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Welcome To The International Congress on Obesity, Vancouver 2016

ICO2016This weekend sees the start of the XIII International Congress on Obesity (ICO), hosted by the World Obesity Federation in partnership with the Canadian Obesity Network (CON) in Vancouver, Canada.

As this year’s Congress President, together with World Obesity Federation President Dr. Walmir Coutinho, it will be our pleasure to welcome delegates from around the world to what I am certain will be a most exciting and memorable event in one of the world’s most beautiful and livable cities.

The program committee, under the excellent leadership of Dr. Paul Trayhurn, has assembled a broad and stimulating program featuring the latest in obesity research ranging from basic science to prevention and management.

I can also attest to the fact that the committed staff both at the World Obesity Federation and the Canadian Obesity Network have put in countless hours to ensure that delegates have a smooth and stimulating conference.

The scientific program is divided into six tracks:

Track 1: From genes to cells

  • For example: genetics, metagenomics, epigenetics, regulation of mRNA and non–coding RNA, inflammation, lipids, mitochondria and cellular organelles, stem cells, signal transduction, white, brite and brown adipocytes

Track 2: From cells to integrative biology

  • For example: neurobiology, appetite and feeding, energy balance, thermogenesis, inflammation and immunity, adipokines, hormones, circadian rhythms, crosstalk, nutrient sensing, signal transduction, tissue plasticity, fetal programming, metabolism, gut microbiome

Track 3: Determinants, assessments and consequences

  • For example: assessment and measurement issues, nutrition, physical activity, modifiable risk behaviours, sleep, DoHAD, gut microbiome, Healthy obese, gender differences, biomarkers, body composition, fat distribution, diabetes, cancer, NAFLD, OSA, cardiovascular disease, osteoarthritis, mental health, stigma

Track 4: Clinical management

  • For example: diet, exercise, behaviour therapies, psychology, sleep, VLEDs, pharmacotherapy, multidisciplinary therapy, bariatric surgery, new devices, e-technology, biomarkers, cost effectiveness, health services delivery, equity, personalised medicine

Track 5: Populations and population health

  • For example: equity, pre natal and early nutrition, epidemiology, inequalities, marketing, workplace, school, role of industry, social determinants, population assessments, regional and ethnic differences, built environment, food environment, economics

Track 6: Actions, interventions and policies

  • For example: health promotion, primary prevention, interventions in different settings, health systems and services, e-technology, marketing, economics (pricing, taxation, distribution, subsidy), environmental issues, government actions, stakeholder and industry issues, ethical issues

I look forward to welcoming my friends and colleagues from around the world to what will be a very busy couple of days.

For more information on the International Congress on Obesity click here

For more information on the World Obesity Federation click here

For more information on the Canadian Obesity Network click here

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Common Scientific And Statistical Errors In Obesity (And Other) Research

sharma-obesity-statisticsThe quality of scientific evidence can only ever be as good as the research methodology and data analyses that goes into creating this evidence.

While poor research methodology, flawed statistical analyses and overstating of findings is by no means particular to obesity research, the wide public interest in the topic of obesity (causes, prevention, treatments) means that flawed outcomes from flawed studies get transmitted to a much larger audience of individuals with a keen interest in this topic.

Thus, the danger of flawed research contributing to widely held misconceptions about obesity can directly lead to poor public policy and ineffective interventions that perhaps have a much broader impact that in other fields of health research.

Thus, it is admirable, that the latest issue of OBESITY features three articles on issues related to the quality of research in this field, highlighting some of the most common and pervasive methodological shortcomings of much of the work.

Thus, for e.g. a paper by Brandon George and colleagues list the 10 most common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research.

These include, in no particular order, issues related to

1) misinterpretation of statistical significance,
2) inappropriate testing against baseline values,
3) excessive and undisclosed multiple testing and “P-value hacking,”
4) mishandling of clustering in cluster randomized trials,
5) misconceptions about nonparametric tests,
6) mishandling of missing data,
7) miscalculation of effect sizes,
8) ignoring regression to the mean,
9) ignoring confirmation bias, and
10) insufficient statistical reporting.

The authors go on to explain each of these errors, citing specific examples from the literature on each.

Most importantly, they also discuss ways to identify such errors and (even better) minimise or avoid them.

As most of these problems are related to statistical handling of the data, the authors passionately argue for the inclusion or at least consultation of statisticians in the both the research and reporting stages of the scientific process, to hopefully produce higher quality, more valid, and more reproducible results.

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How Much Of The Confusion In Childhood Obesity Studies Is Simply Regression To The Mean?

sharma-obesity-statisticsWhen it comes to childhood obesity interventions, there is much confusion as to what works and what doesn’t.

Thus, for every study showing that a given “intervention” (e.g. school intervention programs, exercise programs, removing vending machines, etc. ) changes weight measures, there is at least another study showing that it doesn’t.

Although this problem is by no means specific to research in childhood obesity, for reasons stated below, research in this area appear to be particularly prone to this problem.

Now, a paper by Cockrell Skinner Asheley and colleagues, published in Childhood Obesity, suggests that much of this confusion may simply be due to the statistical phenomenon of “regression to the mean” (RTM).

As readers may be well aware, regression to the mean refers to the well-described phenomenon that “outliers” (up or down) tend to “regress” towards the mean on repeated measures.

Or as the authors explain,

“Today, RTM is often conceptualized primarily in the context of measurement error or repeated measures. Blood pressure provides a reasonable example. If one measure of blood pressure is obtained and is either much higher or lower than the mean, a second measure will likely be closer to the mean. If conceptualized as measurement error, then an average of multiple measures is often used to reduce measurement error, thereby also reducing regression to the mean.”

Repeated measures however do not solve the problem when the measured values actually do change over time (as in a child’s body weight). As the authors note,

“However, this does not address changes in the true value of the variable over time, which are not due to measurement error. Whenever two variables are not perfectly correlated (such as blood pressure at two time points), there will always be RTM when measured in terms of standardized variables. This occurs regardless of measurement error, the order of measurement, and whether the two variables are repeated measures of the same construct. Additionally, as noted by Barnette et al., regression to the mean can occur in nonnormal distributions and those that are not continuous. For example, RTM can occur in binary data and cause subjects to change categories without a change in their actual status.”

While this issue tends to affect all types of research, which is why every experiment would ideally have rigorous controls and the most robust research methods generally use some form of randomisation, this is particularly difficult in studies in childhood obesity.

“Many intervention efforts, including policy changes and community-based interventions, do not easily lend themselves to the gold standard randomized, controlled trial (RCT) designs. Quasi-experimental designs provide stronger evidence than do uncontrolled interventions in which investigators simply look at change from baseline in a single group of treated cases. These designs, which lack the element of randomization, are common in pediatric obesity research, and include cohort studies, regression discontinuity, and panel analysis.”

“One of the most common errors associated with RTM, particularly in the obesity literature, is concluding an intervention is effective when the study design does not permit such a conclusion. Reports of school-based interventions commonly ignore this effect of RTM, reporting reductions in BMI z-score and prevalence of obesity, with no comparator other than baseline. Community-based interventions also claim success in reducing weight and blood pressure even when lacking a control group, as do many clinic treatment studies.

The researchers give a number of examples from the childhood obesity literature, where “findings” can easily be explained by RTM and highlight some of the erroneous conclusions that can be made when studies lack control groups or no consideration is given to RTM in power calculations or data analyses.


“RTM can also be mistaken for evidence of differential effects of treatment as a function of baseline values on the outcome variable. Differential RTM indicates that RTM will be greater among groups defined as being further from the mean than other groups. One example involves weight gain among patients taking antipsychotics. Some studies noted that patients with higher baseline BMI gained less weight when taking an antipsychotic drug than did those with lower baseline BMI. Although this was initially interpreted to mean that the drugs caused less weight gain among persons who were more obese at baseline and thereby mitigated concerns about drug-induced weight gain, subsequent analyses showed that there was no evidence of such differential effects of the drugs as a function of baseline BMI, but rather just different expected weight changes as a function of baseline BMI, as is expected solely from RTM. Erroneous comparison of nonequivalent groups can also be seen when investigators report greater declines in BMI among study participants with higher baseline BMI compared to those with lower baseline BMI, and label it as evidence for differential treatment efficacy by baseline BMI.”

The authors then go on to suggest several ways in which to correct analyses for such effects or to better design studies and statistical analyses to avoid erroneous interpretation of findings (both positive and negative). In all of this, the importance of proper controls is paramount.

This issue is far from trivial  as many costly but ineffective policy or treatment interventions may be implemented based on “promising” findings that are simply attibutable RTM.

On the other hand, interventions that are in fact effective may fail to be implemented or be discarded because RTM masks their actual benefits.

Not least, failing to consider RTM in the design, implementation and analyses of research (particularly the type of research that by its nature lacks proper controls) can be a huge waste of valuable research funding and resources.

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Obesity Network Launches Online Advanced Obesity Course

ABOM logoOne of the Canadian Obesity Network’s key missions is to advance knowledge about evidence-based obesity management amongst health professionals – not least physicians.

Thus, I am excited to see that the Network has now launched a new online Advanced Obesity Management Program that will help physicians (and other health professionals) build upon their existing knowledge base to better support their patients in obesity prevention and treatment.

Developed in collaboration with the MDBriefCase, this multi-part program will allow practitioners to acquire advanced knowledge relevant to their practice.

The course is specifically geared towards preparing for the American Board of Obesity Medicine (ABOM) exam.

The first two modules are now online, the remainder are expected by May 2016.

The entire program is approved for 10 Mainpro-M1 CME Credits.

You can save 25% when you use Coupon Code “AOBlaunch” before March 31, 2016.

To sign up for the course click here

Edmonton, AB

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Overview of Obesity Management in Primary Care

sharma-obesity-doctor-kidGiven the vast number of individuals who could potentially benefit from effective long-term obesity management, there is no option but to manage most of this problem in primary care settings.

While this approach can be highly effective, it does require training, resources and ongoing (lifelong?) interventions (not unlike most other chronic diseases).

Now a rather comprehensive paper by Soleyman and colleagues from the University of Birmingham, Alabama, published in Obesity Reviews provides an overview of obesity management in primary care.

As readers are well aware, our body weight are tightly regulated by a complex neuroendocrine system and defends us agains weight loss through a multi-faceted physiological response to prevent further weight loss and restore body weight.

As the authors note,

“To maintain weightloss, individuals must adhere to behaviours that oppose these physiological adaptations and the other factorsfavouring weight regain. However, it is difficult for peoplewith obesity to overcome physiology with behaviour over the long term. Common reasons for weight regain include decreased caloric expenditure, decreased self-weighing frequency, increased caloric intake, increased fat intake and eating disinhibition over time.”

The paper provides a succinct overview of the evidence supporting behavioural, medical and surgical obesity treatments.

It also reiterates the basic principles of obesity management as outlined in the various guidelines:

1. Obesity is a chronic disease that requires long-term management. It is important to approach patients with information regarding the health implications.

2. The goal of obesity treatment is to improve the health of the patient, and it is not intended for cosmetic purposes.

3. The cornerstone of therapy is comprehensive lifestyle intervention from informed PCPs or other healthcare professionals.

4. The initial goal of therapy is a weight loss of 5–10% in most patients, as this is sufficient to ameliorate many weight-related complications. However, weight loss of ≥10% may be needed to improve certain weight-related complications, such as obstructive sleep apnoea.

5. Consideration should be given to the use of a weight-loss medication or possible bariatric surgery, as the addition of these treatment modalities to lifestyle therapy can promote greater weight loss and maintain the weight loss for a longer period of time.

6. It is important for clinicians to evaluate the patient for weight-related complications, that can be improved by weight loss, and to consider such patients for more aggressive treatment.

These recommendations (with minor variations) are also very much in line with the 5As of Obesity Management framework championed by the Canadian Obesity Network.

As for how to get more primary care clinics to actually implement these approaches, the authors note that,

“Primary care practitioners need to address the problem of obesity in their patients, just as they would with any other chronic condition such as hypertension or type 2 diabetes, and to ensure that their patients are aware of the health risks of obesity.”

Again something that the Canadian Obesity Network is working hard to promote in this country.

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