Anthropometric Profile, Overweight/Obesity Prevalence, and Socioeconomic Impact in Moroccan Children Aged 6–12 Years Old with Autism Spectrum Disorder
Bibliographic record
Abstract
BACKGROUND: In addition to the inherent challenges of their condition, children with autism spectrum disorder (ASD) are also susceptible to the global obesity epidemic. However, concerning the prevalence of obesity within the Moroccan ASD pediatric population, data remain scarce. METHODS: A total of 258 children (boys = 195) aged 6 to 12 years old (mean = 9.4 ± 1.4) diagnosed with ASD participated in this study. Besides the body mass and height, four significant anthropometric markers for assessing obesity were examined: body mass index (BMI), body surface area (BSA), waist circumference (WC), and waist-to-height ratio (WHtR). Each anthropometric marker was categorized into one of three cardiometabolic risk levels based on the Z-scores and their corresponding percentiles. The distribution was as follows: low risk (≤84th percentile), high risk (85th-94th percentile), and very high risk (≥95th percentile). Subsequently, a multiple regression analysis was employed to develop an algorithm that generates a composite risk score. This score incorporates all the anthropometric variables simultaneously, while also weighting their individual contributions to the cardiometabolic risk. RESULTS: Children with ASD exhibit an anthropometric profile that markedly increases their susceptibility to cardiometabolic issues. While roughly 11% of the general Moroccan child population is overweight or obese, this figure soars to nearly 60% among children with ASD when considering the central adiposity markers. Furthermore, children from middle-class socioeconomic backgrounds display a more than threefold greater risk of developing overweight or obesity compared to their counterparts from lower socioeconomic backgrounds. CONCLUSIONS: This study has, for the first time, provided an up-to-date overview of the cardiometabolic risk in Moroccan children with ASD using traditional anthropometric measurements. The primary risk factor is clearly linked to central (abdominal) adiposity, which is recognized as the most deleterious. This study highlights the need to include general and central obesity markers. This study underscores the importance of incorporating both general and central adiposity markers for a more comprehensive assessment, and it emphasizes the need for closer monitoring within this high-risk population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".