Now, a study by Constantin Gasser and colleagues from Melbourne, Australia, in a paper published in the American Journal of Clinical Nutrition, present a systematic review and meta-analysis of confectionary consumption and overweight in kids.
The researchers identified 19 studies fort their systematic review, 11 of which (∼177,260 participants) were included in the meta-analysis.
Overall, odds of excess weight of kids in the highest category of sweets consumption was about 20% less than in the reference category.
This inverse association was true for both chocolate and nonchocolate confectioneries.
Furthermore, in the longitudinal studies and the randomised controlled trial included in the review, no associations were observed between confectionery consumption and overweight, obesity, or obesity-related outcomes.
Thus, based on data from well over 175,000 kids, there appears to be no relationship between sweets consumption and excess weight – if anything, the relationship is the opposite of what one may expect.
As so often, when data don’t fit the “accepted” hypothesis, the authors are also quick to point out that these findings could well be explained by reverse causality (overweight kids avoiding sweets) or underreporting by heavier kids (a polite way of saying that heavier kids may be less honest about their candy consumption).
On the other hand, it may also well be that regular (non-restrictive) sweet consumption actually does in fact make kids less vulnerable to overeating, simply by ruining their appetite (just as grandma always warned you it would – as in, “No sweets before supper!”).
Overall, the findings remind me of a previous study that failed to find any association between sugary pop consumption and body weight in Ontario and PEI kids (if anything skinny kids in PEI drank more pop than those with excess weight).
Whatever the true answer may be, these findings certainly do not support the notion that sweet or chocolate consumption is a key factor in childhood obesity.
To anyone following the “biological” literature on obesity, it should be pretty evident by now that environmental factors can epigenetically modify genes in ways that allow “information” on environmental exposures in parents to be directly transmitted to their offspring.
Now a paper by Peter Huypens and colleagues from the Helmholtz Zentrum München, Germany, published in Nature Genetics, shows that both maternal and paternal exposure to weight gain induced by a high-fat diet in mice can substantially increase the risk for obesity in their offspring.
The key novelty in this study was the fact that the researchers isolated egg and sperm from both male and female mice that had been exposed to high-fat diets (or not) and used these germ cells in various combinations using in-vitro fertilization to create the offspring that were then implanted into surrogate female mice.
In all cases, risk for obesity as well as signs of insulin resistant tracked with both the male and female exposures, pretty much confirming that diets eaten by mothers and fathers can directly influence “genetic” risk for obesity in the next generation.
If transferable to humans (and there is little reason to doubt that this is the case), these findings suggest that a large proportion of the “heritability” of obesity is due to epigenetic modification that transfers risk from one generation to the next.
This means that efforts to prevent childhood obesity need to focus on the parents rather than the kids – kids born to mothers and fathers who have obesity are already born with a substantial higher risk than those born to lean mothers and fathers.
Perhaps our best chances of tackling obesity in the next generation of kids is to focus efforts on younger adults of child-bearing age.
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.
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.”