Any follower of media reports or even research papers on the relationship between obesity and mortality should be righty confused by now.
Not only are there publications suggesting that the relationship between obesity and mortality isn’t that strong after all and that perhaps the BMI levels associated with the longest survival are somewhere around 30 (and not below 25) but then there is the issue of the obesity paradox, or the finding that among people with chronic (and some acute illnesses), a higher BMI is associated with better survival than being of “normal” weight.
On the other hand, there is overwhelming evidence that higher BMI’s are associated with an increased risk of a wide range of health problems – from diabetes to cancer.
This is not to say that everyone with a higher BMI is sick – they are not! But there is no doubt that the risk of illness does increase with higher BMIs.
In our own study on the Edmonton Obesity Staging System (EOSS), which classifies individuals based on their actual health rather on their BMI, we found that while about 50% of individual in the BMI 25-30 range can be considered healthy (EOSS Stage 0 or 1), this number drops to below 15% for individuals in the BMI 40+ range.
So, if obesity is such a risk factor for disease, why do epidemiological studies struggle to consistently show an effect of obesity on mortality?
Now, a paper by Andrew Stokes and Samuel Preston, published in the Proceedings of the US National Academy of Science, suggests that it is not current weight (as used in many studies) but rather the highest lifetime weight that is most clearly associated with mortality.
Their reasoning is as follows. “Intentional” weight loss in the population is rare (very few people in the general population ever consciously manage to lose a significant amount of weight and keep it off)
In contrast, “unintentional” weight loss, when it occurs is generally a bad sign. Indeed, one of the best indicators of poor prognosis (for almost any health condition) is when someone loses weight. In many cases, this “spontaneous” weight loss can precede overt illness or death by many years.
Thus, the authors argue that most of the literature on this issue is simply confounded by the confusion caused by all the people who have unintentionally lost weight due to an underlying health problem (diagnosed or undiagnosed).
As these people would be at higher risk of death, despite measuring in at a lower weight, they muddy the waters making lower BMI levels look more dangerous (or in comparison higher BMI levels look safer) than they are.
To test their hypothesis, the researcher looked at data from the US NHANES study linked to death registers using four different approaches:
Model 1: BMI measured at the time of survey (this is the method most commonly used in epidemiological studies)
Model 2: The highest reported lifetime BMI at the time of survey
Model 3: Individuals surveyed in their current BMI class who had never been heavier compared to individuals in that BMI class who reported formerly being in a higher BMI class.
Model 4: Individuals surveyed in their current BMI class compared to people who were formerly in that BMI class but had moved to a lower BMI class by the time of the survey.
In both models 1 and 2, there was a greater risk of mortality with higher BMI class, but the relationship was stronger in model 2 (highest lifetime BMI) than in model 1 (current BMI).
In model 3, there was still an increased risk with higher BMI class but within each current BMI class, risk was higher in individuals who had previously belonged to a higher BMI class.
In model 4, mortality also rose with the highest weight achieved but was markedly higher in individuals who lost weight after achieving a particular BMI category compared to those who remained at that maximum.
These findings have important implications for our understanding of the relationship between BMI and mortality.
As the authors note,
“Confining analytic attention to survey BMI alone thus sacrifices important information provided by an individual’s maximum BMI. The poor performance of the survey-only model is especially salient because models using only BMI at survey dominate the set of findings in the literature on the relation between BMI and mortality.”
The errors in not considering highest BMI are not trivial.
“33.9% of individuals in the sample who were normal weight at survey were formerly overweight, and this group had three times the prevalence of diabetes and cardiovascular disease CVD) relative to those who were in the normal-weight category at both max and survey.”
Here is how you would interpret the data,
“Disease prevalence and mortality both rise with increases in maximum BMI and rise even further for those who reach a particular maximum BMI category and then lose weight. These patterns strongly suggest that obesity raises the risk of diabetes and CVD and that, once acquired, these diseases often precipitate weight loss….Only by using weight histories can this pattern of erasure be identified and corrected.”
The use of historical data in determining risk would not be a new concept,
“The introduction of historical data in the analysis of smoking occurred more than a half century ago, when studies began to distinguish among current-, former-, and never-smokers.”
Similarly, in the context of obesity one would need to differentiate between people who currently have obesity, people who previously had obesity, and people who never had obesity.
All of this only works, because in these type of epidemiological studies, “intentional” weight loss, be it through behaviour change, medication or surgery, is so rare as to be non-existent. Virtually all weight loss seen at a population level in “unintentional” and probably related to underlying health issues.
Thus, one should not interpret these findings to mean that someone intentionally losing weight through behavioural, medical or surgical treatments is at a higher risk for mortality – the intervention studies we have on that (this cannot be studied in population studies as there are so few cases of “treated” obesity), suggest otherwise.
For clinicians, these data point to the importance of noting the highest BMI and not just current BMI – if the patient has lost weight (especially if this is not explained by obesity treatment), then this may be a high-risk patient.
Thus, The Lancet should no doubt be commended on partnering with the World Obesity Federation to constitute an international panel of 22 experts under the leadership of Boyd Swinburn (New Zealand) and William Dietz (USA) to
“…stimulate action on obesity and strengthen accountability systems for the implementation of agreed recommendations to reduce obesity and its related inequalities and to develop new understandings of the underlying systems that are driving obesity in order to develop innovative approaches towards making those systems less obesogenic.“
While (perhaps to my surprise) I have previously heard of only one of the panelists (Shiriki Kumanyika, Emeritus Professor of Biostatistics and Epidemiology, University of Pennsylvania), I am sure that all of the panelists bring a wide range of expertise to the table.
The overall mandate of the Commission is rather ambitious, with the following declared goals:
First, the Commission will stimulate action and strengthen accountability systems for the implementation of agreed recommendations to reduce obesity and its related inequalities at global and national levels.
Second, it will develop new understandings of the underlying systems that are driving obesity and also devise innovative approaches to reorient those systems in a sustainable and scalable way to encourage healthy weight.
Third, it will also establish mechanisms for regular, independent reporting on progress towards national and global obesity targets, implementation of recommended policies and actions, and specific systems analyses of obesity drivers and solutions.
Clearly, the Commission has its work cut out for it, as their goal is to address all underlying systems that are driving obesity, including nutrition, physical activity, urban planning, food systems, agriculture, climate change, economics, governance and politics, law, business, marketing and communication, trade and investment, human rights, equity, systems science, consumer advocacy, monitoring and evaluation, Indigenous health, epidemiology, medicine, and health care.
The Commission will have its inaugural meeting in February, 2016, in Washington DC, USA, to determine its work plans.
I guess we should stay tuned to see exactly what that plan will look like.
As Canada’s national representative in the World Obesity Federation (formerly IASO), the Canadian Obesity Network is proud to co-host the 13th International Congress on Obesity in Vancouver, 1-4 May 2016.
The comprehensive scientific program will span 6 topic areas:
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
Early-bird registration is now open – click here
Abstract submission deadline is November 30, 2015 – click here
For more information including sponsorship and exhibiting at ICO 2016 – click here
I look forward to welcoming you to Vancouver next year.
Anyone who has closely followed my writings on this topic will know by now that health for a given individual cannot be measured by simply stepping on a scale (or for that matter using a measuring tape).
There are indeed individuals who appear rather healthy even at BMI levels considered to be well into the obesity range (just how many depends on your definition of “healthy”).
In an article and commentary that appears in the American Journal of Epidemiology, Juan Pablo Rey-López and colleagues from the School for Policy Studies, University of Bristol,UK, argue that the notion of “metabolically healthy obesity” (MHO), if anything is distracting and even counterproductive to public health efforts to prevent obesity.
They argue that,
“the MHO phenotype is not benign and as such has very limited relevance as a public health target.”
Throughout the article, the authors indeed make the oft-heard arguments for a population wide approach based on the notion that even a small left-shift in the weight distribution curve (as popularized by Geoffrey Rose) can have a potentially large influence on the population burden of excess weight.
This is not something anyone would argue with – at least at a population level and when the issue is prevention.
Unfortunately, Rey-López and colleagues then fall into the trap of pooh-poohing the research efforts around better trying to understand exactly why there is such a variation in how excess weight may (or may not) affect an individual’s health.
“More efforts must be allocated to reducing the distal and actual causal agents that lead to weight gain, instead of the current disproportionate scientific interest in the biological processes that explain the heterogeneity of obesity.”
Furthermore, they argue against further investments into obesity treatments:
“Nevertheless, it should be openly recognized that further investments in this predominantly individual approach will not reverse the obesity epidemic, because 1) medical therapies or dramatic lifestyle changes do not modify the distal causes of obesity (i.e., modern processed food and the built environment) and 2) individualized lifestyle modifications are commonly unsuccessful and inaccessible.“
The two facts that are largely ignored in this discussion are 1) that efforts at prevention (no matter how effective) are not helping the millions of people already living with this problem and 2) trying to find better treatments by learning more about the biology of this condition is exactly how we have found treatments for a host of other conditions ranging from diabetes to hypercholesterolemia and that these treatments have indeed allowed millions of people with these conditions to live productive and meaningful lives.
Personally, I find that the line of argument presented by the authors reeks of discrimination against people living with this problem. Thus, I cannot help but think that the authors consider people with obesity a “lost cause” not worthy of the investment into finding or providing better treatments.
Whether or not the discussions about MHO will help advance the field or not is certainly debatable.
Wether pitching prevention against treatment has the potential to actually harm people living with this problem is not.
Nevertheless, for what it is worth, a publication by Ruth Brown and colleagues from York University, Toronto, published in Obesity Research and Clinical Practice, suggests that people today may be more susceptible to obesity than just a few decades ago.
The study looks at self-reported dietary from 36,377 U.S. adults from the National Health and Nutrition Survey (NHANES) between 1971 and 2008 and physical activity frequency data from 14,419 adults between 1988 and 2006 (no activity data was available from earlier years).
Between 1971 and 2008, BMI, total caloric intake and carbohydrate intake increased 10-14%, and fat and protein intake decreased 5-9%.
Between 1988 and 2006, frequency of leisure time physical activity increased 47-120%.
However, for a given amount of caloric intake, macronutrient intake or leisure time physical activity, the predicted BMI was up to 2.3kg/m2 higher in 2006 that in 1988.
So unless there was some major systematic shift in what people were reporting (which seems somewhat unlikely) it is clear that factors other than diet and physical activity may be contributing to the increase in BMI over time – or in other words, it appears that people today, for the same caloric intake and physical activity, are more likely to have a higher BMI than people living a few decades ago.
There are of course several plausible biological explanations for these findings including epigenetics, obesogenic environmental toxins, alterations in gut microbiota to name a few.
If nothing else, these data support the notion that there is more to the obesity epidemic than just eating too much and not moving enough.