Obesity and Overweight Conditions in Children and Adolescents (6–18 Years) and Their Impact on Craniofacial Morphology: A Systematic Review
Bibliographic record
Abstract
Background: Childhood obesity and overweight conditions impact systemic health and craniofacial development. Objectives: This review assessed the influence of elevated body mass index (BMI) on craniofacial morphology, considering age, sex, and ethnicity. Methods: A comprehensive search of Scopus, Web of Science, Embase, Cochrane, PubMed, and OpenGrey was conducted following PRISMA guidelines. Ten cross-sectional studies involving 1383 individuals aged 6 to 18 years were included. The sample comprised 812 females and 571 males, with most studies focusing on adolescents aged 12–18 years of different ethnicities depending on the study. Craniofacial structures were compared between overweight/obese and normal weight groups through cephalometric analysis. Study quality was assessed using the Newcastle–Ottawa Scale (NOS). Results: Overweight and obese individuals showed significant craniofacial changes, including increased anterior cranial base length, maxillary and mandibular dimensions, bimaxillary prognathism, and greater soft tissue thickness. These alterations may be influenced by differences in tissue composition, hormonal fluctuations, fat-to-bone ratio, and metabolic disorders. Variations in skeletal divergence, dental alignment, and airway space were also observed. The methodological quality ranged from moderate to high. Conclusions: Excess weight during growth is linked to distinct craniofacial alterations. Orthodontic diagnostics should integrate metabolic and hormonal considerations to optimize treatment outcomes. These changes should be carefully considered by orthodontists and pediatric dentists. Longitudinal studies are needed to understand the long-term effects of obesity on craniofacial development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".