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152 Presentations
Beyond daily reference intakes: Considerations for nutrient utilization in clinical practice
The importance of anthropometry: The utility of mid- upper arm muscle circumference (MUAC)
Q&A
Identifying nutrient deficiencies: The value of a nutrition-focused physical exam
Combating malnutrition in pediatric clinical practice - Sponsored by Abbott
Welcome words
Announcement of Best Abstracts Award Winners
How should we Analyse Longitudinal Growth?
Abstract
Abstract Body
During infancy and puberty, longitudinal growth in height or weight is complicated to model because the growth curves representing individual growth patterns vary considerably in shape. A popular way to analyse such data is to convert the measurements to z-scores—it removes the age trend and linearises the curves. However it also discards useful information about the shape of the growth curve and complicates the analysis of growth during puberty. This talk focuses instead on the measurement scale, and describes a relatively novel growth model called SITAR (Cole et al, 2010).
Two general features of the growth pattern are useful to summarise: a) the shape of the mean growth curve, and b) the nature and extent to which individuals deviate from the mean curve. But this poses a problem—how can one average curves that differ in shape? The trick is to assume that the curves all have the same underlying shape, and that they differ from each other only in simple ways.
SITAR achieves this by estimating a mean growth curve from which individual growth curves differ in just three respects: size (i.e. the extent to which the individual is consistently taller/shorter or heavier/lighter than average); timing (by how much their age at peak growth velocity is earlier or later than average), and intensity (by how much their average growth velocity is higher or lower than average). So individual growth patterns are summarised on three scales: size = small/big, timing = early/late and intensity = slow/fast. Geometrically, size and timing correspond to shifting the curve down/up and left/right, while intensity rotates the curve. SITAR (SuperImposition by Translation and Rotation) is a nonlinear mixed effects model that estimates size, timing and intensity as random effects, while the mean growth curve is a natural cubic spline. It differs from growth models like Jenss-Bayley, Preece-Baines or Karlberg’s ICP which use parametric forms of curve—the advantage of a cubic spline curve is that its shape is defined purely by the data, and hence can be relatively complex, unconstrained by a prespecified mathematical form.
Applied to studies of height in puberty, SITAR fits the data extremely well. The residual standard deviation is typically less than one tenth of the population standard deviation, so the model explains over 99% of the variance. It estimates mean curves for distance and velocity, from which mean final height and mean ages and values of height velocity at take-off and peak can be extracted. It also compares growth summary statistics across two or more groups. Published examples of SITAR include randomised clinical trials testing the effect on attained height of oxandrolone in girls with Turner syndrome, and of calcium supplementation in boys and girls from The Gambia subsisting on a low calcium diet.
Separately SITAR addresses questions concerning the life course, where individual growth patterns in childhood—as summarised by size, timing and intensity—are viewed as exposures relating to later life adverse outcomes. An example here links the age at peak height velocity to bone health in later life, showing that an early puberty is associated with stronger bones at age 60.
SITAR is very effective at summarising the complexity of growth. This may be because it mimics the biology of growth as championed by J M Tanner, focussing on the growth spurt in terms of peak velocity and age at peak velocity.
Q&A
Incretins for Weight Reduction
Pharmacotherapy in Childhood Obesity- Does GLP-1 receptor agonist is a “game changer”
Abstract
Abstract Body
The increasing number of young patients with obesity worldwide is a major challenge for health care systems in many countries.
The gold standard for the treatment of obesity remains a multimodal conservative treatment regime to improve physical activity & reduce caloric intake.
Unfortunately, with this conservative treatment regime, the impact on body weight is overall modest and the majority of patients regain weight. This is the reason why there is an urgent need to establish new treatment strategies for children and adolescents with obesity in order to reduce the risk for the development of comorbidities, and increased mortality later in life.
The leptin-melanocortin pathway and the incretin system are deeply involved in a complex regulation of food intake. One of the incretins is the glucagon like peptide-1 (GLP-1). Like human GLP-1, liraglutide (Saxenda®) is a glucagon- like peptide 1 receptor (GLP-1R) agonist that works on glucose metabolism and body weight due to various mechanisms: promoting insulin secretion from pancreatic β-cells; reducing glucagon secretion from pancreatic α-cells; improving insulin sensitivity; reducing gastric emptying; and improving central appetite regulation.
A large randomized, double-blind placebo- controlled trial, published by Kelly et al., which consisted of a 56-week treatment period and a 26-week follow-up period evaluated the efficacy and safety of liraglutide (3.0 mg) or placebo subcutaneously once daily, in addition to lifestyle therapy among adolescents 12-18 years of age with obesity and a poor response to lifestyle therapy. The results of the study showed that liraglutide was superior to placebo with regard to the change from baseline in the BMI standard-deviation score at week 56, with a significant higher percentage of patients that achieved a reduction of at least 5% in the BMI in the liraglutide group compared with the placebo group. Major side effects included gastrointestinal symptoms.
After this publication, the Saxenda® has been approved by the FDA in 2021 as an anti-obesity drug in addition to lifestyle therapy in adolescents. Further data from real- life setting are needed to assess the use of the medication in larger number of pediatric patients with obesity, in order to evaluate its efficacy and safety in the long-term and the possibility of prevention of obesity related comorbidities.
New Treatment for Rare Genetic Obesity Disorders
Abstract
Abstract Body
Genetic obesity disorders are severe and disabling disorders with a variety of accompanying symptoms or features. These disorders are rare to extremely rare and can be difficult to diagnose. Genetic obesity disorders are more often diagnosed in children than in adult patient groups. Genetic obesity reflects a heterogeneous group of conditions. They are classically divided into non-syndromic and syndromic obesity. Non-syndromic genetic obesity disorders are often caused by a single gene defect leading to defective leptin-melanocortin pathway. Severe obesity classically presents in the first years of life. Syndromic genetic obesity differs from non-syndromic, as they have additional symptoms, apart from obesity, like neurodevelopmental disorders and/or polymalformative syndrome.Children with rare genetic obesity disorders present with insatiable behavior (hyperphagia) and early-onset obesity. The cause is a genetic defect disrupting the signaling through the melanocortin-4 receptor (MC4R) pathway, in the hypothalamus.
Traditional lifestyle interventions are an important basis of treatment but patients are often treatment resistant. In addition to supportive lifestyle interventions, novel pharmacotherapeutic treatment options have become available. Case series have been described using dextroamphetamine or GLP1 agonist. After EMA approval based on clinical trials, patients with specific genetic obesity disorder can now be treated with the MC4R agonist from the age of 6.
In conclusion drugs targeting central brain pathways of weight regulation and energy expenditure provide new and promising treatment options for patients with genetic obesity disorders.
References:
Hampi S., Hassink S. et al. Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity. Pediatrics(2023),151,https://doi.org/10.1542/peds.2022-060640
2020 Webinar Rare EndoERN MTG growth and obesity. Disorders in the leptin-melanocortin pathway. 7juli2020: https://www.youtube.com/watch?v=isFixGGQjS8
Lotte Kleinendorst*, Ozair Abawi, * Bibian van der Voorn, Mieke H.T.M. Jongejan, Annelies E. Brandsma, Jenny A. Visser, Elisabeth F. C. van Rossum, Bert van der Zwaag, Marielle Alders, Elles M. J. Boon, Mieke M. van Haelst, Erica L.T. van den Akker. Identifying underlying medical causes of pediatric obesity: Results of a systematic diagnostic approach in a pediatric obesity center. https://doi.org/10.1371/journal.pone.0232990.
Martin Wabitsch, Sadaf Farooqi, Christa E Flück, Natasa Bratina, Usha G Mallya, Murray Stewart, Jill Garrison, Erica van den Akker, Peter Kühnen. Natural History of Obesity Due to POMC, PCSK1, and LEPR Deficiency and the Impact of Setmelanotide. April 2022Journal of the Endocrine Society. DOI: 10.1210/jendso/bvac057
Van Schaik J, Schouten-van Meeteren AYN, van der Akker ELT, van Santen HM. Dextroamphetamine treatment in children with hypothalamic Obesity. Front Endocrinol (Lausanne). 2022 Mar 9;13:845937. Doi: 10.3398/fendo.2022.845937
Welling M, de Groot, Kleinendorst, van der Voorn, Burgerhart, van der Valk, van Haelst, van den Akker ELT, van Rossum. Effects of glucagon-like peptide-1 analogue treatment in genetic obesity: A case series. Clin Obes. 2021 Dec;11(6):e12481. https://doi.org/10.1111/cob.12481
Clément K*, van den Akker E* , Argente J, Bahm A, Chung WK, Connors H, De Waele K, Farooqi IS, Gonneau-Lejeune J, Gordon G, Kohlsdorf K, Poitou C, Puder L, Swain J, Stewart M, Yuan G, Wabitsch M*, Kühnen P*. Efficacy and safety of setmelanotide, an MC4R agonist, in individuals with severe obesity due to LEPR or POMC deficiency: single-arm, open-label, multicentre, phase 3 trials. Setmelanotide POMC and LEPR Phase 3 Trial Investigators. Lancet Diabetes Endocrinol. 2020 Dec;8(12):960-970. doi: 10.1016/S2213-8587(20)30364-8. Epub 2020 Oct 30. PMID: 33137293.