Dr. Sharma's Obesity Notes

While there is no doubt that today’s kids on average are heavier than ever before, the question of how best to measure obesity in kids, and perhaps even more importantly, how to best determine the health impacts of excess weight remains unresolved.

As in adults, population studies generally use anthropometric measures such as growth curves or body mass index, but these measure, as in adults, are rarely that useful when it comes to determining the individual risks (if any) associated with excess weight.

Nevertheless, such measures, that apply to populations, although not diagnostic, may well be useful in identifying individuals who may warrant closer examination and follow-up.

A paper by Lisa Kakinami and colleagues from McGill University, Monteal, published in the Canadian Medical Association Journal, now examines the relationship between different growth curve definitions of excess weight and cardiometabolic risk factors in kids.

Specifically, the authors compare the two widely used reference standards, the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) growth curves, which have different definitions of overweight and obesity, in a representative population of 2466 boys and girls aged 9, 13 and 16 years in Quebec.

Despite different definitions of overweight and obesity, both the WHO and CDC growth curves generated virtually identical receiver operating characteristic (ROC) curves for individual or combined cardiometabolic risk factors. Small differences in specificity and sensitivity for detecting risk between the two approaches were deemed negligible.

Thus, the researcher conclude that:

“The WHO growth curves show no significant discriminatory advantage over the CDC growth curves in detecting cardiometabolic abnormalities in children aged 9-16 years.”

This of course, still leaves us with the problem of having to make clinical decisions about individuals. This is where the reported higher sensitivities (by 0.6%-8.6%) and lower specificities (by 2.6%-4.2%) of the WHO curves vs. CDC curves, still means that clinicians have to look at each kid individually to decide the appropriate course of intervention.

It is therefore perhaps of interest to note that the paediatric obesity working group within the Canadian Obesity Network (TROPIC) is currently working on adapting the adult Edmonton Obesity Staging System (EOSS) for use in kids and adolescents.

AMS
Leipzig, Germany

Kakinami L, Henderson M, Delvin EE, Levy E, O’Loughlin J, Lambert M, & Paradis G (2012). Association between different growth curve definitions of overweight and obesity and cardiometabolic risk in children. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne PMID: 22546882

.

Regular readers are well aware of the increasing evidence that points to a major role for sleep deprivation in the current obesity epidemic. Indeed, one of the most evident societal changes coinciding with the epidemic spread of excess weight is the significant reduction in sleeping hours - in both kids and adults.

Now a study by Orfeo Buxton and colleagues from Harvard University, published in Science Translational Medicine, shows just how profoundly sleep restriction and disruption of sleep cycles can affect your metabolism.

The experiments were designed to tested the hypotheses that prolonged sleep restriction with concurrent circadian disruption, as can occur in people performing shift work, impairs glucose regulation and metabolism.

Healthy adults were recruited to spend at least five weeks under controlled laboratory conditions in which they experienced an initial baseline segment of optimal sleep, three weeks of sleep restriction (5.6 hours of sleep per 24 hours) combined with circadian disruption (recurring 28-hour “days”), followed by 9 days of recovery sleep with circadian re-entrainment.

Not only die sleep restriction with concurrent circadian disruption markedly decrease participants’ resting metabolic rates but these interventions also increased plasma glucose concentrations after a meal, due to reduced pancreatic insulin secretion.

Nine days of recovery sleep normalized all of these changes.

Interestingly enough, a recent study by Korean researchers, published in the Journal of Sleep Research, looking at the relationship between sleeping patterns and body weight in almost 1,000 school children (48.2% boys) aged 10 or 11 found that, after adjusting for relevant confounding variables (age, sex, breakfast eating, screen time and parental obesity), longer sleep on weekdays and weekends was associated with 30% decreased odds of excess weight.

Perhaps, more importantly (and in line with the Harvard study), kids who slept little during the week but managed to catch up on their sleep deficit on the weekends also had a lower risk of excess weight.

Together these findings support the notion that sleep hygiene may be an important target for intervention in weight management and, at a population level, may well be an issue that may deserve as much attention and discussion as health eating and physical activity.

Is it time for a Canada Sleep Guide?

AMS
Calgary, Alberta

Buxton OM, Cain SW, O’Connor SP, Porter JH, Duffy JF, Wang W, Czeisler CA, & Shea SA (2012). Adverse metabolic consequences in humans of prolonged sleep restriction combined with circadian disruption. Science translational medicine, 4 (129) PMID: 22496545

Kim CW, Choi MK, Im HJ, Kim OH, Lee HJ, Song J, Kang JH, & Park KH (2012). Weekend catch-up sleep is associated with decreased risk of being overweight among fifth-grade students with short sleep duration. Journal of sleep research PMID: 22494030

Yesterday, I blogged about the rather weak relationship between BMI and health risks in the Manitoba Centre for Health Policy (MCHP) Report on Adult Obesity, and suggested that the results may have looked very different had the same data been analysed using the Edmonton Obesity Staging System.

Today, I want to address another interesting finding of this report, namely, the researcher’s attempts to identify the ’causes’ of obesity in Manitoba.

Variables examined included age, sex, marital status, education, employment, household income, activity restrictions, occupational physical activity, self-perceived life stress, satisfaction with life, self-rated mental health, sense of community, eating fruits and vegetables, physical activity leisure and travel, sedentary activities, current smoking, binge drinking, recent changes to improve health, food insecurity, and regular doctor.

Among these, location of residence, age, sex, education, employment, and marital status were particularly strong predictors of excess weight.

Interestingly, the psychological variables had little additional ‘effect’.

leisure– and travel–time activity level was the most strongly associated variable and showed a dose–response relationship—higher levels of activity were associated with lower likelihood of obesity. Other important variables were smoking (which was associated with a lower likelihood of obesity) and time spent in sedentary activities (more than 30 hours per week was associated with a higher likelihood of obesity).

Notably, only age and geography were significantly related to BMI values in youth.

Apart from the fact that such analyses cannot actually prove ‘causality’ as they are merely associated and therefore assumptions about modifying any of the modifiable variable will in fact reduce BMI, the researcher also made another notable observation:

“It is important to note that despite including many variables, this study was only able to explain a small amount of why people are obese. This means there are other reasons for the recent rises in weight, perhaps changes in our diets or our physical and social environment.”

Indeed, I would easily have predicted that factors not considered in this analysis, including parental BMI, birth weight, maternal weight at inception and birth of the participant, duration of sleep, etc. may well have accounted for some of the increase in obesity.

This should not detract from the importance of factors like sedentariness, stress, food insecurity and other variables that had some influence on obesity rates in this study.

It should, however, make us cautious in accepting the commonly held notion that the ‘root cause’ of obesity is simply increased sedentariness and eating too much.

Clearly, this is not the whole (and perhaps not even the biggest part) of this ’story’.

AMS
Edmonton, Alberta

Earlier this week, the Manitoba Centre for Health Policy (MCHP) released a report on Adult Obesity in Manitoba: Prevalence, Associations, and Outcomes.

The document (and especially the summary) makes an interesting read as it describes the rather complex nature of the epidemic and its impact on Manitobans.

While the analysis of about 35,000 Manitoba adults over the age of 18 who took part in one of three surveys between 1989 and 2008, documents the high prevalence of obesity and the fact that many health conditions are indeed more common in people with higher BMI’s, it also shows that these findings do not readily translate into higher healthcare costs till about a BMI of 33.

Thus, as the report summarizes:

“The Obese group almost always used more healthcare services than the other groups. However, the differences were small and often did not come into play until the very highest BMIs….people in the Obese group visited doctors more often than others. However, they only visited about 15% more overall. As well, the rise in visits only occurred from a BMI of 35 for men and 32 for women.
Likewise, costs of prescription drugs went up quite slowly until very high BMIs were reached. Hospitalizations were higher for those in the Obese group, but only for BMIs at or above 33. Home care use did not differ much either.”

This finding is actually not that surprising or unexpected.

Regular readers will by now be quite familiar with the Edmonton Obesity Staging System (EOSS), which was developed exactly because BMI is such an inadequate measure of risk or health.

Thus, I am confident that applying EOSS to this analysis would produce substantially different results than simply looking at BMI.

Thus, for e.g. our recently published analyses show that about 50% of people in the overweight category actually rank as EOSS 2/3. These individuals would considerably amplify the costs of people within the BMI 25-30 range - probably to the same level as EOSS 2/3 in the Obesity categories, while the obese EOSS 0/1 folks (of which there are about 20% in the BMI 30-35 class) would have costs very much like those of the EOSS 0/1 overweight people.

Such overlap in EOSS stages across BMI levels would readily mask any relationship between BMI and healthcare costs till rather extreme levels of BMI, where very few people will remain with EOSS 0/1 and the costs of being EOSS 2/3/4 would be substantially higher.

Thus, the ability of BMI to explore and interpret the cost of ‘obesity’ is limited, as it misses all of the ‘excess-weight-related’ health problems in the Overweight group while diluting the health care costs in the Obese group due to a substantial number of obese EOSS 0/1 people found in the moderately Obese group.

Thus, although I agree with the findings that higher health-care costs are only identifiable in individuals with moderate to severe obesity, I also sense that this report substantially underestimates the true cost of ‘excess-weight-related’ health problems.

The report also looked at ‘risk factors’ for obesity - a topic that I will comment on in tomorrow’s post.

A Summary of the Report is available here

The Complete Report is available here

AMS
Edmonton, Alberta

This morning, I am attending the annual scientific meeting of the Austrian Obesity Society, where I will be presenting a workshop on the management of severe obesity and a key-note lecture on the Canadian Obesity Network - a uniquely Canadian ’success story’ in terms of fostering professional collaboration and engagement in obesity research, prevention, and management.

Clearly, many of the issues that concern overweight and obese Austrians are very similar to what concerns us in Canada.

Thus, the topics at this meeting run the usual gamut from obstetric complications, gestational diabetes, in-utero programming and its impact on childhood obesity, to the use of behavioural, medical and surgical treatments for obesity.

Other talks focus on the impact of obesity on Austria’s health care system and discuss various aspects of health promotion.

Finally, there is also a whole session on the proposed Austrian Obesity Management guidelines, which emphasize managing both the psychological and somatic aspects of excess weight.

As elsewhere, bariatric surgery is also on the rise in Austria and this is clearly reflected in several talks on surgical management of severe obesity - a topic that will certainly continue to gain importance till we come up with better conservative treatments.

I would like to thank my Austrian colleagues Anita Rieder and Hermann Toplak for inviting me to speak at this meeting and being such wonderful hosts.

AMS
Seggau, Austria