Over the last 50 years, cooperative group trials, improvements in supportive care, and medical advances have improved the outcomes of patients with pediatric cancer significantly. Most recent estimates in the United States, using the Surveillance Epidemiology and End Results database, provide overall survivorship at 5 years of >85% for all those diagnosed with cancer before the age of 20, and over 90% for those diagnosed with acute lymphoblastic leukemia (ALL), the most common cancer in childhood.1 As such, there are an estimated half a million survivors of pediatric cancer living in the United States today.2 Long-term survivorship cohort studies have identified a multitude of specific late effects of various chemotherapeutic agents3,4; and many trends are noted in survivors of pediatric cancers, including increased cardiovascular risk and changes in body composition.5,6 The prevalence of chronic disease in this population is high, with >50% of survivors experiencing at least 1 severe or life-threatening condition by the age of 50.2
Body composition has been a long-standing focus of pediatric cancer research. At diagnosis, body composition and overall nutritional status play important roles in how well children tolerate treatment7–9 and also in their outcomes (eg, relapse risk).7,9–12 Emerging data suggest that changes in body composition that occur early in treatment are likely to persist in long-term survivors, impacting health adversely. Key changes include loss of lean mass (especially muscle mass) and increase in fat mass. Both of these contribute to the development of poor cardiovascular health and associated comorbidities, frailty, and poor quality of life.13,14 Together, these conditions predispose survivors of childhood cancer to increased morbidity and mortality at a younger adult age compared with the general population.4,15
Given these findings, there is strong motivation to understand the pathophysiologic link between changes in body composition and compromised metabolic health, and to identify metabolically at-risk individuals early in their disease trajectory. However, convenient methods for measuring body composition in the clinical setting, like the body mass index (BMI; weight in kilograms over height in meters squared), may be inappropriate for assessing metabolic risk in survivors of pediatric cancer as this measure does not distinguish between muscle and adipose tissue.16,17 Further, the interpretation of body composition data relies on comparison with appropriate population norms and identification of a threshold above which metabolic risk is increased; this remains the focus of much global research.
With this review, we will summarize the current understanding on the evaluation of body composition in the context of pediatric cancer, detail the impact of changes in body composition in this context, and review the pathophysiology underpinning the adverse metabolic impacts of increased body fat, particularly visceral fat, and loss of lean muscle mass (ie, sarcopenia). In closing, we highlight the importance of a harmonized approach to the assessment of body composition and associated metabolic risk in survivors of pediatric cancer.
CLINICAL IMPLICATIONS AND INTERPRETATION OF BODY COMPOSITION IN CHILDHOOD CANCER SURVIVORS
In early single-center studies of survivors of pediatric ALL, increased prevalence of metabolic risk factors such as obesity, hypertension, dyslipidemia, and abnormal glucose metabolism after cancer treatment were identified, with the risk frequently higher in those who had received cranial radiation.14 Many early studies that evaluated obesity solely by BMI did not demonstrate a higher prevalence in pediatric cancer survivors.18–20 However, in studies evaluating fat distribution using alternate measures, increased percent body fat and abdominal adiposity were identified in survivors, findings that were not detected by BMI.18,21,22 In subsequent years, the Childhood Cancer Survivor Study published several reports confirming the single-center studies, namely survivors had minimally different rates of obesity based on BMI compared with their siblings, but did have significantly greater cardiovascular risk factors (odds ratio, 1.6-1.9)5 and increased morbidity and mortality because of chronic health conditions.4–6 These outcomes have been reviewed extensively.14,23–28 Although earlier studies in survivors of pediatric ALL attributed these outcomes to cranial radiation, because of the impact on the hypothalamic-pituitary axis, when focusing solely on contemporary protocols, which avoid cranial radiation, predisposition to increased adiposity and metabolic risk remains.14,29
Accordingly, the etiology of obesity and associated cardiometabolic changes cannot be attributed solely to radiation exposure and may be related to significant changes in body composition that occur with treatment for pediatric cancer. Early studies identified muscle wasting secondary to induction therapy30 and these findings have been confirmed subsequently.31–34 An increase in fat mass and obesity have been well established also in survivors.18,21,22,29 The combination of these findings has led to the identification of ‘sarcopenic obesity’ in survivors of pediatric cancer.32,33 These changes in survivors of pediatric ALL have been attributed previously to the use of corticosteroids because of their known impact on fat distribution35,36 and cranial radiation; indeed, survivors of pediatric brain tumors have similar changes in body composition.37 However, long-term studies of survivors of all types of pediatric cancer and survivors of allogenic hematopoietic stem cell transplantation also demonstrated consistently increased metabolic risk and cardiovascular disease,5,14,27,38,39 suggesting that changes in body composition may be more universal with alternate mechanisms involved. For example, the increased catabolic state associated on occasion with a cancer diagnosis and treatment, and decreased mobility have negative impacts on muscle mass.30,40 An increase in inflammatory mediators41,42 and genetic factors43 may play a role also.
Given the burden of chronic health conditions in pediatric cancer survivors, of which metabolic and cardiovascular disease are common, early identification of and intervention for modifiable risk factors are prioritized highly. This includes a focus on altered body composition that is suspected to be a significant contributor to the development of an altered metabolic phenotype. In noncancer survivor cohorts, the metabolic risk is assessed typically by using markers of obesity (eg, with BMI). Unfortunately, applying this strategy to survivors of pediatric cancer may not be sufficient. Multiple studies have now demonstrated that BMI alone is unable to identify the increased fat mass that accumulates after treatment for pediatric cancer (likely because of the presence of sarcopenic obesity) and that metabolic risk remains elevated when the BMI is in the normal range.18,21,22,32,33,38,44,45 This highlights the importance of considering alternate techniques for assessment of body composition in this population.
CLINICAL AND METABOLIC IMPLICATIONS OF ELEVATED ADIPOSITY
Obesity is defined as an abnormal or excessive fat accumulation that presents a risk to health46 and is classified as a chronic disease by both the World Health Organization (WHO) and The Obesity Society.47 In the last several decades, the rates of obesity in children and adolescents, as defined by BMI, have risen significantly.48–51 With this rise, the prevalence of multiple comorbidities, thought previously to occur only with adult obesity, has increased with higher prevalence with increasing severity of obesity.48,52 Notable cardiometabolic comorbidities include abnormalities in glucose metabolism and/or insulin resistance, dyslipidemia, and hypertension. Together with obesity (centrally distributed adiposity, in particular), this cluster of comorbidities is often termed the metabolic syndrome.50,53 Although there exist several published criteria and consensus definitions of the metabolic syndrome in adults,35,54 definitions in children and adolescents vary widely in their reference standards and thresholds, making it more challenging to identify consistently in this age group.50,53 The presence of metabolic syndrome in adults has been associated strongly with progression to type 2 diabetes mellitus and the development of cardiovascular disease,54,55 and there is increasing evidence that the same is true of children and adolescents.56–58 Further, adolescents with significant obesity are likely to carry their adiposity into adulthood and to be at risk of developing complications earlier than those with adult-onset metabolic syndrome.48–50,59
The clustering of these disorders comprising the metabolic syndrome suggests a unifying cause. The most promising explanation involves the theory of adiposopathy.35,60 Adiposopathy refers to the idea that there is insufficient presence of healthy fat because of either conversion or accumulation of dysfunctional or “sick” fat, leading to the adverse cardiometabolic health outcomes associated with obesity. These changes are driven by rapid growth and hypertrophy of adipocytes, commonly found in obesity, with growth outpacing angiogenesis and inducing relative hypoxia in the adipocytes.61 As in other organs, this leads to fat dysfunction and altered cytokine profiles with multiple downstream effects.
One of the main features of adiposopathy is the presence of ectopic fat. There is an increase in circulating free fatty acids (FFAs) in obese individuals, due largely to a relative increase in lipolysis in dysfunctional fat.62 These circulating FFAs derived from food and lipolysis are deposited in ectopic sites, preferentially in and around the viscera like the liver, large vessels, and the heart. This ectopic deposition increases with age and may be because of persistent positive caloric balance and a lack of adequate adipose tissue for storage.55,61–63 Ectopic fat deposits are far more active metabolically and contribute to the development of increased cardiometabolic risk factors such as hypertension, dyslipidemia, insulin resistance, and inflammation in children and adolescents compared with deposition of fat into subcutaneous adipose tissue, which is more benign.64–67
Additional supporting evidence of dysfunctional ectopic and/or visceral fat rather than sheer fat mass driving metabolic risk is that not all obese adults with similarly elevated BMI or fat mass have the same body shape or metabolic profile.68,69 Although many health care providers consider a percent body fat >23% to 25% in adult men and 30% to 35% in adult women to be a marker of increased metabolic risk,68,70 data supporting this are lacking. Several population studies have attempted to draw correlations between fat percentage and outcome,71–81 with inconsistent results and no agreement on the “fat threshold” that leads to increased metabolic disturbance. In children and adolescents, age- and sex-matched curves (also referred to as Ogden curves) for fat mass percentage have been created and provide a reference to population norms.81–84 Body fat >75th percentile on Ogden curves has correlated most closely with high obesity (BMI >95th percentile) and abnormal lipid profiles,74,83,85 but further data are limited. Total body fat percentage thresholds, above which the rate of accumulating visceral adipose tissue increases sharply, do seem to exist in both adults and adolescents,86,87 and likely correlate better with metabolic risk. This nuance provides the rationale for subcategorization of obesity with clear delineation between those at metabolic risk (both with normal and elevated BMI) and those not at metabolic risk (metabolically healthy and obese). Apart from genetic predisposition, the biggest distinguishing feature of metabolically at-risk individuals is an increased component of visceral fat, independent of BMI.68,69
Although the exact link between increased visceral fat and adiposopathy with the metabolic disease is uncertain, several observations are identified consistently. The release of cytokines from dysfunctional adipocytes (termed adipocytokines) drives an overall inflammatory and prothrombotic state, with broad impacts on the body.35,55,65,88,89 For example, an increase in tumor necrosis factor-alpha and interleukin-6 release affects the microvasculature of adipose tissue, further exacerbating the effects of tissue hypoxia. There is also an increase in plasminogen activator inhibitor-1 and fibrinogen, both of which contribute to a prothrombotic state.61,90 Further, endocrine and paracrine effects of dysfunctional adipose tissue are distinctly different from their healthy equivalents. The evolving hormonal imbalance contributes to the development of insulin resistance and other metabolic derangements.55,60,62,90 Other vascular effects mediated by adipocytokines include recruitment of macrophages and other white blood cells, inducing a local inflammatory response and affecting smooth muscle activation. In the long term, in conjunction with activation of the sympathetic nervous system and mechanical compression, this contributes to the development of hypertension and atherosclerosis.61,90,91
Thus, it is clear that ectopic fat deposition and associated dysfunction are linked intimately with metabolic risk. Given this, and the relationship of metabolic disturbance with fat distribution, it is critical that we identify measurement techniques that consider fat distribution and not just the extent of adiposity in the clinical setting. Further, there is increasing evidence that lean muscle mass may modulate the adverse influence of adiposity.
IT IS NOT JUST ABOUT FAT: SARCOPENIA AND ITS IMPACT ON HEALTH
Just as there is variability in adiposity and fat distribution, individuals have varied muscle mass. Skeletal muscle mass, which constitutes ~40% of body weight in healthy adult men and 30% in women,92 is of particular interest. In adults, the combination of age-related muscle loss and decline in function was defined as sarcopenia and became noteworthy as a significant marker of frailty and as a predictor of mortality.15 Consensus definitions of sarcopenia have emerged in the adult literature93,94 and are based both on decreased height adjusted skeletal muscle mass (<7.23 kg/m2 in men and 5.67 kg/m2 in women) and decreased muscle strength and/or physical impairment, as defined by decreased hand-grip strength or reduced gait speed.
Over time, sarcopenia has been documented increasingly in younger populations95–99 and found to be associated with several disease states.34,99 Low muscle mass can be associated with high adiposity; these individuals thus may meet criteria for obesity as well, leading to the term sarcopenic obesity.100,101 Interestingly, a decrease in skeletal muscle mass has been associated with metabolic syndrome and cardiovascular risk, independent of BMI.36,41,78,95–98,102,103 Use of BMI alone can result in misclassification of individuals with sarcopenic obesity,68 making identification of this syndrome more challenging.
The development of sarcopenia is likely multifactorial and may vary depending on the age and health of the individual.94,104 At a basic level, loss of muscle is because of an imbalance in metabolic requirements and dietary intake; as such, increased catabolic drive without appropriate nutrition can lead to decreased muscle mass. In addition, mechanical factors play a role; decreased weight-bearing activities reduce the need for muscles and can lead to muscle atrophy. Several studies have also explored the synergistic effect of increasing adiposity and decreasing muscle mass.101,105 Though the exact mechanism has yet to be determined, inflammatory mediators, genetic factors, and hormonal changes may be involved.41,93,94,104 What is clear is that disease states or interventions that increase inflammation or fat mass are associated with a decrease in muscle mass.93,99,101
In pediatric patients, sarcopenia research has emerged more recently.12,31,32,34,96,106–110 Several normative data sets for skeletal muscle mass reference ranges in children and adolescents have been published40,111,112; however, a consensus cut-off and commonly accepted definition for what constitutes sarcopenia in this population remains elusive. As well, it is challenging to define a standardized functional component of sarcopenia in children, given that neither hand-grip strength nor gait speed can be investigated with equal ease across the pediatric age spectrum. Thus, it is unclear whether the conclusions of many adult studies apply to the pediatric population. Nevertheless, similar metabolic implications of sarcopenia have been demonstrated in children and adolescents.78,96,109 Further, sarcopenia in patients with pediatric cancer is correlated with worse outcomes than in those with normal muscle mass, both in leukemia and solid tumors,12,34,110 and with an increased risk of infectious complications including invasive fungal disease.31
Taken together, it is clear that all elements of body composition are important to assess when attempting to predict an individual’s metabolic risk. Although easy to use, simple indices such as the BMI are insufficient: it is imperative to consider both skeletal muscle mass and adiposity (fat mass with its distribution) in a given individual. As well, the significant long-term risks of developing cardiovascular disease associated with increased visceral fat and sarcopenia, justify early screening for changes in body composition to identify metabolic risk more easily. Unfortunately, quick ways to estimate these body compartments are not always accurate or applicable. Moreover, defining a threshold for “normal” or for clinical significance is often complex. In children, this often means using age- and sex-matched population norms; correct interpretation of data is therefore dependent also on the reference ranges used.
MEASURING AND INTERPRETING BODY COMPOSITION
Several techniques exist for assessing body size and composition with varying degrees of simplicity, accuracy, and availability. These methodologies can be divided largely into 2 groups—those which rely on specialized equipment and expertise and those that can be performed more quickly in a clinical setting. An excellent review of measurement techniques is provided by Borga et al17 and will be summarized more here and in Table 1.
TABLE 1 -
Relative Advantages and Disadvantages of Various Body Composition Measurement Techniques in the Pediatric Oncology Population
Requiring specialized equipment and expertise
| Imaging techniques
||Accurate representation of and differentiation between different body compartments As close to ‘gold standard’ as possible
||Requires specialized equipment and personnel; takes time Sedation required in young children
||CT and/or MRI is useful in research studies as ‘gold standard’ to which other techniques can be compared May be used preferentially in children who do not require sedation and/or when CT or MRI is required as part of clinical care
| CT scans
||Accurate representation of and differentiation between different body compartments As close to ‘gold standard’ as possible
||Requires specialized equipment and personnel; takes time Sedation required in young children Radiation exposure
||Quick, noninvasive, easily performed
||Not yet validated in terms of accuracy
||Ultrasound warrants further investigation as it may be a very useful tool but is not yet validated
| Dual-energy radiograph absorptiometry (DEXA) scan
||Similar accuracy to MRI and CT with current software Less time requirement (limiting sedation need) and radiation exposure
||Requires specialized equipment and personnel Unable to accurately differentiate between types of abdominal fat (ie, visceral vs. nonvisceral) compared with MRI
||Closest alternative to CT/MRI in terms of accuracy Less radiation and sedation requirements make this a favorable technique when resources allow Recommended technique for in-depth clinical assessment and research purposes
| Densitometry Ex. air displacement, water displacement
||Requires specialized equipment and personnel; not widely available clinically Influenced by variation in body water content Separates only by fat and nonfat mass Accuracy limited by assumptions built into techniques
||Not recommended for routine use
| Isotope dilution
||Requires specialized equipment and personnel Influenced by variation in body water content Separates only by fat and nonfat mass Accuracy limited by assumptions built into techniques
||Not recommended for routine use
| Bioelectrical impedance analysis (BIA)
||Highly portable, minimal cost required Useful in resource-poor environments Limitations in technique can be improved by additional measurements in different body regions
||Influenced by variation in body water content, body position, skin and air temperature, recent food intake, and exercise Separates only by fat and nonfat mass Accuracy limited by assumptions built into techniques
||Most useful in situations that are limited by cost, portability, and time constraints Accuracy can be improved with standardization of environment, physical status, and increased number of measurements; however, remains less accurate than DEXA and MRI Not able to differentiate different components of lean mass (ie, bone from muscle) Not routinely recommended for use in research (not gold standard), but may have use in a clinical setting if available
Widely available anthropometric techniques
| Body mass index (BMI)
||Can be performed easily in a clinical setting, noninvasive No cost requirement, specialized equipment, or personnel
||Unable to accurately differentiate fat mass from lean mass, account for fat distribution, or predict cardiovascular outcome Definition of obesity has variable sensitivity in predicting fat mass in different age groups and populations Weight does not scale with height at a power of 2 in adolescents
||Continues to have a role as a quick screening tool within the clinical setting Should not replace a more detailed body composition measurement or metabolic risk assessment, particularly in populations at risk of sarcopenic obesity
| Triponderal mass index (TMI)
||Can be performed easily in a clinical setting, noninvasive No cost requirement, specialized equipment, or personnel More stable than other indices, less reliance on standardized curves Early investigations suggest improved correlation compared with BMI with: Weight/height scale in adolescents Obesity definition and percent fat mass Visceral fat Metabolic risk
||Like BMI, this is a surrogate marker of obesity and/or adiposity Does not differentiate fat from lean mass, but may be better able to predict visceral fat and metabolic risk Not yet validated for widespread clinical use
||Emerging as a possible measure in the clinical setting as a more accurate screening tool than BMI Should not replace a more detailed body composition measurement or metabolic risk assessment, particularly in populations at risk of sarcopenic obesity Should be considered for inclusion in research studies to deepen our understanding and correlation with other techniques
| Limb anthropometry Ex. triceps skin-fold thickness, mid-upper arm circumference
||Can be performed easily in a clinical setting, noninvasive No cost requirement, specialized equipment, or personnel
||Surrogate marker of adiposity in a specific body region Better reflects subcutaneous fat and may underestimate metabolically relevant visceral fat collections
||Can be considered in conjunction with other measures of body composition but should not be the sole measurement used
| Central adiposity measures Ex. waist circumference, waist-to-hip ratio, waist-to-height ratio
||Can be performed easily in a clinical setting, noninvasive No cost requirement, specialized equipment, or personnel A useful way to screen for central adiposity and visceral fat deposits Waist-to-height ratio >0.5 over 5 y of age correlates with increased metabolic risk
||Surrogate marker of adiposity in a specific body region Unable to differentiate between subcutaneous abdominal fat and visceral fat
||When combined with other screening tools (like BMI), the waist-to-height ratio in particular can be used to help predict metabolic risk Should not replace a more detailed body composition measurement or metabolic risk assessment, particularly in at-risk populations
CT indicates computed tomography; MRI, magnetic resonance imaging.
The accuracy of clinically available measurements and indices in assessing body composition and predicting metabolic risk are determined by comparison with a gold standard. Muscle mass and fat mass can be evaluated using imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI).12,17,97,107,113–115 Unfortunately, in pediatric populations, direct imaging techniques have limited clinical applicability. In particular, unnecessary CT scans are avoided because of the associated radiation risk unless otherwise required clinically, whereas MRI scans often require sedation for a young child to lie still for long enough. Recently, ultrasound has been used to distinguish between subcutaneous and visceral abdominal fat, and findings correlated with markers of metabolic dysfunction.116 This technique warrants further validation and research in diverse pediatric populations.
The preferred clinical alternative is a full body scan using dual-energy radiograph absorptiometry (DEXA). With the availability of suitable software, information about bone, fat, and muscle mass can be extracted from the scan,117 though there are some concerns with its accuracy in detecting changes in visceral adipose tissue compared with MRI.17,118 There are minimal risks associated with DEXA, as the radiation involved on contemporary machines is less than that of a typical plain radiograph of the chest or spine,119 and scanning is relatively fast, minimizing the need for sedation in young children. However, DEXA does require specialized equipment and personnel, and results may be less accurate in certain disease states.117
Alternative measurement techniques divide the body typically into 2 compartments (fat-free and fat). Two classic techniques used rarely in contemporary studies are densitometry and isotope dilution or hydrometry.17 Densitometry measures air or water displacement in a closed environment, as with air displacement plethysmography, and calculates body compartment volumes indirectly using standard density parameters of fat and lean mass. Isotope dilution determines total body water content by measuring urinary or saliva concentration of an inert isotope (typically deuterium) after oral administration.120 Fat-free mass is then estimated using an assumed value of its hydration. Both techniques have significant limitations inherent in their assumptions and rely on normal hydrational status.
Bioelectrical impedance analysis (BIA), however, continues to be in common use, particularly in more resource-poor environments, because of its portability and low cost.17 BIA uses the passage of a small electrical current through the body to measure resistance (impedance). By taking advantage of the variable resistivity of fat and lean mass, volumes of each compartment can be estimated. This technique is limited by the assumptions built into the equations used for estimation, which add a significant amount of error, and results are influenced by variable body water content (hydrational status), body position, air and skin temperature, recent food intake, and physical activity.121 This has been accommodated by measuring BIA in regional parts of the body or by using multiple frequencies.16,80
Simple measurements that correlate well with the measurements described above and that can be performed easily in a clinical setting are highly sought, and include BMI discussed earlier. The original use of BMI was as a descriptor in population statistics, but it grew to importance in the 1970s as a better correlate (albeit only marginally) than weight with percent body fat and presumed health outcomes.122 Since that time, the BMI has become ubiquitous both in medicine and common parlance as a way to identify obesity (>30 kg/m2 in adults), despite its known limitations in being able to distinguish fat from lean mass, account for fat distribution, and predict cardiovascular outcome.17,68,71
Given the variation in height and weight as children and adolescents grow, a single cut-off for BMI in pediatric patients is not feasible. Rather, the BMI of a child is considered relative to an age and sex-specific growth standard. Internationally, the most recent charts published by the WHO in 2004 are considered a gold standard for the growth of healthy, multiethnic children from around the world.123,124 Obesity is defined using weight-for-length measurements until 2 years of age and BMI in older children and adolescents. Weight-for-length or BMI >99.9th percentile is considered obese before the age of 5 with a lower threshold of 97th percentile used after the age of 5.125 In North America, Canadian guidelines recommend using the WHO growth standards adapted for Canadian use,125 whereas in the United States, growth standards published by the Centers for Disease Control are recommended.126 Unfortunately, the Centers for Disease Control’s growth standards are older, do not contain a high proportion of breast-fed babies, and are based on a less ethnically diverse population compared with the updated WHO curves.127 The BMI definition of obesity does seem to correlate with increased fat mass in adolescents and has been linked to increased cardiometabolic risk factors, but its sensitivity varies among different populations.128
Recently, the triponderal mass index (TMI; defined as the mass in kilograms divided by the cube of the height in meters) has emerged as an index of body composition that may predict percent adiposity and classify obesity in adolescents.129 Studies as early as the 19th century identified that weight scaled with height at a substantially higher power than 2 (as used for BMI) in adolescents, with values ranging from 2.5 to 3.5 in various studies,129,130 is a better measure. Some of this may be explained by the direct impact weight can have on height through adolescence, because of the impact fat mass plays on triggering puberty.131 Peterson et al129 demonstrated that the TMI was more stable in both girls and boys 8 to 17 years of age (hovering around 14 kg/m3) than other measures, eliminating the need for a comparison with standardized curves. The 95th percentile of TMI (18.8 kg/m3 in boys and 19.7 kg/m3 in girls) also correlated more closely with a definition of obesity using fat mass (95th percentile on Ogden curves) than BMI, improving the sensitivity and specificity of obesity classification in this population.129 Since Peterson et al’s129 seminal paper, the TMI has been used as a descriptor in numerous studies of healthy children and adolescents, demonstrating relative stability above age 6 years until young adulthood, and consistently correlating better than other indices with measurements of body composition in a number of different ethnic populations.132–136 Given the improvement in accuracy, investigations now have centered on using the TMI to identify adiposity thresholds that predict increased metabolic risk.
De Lorenzo et al133 identified TMI thresholds in a population of Italian children 8 to 17 years of age (14.6 kg/m3 in girls and 14.0 kg/m3 in boys) that correlated with adiposity >75th percentile on Ogden curves. Given previous identification of metabolic risk at this percentile of fat mass74,83 they suggest their TMI threshold may be able to predict metabolic risk. In a population of Colombian youth Ramírez-Vélez et al134 were able to further this research and correlate TMI in 3 cohorts (ages 9 to 12, 13 to 17, and 18 to 25) with a high metabolic score. The age- and sex-specific TMI thresholds they identified to predict increased metabolic risk are lower (ranging from 12.13 to 13.21 kg/m3 for female individuals and 11.19 to 12.19 kg/m3 in male individuals) than those published both by Peterson and colleagues and De Lorenzo and colleagues, suggesting that body fat percentile alone may be insufficient for predicting risk. Additional recent studies in populations 6 to 19 years of age have also examined the correlation between TMI and markers of metabolic health, with variable results,135–138 though with marked differences between studies in what risk threshold is used for comparison. There is emerging evidence to suggest the TMI may be better than BMI at identifying increased risk of visceral fat accumulation and further studies are warranted.139,140
Several additional anthropometric measures are used to provide a more targeted assessment of body size using measurements of specific parts of the body. Skin-fold thickness, especially that of the triceps (triceps skin-fold thickness), and limb circumferences (eg, mid-upper arm circumference) have been used as surrogate markers for assessing percent body fat.114,141 However, these measurements better represent subcutaneous fat deposits, potentially underestimating metabolically relevant visceral fat accumulation. To assess central adiposity a number of measures have been used, and waist circumference and waist-to-hip ratios are the measures used most commonly in adults.68,114 In children and adolescents, a waist-to-height ratio is used more commonly as it removes the impact of varying height.142 Over the age of 5 years, a waist-to-height ratio >0.5 has been shown to be predictive of increased metabolic risk,142–145 though studies in different ethnic populations suggest a lower cut point may be more sensitive.142,146,147 This ratio has demonstrated correlation also with truncal obesity measured by DEXA scans148 and has been used in combination with BMI to identify those more at risk.149
In pediatric cancer survivors, body composition has been assessed by many of the above techniques. The advantages of using imaging and DEXA over BMI have been discussed above.
Anthropometric measures have been used in survivors of ALL44,45,150 and brain tumors39 with some success in demonstrating increased adiposity. It is now well established that, during and after treatment, the risk of increased fat mass and decreased muscle mass in this population is captured poorly by clinical measures, and more detailed assessments using DEXA or imaging techniques are warranted. Recently, the TMI was evaluated also in survivors of childhood brain tumors140; similar to other studies on the TMI, Sims et al demonstrated a strong correlation between TMI and measures of adiposity, including percent body fat measured by BIA and waist-to-height ratio, in survivors of childhood brain tumors and age-matched controls. Given its stronger correlation with percent body fat and potential ability to predict the visceral fat distribution and metabolic risk, the TMI may be more useful in survivors of childhood cancer than BMI and other weight-based indices.
It is clear that, despite there being several methods for estimating adiposity, many of these techniques classify individuals as obese or nonobese inaccurately and do not describe fat distribution accurately when compared with gold-standard measurements of body composition. As well, many individuals with a higher fat percentage also have lower muscle mass and these are captured inadequately by simple clinical measures such as the BMI. Given that metabolic health seems to be correlated not just with increased adiposity but with increased visceral fat and that muscle mass may be protective, using weight or BMI alone to predict metabolic risk is insufficient.
It is especially important to consider these factors in research involving body composition and metabolic risk in pediatric cancer survivors because of the high prevalence of sarcopenic obesity and related changes as a result of treatment (Fig. 1). Emerging evidence suggests that the TMI may be more sensitive at detecting both percent adiposity and visceral adiposity in healthy children and adolescents, but there remains ongoing controversy as to appropriate TMI thresholds to identify and predict metabolic risk.
Further body composition research and correlative studies are needed in survivors of pediatric cancer using appropriate gold-standard measurement techniques to improve our understanding of the significance of adiposity and sarcopenia in this population and to identify potential thresholds predictive of metabolic risk. In the future, early identification of these changes in body composition may allow interventions that could ameliorate or prevent the long-term metabolic and cardiovascular risks experienced by survivors of pediatric cancer.
In this paper, we have provided a broad perspective on the significance of adiposity and its distribution in the body on metabolic risk, particularly as it applies to survivors of pediatric cancer. We have highlighted the challenges that clinical researchers face in attempting to measure the body composition of their subjects accurately, particularly in pediatric populations. We encourage those interested in pursuing research in this field to consider the limitations of many of these measures and suggest that DEXA scans likely offer the most robust measures of body composition (including both fat mass and lean body mass), balancing risk with benefit in pediatric populations. Future research should seek to standardize the approach to measuring body composition to better compare studies that will allow more accurate determination of thresholds that may predict and identify metabolic risk. In addition, an assessment of body composition shortly after completion of treatment for pediatric cancer and/or through after-care programs should be considered as information that may help to predict cardiovascular risk in survivors and provide opportunities for intervention.
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