Dr. Sharma's Obesity Notes

While I am on a brief holiday in Berlin, I thought I’d rerun a few earlier posts that discuss the issue of measuring obesity and how such measures may or may not be helpful in obesity management - as many readers may not have seen these posts before, comments are very much appreciated.

The following was first posted on November 18, 2008

This week’s New England Journal of Medicine, features an article by Tobias Pischon on behalf of the EPIC (European Prospective Investigation into Cancer and Nutrition) investigators on the relationship between BMI, waist circumference, waist-to-hip ratio and mortality.

To me, this paper is of considerable interest - not least, because Tobias was one of my students back in Germany, who did his MD thesis on the effect of salt intake and obesity on chronic kidney transplant rejection under my supervision.

Of course, this paper also deals with a topic that I have often blogged about - i.e. the relationship between anthropometric measures and morbidity and mortality.

Pischon and colleagues studied 359,387 participants from nine countries during a mean follow-up of 9.7 years. After adjustments for age, educational level, smoking status, alcohol consumption, and physical activity, the lowest risks of death related to BMI were observed at a BMI of 25.3 for men and 24.3 for women.

After adjustment for BMI, relative risks among men and women in the highest quintile of waist circumference were 2.05 and 1.78, respectively, and in the highest quintile of waist-to-hip ratio, the relative risks were 1.68 and 1.51, respectively.

BMI remained significantly associated with the risk of death in models that included waist circumference or waist-to-hip ratio (P<0.001).

This study, essentially confirms what was already known, namely that the impact of excess body fat on mortality depends not only on the amount of excess fat (BMI) but also on its distribution (waist circumerence, waist-to-hip ratio).

Importantly, the measures of fat distribution are predictive of risk even in normal weight individuals with lower BMI’s, which challenges the use of cutoff points to define abdominal obesity.

On the other hand, as BMI increases, measuring fat distribution adds little to determining obesity-related risk. (which is why obesity guidelines do not recommend measuring waist cirumference in individuals with a BMI>40).

AMS
Edmonton, Alberta

While I am on a brief holiday in Berlin, I thought I’d rerun a few earlier posts that discuss the issue of measuring obesity and how such measures may or may not be helpful in obesity management - as many readers may not have seen these posts before, comments are very much appreciated.

The following was first posted on October 31, 2008

As most readers of these pages probably know, the current definition of obesity is based on the body-mass-index, a number, which, in populations, nicely correlates with body fat.

However, as body fat alone is not the entire picture, other indices that include measurements of fat distribution such as waist circumference or wait-to-hip ration have been suggested, along with cut-offs that would help identify, who is “obese” and who is not.

But how reliably can these indices be measured in clinical practice (as recommended in obesity guidelines)?

This question was addressed by Paul Sebo and colleagues from the Geneva University Hospitals, Switzerland, in a paper just published in Preventive Medicine.

In this study, repeated anthropometric measurements were performed by 12 primary care physicians on 24 adult volunteers, men and women, with an average BMI of 28. While inter-observer reliability for weight, height, and derived BMI were excellent (R>0.99), they were unsatisfactory for waist circumference (R=0.92), hip circumference (R=0.76) and waist-to-hip-ration (R=0.51).

With BMI, only 1% of the volunteers were misclassified as overweight or obese, whereas the use of WC and WHR lead to misclassification in 6% and 23% respectively.

Reliability for the measurements improved after a one-hour training in anthropometric measurements, but the proportion who were misclassified remained high for WC (5%) and WHR (9%).

So, apparently, even with “Swiss precision”, anything that goes beyond height and weight is too complicated to reliably classify obesity in primary care.

But the real question here is whether or not ANY anthropometric measurement can reliably detect who is threatened of affected by excess weight (my definition of obesity). I have argued before that BMI, although fine for population studies, is not useful when making individual decisions about patients.

Not only is there a wide range in individual variability in the actual body fat present in individuals of the same BMI, but, more importantly, there is a huge variability on how that excess fat actually affects that individual’s health.

We have recently proposed the Edmonton Obesity Staging System, which we now use to supplement BMI measurements with stages that reflect the degree of comorbidity and/or reduction in functional status attributable to the excess weight.

Blindly basing decisions to treat or not-to-treat on BMI alone will result in treating a lot of people who have little to gain, while missing out on many who are clearly threatened or affected by excess body fat.

AMS
Edmonton, Alberta

While I am on a brief holiday in Berlin, I thought I’d rerun a few earlier posts that discuss the issue of measuring obesity and how such measures may or may not be helpful in obesity management - as many readers may not have seen these posts before, comments are very much appreciated.

The following was first posted on July 30, 2008

Body mass index (BMI) is currently widely recommended and used as the best measure of obesity both in population and clinical studies. It dates back to the Belgian statistician Adolphe Quételet, who between 1830 and 1850 described this index as a way to characterize the level of adiposity in sedentary adults.

But how accurate is this index really to identify individuals with excess body fat?

This question was recently addressed by Abel Romero-Corral and colleagues from the Mayo Clinic, MN, USA, who analysed the relationship between BMI and body fat percent (BF%) as measured by bioelectrical impedence in 13,601 subjects (age 20-79.9 years; 49% men) from the Third National Health and Nutrition Examination Survey (Int J Obesity).

In this study, the authors defined obesity based on the World Health Organization (WHO) reference standard for obesity of BF%>25% in men and >35% in women.

BMI-defined obesity (>=30) was present in 19% of men and 25% of women, while BF%-defined obesity was present in 44% of men and 52% of women.

A BMI>=30 had a high specificity (men=95%, women=99%), but a poor sensitivity (men=36%, women=49%) to detect BF%-defined obesity. This means that while the BMI definition does identify the vast majority of men and women who have increased body fat, it also misses a significant number of individuals who have high percent body fat and would be considered obese by the BF% definition.

The diagnostic performance of BMI diminished as age increased and in the intermediate range of BMI (25-29.9), BMI failed to discriminate between BF% and lean mass in both sexes.

The authors conclude that accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. Thus, the currently recommended BMI cutoff of >=30 kg for obesity has good specificity but misses more than half the people with excess fat.

The scary part of these results of course is in the fact that based on actual BF% the prevalence of obesity in this population doubled! On the other hand, we know that %body fat or body composition alone is not a particularly reliable measure of health.

I prefer to continue using my operational clinical definition of obesity: the presence of excess body fat that threatens or affects your health.

Given the wide variation in the inter-individual susceptibility to develop adiposity-related health problems, the diagnosis of obesity and the question of whether or not reducing the proportion of body fat will indeed benefit your health will always remain a matter of clinical judgement.

AMS
Duschesnay, Quebec

While I am on a brief holiday in Berlin, I thought I’d rerun a few earlier posts that discuss the issue of measuring obesity and how such measures may or may not be helpful in obesity management - as many readers may not have seen these posts before, comments are very much appreciated.

The following was first posted on July 28, 2008

Don’t worry - I am not going to take off on a discussion about whether obesity is a disease or “simply” a risk factor. I am also not going to discuss again obesity definitions - anthropometric or otherwise.

Today’s post is simply about an analogy that may help sharpen our clinical thinking around excess weight.

Think of someone who has an elevated plasma creatinine level (a marker of kidney failure) - the elevated creatine definitely tells us that there is something “wrong” with the kidneys, but that’s about it. From the creatine level alone we can certainly tell that the kidneys are failing in their excretory function, but we cannot tell what is causing the kidneys to fail - is it a pre-renal, intra-renal or post-renal problem? We can probably list a 100 reasons why kidneys could fail and obviously the treatment (apart from some very general principles) will very much depend on the cause, i.e. the actual diagnosis.

In many ways, one can look at excess body fat simply as a sign or symptom of the fact that there is a something “wrong” with energy homeostasis. The excess body fat tells us nothing about what the problem is - sure, it’s either excessive food intake or reduced energy expenditure - but that is like saying that the creatinine levels are elevated because the kidney is not excreting properly. I can think of a long list of reasons or factors that would contribute to excessive caloric intake or reduced energy expenditure: sociocultural factors, psychological factors, biomedical factors - figuring out what exactly is causing the energy imbalance is the real problem.

Only when we find what is causing the excessive intake will we have made a diagnosis of what is causing the problem - a few specific examples could include: poor meal planning, peer pressure, hedonic overeating, depression, obesogenic medications, binge eating disorder, defective satiety signaling, etc. The point is that till we know what is causing the overeating, we can’t fix it, which means we will have little success in treating the weight problem and will be limited to a “symptomatic” approach - just eat less!

Similarly, when the problem appears to be lack of activity, again the question is what exactly is causing the problem. Obviously if the problem is lack of time our approach will hopefully be very different than if the problem is back pain or lack of motivation (a possible symptom of sleep apnea, exhaustion or depression). A “symptomatic” but useless approach would be to simply recommend 10,000 steps. No better than offering an ice-pack to someone with a fever.

Just as the term “kidney failure” only tells us that there is something “wrong” with the kidneys the term “obesity” only tells us that there is something “wrong” with energy homeostasis.

In itself, neither the term “kidney failure” nor “obesity” is a real diagnosis - they are only helpful if they prompt further investigation into what might have or is still causing the problem. Only when we find the cause will we be on our way to solving the problem.

AMS
Edmonton, Alberta

While I am on a brief holiday in Berlin, I thought I’d rerun a few earlier posts that discuss the issue of measuring obesity and how such measures may or may not be helpful in obesity management - as many readers may not have seen these posts before, comments are very much appreciated.

The following was first posted on October 15, 2007