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.
Now, an interesting paper by Charles Spence and colleagues from Oxford University, published in Brain and Cognition, makes a strong case for how exposure to images of desirable foods (which they label ‘food porn’, or ‘gastroporn’) via digital interfaces might be inadvertently exacerbating our desire for food (what they call ‘visual hunger’).
In their paper, the authors review the growing body of cognitive neuroscience research demonstrating the profound effect that viewing such images can have on neural activity, physiological and psychological responses, and visual attention, especially in the ‘hungry’ brain.
Beginning with a brief discussion of evolutionary aspects of vision and food, the authors remind us that,
“Foraging – the search for nutritious foods – is one of the brain’s most important functions. In humans, this activity relies primarily on vision, especially when it comes to finding those foods that we are already familiar with. In fact, it has been suggested that trichromatic colour vision may originally have developed in primates as an adaptation that facilitated the selection of more energy-rich (and likely red) fruits from in-amongst the dark green forest canopy.”
“The brain is the body’s most energy-consuming organ, accounting for somewhere in the region of 25% of blood flow, or rather, 25% of the available consumed energy. Note that this figure is even higher in the newborn human, where the brain absorbs up to two thirds of the energy that is consumed by the developing organism. As Brown notes: “In embryos, the first part of the neocortex to develop is the part which will represent the mouth and tongue…” As the brain grew in size over the course of human evolution, the demands on the visual system to efficiently locate nutrients in the environment would likely also have increased.”
This notion is not trivial given our current environmental exposure to a multitude of food images:
“Our brains learnt to enjoy seeing food, since it would likely precede consumption. The automatic reward associated with the sight of food likely meant another day of sufficient nutrients for survival, and at the same time, the physiological responses would prepare our bodies to receive that food. Our suggestion here is that the regular exposure to virtual foods nowadays, and the array of neural, physiological, and behavioural responses linked to it, might be exacerbating our physiological hunger way too often. Such visual hunger is presumably also part of the reason why various food media have become increasingly successful in this, the digital age.”
And the influence of food media is widespread:
“Every day, it feels as though we are being exposed to ever more appetizing (and typically high calorie) images of food, what some (perhaps pejoratively) call ‘gastroporn’ or ‘food porn’. Moreover, the shelves of the bookstores are increasingly sagging under the weight of all those cookbooks filled with high-definition and digitally-enhanced food images. It has been suggested that those of us currently living in the Western world are watching more cookery shows on TV than ever before. Such food shows often glamorize food without necessarily telling a balanced story when it comes to the societal, health, and environmental consequences of excess consumption.”
And let’s not forget facebook and Instagram:
“At the same time, the last few years have seen a dramatic rise in the dining public’s obsession with taking images of the foods that they are about to eat, often sharing those images via their social media networks. The situation has reached the point now that some chefs are considering whether to limit, or even, on occasion, to ban their customers from taking photographs of the dishes when they emerge from the kitchen. However, one restaurant consultant and publisher has recently suggested that the way food looks is perhaps more important than ever: “I’m sure some restaurants are preparing food now that is going to look good on Instagram”.
The paper goes on to discuss at length the evidence that exposure to images of foods can alter cognitive responses and create the need for constant dietary restraint, which may be more difficult for some than others.
But not all images of food have these effects:
“These results support the view that people rapidly process (i.e. within a few hundred milliseconds) the fat/carbohydrate/energy value or, perhaps more generally, the pleasantness of food. Potentially as a result of high fat/high carbohydrate food items being more pleasant and thus having a higher incentive value, it seems as though seeing these foods results in a response readiness, or an overall alerting effect, in the human brain.”
As for the parts of the brain that are stimulated by exposure to food images – pretty much all of it. Thus, in one study:
“…the results revealed that obese individuals exhibited a greater increase in neural activation in response to food as compared to non-food images, especially for high-calorie foods, in those brain regions that are associated with reward processing (e.g., the insula and OFC), reinforcement and adaptive learning (the amygdala, putamen, and OFC), emotional processing (the insula, amygdala, and cingulate gyrus), recollective and working memory (the amygdala, hippocampus, thalamus, posterior cingulate cortex, and caudate), executive functioning (the prefrontal cortex (PFC), caudate, and cingulate gyrus), decision making (the OFC, PFC, and thalamus), visual processing (the thalamus and fusiform gyrus), and motor learning and coordination, such as hand-to-mouth movements and swallowing (the insula, putamen, thalamus, and caudate).”
But this knowledge is not all bad. There is also some evidence that digital manipulation of images of vegetables and other healthy foods can make them more attractive and thus hopefully increase their consumption. Whether or not this would actually work in practice remains to be seen.
“Given the essential role that food plays in helping us to live long and healthy lives, one of the key challenges outlined here concerns the extent to which our food-seeking sensory systems/biology, which evolved in pre-technological and food-scarce environments, are capable of adapting to a rapidly-changing (sometimes abundant) food landscape, in which technology plays a crucial role in informing our (conscious and automatic) decisions.”
Are you affected by exposure to foodporn? Is this really a problem?
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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.
For readers, who like showing images that demonstrate just how increasingly prevalent obesity is across the US, here are the 2014 obesity maps released by the US Centre for Disease Control this week.
Not that much new (unless you want to quibble about a couple of percent points here or there) – the situation is bad, with no sign of getting any better (no surprise here).
Here are the basic facts:
- No state had a prevalence of obesity less than 20%.
- 5 states and the District of Columbia had a prevalence of obesity between 20% and <25%.
- 23 states, Guam and Puerto Rico had a prevalence of obesity between 25% and <30%.
- 19 states had a prevalence of obesity between 30% and <35%.
- 3 states (Arkansas, Mississippi and West Virginia) had a prevalence of obesity of 35% or greater.
- The Midwest had the highest prevalence of obesity (30.7%), followed by the South (30.6%), the Northeast (27.3%), and the West (25.7%).
What else can one say?