Arya M. Sharma, MD

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

One of the interesting epidemiological findings in virtually every population study, is that the prevalence of overweight and obesity in men and women, with a few minor differences in age distribution, is virtually identical.

Yet, both commercial and non-commercial weight loss programs as well as bariatric centres are overwhelmingly frequented by women.

Interestingly, research studies on weight loss and obesity management are also almost exclusively done in women.

This, not surprising, but important fact, is elegantly demonstrated in a paper by Sherry Pagoto and colleagues from the University of Massachusetts, published in the latest issue of OBESITY, which examined the inclusion of men in randomised controlled trials of lifestyle weight loss interventions.

Their analysis of 244 studies with a total of 95,207 participants published in the last 10 years (1999-2009), revealed on average 27% male vs. 73% female participants.

Interestingly, trials recruiting a diseased sample included a larger proportion of males than those not targeting a disease (35% vs. 21%).

About 32% of trials used exclusively female samples, whereas only 5% used exclusively male samples .

No studies in the past 10 years specifically targeted minority males as a result of which ethnic males composed 1.8% of total participants in US studies.

Only 24% of studies that underrepresented males provided a reason for doing so.

This of course has major clinical implications, as it means that very little is known about the efficacy or effectiveness of lifestyle interventions in men and virtually nothing is known about weight management in non-white men.

Based on these data, evidence-based lifestyle management of obesity in men is virtually impossible and appears to be a major knowledge gap.

While I appreciate the many reasons why women are so much more likely to participate in weight loss studies and seek out obesity treatments, the fact remains that for men seeking (and perhaps requiring) such treatments, there is virtually no knowledge base on which to make any kind of recommendations.

This gender discrepancy that is perhaps unique for a non-gender related health problem (remember obesity is as common in men as in women), is something that may have to be addressed in future funding-policy decisions.

Currently, there are ‘affirmative action’ type rules in place to ensure fair representation of women in clinical trials - in fact the Canadian Institutes of Health Research has a whole institute dedicated to addressing the issue of gender and health and it appears that much of its activities appear dedicated to studying issues of particular significance for womens’ health.

This discrepancy in gender distribution in obesity research may be a reason to prompt specific initiatives by this and other institutes to ensure that future studies on obesity management (be it lifestyle or other interventions) include a proportion of men that equitably represents the prevalence of excess weight and the burden of obesity in the male population.

AMS
Edmonton, Alberta

Pagoto SL, Schneider KL, Oleski JL, Luciani JM, Bodenlos JS, & Whited MC (2011). Male Inclusion in Randomized Controlled Trials of Lifestyle Weight Loss Interventions. Obesity (Silver Spring, Md.) PMID: 21633403

As in most countries, the population levels of obesity in Canada vary considerably from province to province (as they do within provinces). Although there are many “theories” on why this may be the case there has been little work done on trying to unravel the “explained” and “unexplained” regional variation based on a comparison of factors known to affect obesity levels such as socio-economic status, urban-rural distribution, and other variables.

This issue was now addressed by Daniel Dutton and Lindsay McLaren from the University of Calgary, Alberta, in a paper just published in OBESITY.

Using data from the nationally representative Canadian Community Health Survey (CCHS) (2004), the researchers attempted to decompose the difference in mean BMI between regions, into differences explained by different levels of the covariates between regions and a share explained by those covariates having different effects on BMI in the different regions.

Canada was split into five regions for this analysis: British Columbia, the Prairies (Alberta, Saskatchewan, and Manitoba), Ontario, Quebec, and the Atlantic provinces (Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland). The Atlantic provinces, which currently have the highest obesity rates in Canada, were used as the reference group.

While some differences between provinces (e.g., average BMI for males in Quebec compared to the Atlantic provinces) are mostly explained by the different levels of socio-demographic and behavioral covariates, others (e.g., average BMI for females in Quebec compared to the Atlantic provinces) are mostly explained by the different effects of the covariates on BMI.

One example of a surprising difference between regions is that the impact of increased fruit and vegetable consumption on BMI is substantially stronger in Ontario and Quebec women than in Atlantic women.

The authors have the following explanation to offer regarding this finding:

“One possibility is that the quality of fruits and vegetables consumed differs by region. For example, fruit servings in the Atlantic provinces may consist of more canned fruits (due to a climate less conducive to growing a variety of fruits or geographical distance affecting the efficiency of transporting perishables to the region), which are often packed in syrup, adding to the calorie count, compared to fresh fruits. Another plausible explanation is that consumption of other foods varies regionally, and differentially offsets the impact of fruit and vegetable consumption. For example, if high levels of consumption of fruits and vegetables in the Atlantic region are associated with higher consumption of food overall (including less healthy foods), perhaps reflecting dietary social norms, then we would observe different returns to the consumption of fruits and vegetables.”

Thus, even if covariates (e.g. promoting the consumption of fruit and vegetables) were made to be identical in the different regions, the difference in average BMI between regions would still persist.

As the authors note:

“Thus, targeting covariates in different regions through plans like physical activity or nutrition policy, income equalization, or education subsidies will have ambiguous effects for addressing disparate obesity levels, being plausible policy options in some regions but less so in others.”

Therefore, while some drivers of obesity may best be addressed by federal policies, each region may have to adopt their own strategies to fully address the obesity problem - what works well in one province may have little to no effect in others.

It appears that what applies in clinical practice, also applies for efforts at the regional level: one size does not fit all.

AMS
Edmonton, Alberta

Dutton DJ, & McLaren L (2011). Explained and Unexplained Regional Variation in Canadian Obesity Prevalence. Obesity (Silver Spring, Md.) PMID: 21253004

Visualising vast epidemiological datasets as meaningful graphics is not always simple or straightforward.

Today, I would like to share with you a link to the Public Health Agency of Canada’s Chronic Disease Infobase.

It allows anyone to produce charts and graphics that profile the epidemiology of major non-communicable diseases in Canada, including obesity, cancers, and cardiovascular and respiratory diseases - by province/territory and by regional health unit.

The data sets include demographic, mortality, morbidity, risk factor and related health care data.

According to the Agency: “Chronic Disease Infobase uses advanced information technology to provide dynamic access to an extensive database.”

Every page that is generated retrieves data from the database on the fly, which means that every time you come back to Chronic Disease Infobase, you may get new information; since the database is updated as data become available.

Chronic Disease Infobase provides several display options. Multiple area comparisons, morbidity and mortality time trends, birth cohort mortality trends and proportional mortality trends are just some of the options. Thematic mapping with user friendly options is also available.

For readers more interested in global data, check out the WHO’s Global Infobase, which has similar tools for a global perspective.

Warning: These sites can be tremendous time killers - perfect for fellow procrastinators!

Enjoy!

AMS
Edmonton, Alberta

Hat Tip to Reader Billie-Jean for bringing this great tool to my attention!

Yesterday, I attended the annual Spring Meeting of CANNeCTIN (Canadian Network and Centre for Trials Internationally), a national network funded by the CIHR/CFI Clinical Research Initiative program to improve the prevention and treatment of cardiac and vascular diseases and diabetes.

CANNeCTIN is jointly led by Dr. Salim Yusuf, from Hamilton Health Sciences and McMaster University, and Dr. John Cairns, from the University of British Columbia. CANNeCTIN facilitates the development, conduct and leadership of large international clinical trials, registries and epidemiologic studies across Canada and the world.

As it so happens, yesterday, also saw the online publication in Diabetes Care of a paper I was involved in during my time in Hamilton on the ethnic variation of risk factors associated with obesity.

In this paper, we looked at the relationship between body weight (BMI), adipokines, and insulin resistance in 1,176 South Asian, Chinese, Aboriginal, and European Canadians in the SHARE study (Study of Health Assessment and Risk in Ethnic groups).

Adjusted mean adiponectin (a protein secreted by fat cells that improves insulin sensitivity) concentration was significantly higher in Europeans [12.9] and Aboriginals [11.8] compared to South Asians [8.8] and Chinese [8.5].

Serum leptin levels were also significantly higher in South Asians [11.8] and Aboriginals [11.1] compared to Europeans [9.2] and Chinese [8.3].

BMI and waist circumference were inversely associated with adiponectin in every group except the South Asians.

The increase in HOMA-IR (a measure of insulin resistance) for each given decrease in adiponectin was larger among South Asians and Aboriginals compared to Europeans.

Interestingly, a high glycemic index diet was associated with a larger decrease in adiponectin among South Asians and Aboriginals, and a larger increase in HOMA-IR among South Asians relative to other groups.

This study clearly shows that South Asians have the least favourable adipokine profile of the studied ethnic groups, and like the Aboriginal people, display a greater increase in insulin resistance with decreasing levels of adiponectin.

The reasons for these differences are not clear but we are studying possible mechanisms to explain these findings in South Asians in a “molecular” version of this study.

AMS
Hamilton, Ontario

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Mente A, Razak F, Blankenberg S, Vuksan V, Davis AD, Miller R, Teo K, Gerstein H, Sharma AM, Yusuf S, Anand SS, & for the SHARE, SHARE-AP investigators (2010). Ethnic variation in adiponectin and leptin levels and their association with adiposity and insulin resistance. Diabetes care PMID: 20413520