times, or reported on more data as they collected it).

In a sample of 156 women with PCOS, these researchers reported an average BMR of 1415.7 Calories, with a standard deviation of 672.9. Furthermore, they reported the range of values: the lowest reported BMR was 328.2 Calories, and the highest was 3969 Calories. Stated simply, those values are impossible in this population. Outside of research from this single group, the lowest adult BMRs I’ve encountered in the literature were from patients with severe anorexia; the very lowest BMRs in this population were just north of 2 kilojoules per minute (or about 700 Calories per day). The highest single BMR I’ve encountered was from an extremely muscular collegiate male athlete, with a BMR of around 3700-3800 Calories per day. There’s no other way to say this: the single research finding that led to the popular notion that women with PCOS have lower BMRs is based on obviously bad data.

To be clear, we’re not implying that anyone knowingly did anything unethical here. The data is bad, but we don’t think it’s bad as a result of ill intent. We suspect this research had the same lifecycle of most low-quality but highly cited research. People collected low-quality data. They found illusions that looked like clear patterns in the resulting noise (if you collect enough data, you should expect to find spurious “statistically significant” results purely by chance). Peer review is a poor system for catching these sorts of errors, thus allowing the results to get published. Then, due to a combination of excessive faith in results that have been peer reviewed, insufficient statistical training to spot these types of issues, and the constraints of writing under time pressure, journalists, bloggers, social media users, and even other scientists fail to recognize the problem and continue citing the research.

Stated another way, it would take less than a minute to skim the abstract of the study, see the reported finding that women with PCOS have BMRs that are dramatically lower than women without PCOS, and share an eye-popping finding that feels true (women with PCOS do have a range of metabolic hurdles to overcome, and do often have very understandable struggles with weight management as a result) and that has the imprimatur of scientific legitimacy. On the flip side, identifying the problems with the study required 1) a thorough knowledge of the rest of the research on this topic to first know that something might be up with it, 2) enough statistical knowledge and familiarity with other metabolism research for the large standard deviations to jump out, and 3) hours of legwork to learn more about the device used to assess BMR in the study, and to gather and fully analyze the rest of the research on the topic.

A small rant: Women with PCOS deserve better

Hi. This is Greg. Leigh did the vast majority of the work for this article. I helped out a bit with the statistical analysis. But, I did want to share a few personal thoughts after diving into this body of research to help out with this article.

I do a lot of deep dives into various bodies of research. I’m in the middle of one such deep dive for this BMR series. Most of the bodies of research I dig into relate to health and fitness in some way. Some are good (research on sex differences in muscle growth: surprisingly good!). Many are bad (a lot of the research on dietary supplements…boy howdy). Some are extremely bad. All of which is to say, this isn’t my first rodeo, and I have many points of reference to compare this body of research to.

Top to bottom, this body of research is particularly rough. This article has already said plenty about the Georgopoulos study that most conversation is centralized around, but that’s really just the tip of the iceberg.

The next most-discussed study online (which is the study used to generate the automatic snippet for Google, as of the time of writing) is a conference abstract, claiming that “Patients with PCOS have lower basal metabolic rate (BMR) even when controlled for BMI. … Therefore, PCOS patients may be at risk of lower metabolism that can lead to obesity related to PCOS.”

The most-discussed study that people will share as evidence that women with PCOS don’t have lower BMRs … is the exact same study. Between publishing the abstract and submitting the paper for peer review, I guess the researchers re-ran their analyses, and came away with the opposite result: “After adjusting for age and BMI, there was no significant difference in BMR between PCOS subjects and controls. BMR was also comparable in a secondary analysis comparing PCOS women with and without insulin resistance.”

The problem with both of these studies is that neither of them actually measured BMR. They estimated body composition using BIA (which isn’t even a great method of estimating body composition), and then estimated BMR from the body composition estimate. In other words, the study is actually saying, “After adjusting for age and BMI, women with and without PCOS had similar body composition. So, we’ll just assume they have similar BMRs as well.”

Three other studies claiming to assess differences in BMR between women with and without PCOS also didn’t actually assess differences in BMR. Two others used body composition as the same sort of extremely rough proxy for BMR (if you’re actually studying body composition, just say you’re studying body composition!). One used an armband that is known to do a poor job of estimating BMR – the armband overestimates energy expenditure when sedentary by a factor of two!

But, the hits keep coming. Three other studies came from the same lab that published the Georgopoulos study (one, two, three). They all have the same issues – they all measured BMR using the same device, and they all reported implausibly large standard deviations. And, they mostly have the same feel to them – find some factor to subdivide your study population (oftentimes based on genotype instead of insulin sensitivity), find an implausibly low BMR in a tiny sub-sample of subjects (that’s almost certainly just noise; I strongly suspect that a statistically significant result finally popped out after trying a dozen other ways to sub-divide the subjects that didn’t produce statistically significant results), publish it, collect more low quality data, rinse and repeat.

Oh, and there was a meta-analysis on BMR in women with and without PCOS that was only published as a conference poster, but it still shows up pretty high in the search results. It failed to find most of the research that had been published at the time (the authors should have turned up 10 studies, given the types of studies they were willing to include. They found 3). The three studies included were Georgopoulos (for which they dramatically miscalculated the effect size, probably because they couldn’t tell the difference between standard deviations and standard errors. The effect size should have been -0.46. Instead, it was -10.25), Koika (which doesn’t even contain a comparison between women with and without PCOS. This is a complete head scratcher), and Churchill (which is one of the studies that didn’t even measure BMR). Somehow, they found a way to exclude Churchill “after bias and weighting control.” This is very confusing, since the miscalculated Georgopoulos effect size is nearly 80x larger than the other two; I’m not aware of any statistical procedure that would justify keeping Georgopoulos and excluding Churchill. So, they wound up with a “meta-analysis” of two studies, which were probably both based on the same sample of subjects, and one of those studies didn’t even measure BMR in women without PCOS. It’s not an exaggeration to say that this is the most tragically flawed attempt at a meta-analysis I’ve ever seen (which is really saying something). I feel a little bad being this hard on a conference poster … but not that bad. It’s a real stinker.

To be clear, there is high-quality research on this topic. Segal, Rodrigues, Doh, Graff, Bruner, Robinson, Broskey, Moran, Larsson, and Cosar all deserve their flowers. But, approximately half of the research in this area is either hot garbage, or not even measuring what it claims to measure. That’s a really bad ratio.

And, that really bothers me, because women with PCOS do face a lot of unique challenges, and they need to sort through a lot of misinformation from health, fitness, and wellness influencers to find reliable information about their condition. Actual research on the topic should be a safe port in the storm, but it’s not. This body of research isn’t even “garden variety” bad – as a whole, it’s particularly awful. And that sucks. So, if I was a bit less tactful than normal when describing the shortcomings of this body of research, that’s why.

Last note: I re-ran the analyses of average height, weight, age, and BMR excluding all four studies from the lab that published the Georgopoulos study. The remaining subjects were a bit heavier, a bit older, and had slightly higher BMRs, but, the overall takeaway from that analysis didn’t change. None of the studies assessing fat-free mass came from that lab, and none of the other studies from the lab were included in the primary meta-analysis because they didn’t include comparisons to non-PCOS control groups.

Weighted average height, weight, age, and BMR of the remaining 249 women with PCOS

What do other studies say regarding insulin resistance and BMR?

To close out this article, we should circle back to discuss one aspect of the Georgopoulos study that we haven’t addressed yet: whether increased insulin resistance resulting from PCOS reduces BMR.

So, let’s briefly look at the research examining the impact of insulin resistance on basal metabolic rate in women. Do other studies suggest that low insulin resistance in women decreases BMR?  

For starters, a study by Drabsch et al found that in women, greater insulin resistance was associated with a slightly higher BMR. Furthermore, while not all type 2 diabetics have insulin resistance, the vast majority do, and there are studies that continue to show that BMR is not decreased (and is often slightly increased) in people with type 2 diabetes – here and here.

But, we don’t even need to look outside of the PCOS research. In all of the studies discussed above that measured at least one marker of insulin sensitivity, the women with PCOS had elevated markers of insulin resistance. Graff, Broskey, and Moran all reported elevated HOMA-IR values, Doh reported slower glucose disposal in a euglycemic-hyperinsulinemic clamp protocol, Robinson also reported slower glucose disposal after insulin infusions, and Cosar reported elevated fasting insulin levels. In the body of research discussed above, which found that women with PCOS have normal BMRs, most of the women with PCOS had poor insulin sensitivity. 

Is reduced insulin sensitivity a factor for PCOS in general?

A 2016 systematic review and meta-analysis by Cassar et al examined insulin resistance in women with polycystic ovary syndrome (PCOS) using euglycemic–hyperinsulinemic clamp studies (considered the gold standard for assessing insulin sensitivity). They looked at data from 28 studies including 741 women with PCOS and 1,224 controls. The study found that women with PCOS had 27% lower insulin sensitivity compared to controls, independent of BMI – though elevated BMI further reduced insulin sensitivity by 15% in PCOS women compared to controls. 

In the Doh et al study featured in this article’s meta-analysis, women with PCOS also had decreased insulin sensitivity. For clarity, insulin increases the body’s response to certain hormones, leading to higher levels of androgens, and this increased androgen activity is linked to insulin resistance. Women with PCOS show a stronger insulin response to glucose compared to healthy women, regardless of obesity status.

We also see in Graff et al and Larsson et al that caloric intake and glycemic load is higher. In general, it would appear that decreased insulin sensitivity, even regardless of obesity, is an issue for women with PCOS. However, in the end, that insulin resistance does not appear at this time to be a factor itself in decreased BMR. 

Of course there are other metabolic factors at play than insulin resistance for women with PCOS. PCOS is associated with an increased risk of metabolic syndrome, which encompasses much more than just insulin resistance. But even the research on metabolic syndrome suggests people with metabolic syndrome may have slightly higher – not lower – metabolic rates.

When we take a broader view of different issues, it becomes clear that focusing on BMR might not be particularly fruitful when trying to understand why many women with PCOS struggle with weight management. We simply aren’t seeing in other studies that insulin resistance or trending metabolic factors in women with PCOS create the dramatic difference in BMR that the 2009 study shows. 

That said, it does appear that insulin sensitivity (among other factors) is a valid problem with PCOS that is possibly present regardless of BMI, which brings me to my closing point.

What’s the goal of an article like this? How does it help?

Hi, this is Leigh. To close this article, I wanted to convey a clear sentiment. I always worry that articles like this might come across as belittling the concerns of those dealing with weight management challenges due to a disorder or syndrome. Not to come across as pulling a card, but I have PCOS — and boy, do I have it all. I put the ‘S’ in syndrome, fitting all three Rotterdam criteria (my ultrasound is a sea of bumpy follicles). My point is that this article aims to do the opposite of dismissing real issues. 

I’m a big believer in not suffering the consequences of misplaced priorities. I think there are a lot of minefields in diet management and PCOS, and I think time would be better spent focusing on those issues. For example, while reduced BMR does not seem to be a significant factor, decreased daily physical activity in those with PCOS has shown to be a trend here, here, and here. Women with PCOS also have a higher chance of being affected by mental health issues. And as discussed, reduced insulin sensitivity mixed with a propensity for high glycemic intake is also a repeating occurrence.

All these things could be addressed with better education on these topics versus the distraction of BMR hyperbole. We could discuss the roles that medication and lifestyle play in the treatment of PCOS. Wouldn’t it be great if more attention was paid to mental health, dietary practices, or the benefits of exercise?

Ultimately, the data in this article gives a different level of clarity and understanding of the topic, allowing us to say, “There’s a lot of interesting things going on here, but BMR probably isn’t one of them. What should we focus on next?” 

The post No, PCOS Doesn’t Lower BMR (Scientific Review) appeared first on MacroFactor.

Based on my time in the fitness industry talking with other professionals, working with clients, and generally monitoring the chatter of people online, I think most people tacitly think that human BMRs fall within a relatively narrow range. Very frequently, I’ll see the idea floated that the average female BMR is around 1400 Calories, the average male BMR is around 1800 Calories, and it’s very rare for BMRs to fall outside the range of 1,200-2,200 Calories per day.

There’s a bit of truth to that idea … but only a bit. It is true that most BMRs fall between 1200 and 2200 Calories per day, but it’s not particularly uncommon for BMRs to fall outside of that range. And, at the extremes, BMRs can be much lower than 1,200 Calories per day, and much higher than 2,200 Calories per day.

Starting with the extremes, you can find published instances of BMRs below 800 Calories per day (in the case of patients with anorexia), or exceeding 3700 Calories per day (in the case of extremely muscular athletes).

But, we don’t even need to look at the extremes to see BMRs that fall below 1,200 Calories per day, or that exceed 2,200 Calories per day. For this point, let’s turn our attention to two large, high-quality studies by Mifflin et al and Pavlidou et al. Both of these studies assessed BMR in large samples of people who were more-or-less representative of people in the general population. The subjects in the Mifflin study were a little lighter, on average, than people today (since the study was from 1990), and the subjects in the Pavlidou study were a little heavier, on average, than most folks, so blending these two studies should give us a pretty good overview of the “normal” range of BMR values.

In the Mifflin study, the lowest female BMR was 927 Calories, and the lowest male BMR was 1030 Calories. In the Pavlidou study, the lowest female BMR was 908 Calories, and the lowest male BMR was 1039 Calories.

Similarly, the highest female BMR in the Mifflin study was 2216 Calories, and the highest male BMR was 2849 Calories. In the Pavlidou study, the highest female BMR was 2492 Calories, and the highest male BMR was 2595 Calories.

So, even within the general population, it’s not unheard of for women to have BMRs below 1000 Calories per day, and for men to have BMRs below 1100 Calories per day, nor is it unheard of for women to have BMRs exceeding 2200 Calories per day, and for men to have BMRs exceeding 2500 Calories per day.

But, even then, you could say that we’re no longer dealing with the extremes of extreme populations, but we are still focusing on extremes (the highest and lowest values) within more “normal” populations. So, let’s go one step further.

We can estimate how common or how rare an observation should be if we know the statistical distribution describing those observations. In other words, for data that’s roughly normally distributed (which BMR data is), if we know the mean and standard deviation describing the data, we can estimate how common or uncommon an occurrence should be, using the normal probability density function.

In the Mifflin study, women had BMRs of 1348 ± 214 Calories (mean ± standard deviation), and men had BMRs of 1776 ± 297 Calories. In the Pavlidou study, women had BMRs of 1533 ± 308 Calories, and men had BMRs of 2006 ± 346 Calories. When we pool the results of these two studies, we get the following distributions: for women, 1440 ± 280 Calories; for men, 1890 ± 340 Calories; and for both sexes combined, 1665 ± 380 Calories.

With that information, we can estimate how frequently we should observe various high or low BMR values. Of note, we shouldn’t extrapolate too far since we don’t know the precise skewness and kurtosis values for these samples, but any values within 1.5-2 standard deviations from the mean should be relatively common. Since these are likely skew right distributions (there’s simply a lot more room for BMR values to be very high than very low, because there’s a much larger size difference between the largest people and average-sized people than between the very smallest people and average-sized people), I’ll roughly define the “normal” range as values between 1.5 standard deviations below the mean, and 2 standard deviations above the mean. In other words, if you were in a room with 100 people, you should expect to find a handful people with BMRs below the bottom end of these ranges, or above the top end of these ranges.

So, even when conservatively describing the range of normal BMR values, it becomes clear that BMRs below 1200 Calories or above 2200 Calories per day aren’t terribly uncommon. A BMR of 1200 Calories per day is a little less than 1 standard deviation below the mean for women, and a BMR above 2200 calories per day is a little less than 1 standard deviation above the mean for men. Stated another way, you should expect approximately 1 in 5 women to have a BMR below 1200 Calories per day, you should expect about 1 in 5 men to have a BMR above 2200 Calories per day, and you should expect about 1 in 5 people to have a BMR below 1200 Calories per day or above 2200 Calories per day.

Of course, this is all still one step removed from being useful and applicable for you. It might be nice to learn about the range of human BMRs generally, but you’re probably a bit more interested in the likelihood that you have a particularly high or low BMR. Clearly, BMRs below 1200 Calories per day are going to be more common for smaller people, and BMRs above 2200 Calories per day are going to be more common for larger people.

Thankfully, we can model that easily enough. For the charts below, I used the weight-based version of the Oxford/Henry BMR formula to model average BMR as a function of weight. From there, based on the data discussed in this article, I assumed that the distribution of differences between actual and predicted BMR values has a standard deviation of 200 Calories. Finally, all that was left to do was calculate the probability of having a BMR below 1200 Calories, above 2200 Calories, or between 1200 and 2200 Calories per day at each body weight using normal probability density functions.

I realize these graphs are a bit busy, but these are some key points and landmarks:

1. It’s very uncommon for women to have BMRs above 2200 Calories per day, but BMRs below 1200 should be relatively common at lower body weights.
2. Women who weigh 65kg have a ~1-in-5 chance of having a BMR below 1200, with the likelihood increasing at even lower body weights. Other landmarks: at 80kg, there’s only a ~1-in-20 chance, but at 50kg, it’s closer to a 50/50 proposition (i.e., 1200 Calories is an average BMR for 50kg women).
3. BMRs below 1200 Calories are considerably less common for men, but also aren’t terribly uncommon at body weights below 60kg.
4. BMRs above 2200 Calories are considerably more common for men. 100kg men have a ~1-in-5 chance of having a BMR above 2200 Calories per day, and above 115kg, most men should expect to have BMRs above 2200 Calories per day.
5. The body weights at which you’re the least likely to have a BMR below 1200 or above 2200 Calories per day: about 100kg for women and about 75kg for men.

To wrap this article up, I wanted to state my main reason for writing it: I think a lot of people wind up confused, frustrated, and disappointed because they underestimate the variability of human physiology. If you think that we’re all extremely similar, then it’s easy to spin your wheels trying to follow standard advice and heuristics that don’t apply quite as broadly as most people assume. And, if you fall outside the “normal” range, it’s easy to think that there’s something wrong with you, especially if you’ve been led to believe that the “normal” range describes >99% of individuals. But in this case, the “norms” don’t describe >99% of individuals – they apply to about 80%. It’s not at all uncommon to have a BMR below 1200 calories per day, or above 2200. About 1 in 5 people fall outside that range (especially women who are a bit smaller than average, or men who are a bit larger than average). So, standard weight loss or weight gain advice and heuristics tailored to the “average” person frequently don’t apply to a lot of people. Understand it, embrace it, and don’t be afraid of a bit of self-experimentation.

The post The (Surprisingly Wide) Range of BMRs in Adults appeared first on MacroFactor.

We have that confidence for a few reasons:

1. We’re starting from a good spot. Based on a thorough review of the research, we’re confident that the Oxford/Henry equations and the 1991 Cunningham equation are the best “off the shelf” BMR equations out there right now. They’re what we’re using to ground our new equations, which won’t deviate too far from that strong foundation.
2. Both of these equations were designed to be easy to use in an era when everyone didn’t have a computer in their pocket. As a result, they don’t make use of (relatively simple) mathematical functions that are known to help with more accurately estimating BMR – especially for relatively small or relatively large people.
3. There’s room to improve on these equations because they still have blind spots. The Oxford/Henry and Cunningham equations are great, but they’re not applicable to every population. For instance, they both reliably underestimate BMR in muscular athletic populations.
4. There’s low-hanging fruit to pick. As we’ve discussed in this series, metabolic adaptation decreases BMR below what would be expected during weight loss, but no popular BMR equations account for it.
5. Other equations that account for age do so linearly, despite the fact that BMR decreases more gradually through most of adulthood, and more aggressively past 60 years old.

So, let’s dive in.

Scaling metabolism

Way back in the first article of this series, I cited a paper by Wang and colleagues that modeled BMR as a function of fat-free mass based on relationships observed across species, and based on the scaling of organ mass with body size in humans. They found that these relationships could be approximated with linear equations that were very similar to the 1991 Cunningham equation:  BMR = 21.6 × Fat-Free Mass + 370. However, the actual modeled equations were non-linear – the linear equations just served as “close enough” approximations for most people.

In the second article of this series, I cited research by Müller and colleagues, illustrating how high-metabolic-rate tissue mass scales with body size. Again, their data revealed a nonlinear relationship.

In the third article in this series, I cited research by Pontzer and colleagues, modeling how BMR scales similarly with fat-free mass in both men and women. These cutting-edge metabolism researchers also chose to model the relationship with non-linear equations.

All three of these studies reveal a well-understood principle of metabolism: metabolic rates scale allometrically. 

Allometric scaling describes how various characteristics scale across organisms of different sizes, and why those scaling relationships exist. As it relates to metabolism, when organisms get larger, they tend to slow down … relatively speaking. An elephant obviously has a higher BMR than a shrew, but per unit of body mass, the elephant’s BMR is much, much, much lower. But, this decrease as organisms get larger is nonlinear, so allometric relationships are described by power law functions, rather than linear equations.

Obviously the size difference between the largest and smallest humans isn’t nearly as vast as the size difference between an elephant and a shrew, but humans do differ in size considerably. The very largest human adults are more than five times larger than the very smallest human adults. In other words, we span a large enough size range to warrant allometric scaling. Linear equations describe human metabolism well for most relatively normal-sized people, but they’ll tend to overestimate BMR for particularly small people, and particularly large people (and slightly underestimate BMR for normal-sized people).

This was perhaps best illustrated in a study by Bowes and colleagues. They analyzed the data in the Schofield/FAO database, and found that non-linear allometric equations described the relationship between body size and BMR far better than linear relationships.

So, when modifying the Oxford/Henry equations – which predict BMR based on height, weight, age, and sex – we need to examine the allometric relationship between BMR and weight, and the allometric relationship between BMR and height. And, for modifying the Cunningham equation – which predicts BMR based on fat-free mass – we need to examine the allometric relationship between BMR and fat-free mass, and potentially consider the role of fat mass as well.

Starting with fat-free mass, the traditional view that was originally advanced by Kleiber (the founder of this field of research) is that BMR scales with fat-free mass raised to the power of 0.75. Since then, there’s been a growing contingent of researchers advocating for the perspective that BMR in humans scales with fat-free mass raised to the power of 0.66. Although I genuinely find the back-and-forth fascinating, this isn’t the time to go too far into the weeds on the topic. So, for our purposes here, I think (hope) all parties can agree that scaling to the power of 0.7 neatly splits the difference, while offering a considerable improvement over linear scaling.

Moving onto fat mass, there’s a strong case to be made for incorporating fat mass into body composition-based equations. Research indicates that people with more fat mass may have slightly more high-metabolic-rate organ mass per unit of fat-free mass. So, while fat mass itself doesn’t contribute THAT much to BMR, it’s still indicative of a shifting relationship between fat-free mass and BMR. That should be fairly intuitive if you think about it for a moment. If two people have an identical amount of fat-free mass, but one of them weighs 80kg, and the other weighs 150kg, I think we’d all expect the person who weighs 150kg to have a higher BMR. Unfortunately, there’s not as much theory to draw upon for determining the correct scaling exponent to describe the relationship between fat mass and BMR (after accounting for variation in fat-free mass), beyond saying that the effect (and therefore the exponent) should be quite small. However, a recent large study found that a scaling exponent of 0.066 worked well, and that seems like a perfectly reasonable value.

For body weight, there’s more applicable research. Researchers rarely explicitly report allometric analyses of human metabolic rates, but you can easily estimate and model the allometric relationships implied by linear BMR equations (essentially reversing the process Wang and colleagues used in their study). You can sometimes find implied scaling exponents for men that are close to 0.7, and values for women that are around 0.4. But, values of around 0.6 for men and 0.5 for women are most common. I tested the effect of using different scaling values for men and women, but the impact of using 0.6 for men and 0.5 for women wasn’t meaningfully different than just using 0.55 for both sexes. So, in the interest of parsimony, we’re going with a universal scaling exponent of 0.55. Much like the debate regarding scaling exponents between 0.66 and 0.75 for fat-free mass, any value between 0.4 and 0.7 is a clear improvement over the scaling value of 1 implied by linear equations.

Finally, height: the allometric relationship between height and BMR hasn’t received much research attention. However, we know that both total fat-free mass and high-metabolic-rate organ mass scales strongly with height – both of which are strong predictors of BMR. Fat-free mass scales with height raised to the power of approximately 2. A case could be made for using slightly different exponents for different populations, but once again, a value of 2 is a clear improvement over the value of 1 implied by linear equations.

So, rather than using linear equations, we’ll improve upon the Oxford/Henry and Cunningham equations by scaling BMR to weight raised to the power of 0.55, height raised to the power of 2, and fat-free mass raised to the power of 0.7. We’ll also include fat mass in our replacement for the Cunningham equation, scaling fat mass to the power of 0.066.

Accounting for athletes

As discussed in a prior article in this series, athletes have higher BMRs per unit of fat-free mass than non-athletes. The difference grows as fat-free mass increases, primarily because athletes with large amounts of fat-free mass have larger high-metabolic rate organs than non-athletes with large amounts of fat-free mass.

Because of this, the relationship between fat-free mass and BMR follows an essentially linear relationship in athletes. When I tested an allometric relationship, the scaling exponent to describe the relationship between fat-free mass and BMR was 0.932, which is considerably higher than the values we observe in the general population.

In testing on the dataset, I also found that incorporating fat mass into the equation slightly improved model fit, but I excluded it as a term in the final equation. It seemed to only tangibly improve predictions by reducing BMR estimates for athletes who were likely underfueling to maintain particularly lean physiques; for example, the largest negative residuals in the studies I analyzed came from a study on high-level ballet dancers who were extremely lean.

Since there was only one study on older athletes in my dataset, and since the athletes in that study had BMRs that were comparable to younger athletes, I determined that there was insufficient reason and evidence to justify including an age term.

Metabolic adaptation

Our article on the impact of weight loss on BMR already covered the topic of metabolic adaptation extensively. Even though metabolic adaptation is well-understood and extensively researched, standard BMR prediction equations don’t include a term to account for it. So, factoring in the effect of metabolic adaptation is a bit of low-hanging fruit to improve the predictive accuracy of our equations.

With reasonable weight loss interventions, metabolic adaptation of about 5% is pretty typical. Furthermore, greater weight loss tends to increase metabolic adaptation, and persistent metabolic adaptation of about 3-5% seems to be fairly normal following extensive weight loss and subsequent weight maintenance.

So, if you’re in an energy deficit, your predicted BMR will be 5% lower using our equations. And, if your current body weight is more than 10% below your peak body weight, your predicted BMR will be an additional 3% lower. A case could be made for more aggressive values (especially if you’re maintaining a very aggressive deficit, or getting extremely lean), but I think it’s prudent to err on the side of caution. Even conservatively accounting for metabolic adaptation is a clear improvement over not accounting for it at all, and I think the risk of erring too low (and thus accidentally recommending an energy deficit that would be far too aggressive) exceeds the risk of erring a bit high.

The non-uniform impact of age

As we discussed in the fourth article in this series, BMR (adjusted for body size and body composition) decreases very gradually throughout most of adulthood. However, the rate of decline approximately doubles or triples beyond the age of 60. So, the “age” term in our formulas will reflect this fact.

Putting it all together

For modifying the Oxford/Henry and Cunningham equations, I started by calculating estimated BMRs for the participants in the NHANES body composition database. This was the largest representative dataset I could get my hands on that had body composition data for all subjects.

On average, the Oxford/Henry equations produced slightly higher BMR estimates than the Cunningham equation. Since both are high-quality equations that go about estimating BMR in slightly different ways, I averaged the estimates of the two equations for each subject (the product of two good estimates should be another good estimate), and used the resulting values as the dataset to develop new equations against. This ensures that, on average, our improved equation using height and weight, and our improved equation using fat mass and fat-free mass will produce similar BMR estimates for people with more-or-less “normal” body composition for their age, height, weight, and sex.

Our improved equation based on height, weight, age, and sex is as follows:

BMR = 129.6 × Weight^0.55 + 0.011 × Height² – [1.96;4.9] × Age – 213.8 × Sex

Weight is in kilograms, height is in centimeters, and for sex, male = 0 and female = 1. BMR is reduced by 1.96 Calories for each year up to 60 years old, and 4.9 Calories for each year past 60 years old

Our improved equation based on fat-free mass and fat mass is as follows:

BMR = 50.2 × FFM^0.7 + 40.5 x (FFM^0.7 × FM^0.066) – [1.1;2.75] × Age

FFM = Fat-Free Mass in kg, FM = Fat Mass in kg. BMR is reduced by 1.1 Calories for each year up to 60 years old, and 2.75 Calories for each year past 60 years old

For this equation, I tested fat-free mass in isolation, fat-free mass and fat mass as independent terms, and the interaction between fat-free mass and fat mass. The prior research on the topic found that the interaction of fat-free mass and fat mass was a strong predictor, which my testing confirmed. The inclusion of allometrically scaled fat mass as an independent term didn’t improve model fit, but the combination of fat-free mass and the interaction term performed better than either term in isolation.

Our improved equation for athletes is as follows:

BMR = 40.4 × FFM^0.932

FFM = Fat-Free Mass in kg

Functionally, “athlete” is defined here as anyone spending at least seven hours per week engaged in intense exercise.

Finally, adjustments for metabolic adaptation:

If you’re presently in an energy deficit, the BMR estimate is reduced by 5% to account for the impact of metabolic adaptation (i.e. the output of the formula is multiplied by 0.95). Furthermore, if your current body weight is more than 10% lower than your highest body weight, the BMR estimate is reduced by 3%. So, if you’re currently losing weight and your current weight is more than 10% lower than your highest body weight, multiply the output of the formula by 0.92. If you’re currently weight-stable or gaining weight, but your current body weight is more than 10% lower than your highest body weight, multiply the output of the formula by 0.97.

Naturally, we don’t expect you to do all of that math by hand, nor should you need to type the equations into your phone calculator. We’ve made a BMR calculator that will do all of these calculations for you, and compare the results to other popular BMR formulas. These are also the equations we’ll use to estimate your BMR (in order to initially estimate your total daily energy expenditure) in the MacroFactor app. 

Looking ahead

This concludes the core of this series, but there are still a couple things to look forward to.

First, we have a couple more articles planned to round out common questions people have about BMR: one will tackle the range of human BMRs, and one will discuss whether women with PCOS have lower BMRs.

Second, we believe that these are currently the best BMR equations out there, but there’s still room for improvement. Namely, it’s high time for someone to repeat the process Cunningham carried out in 1991, and Henry carried out in 2005: the world needs another fully comprehensive review and meta-analysis of the BMR literature. We at MacroFactor will be heading up and funding that endeavor in the coming year.

Stay tuned.

The post The Reasoning and Methodology Behind MacroFactor’s New BMR Equations appeared first on MacroFactor.

We’re approaching the end of our BMR series. We’ve discussed the (current) best formulas to estimate BMR, we’ve covered the determinants of BMR, and we’ve addressed how sex, age, and athletic status, and weight loss impact BMR. Now, we’ve reached the final factor that influences BMR: weight gain.

Type and amount of tissue gained

For starters, gaining body tissue will increase BMR, proportional to the metabolic rates of the tissues that are gained. In other words, muscle has a BMR of about 13 Calories per kilogram, adipose tissue has a BMR of about 4.5 Calories per kilogram, and the liver has a BMR of about 200 Calories per kilogram. So, if you gained 5kg, including 3kg of muscle, 1.9kg of fat, and 100g of liver tissue, you’d expect your BMR to increase by about 67-68 Calories per day due to the composition of the tissue you gained. Gaining the same 5kg, while only gaining 5kg of fat, would only be expected to increase BMR by 22-23 Calories.

So, the impact of weight gain on BMR will depend, in part, on the type and amount of tissue you gain. If you don’t gain much weight, you shouldn’t expect your BMR to change by very much, whereas greater weight gain generally brings larger increases in BMR. Furthermore, if you gain a considerable amount of weight, but you primarily gain fat tissue, you should expect to see a smaller increase in BMR as a result, since fat tissue has such a low tissue-specific BMR. However, if you experience a larger increase in fat-free mass (especially in the form of high-metabolic rate tissues), you should expect to see a considerably larger increase in BMR.

Thus, to maximize increases in BMR as you gain weight, you should aim to gain weight gradually, since faster rates of weight gain and larger energy surpluses lead to larger gains of fat mass (per unit of weight gain) than slower rates of weight gain and smaller energy surpluses. Furthermore, exercise – and resistance training in particular – will also help you gain more fat-free mass and less fat mass per unit of weight gain, further increasing your BMR. Finally, maintaining a relatively high protein intake will help you gain more fat-free mass as you gain weight, especially when paired with exercise and a relatively slow rate of weight gain. All three of these things will help your BMR increase to a larger extent as you gain weight.

Does weight gain also cause “metabolic adaptation”?

In our prior article about the impact of weight loss on BMR, we discussed “metabolic adaptation” extensively. As a brief refresher: in a weight loss context, metabolic adaptation refers to decreases in BMR in excess of what you’d expect, based on the amount and composition of tissue lost. In other words, if the mix of fat mass and fat-free mass you lost would be expected to decrease your BMR by 60 Calories, but your BMR actually decreases by 100 Calories, the 40 Calorie difference is attributable to metabolic adaptation.

So, does a similar thing happen when you gain weight?

Yes and no.

Plenty of studies find that BMR increases rapidly when people enter an energy surplus, in excess of what would be predicted by the amount of weight gained. This sounds a lot like metabolic adaptation during weight loss.

However, during weight loss, metabolic adaptations tend to plateau or increase over time. If your BMR is 5% lower than would be predicted after you lose 4% of your initial body weight, it will generally be at least 5% lower than would be predicted after you’ve lost 8% of your initial body weight.

With weight gain, on the other hand, these early “metabolic adaptations” decrease in magnitude over time. In other words, your BMR may jump up by 10% when your weight has only increased by 1%, so it would appear that you’ve experienced “metabolic adaptation” of about 9%. But, by the time your body weight has increased by 5%, your BMR is still only about 10% higher than it was before you started gaining weight, meaning “metabolic adaptation” has dropped to 5%. Once your body weight has increased by 10%, your BMR is still only about 10% higher than it was before you started gaining weight, meaning “metabolic adaptation” has dropped to zero. Past that point, your BMR tends to increase in proportion with the tissue you gain.

A 2012 study by Harris and colleagues illustrated this point well. It involved eight weeks of overfeeding, with the researchers assessing BMR on a weekly basis. Over the first two weeks, BMR increased by around 100 Calories per day, while weight only increased by about 2kg. Past that point, subjects kept gaining weight, but their BMRs essentially plateaued. From week 4 to week 8, the average BMR was between 1772 and 1801 Calories. In week 1, subjects burned about 30.9 Calories per kilogram of fat-free mass. In week 4, they were burning 31.5 Calories per kilogram of fat-free mass. By week 8, their relative BMR was basically identical to baseline: 31.0 Calories per kilogram of fat-free mass. Furthermore, their BMR per unit of weight was actually slightly lower at the end of the study (25.1 Calories per kilogram of body mass) than the start of the study (25.8 Calories per kilogram of body mass).

The researchers also reviewed and analyzed 21 other studies that had assessed changes in BMR following weight gain. Across the board, regardless of study duration, overfeeding appeared to result in an average increase in BMR of around 5-10%, on average. But, in shorter studies, this 10% increase was disproportionate to gains in weight and/or fat-free mass. In longer studies, it wasn’t.

So, you don’t really experience a “metabolic adaptation” to weight gain. Rather, it appears that shifting into an energy surplus gives you a short-term metabolic boost. After you’ve been in an energy surplus for a couple months, your BMR more-or-less returns to “normal” again. Per unit of fat-free mass, your BMR when gaining weight is about the same as when maintaining weight. Your total BMR will increase as you gain weight, but the increases will generally be proportional to the tissue you gain.

To be clear, if you shift directly from weight loss to weight gain, this effect can feel dramatic. If your BMR was 5-10% lower than would be expected as a result of (actual) metabolic adaptations to weight loss, shifting into an energy surplus can wipe out that 5-10% decrease, and may (temporarily) boost your BMR by an additional 5-10%. I suspect that’s one of the main reasons why there are so many positive anecdotes for “reverse dieting.” But, half of the effect simply comes from getting out of an energy deficit (i.e., it could be achieved by simply returning to weight maintenance), and the other half is a short-term adaptation that goes away as you continue gaining weight.

There is one more consideration that could impact the perceived relationship between weight gain and metabolic rate. As discussed in a previous article in this series, athletes have higher BMRs than non-athletes, even when controlling or accounting for differences in total fat-free mass. Furthermore, when people start exercising, we observe increases in BMR that exceed what we’d predict based on changes in body composition. So, if you’re a naturally skinny person who starts trying to gain weight at the same time that you begin exercising consistently for the first time, it’s very possible that you will experience a large increase in your BMR. However, the “excess” increase you experience is more attributable to the introduction of exercise than to the weight gain itself. 

Wrapping it up

This article is considerably shorter than the article on BMR adaptations to weight loss because there’s much less that needs to be said. Weight gain does increase your BMR, because it takes more energy to maintain a larger body. However, the increases in BMR are predictable and roughly proportional to the amount of weight you gain and the composition of the weight you gain. For unknown reasons, people do tend to experience a relatively short-term BMR “boost” when they start gaining weight, but this effect is short-lived and distinct from the metabolic adaptations that occur and persist as people lose weight.

And with that, we’re nearing the end of our BMR series. The final article in the main part of this series will tie together everything we’ve covered, and discuss how we can use all of this information to improve upon the (current) best BMR formulas that we covered all the way back in the first article, in order to help you estimate your BMR as accurately as possible. You can also try our BMR calculator, which incorporates all of the information covered in this series, in order to estimate your BMR as accurately as possible.

The post How Weight Gain Affects BMR appeared first on MacroFactor.

We’re approaching the end of our BMR series. We’ve discussed the (current) best formulas to estimate BMR, we’ve covered the determinants of BMR, and we’ve addressed how sex, age, and athletic status impact BMR. Now, we’ve reached our final pair of factors that influence BMR: weight gain and weight loss. This article will address weight loss, and the next will address weight gain.

Type and amount of tissue lost

For starters, losses of body tissue will decrease BMR, proportional to the metabolic rates of the tissues that are lost. In other words, muscle has a BMR of about 13 Calories per kilogram, adipose tissue has a BMR of about 4.5 Calories per kilogram, and the liver has a BMR of about 200 Calories per kilogram. So, if you lost 5kg, including 3kg of muscle, 1.9kg of fat, and 100g of liver tissue, you’d expect your BMR to decrease by about 67-68 Calories per day due to the composition of the tissue you lost. Losing the same 5kg, while only losing 5kg of fat, would only be expected to decrease BMR by 22-23 Calories.

So, the impact of weight loss on BMR will depend, in part, on the type and amount of tissue you lose. If you don’t lose much weight, you shouldn’t expect your BMR to change by very much, whereas greater weight loss generally brings larger decreases in BMR. Furthermore, if you lose a considerable amount of weight, but you primarily lose fat tissue, you should expect to see a smaller decrease in BMR as a result, since fat tissue has such a low tissue-specific BMR. However, if you experience a larger decrease in fat-free mass (especially in the form of high-metabolic rate tissues), you should expect to see a considerably larger decrease in BMR.

Thus, to mitigate reductions in BMR as you lose weight, you should aim to lose weight gradually, since faster rates of loss and larger energy deficits lead to larger losses of fat-free mass (per unit of weight loss) than slower rates of weight loss and smaller energy deficits. Furthermore, exercise – and resistance training in particular – will also help you lose more fat and less fat-free mass per unit of weight loss, further mitigating reductions in BMR. Finally, maintaining a relatively high protein intake will help you preserve more fat-free mass as you lose weight, especially when paired with exercise and a relatively slow rate of weight loss. If you do all three, you might even be able to gain fat-free mass as you lose weight. All three of these things will help cushion the reductions in BMR that accompany weight loss.

Metabolic Adaptations to Weight Loss

Unfortunately, the amount and composition of the tissues you gain or lose only tell you half of the story. As you lose weight, your BMR tends to decrease more than would be predicted due to tissue losses. This phenomenon is referred to as “metabolic adaptation.” 

The research on metabolic adaptation can be a bit muddled at first glance, because the term is sometimes used to refer to a few different concepts that are extremely similar, but meaningfully distinct.

1. Sometimes “metabolic adaptation” refers to decreases in BMR that are larger than would be predicted based on changes in total fat-free mass (or changes in total fat mass and total fat-free mass).
2. Sometimes “metabolic adaptation” refers to decreases in BMR that are larger than would be predicted based on granular body composition changes, including gains or losses in specific organ masses.
3. Sometimes “metabolic adaptation,” or the more general term “adaptive thermogenesis,” is used to refer to all reductions in energy expenditure (including decreases in energy expenditure due to changes in activity patterns) that occur during weight loss. That’s not what we’ll be focusing on in this article.

This is an important distinction, because research using the first definition (reductions in BMR adjusted for changes in total fat-free mass) tends to report greater metabolic adaptation than research using the second definition (reductions in BMR adjusted for changes in granular body composition). Ultimately, the first definition is more relevant for most people in most contexts, whereas the second definition is more useful for researchers who are interested in gaining a deeper understanding of the physiology underpinning the phenomenon (and the third definition is relevant for an entirely different conversation).

To illustrate, let’s assume that you lose 10kg, including 3kg of fat-free mass. Based on the 1991 Cunningham equation, you’d expect a 3kg decrease in fat-free mass to result in a BMR decrease of 21.6 × 3 = 64.8 Calories. But, let’s assume that your BMR instead decreases by 100 Calories.

Using the first definition, you’d say that your BMR decreased by about 65 Calories due to the loss in fat-free mass, and the additional 35 Calorie decrease is due to metabolic adaptation.

However, an MRI might reveal that your liver and kidneys decreased in size as you lost 10kg. This loss of high-metabolic-rate tissue would lead one to anticipate a larger decrease in BMR than the “raw” decrease in total fat-free mass would predict. So, based on the granular body composition changes that occurred with weight loss, you might expect your BMR to decrease by 85 Calories.

So, using the second definition, you’d say that your BMR decreased by 85 Calories due to the specific composition of the tissues you lost, and the additional 15 Calorie decrease is due to metabolic adaptation.

Ultimately, I don’t think either definition is inherently better or worse. It’s just useful to be aware of the distinction.

The second definition is extremely useful for researchers who are interested in understanding why BMR decreases with weight loss. With the first definition, you can’t disambiguate between decreases in BMR resulting from the loss of high-metabolic-rate tissues, and decreases in BMR resulting from a generalized metabolic slowing in your remaining tissues. But, if you fully itemize specific tissue losses, the excess decrease in BMR after accounting for losses in each specific tissue compartment can more confidently be attributed to generalized metabolic slowing (i.e. “true” metabolic adaptation).

However, as mentioned before, I tend to think the first definition is a more useful definition for most people, most of the time. You might be able to roughly estimate your body fat percentage (and, by extension, your total fat-free mass) as you lose weight, but it’s extremely unlikely that you’ll get full-body MRIs and estimate the mass of each of your organs before and after a weight loss attempt. If your BMR decreases by 200 Calories when you’d expect it to decrease by 100, you’ll probably have no way of knowing if you lost a bit more high-metabolic-rate tissue than expected, or if your remaining tissues experienced a decreased rate of energy expenditure. All you’ll know is that your BMR decreased by 100 Calories more than you thought it would.

A 2022 study by Martin and colleagues helps illustrate these distinctions. Over 12 months, 109 subjects lost an average of 8kg of total body mass, including 7.2kg of fat mass. During that time, BMR decreased by an average of about 100 Calories per day.

Based on the minimal decrease in lean mass observed (-0.8kg), you’d expect BMR to only decrease by about 17 Calories according to the Cunningham equation (21.6 Calories per kilogram of fat-free mass x 0.8 kg of fat-free mass = 17.28 Calories). So, you’d conclude that BMR decreased by about 17 Calories due to decreases in fat-free mass, and the additional 83 Calorie decrease was due to metabolic adaptation.

If you wanted to directly account for decreases in fat mass as well, you might predict a further decrease of 4.5 Calories per kilogram of fat lost (32.4 Calories, since fat mass decreased by 7.2 kilograms), for a total decrease of about 50 Calories. So, you’d conclude that BMR decreased by about 50 Calories due to losses in fat mass and fat-free mass, and the additional 50 Calorie decrease was due to metabolic adaptation.

Finally, you could do what the researchers in the study did: take full-body MRIs to estimate changes in specific organ masses. The subjects lost about 100g of high-metabolic-rate organ tissue, leading to a slightly larger predicted decrease in BMR: about 60 Calories per day. So, the researchers concluded that BMR decreased by about 60 Calories due to specific tissue losses, and the additional 40 Calorie decrease was due to metabolic adaptation.

So, how much metabolic adaptation actually occurred? Was it 83 Calories, 50 Calories, or 40 Calories?

For metabolism researchers, probably 40 Calories. For you, the reader … probably either 83 Calories or 50 Calories, but I also truly don’t think the answer to the question matters. Either way, your BMR decreased more than would be expected, given how much weight (and how little fat-free mass) you lost.

How much metabolic adaptation occurs?

Using the first definition of metabolic adaptation provided above, we tend to see metabolic adaptation on the scale of around 5-10%; in other words, during weight loss, BMR decreases to a level that’s 5-10% lower than would be predicted, based on changes in total fat mass and fat-free mass. So, if your BMR before weight loss was 2200 Calories per day, and you lose an amount of tissue that would be expected to decrease your BMR to 2000 Calories per day, your BMR will likely be about 5-10% lower than that: closer to 1800-1900 Calories per day. 

You can certainly find reports of significantly greater metabolic adaptation, especially when people are exposed to very large energy deficits, or when they achieve extreme levels of leanness. For instance, metabolic adaptation was perhaps most famously observed in the infamous Minnesota semi-starvation study, where men were fed approximately 1200 Calories per day for six months while doing hard labor. In that study, BMR decreased to a level that was more than 20% lower than would be expected, based on changes in total fat mass and fat-free mass. Similarly, a case study in a competitive bodybuilder found that his BMR decreased from approximately 2500 to 1400 Calories per day during the leadup to a bodybuilding contest, despite only losing about 3kg of fat-free mass. His BMR dropped from about 28.5 Calories per kg of fat-free mass to 16.5 Calories per kilogram of fat-free mass, which suggests that he experienced metabolic adaptation of approximately 40%. But, during this time, he got down to 4.5% body fat, and his resting heart rate dropped all the way to 27 beats per minute; he was clearly in a much more physiologically extreme state than most people would experience when dieting (and, for what it’s worth, most physique athletes don’t experience quite that much metabolic adaptation when dieting).

On the flip side, considerably less metabolic adaptation is frequently observed when dieting. In the Martin study cited above, a sizeable minority of the subjects actually experienced a (typically small) increase in BMR despite tissue losses. However, most of these subjects didn’t lose a ton of weight to begin with, and their rate of weight loss was very slow (less than 1kg per month).

But, on the whole, metabolic adaptation of about 5% is fairly typical when you’re not losing much weight (<10% of initial body weight), and when weight loss is fairly slow. Conversely, metabolic adaptation of about 10% is more typical with greater total weight loss and faster rates of weight loss (on top of the expected BMR decreases due to larger losses of lean mass).

As previously mentioned, there’s considerable inter-individual variability – some people will experience more metabolic adaptation, and some people experience less. Some people even experience (typically small) increases in BMR during (typically minor) weight loss. Furthermore, if you plan to go on a complete crash diet, or if you plan to diet to extreme levels of leanness, you might experience metabolic adaptation exceeding 15% or even 20%. But, for most people, most of the time, 5% is fairly typical when losing weight gradually, and when losing less than 10% of your initial body weight, and 10% is fairly typical for more aggressive diets (in terms of either rate of weight loss, or total weight loss achieved).

Why does metabolic adaptation occur?

There are two different ways to answer the question posed by this section header: a teleological answer, and a physiological answer.

The teleological answer¹ relates to survival of the species. During periods of famine, the human body evolved to conserve energy, in order to increase the probability that an organism will survive and reproduce. Your body doesn’t “know” that you want to fit into a smaller pants size or achieve a shredded six pack. It just knows that you’re consistently consuming less energy than you’re burning, and if that trend continues, you won’t be able to pass on your genes. So, it conserves energy as much as it can in an attempt to keep you alive.

The physiological answer is still being investigated. I don’t think we have a full accounting of precisely why metabolic adaptation occurs, but we do at least have a partial answer.

The “classic” explanation is that fat loss decreases levels of a hormone called leptin. Leptin is sensitive to both short-term energy status (are you in an energy deficit or surplus?) and long-term energy status (are your total fat stores higher or lower than they used to be?). Decreased leptin concentrations have a wide range of effects that make weight loss more difficult, including a down-regulation of energy expenditure.

Other research has found that metabolic adaptation is related to changes in thyroid hormone levels. Thyroid hormones – specifically T3 – regulate energy expenditure at the tissue level.

Furthermore, mitochondrial adaptations may influence metabolic adaptation. On average, human mitochondria are about 40% efficient. In other words, they convert about 40% of the chemical energy in food into ATP that can be used to power various cellular processes – the rest of the energy is lost as heat. But, the “40% efficiency” heuristic isn’t an immutable rule – mitochondria can adapt to be more efficient or less efficient in response to a variety of stimuli. For our purposes here, there’s some research (primarily in rodents, though there is a bit of human evidence) indicating that weight loss increases mitochondrial efficiency. That’s “good” in a general sense – all else being equal, greater mitochondrial efficiency enhances physical performance – but it also directly decreases the amount of energy you expend per unit of fat or carbohydrate your body metabolizes.

Finally, if we include losses in organ mass as a component of metabolic adaptation (using the first definition above) … losses in organ mass contribute to disproportionate decreases in BMR per unit of fat-free mass. Losses in organ mass may be driven by some of the hormonal changes mentioned above, or they may simply be the result of eating (and typically burning) less. If you’re consuming and producing fewer biomolecules that your liver needs to metabolize, and you’re producing fewer waste products your kidneys need to filter, it shouldn’t be too surprising if they shrink a bit in response to a reduced workload.

Can we reduce metabolic adaptation?

The biggest thing you can do to reduce metabolic adaptation is to lose weight at a slower rate.

In a 2020 meta-analysis by Ashtary-Larky and colleagues, the researchers pooled the results of seven studies examining the effects of gradual weight loss (averaging about 0.5kg per week) and rapid weight loss (about 1.25kg per week) leading to similar amounts of total weight loss (about 7.5kg). On average, rapid weight loss led to a loss of 1.6kg of fat-free mass, and a reduction in BMR of 137 Calories per day – about 100 Calories more than you’d expect, given the typical loss of fat-free mass. On the other hand, gradual weight loss led to a loss of 0.6kg of fat-free mass, and a reduction in BMR of 87.5 Calories per day – about 75 Calories more than you’d expect, given the typical loss of fat-free mass.

So, a slower rate of weight loss meant that it took subjects about 2-3 times longer to achieve their weight loss target, but they lost a bit less fat-free mass in the process, and experienced about 25% less metabolic adaptation.

A 2024 meta-analysis by Poon and colleagues had similar results. This meta-analysis examined the effects of continuous versus intermittent dieting strategies. But, the net effect of most of the intermittent dieting strategies was simply to reduce the average rate of weight loss. For example, a continuous dieting strategy may involve 18 straight weeks of maintaining a daily deficit of 500 Calories, while an intermittent strategy may involve 36 weeks alternating between a 500 Calorie daily deficit for two weeks, followed by two weeks at maintenance (twice the duration, but the average daily deficit is just 250 Calories per day).

Total weight loss was similar with both continuous and intermittent strategies (about 5-5.5kg), and losses of fat-free mass were similar as well (0.99kg with intermittent, and 0.65kg with continuous). But, despite losing slightly more fat-free mass, subjects in the intermittent dieting groups experienced smaller reductions in BMR (about 39 Calories, versus about 92 Calories with continuous strategies).

And … that’s about it. The two other strategies we tend to look toward to get us out of most metabolic jams are exercise and increased protein intake. Unfortunately, they’re not of much help in this instance.

To be clear, higher protein intakes do help you maintain more fat-free mass when dieting. But, metabolic adaptation refers to decreases in BMR in excess of losses in fat-free mass. So, higher protein intakes will help you maintain a higher BMR because they’ll help you maintain more fat-free mass when dieting, but I’m not aware of any research showing that higher protein intakes reduce metabolic adaptation.

As for exercise, there’s an entire relevant body of literature that’s basically investigating the impact of energy deficits in athletes: RED-S research. RED-S stands for Relative Energy Deficiency in Sport. It’s not worth getting into the nuances of RED-S in this article, but a defining characteristic of RED-S is low energy availability. Energy availability in this context is calculated by subtracting exercise energy expenditure from total energy intake, and dividing the resulting value by fat-free mass. Values under 30 Calories per kilogram of fat-free mass are defined as “Low Energy Availability.” Since BMRs are typically around 30 Calories per kilogram of fat-free mass, being in a low energy availability state essentially means being in an energy deficit … unless you have a reduced BMR due to chronically low energy availability. 

RED-S and low energy availability in athletes are reliably associated with lower BMRs in both male and female athletes. In fact, having a BMR that’s at least 10% lower than would be expected (based on the athlete’s fat-free mass) is frequently used as a screening tool to assess an athlete’s risk of RED-S.

So, much like higher protein intakes, exercise will help you maintain more fat-free mass when dieting, and as a result, it will likely lead to smaller total BMR reductions while you’re losing weight. But, the RED-S research suggests that people who exercise a lot experience about the same magnitude of metabolic adaptation as everyone else.

Does metabolic adaptation last forever? Are you “damaging” or “crashing” your metabolism?

Thankfully, no. Most of the metabolic adaptation that occurs during weight loss is reversed once you stop losing weight and return to energetic maintenance.

All the way back in 1999, Astrup and colleagues meta-analyzed the studies comparing BMRs in formerly obese subjects (who’d previously lost a substantial amount of weight) to matched control subjects who’d never been obese. They found that BMRs were only about 3-5% lower in formerly obese subjects than subjects who’d never been obese.

In the same year, a study examined the BMRs of individuals in the National Weight Control Registry (NWCR). To join the NWCR, you need to lose at least 30 pounds, and maintain that weight loss for at least one year. When compared to weight-matched control, the subjects from the NWCR had virtually identical BMRs.

More recently, Nunes and colleagues performed a systematic review in 2021, investigating the impact of weight loss on metabolic adaptation. They found that most (though not all) studies report a significant reduction in metabolic adaptation – or even a complete abolishment of metabolic adaptation – after a period of neutral energy balance and weight stability.

So, although metabolic adaptation of about ~5-10% is typically observed during periods of significant weight loss, most of that effect goes away once you spend some time at energetic maintenance. Until an updated meta-analysis is conducted, I think the results of the Astrup meta-analysis give us a decent estimate of the typical metabolic adaptation that persists following significant weight loss.

To be clear, your total BMR will likely decrease quite a bit if you lose a lot of weight, but MOST of that reduction will simply be the result of now living in a smaller body. To illustrate, your BMR before significant weight loss may have been 2000 Calories per day. By the end of your diet, it decreased to 1500 Calories per day. After a period of weight maintenance and neutral energy balance, it stabilizes at 1600 Calories per day. So, in total, it decreased by 400 Calories. But, the average BMR for people with a similar body size and body composition (who’d never needed to lose a significant amount of weight) may by 1650 Calories per day. Thus, the total persistent reduction in BMR may be fairly large, especially if you lose quite a lot of weight, but the persistent metabolic adaptation (reductions in BMR below what would be expected) may be quite small, or even nonexistent. You’re not “crashing” or “damaging” your metabolism – metabolic adaptation is a perfectly normal part of weight loss, and it largely reverses once you stop losing weight. 

What about “The Biggest Loser” study?

Before wrapping this article up, I’d be remiss if I didn’t at least mention the infamous “Biggest Loser” study. Many people believe that BMRs are permanently reduced to a dramatic degree following weight loss, and this is the study most strongly influencing those beliefs.

Just for context, “The Biggest Loser” was an American TV show that turned fat shaming into a twisted form of entertainment and crash dieting into a competitive sport. It’s thankfully off the air now, but during its run, contestants would eat as little as possible and exercise as much as possible to lose up to 250 pounds over 30 weeks. 

In 2016, a study was published detailing metabolic and body composition changes during one season of the Biggest Loser competition, with a follow-up six years later.

On average, subjects weighed about 150kg before the competition, 90kg at the end of the competition, and 130kg six years after the competition. They started with 75.5kg of fat-free mass, ended the competition with 64.4kg of fat-free mass, and had 70.2kg of fat-free mass six years later. Finally, their average BMR was reported to be about 2600 Calories per day before the competition, 2000 Calories per day after the competition, and 1900 Calories per day six years later.

Based on a regression model developed from the subjects’ pre-study data, it was reported that subjects experienced 275 Calories of metabolic adaptation during the competition. Furthermore, instead of metabolic adaptation attenuating over time (as subjects re-gained weight), it was reported that metabolic adaptation actually increased to nearly 500 Calories over the next six years.

The only two things that truly need to be said about this study are:

1. It’s a pretty notable outlier in the scientific literature, both in terms of the total magnitude of metabolic adaptation that occurred, and since it reported that metabolic adaptation increased over time, instead of attenuating.
2. The way subjects lost weight was particularly extreme. So, if you don’t intend to crash diet, do intense exercise for over three hours per day, and lose an average of 2kg per week (or up to nearly 4kg per week for the contestant that lost the most weight), you should probably pay more attention to the larger body of research using much more reasonable approaches to weight loss.

But, there are two more things worth pointing out about this study.

First, the subjects had extremely high BMRs at baseline. To bring back the graph showing BMRs as a function of fat-free mass in athletes from a prior article in this series, you can see where the Biggest Loser contestants would fall at baseline, relative to the trendline.

As you can see, their baseline BMRs would be quite a bit above average for competitive athletes, and extremely high for people who had relatively low levels of activity before they went on the show. According to the 1991 Cunningham equation, their predicted BMRs at baseline would be almost exactly 2000 Calories per day (meaning their measured BMRs were about 600 Calories higher than would be predicted, based on their fat-free mass).

So, to the extent that metabolic adaptation occurred and persisted in the Biggest Loser participants, their BMRs were still somewhere between “very high” and “extremely average” relative to their fat-free mass at all time points. Thus, one potential explanation for the outlier findings in this study is just that the baseline BMR values were unrealistically high for some reason, and they settled back to more normal values in subsequent measurements. For instance, short-term overfeeding can elevate BMR by about 10% (as we’ll discuss in the next article), and I find it plausible that the contestants may have tried to “bulk up” a bit before going on the show to make it a bit easier to lose a lot of weight during the early weeks and avoid elimination. If something like that occurred (or if baseline BMR was unrepresentative for any other reason), that would mean subsequent estimations of “metabolic adaptation” would all be overstated.

Second, and more importantly, different metabolic carts were used to assess BMR throughout the study. The MAX-II metabolic cart was used for the baseline and post-competition measurements, and a ParvoMedics cart was used for the six-year follow-up. That may not mean much to most readers, but if you’ve spent much time reading metabolism research, you’re definitely familiar with ParvoMedics; they’re one of the top manufacturers of indirect calorimeters, and Parvo carts are trusted by metabolism researchers world-wide due to their excellent track record of accuracy and reliability. But, much like myself, I suspect you weren’t familiar with the MAX-II – this was the first study I’d seen using this particular metabolic cart. 

Two years after The Biggest Loser study was published, a validation study by Kaviani and colleagues showed why the MAX-II isn’t frequently used in research. It found that the MAX-II overestimates VO2 by about 7%, and VCO2 by about 4.5% (whereas both Parvo carts tested in the same study had errors of at most 1.2%). So, the MAX-II would be expected to overestimate BMR by around 6.5%.²

Given the bias of the MAX-II cart used to take the pre- and post-competition measurements, the magnitude of the persistent metabolic adaptation was likely quite a bit smaller than the study reported. Instead of a baseline BMR of about 2600 Calories per day, the actual average baseline BMR was likely closer to 2435 Calories per day. Instead of a post-competition BMR of about 2000 calories per day, the actual average post-competition BMR was likely closer to 1865 Calories per day. So, rather than decreasing by nearly 100 Calories per day while subjects re-gained weight over the next six years, that would mean their BMRs actually slightly increased from about 1865 to about 1900 Calories over the next six years. That still wouldn’t paint the rosiest picture in the world, but it would slash the persistent metabolic adaptation by about a third – from about 500 Calories to about 330 Calories.

Put it all together, and a slightly different picture emerges: even with the errors produced by the MAX-II cart, the contestants STILL had quite high BMRs at baseline. Following the competition, they experienced some metabolic adaptation. Over the next six years, their BMRs increased a bit as they regained weight, though perhaps still not quite as much as one would predict. However, both post-competition, and six years later, they were experiencing around 300 Calories of metabolic adaptation – a little less than 300 immediately post-competition, and a little more than 300 six years later, but the difference isn’t worth writing home about. Furthermore, their BMRs at both post-competition time points were perfectly normal compared to the general population, and maybe slightly on the high side, relative to their fat-free mass.

In short, this study doesn’t show that being on the Biggest Loser totally and permanently cratered the participants’ metabolisms. It does still suggest that a significant amount of metabolic adaptation occurred, and that the metabolic adaptations persisted – and perhaps even increased – for the next six years. But, the study likely overstated the magnitude of the persistent metabolic adaptation by nearly 50% (it was likely closer to 330 Calories than 500 Calories), due to issues with the metabolic cart used to take the baseline BMR measurements. Furthermore, at all time points, the contestants had normal-to-high BMRs relative to the general population, and relative to their fat-free mass. And, just to reiterate, the results of this study do still run counter to most of the research on the topic, and the approach to weight loss used on the Biggest Loser is still unrepresentative of how most people lose weight. So, it would be unwise to assume that the Biggest Loser study is representative of what happens following weight loss in most populations, most of the time.

Wrapping it up

This article was quite long, so I think we’re due for a brief recap:

1. Your BMR will likely decrease as you lose weight. However, most of that decrease is simply due to losses in body tissue. It takes less energy to power a smaller body.
2. Preserving fat-free mass by losing weight gradually, exercising (and resistance training in particular), and eating plenty of protein will help you lose more fat and less fat-free mass, which will mitigate reductions in BMR.
3. Metabolic adaptation – reductions in BMR in excess of what you’d predict based on the magnitude and composition of the tissue you lose – is a normal part of weight loss.
4. Metabolic adaptation of about 5-10% is pretty typical. More metabolic adaptation is generally seen with greater total weight loss, and faster rates of weight loss.
5. Reducing your rate of weight loss can help mitigate metabolic adaptation. Smaller consistent energy deficits and intermittent dieting strategies seem to be similarly effective at reducing metabolic adaptation.
6. Metabolic adaptation mostly goes away when you return to maintenance and your weight stabilizes. Though, slight metabolic adaptation may persist – around 3-5% seems typical (which usually amounts to less than 100 Calories per day), though many studies report that it goes away entirely.
7. Don’t let your perception of metabolic adaptation be dictated by the Biggest Loser study. It used an extreme intervention, it’s an outlier in the literature, and its findings were likely exaggerated due to an equipment issue.

The next article in this series will discuss how weight gain affects BMR. Then, we’ll wrap up the main part of this series by using everything we’ve learned to improve upon the (current) best BMR equations. You can also try our BMR calculator, which incorporates all of the information covered in this series, in order to estimate your BMR as accurately as possible.

1. Teleology is the branch of philosophy interested in the purpose a phenomenon serves, rather than the specific cause of the phenomenon ↩︎
2. To give the researchers some credit, they did do some extremely preliminary validation work to justify their use of the MAX-II cart, but not enough to give me much confidence. Of the eight measurements they took with the MAX-II, half of them produced errors of around 8-12%. See the supplemental materials in the study. ↩︎

The post How Weight Loss Affects BMR appeared first on MacroFactor.

Your basal metabolic rate (BMR) tells you how much energy your body burns to just “keep the lights on” – it’s the energy used to power the basic functions of your vital organs, to accomplish sufficient protein and cell turnover to keep your tissues functioning properly, and more. If you didn’t leave your bed all day and didn’t move a muscle, your basal metabolic rate is the amount of energy you’d still burn in a day.

There’s a general belief that athletes have higher BMRs than non-athletes because they have more muscle mass due to training. And, while it’s true that athletes do have higher BMRs, differences in muscle mass are far from the primary reason for the difference.

In a previous article in this series, we discussed the determinants of your BMR. Just to recap, your BMR is determined by the tissues composing your body, and the specific metabolic rates of those different tissues. When you split your BMR out on a tissue-by-tissue basis, it becomes clear that differences in muscle mass have a relatively small impact on your overall BMR. Muscle has a tissue-specific metabolic rate of about 13.5 Calories per kilogram. So, if you gained or lost a large amount of muscle mass – say, 5 kilograms or 11 pounds – that would only increase or decrease your BMR by about 67 Calories per day. That’s not nothing, but it’s a fairly small difference in the grand scheme of things.

Most of your BMR is determined by the mass of your high-metabolic-rate organs: your brain, heart, kidneys, and liver. These tissues all have BMRs that are about 15-33 times higher than the BMR of skeletal muscles – about 200-440 Calories per kilogram, versus 13.5 for muscle. And, as we covered in a previous article, high-metabolic-rate tissue mass doesn’t scale linearly with total lean mass. In other words, larger people generally have larger hearts, livers, kidneys, and brains, but the difference is considerably smaller than the difference in total fat-free mass. If person A has twice as much fat-free mass as person B, their high-metabolic-rate organs might only be 50% larger.

Because of this, BMR per unit of fat-free mass tends to decrease as fat-free mass increases. People with around 40kg of fat-free mass typically have BMRs of about 31 Calories per kilogram of fat-free mass, whereas people with 80kg of fat-free mass typically have BMRs of about 26 Calories per kilogram of fat-free mass. As total fat-free mass increases, the ratio of low-metabolic-rate tissues (like muscle, bone, and the lean component of adipose tissue) to high-metabolic-rate tissues (like brain, heart, liver, and kidneys) tends to increase.

If you’ve been following along with this series, I’m sure you’re already familiar with all of the information up to this point in the article. But, this recap is important, because it sets the stage for discussing how athletes differ from non-athletes.

The most important factor contributing to higher BMRs in athletes

The main reason athletes have higher BMRs than non-athletes is that everything I’ve covered in this article – and most of the articles in this series up to this point – doesn’t really apply to athletes. Larger and smaller athletes burn about the same amount of energy per unit of fat-free mass.

This was most clearly demonstrated in a pair of studies from Japan. The researchers analyzed body composition and BMR in 57 male athletes in one study, and 93 female athletes in the other. In both studies, they split athletes into three groups (small, medium, and large) based on their fat-free mass. They found that BMR per unit of fat-free mass was basically the same in all three groups in both studies. Furthermore, comparing between studies, BMR per unit of fat-free mass was basically the same in the male and female athletes (which runs counter to what we observe in the general population – women tend to have higher BMRs per unit of fat-free mass in non-athletes).

A follow-up study from the same group of researchers tells us why larger athletes have the same BMR per unit of fat-free mass as smaller athletes: in athletes, most high-metabolic rate organs do scale linearly with body size. In athletes with fat-free masses ranging from about 57kg to 85kg, muscle, liver, kidney, and heart mass all scaled linearly with total fat-free mass. So, at all body sizes, each of these tissues had a consistent relative contribution to total BMR. The one exception was the brain, which didn’t scale as strongly with total fat-free mass.

In other words, large athletes with 50% more total fat-free mass than small athletes also had hearts, livers, and kidneys that were about 50% larger, which runs counter to what we observe in non-athletes. However, athletes with more fat-free mass only had slightly more brain mass than non-athletes. As a result, you’d still expect smaller athletes to have slightly higher BMRs per unit of fat-free mass than larger athletes (which is what these studies observed), but the difference is much smaller than the one that’s observed in the general population.

These results are bolstered by an earlier study by Midorikawa and colleagues. In this study, sumo wrestlers were compared to untrained controls. The sumo wrestlers had much higher BMRs, but both groups had similar BMRs per unit of fat-free mass. Again, the researchers found that the mass of most high-metabolic rate organs (the heart, liver, and kidneys) accounted for similar proportions of total fat-free mass in both groups, explaining the similarities in BMRs per unit of fat-free mass. Much like the previous study, the brain was the one exception – brain mass was pretty similar in both groups (meaning brain mass per unit of total FFM was a bit lower in the sumo wrestlers).

So, the main reason athletes buck the trend discussed in previous articles is that athletes with large amounts of fat-free mass have granular body compositions that are different from non-athletes with large amounts of fat-free mass. Non-athletes with large amounts of fat-free mass have disproportionately more low-metabolic rate fat-free tissue than people with less fat-free mass, whereas the ratio of high- to low-metabolic rate fat-free tissue remains remarkably consistent in athletes with differing amounts of total fat-free mass.

The effects of recent training

When you have your BMR measured, you’re asked to follow quite a few pre-assessment guidelines (you should be, at least). You should be in a fasted state, have no stimulants in your system, use the bathroom before the test, and avoid strenuous exercise for at least 48 hours before the test.

All of these factors are important, because they can all skew the results of your BMR test. If you’ve eaten recently, the thermic effect of feeding (the energy you burn to digest food) will artificially elevate your resting energy expenditure. Stimulants boost your resting energy expenditure slightly. Anxiety from feeling the urge to urinate can elevate your energy expenditure a bit. And … strenuous exercise can elevate your resting energy expenditure for a day or two.

The degree to which exercise elevates your BMR will depend on how long and how strenuous your workout was. You may experience no increase at all following a relatively easy, relatively low-volume workout, you may experience an increase of 100 Calories for the next day or two following a harder workout. This increase may be due to the increased energy cost of repairing muscle damage, increases in sympathetic nervous system activity, and the general biochemical cost of returning to homeostasis (metabolizing waste products, resolving inflammatory responses, converting lactate back to glucose, etc.). You can find some outlier studies suggesting that this increase can be 400+ calories, but most studies find elevations in the range of 50-150 calories.

Most of the research on this phenomenon has been conducted in untrained subjects, but there’s evidence for it in elite athletes as well. For example, cyclists competing in the Ardennes classics (~250km/150mile one-day bike races through mountainous terrain) had their BMRs measured before a race, and the morning after a race. Their average BMR before the race was about 1936 calories (already pretty high, since they weighed just 67kg, on average). The morning after the race, their BMRs were elevated to 2055 Calories.

Most of the research assessing BMR in elite athletes will note that the athletes were required to refrain from strenuous exercise for either 24 or 48 hours prior to BMR measurements. I have some level of skepticism about how many athletes actually follow through with that requirement. People lie to researchers sometimes, and athletes who are accustomed to training every day may not want to take two days off of training to participate in a study, especially if they don’t fully understand the reason they’d need to take time off in the first place. I also have some level of skepticism about whether BMR fully returns to baseline after 48 hours in athletes who train hard day-in and day-out for months or years; we observe the BMR returns to baseline within 48 hours after a single challenging workout, but I find it plausible that the elevation may last for longer if someone’s trained hard on four of the past five days. Some older research in athletes has suggested that it may take up to five days.

However, I ultimately think that those considerations are purely academic. If athletes’ “true” BMRs (i.e. their BMRs if they were fully recovered from exercise) are slightly lower than the BMRs reported in the research on the topic, I’m not sure it matters too much, because athletes spend most of their time training consistently. If you spend the vast majority of your time <48 hours removed from your last workout, you could easily make the case that the BMR elevations associated with recovery from training are just a part of your “normal” BMR. Or, on the flip side, if athletes’ BMRs have fully returned to baseline before being measured, that would mean that the research may slightly underestimate athletes’ day-to-day BMRs (which would be elevated following workouts on most days). But, since post-training elevations in BMR tend to be around 100 Calories per day, this would ultimately be a relatively small difference.

Yes, muscle too

I’d be remiss if I didn’t call this out: exercise generally increases muscle mass, and athletes in most sports have more muscle than untrained individuals. I realize that I downplayed the importance of muscle mass earlier in the article, because it’s falsely assumed to be the only factor (or at least the primary factor) explaining why athletes generally have higher BMRs, and I wanted to push back against that erroneous belief. But, it certainly is a factor. If two people are the same height and weight, and one has 5kg more muscle and 5kg less fat (on par with the body composition differences we tend to observe when comparing athletes and non-athletes), you’d expect the individual with more muscle mass and less fat mass to have a BMR that’s 40-50 Calories higher.

To be clear, that’s a very real effect. But, it pales in comparison to the other two factors discussed above when comparing two individuals who are roughly the same height and weight. 

Now, if you compared a very large, very muscular 100kg athlete to a much smaller, much less muscular 60kg individual, of course the muscular athlete will have a much higher BMR. But, that’s an apples to oranges comparison, and most of that difference will be accounted for by differences in high-metabolic rate organ masses, with differences in muscle mass playing a smaller role.

I’m getting slightly ahead of myself, but let’s assume we were comparing an athlete to a non-athlete, and both weigh 75kg. The non-athlete has 20% body fat and 60kg of fat-free mass, while the athlete has 13% body fat, 5 additional kilograms of muscle mass, and 65kg of fat-free mass in total. Based on the 1991 version of the Cunningham equation (one of the best BMR equations for non-athletes), you’d predict the non-athlete to have a BMR of 1666 Calories. Based on the equation that will be developed and discussed below, you’d predict the athlete to have a BMR of 1986.5 Calories. That’s a difference of about 320 Calories. The 5kg difference in muscle mass only explains about 20% (65 Calories) of that difference.

So, how much higher are BMRs in athletes?

To answer the question posed in the section header, I performed a meta-regression. In the interest of full transparency, I didn’t do a full systematic literature search to identify every study that could have been included. But, I was able to lean on two recent studies that did do systematic literature searches: a 2023 systematic review by Martinho and colleagues, and a 2023 meta-analysis by O’Neill and colleagues. Both of these papers found all of the studies that assessed BMR in athletes and compared those measurements to pre-existing BMR prediction equations. Plenty of studies measure BMR but don’t compare those measurements to a prediction equation, so I supplemented the studies from those review papers with some Pubmed searching of my own. As mentioned, this was not a fully systematic search. I’m sure I missed several studies. But, I was targeting a total count of around 1500-2000 total athletes. Increases in statistical precision scale nonlinearly with sample sizes. Margins of error with polling data illustrate this pretty well, as you can see below. More data does continue increasing precision, but you hit a point of diminishing returns.

So, I was content to sift through several hundred Pubmed records instead of several thousand. I’m quite confident that this analysis includes enough of the studies on the topic to fairly and accurately describe the research on BMR in athletes with sufficient precision.

For a study to be included, it needed to assess both BMR and body composition in healthy adult athletes without any known medical conditions. I included the body composition requirement because the athletes in these studies tended to have remarkably homogeneous body composition. Almost all of the men were between 10-20% body fat, and almost all of the women were between 17-27% body fat, so an equation using height, weight, and age (instead of fat-free mass) wouldn’t generalize outside of those bounds anyways.

I ultimately turned up 50 groups comprising 1950 total athletes (1146 men, and 804 women) across 29 studies. They ran the gamut from Olympians to recreational lifters, and from professional cyclists to bodybuilders to sumo wrestlers. You can see the sample size, and average age, height, weight, fat-free mass, body fat percentage, and BMR of all of the included studies in the tables below.

If you’d like to dig into any of the studies for yourself, the table below has clickable links for all of the included studies:

Individual subject data wasn’t available, so I performed a meta-regression on the group mean fat-free mass and BMR values, weighted by sample sizes.

The best-fit regression lines for only male and only female athletes didn’t meaningfully differ from the regression line for athletes of both sexes (which is what we’d expect, based on similar research in non-athletes). The linear equation predicting BMR in athletes was:

BMR = 28.9 × Fat-Free Mass (kg) + 108

Alternately, if you’re an athletic man between 10-20% body fat, or an athletic woman between 17-27% body fat, you could use this equation, which was calculated via multiple regression (again, this should not be expected to generalize very far outside of those body composition bounds).

BMR = 21.2 × Weight (kg) + 6.7 × Height (cm) – 2.9 × age – 95 × sex* – 764

*0 if male, 1 if female

Evaluating the results

When comparing this equation to the 1991 Cunningham equation (the best equation for non-athletes that predicts BMR from fat-free mass), you can see that the two equations produce pretty similar BMR predictions at relatively low levels of fat-free mass, but they diverge as fat-free mass increases. At 40kg of fat-free mass, the predictions only differ by 2.4%. At 85kg of fat-free mass, the MacroFactor equation generates a prediction that’s over 16% higher.

This is exactly what you should expect, given the information covered thus far in this series. In non-athletes, high-metabolic rate tissue comprises a smaller and smaller percentage of total fat-free mass as total fat-free mass increases. In athletes, on the other hand, heart, kidney, and liver mass scale proportionally with total fat-free mass, and only relative brain mass (as a percentage of total fat-free mass) decreases as total fat-free mass increases. So, you should anticipate that BMR per unit of fat-free mass should decrease much less in athletes as total fat-free mass increases.

Digging a layer deeper, I roughly categorized the athletes in these studies as “elite” or “non-elite.” I’ll readily admit that this is a somewhat subjective characterization. For example, are Division III collegiate athletes “elite” athletes? Compared to pros, absolutely not. Compared to the vast majority of humans, absolutely. When in doubt, I typically gave them the nod if they competed at a level of sport that most people would be unable to reach (even if that was a sub-professional level), or if the study specifically noted that they trained for more than an average of 2 hours per day (14+ hours per week).

Ultimately, it appears that athletes have higher BMRs than non-athletes regardless of competitive achievement. The BMRs of the elite and non-elite athletes in these studies weren’t discernibly different.

In fact, this isn’t a particularly surprising finding. A BMR equation that tends to perform pretty well in athletes is the 1980 version of the Cunningham equation (BMR = 22 × FFM + 500). It produces comprehensively higher BMR estimates than the 1991 Cunningham equations (BMR = 21.6 × FFM + 370), and tends to overestimate BMR in the general population. However, it was developed from data collected from a general population sample back in 1918, when typical levels of physical activity were considerably higher, but rates of structured sport participation were considerably lower. Another equation that performs well for high-level competitive athletes is the ten Haaf equation (BMR = 22.8 × FFM + 484), despite the fact that it was developed from a sample of recreational athletes. So, it appears that people who are generally active and exercise regularly tend to have higher BMRs than people who have a more sedentary lifestyle, but you don’t necessarily need to train like a professional athlete to reap the benefits.

Next, let’s turn our attention to the types of sports these athletes participated in. I categorized all of the studies as including strength/power athletes, endurance athletes, or “mixed” (for the most part, these were studies that included athletes from a variety of different sports).

In general, athletes in both strength/power sports and endurance sports tended to have slightly higher BMRs than athletes in sports with mixed demands, or athletes in studies that included a variety of different sports. For what it’s worth, I personally wouldn’t read too far into that – most of the studies were categorized as “mixed,” and one study in strength/power athletes or endurance athletes that finds a particularly low average BMR could flip the trend. Furthermore, I should point out that the research on endurance athletes firmly counters the common (but completely spurious) claim that “cardio crashes your metabolism.” It quite clearly doesn’t.

Aging

In our previous article about the impact of age of BMR, I hinted that the next article in this series might have suggestions about how we can stymie age-related declines in BMR.

By now, it should be clear: exercise and staying active.

Unfortunately, only one study included in this analysis used a sample of subjects with an average age north of 40. However, that study by Frings-Meuthen and colleagues is perfectly at home in the rest of this body of research. These athletes were in their mid-50s, on average, and their BMRs were only slightly below the general trendline.

Furthermore, after adjusting for height, fat-free mass, fat mass, and sex, the researchers found that each year of age was associated with a decrease in BMR of just 0.6 Calories. The athletes in this study ranged from 35 to 84 years old. As we discussed in the last article, BMR corrected for similar factors tends to decrease at a rate of about 2 Calories per year up to 60 years old, and 4-5 Calories per year thereafter. So, not only did they have BMRs that were comparable to younger athletes – this study also suggests that age-related declines in BMR per unit of fat-free mass occur at about one-third to one-eighth the usual rate in Masters athletes. And of course, that’s before even mentioning how regular exercise can help you build or maintain muscle throughout your life, countering the typical age-related losses in strength and muscle mass.

I should note that the researchers suggested that the subjects in their study may have had artificially elevated BMRs, because BMR was assessed in a room that was 27.5℃ (which is warmer than you’d typically keep a room for metabolic testing). However, assuming the subjects were wearing light clothing, 27.5℃ is still within the thermoneutral zone (the range of temperatures where your body can easily regulate temperature without the need to expend additional energy). Elevations in resting energy expenditure only start to reliably occur at considerably higher ambient temperatures. Furthermore, other research in both men and women has also found that older athletes have BMRs (adjusted for body composition) that are more similar to younger athletes than to sedentary older adults.

Differences vs. Changes

Before wrapping up, I’d like to address the topic of changing your metabolic rate in response to exercise. It’s possible that everything in this article has been fool’s gold, after all. “Athletes have higher BMRs” doesn’t necessarily imply, “If you start exercising more, that will increase your BMR.” It may feel like an obvious leap to make, but to apply a bit of skepticism, I’m sure you’d feel differently about the statement, “Professional basketball players have an average height of 6’6”, so playing basketball will make you taller.”

It’s possible that people who are athletes were able to become athletes because they were people who naturally had larger hearts to pump more blood, larger livers to metabolize the metabolic byproducts of exercise, and larger kidneys to flush metabolic waste out of their systems.

So, does exercise actually increase your BMR?

Yes.

A 2020 meta-analysis by MacKenzie-Shalders and colleagues analyzed the research on changes in BMR after exercise interventions. Most of the included studies lasted for around 12 weeks, and subjects experienced an average increase in BMR of about 80 Calories. As discussed above, that’s a much larger increase than you could reasonably expect due to increases in muscle mass – for an 80 Calorie increase in BMR, you’d need to build about 6kg of muscle in 12 weeks (a 1.5kg increase in fat-free mass is more typical with resistance training, and less with aerobic training). Furthermore, research also suggests that athletes’ BMRs decrease by about 5-10% if they stop training, so we have evidence of adaptability going both directions.

Furthermore, it’s well-known that the heart increases in size (in a benign or beneficial manner) as an adaptation to exercise. I’m not aware of much direct evidence of (benign or beneficial) kidney and liver growth with long-term exercise training, but I find it plausible, and there is a bit of human evidence for the phenomenon. For instance, elevated biomarkers in liver function tests following an intense exercise bout may suggest that exercise stresses the liver (again, in a benign or beneficial way; exercise is good for the liver) in a way that would lead to adaptations over time.

Finally, as previously discussed, we don’t just observe higher BMRs in elite athletes (who may have physiological gifts that aren’t available to most of us). We also observe similarly elevated BMRs in recreational lifters, totally normal CrossFit participants, and recreational athletes.

So, I can’t confidently say that exercise will fully close the gap between your BMR and the BMR of a professional athlete. But, I can confidently say that exercise does at least narrow the gap, both because it helps you build more lean tissue, and because it likely increases your BMR per unit of fat-free mass.

Wrapping it up

At this point, our BMR series is winding down. We’ve discussed the best BMR prediction equations, covered determinants of BMR, addressed how age and sex impact BMR, and now we’ve gone through the research about BMR in athletes (and, by extension, how exercise impacts your BMR). The next articles will cover weight loss and weight gain. Then, we’ll wrap up the main part of this series by using everything we’ve learned to improve upon the (current) best BMR equations. You can also try our BMR calculator, which incorporates all of the information covered in this series, in order to estimate your BMR as accurately as possible.

The post How (and Why) Athletes’ BMRs Differ from Non-Athletes appeared first on MacroFactor.

There’s a general belief that “your metabolism slows down as you age.” In this article, we’ll dig into the research around that topic to determine how true it is. As you’ll see, your metabolism does slow down as you age, but probably not in quite the way (or at quite the rate) most people would suspect. Then, we’ll briefly discuss where the perception of cratering metabolisms with aging comes from.

The First Metabolic Slowdown

I’m sure you’ve heard people say, “I could eat everything in sight as a kid (or teenager) and not gain weight” or “my metabolism tanked when I entered my 20s.” And, as it turns out, research actually supports those observations … at least to some degree.

As covered in a previous article, smaller people tend to have a higher BMR per unit of fat-free mass, because more of their fat-free mass is composed of organs with extremely high metabolic rates. For instance, muscles have a BMR of about 13 Calories per kilogram, but the heart and the kidneys have a BMR of about 440 Calories per kilogram, the brain has a BMR of about 240 Calories per kilogram, and the liver has a BMR of about 200 Calories per kilogram. The size of these high-metabolic-rate organs doesn’t scale 1:1 with body size – someone with 50% more total fat-free mass may only have 30% more high-metabolic-rate tissue.

So, children and teenagers have considerably higher BMRs for their size than adults do, in part because their high-metabolic-rate organs are larger relative to their body size. This is pretty intuitive if you just look at the size of a baby’s head. A baby’s brain is around 25-30% as large as an adult’s brain, even if the baby itself is only about 5% as large as an adult. So, relative to its body size, its brain is 5-6 times larger than an adult’s brain. As you grow, the rest of your body grows a bit faster than your high-metabolic-rate organs do, but children and adolescents continue to have a relatively greater proportion of high-metabolic-rate tissue mass until adulthood. Nearly 20% of an infant’s total body mass is comprised of high-metabolic rate tissue – the corresponding figure is usually around 5-7% for adults.

Due to relative differences in organ sizes, you’d expect infants to burn about 85% more energy than adults per unit of fat-free mass, with the difference gradually shrinking throughout childhood and adolescence.

But, relative organ size is only half of the equation. Children and adolescents also have another major metabolic advantage: their bodies are growing. Physical growth is an energetically costly process. Adults’ BMRs reflect the energy cost required to maintain their body’s current tissues. For children and adolescents, BMR reflects the energy cost required to maintain their body’s current tissues, and the energy cost of synthesizing a lot of new tissue, quite quickly. 

As a result, children and adolescents have BMRs that are up to 50% higher than you’d expect based on the quantity and composition of their fat-free mass. Consequently, instead of burning about 85% more energy than adults per unit of fat-free mass, infants burn about 135% more energy per unit of fat-free mass than adults do! This difference gradually decreases throughout adolescence, but teenagers still burn about 25% more energy per unit of fat-free mass than adults.

So, your relative metabolic rate does slow down quite a bit once you stop growing. Your absolute metabolic rate may not change very much, but your adult BMR at 80kg or 180lb may be the same as your adolescent BMR at 70kg or 160lb, which can feel a bit jarring. Growing up, you needed to eat more and more and more as your body grew. When entering your 20s, you’re slightly larger than you were in your teens, but your BMR is about the same as it was 5-10 kilograms (or 10-20 pounds) ago. Even though your absolute BMR may not change very much, I can understand why it might feel like it was cratering.

The (Very) Gradual Slide

Another common refrain is that your metabolism slows down in middle age. There’s a shred of truth to that idea – your metabolism does slow a little bit throughout adulthood – but I suspect most people dramatically overestimate the impact.

For starters, in the absence of exercise (and strength training in particular), adults lose about 1% of their lean mass each year after the age of ~30, with losses accelerating past the age of ~40. However, most of that loss comes from low-metabolic-rate tissues (primarily muscle mass). So, this loss of lean mass does contribute to a gradual decline in BMR … but it’s a very gradual decline. The average young woman has around 21kg of muscle mass, and the average young man has about 33kg of muscle mass. Losing 200-300 grams of muscle tissue in a year may be an eventual harbinger for sarcopenia or osteoporosis, but it would only be expected to decrease your BMR by about 3-4 Calories per year, which would hardly be perceptible.

Furthermore, when accounting for the granular composition of people’s tissues, BMR experiences a further very gradual decline throughout adulthood. In other words, it appears that all of your organs “slow down” a little bit as you age. Research by Wong and colleagues suggests that most of your tissues burn about 1-2% less energy per unit of mass in middle age (31-50 years old) compared to young adulthood (21-30 years old).

Research by Geisler and colleagues paints a similar picture. It suggests that, after adjusting for both the amount and the granular composition of subjects’ tissues, BMR slightly decreased year over year throughout adulthood. Now, the decrease was tiny – about 2 Calories per year on average, but it was there. So, adults between 18-39 years old had BMRs that were (on average) about 25 calories higher than would be predicted based on their fat-free mass, while adults between 40-59 years old had BMRs that were about 10-15 calories lower than would be predicted based on FFM.

Pontzer and colleagues performed a similar analysis with an even larger sample size. Even after accounting for age-related changes in the composition of low- versus high-metabolic-rate tissues, they found that BMR per unit of fat-free mass slowly decreased throughout adulthood. Overall, it was about 8-9% lower at age 60 than at age 20.

So, zooming out, the idea that your metabolism slows down through middle age is technically true, both because you tend to lose some muscle mass, and because all of your organs gradually “slow down.” But, when people talk about their metabolism slowing down when they reach middle age, I think they expect there to be a more dramatic change. You’ll commonly hear statements suggesting that your metabolism falls off a cliff when you enter your 40s, but the data doesn’t support that notion.

If you compared your BMR at age 20 to your BMR at age 60, there might be a pretty notable difference. If your BMR decreases by 3-4 calories per year due to a loss of muscle mass after age 30, and your BMR adjusted for fat-free mass decreases by an additional ~2 Calories per year, your BMR at age 60 might be 150-200 Calories per day slower than your BMR at age 20.

But, it’s worth noting that this is a very gradual process. Year to year, its effects should be imperceptible – you’re not going to notice if your daily BMR is 5 Calories lower this year than last year. Furthermore, exercise (and, in particular, resistance training) should be able to stave off most of this decrease by mitigating (or reversing) age-related losses in muscle mass.

The Not-So-Groovy 60s (and Beyond)

Between the ages of 20 and 60, your BMR primarily decreases due to losses in muscle mass (particularly if you’re not exercising). Research suggests that the metabolic rates of each of your tissues also slows down very gradually, but that effect is miniscule. However, past age 60, BMRs begin slowing down more rapidly.

To be clear, there’s nothing “magic” about turning 60 years old. You can find studies identifying a metabolic inflection point closer to 55 years old, and others identifying an inflection point closer to 65 years old, but it’s always around 60 years old. I’m sure it varies person to person, due to the fact that biological aging and chronological aging aren’t identical processes. On a biological level, a 60-year-old might have signs of aging that are closer to what is typically observed in a 50-year-old or a 70-year-old. But, on average, we tend to see metabolic characteristics begin to shift as people enter their seventh decade of life.

This effect isn’t dramatic and immediate. Rather, BMR beyond 60 years old continues decreasing in much the same manner as it decreased between 20 and 60 years old, but the rate of decrease begins accelerating.

For starters, the loss of muscle mass continues at about the same absolute rate (or potentially at a slightly faster rate), but each yearly loss of 200-300g of muscle tissue has a larger relative effect. If you had 30kg of muscle tissue at 30 years old, losing 250g of muscle tissue would represent a loss of 0.83% of your total muscle mass. Once you’ve lost 5kg of muscle due to these gradual decreases, losing a further 250g of muscle tissue would represent a loss of 1% of your remaining muscle tissue. Once you’re down to 20kg of muscle mass, losing an additional 250g would represent a loss of 1.25% of your remaining muscle tissue.

But, much more importantly, the BMRs of your body’s tissues begin to slow down considerably faster after about 60 years old.

Going back to the study by Wong and colleagues, we saw that organ-specific BMRs decreased by about 1-2% between young adulthood and middle age. But, the same study also examined organ-specific BMRs in a 51-73-year-old cohort. It found that the older adults had organ-specific BMRs that were about 2-3% lower than the middle-aged cohort. This is approximately twice as large as the difference between young adults and middle-aged adults.

The study by Geisler and colleagues had similar findings. After adjusting for differences in fat-free mass, 40-59-year old adults had BMRs that were only 10-15 calories lower than would be predicted. In contrast, 60-69-year old adults had BMRs that were about 50 Calories lower than would be predicted, and above age 70, BMRs were about 120 Calories lower than would be predicted. So, from 20-60 years old, BMR per unit of fat-free mass decreased by about 2 Calories per year, but above 60 years old, the decrease is closer to 4-5 Calories per year.

Finally, the study by Pontzer found that, after adjusting for the composition of low- versus high-metabolic-rate tissues, BMR per unit of fat-free mass decreased by about 13% between 60 and 80 years old. So, the relative rate of decline was about 0.2% per year from 20-60 years old, and about 0.65% per year beyond age 60.

Add it all up, and your BMR decreases about 3 times faster above age 60 than it decreased throughout young adulthood and middle age. Again, this isn’t an immediate night-and-day difference. Your metabolism isn’t going to fall off a cliff between your 60th and 61st birthdays. But, it does mean that the metabolic slowdown you can expect between age 60 and 70 should be similar to the metabolic slowdown you experienced between age 30 and age 60.

What explains the perception that BMRs slow down dramatically faster, and dramatically sooner?

So, just to recap: your BMR does tend to decrease throughout adulthood, but the decrease is very gradual until approximately 60 years old. Your BMR doesn’t fall off a cliff when you reach your 20s, or your 30s, or your 40s, or even your 50s. Even past 60 years old, the year-over-year decrease isn’t particularly dramatic – you’d probably notice a 100-150 Calories decrease over the course of a decade, but you probably wouldn’t notice a 10-15 Calorie decrease over the course of a year. So, why do so many people believe that their metabolism dramatically slowed down when they entered their 20s or 30s or 40s?

This isn’t a particularly sexy answer, but it mostly comes down to lifestyle and physical activity levels.

As we get older, our activity levels tend to decrease. The change isn’t particularly large for light physical activity until your 50s or 60s, but levels of moderate-to-vigorous physical activity decrease dramatically from childhood to adolescence to young adulthood, and tend to further decline as we age. Furthermore, total sedentary time tends to increase as we age.

Thinking back on my own life, I found that I had a much harder time controlling my weight once I graduated college. I maintained a consistent exercise habit, but my lifestyle significantly shifted. In college, I’d spend at least an hour each day traversing a reasonably large campus on foot. After graduation, I moved to an apartment that didn’t have anything within walking distance, so I drove everywhere, and my daily step count probably decreased by at least 75%. So, my personal experience didn’t perfectly match the population-level trends – my light activity took a nosedive while I maintained my previous levels of moderate-to-vigorous physical activity – but my total activity levels certainly decreased. If I didn’t know any better, I might have thought my increased struggles with weight management reflected a precipitous drop in my BMR. But, realistically, I was just moving around less.

I suspect this dynamic explains most (if not all) of the perception that your metabolism dramatically slows down at a certain stage in your life. Even though shifts in lifestyle, activity levels, and sedentary time look like they occur gradually at the population level, those changes can be swift and dramatic for individuals. Here are some examples of how physical activity levels can rapidly change in adulthood:

1. You may experience a precipitous drop in light physical activity after graduating college, since you don’t need to walk between classes anymore.
2. Your moderate-to-vigorous physical activity may rapidly decrease because you aged out of competitive sports, or an injury sidelined you from participation in some intense physical activity.
3. Activity levels can dramatically change because you get a promotion from an entry-level job that keeps you on your feet all day, to a management position that puts you behind a desk.
4. Time constraints and exhaustion after having your first child might seriously curtail your ability or desire to work out as hard or for as long as you used to.
5. Your activity levels may have take a nosedive when you move from a walkable city to suburbia.

In short, most perceptions of rapid metabolic slowdowns are likely the result of increases in sedentary time, or decreases in activity levels. These shifts can certainly put a big dent in your total energy expenditure, even if they don’t actually affect your metabolism very much.

Wrapping Up

This article may have been a bit of a downer, but you should stick around for the next article in this series. It will discuss BMR in athletes, but, even if you wouldn’t consider yourself an athlete, it will also discuss how we might be able to mitigate a pretty hefty chunk of the age-related decrease in BMR.

After that, future articles will discuss how weight gain and weight loss affect your BMR, and how we can use all of the information covered in this series to improve on the (current) best BMR prediction equations.

The post Does Your Metabolism Slow Down With Age? appeared first on MacroFactor.

I don’t think it will come as a surprise to anyone that men tend to have higher BMRs than women. So, why is that?

If you’ve read the earlier articles in this series, you’ll know that BMR scales with fat-free mass: in general, people who have more fat-free mass tend to have higher BMRs. So, is that the only reason why men tend to have higher BMRs? Or does the relationship between fat-free mass and BMR differ between the sexes?

Ultimately, it appears that differences in fat-free mass can fully explain sex differences in basal metabolic rates.

This was first observed by Cunningham in 1980. He was re-analyzing the data of 223 subjects used to develop the Harris-Benedict BMR equations, and found that fat-free mass worked well as the sole predictor of BMR. In equations using factors like height and weight, he found that men have higher BMRs per unit of height or per unit of weight (in other words, if a man and a woman are both 170cm tall and weigh 70kg, you’d expect the man to have a higher BMR). But, male and female BMR could be predicted using the same equation when scaling to fat-free mass; it didn’t appear that the relationship between fat-free mass and BMR differed between the sexes (in other words, if a man and a woman both have 50kg of fat-free mass, you’d expect them to have the same BMR).

Subsequent research has supported this initial finding. For instance, a 1990 study by Mifflin and colleagues analyzed data collected from almost 500 subjects (247 women and 251 men). They found that the equations describing the relationship between fat-free mass and BMR were very similar in both sexes, so the “final” version of the FFM-based Mifflin-St Jeor equation didn’t differentiate between men and women.

A 2002 study by Heymsfield and colleagues, analyzing the data of 131 men and 158 women, had similar results:

Finally, for an even larger and even more recent example, a 2021 study by Pontzer and colleagues included an analysis of nearly 1,700 subjects in the doubly labeled water database, and it also found that the relationship between fat-free mass and BMR was the same in men and women.

I could bore you with dozens of additional examples, but I think you get the point: research consistently finds that fat-free mass is the best single predictor of BMR, and it finds that the relationship between fat-free mass and BMR is the same in both sexes. This has been a stable and reliable finding in large-sample research studies spanning the last four decades. That’s why our preferred equation for predicting BMR from fat-free mass (the 1991 version of the Cunningham equation) applies equally to both sexes.

Interestingly, that also means that women generally have a slightly higher energy expenditure per unit of fat-free mass than men do.

As we covered in a previous article, energy expenditure is associated with fat-free mass, but it’s directly determined by the specific tissues composing the body, and the individual metabolic rates of those tissues. Some lean tissues – like the heart, brain, kidneys, and liver – burn a ton of energy at rest. Others – like muscles, bones, and the lean component of adipose tissue – are still lean tissues, but they burn relatively few calories at rest (per unit of mass, at least).

As total lean mass increases, the composition of that lean mass changes. People with less total lean mass tend to have a higher percentage of high-metabolic-rate tissues than people with more total lean mass. That applies within each sex (i.e. smaller men tend to have a higher BMR per unit of fat-free mass than larger men, and smaller women tend to have a higher BMR per unit of fat-free mass than larger women), and it also applies between sexes.

To illustrate, men tend to have about 30-35% more total fat-free mass than women (the average man has around 60kg of fat-free mass, and the average woman has around 45kg of fat-free mass). But, men tend to have brains that are only about 10-15% larger, kidneys that are only 5-10% larger, and hearts that are only about 20% larger. So, since women have slightly more high-metabolic-rate tissue per unit of fat-free mass, they also tend to have a slightly higher BMR per unit of fat-free mass. On average, women burn around 30 Calories per kilogram of fat-free mass, while men burn around 27.5-28 Calories per kilogram of fat-free mass.

Of course, you may not know your body-fat percentage or how much fat-free mass you have. So, how does sex affect the relationship between total body weight and BMR?

Looking back at the study by Mifflin and colleagues cited above, the researchers found that men burned an average of 166 more Calories than women per unit of body weight. This is reflected by the equations they developed for predicting BMR from height, weight, age, and sex:

Mifflin-St Jeor equation for men:

BMR = 10 × weight (in kg) + 6.25 × height (in cm) – 5 × age + 5

Mifflin-St Jeor equation for women:

BMR = 10 × weight (in kg) + 6.25 × height (in cm) – 5 × age – 161

The equation is the same until the final term – after multiplying weight by 10, height by 6.25, and age by 5, you add 5 Calories for men, and subtract 161 Calories for women, for a difference of 166 Calories.

Other research has taken a similar approach to predicting BMR from height, weight, age, and sex. The difference of 166 calories reported by Mifflin is a pretty middle-of-the-road value. You can find values as low as 77 Calories, or as high as 241 Calories, but an average difference of about 150-200 Calories is pretty typical.

However, the gap is probably a bit larger at higher body weights, and smaller at lower body weights. In a study by Müller and colleagues, researchers found that the gap between men’s and women’s BMR (scaled to body mass) was larger for subjects with an overweight or obese BMI than for subjects with a BMI below 25. 

This is corroborated by research by Henry and colleagues (the research used to develop the Oxford/Henry BMR equations discussed earlier in this series) on over 11,000 subjects. At very low body weights, men and women may have BMRs that differ by less than 100 Calories, but the gap grows as body weight increases. When comparing men and women at the same body mass, men burn about 10% more calories than women at 50kg of body mass, about 15% more at 85kg of body mass, and the gap gradually continues growing from there. This gap increases as body weight increases primarily because sex differences in body composition get larger at higher body weights.

So, let’s briefly recap to wrap up this article:

1. Fat-free mass is the primary predictor of BMR, and the relationship between fat-free mass and BMR is the same in both sexes. In other words, a man and a woman with the same amount of fat-free mass would be expected to have the same BMR.
2. Smaller people generally have higher BMRs per unit of fat-free mass, because people with less fat-free mass tend to have more high-metabolic-rate tissue per unit of fat-free mass. Because of this, women generally have higher BMRs per unit of fat-free mass than men do.
3. Per unit of total body mass, men’s BMRs are generally about 150-200 Calories higher than women’s, but the gap is a bit smaller at lower body weights and larger at higher body weights.

Other articles in this series will continue exploring this topic, discussing how age impacts BMR, why athletes have higher BMRs (it’s not just a matter of having more muscle mass!), and how weight gain and weight loss affect your BMR. After that, we’ll explore how we can use all of this information to improve on the (current) best BMR prediction equations. You can also try our BMR calculator, which incorporates all of the information covered in this series, in order to estimate your BMR as accurately as possible.

The post The Impact of Sex on Basal Metabolic Rate appeared first on MacroFactor.

When you look at the equations used to estimate BMR (you can read more about the best BMR equations here), you’ll find that almost all of them predict your BMR based on either 1) your age, height, weight, and sex, or 2) the amount of fat-free mass you have. But, those factors are merely associated with basal metabolic rate. They’re used to estimate BMR because they’re characteristics that are relatively easy to measure or estimate, and they’re predictive of the actual determinants of BMR.

So, what are the actual determinants of BMR?

1. Body composition (but not in the way you’d probably expect)
2. The specific metabolic rates of your body’s various tissues

Each tissue in your body has its own basal metabolic rate. Some tissues require a lot of energy to function normally, while others require very little. You’ve probably heard that “muscle burns more calories than fat,” and that’s technically true, but it matters less than you likely expect.

Your muscles have a tissue-specific BMR of about 13 Calories per kilogram, and your fat tissue has a BMR of about 4.5 Calories per kilogram. So, if you lost 10kg of fat and gained 10kg of muscle, you’d experience an enormous change in “big picture” body composition, but your BMR would only be expected to increase by about 85 Calories per day. That’s not nothing, but it’s also not a dramatic change.

The real metabolic heavy hitters are your heart, kidneys, brain, and liver.

In contrast to your muscle and fat tissue, your heart and kidneys have a BMR of about 440 Calories per kilogram, your brain has a BMR of about 240 Calories per kilogram, and your liver has a BMR of about 200 Calories per kilogram. Collectively, they generally account for less than 5% of total body mass, while typically accounting for more than 50% of your total BMR. 

So, when I say that body composition is one of the determinants of BMR, I don’t just mean that body-fat percentage or total fat-free mass are direct determinants of BMR. Rather, I mean that the actual granular composition of your body determines your BMR. If two people weigh 75kg and have 60kg of fat-free mass, but one of them has kidneys, a liver, and a heart that are 20% larger than average, and the other has kidneys, a liver, and a heart that are 20% smaller than average (comfortably within the range of normal inter-individual variability), their “big-picture” body composition would be the same, but a granular assessment of the tissues composing their body would reveal considerable differences in body composition. As a result, these two people would be expected to have BMRs that differed by about 230 Calories per day. In other words, slight variations in organ mass can have almost a 3-times larger impact on BMR than losing 10kg of fat and gaining 10kg of muscle. 

As mentioned in a previous article, even the best BMR prediction equations using factors like age, sex, height, weight, age, and fat-free mass have the potential to under- or over-estimate BMR by at least 300-400 Calories per day. These equations work as well as they do (producing reasonable ballpark estimates for most people) because age, sex, height, weight, age, and/or fat-free mass are associated with granular body composition – males, younger people, larger people, and people with more fat-free mass have more total tissue, and they also tend to have more “high metabolic rate” tissue.

But, on the flip side, these equations can sometimes significantly over- or under-estimate BMR because all of those factors are more strongly predictive of low-metabolic-rate tissue mass than high-metabolic-rate tissue mass. In other words, if I know your body mass and have a rough idea of your body-fat percentage, I can estimate how much fat mass you have with reasonable accuracy, and I can predict how much muscle mass you have with a high degree of accuracy. And, since those tissues don’t burn very much energy at rest, any errors in my ability to estimate your total fat mass and total muscle mass would have a relatively small impact on my ability to estimate your basal metabolic rate. But, I wouldn’t be able to predict your heart, brain, kidney, and liver mass with nearly as much accuracy. Since those tissues do burn a ton of energy at rest, any errors in my ability to estimate your organ masses would have a relatively large impact on my ability to estimate your basal metabolic rate.

However, in studies where researchers perform MRIs to estimate organ volume and organ mass, they can estimate BMR far more accurately. Instead of having a typical error of about 150-200 Calories, with the largest errors exceeding 400 Calories, the typical error shrinks to 60-100 Calories, and the largest errors rarely exceed 150-200 Calories. In other words, by more precisely estimating organ masses using MRI, instead of (tacitly) very roughly estimating organ masses using factors like height, weight, age, sex, and total fat-free mass, you can predict BMR with about 2-2.5-times more accuracy. This same general principle applies to estimating changes in BMR with weight loss.

From there, the remaining errors are primarily due to differences in organ-specific BMRs (your kidneys may burn 5% more energy per unit of mass than someone else’s kidneys, for instance), and errors in estimating organ masses from MRIs, but there’s not THAT much error left to account for.

So, here are the basic takeaways:

1. Your BMR is determined by your granular body composition, and the specific metabolic rates of the tissues composing your body.
2. So, by estimating someone’s BMR, you’re functionally making predictions about their granular body composition.
3. Variance in the size of high-metabolic-rate tissues, like the brain, liver, kidneys, and heart, has a much larger impact on BMR than variance in low-metabolic-rate tissues like muscle, fat, and bone.
4. Factors like age, sex, height, weight, and total fat-free mass are more predictive of low-metabolic-rate tissue mass than high-metabolic-rate tissue mass. So, it should be unsurprising that even the best BMR equations can sometimes significantly under- or overestimate BMR.

Before wrapping up, I’d like to point out a fun fact about body size and granular body composition that’s baked into BMR prediction equations.

As you’ll recall from from a previous article, the 1991 Cunningham equation predicts BMR using this formula:

BMR = 21.6 × Fat-Free Mass (kg) + 370

So, you might ask, “where does the  ‘+ 370’ come from?” Literally, it would imply that someone with 0kg of fat-free mass would still have a BMR of 370 Calories per day. Obviously that’s not the case – a prediction equation really only needs to “work” within a range of reasonable values, and most adults have at least 30-35kg of fat-free mass. But, it also (and much more reasonably) implies that BMR per unit of fat-free mass decreases as fat-free mass increases.

To illustrate, this formula would predict that someone with 40kg of fat-free mass would have a BMR of 1234 Calories per day: 30.85 Calories per kilogram of fat-free mass. Similarly, it would predict that someone with 80kg of fat-free mass would have a BMR of 2098 Calories per day: 26.23 Calories per kilogram of fat-free mass.

So, is that actually true? Does this formula accurately reflect reality?

Yep! A 2002 study by Heymsfield and colleagues illustrates this well. In a sample of 289 subjects, BMR was positively associated with fat-free mass (more fat-free mass = higher BMR), but BMR per kilogram of fat-free mass was negatively associated with fat-free mass (more fat-free mass = lower BMR per unit of fat-free mass).

Furthermore, they found that as fat-free mass increased, more and more fat-free mass came from low-metabolic-rate tissues (muscle, bone, and the lean component of adipose tissue).

A 2011 study by Müller and colleagues lets us extend these findings further. The researchers used MRI to assess the granular body composition of 262 subjects, allowing us to model out how high-metabolic-rate tissue mass changes as fat-free mass changes. The findings illustrate that the percentage of your fat-free mass comprised of high-metabolic-rate tissues (brain, heart, liver, and kidneys) is expected to decrease as total fat-free mass increases, primarily due to the brain and liver accounting for smaller percentages of total fat-free mass.

So, you now know what actually determines your basal metabolic rate: your granular body composition, and the specific metabolic rates of the individual tissues composing your body. You also now know why BMR per unit of fat-free mass generally decreases as fat-free mass increases, and you know what accounts for most of the potential for error in standard BMR equations: ultimately, it mostly comes down to your high-metabolic-rate organs. Factors like height, weight, age, sex, and fat-free mass can’t predict the masses of high-metabolic-rate tissues as accurately as they can predict the masses of low-metabolic-rate tissues, but those high-metabolic rate tissues account for most of your BMR. Furthermore, high-metabolic-rate tissues account for less and less of your total fat-free mass as fat-free mass increases.

Other articles in this series will dig deeper into this topic, discussing how factors like age and sex impact BMR, why athletes have higher BMRs (it’s not just a matter of having more muscle mass!), and how weight gain and weight loss affect your BMR. After that, we’ll explore how we can use all of this information to improve on the (current) best BMR prediction equations. You can also try our BMR calculator, which incorporates all of the information covered in this series, in order to estimate your BMR as accurately as possible.

The post What Determines Your Basal Metabolic Rate? appeared first on MacroFactor.

Your basal metabolic rate (BMR) tells you how much energy your body burns to just “keep the lights on” – it’s the energy used to power the basic functions of your vital organs, to accomplish sufficient protein and cell turnover to keep your tissues functioning properly, etc. If you didn’t leave your bed all day, and didn’t move a muscle, your basal metabolic rate is the amount of energy you’d still burn in a day. ¹

To estimate your BMR, you just need to plug some basic demographic and anthropometric information (like height, weight, age, sex, and/or fat-free mass) into a formula, and the formula will spit out an estimate of your BMR. So, which equation should you use?

When you dig through the research on the topic, you’ll find a lot of proposed equations for estimating basal metabolic rate. In fact, a 2013 study found 248 different BMR equations, and there are doubtlessly many more that have been published since then. But, there are two that generally perform the best in most populations.

If you don’t know your body-fat percentage, the Oxford/Henry equation(s) are your best bet. If you do know your body-fat percentage, the 1991 Cunningham equation is generally the way to go.

The Oxford/Henry Equations are as follows:

The 1991 Cunningham equation, on the other hand, is the same for everyone:

BMR = 21.6 × Fat-Free Mass (kg) + 370

Or

BMR = 9.8 × Fat-Free Mass (lb) + 370

If you don’t know your fat-free mass (FFM), but you do know your body-fat percentage, you can calculate your fat-free mass using this equation:

FFM = Body Weight × (1 – Body-Fat Percentage)

Why are these the two best equations in most populations?

Oxford/Henry

The Oxford/Henry Equations were developed using the most data, and they have the strongest support in subsequent research.

Back in the 1980s, the UN and the World Health Organization wanted to develop equations that could be used to estimate energy expenditure in a diverse array of populations. Global food insecurity and malnutrition were even bigger problems then than they are now, and obesity rates were starting to trend up in developed nations, so developing accurate equations to predict BMR (which could then be used to predict total energy needs) seemed like a pressing concern.

The resulting FAO/WHO/UNU equations (sometimes referred to as the Schofield equations) are still frequently used, but they have one significant problem: they reliably tend to overestimate BMR, especially in smaller people.

These equations were developed from a database consisting of data from 7,173 subjects, but nearly 50% of the data (from 3,388 subjects) came from just 9 old Italian studies that were conducted between 1936 and 1942. And, as it turns out, one of two things is true: either 1) Italians during this era had exceptionally high basal metabolic rates, or 2) Italian fascists weren’t particularly good at doing metabolism research.

In 2005, researchers re-examined the FAO/WHO/UNU equations, the underlying data used to develop these equations, and the additional data that had been published during the intervening decades. They found that, compared to research in virtually all other populations, the Italian subjects had BMRs that were about 10% higher than other populations with the similar characteristics. Since those Italian subjects were relatively small, and massively overrepresented in the database used to develop the FAO/WHO/UNU equations, they’re the primary reason why the resulting equations were subsequently found to overestimate BMR, especially in smaller people. The image below illustrates this divergence in young women, but similar differences were observed for other age and sex cohorts.

So, the researchers excluded those non-representative Italian studies, added data from an additional 7,000+ subjects from studies published between 1985 and 2005, and updated the equations using a larger (10,552 total subjects) and more representative sample that wasn’t unduly influenced by any overrepresented subpopulations. This is a much larger population than the samples used to develop other popular BMR equations using height, weight, and age, such as the Harris-Benedict equation (239 subjects), the revised Harris-Benedict equation (337 subjects), and the Mifflin-St Jeor equations (498 subjects).

Since the Oxford/Henry equations were developed, a meta study found that the Oxford/Henry equations had the best combination of low error (small average deviations between measured and predicted BMRs) and low bias (not systematically over- or under-estimating BMR in particularly large or particularly small people) across both sexes. Similarly, another huge study with nearly 17,000 subjects found that the Oxford/Henry equations were among the best-performing equations for people in all BMI categories. Finally, a 2022 meta-analysis found that the FAO/WHO/UNU equations performed best in people with overweight and obesity, but that review included relatively few studies that used the Oxford/Henry equations. However, BMR estimates provided by the FAO/WHO/UNU equations and the Oxford/Henry equations tend to converge at higher body weights and BMIs (in other words, if the FAO/WHO/UNU equations perform well in people with obesity, the Oxford/Henry equations do too).

Overall, in most populations, the Oxford/Henry equations are the best BMR equations based on height, weight, age, and sex.

Cunningham, 1991

Much like the Oxford/Henry equations, the 1991 version of the Cunningham equation is the result of synthesizing data from multiple other studies. Cunningham had first developed an equation for estimating BMR from fat-free mass in 1980. In the intervening decade, more research groups investigated the relationship between fat-free mass (FFM) and BMR, allowing Cunningham to systematically analyze the results from a total population of 1482 subjects. The studies included both males and females, with a pretty even mix of lean and obese subjects.

Subsequent research has supported the validity of the 1991 Cunningham equations. For instance, a decade after Cunningham’s study, Wang and colleagues analyzed the FFM/BMR relationship in the published research (which included another seven studies that came out after Cunningham’s equation was published). It was a somewhat less rigorous analysis – I don’t believe they applied weightings based on the number of subjects in each study – but it found that the “average” equation to predict BMR from FFM was BMR = 21.5 * FFM + 407, which is practically indistinguishable from Cunningham’s equation.

Furthermore, they modeled the theoretical relationship between FFM and BMR that was revealed from animal research spanning a range of 7 orders of magnitude; when scaled to body size, metabolic rates are shockingly consistent and predictable between species. Wang and colleagues found that the theoretical relationship between FFM and BMR in animals of all sizes could be modeled with this equation: BMR = 21.7 * FFM + 374². Again, that’s virtually indistinguishable from the 1991 Cunningham equation.

Finally, to lend support to both the 1991 Cunningham equation and the Oxford/Henry equations, both equations produce comparable BMR estimates. Essentially, if the Oxford/Henry equations are good, and the 1991 Cunningham equation produces similar estimates, that suggests that the 1991 Cunningham equation is also pretty good (and vice versa). Using the NHANES body composition cohort, I calculated the estimated BMR for all participants using both the Oxford/Henry equations and the 1991 Cunningham equation. They produced estimates that differed by less than 100 Calories, on average.

Which equation should you use?

If both the Oxford/Henry and the 1991 Cunningham equation are generally good, and typically produce similar BMR estimates, which one should you use?

If you don’t have a decent idea of your body composition, the Oxford/Henry equations are probably the way to go. However, if you do have a pretty good idea of your body composition, the 1991 Cunningham equation is likely the better option. Fat-free mass is by far the most important predictor of BMR. Equations like the Oxford/Henry equations work well because sex, height, and weight are reliably associated with fat-free mass. So, in essence, the Oxford/Henry equation is tacitly predicting your fat-free mass to then predict your BMR. But, if you think you can estimate your body-fat percentage with a reasonable degree of accuracy (within about 5% or so), using the 1991 Cunningham equation essentially lets you go “straight to the source,” and estimate BMR directly from FFM.

Ultimately, the choice of equation shouldn’t make that large of a difference for most people, most of the time. There are a few exceptions, however.

First, if you think your body-fat percentage is considerably higher or considerably lower than average for people of your age, height, weight, and sex, the 1991 Cunningham equation is likely a much better option than the Oxford/Henry equations. If you’re 15% body fat, and the average person of your height, weight, age, and sex is 30% body fat, the Oxford/Henry equations would likely underestimate your BMR.

Second, if you’re over 60 years old, the Oxford/Henry equation corresponding to your age and sex is probably the better option. The relationship between fat-free mass and BMR remains fairly stable through most of your adult life, but BMR per unit of fat-free mass begins declining more rapidly past the age of 60 (which we’ll discuss in more detail later in this series). So, the 1991 Cunningham equation would likely reliably overestimate your BMR.

Third, if you’re an athlete, both of these equations are likely to underestimate your BMR. As we’ll discuss later in this series, athletes tend to have considerably higher BMRs than non-athletes, even when accounting for differences in body size and body composition.

Just how accurate are these equations?

Even though the Oxford/Henry and 1991 Cunningham equations are the cream of the crop, don’t expect them to perfectly nail your BMR. As discussed in a previous MacroFactor article, even the best BMR equation can produce relatively large under- or over-estimates. You can be reasonably confident that the value produced by these equations will be within about 150-200 Calories of your actual BMR, and you can be quite confident that the value produced by these equations will be within about 300-400 Calories of your actual BMR. In other words, if these equations estimate that your BMR is 1600 Calories per day, there’s about a two-thirds chance that your actual BMR is between 1400-1450 Calories on the low end, 1750-1800 Calories on the high end, and a 95% chance that your actual BMR is between 1200-1300 Calories on the low end, and 1900-2000 Calories on the high end.

Final notes

You may know the 1991 Cunningham equation as the Katch-McArdle equation. They’re the same equation. It was developed by Cunningham, and popularized by Katch and McArdle in their exercise physiology textbook. Since it was developed by Cunningham, I’m giving him the credit.

If you’re only semi-confident in your ability to estimate your body composition, there’s no problem with calculating your BMR using both the Cunningham and Oxford/Henry equations, and averaging the two values.

The Mifflin-St Jeor equation deserves an honorable mention. For formulas based on height, weight, age, and sex, I think it’s probably the second best, after the Oxford/Henry equations.

Finally, if you’d like to learn more about the determinants of basal metabolic rate, to better understand the (surprisingly cool) theory and physiology underpinning these equations, you’ll really enjoy the next article in this series. Subsequent articles in this series will also discuss how factors like age, sex, and weight loss impact BMR, and we’ll wrap it up by using all of that data to improve on the BMR equations covered in this article. We’ve already published a BMR calculator with these new equations that you can try out for yourself, but the articles explaining the rationale methodology used to develop our new BMR equations will be published over the next three weeks. Stay tuned!

1. There are several different terms that describe similar but slightly different concepts, including basal metabolic rate (BMR) or energy expenditure (BEE), sleeping metabolic rate (SMR) or energy expenditure (SEE), and resting metabolic rate (RMR) or energy expenditure (REE). For the purpose of this series, we’re using all of these terms interchangeably. Technically, this series is mostly about RMR/REE, which is the amount of energy your body actually burns at rest most of the time. BMR is the amount of energy you burn first thing in the morning, and measuring BMR requires subjects to sleep in the lab overnight. Sleeping metabolic rate is, quite intuitively, the amount of energy you burn while sleeping, which also requires subjects to sleep in the lab overnight. BMR is usually a little lower than RMR, and SMR is usually a little lower than BMR, but all three values scale with each other (i.e., you’re not going to have a really high RMR and a really low SMR), and most research on the topic assesses RMR since the subject burden of measuring RMR is much lower than BMR or SMR. RMR is also more broadly representative of your “normal” metabolic rate, since most people spend most of their time awake. The reason we opted to use the term “BMR” is just that it’s the term that more people are familiar with, and these terms are all used interchangeably by most non-academics. ↩︎
2. The actual equation is a nonlinear allometric scaling equation. This is the linear approximation corresponding to the range of FFMs typical in humans. ↩︎

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