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
There are now more overweight than underweight people in the world today and there is little hope that there will be any noticeable reduction in this trend till 2025.
That essentially is the key message from a “landmark” paper with data from almost 20 million people (and a similar number of authors) from around the world published in The Lancet.
The researchers looked at data from 1698 population-based data sources from 200 countries for obesity prevalence data between 1975 and 2014.
The paper has wonderful maps and graphics that I am sure will find their way into many presentations on obesity (including mine) to demonstrate both the magnitude and ubiquity of the problem (not to say that underweight still remains a substantial problem in many parts of the world with problems at both ends of the weight spectrum often co-existing within the same countries).
Most alarmingly, as the authors point out, is the trend for severe obesity which will soon affect as many as 6-9% of the population in some high- and middle-income countries.
According to the authors,
“Even anti-hypertensive drugs, statins, and glucose-lowering drugs will not be able to fully address the hazards of such high BMI levels, and bariatric surgery might be the most effective intervention for weight loss and disease prevention and remission.”
Now may may well be true but it is also highly unrealistic.
At the rates that bariatric surgery is currently accessible in most countries, it will only take a 100 years (or more) to treat everyone who already meets the criteria today.
What we really need are better medical treatments that are effective, safe and scaleable (in the same manner that we have scaled the use of anti-hypertensive drugs, statins and glucose-lowering drugs to address hypertension, dyslipidemia or diabetes, respectively).
Better obesity treatments will be desperately needed while we wait for population interventions to hopefully begin reducing obesity rates by 2025.
San Diego, CA
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.
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.
A blood pressure that is too high can kill you – so can a blood pressure that is too low.
A blood sugar that is too high can kill you – so can a blood sugar that is too low.
It turns out that BMI is no different – too high and too low both carry a risk – a risk, however, that is substantially confounded by actual body fat%, which is not reliably measured by BMI.
This is basically the message in a paper by my colleagues Raj Padwal and co from the University of Alberta in a paper published in the Annals of Internal Medicine.
The researchers looked at data from about 50,000 women and 5,000 men (mean age, 63.5 years; mean BMI, 27.0 kg/m2) referred for bone mineral density (BMD) testing with dual-energy x-ray absorptiometry (DXA), which they linked to administrative databases.
Given the size and demographics of the cohort, death occurred in almost 5000 women over a median of 6.7 years and 1000 men over a median of 4.5 years.
Women in the lowest BMI and body fat% quintiles had a 40% higher risk of dying (compared to quintile 3). Risk of dying were also about 20% greater in the highest body fat% quintile for women.
Similarly in men, both low BMI (HR, 1.45 for quintile 1) and high body fat percentage (HR, 1.59 for quintile 5) were associated with increased mortality.
The exciting bit about this study is that the researchers had both BMI and body fat% available to them and were able to show that both variables independently of each other contribute to mortality risk.
Thus, the worst possible combination in both men and women was low BMI and high body fat%.
Or, as the authors put it,
“Low BMI and high body fat percentage were both associated with increased all-cause mortality. Mortality increased as BMI decreased and body fat percentage increased…..Thus, our results suggest that BMI may be an inappropriate surrogate for adiposity, and this limitation may explain the presence of the obesity paradox in many studies.”
As the authors discuss, these finding should have clinical implications as they clearly demonstrate the limitations of BMI as a measure of health risk.
“..our findings underscore that the risk for all-cause mortality increases with both increasing adiposity and decreasing BMI in a general population of middle-aged and older adults. These findings also suggest the importance of using direct measures of adiposity when building prognostic or even exploratory models.”