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Relative fat mass (RFM) as a new estimator of whole-body fat percentage
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cdjs77
Posts: 176 Member
in Debate Club
An interesting new article I came across this morning, with an improved formula for estimating body fat percentages based on a recent study.
https://www.nature.com/articles/s41598-018-29362-1
Abstract:
"High whole-body fat percentage is independently associated with increased mortality. We aimed to identify a simple anthropometric linear equation that is more accurate than the body mass index (BMI) to estimate whole-body fat percentage among adult individuals. National Health and Nutrition Examination Survey (NHANES) 1999–2004 data (n = 12,581) were used for model development and NHANES 2005–2006 data (n = 3,456) were used for model validation. From the 365 anthropometric indices generated, the final selected equation was as follows: 64 − (20 × height/waist circumference) + (12 × sex), named as the relative fat mass (RFM); sex = 0 for men and 1 for women. In the validation dataset, compared with BMI, RFM better predicted whole-body fat percentage, measured by dual energy X-ray absorptiometry (DXA), among women and men. RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men. RFM reduced total obesity misclassification among all women and all men and, overall, among Mexican-Americans, European-Americans and African-Americans. In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European or African ethnicity."
https://www.nature.com/articles/s41598-018-29362-1
Abstract:
"High whole-body fat percentage is independently associated with increased mortality. We aimed to identify a simple anthropometric linear equation that is more accurate than the body mass index (BMI) to estimate whole-body fat percentage among adult individuals. National Health and Nutrition Examination Survey (NHANES) 1999–2004 data (n = 12,581) were used for model development and NHANES 2005–2006 data (n = 3,456) were used for model validation. From the 365 anthropometric indices generated, the final selected equation was as follows: 64 − (20 × height/waist circumference) + (12 × sex), named as the relative fat mass (RFM); sex = 0 for men and 1 for women. In the validation dataset, compared with BMI, RFM better predicted whole-body fat percentage, measured by dual energy X-ray absorptiometry (DXA), among women and men. RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men. RFM reduced total obesity misclassification among all women and all men and, overall, among Mexican-Americans, European-Americans and African-Americans. In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European or African ethnicity."
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Replies
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So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.1 -
How about just using a DEXA, to determine the difference between how much of us' bone & fat?1
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funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.1 -
Stockholm_Andy wrote: »funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.
This^.
It's meant to estimate who is actually at an unhealthy BF%, not necessarily what your BF% is. This measurement showed fewer "false negative" cases of obesity, meaning fewer people who were identified as healthy when they were actually obese. Also, the statistic is designed to minimize outliers while still be accurate, so there are not likely to be "many" outliers.
Also, statistically, it does take frame size into account, just not directly. When you make a statistical model, you fit it to the data you have, which in this case takes into account gender, height, and waist size. They then use these statistics to compute a model which fits the distribution of actual BF% as shown by DXA. If the variance of BF% within height/waist-size categories was high (i.e. varied significantly by build), then the model would not be statistically significant, and this wouldn't be published in Nature.SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because DXA scans are expensive and not always available.
The scientists here actually used DXA scans to verify the accuracy of their measurements, but the purpose of these "quick-and-dirty" assessments is to identify cases which are likely to need further investigation. It's pointless and a waste of resources to do a DXA scan for everyone that walks into a doctor's office. In some countries, they may not even have this technology available to them. This measurement, like BMI, was meant to tell doctors whether or not it is worth looking into someone's weight further.
Like BMI, this isn't meant to be a perfect "diagnosis" for obesity. It is meant as a starting point for looking into it as a possibility.1 -
Stockholm_Andy wrote: »funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.
This^.
It's meant to estimate who is actually at an unhealthy BF%, not necessarily what your BF% is. This measurement showed fewer "false negative" cases of obesity, meaning fewer people who were identified as healthy when they were actually obese. Also, the statistic is designed to minimize outliers while still be accurate, so there are not likely to be "many" outliers.
Also, statistically, it does take frame size into account, just not directly. When you make a statistical model, you fit it to the data you have, which in this case takes into account gender, height, and waist size. They then use these statistics to compute a model which fits the distribution of actual BF% as shown by DXA. If the variance of BF% within height/waist-size categories was high (i.e. varied significantly by build), then the model would not be statistically significant, and this wouldn't be published in Nature.SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because DXA scans are expensive and not always available.
The scientists here actually used DXA scans to verify the accuracy of their measurements, but the purpose of these "quick-and-dirty" assessments is to identify cases which are likely to need further investigation. It's pointless and a waste of resources to do a DXA scan for everyone that walks into a doctor's office. In some countries, they may not even have this technology available to them. This measurement, like BMI, was meant to tell doctors whether or not it is worth looking into someone's weight further.
Like BMI, this isn't meant to be a perfect "diagnosis" for obesity. It is meant as a starting point for looking into it as a possibility.
For where it's available & for where insurance companies're using it to determine rates, wouldn't it be cost effective to pay for the scan than the extra insurance?1 -
SandSeaSkySoul wrote: »Stockholm_Andy wrote: »funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.
This^.
It's meant to estimate who is actually at an unhealthy BF%, not necessarily what your BF% is. This measurement showed fewer "false negative" cases of obesity, meaning fewer people who were identified as healthy when they were actually obese. Also, the statistic is designed to minimize outliers while still be accurate, so there are not likely to be "many" outliers.
Also, statistically, it does take frame size into account, just not directly. When you make a statistical model, you fit it to the data you have, which in this case takes into account gender, height, and waist size. They then use these statistics to compute a model which fits the distribution of actual BF% as shown by DXA. If the variance of BF% within height/waist-size categories was high (i.e. varied significantly by build), then the model would not be statistically significant, and this wouldn't be published in Nature.SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because DXA scans are expensive and not always available.
The scientists here actually used DXA scans to verify the accuracy of their measurements, but the purpose of these "quick-and-dirty" assessments is to identify cases which are likely to need further investigation. It's pointless and a waste of resources to do a DXA scan for everyone that walks into a doctor's office. In some countries, they may not even have this technology available to them. This measurement, like BMI, was meant to tell doctors whether or not it is worth looking into someone's weight further.
Like BMI, this isn't meant to be a perfect "diagnosis" for obesity. It is meant as a starting point for looking into it as a possibility.
For where it's available & for where insurance companies're using it to determine rates, wouldn't it be cost effective to pay for the scan than the extra insurance?
Of course it would.
However, not all countries have health care based on medical insurance, scanning every single person would just be an extra drain on the national health system. Why not use as a really simple formula to decide which people statistically are likely to need some help with their weight.
Further tests and/or resources could then be aimed at them....
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SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because anthropomorphic linear (with a measuring tape) is cheaper than a DEXA. The abstract indicates that the new formula is better than BMI for all men and all women.2 -
Wait. Am I understanding this correctly? The formula is 64 − (20 × height/waist circumference) + (12 × sex)? If I am calculating this correctly (and it's early, so I might not be) this formula predicts I am at 31% body fat. With a BMI of 21.1
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Yup, @mousepotato1975, I ran those numbers and came out 29bf at a BMI of 20.2.
It could (I suppose) be a good indicator of abdominal fat, which could indicate an excess of visceral fat, or not.
It would take a drop of 2" off my waist (26 to 24) to drop my body fat to 25 as per this estimation.
What if I had a narrow waist and carried all my fat in my hips and legs, like many women do, how would the accuracy be better than a BMI for general population purposes.
Not arguing, just trying to understand why this would be better.
Cheers, h.2 -
Agreed @middlehaitch I may still have a few vanity pounds to lose, but I hardly think I'm 31% body fat at 5'3" and 115-118 pounds!1
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Yeah, they already did this in the military. If you failed the scale/BMI you got taped... but it was not just based off of your waist measurements - they also measured your neck.0
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Reading the abstract, it does say it's less accurate for older people (I think I remember the accuracy starts deteriorating somewhere around 50 yrs of age).
Here it is:"We found a progressive decline in body weight, height and fat-free mass after 50 years of age, and a steeper decline in fat mass and waist circumference after 70 years of age among women and men (Supplementary Fig. 5), which coincided with the lower predicting ability of all models in older individuals."
I got a DEXA scan done in February, so I can use those results to compare to the formula. For reference, I'm 67 and female, so I would anticipate the numbers would have some error.
BMI: 18.6
Weight: 105
Height: 63 inches
Waist: 31.1
64-(20*2.02)+12 = 35.5
So
35.5% body fat by the formula
24.8 % body fat by DEXA
In my case, this is a considerable difference, and would indicate a fairly alarming bodyfat percent compared either to BMI or DEXA results. If this result is typical of what we would expect to find in an older population (no way to tell without a standard deviation in accuracy by age analysis), I think that leads to some significent limitations on how this method could be used with the large percentage of the population who are over 50, even if it's more accurate for younger people.
Just my thoughts
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I still stand by the DEXA but of course, just for those that BMI and/or RFM otherwise'd be inaccurate for! However that'd still be many, simply because of the natural varieties of body composition alone, especially among females! Take a gander at all of the responses to the threads here concerning women, whom're having difficulty finding clothes that fit them adequately!0
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I've lost more than half of my highest body weight. I'm in the healthy BMI range but I've got loose skin with attached subcutaneous fat many places, especially in my abdominal area. My waist skews to a larger measurement than it would after, say, a Fleur-de-Lis abdominoplasty.
I've played the game of getting down to a healthy BMI so now they want to move the goalposts? Normal weight obesity is the new skinny fat.1 -
SandSeaSkySoul wrote: »I still stand by the DEXA but of course, just for those that BMI and/or RFM otherwise'd be inaccurate for! However that'd still be many, simply because of the natural varieties of body composition alone, especially among females! Take a gander at all of the responses to the threads here concerning women, whom're having difficulty finding clothes that fit them adequately!
If you point is that a DEXA scan is more accurate than this formula then you are 100% correct.
You are after all comparing a machine that costs tens of thousands of dollars to a tape measure.
The researchers didn't aim to come up with something more accurate than a DEXA there aim was to find something, on average, more accurate then BMI. Which they've shown they have.
The beauty is you only need a single piece of equipment. A tape measure. BMI needed both a tape measure and a scale.1 -
I've lost more than half of my highest body weight. I'm in the healthy BMI range but I've got loose skin with attached subcutaneous fat many places, especially in my abdominal area. My waist skews to a larger measurement than it would after, say, a Fleur-de-Lis abdominoplasty.
I've played the game of getting down to a healthy BMI so now they want to move the goalposts? Normal weight obesity is the new skinny fat.
Just because someone has suggested a more accurate was of estimating obese levels in the general population doesn't mean you've suddenly gained body fat and become obese again.
Congratulations by the way on losing half your body weight that is an amazing achievement.0 -
So is the formula supposed to predict my BF% ? I've never attempted to measure my BF one way or the other. I'm 5'4" (and 1/2 dammit) 128lbs. Puts me at a BMI of 22 and an RFM of 30. I'm 45 for whatever that's worth. I guess that's pretty consistent, maybe a touch on the high side. I'd prefer 29% though, it sounds better
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SandSeaSkySoul wrote: »Stockholm_Andy wrote: »funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.
This^.
It's meant to estimate who is actually at an unhealthy BF%, not necessarily what your BF% is. This measurement showed fewer "false negative" cases of obesity, meaning fewer people who were identified as healthy when they were actually obese. Also, the statistic is designed to minimize outliers while still be accurate, so there are not likely to be "many" outliers.
Also, statistically, it does take frame size into account, just not directly. When you make a statistical model, you fit it to the data you have, which in this case takes into account gender, height, and waist size. They then use these statistics to compute a model which fits the distribution of actual BF% as shown by DXA. If the variance of BF% within height/waist-size categories was high (i.e. varied significantly by build), then the model would not be statistically significant, and this wouldn't be published in Nature.SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because DXA scans are expensive and not always available.
The scientists here actually used DXA scans to verify the accuracy of their measurements, but the purpose of these "quick-and-dirty" assessments is to identify cases which are likely to need further investigation. It's pointless and a waste of resources to do a DXA scan for everyone that walks into a doctor's office. In some countries, they may not even have this technology available to them. This measurement, like BMI, was meant to tell doctors whether or not it is worth looking into someone's weight further.
Like BMI, this isn't meant to be a perfect "diagnosis" for obesity. It is meant as a starting point for looking into it as a possibility.
For where it's available & for where insurance companies're using it to determine rates, wouldn't it be cost effective to pay for the scan than the extra insurance?
Would it be cheaper to give everyone that walks into a doctor's office a DXA scan? No. This formula is meant to identify those who are at risk for obesity, they could then get a DXA scan to confirm, but it would be a waste of money to have DXA scans for a, for example, 175 cm, 57 kg, highly-active woman. In order to figure out which of these cases can be ruled out we need a way to measure, like BMI or RFM.Mouse_Potato wrote: »Wait. Am I understanding this correctly? The formula is 64 − (20 × height/waist circumference) + (12 × sex)? If I am calculating this correctly (and it's early, so I might not be) this formula predicts I am at 31% body fat. With a BMI of 21.
It's not meant to predict body fat, it's meant to predict if you fit in the healthy range of body fat percentages. For example, my measurement was 16.7, and I doubt I have 16.7% body fat. However, the number would indicate, based on their study, that I am within the healthy range, which I would say is correct. Also, The study noted RFM has fewer "false negatives" than BMI, which means fewer people classified as healthy when they are not. So it is possible that there are people who would fall within the healthy range for BMI but not for RFM or actual BF% (not that you're one of them based on your profile photo!).0 -
SandSeaSkySoul wrote: »Stockholm_Andy wrote: »funjen1972 wrote: »So this formula assumes every individual with the same height and waist circumference has the same amount of body fat. The issue I see is it does not take build (or frame shape/size) into account. It gives a very pear shaped women with tiny waist and large hips has the same body fat percentage as a very fit women with the same size waist.
The problem is we are trying to develop a single formula for humans who I have a gazillion variables. Although it states this is more accurate than BMI, there will still be many outliers.
You are correct, of course, there will be outliers with any generalised formula. The authors demonstrated that:-
RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men.
They're not saying it's 100% accurate but that it's more accurate than BMI. As you say the additional variable they've introduced (waist size) is a better correlation for men than women as the latter tend to have more variation in fat storage.
I've seen a lot of online calculators which take into account hip size (and other measurements) for women but not for men.
However, if you are an outlier I would suggest you probably know already right? I mean you'd have to be pretty jacked to show a BMI as obese with a low BF%.
This^.
It's meant to estimate who is actually at an unhealthy BF%, not necessarily what your BF% is. This measurement showed fewer "false negative" cases of obesity, meaning fewer people who were identified as healthy when they were actually obese. Also, the statistic is designed to minimize outliers while still be accurate, so there are not likely to be "many" outliers.
Also, statistically, it does take frame size into account, just not directly. When you make a statistical model, you fit it to the data you have, which in this case takes into account gender, height, and waist size. They then use these statistics to compute a model which fits the distribution of actual BF% as shown by DXA. If the variance of BF% within height/waist-size categories was high (i.e. varied significantly by build), then the model would not be statistically significant, and this wouldn't be published in Nature.SandSeaSkySoul wrote: »How about just using a DEXA, to determine the difference between how much of us' bone & fat?
Because DXA scans are expensive and not always available.
The scientists here actually used DXA scans to verify the accuracy of their measurements, but the purpose of these "quick-and-dirty" assessments is to identify cases which are likely to need further investigation. It's pointless and a waste of resources to do a DXA scan for everyone that walks into a doctor's office. In some countries, they may not even have this technology available to them. This measurement, like BMI, was meant to tell doctors whether or not it is worth looking into someone's weight further.
Like BMI, this isn't meant to be a perfect "diagnosis" for obesity. It is meant as a starting point for looking into it as a possibility.
For where it's available & for where insurance companies're using it to determine rates, wouldn't it be cost effective to pay for the scan than the extra insurance?
Would it be cheaper to give everyone that walks into a doctor's office a DXA scan? No. This formula is meant to identify those who are at risk for obesity, they could then get a DXA scan to confirm, but it would be a waste of money to have DXA scans for a, for example, 175 cm, 57 kg, highly-active woman. In order to figure out which of these cases can be ruled out we need a way to measure, like BMI or RFM.Mouse_Potato wrote: »Wait. Am I understanding this correctly? The formula is 64 − (20 × height/waist circumference) + (12 × sex)? If I am calculating this correctly (and it's early, so I might not be) this formula predicts I am at 31% body fat. With a BMI of 21.
It's not meant to predict body fat, it's meant to predict if you fit in the healthy range of body fat percentages. For example, my measurement was 16.7, and I doubt I have 16.7% body fat. However, the number would indicate, based on their study, that I am within the healthy range, which I would say is correct. Also, The study noted RFM has fewer "false negatives" than BMI, which means fewer people classified as healthy when they are not. So it is possible that there are people who would fall within the healthy range for BMI but not for RFM or actual BF% (not that you're one of them based on your profile photo!).
I didn't see anything in the link to say what the healthy range is though, did I miss that?
ETA: I see that >33.9 is correlated with obesity, but I'm curious if there is a "healthy range" suggested or if it's just obese/not-obese.1 -
They refrain from specifying a healthy range.Our study was cross-sectional and used a single-point measurement of each anthropometric. Thus, our study was not designed to propose RFM cut-points for the diagnosis of obesity. We defined obesity using arbitrary cut-points of DXA-measured body fat percentage to compare obesity misclassification by RFM and BMI. Sensitivity analysis showed RFM had better diagnostic accuracy for obesity than BMI among men regardless the cut-point used to define obesity.
As for whether or not RFM is supposed to correspond to %BodyFat; it sort of reads that way but they never clearly specify.
and elsewhere they stateTo date, there is no consensus on the diagnosis of obesity based on body fat percentage. Thus, to define obesity based on body fat percentage we used arbitrary cut-points of DXA-measured body fat percentage: ≥33.9% for women and ≥22.8% for men (corresponding cut-points between the first and second quintiles for each sex)An RFM ≥33.9 for women and ≥22.8 for men showed a high sensitivity to identify individuals with obesity, 95.0% and 96.2%, respectively.
Given the wording and that the numbers sync up it does sort of feel like RFM is meant to approximate %BodyFat, at least more specifically than BMI. Although they do point out that accuracy was lower among lower body fat individuals.In the validation dataset, the performance of RFM to estimate DXA-measured body fat percentage was overall more consistent than that of BMI among women and men, across ethnic groups, young, middle-age and older adults, and across quintiles of body fat percentage, although the accuracy of RFM was lower among individuals with lower body fatness.
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