Beyond body mass index: Body composition in children and adolescents with type 1 diabetes, clinical implications and perspectives

Abstract

Body composition assessment has emerged as a complement to anthropometric evaluation in children and adolescents with type 1 diabetes (T1D). Accumulating evidence demonstrates that body composition alterations may develop, despite a normal body mass index, highlighting the limitations of body mass index as a standalone metric for detecting metabolically significant changes. This review aims to synthesise evidence on the factors determining body composition, measurement techniques and clinical implications in youth with T1D, focusing on adiposity, lean body mass and fat distribution. Available evidence suggests greater total adiposity in youth with T1D than in healthy peers, whereas lean body mass does not consistently differ between groups. The evidence for central fat distribution is heterogeneous, and direct paediatric data on visceral and ectopic fat remain limited. Predominantly, observational studies have reported associations between body composition measures and glycaemic control, insulin sensitivity, cardiometabolic risk markers and physical fitness. However, their incremental clinical value beyond anthropometry remains to be established. Bioelectrical impedance analysis is a feasible tool for assessing body composition. However, the absence of standardised body composition metrics and limited paediatric reference data limit its clinical implementation. Future longitudinal studies, standardised assessment protocols and reference standards are required to establish how body composition assessment can support individualised paediatric diabetes care.

Key Words: Adiposity; Bioelectrical impedance analysis; Body composition; Cardiometabolic risk; Glycaemic control; Lean body mass; Type 1 diabetes

Core Tip: Alterations in body composition are increasingly recognised as an important yet underappreciated aspect of type 1 diabetes in childhood and adolescence. Conventional anthropometric measures provide only a partial picture of metabolic health, whereas body composition assessment offers additional insight into adiposity, insulin sensitivity and cardiometabolic risk. This review summarises the current evidence, discusses the methodological challenges that limit widespread clinical implementation and highlights future research priorities. Standardised paediatric assessment protocols and robust reference values will be essential to embed body composition assessment into individualised diabetes care.

INTRODUCTION

Type 1 diabetes (T1D) is one of the most common chronic diseases of childhood and adolescence, affecting approximately 1.8 million children and adolescents worldwide, with more than 219000 new cases diagnosed annually[1]. Over the past two decades, major advances in modern diabetes technologies, including continuous glucose monitoring (CGM), insulin pump therapy and automated insulin delivery (AID) systems, have transformed the management of paediatric T1D. The widespread adoption of these technologies has redefined contemporary standards of care, enabling more effective glycaemic management while reducing the burden of diabetes[2,3]. In parallel, a growing body of evidence from both randomised controlled trials and real-world studies consistently supports the efficacy of AID systems in optimising glycaemic outcomes by increasing time in range, reducing hyperglycaemia and hypoglycaemia and facilitating the attainment of recommended glycaemic targets in children and adolescents with T1D[4-6].

Notwithstanding these therapeutic advances, new metabolic challenges have emerged. Overweight and obesity have become increasingly prevalent among children and adolescents with T1D, with international registry data indicating that nearly one-third of affected youth are overweight or obese, mirroring the secular trends observed in the broader paediatric population[7,8]. This shifting phenotype has fuelled growing acceptance of the term “double diabetes”, describing a subset of patients with T1D exhibiting insulin-resistant features traditionally associated with type 2 diabetes[9,10]. In addition to excess adiposity, puberty, exogenous insulin therapy and lifestyle-related factors may impair insulin sensitivity, resulting in increased insulin requirements and suboptimal metabolic control[11,12]. Consequently, the management of paediatric T1D has gradually broadened beyond the pursuit of glycaemic targets to encompass the prevention of obesity and cardiometabolic complications, with growing emphasis on comprehensive metabolic screening and structured lifestyle interventions[12,13].

Traditionally, nutritional status in children and adolescents with T1D has been assessed using simple anthropometric indices, particularly body mass index (BMI), owing to its simplicity and widespread adoption in both clinical practice and research. Although BMI remains a valuable screening tool for identifying underweight, overweight and obesity, it provides only an indirect estimate of adiposity and does not distinguish fat mass (FM) from fat-free mass (FFM) or provide information on regional fat distribution[14,15]. Consequently, children with similar BMI values may exhibit markedly different body composition profiles and cardiometabolic risk, while longitudinal changes in BMI do not necessarily reflect changes in adiposity[16,17]. Body-composition assessment can provide additional information on FM, lean tissue and regional fat distribution beyond BMI, although its incremental clinical value in paediatric T1D remains to be established.

Recent reviews suggest greater adiposity in youth with T1D than in healthy peers and describe associations with glycaemic and cardiometabolic measures, although findings vary across studies[18-20]. Nevertheless, the existing literature exhibits considerable heterogeneity, driven by variation in study cohorts, evaluation methodology and body composition metrics, thereby limiting direct comparisons across studies. A comprehensive understanding of the determinants, measurement techniques and clinical relevance of body composition is therefore essential for establishing its potential integration into standard care and long-term management for children and adolescents with T1D. Accordingly, this review aims to summarise the current evidence critically on body composition in youth with T1D, discuss its clinical implications, highlight existing knowledge gaps and explore future perspectives for research and clinical practice.

An overview of the scope and structure of the review is presented in Figure 1, which summarises the principal determinants of body composition, available assessment methods, predominant body-composition phenotype, associated clinical implications, current knowledge gaps and future research perspectives in paediatric T1D.

Figure 1 Review content overview. Most paediatric evidence is observational and heterogeneous. Direct imaging evidence on visceral and ectopic fat is limited, and bioelectrical impedance analysis-derived visceral-fat estimates should not be interpreted as direct anatomical measurements. BIA: Bioelectrical impedance analysis; DXA: Dual-energy X-ray absorptiometry; MRI: Magnetic resonance imaging; CT: Computed tomography; BMI: Body mass index.

LITERATURE SEARCH

This narrative review was informed by a literature search conducted in PubMed/MEDLINE and Scopus from database inception to 20 August 2026. The search combined terms relating to T1D (“type 1 diabetes”, “T1D”), the paediatric population (“child*”, “adolescen*”, “youth”, “pediatric*”, “paediatric*”), and body composition (“body composition”, “body fat percentage”, “fat mass”, “fat-free mass”, “lean body mass”, “fat mass index”, “lean mass index”, “visceral fat”, “visceral adiposity”, “bioelectrical impedance”, “bioimpedance”, “dual-energy X-ray absorptiometry”, “DXA”, “DEXA”). Terms within each group were combined with OR, and the three groups with AND. The search identified 378 records in PubMed/MEDLINE and 416 in Scopus. After duplicates were removed by matching DOIs and titles, 505 unique records remained.

The first author performed the literature search and initial evidence selection, whereas potentially relevant publications and uncertain cases were critically reviewed with the senior author. Studies focusing on children and adolescents with T1D were prioritised, whereas adult studies were considered only when paediatric evidence was limited. Because this was a narrative rather than a systematic review, no formal risk-of-bias instrument was applied. Evidence was instead critically appraised according to study design, population characteristics, sample size, the body composition assessment method used, adjustment for relevant confounders, directness to the paediatric population and consistency with the broader literature. The narrative design and the absence of formal risk-of-bias assessment should be considered when interpreting the conclusions of this review.

No relevant non-English publications were identified by the search. Therefore, all publications included in this review were in English. Conference abstracts were treated as preliminary evidence and interpreted cautiously because of their limited methodological detail. They were used to complement the discussion rather than to support the main conclusions.

DETERMINANTS OF ALTERED BODY COMPOSITION IN CHILDREN AND ADOLESCENTS WITH T1D

Physiological determinants in body composition during growth and puberty

Body composition changes dynamically throughout childhood and adolescence, reflecting the complex interplay among growth, development and endocrine regulation[15,21-23]. During normal growth, both FM and FFM increase progressively. However, their relative proportions vary substantially according to age, sex and pubertal stage[22,23]. Of all stages of development, puberty is the period during which body composition undergoes the most pronounced physiological changes, largely driven by growth hormone, insulin-like growth factor-1 and sex steroids[21,22]. Consequently, boys typically experience a marked increase in lean body mass and skeletal muscle, accompanied by a relative decline in body fat percentage (BF%), whereas girls exhibit proportionally greater FM accumulation, particularly in the gluteofemoral region, while demonstrating a smaller increase in lean body mass[21-23].

These sex-specific developmental trajectories are not fully captured by chronological age alone, since the timing and tempo of puberty and the pubertal growth spurt vary considerably among individuals. Thus, children of the same age may differ markedly in Tanner stage, growth velocity and the expected accrual and distribution of fat and lean mass[21-23]. In youth with T1D, this developmental variability is superimposed on the physiological decline in insulin sensitivity during puberty, which is often accompanied by rising insulin requirements and may further complicate the relationships among insulin exposure, glycaemic control and body composition[11]. Consistent with these developmental differences, paediatric T1D studies have shown distinct sex-specific body composition profiles, with girls generally exhibiting greater total and central adiposity than boys[20]. Therefore, body composition findings should be interpreted against sex- and maturation-specific reference data, and studies should report and, where appropriate, stratify or adjust their analyses for Tanner stage, pubertal timing and growth velocity. Failure to account for these factors may result in the attribution of physiological developmental differences to T1D or its treatment. These developmental considerations provide a framework for interpreting the disease- and treatment-related determinants discussed in the following section.

Disease- and treatment-related determinants

Alterations in body composition begin even before the clinical onset of T1D, because absolute insulin deficiency induces a profound catabolic state. In the absence of insulin, anabolic pathways are suppressed, whereas lipolysis, proteolysis and hepatic glucose production are stimulated, resulting in progressive depletion of both FM and lean body mass[24]. Consequently, children often present with marked weight loss and reduced muscle mass at diagnosis, the magnitude of which depends largely on the duration and severity of the metabolic derangement. Following the initiation of insulin therapy, reversal of this catabolic state restores anabolic metabolism, leading to rapid weight recovery during the first months after diagnosis[24-26].

Weight recovery after insulin initiation reflects the reversal of the catabolic state present at diagnosis. A longitudinal study reported that body composition normalised within six weeks of treatment and remained stable at one year[25]. Furthermore, a recent systematic review and meta-analysis confirmed that children and adolescents with T1D consistently have higher body fat than their healthy peers, despite considerable inter-study heterogeneity[19]. In line with the escalating rates of overweight and obesity among patients with T1D, this altered body composition phenotype has gained clinical relevance, highlighting that body weight alone fails to describe metabolic health status adequately[27]. Collectively, these observations suggest that the trajectory of body composition following diagnosis extends beyond simple weight restoration, reflecting a multifaceted interplay among disease-specific, therapeutic and developmental processes.

Beyond the reversal of the initial catabolic state, mechanistic evidence, derived largely from physiological, experimental and adult T1D studies, suggests potential pathways by which exogenous insulin therapy could be linked to long-term alterations in body composition. Under physiological conditions, insulin is secreted by pancreatic beta cells directly into the portal circulation, exposing the liver to substantially higher insulin concentrations than peripheral tissues[28]. In contrast, subcutaneous insulin administration bypasses the portal system, producing relative peripheral hyperinsulinaemia, which may favour lipid storage, suppress lipolysis and be linked to reduced insulin sensitivity within skeletal muscles[29,30]. These altered insulin kinetics may shift the metabolic balance towards adipose tissue deposition rather than lean mass accretion, especially when compounded by excessive caloric intake or sedentary behaviour[27,30]. These mechanisms have been proposed as possible explanations for the observed relationships among insulin exposure, adiposity and insulin requirements, despite the limited paediatric evidence[9,27].

These mechanisms are likely to reinforce one another rather than to act independently. Because subcutaneous insulin enters the systemic rather than the portal circulation, the liver is exposed to relatively less insulin. In contrast, skeletal muscle and adipose tissue are exposed to relatively more insulin than under physiological conditions. Reduced hepatic insulin exposure may lead to less complete suppression of endogenous glucose production, prompting higher insulin doses and further increasing peripheral insulin levels. In adipose tissue, insulin promotes triglyceride storage and suppresses lipolysis. As adiposity increases, however, greater release of free fatty acids and inflammatory signalling may promote lipid deposition in skeletal muscle and interfere with insulin signalling and mitochondrial energy metabolism. This may further reduce insulin sensitivity and increase insulin requirements, creating a self-reinforcing cycle. Supporting this model, paediatric clamp studies have shown hepatic, adipose and peripheral insulin resistance in youth with T1D even in the absence of conventional metabolic-syndrome features, whereas impaired skeletal-muscle mitochondrial recovery has been associated with lower peripheral insulin sensitivity[31,32]. Pubertal insulin resistance, hyperglycaemia, physical inactivity and a positive energy balance may further amplify this cycle. Thus, higher insulin requirements may represent both a potential contributor to and a marker of the evolving insulin-resistant phenotype[11,27,29,30,33].

Modifiable lifestyle determinants

In addition, several modifiable lifestyle factors may strongly influence body composition in children and adolescents with T1D. Among these, physical activity, dietary habits and the use of modern diabetes technologies are potentially modifiable determinants that may either mitigate or exacerbate alterations in adiposity and lean body mass.

Regular physical activity supports skeletal muscle development and a healthy body composition throughout childhood and adolescence and is particularly important for youth with T1D. In this population, higher physical activity levels have been associated with greater lean mass, lower adiposity and higher insulin sensitivity[34-37], whereas recent interventional evidence indicates that structured resistance training can improve selected musculoskeletal outcomes[38]. Conversely, prolonged sedentary behaviour has been associated with increased adiposity and compromised metabolic outcomes, although evidence explicitly addressing its effect on body composition in children and adolescents with T1D is still scarce[35,36]. Despite these well-recognised benefits, many children and adolescents with T1D fail to achieve recommended levels of physical activity, primarily because of concerns regarding hypoglycaemia, glycaemic variability and the complexity of managing insulin and carbohydrate requirements around exercise[34,37,39]. Therefore, promoting safe, routine physical activity should be considered an essential component of strategies to sustain healthy body composition and optimise long-term metabolic health in youth with T1D.

Nutrition is another key modifiable determinant of body composition in youth with T1D. Adequate energy and nutrient intake is essential for supporting normal growth, preserving lean body mass and preventing excess adiposity accumulation throughout childhood and adolescence[13,35,40]. However, nutritional management in T1D poses challenges, because dietary decisions are closely integrated with insulin adjustments, carbohydrate counting and the constant need to avoid and treat hypoglycaemia. Consequently, compensatory eating habits, excessive carbohydrate intake during hypoglycaemia rescue episodes and habitual excess carbohydrate consumption may inadvertently promote a positive energy balance and contribute to increased adiposity over time[40,41]. Emerging evidence further suggests that adherence to healthy dietary patterns, most notably the Mediterranean diet, may be associated with a more favourable body composition and better metabolic outcomes[35,42].

The widespread adoption of CGM, insulin pump therapy and AID systems has markedly reshaped the management of paediatric T1D and may indirectly influence body composition through multiple mechanisms. Improved glycaemic control, reduced hypoglycaemia and greater treatment flexibility may facilitate regular physical activity and reduce compensatory carbohydrate intake, thereby promoting more favourable health behaviours[5,43]. Conversely, improved metabolic efficiency and the optimisation and facilitation of insulin delivery have raised concerns regarding potential weight gain following the initiation of advanced diabetes technologies, particularly AID systems[43,44]. Nevertheless, the available evidence remains inconsistent, and studies that specifically evaluate changes in body composition, rather than isolated variations in body weight or BMI, are scarce. Although modern diabetes technologies have revolutionised glycaemic management, further prospective studies incorporating standardised body-composition assessments are required to determine their long-term effects on adiposity and lean body mass.

The interpretation of body composition findings in paediatric T1D requires careful attention to the interrelationships among developmental, disease-related, treatment-related and lifestyle factors. Chronological age alone may not adequately reflect biological maturation. Overlooking pubertal stage and timing, growth velocity and sex-specific developmental trajectories may lead to physiological differences being ascribed to T1D or its treatment[21-23]. Diabetes-related variables are also closely interrelated: At a given age, an earlier diagnosis generally implies longer exposure to T1D and insulin therapy, whereas higher insulin requirements may reflect differences in the treatment regimen, glycaemic control or underlying insulin resistance and adiposity, complicating the direction of the observed associations[19,27]. Physical activity, dietary intake, sedentary behaviour and sleep may similarly cluster rather than act independently, whereas the predominantly cross-sectional evidence limits assessment of their individual contributions[35]. The socioeconomic context may further modify these relationships. In a longitudinal analysis from the SEARCH study, several indicators of lower socioeconomic status were associated with less favourable estimated body-fat trajectories, particularly among girls[45]. Because the extent of adjustment for these variables varies considerably across studies, residual confounding contributes to the heterogeneity of the available evidence. Collectively, these physiological, disease-related and lifestyle determinants interact throughout childhood and adolescence and should be considered when interpreting body composition trajectories in youth with T1D.

METHODS FOR ASSESSING BODY COMPOSITION

Several methods are available for assessing body composition in children and adolescents, and these differ substantially in their underlying principles, accuracy, feasibility and clinical applicability. The main characteristics, advantages, limitations and potential clinical applications of these methods are summarised in Table 1.

Table 1 Body-composition assessment methods in paediatric type 1 diabetes.

Method	Accuracy	Reproducibility	Radiation	Cost/resources	Availability	Paediatric T1D validation	Regional fat	Serial monitoring
Anthropometry (BMI, WC, skinfolds)	Indirect assessment	High for height and weight, operator-dependent for WC and skinfolds	None	Minimal equipment	Widespread	Not applicable for direct body composition	Indirect central adiposity	Suitable for growth and weight trends only
BIA	Equation- and hydration-dependent estimates	Acceptable under standardized conditions with the same device	None	Dedicated device, lower resource requirements than imaging	Variable	Limited	Device-dependent indirect estimates, not interchangeable with MRI- or CT-measured VAT	Conditional on standardized protocol
DXA	Precise for total and regional tissues, not universal for all compartments	High within the same device and software	Low-dose	Specialized equipment and personnel	Selected centres	Limited	Trunk fat, VAT estimation with selected systems	Limited mainly by resources and access
MRI	Direct assessment of specific tissue compartments	High with standardized protocols	None	Specialized equipment and personnel	Specialized centres	Limited	Direct VAT, SAT and ectopic fat assessment	Limited mainly by resources and access
CT	High spatial resolution	High with standardized protocols	Ionizing radiation	Specialized equipment and personnel	Specialized centres	Not established	Direct VAT, SAT and muscle assessment	Unsuitable for routine serial monitoring
ADP	Model-based total FM and FFM assessment	Good under standardized conditions	None	Dedicated specialized equipment	Mainly research centres	Not established	None	Feasible but access-limited

Accuracy and reproducibility depend on the measured compartment, device, prediction algorithm and acquisition protocol. Cost and availability are described in relative terms because they vary across healthcare settings. T1D: Type 1 diabetes; BMI: Body mass index; WC: Waist circumference; BIA: Bioelectrical impedance analysis; MRI: Magnetic resonance imaging; CT: Computed tomography; VAT: Visceral adipose tissue; DXA: Dual-energy X-ray absorptiometry; SAT: Subcutaneous adipose tissue; ADP: Air displacement plethysmography; FFM: Fat-free mass; FM: Fat mass.

Anthropometric methods

Anthropometric assessment remains the first-line approach to evaluating nutritional status in children and adolescents, because of its simplicity, low cost and widespread availability. Standard measurements include body weight, height, BMI, waist circumference and skinfold thickness, all of which are non-invasive, easily applicable in routine clinical practice and supported by age- and sex-specific paediatric reference standards[46,47].

BMI is the most widely used anthropometric index in paediatric practice, and is routinely expressed as age- and sex-specific percentiles or z-scores[48]. It is an effective screening tool for identifying underweight, overweight and obesity, and has demonstrated prognostic utility for adult overweight and cardiometabolic risk[49,50]. However, because BMI reflects gross body size rather than body composition, it should be interpreted alongside complementary methods when a more detailed assessment of adiposity and lean body mass is warranted.

Waist circumference and skinfold thickness provide additional anthropometric information beyond BMI. Waist circumference is a surrogate marker of central adiposity and has been linked to visceral fat accumulation and cardiometabolic risk, whereas skinfold thickness estimates subcutaneous fat stores using standardised prediction equations[46,47,51]. Nevertheless, both techniques are indirect measures of body composition and have acknowledged limitations, including inter-operator variability, reduced precision in individuals with obesity and an inability to quantify body fat compartments directly. Collectively, anthropometric measurements provide valuable screening information but are ideally supplemented by methods that directly quantify body composition.

Bioelectrical impedance analysis

Bioelectrical impedance analysis (BIA) has emerged as one of the most widely used methods for assessing body composition because it is rapid, non-invasive, inexpensive and free of ionising radiation. By measuring the body's opposition to a low-amplitude electrical current and applying validated prediction equations, BIA provides estimates of FM, FFM, BF% and related indices, including FM index (FMI) and FFM index (FFMI)[52-54]. Modern multifrequency devices can also estimate skeletal muscle mass and segmental body composition, and some provide visceral fat estimates derived from proprietary prediction algorithms. These are indirect estimates and should not be interpreted as direct anatomical measurements of visceral adipose tissue or used interchangeably with magnetic resonance imaging (MRI) or computed tomography (CT)-derived measurements[53,54].

Compared with conventional anthropometric assessment, BIA provides more detailed estimates of body composition while retaining important operational advantages, including portability, short examination time and the absence of radiation. These characteristics make repeated measurements feasible during childhood and adolescence. However, feasibility should not be equated with established clinical utility or interchangeability across devices. BIA estimates are influenced by hydration status, recent food intake, physical activity and the device-specific prediction equations used. Serial measurements should therefore be performed under standardised conditions, preferably using the same device, software and prediction equation, because values obtained using different systems may not be directly comparable[54,55].

In children and adolescents with T1D, BIA may identify alterations in fat and lean mass that remain undetected by BMI alone[26]. Nevertheless, paediatric T1D-specific validation and longitudinal outcome data are scarce. BIA may therefore be considered an adjunctive tool in research and in clinical settings where standardised serial assessments can be ensured; however, current evidence does not support its universal implementation for routine longitudinal monitoring.

Dual-energy X-ray absorptiometry

Dual-energy X-ray absorptiometry (DXA) is a widely used reference technique for body composition assessment, providing precise and reproducible estimates of total and regional FM, lean soft tissue and bone mineral content with minimal radiation exposure[56,57]. However, it is not a universal gold standard across all body compartments. Multicomponent models remain criterion methods for total-body composition, whereas MRI and CT provide a more direct assessment of visceral and ectopic fat[56,58].

The interpretation of DXA measurements in children requires age-, sex- and pubertal stage-specific reference data, because body composition changes substantially throughout growth and development. In recent years, paediatric reference charts for DXA-derived parameters, including FMI, lean mass index and appendicular lean mass index, have become available, facilitating standardised interpretation and comparison across both clinical and research settings[57,59,60]. Nevertheless, variations among devices, software versions and manufacturers may still limit comparability across studies. In addition, the cost, limited availability, need for trained personnel, and exposure to low-dose ionising radiation restrict the routine use of DXA for longitudinal monitoring in children and adolescents.

Other body composition assessment techniques

MRI, CT, and air displacement plethysmography provide highly accurate assessments of body composition and are used primarily in research settings. MRI provides the most accurate assessment of visceral and ectopic fat without exposure to ionising radiation. CT also provides detailed anatomical imaging, but the radiation exposure limits its use in paediatric populations[46,56]. Air displacement plethysmography estimates body composition from body volume and body density and has shown good agreement with DXA, although its availability is restricted[47,56]. Because of their cost, limited accessibility and technical requirements, these methods are not routinely used for body composition assessment in paediatric populations but remain valuable research tools and reference techniques for validation studies.

Practical clinical considerations

Current American Diabetes Association Standards recommend the assessment of height, weight and BMI, together with growth and pubertal development, as part of the initial and follow-up evaluation of children and adolescents with diabetes[61]. Building on this minimum standard, and drawing on the available evidence and the authors’ clinical experience, we propose a pragmatic framework for considering additional body composition assessment, acknowledging that it has not been formally validated in paediatric T1D. Waist circumference may be added when feasible, and additional assessment may be considered when conventional anthropometry is discordant with the clinical phenotype or when excess adiposity, reduced muscle mass, abnormal growth or skeletal health concerns are suspected. In such cases, standardised BIA may be the most feasible initial adjunct, whereas DXA may be considered when more precise regional body composition or bone assessment is clinically important. Body composition findings should always be interpreted alongside anthropometric, pubertal and metabolic data, and should not independently determine clinical management. Overall, the choice of body composition assessment method must align with clinical aims, patient profiles and available resources, balancing accuracy against feasibility.

CLINICAL EVIDENCE ON BODY COMPOSITION IN CHILDREN AND ADOLESCENTS WITH T1D

Table 2 presents selected studies that illustrate the main clinical domains discussed in this review. Priority was given to paediatric systematic reviews and meta-analyses, longitudinal cohorts and intervention studies. Cross-sectional studies were also included to illustrate contemporary findings on glycaemic associations and regional adiposity, particularly where longitudinal evidence was limited. Selection also took account of sample size, assessment method and relevance to the clinical questions addressed.

Table 2 Summary of key paediatric evidence on body composition in type 1 diabetes.

Clinical domain	Ref.	Design and population	Method	Main findings
Adiposity and trajectories	Zheng et al[19], 2022	Systematic review and meta-analysis, 24 studies, 1017 youth with T1D and 1045 controls	DXA	Youth with T1D had 1.2 kg more fat mass and 2.3 percentage points higher body-fat percentage. Lean mass did not differ. Earlier onset and higher insulin dose were associated with larger differences
	Davis et al[25], 2012	Prospective cohort, 30 newly diagnosed children and 14 controls, assessments at diagnosis, 6 weeks and 1 year	Whole-body DXA	The lower BMI and body-fat percentage observed at diagnosis normalised by 6 weeks after insulin initiation and remained stable at 1 year. Sex differences persisted
	Lipsky et al[73], 2016	Secondary longitudinal analysis of a dietary RCT, 136 youth, 18-month follow-up	DXA and CGM	Adiposity was not associated with HbA1c but was associated with more frequent hyperglycaemic excursions and lower 1,5-anhydroglucitol
	Kahkoska et al[45], 2018	Longitudinal SEARCH cohort, 742 youth with T1D, approximately 9 years of follow-up	Anthropometry-derived estimated body-fat percentage	Lower socioeconomic indicators were associated with less favourable adiposity trajectories, particularly among girls
	Halvadzhiyan et al[64], 2026	Retrospective comparison of two single-centre cohorts assessed 20 years apart, n = 293	BIA and anthropometry	The contemporary cohort showed a trend toward higher BMI and greater fat-free mass. Girls consistently had higher fat mass. CGM use was associated with lower BMI, HbA1c and insulin dose
Glycaemic and metabolic associations	Maffeis et al[78], 2026	Cross-sectional multicentre study, 364 children and adolescents aged 6-18 years	BIA, anthropometry and CGM	FMI, body-fat percentage and waist-to-height ratio were positively associated with HbA1c and inversely with time in range. FMI and waist-to-height ratio remained independently associated with achievement of glycaemic targets
	Torre et al[72], 2026	Cross-sectional single-centre study, 133 fully pubertal adolescents	BIA and CGM	BIA-derived visceral-fat estimates correlated weakly with current and mean 12-month HbA1c. The association with mean 12-month HbA1c was not significant after adjustment. FFM% correlated weakly with glucose CV and sensor glucose SD. TIR was inversely associated with estimated BMR in a separate multivariable model with BMR as the outcome
Fat distribution and vascular markers	Rangel et al[67], 2025	Cross-sectional study, 78 children and adolescents aged 5-18 years	Segmental BIA	Nearly half had excess adiposity, including about 11% of participants with normal BMI. Girls had higher body-fat percentage and a lower muscle-to-fat ratio. Planned exercise was associated with a more favourable profile
	Ozde et al[68], 2026	Cross-sectional case-control study, 150 youth with T1D and 152 age- and sex-matched controls	Ultrasound EATT/PrATT, cIMT and eGDR	Epicardial and perirenal fat thicknesses were higher in T1D and were associated with lower estimated insulin sensitivity, inflammatory markers, hepatic steatosis and cIMT
	Tas et al[71], 2026	Cross-sectional study, 49 late-pubertal youth with T1D	MRI hepatic fat, DXA and cIMT	Hepatic fat was low and was not associated with cIMT. Several adiposity and body-composition measures, rather than liver fat, were associated with cIMT
Muscle and physical function	Huerta-Uribe et al[84], 2024	Prospective cohort, 83 youth aged 6-18 years, 1-year follow-up	DXA and handgrip strength	Persistently higher handgrip strength was associated with a more favourable cardiometabolic profile and lower adiposity. Muscle strength provided information distinct from lean mass
	Muñoz-Pardeza et al[38], 2026	24-week parallel RCT, 62 youth aged 8-18 years	DXA, progressive resistance training	Training increased lean mass by 0.88 kg and whole-body-less-head bone mineral content by 32.4 g and reduced probable sarcopenia risk. Fat mass and anthropometry were unchanged
Skeletal health	Loxton et al[86], 2021	Systematic review and meta-analysis, 46 studies, 2617 youth with T1D and 3851 controls	DXA, pQCT and quantitative ultrasound	Youth with T1D had lower bone mineral measures across several skeletal sites and imaging modalities than healthy controls
	Mondkar et al[87], 2026	Retrospective longitudinal cohort, 200 children and adolescents, 4-year follow-up	DXA and pQCT	Total-body-less-head areal BMD z-score and trabecular volumetric BMD declined over time. Longer diabetes duration and adverse metabolic factors were associated with poorer skeletal outcomes

T1D: Type 1 diabetes; DXA: Dual-energy X-ray absorptiometry; BMI: Body mass index; RCT: Randomised controlled trial; CGM: Continuous glucose monitoring; HbA1c: Glycated haemoglobin; BIA: Bioelectrical impedance analysis; FMI: Fat mass index; CV: Coefficient of variation; TIR: Time in range; BMR: Basal metabolic rate; FFM: Fat-free mass; EATT: Epicardial adipose tissue thickness; PrATT: Perirenal adipose tissue thickness; cIMT: Carotid intima-media thickness; eGDR: Estimated glucose disposal rate; MRI: Magnetic resonance imaging; pQCT: Peripheral quantitative computed tomography; BMD: Bone mineral density.

Increased adiposity

Increased adiposity is the most consistent body composition abnormality in children and adolescents with T1D. The strongest evidence to date derives from the systematic review and meta-analysis by Zheng et al[19], which pooled data from 24 studies and more than 2000 participants and demonstrated that youth with T1D had 1.2 kg more FM and 2.3 percentage points higher BF% than healthy controls, corresponding to a relative difference of approximately 9%, although with substantial inter-study heterogeneity. Meta-regression analyses further showed that a younger age at diagnosis and a higher daily insulin dose were associated with greater adiposity, whereas glycated haemoglobin (HbA1c) and diabetes duration were not significantly related to adiposity. These results suggest that insulin requirements may be related to differences in adiposity, although the direction of this relationship cannot be determined from the available evidence.

Subsequent case-control studies using BIA have yielded heterogeneous findings. In a prospective study, Giardinelli et al[26] found no significant differences in FM%, FFM%, FMI or other conventional BIA-derived body composition parameters between children and adolescents with T1D and Tanner stage-matched controls. However, the phase angle was significantly lower in the T1D group. In contrast, Nsamba et al[62] demonstrated significantly higher FMI, greater skinfold thicknesses and an increased waist-to-hip ratio in Ugandan children with T1D, whereas FFMI remained comparable with that of healthy controls, indicating that the observed differences were primarily attributable to excess adiposity rather than reduced lean tissue. Similar findings were reported by Akın Kağızmanlı et al[63], who observed significantly higher BF% and FM despite comparable BMI, waist-to-height ratio (WHtR) and tri-ponderal mass index between children with T1D and healthy peers. Notably, children with a disease duration of at least five years showed even greater body fat accumulation, suggesting that adiposity may increase progressively with disease duration.

A recent comparison of two cohorts assessed at the same centre 20 years apart also identified sex- and time-related differences in body composition, including higher BMI in contemporary prepubertal boys and consistently higher FM in girls than in boys[64]. Earlier paediatric studies reported more heterogeneous findings. In a preliminary Greek case-control study, Galli-Tsinopoulou et al[65] found no significant differences in BF% or FMI between children with T1D and controls. However, FFMI was significantly higher in the diabetes group. These discrepancies probably reflect variations in study populations, sample sizes, disease duration and body composition methodology. Nevertheless, taken together, the available evidence consistently supports increased adiposity as the predominant body composition phenotype in youth with T1D. These findings indicate that increased adiposity may be present despite comparable BMI values, further highlighting the inadequacy of relying solely on BMI to precisely identify excess body fat in children and adolescents with T1D[26,62,63].

Lean body mass

In contrast to the consistent evidence for increased adiposity, alterations in lean body mass are less pronounced in children and adolescents with T1D. Zheng et al[19] did not demonstrate significant overall differences in lean body mass between patients and healthy controls, although considerable heterogeneity was observed across the individual studies. Overall, the available evidence suggests that lean body mass is generally preserved.

Two longitudinal studies have provided important insight into the evolution of lean body mass after diagnosis. Davis et al[25] demonstrated that children with newly diagnosed T1D presented with reduced BMI and body fat because of the catabolic effects of insulin deficiency, with body composition normalising within the first weeks after the initiation of insulin therapy and remaining stable after one year. In a conference abstract, Tewari et al[66] reported significantly lower FM and FMI at diagnosis, whereas FFM remained largely preserved. Following 10-12 weeks of insulin therapy, both FM and FFM increased significantly. However, FM remained lower than in healthy controls, indicating that recovery of body composition is a dynamic process that continues beyond the initial phase of treatment.

The evidence from observational studies is more inconsistent. Giardinelli et al[26] reported comparable fat- and lean-mass indices between youth with T1D and Tanner stage-matched controls. Nsamba et al[62] found no significant differences in FFMI between groups despite significantly higher FMI in children with T1D. In contrast, Galli-Tsinopoulou et al[65] reported significantly higher FFMI in children with T1D, whereas BF% and FMI were comparable to those of healthy controls. The heterogeneity of these findings is probably attributable to differences in study populations and methodology, disease characteristics and the body composition indices selected. Importantly, absolute lean mass, FFM and height-adjusted measures such as FFMI are not interchangeable and may capture different aspects of body composition. Beyond methodological differences, variation in sex distribution, pubertal stage and habitual physical activity may contribute substantially to between-study inconsistency. Puberty is accompanied by sex-specific lean-mass accrual, whereas habitual physical activity and resistance exercise influence muscle development. Failure to account for these factors may therefore obscure or exaggerate T1D-related differences[21-23,34-37]. Overall, current evidence indicates that lean body mass is relatively well preserved in most children and adolescents with T1D.

Regional, visceral and ectopic fat distribution

In addition to total adiposity, regional fat distribution has attracted growing attention as a potential correlate of metabolic risk. However, its added value beyond total FM remains insufficiently established in paediatric T1D. In the recent meta-analysis, several DXA-based studies demonstrated greater trunk fat and central adiposity in children and adolescents with T1D than in healthy controls, even in the absence of substantial differences in BMI[19].

More recent studies using BIA have further supported these observations. In the Ugandan case-control study, children with T1D had a significantly higher waist-to-hip ratio, together with greater skinfold thicknesses, indicating increased central fat accumulation despite preserved FFM[62]. Similarly, Rangel et al[67] found that almost half of adolescents with T1D had excess body fat, whereas approximately 11% of those with a normal BMI were classified as having excess adiposity by BIA. BF% was strongly correlated with BIA-estimated visceral fat, and girls demonstrated a less favourable body composition profile, characterised by higher BF% and lower muscle-to-fat ratio than boys.

Evidence concerning ectopic fat depots in paediatric T1D is more limited. A recent ultrasound-based study showed greater epicardial and perirenal adipose tissue thickness in youth with T1D than in controls, with both depots associated with lower estimated insulin sensitivity and poorer inflammatory and vascular profiles[68]. Data regarding hepatic fat are inconsistent. Regnell et al[69] found no hepatic steatosis and slightly lower total hepatic fat in children with T1D than in controls, although the segmental distribution of hepatic fat differed between groups. Conversely, among lean adolescents with T1D, Atwa et al[70] reported an independent association between ultrasonographically detected fatty liver and increased carotid intima-media thickness. A recent MRI-based study found low hepatic fat levels, no cases of steatosis and no association between hepatic fat and carotid thickness[71]. Differences in population characteristics and imaging methodology may, in part, account for these discrepancies.

Although current evidence remains sparse, assessment of regional, visceral and ectopic fat may provide information beyond total adiposity. Trunk fat, BIA-derived visceral-fat estimates, imaging-based measurements of visceral adipose tissue and specific ectopic fat depots represent different measures and should not be treated as interchangeable. Prospective paediatric studies using standardised imaging protocols are needed to establish the clinical and prognostic value of directly assessed fat depots.

CLINICAL IMPLICATIONS OF BODY COMPOSITION IN PAEDIATRIC T1D

The evidence summarised in the following sections is primarily derived from paediatric cohorts. Where paediatric data are limited, findings from adults with T1D are included to provide complementary mechanistic or clinical context and are identified explicitly as such.

Body composition, glycaemic control and insulin resistance

Rather than serving merely as an indicator of nutritional status, body composition is an important correlate of metabolic health in children and adolescents with T1D. Although conventional anthropometric indices, such as BMI, retain their value as screening tools, studies have examined the associations of total adiposity and regional fat distribution with glycaemic control, insulin sensitivity and insulin requirements. Findings vary across populations and assessment methods, and the evidence that body composition measures consistently provide additional information beyond anthropometry remains limited[19,72-78].

One of the largest paediatric studies to address this topic was recently published by Maffeis et al[78], who evaluated 364 children and adolescents with T1D using BIA. Higher FM%, FMI and WHtR were all significantly associated with higher HbA1c and lower time in range. Importantly, both FMI and WHtR remained independently associated with achieving recommended glycaemic targets after adjustment for age, sex, pubertal stage, diabetes duration, insulin dose and treatment modality. These findings indicate that excess adiposity is associated with less favourable glycaemic outcomes, independent of several traditional clinical variables, and highlight the potential value of body composition assessment in identifying less favourable metabolic profiles.

Recent studies further suggest that the regional distribution of adipose tissue may provide information beyond total FM for metabolic outcomes. In an exploratory study of adolescents with T1D, Torre et al[72] reported weak positive correlations between BIA-derived visceral-fat estimates and both current and mean 12-month HbA1c. However, the association with mean 12-month HbA1c was no longer significant after adjustment for age, sex, diabetes duration, BMI and AID use. FFM percentage showed weak positive correlations with glucose coefficient of variation and sensor glucose standard deviation. In a separate multivariable analysis, time in range was inversely associated with BIA-estimated basal metabolic rate, which was the outcome variable. These findings should be interpreted with caution, given their exploratory nature and the lack of an independent association between visceral fat estimates and mean 12-month HbA1c. Semenova et al[74] reported significant associations between body composition parameters, estimated glucose disposal rate (eGDR) and several CGM-derived measures of glucose control and variability in an adult T1D cohort, supporting the close interplay between body composition, insulin sensitivity and glucose homeostasis in T1D, although these findings cannot be extrapolated directly to children and adolescents.

The relationship between body composition and glycaemic control, however, extends beyond HbA1c. In an 18-month longitudinal study using DXA, Lipsky et al[73] found no significant association between BMI or DXA-derived adiposity measures and HbA1c, yet greater BF% and trunk fat were associated with an increased duration of hyperglycaemia and with diminished circulating 1,5-anhydroglucitol levels, indicating more pronounced hyperglycaemic excursions. Such results suggest that associations between adiposity and glycaemic measures may differ according to the outcome assessed, supporting the inclusion of CGM-derived metrics alongside HbA1c in studies of body composition.

Body composition is also closely related to insulin sensitivity and insulin requirements. In the recent systematic review and meta-analysis by Zheng et al[19], higher daily insulin dose was independently associated with greater BF%, whereas HbA1c and diabetes duration showed no significant association with adiposity. These findings are consistent with a potentially bidirectional relationship among adiposity, insulin sensitivity and insulin requirements. However, the direction of these relationships cannot be determined from the predominantly observational evidence. In the study by Elhabashy et al[76], which combined BIA assessment of body composition with MRI quantification of regional adipose tissue, adolescents with insulin resistance had significantly higher total body fat, visceral fat percentage and visceral-to-subcutaneous fat ratio than insulin-sensitive individuals, despite similar diabetes duration and insulin treatment. Moreover, visceral adiposity retained an independent association with insulin resistance and adverse metabolic profiles, suggesting a potential association between regional fat distribution and metabolic abnormalities within this cohort, without establishing causality or incremental clinical utility. These observed associations are supported by recognised physiological mechanisms. Skeletal muscle is responsible for most insulin-mediated glucose disposal under physiological conditions and is therefore the principal determinant of peripheral insulin sensitivity. Consequently, preservation of lean body mass may contribute to improved glucose uptake and overall metabolic homeostasis[77,79].

In addition to conventional body composition indices, recent investigations have explored novel biomarkers derived from BIA. García-Poblet et al[75] demonstrated that lower phase angle values, an index of cellular integrity and nutritional status, were associated with poorer glycaemic control, less favourable body composition and higher triglyceride-glucose index in adolescents with T1D. These findings suggest that impedance-derived parameters may complement traditional measures of adiposity when assessing metabolic health. Although their clinical utility remains to be established, such biomarkers could enhance future risk stratification.

Overall, current evidence indicates that body composition is important to metabolic health profiling in paediatric T1D. Higher adiposity has been associated with poorer glycaemic measures, lower insulin sensitivity and higher insulin requirements in some studies, but the findings are inconsistent, and evidence specifically concerning visceral fat is scarce. Emerging impedance-derived biomarkers also require further validation. These observations suggest that body composition assessment may complement conventional anthropometry by providing a more detailed characterisation of the metabolic phenotype in paediatric T1D.

Body composition and cardiometabolic risk

Beyond its association with glycaemic control and insulin resistance, body composition has emerged as an important correlate of cardiometabolic health in individuals with T1D. Studies have reported associations between adiposity measures and dyslipidaemia, hypertension, metabolic syndrome or subclinical vascular abnormalities, although the evidence includes adult cohorts and predominantly cross-sectional analyses. The extent to which regional fat measures improve cardiovascular risk assessment beyond BMI in paediatric T1D remains uncertain[19,76,80-82].

Some paediatric studies have examined associations between measures of regional adiposity and cardiometabolic risk markers. Elhabashy et al[76] reported that adolescents with T1D and insulin resistance exhibited significantly higher total BF%, visceral fat percentage and visceral-to-subcutaneous fat ratio than insulin-sensitive individuals. In that study, visceral and subcutaneous adipose tissue were quantified using MRI. Within the same cohort, participants with microvascular complications had greater visceral adiposity. Visceral-fat measures correlated with the waist-to-hip ratio, eGDR and low-density lipoprotein cholesterol. Waist-to-hip ratio and eGDR were identified as independent correlates in multivariable analyses. These data suggest that regional fat accumulation may be an important marker of adverse cardiometabolic health, independent of glycaemic control and insulin therapy.

Studies in broader age groups provide additional, although indirect, evidence regarding the cardiometabolic relevance of regional adiposity in T1D. Zeng et al[80] used BIA to assess body composition in individuals with T1D. The BIA-derived visceral fat index was associated with lower estimated insulin sensitivity and with the presence of the metabolic syndrome. This device-derived index is an indirect estimate rather than an imaging-based measurement of visceral adipose tissue. In a large multicentre cohort of adults with T1D, Salle et al[81] showed that WHtR, a surrogate marker of central adiposity, was more strongly associated with estimated 10-year cardiovascular risk than BMI. Individuals with WHtR ≥ 0.5 demonstrated a significantly worse cardiovascular risk profile despite similar BMI values, supporting the view that abdominal fat distribution yields clinically meaningful information. In addition, García-Poblet et al[75] demonstrated that lower phase angle values were associated with higher triglyceride-glucose index and an adverse body composition profile, indicating that BIA-derived biomarkers may complement conventional body composition parameters in identifying individuals at increased cardiometabolic risk.

In another adult DXA-based study, Abd El Dayem and Battah[82] demonstrated that hypertensive individuals with T1D exhibited significantly higher total fat percentage, abdominal fat percentage and fat-to-lean ratio than normotensive patients. Moreover, abdominal fat percentage was independently associated with mean arterial pressure and systolic blood pressure. In contrast, abdominal fat, diabetes duration and fat-to-lean ratio were independently associated with diastolic blood pressure. These findings further support central adiposity as a potential marker of blood-pressure-related cardiovascular risk beyond overall adiposity.

The observed clinical associations are biologically plausible. Visceral adipose tissue is increasingly recognised as a metabolically active endocrine organ that secretes pro-inflammatory cytokines, adipokines and free fatty acids, promoting chronic low-grade inflammation, endothelial dysfunction and impaired insulin signalling. These processes contribute to insulin resistance, vascular remodelling and atherosclerotic changes, providing biological plausibility for reported associations between central adiposity and cardiometabolic abnormalities, without establishing these pathways as causal in paediatric T1D[79,83].

Taken together, paediatric cross-sectional evidence suggests that central and visceral adiposity may be associated with a less favourable metabolic profile. Adult cohorts provide additional, although indirect, support for the cardiometabolic relevance of regional adiposity. However, prospective paediatric evidence linking these measures to subsequent cardiovascular outcomes remains limited, precluding conclusions regarding their clinical utility for cardiovascular risk prediction.

Body composition, skeletal muscle, skeletal health and physical fitness

Although most studies have focused on adiposity, skeletal muscle represents an equally important component of body composition influencing both metabolic health and physical performance in people with T1D. Therefore, the assessment of body composition should include evaluation of lean tissue compartments rather than focusing exclusively on adiposity distribution. Because skeletal muscle is the principal site of insulin-mediated glucose disposal, alterations in muscle mass and function may influence not only physical performance but also overall metabolic health[77].

Lean mass, skeletal muscle mass, muscle quality, muscle strength and physical fitness are related but distinct constructs. DXA- and BIA-derived estimates of lean mass primarily reflect tissue quantity and do not directly assess muscle quality or contractile function. Muscle strength requires functional testing, such as handgrip strength, whereas physical function and fitness also encompass mobility, muscular endurance and cardiorespiratory capacity. Accordingly, low lean mass alone is insufficient to define sarcopenia, particularly in paediatric populations, where standardised diagnostic criteria are lacking[84,85].

Research indicates that both skeletal muscle quantity and muscle function are key determinants of body composition and cardiometabolic health. Huerta-Uribe et al[84] assessed adolescents with T1D using DXA together with handgrip strength testing. Adolescents with lower appendicular skeletal muscle mass and reduced handgrip strength exhibited a less favourable body composition profile, characterised by higher visceral and subcutaneous adipose tissue, as well as poorer cardiometabolic characteristics. Notably, muscle strength was more closely associated with metabolic health than lean mass alone, suggesting that the assessment of muscle function may complement other body composition measurements.

More recently, a 24-week randomised controlled trial by Muñoz-Pardeza et al[38] demonstrated that progressive resistance training in youth with T1D resulted in modest gains in lean mass and bone mineral content, and a lower risk of probable sarcopenia, without significant changes in FM. These findings provide direct paediatric interventional evidence that structured resistance exercise may improve selected musculoskeletal outcomes.

Growth and puberty are also critical periods for skeletal development and peak bone-mass accrual. A meta-analysis of 46 studies involving 6468 participants demonstrated lower bone mineral density at several skeletal sites in youth with T1D than in healthy controls[86]. This observation is further supported by a recent 4-year longitudinal DXA and peripheral quantitative CT study showing progressive deterioration in bone density and geometry in children and adolescents with T1D[87]. Combined assessment of lean mass and bone mineral parameters may therefore provide a more comprehensive characterisation of muscle-bone development. Nevertheless, DXA-derived bone mineral content and density do not capture bone microarchitecture or quality and should not be considered a complete assessment of skeletal health.

Habitual physical activity also helps to develop a healthier body composition, as documented mainly in adult cohorts. In a DXA-based study, Brazeau et al[88] demonstrated that adults with T1D who maintained a more active lifestyle had a significantly lower BMI, together with lower total and trunk FM, than sedentary individuals, despite similar glycaemic control and insulin requirements. More recently, Helleputte et al[89] demonstrated that higher levels of physical activity and greater exercise capacity were associated with lower visceral adiposity, higher estimated insulin sensitivity, lower daily insulin dose and a more favourable cardiovascular profile. Although documented in adult cohorts, these observations underscore the close interaction between physical activity, body composition and metabolic health. Moreover, Hanamura et al[90] observed that adults with childhood-onset T1D reported lower participation in regular exercise from school age onwards. They also exhibited higher BF% despite preserved skeletal muscle mass, suggesting that long-term physical activity habits may influence body composition trajectories throughout life[90].

Overall, skeletal muscle and physical fitness are important but frequently overlooked aspects of health in T1D. However, lean mass, muscle quality, muscle strength and physical fitness are distinct constructs and should not be used interchangeably. Preservation of skeletal muscle mass together with adequate muscle function and regular physical activity is associated with a more favourable metabolic phenotype. In contrast, reduced muscular fitness is accompanied by increased adiposity and less favourable cardiometabolic characteristics.

KNOWLEDGE GAPS AND FUTURE DIRECTIONS

Despite the growing body of evidence that supports the clinical relevance of body composition assessment in children and adolescents with T1D, several important limitations continue to hinder its adoption in routine clinical practice. The available evidence exhibits considerable methodological heterogeneity, making comparisons across studies difficult and limiting the development of evidence-based clinical recommendations. Variability in study populations, including disease duration, pubertal status and methodological differences, has resulted in inconsistent reporting of body composition indices, making it difficult to compare findings across studies or generalise them to broader populations.

Although current associations support the clinical relevance of body composition, the incremental value of its assessment beyond conventional anthropometry and its effect on clinical decision-making and outcomes remain to be established. A key limitation is the lack of consensus on which body composition parameters are the most clinically meaningful. Studies have variously reported FM, BF%, FMI, FFM, FFMI, appendicular lean mass, visceral adiposity, phase angle and other impedance-derived indices, often without direct comparison between methods or standardised reporting. Consequently, the optimal metrics for risk stratification and longitudinal monitoring in paediatric T1D remain poorly defined. Future studies should establish standardised assessment protocols and identify a core set of body composition outcomes that can be applied consistently across research and clinical settings.

An additional barrier to the broader implementation of body composition assessment is the limited availability of normative paediatric reference data for several indices, particularly those derived from BIA. Although age- and sex-specific reference values are available for many DXA-derived body composition parameters, comparable normative datasets remain scarce for several BIA-derived measurements and emerging biomarkers, including phase angle[60,91,92]. Existing reference data are largely constrained by small sample sizes, narrow age cohorts or single-centre populations, limiting their applicability across different ethnic groups and stages of growth and pubertal development[93,94]. Establishing robust age-, sex- and pubertal stage-specific reference standards is therefore essential before BIA-derived parameters can be widely incorporated into routine paediatric diabetes care.

Furthermore, the available literature is constrained by a heavy reliance on cross-sectional data, which precludes the assessment of causality. Although associations between body composition indices, glycaemic measures and cardiometabolic risk markers have been reported, findings are heterogeneous, and cross-sectional studies cannot establish temporal sequence or causality. Limited longitudinal follow-up from diagnosis through puberty into early adulthood, together with the small number of intervention studies assessing changes in body composition and clinically relevant outcomes, restricts our understanding of these relationships. Large-scale, multicentre, prospective studies are therefore needed to define the natural trajectory more clearly of body composition alterations throughout the course of T1D, facilitating the identification of effective preventative and therapeutic strategies.

Current evidence also remains largely focused on traditional metabolic outcomes such as HbA1c, insulin requirements and insulin sensitivity, whereas considerably fewer studies have investigated the relationship between body composition and vascular health, early cardiovascular alterations, or long-term diabetes complications. Future research should integrate body composition assessment with CGM metrics, vascular imaging and emerging metabolic biomarkers to better understand the complex interactions among adiposity, skeletal muscle and long-term clinical outcomes.

Ultimately, the future of body composition assessment in paediatric T1D is closely aligned with the principles of precision medicine. The growing availability of portable BIA devices, advances in imaging techniques and the integration of digital health technologies provide new opportunities for individualised risk stratification and personalised management. Combining body composition assessment with CGM, clinical characteristics, lifestyle factors and artificial intelligence-based predictive models may ultimately help inform individualised nutritional and exercise strategies. If the current methodological and evidentiary limitations are addressed, body composition assessment may become a useful adjunct to conventional anthropometry in selected clinical settings, supporting more individualised therapeutic strategies.

CONCLUSION

Body composition has emerged as an important component of the comprehensive assessment of children and adolescents with T1D, providing clinically relevant information beyond basic anthropometric measures. Available evidence suggests greater total adiposity in youth with T1D than in healthy peers, whereas lean body mass does not consistently differ between groups. The findings concerning central fat distribution are heterogeneous, and direct evidence of increased visceral or ectopic fat in paediatric T1D is scarce. Excess total adiposity may be present despite a normal BMI, highlighting the limitations of relying exclusively on conventional anthropometric indices to assess nutritional and metabolic health.

Predominantly observational paediatric studies have reported associations between body composition parameters and glycaemic control, insulin sensitivity, insulin requirements and physical fitness, although findings vary across studies. Cross-sectional paediatric studies also suggest a relationship between regional adiposity and adverse cardiometabolic profiles. However, the evidence linking these measures to subsequent cardiovascular outcomes in youth remains limited and derives partly from adult cohorts. Regional fat distribution may nevertheless provide information beyond total adiposity, whereas emerging BIA-derived biomarkers require further paediatric validation.

Despite these encouraging findings, important challenges must be addressed before incorporating body composition assessment into routine clinical practice. The current literature is limited by methodological heterogeneity, the predominance of cross-sectional studies, inconsistent body composition indices and the limited availability of standardised paediatric reference values for several indices, particularly those derived from BIA. These limitations currently preclude universal routine implementation. When BIA is used for serial assessment, measurement conditions, including hydration status, recent food intake and physical activity, should be standardised, and the same device, software and prediction equation should preferably be used.

With diabetes care increasingly shifting towards a precision medicine paradigm, body composition assessment could contribute to individualised clinical management. As a complement to rather than a replacement for conventional anthropometry, it may provide additional phenotypic information to support more comprehensive assessment and individualised lifestyle strategies. Ongoing research should clarify its clinical utility and define optimal pathways for incorporating body composition assessment into routine clinical practice and future evidence-based guidelines.