ResProt JMIR Res Protoc JMIR Research Protocols 1929-0748 JMIR Publications Toronto, Canada v10i1e21256 32750002 10.2196/21256 Protocol Protocol Metabolic Syndrome Parameters, Determinants, and Biomarkers in Adult Survivors of Childhood Cancer: Protocol for the Dutch Childhood Cancer Survivor Study on Metabolic Syndrome (Dutch LATER METS) Eysenbach Gunther Waespe Nicolas Pluimakers Vincent MD 1
Princess Maxima Center for Pediatric Oncology Heidelberglaan 25 Utrecht, 3584CS Netherlands 31 613348669 v.g.pluimakers@prinsesmaximacentrum.nl
https://orcid.org/0000-0002-3066-3951 Fiocco Marta PhD 1 2 3 https://orcid.org/0000-0001-5588-0277 van Atteveld Jenneke MD 1 https://orcid.org/0000-0002-4436-0089 Hobbelink Monique MD 4 https://orcid.org/0000-0003-3435-917X Bresters Dorine MD, PhD 1 https://orcid.org/0000-0002-5417-4159 Van Dulmen-den Broeder Eline PhD 1 5 https://orcid.org/0000-0002-8870-6539 Van der Heiden-van der Loo Margriet PhD 6 https://orcid.org/0000-0001-5767-854X Janssens Geert O MD, PhD 1 4 https://orcid.org/0000-0002-0331-713X Kremer Leontien MD 1 5 https://orcid.org/0000-0002-7619-7845 Loonen Jacqueline MD, PhD 7 https://orcid.org/0000-0002-9963-8367 Louwerens Marloes MD 2 https://orcid.org/0000-0002-6748-3540 Van der Pal Helena MD, PhD 1 https://orcid.org/0000-0003-2253-2115 Ronckers Cécile PhD 1 5 8 https://orcid.org/0000-0003-3524-4657 Van Santen Hanneke MD, PhD 1 9 https://orcid.org/0000-0002-6773-6931 Versluys Birgitta MD, PhD 1 9 https://orcid.org/0000-0002-4283-5503 De Vries Andrica MD, PhD 1 10 https://orcid.org/0000-0003-0964-0007 Van den Heuvel-Eibrink Marry MD 1 https://orcid.org/0000-0002-7760-879X Neggers Sebastian MD, PhD 1 11 https://orcid.org/0000-0002-7698-0282 Princess Maxima Center for Pediatric Oncology Utrecht Netherlands Department of Internal Medicine Leiden University Medical Center Leiden Netherlands Mathematical Institute Leiden University Leiden Netherlands Department of Radiation Oncology University Medical Center Utrecht Utrecht Netherlands Department of Pediatric Oncology Emma Children’s Hospital/Amsterdam University Medical Center Amsterdam Netherlands Dutch Childhood Oncology Group Utrecht Netherlands Department of Hematology Radboud University Medical Center Nijmegen Netherlands Institute of Biostatistics and Registry Research Brandenburg Medical School Theodor Fontane Neuruppin Germany Department of Pediatric Oncology and Hematology Wilhelmina Children’s Hospital/University Medical Center Utrecht Utrecht Netherlands Department of Pediatric Oncology/Hematology Sophia Children’s Hospital/Erasmus Medical Center Rotterdam Netherlands Department of Endocrinology Erasmus Medical Center Rotterdam Netherlands Corresponding Author: Vincent Pluimakers v.g.pluimakers@prinsesmaximacentrum.nl 1 2021 27 1 2021 10 1 e21256 9 6 2020 4 7 2020 18 8 2020 10 11 2020 ©Vincent Pluimakers, Marta Fiocco, Jenneke van Atteveld, Monique Hobbelink, Dorine Bresters, Eline Van Dulmen-den Broeder, Margriet Van der Heiden-van der Loo, Geert O Janssens, Leontien Kremer, Jacqueline Loonen, Marloes Louwerens, Helena Van der Pal, Cécile Ronckers, Hanneke Van Santen, Birgitta Versluys, Andrica De Vries, Marry Van den Heuvel-Eibrink, Sebastian Neggers. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 27.01.2021. 2021

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

 Background

Potential late effects of treatment for childhood cancer include adiposity, insulin resistance, dyslipidemia, and hypertension. These risk factors cluster together as metabolic syndrome and increase the risk for development of diabetes mellitus and cardio- and cerebrovascular disease. Knowledge on risk factors, timely diagnosis, and preventive strategies is of importance to prevent cardio- and cerebrovascular complications and improve quality of life. Currently, no national cohort studies on the prevalence and determinants of metabolic syndrome in childhood cancer survivors, including biomarkers and genetic predisposition, are available.

 Objective

The objectives of the Dutch LATER METS study are to assess 1) the prevalence and risk factors of metabolic syndrome and its separate components, and 2) the potential diagnostic and predictive value of additional biomarkers for surveillance of metabolic syndrome in the national cohort of adult long-term survivors of childhood cancer.

 Methods

This is a cross-sectional study based on recruitment of all survivors treated in the Netherlands between 1963 and 2002. Metabolic syndrome will be classified according to the definitions of the third Adult Treatment Panel Report of the National Cholesterol Education Program as well as the Joint Interim Statement and compared to reference data. Dual-energy x-ray absorptiometry scans were performed to assess body composition in more detail. The effect of patient characteristics, previous treatment, and genetic variation on the risk of metabolic syndrome will be assessed. The diagnostic and predictive value of novel biomarkers will be tested.

 Results

Patient accrual started in 2016 and lasted until April 2020. A total of 2380 survivors from 7 pediatric oncology hospitals have participated. From July 2020, biomarker testing, single nucleotide polymorphism analysis, and data analysis will be performed.

 Conclusions

The Dutch LATER METS study will provide knowledge on clinical and genetic determinants of metabolic syndrome and the diagnostic value of biomarkers in childhood cancer survivors. The results of this study will be used to optimize surveillance guidelines for metabolic syndrome in survivors based on enhanced risk stratification and screening strategies. This will improve diagnosis of metabolic syndrome and prevent complications.

 International Registered Report Identifier (IRRID)

DERR1-10.2196/21256

 metabolic syndrome childhood cancer survivor Dutch Childhood Cancer Survivor Study methodology Dutch LATER METS Introduction

Due to increasing survival of patients with childhood cancer, late side effects have become more prominent. Potential late effects include adiposity, insulin resistance, dyslipidemia, and hypertension, which cluster together as metabolic syndrome. Metabolic syndrome is associated with a higher risk of diabetes mellitus, as well as cardio- and cerebrovascular morbidity and mortality later in life [1-3]. The separate components are in themselves risk factors for diabetes and cardiovascular disease but, when coexisting, the components can aggravate each other, leading to an even higher risk of diabetes and cardiovascular disease [4,5].

Studies in childhood cancer survivors have reported a prevalence of metabolic syndrome of over 30% after 25 years follow-up, substantially higher compared to age- and sex-matched controls (odds ratio 1.76) [6,7]. This apparent risk difference for metabolic syndrome further increases the elevated risks for cardiovascular outcomes and endothelial damage from anthracyclines, alkylating agents, and irradiation [8,9]. Consequently, the mortality due to coronary and cerebrovascular disease in long-term survivors is up to 12.7 times higher than in the general population [10-13]. The fact that metabolic syndrome can be subclinical for many years emphasizes the need for timely identification of metabolic syndrome in survivors and early intervention strategies. Lifestyle and diet advice, exercise, and medication may prevent the development of diabetes and cardio- and cerebrovascular disease, improving survival rates and quality of life.

Several underlying conditions have been reported to increase the risk for (components of) metabolic syndrome in survivors: growth hormone deficiency, pancreatic beta cell dysfunction, hypogonadism, hypothyroidism, and altered body composition with increased intra-abdominal fat [14-19]. Hence, an increased risk of metabolic syndrome might be associated with treatment for a brain tumor, treatment with radiotherapy, intensive chemotherapy, nephrectomy, adrenalectomy, or stem cell transplantation [7,16,20-32]. The effects of other potentially harmful treatments, for example corticosteroids, and patient-related factors such as sex, age, body mass index at diagnosis, and lifestyle, are still not clear [3]. Also, heterogeneity in incidence of metabolic syndrome among homogeneously treated survivors suggests a role of genetic susceptibility [33,34]. A few studies using candidate gene approaches [24,35] as well as one genome-wide association study [36] have identified genetic variants that might be associated with development of metabolic syndrome and its components in survivors. Results based on these studies have not yet been replicated or functionally validated.

Multiple definitions of metabolic syndrome have been developed over the past years. The two most commonly used are those of the third Adult Treatment Panel Report of the National Cholesterol Education Program [37] and the Joint Interim Statement of the International Diabetes Federation; National Heart, Lung, and Blood Institute; and the American Heart Association [38]. Both definitions overlap largely but they differ in waist circumference cut-off point (Table 1). Apart from the 4 components, pro-inflammatory and prothrombotic markers have been reported to be relevant biomarkers of metabolic syndrome, as has hyperuricemia [39,40].

Adequate assessment of metabolic syndrome in survivors using the National Cholesterol Education Program and Joint Interim Statement definitions has specific challenges, particularly after abdominal radiotherapy. It has been shown that body mass index and waist circumference underestimate adiposity due to deformation of spine, muscles, and fat, particularly in past treatment eras when higher radiotherapy doses and larger fields were used [21,41,42]. Similarly, adiposity can be disguised due to sarcopenic obesity after stem cell transplantation [43,44]. Body composition can be more reliably measured by dual-energy x-ray absorptiometry, but this is time consuming and expensive to be implemented for standard follow-up of all survivors. Serum biomarkers may be more cost-effective surrogate markers for metabolic syndrome. In smaller survivor cohorts and in the general population, biomarkers other than triglycerides and high-density lipoprotein cholesterol that have been proposed as predictors of metabolic syndrome include low-density lipoprotein, apolipoprotein-B, leptin, adiponectin, uric acid, and C-reactive protein [39,45-50].

So far, large studies on clinically diagnosed metabolic syndrome in survivors are scarce. Two large multicenter cohort studies with clinically diagnosed metabolic syndrome are the American St. Jude Lifetime (SJLIFE, all types of childhood cancer) [6,7] and the French Leucémies de l'Enfant et l'Adolescent (leukemia) [31,51,52] studies. Other studies have yielded heterogeneous and sometimes conflicting results and can be difficult to compare. This may be due to metabolic syndrome components being analyzed only separately, or due to small patient cohorts, a questionnaire based or retrospective design, insufficient treatment data (eg, only childhood cancer diagnosis is known, not treatment), and short follow-up (metabolic syndrome risk increases continuously with age, so a follow-up of 10-20 years likely underestimates this) [22,32,53-55]. In addition, comparison of study outcomes can be difficult due to the use of different classifications. Currently, no studies in national cohorts on prevalence and determinants of metabolic syndrome in childhood cancer survivors, including biomarkers and genetic predisposition to metabolic syndrome, are available.

Here we describe the methodology of the Dutch LATER METS study in the adult cohort of survivors treated between 1963 and 2002. This nationwide study assesses metabolic syndrome prevalence, clinical and genetic risk factors, and the diagnostic and predictive value of additional biomarkers. The results of this study will be used to identify survivors at risk and to optimize surveillance guidelines.

NCEP-ATP IIIa and JISb classifications of metabolic syndrome, and alternative classification with adiposity measured by dual-energy x-ray absorptiometry scan.

Required for diagnosis (≥3) Measurement NCEP-ATP III JIS Alternative with dual-energy x-ray absorptiometry scan
Adiposity Waist circumference (cm) >102c/88d ≥94c/80d,e Body fat Z-score >2
Insulin resistance Fasting glucose (mmol/L) ≥5.5 or treatment ≥5.5 or treatment ≥5.5 or treatment
Dyslipidemia Triglycerides (mmol/L) ≥1.7 or treatment ≥1.7 or treatment ≥1.7 or treatment
High-density lipoprotein cholesterol (mmol/L) <1.0c/1.3d or treatment <1.0c/1.3d or treatment <1.0c/1.3d or treatment
Hypertension Blood pressure (mmHg) ≥130/85 or treatment ≥130/85 or treatment ≥130/85 or treatment

aNCEP-ATP III = National Cholesterol Education Program Adult Treatment Panel III.

bJIS = Joint Interim Statement of International Diabetes Federation; National Heart, Lung, and Blood Institute; and the American Heart Association.

cMen.

dWomen.

eCut-off for Caucasian population.

 Methods Objectives

The objectives of this study are to assess 1) the prevalence and risk factors (patient characteristics, previous treatment, and genetic variation) of metabolic syndrome and its separate components, compared to reference data, and 2) the potential diagnostic and predictive value of novel biomarkers for surveillance of metabolic syndrome in the national cohort of adult long-term survivors of childhood cancer.

 Study Population and Design

The Dutch LATER METS study is part of the nationwide Dutch LATER study (Figure 1). This study started accrual in all 7 pediatric oncology centers in the Netherlands in 2016, thereby inviting the national cohort of all survivors treated in these hospitals between 1963 and 2002 to participate. Survivors were identified from registries of children with newly diagnosed cancer that are maintained in each of the 7 pediatric oncology centers in the Netherlands. This study merged the available information to create a specific childhood cancer survivors registry containing all registered survivors. Dependent on completeness of the sources in the centers, the starting year varied from 1963 to 1977. The LATER METS study was approved by the Medical Research Ethics Committee of the Amsterdam University Medical Center (registered at toetsingonline.nl, NL32117.018.10).

In the Dutch LATER study, data from 15 substudies of late effects were collected, including cardiotoxicity, bone density, frailty, growth hormone deficiency, renal toxicity, fatigue, and psychological late effects. Individuals who survived at least 5 years after diagnosis of histologically confirmed malignancies (as defined in the 3rd edition of the International Classification of Childhood Cancer [56]) or Langerhans cell histiocytosis, were treated with chemotherapy or radiotherapy, and were between 0 and 17 years of age at diagnosis were invited. Exclusion criteria were treatment for a malignancy in the past year and living abroad.

For all eligible survivors, prior to the visit of the late-effects clinic, sex; date of birth; date of cancer diagnosis; and detailed data on cancer type and treatment, including chemotherapy regimens and doses, radiotherapy fields and (fractionated) dose, stem cell transplantation and corticosteroid treatments, were collected in a pseudonymized, web-based, central database. This includes primary diagnosis as well as, if present, recurrences and subsequent malignancies.

Subsequently, data collection for all studies was combined with the survivors’ regular care visit to the late-effects clinic for the majority of survivors. Before the visit, survivors received information about the study, sent by mail by the study personnel. If they agreed to participate, study data was collected by the treating physician or the study personnel.

The entire cohort, at formation in 2008, contained 6165 eligible survivors. By mail, survivors were provided the option to opt-out of future study participation. For the Dutch LATER study, the cohort was frozen in 2016, leaving 5160 subjects eligible. For the LATER METS study, only adults (n=4741) were invited. Inclusion took place until April 2020. Written informed consent was obtained from all study participants.

Overview of the Dutch LATER study cohort and embeddedness of the Dutch LATER METS study cohort within the underlying cohort. Percentages indicate proportion of Dutch LATER cohort (N=6165).

 Reference Population

Normative data from the Dutch Lifelines study cohort will serve as reference population [57]. This is a 3-generation cohort of 167,000 inhabitants (10%) of the north of the Netherlands, from whom, among other data, the following parameters relevant to our study were collected between 2006 and 2013: age, sex, height, weight, waist and hip circumference, blood pressure, comorbidities, medication use, smoking, physical activity, high-density lipoprotein, triglycerides, glucose, apolipoprotein-B, low-density lipoprotein, total cholesterol, uric acid, and high sensitivity C-reactive protein. We aim to use a subset of this reference cohort as controls that have the same age and sex distribution as our study cohort.

 Data Collection Data Collected Before Visit of Late-Effects Clinic

An overview of collected variables is presented in Table 2. In addition to the previously mentioned data, the following variables relevant for the Dutch LATER METS study were extracted from the medical records: height and weight at cancer diagnosis and relevant comorbidities.

 Data Collected at Visit of Late-Effects Clinic

Weight was measured without shoes and with light clothing on an electronic scale to the nearest 0.1kg. Height was measured without shoes to the nearest centimeter. Body mass index was calculated from weight and height. Waist circumference was measured in the middle between the lower rib and iliac crest to the nearest centimeter. Hip circumference was measured at the greater trochanter to the nearest centimeter. Waist/hip ratio was calculated. Blood pressure was measured after at least 5 minutes rest with an electronic oscillometric meter (the mean of two measurements).

Survivors completed a general health questionnaire, containing questions about comorbidities, current medication use, smoking and alcohol habits, education level, and family history of diabetes mellitus and cardiovascular disease. They also completed the Short Questionnaire to Assess Health enhancing physical activity [58]. Total body dual-energy x-ray absorptiometry scans (Hologic and Lunar types) were used to assess body composition [41]. These measurements include fat percentage and lean body mass. The 6-minutes walking test was performed in a subset of the survivors (those treated in the Sophia children’s hospital/Erasmus Medical Center, Rotterdam) as a measure of functional exercise capacity [59,60].

Overview of collected variables.

Collection period Category Variable Unit(s) or categories
Collected before visit of late-effects clinic Childhood cancer type and treatment Primary childhood cancer diagnosis ICCC-3a classification
Treatment protocol Name and arm
Chemotherapy, per regimen TCDb
Radiotherapy field
  Cranial/craniospinal TCD, fractions (if applicable)
  Total body TCD, fractions (if applicable)
  Abdominal TCD, fractions (if applicable)
  Pancreas involvement TCD, fractions (if applicable)
Surgery procedure
  Autologous stem cell transplantation Yes / No, conditioning regimen
  Allogeneic stem cell transplantation Yes / No, conditioning regimen
Relapse Yes / No
Patient characteristics Sex Male / Female
Date of birth Date
Date of childhood cancer diagnosis Date
Date of study measurements (follow-up date) Date
Medical history Height at cancer diagnosis Centimeter
Weight at cancer diagnosis Kilogram
Growth hormone deficiency Yes / No
Growth hormone replacement Yes / No
Hypothyroidism Yes / No
Hypogonadism Yes / No
Hypocortisolism with steroid replacement Yes / No
Collected at visit of late-effects clinic Physical examination Height Centimeter
Weight Kilogram
Waist circumference Centimeter
Hip circumference Centimeter
Blood pressure mmHg
General health questionnaire Does the survivor have or has the survivor experienced
  High cholesterol Yes / No, age at diagnosis
  Hypertension Yes / No, age at diagnosis
  Diabetes mellitus Yes / No, age at diagnosis
  Myocardial infarction Yes / No, age at diagnosis
  Stroke Yes / No, age at diagnosis
Medication use Type, dose, age at start
Smoking status Yes / Former / No
Cardiovascular disease in family Relative, type of disease, age at diagnosis
Questionnaire to Assess Health enhancing physical activity N/Ac N/A
Dual-energy x-ray absorptiometry scan Total body fat Percentage
Z-score total body fat Z-score
Lean body mass Kilogram per m2
Appendicular lean body mass Kilogram per m2
6-minute walking test N/A Meter
Data determined from stored samples Serum biomarkers
  High-density lipoprotein mmol/L
  Low-density lipoprotein mmol/L
  Total cholesterol mmol/L
  Apolipoprotein-B g/L
  Glucose mmol/L
  Insulin pmol/L
  Adiponectin ug/mL
  Leptin ng/mL
  Uric acid mmol/L
  High sensitivity C-reactive protein mg/L
  IL-6d pg/mL
  hsTNFae pg/mL
  IL-1f pg/mL
  IGF-1g ug/L
  Creatinine mg/mmol
  Urea mmol/L
  LHh U/L
  FSHi U/L
  AMHj ug/L
  Estradiol pmol/L
  Testosterone nmol/L
DNA from blood/saliva N/A

aICCC-3: International Classification of Childhood Cancer, edition 3.

bTCD: total cumulative dose.

cN/A: not applicable.

dIL-6: interleukin-6.

ehsTNF: high-sensitivity tumor necrosis factor alpha.

fIL-1: interleukin-1.

gIGF-1: insulin-like growth factor 1.

hLH: luteinizing hormone.

iFSH: follicle stimulating hormone.

jAMH: anti-Müllerian hormone.

 Data Determined From Stored Samples

Venous blood samples were drawn after overnight fasting and stored at -80°C in the biobank. To assess dyslipidemia, a lipid spectrum will be measured, consisting of triglycerides, high-density lipoprotein, low-density lipoprotein, total cholesterol, and apolipoprotein-B. Insulin resistance will be assessed by measuring glucose and insulin. Additionally, adiponectin, leptin, and uric acid will be measured. Inflammatory markers include high sensitivity C-reactive protein, interleukin-6, high sensitivity tumor necrosis factor alpha, and interleukin-1. The following possible confounders will be measured: insulin-like growth factor 1, kidney function (creatinine, urea), sex hormones (luteinizing hormone, follicle stimulating hormone, anti-Müllerian hormone in women, estradiol in women, testosterone in men), thyroid function (thyroid-stimulating hormone, free thyroxine), cortisol.

DNA for analysis of single nucleotide polymorphisms will be isolated from blood or, in survivors who received allogeneic stem cell transplantation, saliva. Saliva was obtained by spitting into a collection tube (Oragene kit) after not drinking or eating for 30 minutes.

 Metabolic Syndrome Definition

Metabolic syndrome will be classified according to definitions by the third Adult Treatment Panel Report of the National Cholesterol Education Program [37] and the Joint Interim Statement of the International Diabetes Federation; National Heart, Lung, and Blood Institute; and the American Heart Association [38] (Table 1). Should these criteria be updated during our analysis, we will strive to take these adjustments into account.

 Risk of Bias

Sex, date of birth, date of cancer diagnosis, and disease and treatment data are also available for nonparticipating survivors. Hence, comparing participating and nonparticipating survivors in order to determine the risk of selection bias is feasible. We will also compare these data between survivors with complete and incomplete data to judge the risk of attrition bias. Neither physician nor study personnel were blinded to the exposures of the survivors. Objectively measurable outcomes will reduce the risk of bias in this setting.

 Statistical Analysis Prevalence of Metabolic Syndrome

The percentage of subjects with metabolic syndrome and the separate components will be assessed in survivors and in the Lifelines reference cohort according to both aforementioned metabolic syndrome definitions. Both cohorts will be compared by Chi-square (or Fisher exact) test. The relative risk for survivors to develop metabolic syndrome, compared to Lifelines reference data, will be calculated by employing a log-binomial regression model. The agreement between both metabolic syndrome definitions will be investigated with kappa statistic, in the whole cohort and stratified by sex.

A total body fat percentage of more than two standard deviations above the mean, as assessed by dual-energy x-ray absorptiometry, will be used as the most reliable marker for adiposity. We will estimate the correlation between waist circumference and fat percentage measured by dual-energy x-ray absorptiometry scan, and we will compare overweight classification with both definitions.

 Risk Factors

Treatment-related risk factors for occurrence of metabolic syndrome and the separate components will be assessed using multiple uni- and multivariable logistic regression models. Based on literature, an initial model will be built with cranial radiotherapy, abdominal radiotherapy, and alkylating agents (total alkylating dose calculated using cyclophosphamide equivalent dose [61]) as treatment-related independent variables, and age, sex, follow-up time, and smoking as patient-related independent variables. The effect of potential additional risk factors will be assessed by adding them to the initial model, and variables with a P value <.20 will be kept in the final model. These potential risk factors include all other chemotherapy agents (type and total cumulative dose), other radiotherapy fields (body location and dose), corticosteroids, education level, family history, physical activity, functional exercise capacity, and comorbidities.

We will also investigate different abdominal radiotherapy fields involved (pancreas, liver), and the influence of stem cell transplantation conditioning regimens. We will also study patient- and treatment-related risk factors for the outcome underdiagnosis of overweight measured by waist circumference.

 Biomarkers

Biomarker values will be reported, with reference values from the local laboratory where the samples are measured. This will be compared to Lifelines reference data by Chi-square (or Fisher exact) test. A risk factor analysis of altered biomarker values will be performed similarly to the abovementioned strategy for risk factor analysis of metabolic syndrome occurrence.

The diagnostic and predictive value of the biomarkers to detect metabolic syndrome will be investigated in multiple steps. We will stratify the survivors by metabolic syndrome presence or absence and compare mean or median values with the t test or Mann-Whitney U test. We will evaluate sensitivity and specificity and positive and negative predictive value based on the reference values of the local laboratory where the samples are measured. We will compare the area under the curve for a model with metabolic syndrome components and for a model with each biomarker added in order to investigate the additional diagnostic value of the novel biomarkers. We will build multivariable logistic regression models with metabolic syndrome as dependent variable and the biomarker as independent variable. In these models, we will also include metabolic syndrome components as covariates in order to investigate the independent predictive value of the novel biomarkers. We will estimate the metabolic syndrome risk by including the biomarker as categorical as well as continuous variable.

Correlation (Pearson or Spearman) between biomarkers and fat percentage by dual-energy x-ray absorptiometry scan will be used to measure the potential use as surrogate markers for adiposity.

 Genetic Susceptibility Analysis

Genotyping will be performed with the Infinium Global Screening Array [62] on DNA isolated from blood or, in post-stem cell transplantation survivors, saliva. Quality control of the genotype data will be performed following a standardized protocol [63] including filtering based on call rate (excluded when <0.975 for either single nucleotide polymorphism or individual call rate), Hardy-Weinberg equilibrium, excess heterozygosity, gender mismatches, and familial relationships. Genetic ancestry will be assessed based on principal component analysis. Imputation will be performed with the Michigan Imputation Server using standard settings [64] with reference panel Haplotype Reference Consortium version r1.1 [65].

The single nucleotide polymorphism analysis will be performed with the RVtests software package [66] using multiple logistic regression models with metabolic syndrome and its separate components as outcomes. The initial analysis will be adjusted for age at follow-up, sex, and genetic ancestry. Then, potentially relevant covariates will be added to the model using forward selection to study whether they influence the single nucleotide polymorphism analysis; if so, they will be kept in the model. These covariates include: body mass index at follow-up, comorbidities (growth hormone deficiency, hypogonadism, diabetes mellitus, and hypothyroidism), cranial and abdominal radiotherapy, and alkylating agents (cyclophosphamide equivalent dose). We will also perform a time-to-event analysis (with left-censoring) on identified hits in order to get clinically relevant effect estimates.

Quality control of the single nucleotide polymorphism analysis will be performed with the EasyQC package using standard settings [67]. This includes filtering based on minor allele frequency (excluded when <0.05) and imputation quality (excluded when <0.3).

Visualization of the genetic associations and annotation of biological function for the top single nucleotide polymorphisms will be performed with the FUMA platform [68]. Findings will be replicated in available independent international cohorts.

 Results Patient Accrual

Patient accrual started in 2016 and lasted until April 2020. A total of 2380/4741 survivors have participated (participation rate 50.2%). From July 2020, biomarker testing, single nucleotide polymorphism analysis, and data analysis will be performed.

 Power Calculation

We performed a power calculation with an expected prevalence of metabolic syndrome in our study cohort of 30%. This percentage is based on results from the SJLIFE cohort, in which the prevalence of clinically diagnosed metabolic syndrome in 1598 survivors, after a mean of 25.6 years since diagnosis, was 31.8% [6]. This is the only large cohort study so far with clinically diagnosed metabolic syndrome in survivors of heterogeneous malignancies with a follow-up time comparable to that of our cohort.

Based on the sample size of 2380 survivors, expected metabolic syndrome prevalence of 30%, power of 80%, and type I error of .05, we will have sufficient power to detect an approximate 3% difference in metabolic syndrome prevalence with the reference cohort. For risk factor analysis among survivors, the minimum detectable difference will depend on in how many survivors the risk factor (eg, treatment regimen) is present. For example, if the risk factor is present in 10%, 25%, or 50% of survivors, a minimum difference of approximately 9%, 7%, or 6%, respectively, can be detected.

A genetic power calculator was used to estimate the relative risk that can be found in the genetic susceptibility analysis for an assumed minor allele frequency of 0.25 [69]. Based on the sample size of 2380, metabolic syndrome population prevalence of 15% [70], a power of 80%, a type I error of 5X10–8, and a case-control ratio of 1:2, the relative risk per high risk allele that can be found is 1.5.

 Discussion

In the current study, we will assess the prevalence and patient- and treatment-related risk factors for metabolic syndrome and its separate components in adult survivors of childhood cancer, as well as the additional diagnostic value of novel biomarkers for surveillance, and the genetic susceptibility to (treatment-related) metabolic syndrome by single nucleotide polymorphism analysis.

A total of 2380 survivors have participated in the study. This corresponds to 38.6% of all survivors (N=6165) in the Dutch LATER cohort, and a participation rate of 50.2% of invited adult survivors (n=4741). The definitive numbers of refusals, nonresponders, deaths or otherwise excluded subjects are not available yet. We will report these in the paper with the results of our study.

Strengths of this study include the availability of a national cohort of survivors, the availability of comprehensive disease and treatment data, and the clinical assessment of late effects, in addition to questionnaire based endpoints. So far, the role of biomarkers and genetic susceptibility to metabolic syndrome has not been well defined in survivors. We specifically intend to use dual-energy x-ray absorptiometry scans and relevant biomarkers (those with a high independent diagnostic or prognostic value, and a high correlation with fat percentage on dual-energy x-ray absorptiometry scan) to enable identification of survivors at risk for metabolic syndrome, in whom waist circumference measurement is not feasible due to abdominal radiotherapy.

In conclusion, our study will provide knowledge on clinical and genetic determinants of metabolic syndrome and the diagnostic value of biomarkers in adult childhood cancer survivors. The results of this study will be used to optimize surveillance guidelines for metabolic syndrome among survivors, based on enhanced risk stratification and screening strategies. This will improve the diagnosis of metabolic syndrome and prevent complications, thereby improving quality of life.

None declared.

 de Haas Ec Oosting SF Lefrandt JD Wolffenbuttel BH Sleijfer DT Gietema JA The metabolic syndrome in cancer survivors The Lancet Oncology 2010 02 11 2 193 203 10.1016/S1470-2045(09)70287-6 20152771 S1470-2045(09)70287-6 Rosen GP Nguyen H Shaibi GQ Metabolic syndrome in pediatric cancer survivors: a mechanistic review Pediatr Blood Cancer 2013 12 60 12 1922 8 10.1002/pbc.24703 23913590 Pluimakers VG van Waas M Neggers SJCMM van den Heuvel-Eibrink MM Metabolic syndrome as cardiovascular risk factor in childhood cancer survivors Crit Rev Oncol Hematol 2019 01 133 129 141 10.1016/j.critrevonc.2018.10.010 30661649 S1040-8428(18)30204-X Reaven GM Banting lecture 1988. Role of insulin resistance in human disease Diabetes 1988 12 37 12 1595 607 10.2337/diab.37.12.1595 3056758 DeFronzo RA Ferrannini E Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease Diabetes Care 1991 03 14 3 173 94 10.2337/diacare.14.3.173 2044434 Smith WA Li C Nottage KA Mulrooney DA Armstrong GT Lanctot JQ Chemaitilly W Laver JH Srivastava DK Robison LL Hudson MM Ness KK Lifestyle and metabolic syndrome in adult survivors of childhood cancer: a report from the St. Jude Lifetime Cohort Study Cancer 2014 09 01 120 17 2742 50 10.1002/cncr.28670 25070001 PMC4165406 Nottage KA Ness KK Li C Srivastava D Robison LL Hudson MM Metabolic syndrome and cardiovascular risk among long-term survivors of acute lymphoblastic leukaemia - From the St. Jude Lifetime Cohort Br J Haematol 2014 05 165 3 364 74 10.1111/bjh.12754 24467690 PMC4271734 van der Pal HJ van Dalen EC van Delden E van Dijk IW Kok WE Geskus RB Sieswerda E Oldenburger F Koning CC van Leeuwen FE Caron HN Kremer LC High risk of symptomatic cardiac events in childhood cancer survivors J Clin Oncol 2012 05 01 30 13 1429 37 10.1200/JCO.2010.33.4730 22473161 JCO.2010.33.4730 Brouwer CAJ Postma A Hooimeijer HLH Smit AJ Vonk JM van Roon AM van den Berg MP Dolsma WV Lefrandt JD Bink-Boelkens MTE Zwart N de Vries EGE Tissing WJE Gietema JA Endothelial damage in long-term survivors of childhood cancer J Clin Oncol 2013 11 01 31 31 3906 13 10.1200/JCO.2012.46.6086 24062395 JCO.2012.46.6086 Reulen RC Winter DL Frobisher C Lancashire ER Stiller CA Jenney ME Skinner R Stevens MC Hawkins MM British Childhood Cancer Survivor Study Steering Group Long-term cause-specific mortality among survivors of childhood cancer JAMA 2010 07 14 304 2 172 9 10.1001/jama.2010.923 20628130 304/2/172 Schindler M Spycher BD Ammann RA Ansari M Michel G Kuehni CE Swiss Paediatric Oncology Group (SPOG) Cause-specific long-term mortality in survivors of childhood cancer in Switzerland: A population-based study Int J Cancer 2016 07 15 139 2 322 33 10.1002/ijc.30080 26950898 PMC5071665 Armstrong GT Liu Q Yasui Y Neglia JP Leisenring W Robison LL Mertens AC Late mortality among 5-year survivors of childhood cancer: a summary from the Childhood Cancer Survivor Study J Clin Oncol 2009 05 10 27 14 2328 38 10.1200/JCO.2008.21.1425 19332714 JCO.2008.21.1425 PMC2677921 Bagnasco F Caruso S Andreano A Valsecchi MG Jankovic M Biondi A Miligi L Casella C Terenziani M Massimino M Sacerdote C Morsellino V Erminio G Garaventa A Faraci M Micalizzi C Garrè ML Pillon M Basso G Biasin E Fagioli F Rondelli R Pession A Locatelli F Santoro N Indolfi P Palumbo G Russo G Verzegnassi F Favre C Zecca M Mura R D'Angelo P Cano C Byrne J Haupt R OTR-AIEOP Registry Late mortality and causes of death among 5-year survivors of childhood cancer diagnosed in the period 1960-1999 and registered in the Italian Off-Therapy Registry Eur J Cancer 2019 03 110 86 97 10.1016/j.ejca.2018.12.021 30772657 S0959-8049(18)31576-4 Chemaitilly W Cohen LE Diagnosis of endocrine disease: Endocrine late-effects of childhood cancer and its treatments Eur J Endocrinol 2017 04 176 4 R183 R203 10.1530/EJE-17-0054 28153840 EJE170054 Friedman DN Hilden P Moskowitz CS Suzuki M Boulad F Kernan NA Wolden SL Oeffinger KC Sklar CA Cardiovascular Risk Factors in Survivors of Childhood Hematopoietic Cell Transplantation Treated with Total Body Irradiation: A Longitudinal Analysis Biol Blood Marrow Transplant 2017 03 23 3 475 482 10.1016/j.bbmt.2016.12.623 28040534 S1083-8791(16)31182-X PMC5325052 de Vathaire F El-Fayech C Ben Ayed FF Haddy N Guibout C Winter D Thomas-Teinturier C Veres C Jackson A Pacquement H Schlumberger M Hawkins M Diallo I Oberlin O Radiation dose to the pancreas and risk of diabetes mellitus in childhood cancer survivors: a retrospective cohort study Lancet Oncol 2012 10 13 10 1002 10 10.1016/S1470-2045(12)70323-6 22921663 S1470-2045(12)70323-6 Wei C Thyagiarajan M Hunt L Cox R Bradley K Elson R Hamilton-Shield J Stevens M Crowne E Reduced beta-cell reserve and pancreatic volume in survivors of childhood acute lymphoblastic leukaemia treated with bone marrow transplantation and total body irradiation Clin Endocrinol (Oxf) 2015 01 82 1 59 67 10.1111/cen.12575 25132503 van Dorp W Blijdorp K Laven JSE Pieters R Visser JA van der Lely AJ Neggers SJCMM van den Heuvel-Eibrink MM Decreased ovarian function is associated with obesity in very long-term female survivors of childhood cancer Eur J Endocrinol 2013 06 168 6 905 12 10.1530/EJE-13-0114 23557987 EJE-13-0114 de Haas EC Altena R Boezen HM Zwart N Smit AJ Bakker SJL van Roon AM Postma A Wolffenbuttel BHR Hoekstra HJ van Leeuwen FE Sleijfer DT Gietema JA Early development of the metabolic syndrome after chemotherapy for testicular cancer Ann Oncol 2013 03 24 3 749 55 10.1093/annonc/mds527 23131388 S0923-7534(19)37149-2 Darzy K Shalet S Hypopituitarism following Radiotherapy Revisited Endocr Dev 2009 15 1 24 10.1159/000207607 19293601 van Waas M Neggers SJCMM Raat H van Rij CM Pieters R van den Heuvel-Eibrink MM Abdominal radiotherapy: a major determinant of metabolic syndrome in nephroblastoma and neuroblastoma survivors PLoS One 2012 7 12 e52237 10.1371/journal.pone.0052237 23251703 PONE-D-12-18634 PMC3522621 Mostoufi-Moab S Seidel K Leisenring WM Armstrong GT Oeffinger KC Stovall M Meacham LR Green DM Weathers R Ginsberg JP Robison LL Sklar CA Endocrine Abnormalities in Aging Survivors of Childhood Cancer: A Report From the Childhood Cancer Survivor Study J Clin Oncol 2016 09 20 34 27 3240 7 10.1200/JCO.2016.66.6545 27382091 JCO.2016.66.6545 PMC5024546 Belle FN Weiss A Schindler M Goutaki M Bochud M Zimmermann K von der Weid N Ammann RA Kuehni CE Overweight in childhood cancer survivors: the Swiss Childhood Cancer Survivor Study Am J Clin Nutr 2018 01 01 107 1 3 11 10.1093/ajcn/nqx006 29381792 4825203 Wilson CL Liu W Yang JJ Kang G Ojha RP Neale GA Srivastava DK Gurney JG Hudson MM Robison LL Ness KK Genetic and clinical factors associated with obesity among adult survivors of childhood cancer: A report from the St. Jude Lifetime Cohort Cancer 2015 07 01 121 13 2262 70 10.1002/cncr.29153 25963547 PMC4641835 van Santen HM Geskus RB Raemaekers S van Trotsenburg ASP Vulsma T van der Pal HJH Caron HN Kremer LCM Changes in body mass index in long-term childhood cancer survivors Cancer 2015 12 01 121 23 4197 204 10.1002/cncr.29614 26287726 Meacham LR Sklar CA Li S Liu Q Gimpel N Yasui Y Whitton JA Stovall M Robison LL Oeffinger KC Diabetes mellitus in long-term survivors of childhood cancer. Increased risk associated with radiation therapy: a report for the childhood cancer survivor study Arch Intern Med 2009 08 10 169 15 1381 8 10.1001/archinternmed.2009.209 19667301 169/15/1381 PMC3529471 Felicetti F D'Ascenzo F Moretti C Corrias A Omedè Pierluigi Marra WG Arvat E Fagioli F Brignardello E Gaita F Prevalence of cardiovascular risk factors in long-term survivors of childhood cancer: 16 years follow up from a prospective registry Eur J Prev Cardiol 2015 06 22 6 762 70 10.1177/2047487314529348 24691151 2047487314529348 Kooijmans EC Bökenkamp Arend Tjahjadi NS Tettero JM van Dulmen-den Broeder E van der Pal HJ Veening MA Early and late adverse renal effects after potentially nephrotoxic treatment for childhood cancer Cochrane Database Syst Rev 2019 03 11 3 CD008944 10.1002/14651858.CD008944.pub3 30855726 PMC6410614 Gibson TM Li Z Green DM Armstrong GT Mulrooney DA Srivastava D Bhakta N Ness KK Hudson MM Robison LL Blood Pressure Status in Adult Survivors of Childhood Cancer: A Report from the St. Jude Lifetime Cohort Study Cancer Epidemiol Biomarkers Prev 2017 12 26 12 1705 1713 10.1158/1055-9965.EPI-17-0510 29167278 1055-9965.EPI-17-0510 PMC5788007 van Waas M Neggers SJCMM van Eck JP van Noesel MM van der Lely A de Jong FH Pieters R van den Heuvel-Eibrink MM Adrenal function in adult long-term survivors of nephroblastoma and neuroblastoma Eur J Cancer 2012 05 48 8 1159 66 10.1016/j.ejca.2012.02.046 22513228 S0959-8049(12)00183-9 Oudin C Simeoni M Sirvent N Contet A Begu-Le Coroller A Bordigoni P Curtillet C Poirée Maryline Thuret I Play B Massot MC Chastagner P Chambost H Auquier P Michel G Prevalence and risk factors of the metabolic syndrome in adult survivors of childhood leukemia Blood 2011 04 28 117 17 4442 8 10.1182/blood-2010-09-304899 21278355 blood-2010-09-304899 Chow EJ Simmons JH Roth CL Baker KS Hoffmeister PA Sanders JE Friedman DL Increased cardiometabolic traits in pediatric survivors of acute lymphoblastic leukemia treated with total body irradiation Biol Blood Marrow Transplant 2010 12 16 12 1674 81 10.1016/j.bbmt.2010.05.016 20685399 S1083-8791(10)00229-6 PMC2975816 Clemens E van der Kooi ALF Broer L van Dulmen-den Broeder E Visscher H Kremer L Tissing W Loonen J Ronckers CM Pluijm SMF Neggers SJCMM Zolk O Langer T Zehnhoff-Dinnesen AA Wilson CL Hudson MM Carleton B Laven JSE Uitterlinden AG van den Heuvel-Eibrink MM The influence of genetic variation on late toxicities in childhood cancer survivors: A review Crit Rev Oncol Hematol 2018 06 126 154 167 10.1016/j.critrevonc.2018.04.001 29759558 S1040-8428(17)30567-X Gramatges MM Bhatia S Evidence for Genetic Risk Contributing to Long-Term Adverse Treatment Effects in Childhood Cancer Survivors Annu Rev Med 2018 01 29 69 247 262 10.1146/annurev-med-041916-124328 29029584 van Waas M Neggers SJCMM Uitterlinden AG Blijdorp K van der Geest IMM Pieters R van den Heuvel-Eibrink MM Treatment factors rather than genetic variation determine metabolic syndrome in childhood cancer survivors Eur J Cancer 2013 02 49 3 668 75 10.1016/j.ejca.2012.09.007 23036851 S0959-8049(12)00721-6 England J Drouin S Beaulieu P St-Onge P Krajinovic M Laverdière Caroline Levy E Marcil V Sinnett D Genomic determinants of long-term cardiometabolic complications in childhood acute lymphoblastic leukemia survivors BMC Cancer 2017 11 10 17 1 751 10.1186/s12885-017-3722-6 29126409 10.1186/s12885-017-3722-6 PMC5681795 National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report Circulation 2002 12 17 106 25 3143 3143 10.1161/circ.106.25.3143 Alberti KGMM Eckel RH Grundy SM Zimmet PZ Cleeman JI Donato KA Fruchart J James WPT Loria CM Smith SC International Diabetes Federation Task Force on EpidemiologyPrevention Hational Heart‚ Lung‚Blood Institute American Heart Association World Heart Federation International Atherosclerosis Society International Association for the Study of Obesity Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity Circulation 2009 10 20 120 16 1640 5 10.1161/CIRCULATIONAHA.109.192644 19805654 CIRCULATIONAHA.109.192644 Koh KK Han SH Quon MJ Inflammatory markers and the metabolic syndrome: insights from therapeutic interventions J Am Coll Cardiol 2005 12 06 46 11 1978 85 10.1016/j.jacc.2005.06.082 16325028 S0735-1097(05)02188-1 Kanbay M Jensen T Solak Y Le M Roncal-Jimenez C Rivard C Lanaspa MA Nakagawa T Johnson RJ Uric acid in metabolic syndrome: From an innocent bystander to a central player Eur J Intern Med 2016 04 29 3 8 10.1016/j.ejim.2015.11.026 26703429 S0953-6205(15)00423-9 PMC4826346 Blijdorp K van den Heuvel-Eibrink MM Pieters R Boot AM Delhanty PJD van der Lely A Neggers SJCMM Obesity is underestimated using body mass index and waist-hip ratio in long-term adult survivors of childhood cancer PLoS One 2012 7 8 e43269 10.1371/journal.pone.0043269 22905245 PONE-D-12-14510 PMC3419210 Karlage RE Wilson CL Zhang N Kaste S Green DM Armstrong GT Robison LL Chemaitilly W Srivastava DK Hudson MM Ness KK Validity of anthropometric measurements for characterizing obesity among adult survivors of childhood cancer: A report from the St. Jude Lifetime Cohort Study Cancer 2015 06 15 121 12 2036 43 10.1002/cncr.29300 25728221 PMC4457576 Inaba H Yang J Kaste SC Hartford CM Motosue MS Chemaitilly W Triplett BM Shook DR Pui C Leung W Longitudinal changes in body mass and composition in survivors of childhood hematologic malignancies after allogeneic hematopoietic stem-cell transplantation J Clin Oncol 2012 11 10 30 32 3991 7 10.1200/JCO.2011.40.0457 23032628 JCO.2011.40.0457 PMC3675688 Wei C Thyagiarajan MS Hunt LP Shield JPH Stevens MCG Crowne EC Reduced insulin sensitivity in childhood survivors of haematopoietic stem cell transplantation is associated with lipodystropic and sarcopenic phenotypes Pediatr Blood Cancer 2015 11 62 11 1992 9 10.1002/pbc.25601 25989749 Parish S Offer A Clarke R Hopewell JC Hill MR Otvos JD Armitage J Collins R Heart Protection Study Collaborative Group Lipids and lipoproteins and risk of different vascular events in the MRC/BHF Heart Protection Study Circulation 2012 05 22 125 20 2469 78 10.1161/CIRCULATIONAHA.111.073684 22539783 CIRCULATIONAHA.111.073684 Mach F Baigent C Catapano AL Koskinas KC Casula M Badimon L Chapman MJ De Backer GG Delgado V Ference BA Graham IM Halliday A Landmesser U Mihaylova B Pedersen TR Riccardi G Richter DJ Sabatine MS Taskinen M Tokgozoglu L Wiklund O ESC Scientific Document Group 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk Eur Heart J 2020 01 01 41 1 111 188 10.1093/eurheartj/ehz455 31504418 5556353 Ketterl TG Chow EJ Leisenring WM Goodman P Koves IH Petryk A Steinberger J Baker KS Adipokines, Inflammation, and Adiposity in Hematopoietic Cell Transplantation Survivors Biol Blood Marrow Transplant 2018 03 24 3 622 626 10.1016/j.bbmt.2017.11.024 29197678 S1083-8791(17)30871-6 PMC5831197 Skoczen S Tomasik PJ Bik-Multanowski M Surmiak M Balwierz W Pietrzyk JJ Sztefko K Gozdzik J Galicka-Latała D Strojny W Plasma levels of leptin and soluble leptin receptor and polymorphisms of leptin gene -18G > A and leptin receptor genes K109R and Q223R, in survivors of childhood acute lymphoblastic leukemia J Exp Clin Cancer Res 2011 06 01 30 64 10.1186/1756-9966-30-64 21631924 1756-9966-30-64 PMC3127776 Im J Kim S Lee J Shim J Lee H Lee D Association between hypoadiponectinemia and cardiovascular risk factors in nonobese healthy adults Metabolism 2006 11 55 11 1546 50 10.1016/j.metabol.2006.06.027 17046559 S0026-0495(06)00247-2 Cicero AFG Fogacci F Giovannini M Grandi E Rosticci M D'Addato S Borghi C Serum uric acid predicts incident metabolic syndrome in the elderly in an analysis of the Brisighella Heart Study Sci Rep 2018 08 01 8 1 11529 10.1038/s41598-018-29955-w 30068918 10.1038/s41598-018-29955-w PMC6070523 Saultier P Auquier P Bertrand Y Vercasson C Oudin C Contet A Plantaz D Poirée Marilyne Ducassou S Kanold J Tabone M Dalle J Lutz P Gandemer V Sirvent N Thouvenin S Berbis J Chambost H Baruchel A Leverger G Michel G Metabolic syndrome in long-term survivors of childhood acute leukemia treated without hematopoietic stem cell transplantation: an L.E.A. study Haematologica 2016 12 101 12 1603 1610 10.3324/haematol.2016.148908 27515247 haematol.2016.148908 PMC5479621 Oudin C Auquier P Bertrand Y Contet A Kanold J Sirvent N Thouvenin S Tabone M Lutz P Ducassou S Plantaz D Dalle J Gandemer V Beliard S Berbis J Vercasson C Barlogis V Baruchel A Leverger G Michel G Metabolic syndrome in adults who received hematopoietic stem cell transplantation for acute childhood leukemia: an LEA study Bone Marrow Transplant 2015 11 50 11 1438 44 10.1038/bmt.2015.167 26191949 bmt2015167 Holmqvist AS Olsen JH Andersen KK de Fine Licht S Hjorth L Garwicz S Moëll C Anderson H Wesenberg F Tryggvadottir L Malila N Boice JD Hasle H Winther JF ALiCCS study group Adult life after childhood cancer in Scandinavia: diabetes mellitus following treatment for cancer in childhood Eur J Cancer 2014 04 50 6 1169 75 10.1016/j.ejca.2014.01.014 24507548 S0959-8049(14)00048-3 Bhakta N Liu Q Ness KK Baassiri M Eissa H Yeo F Chemaitilly W Ehrhardt MJ Bass J Bishop MW Shelton K Lu L Huang S Li Z Caron E Lanctot J Howell C Folse T Joshi V Green DM Mulrooney DA Armstrong GT Krull KR Brinkman TM Khan RB Srivastava DK Hudson MM Yasui Y Robison LL The cumulative burden of surviving childhood cancer: an initial report from the St Jude Lifetime Cohort Study (SJLIFE) The Lancet 2017 12 09 390 10112 2569 2582 10.1016/S0140-6736(17)31610-0 28890157 S0140-6736(17)31610-0 PMC5798235 Prasad M Arora B Chinnaswamy G Vora T Narula G Banavali S Kurkure P Nutritional status in survivors of childhood cancer: Experience from Tata Memorial Hospital, Mumbai Indian J Cancer 2015 52 2 219 23 10.4103/0019-509X.175814 26853412 IndianJournalofCancer_2015_52_2_219_175814 Steliarova-Foucher E Stiller C Lacour B Kaatsch P International Classification of Childhood Cancer, third edition Cancer 2005 04 01 103 7 1457 67 10.1002/cncr.20910 15712273 Lifelines cohort and biobank Lifelines 2020-05-01 https://www.lifelines.nl/ Wendel-Vos GCW Schuit AJ Saris WHM Kromhout D Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity J Clin Epidemiol 2003 12 56 12 1163 9 10.1016/s0895-4356(03)00220-8 14680666 S0895435603002208 ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories ATS statement: guidelines for the six-minute walk test Am J Respir Crit Care Med 2002 07 01 166 1 111 7 10.1164/ajrccm.166.1.at1102 12091180 Casanova C Celli BR Barria P Casas A Cote C de Torres JP Jardim J Lopez MV Marin JM Montes de Oca M Pinto-Plata V Aguirre-Jaime A Six Minute Walk Distance Project (ALAT) The 6-min walk distance in healthy subjects: reference standards from seven countries Eur Respir J 2011 01 37 1 150 6 10.1183/09031936.00194909 20525717 09031936.00194909 Green DM Nolan VG Goodman PJ Whitton JA Srivastava D Leisenring WM Neglia JP Sklar CA Kaste SC Hudson MM Diller LR Stovall M Donaldson SS Robison LL The cyclophosphamide equivalent dose as an approach for quantifying alkylating agent exposure: a report from the Childhood Cancer Survivor Study Pediatr Blood Cancer 2014 01 61 1 53 67 10.1002/pbc.24679 23940101 PMC3933293 Illumina Infinium Global Screening Array-24 v3.0 BeadChip 2020 2020-05-01 https://www.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html Anderson CA Pettersson FH Clarke GM Cardon LR Morris AP Zondervan KT Data quality control in genetic case-control association studies Nat Protoc 2010 09 5 9 1564 73 10.1038/nprot.2010.116 21085122 nprot.2010.116 PMC3025522 Das S Forer L Schönherr Sebastian Sidore C Locke AE Kwong A Vrieze SI Chew EY Levy S McGue M Schlessinger D Stambolian D Loh P Iacono WG Swaroop A Scott LJ Cucca F Kronenberg F Boehnke M Abecasis GR Fuchsberger C Next-generation genotype imputation service and methods Nat Genet 2016 10 48 10 1284 1287 10.1038/ng.3656 27571263 PMC5157836 McCarthy S Das S Kretzschmar W Delaneau O Wood AR Teumer A Kang HM Fuchsberger C Danecek P Sharp K Luo Y Sidore C Kwong A Timpson N Koskinen S Vrieze S Scott LJ Zhang H Mahajan A Veldink J Peters U Pato C van Duijn CM Gillies CE Gandin I Mezzavilla M Gilly A Cocca M Traglia M Angius A Barrett JC Boomsma D Branham K Breen G Brummett CM Busonero F Campbell H Chan A Chen S Chew E Collins FS Corbin LJ Smith GD Dedoussis G Dorr M Farmaki A Ferrucci L Forer L Fraser RM Gabriel S Levy S Groop L Harrison T Hattersley A Holmen OL Hveem K Kretzler M Lee JC McGue M Meitinger T Melzer D Min JL Mohlke KL Vincent JB Nauck M Nickerson D Palotie A Pato M Pirastu N McInnis M Richards JB Sala C Salomaa V Schlessinger D Schoenherr S Slagboom PE Small K Spector T Stambolian D Tuke M Tuomilehto J Van den Berg LH Van Rheenen W Volker U Wijmenga C Toniolo D Zeggini E Gasparini P Sampson MG Wilson JF Frayling T de Bakker PIW Swertz MA McCarroll S Kooperberg C Dekker A Altshuler D Willer C Iacono W Ripatti S Soranzo N Walter K Swaroop A Cucca F Anderson CA Myers RM Boehnke M McCarthy MI Durbin R Haplotype Reference Consortium A reference panel of 64,976 haplotypes for genotype imputation Nat Genet 2016 10 48 10 1279 83 10.1038/ng.3643 27548312 ng.3643 PMC5388176 Zhan X Hu Y Li B Abecasis GR Liu DJ RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data Bioinformatics 2016 05 01 32 9 1423 6 10.1093/bioinformatics/btw079 27153000 btw079 PMC4848408 Heid I, Winkler T, Gorski M, Olden M Easy QC Universität Regensburg 2020-05-01 https://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/ Watanabe K Taskesen E van Bochoven A Posthuma D Functional mapping and annotation of genetic associations with FUMA Nat Commun 2017 11 28 8 1 1826 10.1038/s41467-017-01261-5 29184056 10.1038/s41467-017-01261-5 PMC5705698 Purcell S Cherny SS Sham PC Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits Bioinformatics 2003 01 19 1 149 50 10.1093/bioinformatics/19.1.149 12499305 Blokstra A Vissink P Venmans L Holleman P van DSY Smit H Verschuren W Measuring The Netherlands A monitoring study of risk factors in the general population-2010. RIVM Rapport 260152001/ 2009 2011