Correlation of FSTL1 and its paralogs with body composition in adult survivors of childhood cancer

Correlation of FSTL1 and its paralogs with body composition in adult survivors of childhood cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Correlation of FSTL1 and its paralogs with body composition in adult survivors of childhood cancer Lucie Štrublová, Filip Zlámal, Tomáš Pískovský, Jan Kučera, Jana Fialová Kučerová, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5637051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract This retrospective cross-sectional study investigated the relationship between body composition and circulating plasma levels of follistatin-like proteins (FSTLs; FSTL1, FSTL4, and FSTL5) in adult survivors of childhood cancer. This is the first study to investigate the association between plasma levels of FSTL4, FSTL5 and skeletal muscle mass. The cohort consisted of 61 CCS (26 females and 35 males) aged 18-36 years (mean age 24.6±4.4 years) who were followed up at the Long-Term Follow-Up Clinic of St. Anne's Hospital in Brno. The mean age at diagnosis was 10.9±4.9 years and the mean time after treatment was 12.0±5.1 years. Body composition was assessed by anthropometric indicators and bioelectrical impedance analysis (BIA; InBody 370). Plasma FSTLs levels were quantified using commercially available ELISA kits. Correlations were examined by linear regression analysis. Significant negative correlation was found between plasma level of FSTL1 and body fat mass index (BFMI) and significant positively correlation was between FSTL1 and skeletal muscle mass index (SMI). Our results suggest that FSTL1 may be potential indicator of adiposity and skeletal muscle loss in CCS. Trial registration: This study was registered on July 29, 2022, at ClinicalTrials.gov (NCT05481229). Health sciences/Biomarkers Health sciences/Oncology FSTL1 FSTL4 FSTL5 adiposity skeletal muscle mass late effects childhood cancer survivors Figures Figure 1 Introduction Significant improvements in childhood cancer survival rates have led to a growing population of long-term survivors. However, these individuals are at substantially increased risk of developing chronic health conditions, including cardiovascular, metabolic and musculoskeletal problems, which significantly impact their quality of life 1 – 9 . Epidemiological studies, such as the Childhood Cancer Survivor Study (CCSS), highlight the pervasive nature of these late effects and underscore the need for a deeper understanding of the underlying mechanisms 2 , 10 , 11 . A key factor contributing to these long-term health problems is altered body composition, which can occur early in cancer treatment and be further affected later in life by the inappropriate lifestyle of childhood cancer survivors 10 , 12 , 13 . Childhood cancer survivors often have increased adiposity and decreased skeletal muscle mass compared to the general population 2 , 11 , 14 – 17 . This adverse change in body composition is associated with an increased risk of metabolic syndrome and other metabolic disorders 9 , 16 , 17 . The interplay between adipose and muscle tissue and their respective secretions of adipokines and myokines is critical to understanding this complex interaction. One of the goals of current science in the field of late effects of cancer treatment is to identify factors and potential biomarkers associated with changes in body composition and to find appropriate preventive or therapeutic measures applicable in the follow-up care of this specific cohort of patients 2 , 18 , 19 . Research into particular adipokines and myokines is essential for elucidating the pathophysiological mechanisms behind these metabolic disorders 20 – 24 . While several signalling molecules that reflect body composition, particularly concerning adiposity, have been studied in detail among childhood cancer survivors 15 , 21 – 23 , the role of many others, including follistatin-like protein 1 (FSTL1) and its paralogs, have yet to be examined 21 , 25 – 28 . FSTL1 and its paralogs are considered signalling molecules that may be potential regulators of metabolism 21 , 25 – 28 . Their levels and function depend on the specific biological context, which means that their effects can vary depending on the environment and the health status of the organism. FSTL1 is the most studied of the FSTL family, while knowledge of follistatin-like protein 4 (FSTL4) and follistatin-like protein 5 (FSTL5) is limited 25 , 26 , 29 , 30 . FSTL1 exerts diverse and often contradictory effects 24 , 26 , 31 . FSTL1 is known to play an important role in the cardiovascular system and its elevated serum levels are associated with several cardiovascular diseases 29 , 30 . Previous research examining the role of FSTL1 in body composition and metabolic disorders has produced inconclusive results. Some studies indicate that elevated FSTL1 levels correlate with metabolic syndrome 24 , 25 , while others report lower levels in severe obesity 31 and significantly reduced FSTL1 levels in muscle tissue of adult patients with tumor cachexia 32 . Despite these findings, the exact mechanisms and roles of FSTL1 in adiposity and muscle mass regulation remain unclear, and further research is required to clarify its function and implications for CCS, particularly those experiencing the complications of altered body composition and associated metabolic disorders. FSTL4 (SPIG1) was originally identified during retinal development in the chicks 29 , 33 . Although its involvement in the CNS has been linked to some neurodegenerative diseases 34 , its expression and role in cardiovascular disease are still uncertain 29 . In the context of body composition, FSTL4 and FSTL5 levels have not yet been sufficiently investigated, and no study has examined the relationship between plasma levels of FSTL4 and FSTL5 and skeletal muscle mass. The primary hypothesis of our study is that altered body composition in adult survivors of childhood cancer, characterized by increased adiposity and decreased skeletal muscle mass, is associated with dysregulated levels of follistatin-like proteins (FSTL1, FSTL4, and FSTL5). Therefore, the aim of this pilot study is to investigate the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 in adult CCS. Material and methods Study design This retrospective cross-sectional study focuses on a sample of 61 childhood cancer survivors followed up at the Long-Term Follow-Up Clinic of St. Anne's University Hospital in Brno, Czech Republic. This study evaluates the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5. All procedures involving human participants were performed in accordance with the ethical standards of St. Anne's University Hospital, Brno. Study setting Between 25 April 2018 and 5 June 2019, we enrolled 61 individuals, 26 women and 35 men, who provided informed consent to participate in the study and who also met all inclusion criteria. Study participants were presented with a two-part questionnaire administered by a researcher during their visit to the clinic. This section included questions about anthropometric data, the survivor's personal history, questions about cancer type and treatment duration. The survivor then completed the second section. This section included questions about socioeconomic status, such as education level. Survivors were also measured by bioelectrical impedance analysis (BIA) at the time of recruitment to the study, and their height, weight, waist and hip circumference were recorded. The collection of biological material was also part of the research protocol. Peripheral venous blood samples were collected into EDTA tubes and processed, obtained plasma was stored at -80 °C until further analysis. Participants Survivors were aged 18-36 years (mean age 24.6±4.4 years) and had been diagnosed with childhood cancer between 1983 and 2011 (for more information on the study cohort, refer to Table 1). The inclusion criteria for participation in this study were as follows: a) had an appointment at the outpatient clinic between April 2018 and June 2019; b) treated with chemotherapy and/or radiotherapy for cancer diagnosed between the ages of 0 and 18 years; c) aged 18 years or older; d) in complete remission of the primary cancer; e) met the BIA measurement criteria; f) they met the conditions for venous blood sampling g) signed an informed consent for the use of their health data for scientific and research purposes. Anthropometric characteristics Body composition was measured using the InBody Model 370. All survivors were instructed on fluid and food intake prior to body composition measurements 35 . The variables used for analysis were skeletal muscle mass index (SMI) and body fat mass index (BFMI). SMI, calculated as kilograms of skeletal muscle mass per square meter (kg/m²), indicates height-normalized skeletal muscle mass. Similarly, BFMI, calculated as kilograms of body fat mass per square meter (kg/m²), indicates height-normalized body fat mass, which addresses issues in interpreting data expressed as percentages of body weight or as absolute values 36 . Total body fat was categorised according to the Obesity Medicine Association (OMA) 37 . According to this classification, an increased amount of fat between 30-34% in women and 25-29% in men is considered risky and pre-obesity. Values ≥35% in women and ≥30% in men were then defined as obesity. Weight was measured to the nearest 0.1 kg on an electronic scale and confirmed by weight measurement using the In Body 370 device. Height was measured using a stadiometer. The body mass index (BMI) was calculated from the height and weight by dividing the weight in kilograms by the height in centimetres squared. BMI was categorised according to the WHO 38,39 ; in Table 1B, BMI 30-40 is presented as "obesity" and BMI ≥ 40 as "extreme obesity". Finally, waist and hip circumferences were measured using a tape measure. Waist circumference (WC) was measured in the horizontal plane at a point marked just above the right subcostal line on the mid-axillary line 40 , with minimal breathing, and classified according to WHO 41 . Based on the measured values of waist and hip circumference was calculated and Waist-to-hip ratio (WHR). WHR was categorised according to the WHO 42 . Height, waist circumference, and hip circumference were recorded to the nearest 0.1 cm. Diagnosis and treatment The cancer diagnoses were divided into 4 diagnosis groups "brain and spinal column tumours", "leukaemia", "lymphoma" and "other solid tumours", the number of survivors in each category is presented in Table 1B. Based on the available information on treatment history, treatments given were categorised as cranial radiation vs no cranial radiation according to their potential effect on the development of adiposity 43 . Cranial radiation included cranial irradiation therapy or whole body irradiation therapy. These data were used to create the dichotomous variable "type of treatment" cranial radiation vs no cranial radiation. FSTL measurement Plasma levels of FSTL1, FSTL4, and FSTL5 in the patient cohort were measured in duplicates using commercially available ELISA kits according to the manufacturer's instructions (Cusabio Technology LLC, cat. nos. CSB-E13516h, CSB-EL009027HU and CSB-EL009028HU, respectively). For FSTL1 and FSTL5 measurements, the plasma samples were diluted three-fold and two-fold in the sample diluent (5% BSA in PBS), respectively. Absorbance was measured on a Spectramax 340PC Microplate Reader (Molecular Devices). The detection limit for FSTL1, FSTL4, and FSTL5 was 3.12, 0.39, and 0.31 ng/mL, respectively. The inter-assay coefficients of variability for all three measured proteins were < 10%. Analytical sample The analysis included a total of 61 survivors. Data analysis The data analysis was performed using statistical software R version 4.0.3 44 . First, descriptive analysis was conducted and basic relationships between variables were assessed. Descriptive characteristics for continuous variables are represented using mean and standard deviation. As regards categorical variables absolute and relative frequencies are used. Normality for continuous variables was checked by statistical tests (like Shapiro-Wilk, Pearson, Anderson-Darling) and also graphically (histogram and Q-Q plot). If data were far from the desired normal distribution, logarithmic transformation was applied (and normality was checked again). The relationship between continuous variables was accessed by pairwise Pearson correlation coefficient. The methods of linear regression models were used to identify which variables were significantly associated with values of FSTLs and also to quantify the strength of the effects. For this purpose, each of natural logarithm of FSTLs was considered as dependent variable and sex, age, BFMI, SMI and cranial radiation are considered as independent variables. And finally, graphical methods were used to check assumptions of each of the regression model (normality, homoscedasticity, independence). Ethics approval The study was approved by the local ethics committee, and approval for this research was granted under registration number IIT/2017/35. The study adhered to the principles outlined in the Declaration of Helsinki. Consent to participate Informed consent was obtained from all individual participants included in the study. Results Patient characteristics Table 1. A) Descriptive characteristics of the sample by sex . Mean (standard deviation) values are presented. B) Distribution of selected categorical variables. Absolute values (percentages) are presented. Table 1A Variable Male (N=35) Female (N=26) Total (N=61) Height (cm) 176.00 (9.95) 167.17 (6.81) 172.20 (9.66) Weight (kg) 72.10 (16.17) 59.70 (9.34) 66.94 (14.97) BMI (kg/m 2 ) 23.14 (4.20) 21.38 (2.80) 22.41 (3.75) Body fat mass (kg) 15.49 (8.77) 17.71 (8.03) 16.43 (8.47) Body fat (%) 20.35 (8.72) 27.98 (8.20) 23.56 (9.25) Body fat mass index – BFMI (kg/m 2 ) 5.00 (2.86) 6.33 (2.80) 5.56 (2.89) Skeletal muscle mass (kg) 32.28 (6.74) 23.90 (3.77) 28.75 (7.01) Skeletal muscle mass (%) 44.74 (5.02) 39.28 (4.69) 42.44 (5.55) Skeletal muscle mass index – SMI (kg/m 2 ) 10.30 (1.48) 8.49 (0.88) 9.54 (1.55) Waist (cm) 84.73 (11.70) 74.44 (9.97) 80.29 (12.05) Hips (cm) 91.53 (10.03) 89.22 (7.55) 90.52 (9.02) Waist-to-hip ratio – WHR (-) 0.92 (0.07) 0.83 (0.06) 0.88 (0.08) Age (y) 24.71 (5.01) 24.55 (3.31) 24.64 (4.33) Time after treatment (y) 12.30 (5.60) 11.69 (4.40) 12.03 (5.09) Age at diagnosis (y) 10.69 (4.45) 11.12 (5.41) 10.87 (4.85) FSTL1 (ng/mL) 53.40 (16.34) 49.65 (29.31) 51.80 (22.64) FSTL4 (ng/mL) 1.03 (0.51) 0.98 (0.86) 1.00 (0.68) FSTL5 (ng/mL) 16.35 (9.89) 15.89 (8.17) 16.15 (9.12) Table 1B Variable Category N (%) Body fat Essential fat 6 (9.84) Fitness 9 (14.75) Athlete 9 (14.75) Acceptable 15 (24.59) Pre-obesity 7 (11.48) Obesity 11 (18.03) Missing 4 (6.56) BMI Underweight 6 (9.84) Normal 40 (65.57) Overweight 12 (19.67) Obesity 2 (3.28) Extreme obesity 0 (0.00) Missing 1 (1.64) WHR Non-obese 27 (44.26) Obese 30 (49.18) Missing 4 (6.56) Education Elementary 18 (29.51) High school 28 (45.90) University 15 (24.59) Cranial radiation Yes 17 (27.87) No 44 (72.13) Type of diagnosis Brain and spinal column tumours 13 (21.31) Leukemia 8 (13.11) Lymphoma 18 (29.51) Other solid tumours 22 (36.06) Body composition and plasma levels of FSTL1, FSTL4 and FSTL5 Linear regression models were used to examine the relationship between BFMI and SMI and plasma levels of FSTL1, FSTL4 and FSTL5. In the next step, a model was developed for FSTL1, FSTL4 and FSTL5 (log-transformed) as a function of sex, age, BFMI and SMI. Results are shown in Table 2 (see Table 2 and Figure 1). The other body composition variables BMI and WHR were not found to be significantly correlated with FSTL1, FSTL4, FSTL5 and were therefore not further used in the model. Table 2. Result of final model for FSTL1. Model for log(FSTL1) b 95% CI for b p Sex female (ref) male -0.140 (-0.396;0.116) 0.278 Age -0.006 (-0.028;0.016) 0.594 BFMI -0.053 (-0.088;-0.018) 0.004 SMI 0.113 (0.031;0.196) 0.008 Cranial radiation no (ref) yes 0.189 (-0.049;0.427) 0.117 F(5,50)=3.140, p=0.015, R 2 =0.239 Results of models for FSLT4 and FSTL5 are not included because these models are not statistically significant (FSTL4: F(5,41)=1.118, p=0.366, R 2 =0.120; FSTL5: F(5,51)=0.349, p=0.881, R 2 =0.033). Table 2 shows the results of the model for FSTL1. Statistically significant relationships between FSTL1 and the independent variables, followed by the characteristics of the overall model. The only significant relationship between FSTLs and body composition was found in the model for FSTL1. BFMI is negatively related to log(FSTL1) whereas skeletal muscle index is positively related to log(FSTL1). Increasing BFMI by 1 kg/m2 decreases level of FSLT1 by 5.1% on average (95% CI: 1.7;8.4%). Increasing SMI by 1 kg/m2 increases level of FSTL1 by 12.0% on average (95% CI: 3.1;21.7%). Effects of sex, age and cranial radiation are not statistically significant. Discussion CCS face changes in body composition, especially an increase in fat mass and a decrease in muscle mass, that are associated with an elevated risk of metabolic syndrome and other metabolic disorders and affect their quality of life. Commonly used measures, such as BMI, do not differentiate between muscle and adipose tissue or assess their distribution. This can easily lead to an underestimate altered body composition. Altered body composition is associated with metabolic disease and cardiovascular problems, potentially resulting in long-term adverse effects on the health and quality of life of CCS 16,19,45–49 . Furthermore, measures of total adiposity (percent fat mass; %FM) and central adiposity, WHR, have been shown to be more reliable predictors of cardiometabolic health and risk than BMI 45,50–52 . Similarly, SMI and BFMI have been shown to be valuable for assessing body composition in CCS, providing insights into the long-term effects of cancer treatments on muscle and fat distribution 19,53 .The aim of this pilot study was to investigate the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 in adult CCS. Our study employed multiple methods to assess body composition: WHR, BMI and BIA. The overall mean WHR of our study population is 0.88 (0.09), in line with results reported by Pluimakers et al., who demonstrated a mean WHR in CCS cohort is 0.88 (0.09) 2 . The high prevalence of abnormal WHR values (49%) in our study population suggests a substantial presence of central adiposity, while BMI indicated overweight or obesity in 23% of our participants. In contrast, BIA revealed a higher prevalence of increased adiposity (pre-obesity and obesity), affecting 30% of the study population. These findings further highlight the limitations of BMI in accurately assessing geographical accumulation of adiposity in childhood cancer survivors. The underestimation of total adiposity by BMI was further brought up by Wang et al. in their study who found that although the prevalence of overweight and obesity according to BMI was almost identical to that of the control group (42.6% vs. 40.4%), the childhood cancer survivors had significantly higher amounts of adipose tissue than the control group on assessment by BIA, dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI). In this study, we examined the association between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 using a linear regression model in the CCS cohort. FSTLs are signalling molecules that are synthesised in different tissues of the body. FSTL1 (also known as TSC-36) was first discovered and described in 1993 as one of six genes regulated by transforming growth factor β (TGFβ1) in mouse osteoblasts 54 . FSTL1 is the most studied of the FSTLs, whereas our knowledge of FSTL4 and FSTL5 is limited 25,26,29,30 . A significant association was found between the plasma level of FSTL1 and BFMI. As the BFMI increase FSTL1 level decrease. This is in line with a study 31 which explains the reduced FSTL1 levels in extremely obese individuals as a possible consequence of reduced adipogenesis, increased adipocyte apoptosis and epigenetic silencing of the FSTL1 gene. The authors of the study suggest that FSTL1 may be a valuable biomarker for predicting future weight gain and assessing the effectiveness of weight loss interventions. In contrast, elevated plasma levels of FSTL1 have been observed in association with pre-existing metabolic diseases. Elevated FSTL1 levels in relation to metabolic complications were studied in detail by Liu et al. in a cohort of obese children, who concluded that elevated serum FSTL1 levels are associated with metabolic fatty liver disease 55 . In the study by Yang et al, elevated plasma FSL1 levels were associated with metabolic syndrome and correlated with WC and visceral obesity 27 . In our study, we did not assess the prevalence of metabolic disorders, but the relationship between WHR as an indicator of increased risk of visceral obesity and plasma FSTL1 levels was not significant. A variable for cranial radiation was also included in our model. Cranial irradiation is strongly associated with changes in body composition, particularly the development of adiposity, in the CSS cohorts 43,56,57 . The effects of cranial radiation were not statistically significant. A significant positive association was found between the plasma level of FSTL1 and SMI. Unfortunately, we cannot compare our results with any available findings from comparable cohorts. De Castro et al. 32 investigated FSTL1 levels in adult cachectic gastric cancer patients and found significantly lower FSTL1 levels in muscle tissue, but no corresponding change in plasma levels. The authors of the study believe that the reduced production of FSTL1 in muscle during cachexia may not be primarily due to reduced production in the muscle itself but may be influenced by other factors that affect the release or clearance of FSTL1 from muscle, such as reduced muscle performance and impaired muscle recovery. Further, FSLT1 synthesis in skeletal muscle tissue may be regulated by inflammatory stimuli, as myotubes show increased secretion of FSTL1 into the culture medium after treatment with interleukin-1 beta and interferon gamma 58 . The results of the different study 59 show a weak or non-significant association between circulating FSTL1 levels and markers of skeletal muscle function (walking speed and handgrip strength) in a cohort of haemodialysis patients. Although a weak positive association between FSTL1 and handgrip strength was observed only in men, this limited association suggests that the effect of FSTL1 on muscle mass and strength in this population may be secondary to other factors known to affect muscle health in haemodialysis patients, such as inflammation, malnutrition and uremic toxins. In line with our study decrease in muscle mass and strength has been documented during childhood cancer treatment, which often persists into adulthood 60 . Muscle weakness and loss of muscle mass are common in these children, not only during active disease but also in surviving patients. These acute effects are likely to be due to a combination of factors, including the cancer itself, intensive multimodal therapy and systemic changes such as inflammation, hormone levels and nutritional status 13 . However, children with cancer have changes in muscle mass that are not associated with changes in bone mineral content, fat mass or total body weight. This suggests that muscle mass should be assessed explicitly and independently of BMI or adiposity 11,15,60 . Oncology treatment, including radiotherapy, chemotherapy and surgery, can lead to the development of sarcopenia, a progressive and generalized skeletal muscle disease which is associated with an increased likelihood of developing adverse consequences, including falls, fractures, physical disabilities and mortality 61 . Sarcopenia is a key factor in the development of frailty and premature aging in CCS 62,63 .These complications can occur independently or cumulatively, and their severity depends on individual patient characteristics and the type and intensity of treatment. Muscle tissue damage occurs through a variety of mechanisms, including acute cellular damage (e.g., impaired cell division, membrane permeability and ion pump function) and chronic processes (e.g., inflammation, ischaemia). In addition, some chemotherapeutic agents (e.g. L-asparaginase, methotrexate, vincristine) may themselves cause a reduction in muscle strength and flexibility. Muscle atrophy, fibrosis and hypoplasia may occur 64–66 . It should be noted that our study did not assess the prevalence of sarcopenia or sarcopenic obesity. Thus, further investigation, including assessment of skeletal muscle functional parameters, would be required to confirm the diagnosis and evaluate sarcopenia or sarcopenic obesity 67 . Physical activity has a positive effect on body composition, which may help to reduce some of the adverse late effects of childhood cancer and its treatment, and may help to prevent metabolic, cardiovascular and musculoskeletal disorders. A systematic review 68 showed that moderate-intensity aerobic exercise stimulates the secretion of FSTL1, which plays an important role in the prevention of atherosclerosis. FSTL1 improves endothelial function, inhibits smooth muscle cell proliferation and reduces vascular wall thickening. Through this mechanism, moderate-intensity aerobic exercise prevents endothelial dysfunction, arterial stiffness and vascular inflammation, thereby preventing the progression of atherosclerosis. Nam et al. demonstrated a key role for FSTL1 in the regulation of lipid metabolism during and after endurance exercise. Significantly elevated levels of FSTL1 were observed during and after exercise and correlated with lean body mass (LBM) and lipolysis 69 . These results correlate with our findings of increasing levels of FSTL1 with increasing SMI. However, the analyses have examined the relationship between FSTL1 and LBM, not SMM as in our study. The question therefore remains to what extent this type of physical activity would lead to FSTL1 production in relation to SMM. Survivors reported low levels of physical activity during adolescence and young adulthood 70 . The result of CCSS showed that CCS were more likely than their siblings not to follow recommendations for physical activity 10 . In this context, survivors are at increased risk of metabolic disorders and cardiovascular disease, which may increase the development of these comorbidities and significantly affect their quality of life 9,14,15,43 . However, the question remains as to whether the development of appropriate recommendations and intervention programmes to promote physical activity, healthy diet and weight reduction can achieve potential changes in FSTL1 expression and promote its tissue protective effect, as is the case with other more studied proteins 71 . Our study has several limitations, including the heterogeneity of the study group and the relatively small sample size and lack of relevant reference values for comparison of our results. These limitations are particularly relevant for patients with brain tumours, who are much more likely to develop metabolic disturbances many years after treatment 15,43,72 . Similarly, endocrine complications are among the most common late effects observed in hematopoietic stem cell transplant (HSCT) survivors 57,73 . Another limitation concerns the WHR assessment. Although the WHR indicated the presence of excessive central adiposity, it is unclear whether this is due to expansion of subcutaneous or visceral fat. Finally, the use of bioelectrical BIA presents also a per se limitation as the changes in the distribution of water in the body affect the accuracy of BIA measurements. Therefore, in obese patients, the method may underestimate the amount of fat-free mass (FFM) 51–53 . However, it could be argued that even the DXA measurements are affected by hydration status 74 . The DXA method can be considered as a reference method in clinical research because it allows a rapid and non-invasive assessment of adipose tissue, skeletal muscle and bone mineral density, but its disadvantages are higher acquisition costs, the need for specialised radiological equipment and poorer feasibility in practice compared to the BIA method 75,76 . BIA and DXA showed a moderate to high correlation in measuring whole-body bone mineral density BMD in adults 77 . DXA could provide deeper insights into the spatial localisation of tissue type, for example visceral and subcutaneous adipose tissue (VAT and SAT), which have been shown to play different roles in the development of obesity-related metabolic complications 56,57 . The use of other imaging modalities, such as computed tomography (CT) or MRI, would further enhance the understanding of fat accumulation in muscle or liver. However, both methods are very expensive, less available in practice, require specialised operators and the CT method is associated with a higher dose of ionising radiation than the DXA method 58,59 . BIA was chosen for this study because it is simple, practical and affordable. It has shown promise in detecting changes in adiposity and body composition in the CCS population. Despite the above limitations, our study addresses a very important and timely issue and is the first pilot study to investigate the association of FSTL1, FSTL4 and FSTL5 with body composition in the specific population of CCS. It is also the first study to investigate the association of FSTL4 and FSTL5 with skeletal muscle mass. CCS represent a highly specific and scarce demographic subpopulation. The rarity of childhood cancer, coupled with survivorship, inherently limits the potential pool of participants. Further research in the specific subpopulation of CCS is complicated by limited sizes of study populations and heterogenous nature of the underlying cancer diseases and/or treatments, which emphasizes a need for larger, robust population studies. Conclusion Our results suggest that FSTL1 is potentially useful for assessing body composition in CCS. FSTL1 levels were negatively correlated with body fat mass index and positively correlated with skeletal muscle mass index. These associations highlight the need for further research into the complex interactions between body composition, cancer treatment, lifestyle factors and long-term health outcomes. Understanding the role of FSTLs in this context is crucial, particularly given the established links between reduced muscle mass and adverse cardiovascular and metabolic outcomes. Future studies may benefit from using larger sample sizes and more representative groups of childhood cancer survivors for each type of diagnosis, as well as longitudinal designs. This approach could improve our understanding of the potential role of these molecules and aid in targeting therapeutic and lifestyle interventions. Declarations Funding information This study was supported by MUNI/A/1370/2022 from Grants Agency of the Masaryk University. Author Contribution Lucie Štrublová, Tomáš Kepák, and Julie Bienertová-Vašků designed the study. Julie Bienert-Vašků received financial support for this study. Lucie Štrublová and Tomáš Kepák recruited the patients. Filip Zlámal and Tomáš Pískovský performed data analysis. Lucie Štrublová wrote the first draft of the manuscript. All authors listed on the title page critically revised and approved the final version of the manuscript. Acknowledgement We thank all the staff of the laboratories of the Institute of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, who processed the research samples, and also to all patients who participated in the study. Data Availability The dataset generated during and/or analysed during the current study is not publicly available due to ethical considerations and participant protection, but it is available from the corresponding author upon reasonable request. References Morales, J. S. et al. Is health status impaired in childhood cancer survivors? A systematic review and meta-analysis. Crit. Rev. Oncol. Hematol. 142 , 94–118 (2019). Pluimakers, V. G. et al. Prevalence, risk factors, and optimal way to determine overweight, obesity, and morbid obesity in the first Dutch cohort of 2338 long-term survivors of childhood cancer: a DCCSS-LATER study. Eur. J. Endocrinol. 189 , 495–507 (2023). Rossi, F. et al. 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B. & Jan, A. B. M. I. Classification Percentile And Cut Off Points. in StatPearls (StatPearls Publishing, 2024). Using a bony. landmark to measure waist circumference - PubMed. https://pubmed.ncbi.nlm.nih.gov/7798573/ Waist circumference and waist. -hip ratio: report of a WHO expert consultation. https://www.who.int/publications/i/item/9789241501491 World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8–11 December 2008. (2011). Casano-Sancho, P. & Izurieta-Pacheco, A. C. Endocrine Late Effects in Childhood Cancer Survivors. Cancers (Basel) . 14 , 2630 (2022). R: The R Project for Statistical Computing. https://www.r-project.org/ Blijdorp, K. et al. Obesity is underestimated using body mass index and waist-hip ratio in long-term adult survivors of childhood cancer. PLoS One . 7 , e43269 (2012). Prado, C. M. M., Wells, J. C. K., Smith, S. R., Stephan, B. C. M. & Siervo, M. Sarcopenic obesity: A Critical appraisal of the current evidence. Clin. Nutr. 31 , 583–601 (2012). Guolla, L., Barr, R., Jaworski, M., Farncombe, T. & Gordon, C. Sarcopenia in long-term survivors of cancer in childhood and adolescence: A cross-sectional study of calf muscle mass by peripheral quantitative computed tomography with an examination of the muscle-bone unit. Pediatr. Blood Cancer . 71 , e30705 (2024). Brinksma, A. et al. Changes in body size and body composition in survivors of childhood cancer: seven years follow-up of a prospective cohort study. Clin. Nutr. 41 , 2778–2785 (2022). Nakayama, H. et al. Sarcopenia and obesity in long-term survivors of childhood leukemia/lymphoma: a report from a single institution. Jpn J. Clin. Oncol. 51 , 1100–1106 (2021). Karlage, R. E. et al. Validity of anthropometric measurements for characterizing obesity among adult survivors of childhood cancer: A report from the St. Jude Lifetime Cohort Study. Cancer 121 , 2036–2043 (2015). Phillips, C. M. et al. Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype. Obes. (Silver Spring) . 21 , E154–161 (2013). Lee, C. M. Y., Huxley, R. R., Wildman, R. P. & Woodward, M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J. Clin. Epidemiol. 61 , 646–653 (2008). Romano, A. et al. Bioelectrical Impedance Analysis of Body Composition in Male Childhood Brain Tumor Survivors. Diseases 12 , 306 (2024). Ohashi, T., Sato, S., Yoshiki, A. & Kusakabe, M. TSC-36 (follistatin-related polypeptide) gene expression in estrogen receptor positive osteoblastic cell line, CDO7F. Calcif Tissue Int. 61 , 400–403 (1997). Liu, L., Li, M., Qin, Y., Liu, L. & Xiao, Y. Serum follistatin like 1 in children with obesity and metabolic-associated fatty liver disease. BMC Endocr. Disord . 24 , 165 (2024). Miller, T. L. et al. Characteristics and determinants of adiposity in pediatric cancer survivors. Cancer Epidemiol. Biomarkers Prev. 19 , 2013–2022 (2010). Roziakova, L. & Mladosievicova, B. Endocrine Late Effects After Hematopoietic Stem Cell Transplantation. Oncol. Res. 18 , 607–615 (2010). Görgens, S. W. et al. Regulation of follistatin-like protein 1 expression and secretion in primary human skeletal muscle cells. Arch. Physiol. Biochem. 119 , 75–80 (2013). Kim, D. K. et al. Clinical implications of circulating follistatin-like protein-1 in hemodialysis patients. Sci. Rep. 13 , 6637 (2023). Goodenough, C. G., Partin, R. E. & Ness, K. K. Skeletal Muscle and Childhood Cancer: Where are we now and where we go from here. Aging Cancer . 2 , 13–35 (2021). Cruz-Jentoft, A. J. et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing . 48 , 16–31 (2019). Ness, K. K. et al. Frailty in childhood cancer survivors. Cancer 121 , 1540–1547 (2015). Ness, K. K. et al. Physiologic frailty as a sign of accelerated aging among adult survivors of childhood cancer: a report from the St Jude Lifetime cohort study. J. Clin. Oncol. 31 , 4496–4503 (2013). Powers, B. E., Gillette, E. L., Gillette, S. L., LeCouteur, R. A. & Withrow, S. J. Muscle injury following experimental intraoperative irradiation. Int. J. Radiat. Oncol. Biol. Phys. 20 , 463–471 (1991). Portlock, C. S., Boland, P., Hays, A. P., Antonescu, C. R. & Rosenblum, M. K. Nemaline myopathy: a possible late complication of Hodgkin’s disease therapy. Hum. Pathol. 34 , 816–818 (2003). van Dijk, I. W. E. M. et al. Evaluation of late adverse events in long-term wilms’ tumor survivors. Int. J. Radiat. Oncol. Biol. Phys. 78 , 370–378 (2010). Donini, L. M. et al. Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement. Obes. Facts . 15 , 321–335 (2022). Damay, V. A., Setiawan, S., Lesmana, R., Akbar, M. R. & Lukito, A. A. Effects of Moderate Intensity Aerobic Exercise to FSTL-1 Regulation in Atherosclerosis: A Systematic Review. Int. J. Angiol. 32 , 1–10 (2023). Nam, J. S. et al. Follistatin-like 1 is a myokine regulating lipid mobilization during endurance exercise and recovery. Obes. (Silver Spring) . 32 , 352–362 (2024). Devine, K. A. et al. Factors Associated with Physical Activity Among Adolescent and Young Adult Survivors of Early Childhood Cancer: A report from the Childhood Cancer Survivor Study (CCSS). Psycho-oncology 27, 613 (2017). Senesi, P., Luzi, L., Terruzzi, I. & Adipokines Myokines, and Cardiokines: The Role of Nutritional Interventions. Int. J. Mol. Sci. 21 , 8372 (2020). Rey-Casserly, C. & Diver, T. Late effects of pediatric brain tumors. CURR. OPIN. PEDIATR. 31 , 789–796 (2019). Orio, F. et al. Endocrinopathies after allogeneic and autologous transplantation of hematopoietic stem cells. ScientificWorldJournal 282147 (2014). (2014). Barreira, T. V. & Tseh, W. The effects of acute water ingestion on body composition analyses via Dual-Energy X-Ray Absorptiometry. Clin. Nutr. 39 , 3836–3838 (2020). Andreoli, A., Scalzo, G., Masala, S., Tarantino, U. & Guglielmi, G. Body composition assessment by dual-energy X-ray absorptiometry (DXA). Radiol. Med. 114 , 286–300 (2009). Achamrah, N. et al. Comparison of body composition assessment by DXA and BIA according to the body mass index: A retrospective study on 3655 measures. PLoS One . 13 , e0200465 (2018). Chuang, C. L. et al. Comparison of whole body bone mineral density measurements between dual energy X-ray absorptiometry and novel bioelectrical impedance analysis. Sci. Rep. 14 , 29127 (2024). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5637051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":437823844,"identity":"7efbe203-b063-4d81-8e9f-a52bafbc0c01","order_by":0,"name":"Lucie Štrublová","email":"","orcid":"","institution":"Masaryk University","correspondingAuthor":false,"prefix":"","firstName":"Lucie","middleName":"","lastName":"Štrublová","suffix":""},{"id":437823845,"identity":"9b154451-c640-4d45-b7f2-a977b25179f7","order_by":1,"name":"Filip Zlámal","email":"","orcid":"","institution":"Masaryk 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10:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5637051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5637051/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03013-8","type":"published","date":"2025-05-30T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79886281,"identity":"3eb57be1-b4c7-4009-ac55-838c42e16230","added_by":"auto","created_at":"2025-04-04 06:03:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193559,"visible":true,"origin":"","legend":"\u003cp\u003eCountour plot output of final model showing relationship of FSTL1 and SMI and BFMI in an illustative example with fixed parameters of 23y-old female with no cranial radiation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5637051/v1/f3990e1c585b8bd84523bc04.png"},{"id":83783029,"identity":"c0268c27-fc13-4088-81ee-71b7eaf51aba","added_by":"auto","created_at":"2025-06-02 16:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":945409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5637051/v1/830c19ac-ea87-422a-8408-a6fa16c4ec77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of FSTL1 and its paralogs with body composition in adult survivors of childhood cancer ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSignificant improvements in childhood cancer survival rates have led to a growing population of long-term survivors. However, these individuals are at substantially increased risk of developing chronic health conditions, including cardiovascular, metabolic and musculoskeletal problems, which significantly impact their quality of life\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Epidemiological studies, such as the Childhood Cancer Survivor Study (CCSS), highlight the pervasive nature of these late effects and underscore the need for a deeper understanding of the underlying mechanisms\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A key factor contributing to these long-term health problems is altered body composition, which can occur early in cancer treatment and be further affected later in life by the inappropriate lifestyle of childhood cancer survivors\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Childhood cancer survivors often have increased adiposity and decreased skeletal muscle mass compared to the general population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This adverse change in body composition is associated with an increased risk of metabolic syndrome and other metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The interplay between adipose and muscle tissue and their respective secretions of adipokines and myokines is critical to understanding this complex interaction. One of the goals of current science in the field of late effects of cancer treatment is to identify factors and potential biomarkers associated with changes in body composition and to find appropriate preventive or therapeutic measures applicable in the follow-up care of this specific cohort of patients\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Research into particular adipokines and myokines is essential for elucidating the pathophysiological mechanisms behind these metabolic disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. While several signalling molecules that reflect body composition, particularly concerning adiposity, have been studied in detail among childhood cancer survivors\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, the role of many others, including follistatin-like protein 1 (FSTL1) and its paralogs, have yet to be examined\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. FSTL1 and its paralogs are considered signalling molecules that may be potential regulators of metabolism\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Their levels and function depend on the specific biological context, which means that their effects can vary depending on the environment and the health status of the organism. FSTL1 is the most studied of the FSTL family, while knowledge of follistatin-like protein 4 (FSTL4) and follistatin-like protein 5 (FSTL5) is limited\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. FSTL1 exerts diverse and often contradictory effects\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. FSTL1 is known to play an important role in the cardiovascular system and its elevated serum levels are associated with several cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Previous research examining the role of FSTL1 in body composition and metabolic disorders has produced inconclusive results. Some studies indicate that elevated FSTL1 levels correlate with metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, while others report lower levels in severe obesity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and significantly reduced FSTL1 levels in muscle tissue of adult patients with tumor cachexia\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Despite these findings, the exact mechanisms and roles of FSTL1 in adiposity and muscle mass regulation remain unclear, and further research is required to clarify its function and implications for CCS, particularly those experiencing the complications of altered body composition and associated metabolic disorders. FSTL4 (SPIG1) was originally identified during retinal development in the chicks\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Although its involvement in the CNS has been linked to some neurodegenerative diseases\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, its expression and role in cardiovascular disease are still uncertain\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In the context of body composition, FSTL4 and FSTL5 levels have not yet been sufficiently investigated, and no study has examined the relationship between plasma levels of FSTL4 and FSTL5 and skeletal muscle mass.\u003c/p\u003e \u003cp\u003eThe primary hypothesis of our study is that altered body composition in adult survivors of childhood cancer, characterized by increased adiposity and decreased skeletal muscle mass, is associated with dysregulated levels of follistatin-like proteins (FSTL1, FSTL4, and FSTL5). Therefore, the aim of this pilot study is to investigate the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 in adult CCS.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cross-sectional study focuses on a sample of 61 childhood cancer survivors followed up at the Long-Term Follow-Up Clinic of St. Anne\u0026apos;s University Hospital in Brno, Czech Republic. This study evaluates the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5. All procedures involving human participants were performed in accordance with the ethical standards of St. Anne\u0026apos;s University Hospital, Brno.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 25 April 2018 and 5 June 2019, we enrolled 61 individuals, 26 women and 35 men, who provided informed consent to participate in the study and who also met all inclusion criteria. Study participants were presented with a two-part questionnaire administered by a researcher during their visit to the clinic. This section included questions about anthropometric data, the survivor\u0026apos;s personal history, questions about cancer type and treatment duration. The survivor then completed the second section. This section included questions about socioeconomic status, such as education level. Survivors were also measured by bioelectrical impedance analysis (BIA) at the time of recruitment to the study, and their height, weight, waist and hip circumference were recorded. The collection of biological material was also part of the research protocol.\u0026nbsp;Peripheral venous blood samples were collected into EDTA tubes and processed, obtained plasma was stored at -80 \u0026deg;C until further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvivors were\u0026nbsp;aged 18-36 years (mean age 24.6\u0026plusmn;4.4 years) and had been diagnosed with childhood cancer between 1983 and 2011 (for more information on the study cohort, refer to Table 1). The inclusion criteria for participation in this study were as follows: a) had an appointment at the outpatient clinic between April 2018 and June 2019; b) treated with chemotherapy and/or radiotherapy for cancer diagnosed between the ages of 0 and 18 years; c) aged 18 years or older; d) in complete remission of the primary cancer; e) met the BIA measurement criteria; f) they met the conditions for venous blood sampling g) signed an informed consent for the use of their health data for scientific and research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody composition was measured using the InBody Model 370. All survivors were instructed on fluid and food intake prior to body composition measurements\u003csup\u003e35\u003c/sup\u003e. The variables used for analysis were skeletal muscle mass index (SMI) and body fat mass index (BFMI). SMI, calculated as kilograms of skeletal muscle mass per square meter (kg/m\u0026sup2;), indicates height-normalized skeletal muscle mass. \u0026nbsp;Similarly, BFMI, calculated as kilograms of body fat mass per square meter (kg/m\u0026sup2;), indicates height-normalized body fat mass, which addresses issues in interpreting data expressed as percentages of body weight or as absolute values\u003csup\u003e36\u003c/sup\u003e. Total body fat was categorised according to the Obesity Medicine Association (OMA)\u003csup\u003e37\u003c/sup\u003e. According to this classification, an increased amount of fat between 30-34% in women and 25-29% in men is considered risky and pre-obesity. Values \u0026ge;35% in women and \u0026ge;30% in men were then defined as obesity. Weight was measured to the nearest 0.1 kg on an electronic scale and confirmed by weight measurement using the In Body 370 device. Height was measured using a stadiometer. The body mass index (BMI) was calculated from the height and weight by dividing the weight in kilograms by the height in centimetres squared. BMI was categorised according to the WHO\u003csup\u003e38,39\u003c/sup\u003e; in Table 1B, BMI 30-40 is presented as \u0026quot;obesity\u0026quot; and BMI \u0026ge; 40 as \u0026quot;extreme obesity\u0026quot;. \u0026nbsp;Finally, waist and hip circumferences were measured using a tape measure. Waist circumference (WC) was measured in the horizontal plane at a point marked just above the right subcostal line on the mid-axillary line\u003csup\u003e40\u003c/sup\u003e, with minimal breathing, and classified according to WHO\u003csup\u003e41\u003c/sup\u003e. Based on the measured values of waist and hip circumference was calculated and Waist-to-hip ratio (WHR). WHR was categorised according to the WHO\u003csup\u003e42\u003c/sup\u003e. Height, waist circumference, and hip circumference were recorded to the nearest 0.1 cm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnosis and treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cancer diagnoses were divided into 4 diagnosis groups \u0026quot;brain and spinal column tumours\u0026quot;, \u0026quot;leukaemia\u0026quot;, \u0026quot;lymphoma\u0026quot; and \u0026quot;other solid tumours\u0026quot;, the number of survivors in each category is presented in Table 1B. Based on the available information on treatment history, treatments given were categorised as cranial radiation vs no cranial radiation according to their potential effect on the development of adiposity\u003csup\u003e43\u003c/sup\u003e. Cranial radiation included cranial irradiation therapy or whole body irradiation therapy. These data were used to create the dichotomous variable \u0026quot;type of treatment\u0026quot; cranial radiation vs no cranial radiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSTL measurement \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma levels of FSTL1, FSTL4, and FSTL5 in the patient cohort were measured in duplicates using commercially available ELISA kits according to the manufacturer\u0026apos;s instructions (Cusabio Technology LLC, cat. nos. CSB-E13516h, CSB-EL009027HU and CSB-EL009028HU, respectively). For FSTL1 and FSTL5 measurements, the plasma samples were diluted three-fold and two-fold in the sample diluent (5% BSA in PBS), respectively. Absorbance was measured on a Spectramax 340PC Microplate Reader (Molecular Devices). The detection limit for FSTL1, FSTL4, and FSTL5 was 3.12, 0.39, and 0.31 ng/mL, respectively. The inter-assay coefficients of variability for all three measured proteins were \u0026lt; 10%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis included a total of 61 survivors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analysis was performed using statistical software R version 4.0.3\u003csup\u003e44\u003c/sup\u003e. First, descriptive analysis was conducted and basic relationships between variables were assessed. Descriptive characteristics for continuous variables are represented using mean and standard deviation. As regards categorical variables absolute and relative frequencies are used. Normality for continuous variables was checked by statistical tests (like Shapiro-Wilk, Pearson, Anderson-Darling) and also graphically (histogram and Q-Q plot). If data were far from the desired normal distribution, logarithmic transformation was applied (and normality was checked again). The relationship between continuous variables was accessed by pairwise Pearson correlation coefficient.\u003c/p\u003e\n\u003cp\u003eThe methods of linear regression models were used to identify which variables were significantly associated with values of FSTLs and also to quantify the strength of the effects. For this purpose, each of natural logarithm of FSTLs was considered as dependent variable and sex, age, BFMI, SMI and cranial radiation are considered as independent variables. And finally, graphical methods were used to check assumptions of each of the regression model (normality, homoscedasticity, independence).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local ethics committee, and approval for this research was granted under registration number IIT/2017/35. The study adhered to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp; 1. A) Descriptive characteristics of the sample by sex . Mean (standard deviation) values are presented. B) Distribution of selected categorical variables. Absolute values (percentages) are presented.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (N=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (N=26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e176.00 (9.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e167.17 (6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e172.20 (9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e72.10 (16.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e59.70 (9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e66.94 (14.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e23.14 (4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e21.38 (2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e22.41 (3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBody fat mass (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.49 (8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e17.71 (8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.43 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBody fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20.35 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27.98 (8.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e23.56 (9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBody fat mass index \u0026ndash; BFMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5.00 (2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e6.33 (2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e5.56 (2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSkeletal muscle mass (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e32.28 (6.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e23.90 (3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e28.75 (7.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSkeletal muscle mass (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e44.74 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e39.28 (4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e42.44 (5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSkeletal muscle mass index \u0026ndash; SMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e10.30 (1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e8.49 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e9.54 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eWaist (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e84.73 (11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e74.44 (9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e80.29 (12.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHips (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e91.53 (10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e89.22 (7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e90.52 (9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eWaist-to-hip ratio \u0026ndash; WHR (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.92 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.83 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.88 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e24.71 (5.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e24.55 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e24.64 (4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTime after treatment (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e12.30 (5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e11.69 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e12.03 (5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge at diagnosis (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e10.69 (4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e11.12 (5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e10.87 (4.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eFSTL1 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e53.40 (16.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e49.65 (29.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e51.80 (22.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eFSTL4 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.03 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.98 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.00 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eFSTL5 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e16.35 (9.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.89 (8.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.15 (9.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBody fat\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEssential fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e6 (9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFitness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e9 (14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAthlete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e9 (14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e15 (24.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePre-obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e7 (11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e11 (18.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e4 (6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e6 (9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e40 (65.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e12 (19.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e2 (3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eExtreme obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e1 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNon-obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e27 (44.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e30 (49.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e4 (6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e18 (29.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e28 (45.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e15 (24.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCranial radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e17 (27.87)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e44 (72.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eType of diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBrain and spinal column tumours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e13 (21.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e8 (13.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e18 (29.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOther solid tumours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e22 (36.06)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody composition and plasma levels of FSTL1, FSTL4 and FSTL5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear regression models were used to examine the relationship between BFMI and SMI and plasma levels of FSTL1, FSTL4 and FSTL5. In the next step, a model was developed for FSTL1, FSTL4 and FSTL5 (log-transformed) as a function of sex, age, BFMI and SMI. Results are shown in Table 2 (see Table 2 and Figure 1). The other body composition variables BMI and WHR were not found to be significantly correlated with FSTL1, FSTL4, FSTL5 and were therefore not further used in the model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u0026nbsp;2. Result of final model for FSTL1.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"533\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 533px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel for log(FSTL1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e95% CI for b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 333px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 333px;\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e(-0.396;0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e(-0.028;0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eBFMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e(-0.088;-0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e(0.031;0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCranial radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 333px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 333px;\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e(-0.049;0.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 533px;\"\u003e\n \u003cp\u003eF(5,50)=3.140, p=0.015, R\u003csup\u003e2\u003c/sup\u003e=0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults of models for FSLT4 and FSTL5 are not included because these models are not statistically significant (FSTL4:\u0026nbsp;F(5,41)=1.118, p=0.366, R\u003csup\u003e2\u003c/sup\u003e=0.120; FSTL5: F(5,51)=0.349, p=0.881, R\u003csup\u003e2\u003c/sup\u003e=0.033).\u003c/p\u003e\n\u003cp\u003eTable 2 shows the results of the model for FSTL1. Statistically significant relationships between FSTL1 and the independent variables, followed by the characteristics of the overall model.\u003c/p\u003e\n\u003cp\u003eThe only significant relationship between FSTLs and body composition was found in the model for FSTL1. BFMI is negatively related to log(FSTL1) whereas skeletal muscle index is positively related to log(FSTL1). Increasing BFMI by 1 kg/m2 decreases level of FSLT1 by 5.1% on average (95% CI: 1.7;8.4%). Increasing SMI by 1 kg/m2 increases level of FSTL1 by 12.0% on average (95% CI: 3.1;21.7%). Effects of sex, age and cranial radiation are not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCCS face changes in body composition, especially an increase in fat mass and a decrease in muscle mass, that are associated with an elevated risk of metabolic syndrome and other metabolic disorders and affect their quality of life. Commonly used measures, such as BMI, do not differentiate between muscle and adipose tissue or assess their distribution. This can easily lead to an underestimate altered body composition. Altered body composition is associated with metabolic disease and cardiovascular problems, potentially resulting in long-term adverse effects on the health and quality of life of CCS\u003csup\u003e16,19,45\u0026ndash;49\u003c/sup\u003e. Furthermore, measures of total adiposity (percent fat mass; %FM) and central adiposity, WHR, have been shown to be more reliable predictors of cardiometabolic health and risk than BMI\u003csup\u003e45,50\u0026ndash;52\u003c/sup\u003e. Similarly, SMI and BFMI have been shown to be valuable for assessing body composition in CCS, providing insights into the long-term effects of cancer treatments on muscle and fat distribution\u003csup\u003e19,53\u003c/sup\u003e.The aim of this pilot study was to investigate the relationship between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 in adult CCS.\u003c/p\u003e\n\u003cp\u003eOur study employed multiple methods to assess body composition: WHR, BMI and BIA. \u0026nbsp;The overall mean WHR of our study population is 0.88 (0.09), in line with results reported by Pluimakers et al., who demonstrated a mean WHR in CCS cohort is 0.88 (0.09)\u003csup\u003e2\u003c/sup\u003e. The high prevalence of abnormal WHR values (49%) in our study population suggests a substantial presence of central adiposity, while BMI indicated overweight or obesity in 23% of our participants. In contrast, BIA revealed a higher prevalence of increased adiposity (pre-obesity and obesity), affecting 30% of the study population. These findings further highlight the limitations of BMI in accurately assessing geographical accumulation of adiposity in childhood cancer survivors. The underestimation of total adiposity by BMI was further brought up by Wang et al. in their study who found that although the prevalence of overweight and obesity according to BMI was almost identical to that of the control group (42.6% vs. 40.4%), the childhood cancer survivors had significantly higher amounts of adipose tissue than the control group on assessment by BIA, dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the association between body composition and plasma levels of FSTL1, FSTL4 and FSTL5 using a linear regression model in the CCS cohort. FSTLs are signalling molecules that are synthesised in different tissues of the body. FSTL1 \u0026nbsp;(also known as TSC-36) was first discovered and described in 1993 as one of six genes regulated by transforming growth factor \u0026beta; (TGF\u0026beta;1) in mouse osteoblasts\u003csup\u003e54\u003c/sup\u003e. \u0026nbsp;FSTL1 is the most studied of the FSTLs, whereas our knowledge of FSTL4 and FSTL5 is limited\u003csup\u003e25,26,29,30\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA significant association was found between the plasma level of FSTL1 and BFMI. As the BFMI increase FSTL1 level decrease.\u0026nbsp;\u0026nbsp;This is in line with a study\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e which explains the reduced FSTL1 levels in extremely obese individuals as a possible consequence of reduced adipogenesis, increased adipocyte apoptosis and epigenetic silencing of the FSTL1 gene. The authors of the study suggest that FSTL1 may be a valuable biomarker for predicting future weight gain and assessing the effectiveness of weight loss interventions. In contrast, elevated plasma levels of FSTL1 have been observed in association with pre-existing metabolic diseases. Elevated FSTL1 levels in relation to metabolic complications were studied in detail by Liu et al. in a cohort of obese children, who concluded that elevated serum FSTL1 levels are associated with metabolic fatty liver disease\u0026nbsp;\u003csup\u003e55\u003c/sup\u003e. In the study by Yang et al, elevated plasma FSL1 levels were associated with metabolic syndrome and correlated with WC and visceral obesity\u003csup\u003e27\u003c/sup\u003e. In our study, we did not assess the prevalence of metabolic disorders, but the relationship between WHR as an indicator of increased risk of visceral obesity and plasma FSTL1 levels was not significant.\u003c/p\u003e\n\u003cp\u003eA variable for cranial radiation was also included in our model. Cranial irradiation is strongly associated with changes in body composition, particularly the development of adiposity, in the CSS cohorts\u003csup\u003e43,56,57\u003c/sup\u003e. The effects of cranial radiation were not statistically significant.\u003c/p\u003e\n\u003cp\u003eA significant positive association was found between the plasma level of FSTL1 and SMI. Unfortunately, we cannot compare our results with any available findings from comparable cohorts. De Castro et al.\u003csup\u003e32\u003c/sup\u003e investigated FSTL1 levels in adult cachectic gastric cancer patients and found significantly lower FSTL1 levels in muscle tissue, but no corresponding change in plasma levels. The authors of the study believe that the reduced production of FSTL1 in muscle during cachexia may not be primarily due to reduced production in the muscle itself but may be influenced by other factors that affect the release or clearance of FSTL1 from muscle, such as reduced muscle performance and impaired muscle recovery. Further, FSLT1 synthesis in skeletal muscle tissue may be regulated by inflammatory stimuli, as myotubes show increased secretion of FSTL1 into the culture medium after treatment with interleukin-1 beta and interferon gamma\u003csup\u003e58\u003c/sup\u003e. The results of the different study\u003csup\u003e59\u003c/sup\u003e show a weak or non-significant association between circulating FSTL1 levels and markers of skeletal muscle function (walking speed and handgrip strength) in a cohort of haemodialysis patients. Although a weak positive association between FSTL1 and handgrip strength was observed only in men, this limited association suggests that the effect of FSTL1 on muscle mass and strength in this population may be secondary to other factors known to affect muscle health in haemodialysis patients, such as inflammation, malnutrition and uremic toxins. In line with our study decrease in muscle mass and strength has been documented during childhood cancer treatment, which often persists into adulthood\u003csup\u003e60\u003c/sup\u003e. Muscle weakness and loss of muscle mass are common in these children, not only during active disease but also in surviving patients. These acute effects are likely to be due to a combination of factors, including the cancer itself, intensive multimodal therapy and systemic changes such as inflammation, hormone levels and nutritional status\u003csup\u003e13\u003c/sup\u003e. However, children with cancer have changes in muscle mass that are not associated with changes in bone mineral content, fat mass or total body weight. This suggests that muscle mass should be assessed explicitly and independently of BMI or adiposity\u003csup\u003e11,15,60\u003c/sup\u003e. Oncology treatment, including radiotherapy, chemotherapy and surgery, can lead to the development of sarcopenia, a progressive and generalized skeletal muscle disease which is associated with an increased likelihood of developing adverse consequences, including falls, fractures, physical disabilities and mortality\u003csup\u003e61\u003c/sup\u003e. Sarcopenia is a key factor in the development of frailty and premature aging in CCS\u003csup\u003e62,63\u003c/sup\u003e.These complications can occur independently or cumulatively, and their severity depends on individual patient characteristics and the type and intensity of treatment. Muscle tissue damage occurs through a variety of mechanisms, including acute cellular damage (e.g., impaired cell division, membrane permeability and ion pump function) and chronic processes (e.g., inflammation, ischaemia). In addition, some chemotherapeutic agents (e.g. L-asparaginase, methotrexate, vincristine) may themselves cause a reduction in muscle strength and flexibility. Muscle atrophy, fibrosis and hypoplasia may occur\u003csup\u003e64\u0026ndash;66\u003c/sup\u003e. It should be noted that our study did not assess the prevalence of sarcopenia or sarcopenic obesity. Thus, further investigation, including assessment of skeletal muscle functional parameters, would be required to confirm the diagnosis and evaluate sarcopenia or sarcopenic obesity\u003csup\u003e67\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhysical activity has a positive effect on body composition, which may help to reduce some of the adverse late effects of childhood cancer and its treatment, and may help to prevent metabolic, cardiovascular and musculoskeletal disorders. A systematic review\u003csup\u003e68\u003c/sup\u003e showed that moderate-intensity aerobic exercise stimulates the secretion of FSTL1, which plays an important role in the prevention of atherosclerosis. FSTL1 improves endothelial function, inhibits smooth muscle cell proliferation and reduces vascular wall thickening. Through this mechanism, moderate-intensity aerobic exercise prevents endothelial dysfunction, arterial stiffness and vascular inflammation, thereby preventing the progression of atherosclerosis. Nam et al. demonstrated a key role for FSTL1 in the regulation of lipid metabolism during and after endurance exercise. \u0026nbsp; Significantly elevated levels of FSTL1 were observed during and after exercise and correlated with lean body mass (LBM) and lipolysis\u003csup\u003e69\u003c/sup\u003e. These results correlate with our findings of increasing levels of FSTL1 with increasing SMI. However, the analyses have examined the relationship between FSTL1 and LBM, not SMM as in our study. \u0026nbsp;The question therefore remains to what extent this type of physical activity would lead to FSTL1 production in relation to SMM. Survivors reported low levels of physical activity during adolescence and young adulthood\u003csup\u003e70\u003c/sup\u003e. The result of CCSS showed that CCS were more likely than their siblings not to follow recommendations for physical activity\u003csup\u003e10\u003c/sup\u003e. In this context, survivors are at increased risk of metabolic disorders and cardiovascular disease, which may increase the development of these comorbidities and significantly affect their quality of life\u003csup\u003e9,14,15,43\u003c/sup\u003e. However, the question remains as to whether the development of appropriate recommendations and intervention programmes to promote physical activity, healthy diet and weight reduction can achieve potential changes in FSTL1 expression and promote its tissue protective effect, as is the case with other more studied proteins\u003csup\u003e71\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations, including the heterogeneity of the study group and the relatively small sample size and lack of relevant reference values for comparison of our results. These limitations are particularly relevant for patients with brain tumours, who are much more likely to develop metabolic disturbances many years after treatment\u003csup\u003e15,43,72\u003c/sup\u003e. \u0026nbsp;Similarly, endocrine complications are among the most common late effects observed in hematopoietic stem cell transplant (HSCT) survivors\u003csup\u003e57,73\u003c/sup\u003e. Another limitation concerns the WHR assessment. Although the WHR indicated the presence of excessive central adiposity, it is unclear whether this is due to expansion of subcutaneous or visceral fat. Finally, the use of bioelectrical BIA presents also a per se limitation as the changes in the distribution of water in the body affect the accuracy of BIA measurements. Therefore, in obese patients, the method may underestimate the amount of fat-free mass (FFM)\u003csup\u003e51\u0026ndash;53\u003c/sup\u003e. However, it could be argued that even the DXA measurements are affected by hydration status\u003csup\u003e74\u003c/sup\u003e. \u0026nbsp;The DXA method can be considered as a reference method in clinical research because it allows a rapid and non-invasive assessment of adipose tissue, skeletal muscle and bone mineral density, but its disadvantages are higher acquisition costs, the need for specialised radiological equipment and poorer feasibility in practice compared to the BIA method\u003csup\u003e75,76\u003c/sup\u003e. BIA and DXA showed a moderate to high correlation in measuring whole-body bone mineral density BMD in adults\u003csup\u003e77\u003c/sup\u003e. DXA could provide deeper insights into the spatial localisation of tissue type, for example visceral and subcutaneous adipose tissue (VAT and SAT), which have been shown to play different roles in the development of obesity-related metabolic complications\u003csup\u003e56,57\u003c/sup\u003e. The use of other imaging modalities, such as computed tomography (CT) or MRI, would further enhance the understanding of fat accumulation in muscle or liver. However, both methods are very expensive, less available in practice, require specialised operators and the CT method is associated with a higher dose of ionising radiation than the DXA method\u003csup\u003e58,59\u003c/sup\u003e. BIA was chosen for this study because it is simple, practical and affordable. It has shown promise in detecting changes in adiposity and body composition in the CCS population.\u003c/p\u003e\n\u003cp\u003eDespite the above limitations, our study addresses a very important and timely issue and is the first pilot study to investigate the association of FSTL1, FSTL4 and FSTL5 with body composition in the specific population of CCS. It is also the first study to investigate the association of FSTL4 and FSTL5 with skeletal muscle mass. CCS represent a highly specific and scarce demographic subpopulation. The rarity of childhood cancer, coupled with survivorship, inherently limits the potential pool of participants. Further research in the specific subpopulation of CCS is complicated by limited sizes of study populations and heterogenous nature of the underlying cancer diseases and/or treatments, which emphasizes a need for larger, robust population studies.\u003c/p\u003e"},{"header":"Conclusion","content":"Our results suggest that FSTL1 is potentially useful for assessing body composition in CCS. FSTL1 levels were negatively correlated with body fat mass index and positively correlated with skeletal muscle mass index. These associations highlight the need for further research into the complex interactions between body composition, cancer treatment, lifestyle factors and long-term health outcomes. Understanding the role of FSTLs in this context is crucial, particularly given the established links between reduced muscle mass and adverse cardiovascular and metabolic outcomes. Future studies may benefit from using larger sample sizes and more representative groups of childhood cancer survivors for each type of diagnosis, as well as longitudinal designs. This approach could improve our understanding of the potential role of these molecules and aid in targeting therapeutic and lifestyle interventions."},{"header":"Declarations","content":"\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThis study was supported by MUNI/A/1370/2022 from Grants Agency of the Masaryk University.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLucie Štrublov\u0026aacute;, Tom\u0026aacute;š Kep\u0026aacute;k, and Julie Bienertov\u0026aacute;-Vašků designed the study. Julie Bienert-Vašků received financial support for this study. Lucie Štrublov\u0026aacute; and Tom\u0026aacute;š Kep\u0026aacute;k recruited the patients. Filip Zl\u0026aacute;mal and Tom\u0026aacute;š P\u0026iacute;skovsk\u0026yacute; performed data analysis. Lucie Štrublov\u0026aacute; wrote the first draft of the manuscript. All authors listed on the title page critically revised and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the staff of the laboratories of the Institute of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, who processed the research samples, and also to all patients who participated in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset generated during and/or analysed during the current study is not publicly available due to ethical considerations and participant protection, but it is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMorales, J. S. et al. Is health status impaired in childhood cancer survivors? 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Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 29127 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FSTL1, FSTL4, FSTL5, adiposity, skeletal muscle mass, late effects, childhood cancer survivors","lastPublishedDoi":"10.21203/rs.3.rs-5637051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5637051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis retrospective cross-sectional study investigated the relationship between body composition and circulating plasma levels of follistatin-like proteins (FSTLs; FSTL1, FSTL4, and FSTL5) in adult survivors of childhood cancer. This is the first study to investigate the association between plasma levels of FSTL4, FSTL5 and skeletal muscle mass. The cohort consisted of 61 CCS (26 females and 35 males) aged 18-36 years (mean age 24.6±4.4 years) who were followed up at the Long-Term Follow-Up Clinic of St. Anne's Hospital in Brno. The mean age at diagnosis was 10.9±4.9 years and the mean time after treatment was 12.0±5.1 years. Body composition was assessed by anthropometric indicators and bioelectrical impedance analysis (BIA; InBody 370). Plasma FSTLs levels were quantified using commercially available ELISA kits. Correlations were examined by linear regression analysis. Significant negative correlation was found between plasma level of FSTL1 and body fat mass index (BFMI) and significant positively correlation was between FSTL1 and skeletal muscle mass index (SMI). Our results suggest that FSTL1 may be potential indicator of adiposity and skeletal muscle loss in CCS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration:\u003c/em\u003e This study was registered on July 29, 2022, at ClinicalTrials.gov (NCT05481229).\u003c/p\u003e","manuscriptTitle":"Correlation of FSTL1 and its paralogs with body composition in adult survivors of childhood cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-04 06:03:43","doi":"10.21203/rs.3.rs-5637051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-02T14:02:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-30T15:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79137223689690234127796944210424202217","date":"2025-04-08T13:06:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T08:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99236060755331346433628209126251810954","date":"2025-04-03T07:51:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T07:36:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T14:24:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-19T21:42:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97115ca8-13eb-4732-b619-5ad00a74acc4","owner":[],"postedDate":"April 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46620356,"name":"Health sciences/Biomarkers"},{"id":46620357,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-06-02T16:05:08+00:00","versionOfRecord":{"articleIdentity":"rs-5637051","link":"https://doi.org/10.1038/s41598-025-03013-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-30 15:57:26","publishedOnDateReadable":"May 30th, 2025"},"versionCreatedAt":"2025-04-04 06:03:43","video":"","vorDoi":"10.1038/s41598-025-03013-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03013-8","workflowStages":[]},"version":"v1","identity":"rs-5637051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5637051","identity":"rs-5637051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}