Appetite-Regulating Hormones and Eating Behaviors in Children with Autism Spectrum Disorder
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that involves deficits in social, behavioral, and communicative domains. As an increasing number of children are diagnosed with ASD, within Canada and globally, there has also been increased findings of higher rates of overweight and obesity among this population. Excessive weight gain is of concern due to the social, physical, and psychological impacts of obesity and its secondary associated disorders. Particularly for individuals with ASD and families, this can lead to an added burden placed onto this already vulnerable population. In order to improve the effectiveness of treatments and curb the development of overweight and obesity in ASD, a more comprehensive understanding of some of the underlying mechanisms such as possible hormonal factors and feeding behaviors is needed. Therefore, the overall objective of this research was to (1) assess the risk factors for unhealthy weight gain and obesity that have been implicated in ASD, (2) examine hormones involved in regulation of appetite and energy balance (leptin, ghrelin, GLP-1, PYY, insulin) and how they may differ based on weight status among children with ASD, and (3) to explore differences in mealtime feeding behaviors among groups of varying weight status with ASD. In chapter 2, risk factors for unhealthy weight gain and obesity were explored among children with ASD. We discussed the role of selective feeding behaviors, which are often related to sensory challenges and specific behavioral phenotypes, such as restricted and repetitive behaviors. We also discussed the research on physical activity opportunities and sedentary behaviors among this population. Parents also often report more barriers to physical exercise due to the social nature of many activities. Furthermore, we discussed the role of genetics and specific genes that have been implicated in both ASD and obesity development. In addition, many children with ASD often present with secondary comorbidities (e.g., depression), and medications to manage these symptoms can further impact weight status. We also discussed emerging factors, which we defined as factors independently associated with increased risk for both obesity and ASD, that have not yet been studied as risk factors for unhealthy weight gain and obesity among children with ASD. The latter included the gut microbiota, endocrine influences, and maternal metabolic disorders. Chapter 3 summarizes the findings of a cross-sectional study comprised of 21 children with ASD between the ages of 5 to 12 years old. Of the recruited children, 15 were of normal weight (NW) status and 6 children were of overweight or obese (OWOB) weight status. Information through anthropometric measurements, blood samples, and questionnaires was collected. The major findings of this study included that under fasting conditions, the group with OWOB weight status was found to have higher leptin concentrations (p=0.018). We also found there were higher reported feeding challenges among the OWOB group (p=0.045). The major findings of this thesis are that a combination of behavioral, lifestyle, and physiological components contribute to overweight and obesity among children with ASD. This research highlights that behavioral and hormonal factors may also contribute to accelerated weight gain among children with ASD, and there is a need for further research to clarify the interplay among these factors in order to better define potential targets for prevention and intervention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".