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Will WHO STOPS Childhood Obesity Promote Weight Bias And Unhealthy Body Image?

sharma-obesity-kids-playing-outsideChildhood obesity is a grave concern and so far community based interventions to prevent it have been rare and far between, with little evidence that any changes (however meagre) are in fact sustainable over time and will actually lead to a reduction in adult obesity.

Thus, the Australian team of Steven Allander and colleagues must be commended on embarking on what I believe will be the first cluster randomized trial in ten communities in the Great South Coast Region of Victoria, Australia to test whether it is possible to: (1) strengthen community action for childhood obesity prevention, and (2) measure the impact of increased action on risk factors for childhood obesity.

According to the trial design published in the International Journal of Environmental Research in Public Health, the WHO STOPS intervention will involve a facilitated community engagement process that: creates an agreed systems map of childhood obesity causes for a community; identifies intervention opportunities through leveraging the dynamic aspects of the system; and, converts these understandings into community-built, systems-oriented action plans.

Ten communities will be randomized (1:1) to intervention or control in year one and all communities will be included by year three.

The primary outcome is childhood obesity prevalence among grade two (ages 7–8 y), grade four (9–10 y) and grade six (11–12 y) students measured using established community-led monitoring system (69% school and 93% student participation rate in government and independent schools).

An additional group of 13 external communities from other regions of Victoria with no specific interventions will provide an external comparison.

All of this makes sense and is highly commendable.

What is shockingly lacking however – at least I see no mention of this in the published study design – is the inclusion of an explicit focus on what such community interventions aimed at reducing childhood obesity, will do to self-esteem and body image of the kids involved and weight bias in the communities overall.

Indeed, I see no mention of anyone with an explicit expertise in weight bias or kids mental health on the panel of researchers involved in this study.

This is concerning, as we now understand well that body image concerns and both implicit and explicit weight bias begin in kindergarten-age kids and must acknowledge that the “moral panic” created around childhood obesity has been accused of further promoting eating disorders, body image issues and weight bias.

Thus, we have here the unique opportunity to study the potential harm that could be done by school “surveillance” programs that assess body weight in kids or by the well-meant education on “healthy activity and healthy eating” that may teach kids that obesity is simply a result of making poor choices and not moving enough (rather than a complex biopsychosocial chronic disease, that is highly resistant to lasting effects of time-limited interventions).

I would sincerely appeal to the researchers involved to amend their study protocol to include changes in weight bias, unhealthy weight obsessions, body image issues, and eating disorders both at the level of the kids and the community overall, to ensure that the well-meant interventions do not inadvertently replace one problem with another – as always, the Devil of public health interventions lies in the unintended consequences.

In fact, if I was on the ethics committee tasked with approving this study, I would insist that an in-depth assessment plan for the potential harm of this intervention be in place before commencement of any study related activities in the relevant communities.

If the overall goal of the WHO STOPS intervention is to have a healthier generation of kids, nothing is more important than fully understanding the potential impact of this intervention on mental health and social attitudes towards kids and adults living with obesity.

Copenhagen, DK


Do Endocrine Disrupters Play A Role in Obesity?

sharma-obesity-universal-theoryThere are now over 1,000 synthesised chemicals that have been characterised as endocrine disrupters – i.e. exogenous chemical entities that interfere with the body’s hormonal function.

Exposure to endocrine disrupters in humans included our diets, personal care products, antimicrobial soaps, household or agricultural pesticides, and cleaning products.

Now, the Endocrine Society has produced its second Scientific Statement on environmental endocrine-disrupting chemicals (EDC-2), published in Endocrine Reviews. (a summary appears in JAMA Internal Medicine)

The major EDC classes reviewed were industrial chemicals (polychlorinated biphenyls [PCBs], dioxins), pesticides, plastics and plasticizers (bisphenol A [BPA] and phthalates), perfluorinated compounds, and flame retardants.

As for the relationship between ECDs and obesity, the authors summarize:

“In animals, several EDCs now referred to as obesogens and diabetogens were associated with obesity and DM2, respectively, with results dependent on the chemical, dosage, and age of exposure. The evidence is strongest for tributyltin, persistent organic pollutants (POPs), pesticides, and BPA. Some EDCs exert actions on adipogenesis, or on pancreatic β- and α-cells. Developmental EDC exposures also led to insulin resistance and hyperinsulinemia and were associated with alterations in serum adiponectin and leptin. Although the mechanisms varied, they included effects mediated via the aryl hydrocarbon receptor, peroxisome proliferator-activated receptor γ (PPARγ), and estrogen receptors (ERs). Furthermore, limited evidence suggests that the hypothalamic control of energy balance may be perturbed. Cross-sectional epidemiological data showed associations between EDCs, obesity, and/or DM2, although causality cannot be inferred. Less is known about EDCs and cardiovascular disease, but emerging work suggests that this merits further research.”

I have little doubt that both exposure and susceptibly varies widely between individuals, however there are currently no tests that would allow us to discern the contribution of ECDs to obesity in a given individual.

Thus, while the magnitude of the contribution of ECDs to any given person’s weight problem may be hard to diagnose and even harder to manage, the findings do remind us that the environmental drivers of obesity may well go beyond just our foodscape and sedentariness.

Free access to the Executive Summary of the Statement is available here

Edmonton, AB


5th Canadian Obesity Summit – Four More Days To Submit Your Abstracts!

banff-springs-hotelEvery two years the Canadian Obesity Network holds its National Obesity Summit – the only national obesity meeting in Canada covering all aspects of obesity – from basic and population science to prevention and health promotion to clinical management and health policy.

Anyone who has been to one of the past four Summits has experienced the cross-disciplinary networking and breaking down of silos (the Network takes networking very seriously).

Of all the scientific meetings I go to around the world, none has quite the informal and personal feel of the Canadian Obesity Summit – despite all differences in interests and backgrounds, everyone who attends is part of the same community – working on different pieces of the puzzle that only makes sense when it all fits together in the end.

The 5th Canadian Obesity Summit will be held at the Banff Springs Hotel in Banff National Park, a UNESCO World Heritage Site, located in the heart of the Canadian Rockies (which in itself should make it worth attending the summit), April 25-29, 2017.

Yesterday, the call went out for abstracts and workshops – the latter an opportunity for a wide range of special interest groups to meet and discuss their findings (the last Summit featured over 20 separate workshops – perhaps a tad too many, which is why the program committee will be far more selective this time around).

So here is what the program committee is looking for:

  • Basic science – cellular, molecular, physiological or neuronal related aspects of obesity
  • Epidemiology – epidemiological techniques/methods to address obesity related questions in populations studies
  • Prevention of obesity and health promotion interventions – research targeting different populations, settings, and intervention levels (e.g. community-based, school, workplace, health systems, and policy)
  • Weight bias and weight-based discrimination – including prevalence studies as well as interventions to reduce weight bias and weight-based discrimination; both qualitative and quantitative studies
  • Pregnancy and maternal health – studies across clinical, health services and population health themes
  • Childhood and adolescent obesity – research conducted with children and or adolescents and reports on the correlates, causes and consequences of pediatric obesity as well as interventions for treatment and prevention.
  • Obesity in adults and older adults – prevalence studies and interventions to address obesity in these populations
  • Health services and policy research – reaserch addressing issues related to obesity management services which idenitfy the most effective ways to organize, manage, finance, and deliver high quality are, reduce medical errors or improve patient safety
  • Bariatric surgery – issues that are relevant to metabolic or weight loss surgery
  • Clinical management – clinical management of overweight and obesity across the life span (infants through to older adults) including interventions for prevention and treatment of obesity and weight-related comorbidities
  • Rehabilitation –  investigations that explore opportunities for engagement in meaningful and health-building occupations for people with obesity
  • Diversity – studies that are relevant to diverse or underrepresented populations
  • eHealth/mHealth – research that incorporates social media, internet and/or mobile devices in prevention and treatment
  • Cancer – research relevant to obesity and cancer

…..and of course anything else related to obesity.

Deadline for submission is October 24, 2016

To submit an abstract or workshop – click here

For more information on the 5th Canadian Obesity Summit – click here

For sponsorship opportunities – click here

Looking forward to seeing you in Banff next year!

Edmonton, AB


Higher BMI In Identical Twins Increases Risk of Diabetes But Not Heart Attacks?

sharma-obesity-blood-sugar-testing1Increased BMI is often touted as a major risk factor for cardiovascular disease. However, this relationship is not as straightforward as most of us believe.

Now a study by Peter Nordström and colleagues, published in JAMA Internal Medicine, reports that a higher BMI in identical twins is associated with a greater risk for type 2 diabetes but not myocardial infarction or death.

The researchers looked at data from 4,046 monzygous twin pairs with discordant BMIs (difference >0.01 units) from the nationwide Swedish twin registry.

During a mean follow-up of 12 years, the rate of myocardial infarcts and deaths were similar in the twins with lower BMI compared to their higher BMI co-twin (5.0% vs. 5.2% and 13.6% vs. 15.6%, respectively).

This lack of difference remained true even when the researchers compared the extremes of BMI discordance and only considered twins with BMI greater than 30.

In contrast, both higher BMI and greater increase in BMI since 30 years before baseline was associated with greater risk of incident diabetes.

Given that diabetes is such a powerful risk factor for cardiovascular disease, one can only wonder why this did not translate into a higher cardiovascular risk in the higher weight twins.

One possible explanation, offered by the authors is that cardiovascular risk may have been well managed in these individuals thus minimizing any increased risk due to diabetes (or other BMI associated risk factors such as dyslipidemia or hypertension).

Indeed, it would probably have required a far larger group of twins (or much longer follow-up) to fully rule out higher cardiovascular risk in these twins.

Let us also not forget that BMI is a rather lousy measure of overall cardiovascular risk.

Thus, which the study is certainly compatible with the (genetics-independant?) role of higher BMI in the risk for diabetes, it certainly should not be interpreted as demonstrating that this increased risk in benign in terms of cardiovascular disease.

Edmonton, AB


How Do People With Obesity Spend Their Time?

time spiralWe live in a time where most of us complain about the lack of it. Thus, I often remind myself that our “fast-food culture” is more a time than a food problem.

Now a study by Viral Patel and colleagues, published in OBESITY, takes a detailed look at how US Americans spend their time according to different BMI categories.

The researchers analyse data from over 28,503 observations of individuals aged 22 to 70 from the American Time Use Survey, a continuous cross-sectional survey on time use in the USA.

In a statistical model that adjusted for various sociodemographic, geographic, and temporal characteristics, younger age; female sex; Asian race; higher levels of education; family income >$75 k; self-employment; and residence in the West or Northeast census regions were all associated with a lower BMI relative to reference categories whereas age 50 to 59 years; Black, Hispanic, or “other” race; and not being in the labor force were associated with a higher BMI.

That said, here are the differences in time use associated with higher BMI:

Although there were no substantial differences among BMI categories in time spent sleeping, overweight individuals experienced almost 20 fewer minutes of sleeplessness on weekends/holidays than individuals with normal weight. Furthermore, there was a U-shaped relationship between BMI and sleep duration such that BMI was lowest when sleep duration was approximately 8 h per day and increased as sleep duration became both shorter and longer. Less sleep on weekends and holidays (5 to 7 h) was also associated with higher BMI than 8 to 9 h or sleep.

There were also no major differences between BMI categories and the odds of participating in work or in the amount of time working. However, working 3-4 h on weekends/holidays was associated with the lowest BMI. Individuals with obesity were more likely to be working between 3:30 a.m. and 7:00 a.m. on weekdays than normal-BMI individuals, again perhaps cutting into restful sleep.

Individuals with obesity were less likely to participate in food and drink preparation than individuals with normal weight on weekdays but spent about the same amount of time eating or drinking as the reference category.

Interestingly, individuals with obesity were more likely than individuals with normal weight to participate in health-related self-care, and overweight individuals spent over 1 h more on weekdays than individuals with normal weight on health-related self-care and also spent an additional 15 min (almost double the time) on professional and personal care services.

While individuals with higher BMI were less likely to participate in sports, exercise, and recreation on weekdays and weekends/holidays compared with individuals with normal weight, those who did participate did not differ from individuals with normal weight in the amount of time spent participating. In contrast, overweight individuals were more likely to attend sports/recreation events during the week and spent an additional 47 min (almost 25% more) on this activity than individuals with normal weight.

Overall, there was a positive and generally linear association between time spent viewing television/movies and BMI, with individuals with obesity more likely to watch television almost all hours of the day during the week and weekends.

On weekends/holidays, individuals with obesity were more likely to participate in care for household children and household adults. It was also observed that individuals with obesity spent an additional 15 min on religious and spiritual activities on weekends/holidays, compared with normal-BMI individuals (who spent 116 min).

While these data are of interest and are largely consistent with the emerging data on the role of optimal sleep duration and the detrimental impact of sedentary activities like television viewing on body weight, we must remember that the data are cross-sectional in nature and cannot be interpreted to imply causality (as, unfortunately, the authors do throughout their discussion).

Also, no correction is made for increasing medical, mental, or functional limitations associated with increasing BMI levels, which may well substantially affect time use including sleep, work, participation in sports or work-related activities.

Thus, it is not exactly clear what lessons one can learn regarding possible interventions – it is one thing to describe behaviours – it is an entirely different thing to try and understand why those behaviours occur in the first place.

Thus, unfortunately, findings from these type of studies too often feed into the simplistic and stereotypical “obesity is a choice” narrative, which does little more than promote weight bias and discrimination.

Edmonton, AB