Mapping the Fat: How Childhood Obesity and Body Composition Shape Obstructive Sleep Apnoea

Abstract

Background/Objectives: Childhood obesity represents a growing public health concern. It is closely associated with obstructive sleep apnoea (OSA), which impairs nocturnal breathing and significantly affects neurocognitive and cardiovascular health. This review aims to analyse differences in fat distribution, anthropometric parameters, and instrumental assessments of paediatric OSA compared to adult OSA to improve the diagnostic characterisation of obese children. Methods: narrative review. Results: While adenotonsillar hypertrophy (ATH) remains a primary cause of paediatric OSA, the increasing prevalence of obesity has introduced distinct pathophysiological mechanisms, including fat accumulation around the pharynx, reduced respiratory muscle tone, and systemic inflammation. Children exhibit different fat distribution patterns compared to adults, with a greater proportion of subcutaneous fat relative to visceral fat. Nevertheless, cervical and abdominal adiposity are crucial in increasing upper airway collapsibility. Recent evidence highlights the predictive value of anthropometric and body composition indicators such as neck circumference (NC), neck-to-height ratio (NHR), neck-to-waist ratio (NWR), fat-to-muscle ratio (FMR), and the neck-to-abdominal-fat percentage ratio (NAF%). In addition, ultrasound assessment of lateral pharyngeal wall (LPW) thickness and abdominal fat distribution provides clinically relevant information regarding anatomical contributions to OSA severity. Among imaging modalities, dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and air displacement plethysmography (ADP) have proven valuable tools for evaluating body fat distribution. Conclusions: Despite advances in the topic, a validated predictive model that integrates these parameters is still lacking in clinical practice. Polysomnography (PSG) remains the gold standard for diagnosis; however, its limited accessibility underscores the need for complementary tools to prioritise the identification of children at high risk. A multimodal approach integrating clinical, anthropometric, and imaging data could support the early identification and personalised management of paediatric OSA in obesity.

Keywords: anthropometric measure; body composition; children; fat distribution; obesity; obstructive sleep apnoea; polysomnography; sleep disordered breathing; ultrasonography.

Figure 1

Estimated probability of severe paediatric OSA (AHI ≥ 10 events·h⁻¹) derived from multivariable logistic regression according to combinations of BMI > 29.2 kg·m⁻², neck circumference > 35.8 cm, and waist circumference ≥ 93.5 cm (n = 152). Bars represent predicted probability (%) with 95% confidence intervals; dashed horizontal lines mark 25%, 50%, and 75% probability thresholds. Abbreviations: BMI, body mass index; NC, neck circumference; WC, waist circumference.

Figure 2

Standardised mean differences (Cohen’s d) in anthropometric and craniofacial parameters between children with and without obstructive sleep apnoea (OSA). The bars show differences in neck, waist, and hip circumferences (cm), and cervicomental and maxillomandibular angles (degrees). Bar colours indicate certainty of evidence: green = high, orange = low, red = very low [101]. Abbreviations: NC, neck circumference; WC, waist circumference; WHR, waist-to-hip ratio.

Figure 3

Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for various body composition indicators and their association with paediatric obstructive sleep apnoea (OSA). Indicators: CFMI = central fat mass index; FFMI = fat-free mass index; FMI = fat mass index; FMR = fat-to-muscle mass ratio. Bar colour coding: red = highly significant (p < 0.001); blue = significant (p < 0.05); grey = non-significant (p > 0.05). The dashed horizontal line at aOR = 1.0 indicates the null association [45].

Figure 4

Pearson correlation coefficients between the log-transformed apnoea–hypopnoea index (log AHI)/obstructive apnoea–hypopnoea index (log oAHI) and adiposity indicators. Variables: BMI z-score (zBMI), total fat mass (FM), body fat percentage (BF%), neck-to-abdominal fat percentage ratio (NAF%). Bars display correlation coefficients, with p-values shown above each bar. Significant associations are indicated [138].

Figure 5

Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for body composition indicators associated with paediatric OSA risk. Indicators: FMR = fat-to-muscle mass ratio; zBMI = BMI z-score; FMI = fat mass index; CFMI = central fat mass index; FM% = fat mass percentage. The dashed vertical line at aOR = 1.0 represents no association. Statistically significant predictors are shown in blue; non-significant ones are shown in grey [45].

Figure 6

Flow-chart of a five-step clinical pathway for stratification and integrated management of paediatric OSA in children with obesity: (1) initial clinical assessment, (2) anthropometric evaluation (BMI, NC, NHR), (3) instrumental screening (overnight oximetry or PSG if ODI ≥ 7.9 h⁻¹), (4) body composition analysis (DXA, BIA, ADP), and (5) multidisciplinary management (ENT specialist, pulmonologist, nutritionist, psychologist). Abbreviations: ADP, air-displacement plethysmography; BIA, bioelectrical impedance analysis; BMI, body mass index; DXA, dual-energy X-ray absorptiometry; ENT, ear–nose–throat specialist; NC, neck circumference; NHR, neck-to-height ratio; ODI, oxygen desaturation index; OSA, obstructive sleep apnoea; PSG, polysomnography.