The biguanide metformin is widely used for the treatment of type 2 diabetes. Metformin has also been shown to slow the progression from pre to full-blown type 2 diabetes. Moreover, metformin can reduce weight gain associated with psychotropic medications and polycystic ovary syndrome.
Now, a randomised controlled trial by M P van der Aa and colleagues from the Netherlands, published in Nutrition & Diabetes suggests that long-term treatment with metformin may stabilize body weight and improve body composition in adolescents with obesity and insulin resistance.
The randomised placebo-controlled double-blinded trial included 62 adolescents with obesity aged 10–16 years old with insulin resistance, who received 2000 mg of metformin or placebo daily and physical training twice weekly over 18 months.
Of the 42 participants (mean age 13, mean BMI 30), BMI was stabilised in the metformin group (+0.2 BMI unit), whereas the control group continued to gain weight (+1.2 BMI units).
While there was no significant difference in HOMA-IR, mean fat percentage reduced by 3% compared to no change in the control group.
Thus, the researcher conclude that long-term treatment with metformin in adolescents with obesity and insulin resistance can result in stabilization of BMI and improved body composition compared with placebo.
Given the rather limited effective options for addressing childhood obesity, this rather safe, simple, and inexpensive treatment may at least provide some relief for adolescents struggling with excess weight gain.
Please support European Obesity Day
European Obesity Day (EOD) takes place this coming Saturday, 21 May, and is aimed at raising awareness and increasing knowledge about obesity and the many other diseases on which it impacts.
EOD is a major annual initiative for the European Association for the Study of Obesity (EASO) and so would like to ask you to support the activities by joining in the conversation on social media. It will help us to reach more of the policymakers, politicians, healthcare professionals, patients and the media who we are targeting with important messages about the need to take obesity more seriously.
There are several ways you can show your support:
Like the European Obesity Facebook Page
Follow EOD on Twitter @EOD2016
Join the conversations on twitter using the hashtag #EOD2016
Pledge your support on the European Obesity Day website
Visit the EOD website to see what we have been doing
Encourage your friends and colleagues to support us too
In line with the Action for a Healthier Future theme for EOD 2016, we hope we can count on your support.
Regular readers are by now familiar with the Edmonton Obesity Staging System (EOSS), that classifies individuals with obesity based on its impact on physical, mental and functional health.
Now, Stasia Hadjiyannakis and colleagues present an adaptation of EOSS for kids, published in Pediatrics and Child Health.
The evidence-informed paediatric clinical obesity staging system (EOSS-P), builds on EOSS for adults and captures the severity of disease, as well as factors that complicate management, within four domains of health most commonly encountered in obesity:
The EOSS-P assesses four main domains that are impacted by obesity and can impact responsiveness to weight management – metabolic, mechanical, mental, milieu:
Metabolic complications of paediatric obesity include glucose dysregulation (including type 2 diabetes [T2D]), dyslipidemia, the metabolic syndrome, nonalcoholic fatty liver disease, hypertension and, in adolescent females, polycystic ovary syndrome. Metabolic complications are often asymptomatic and must be screened for to be identified. Screening should begin at two years of age for lipid disorders, three years of age for hypertension and at 10 years of age or at the onset of puberty, if this occurs earlier, for diabetes. Metabolic complications of obesity can improve significantly through changes in health behaviour with minimal change in BMI.
Biomechanical complications of paediatric obesity include sleep apnea, sleep disordered breathing, gastroesophageal reflux disease, and musculoskeletal pain and dysfunction. The presence of sleep apnea and/or sleep disordered breathing can exacerbate the metabolic complications of obesity, have deleterious neurobehavioural effects, and affect appetite and food intake. Biomechanical complications can be barriers to weight management and affect prognosis. If left inadequately treated, biomechanical complications of obesity can promote further weight gain.
Children and youth with obesity are at risk for social isolation and stigmatization. Childhood psychiatric disorders (eg, depression, anxiety), school difficulties, body dissatisfaction, dysregulated eating behaviours, teasing and bullying have all been linked to paediatric obesity. Children and youth with obesity have consistently reported lower health-related quality of life compared with normative samples. Mental health disorders, as well as some of the pharmacotherapeutic agents that are used to manage them, can complicate weight management, promote weight gain and affect prognosis.
An assessment of the family, school and neighbourhood milieus (the social milieu) is unique to the paediatric staging system and is important given the key role that parents, family members, schools and communities/neighbourhoods play in the health and wellbeing of children and youth. School difficulties and family factors, such as poor parental health, maternal depression, poor family functioning, receipt of social assistance, lack of emotional support, single parenthood and maternal drug use, have been associated with childhood obesity. Exposure to greater levels of psychosocial stress has been associated with higher levels of self-reported illness and negative health outcomes. Parental involvement and support are integral to successful paediatric obesity management.
The EOSS-P can be applied to children with obesity who are ≥2 years of age. The staging system is a tool reliant on clinician ratings, which are based on common clinical assessments including medical history, clinical examination and routine investigations. The EOSS-P is based on the presence and degree of the 4Ms with four stages of increasing health risk severity (0, 1, 2 and 3). The 4Ms are distinct categories, and progression in one of the categories does not necessarily coincide with a concomitant increase in the others. Individuals are assigned the highest stage in which they present with any metabolic, mechanical, mental health or social milieu risk factors.
As the authors note,
“This assessment tool can help support improved clinical and administrative decisions regarding the allocation of resources (ie, human, financial, time) for obesity management, and provide a platform for future research and clinical care designed to individualize therapeutic options.”
I have little doubt that clinicians will welcome this adaptation of EOSS for pediatric care as enthusiastically as they have welcomed the adult version of EOSS.
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
Much of the research on the contribution of screen time, sedentariness, food consumption and other factors comes from cross-sectional or longitudinal studies, where researchers essentially describe correlations and statistical “effect sizes”.
To be at all meaningful, analyses in such studies need to be adjusted for known (or at least likely) confounders (or at least the confounders that happen to available).
No matter how you turn and wind the data, such studies by definition cannot prove causality or (even less likely) predict the outcome of actual intervention studies.
Nevertheless, such studies can be helpful in generating hypotheses.
Thus, for example, I read with interest the recent paper by Lei Shang and colleagues from the University of Laval, Quebec, Canada, published in Preventive Medicine Reports.
The researchers looked at cross-sectional data on 630 Canadian children aged 8-10 years with at least one obese biological parent.
While the overall median daily screen time was about 2.2 hours, longer screen time was associated with higher intake of energy (74 kcal) and lower intake of vegetables & fruit (- 0.3 serving/1000 kcal).
This unhealthy “effect” of screen time on diet appeared even stronger among children with overweight.
Thus, there is no doubt that the study shows that,
“Screen time is associated with less desirable food choices, particularly in overweight children.”
The question of course remains whether or not this relationship is actual causal or in other words, does watching more television lead to an unhealthier diet (I am guessing no one assumes that eating an unhealthier diet leads to more TV watching).
Unfortunately, this is not a question that can be answered by this type of research.
Nor, is this type of research likely to predict whether or not reducing screen time will get the kids to eat better.
Indeed, it doesn’t take a lot of imagination to come up with other explanations for these findings that would not require any assumption of a causal link between eating behaviours and television watching.
For one, TV watching could simply be a surrogate measure for parenting style – perhaps parents that let their kids watch a lot of TV are also less concerned about the food they eat.
And, for all we know, reducing TV time may (e.g. by cutting the kids off from TV – or cutting the parents off from a convenient babysitter) in the end make the kids eating behaviours even worse.
Who knows – that’s exactly the point – who knows?
To be fair, the authors are entirely aware of the limitations of such studies:
“This study was cross-sectional, so no causal inference could be made and the possible mechanism is not clear. Although our data collection strictly followed the detailed manual procedure to guarantee the quality control (QUALITY Cohort Technical Documents, 2011), potential bias and errors may still exist in those self-reported questionnaires. A number of potential confounding factors have been adjusted in the regression models, but the results may still be confounded by other known and unknown factors.”
So, while the findings may well fit into the “narrative” of sedentariness -> unhealthy diets -> obesity, we must remain cautious in not overinterpreting findings from these type of studies or jumping to conclusions regarding policies or other interventions.