Eur J Epidemiol (2014) 29:261–275 DOI 10.1007/s10654-014-9901-8

CANCER

Biomarker patterns of inflammatory and metabolic pathways are associated with risk of colorectal cancer: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Krasimira Aleksandrova • Mazda Jenab • H. Bas Bueno-de-Mesquita • Veronika Fedirko • Rudolf Kaaks • Annekatrin Lukanova • Fra¨nzel J. B. van Duijnhoven Eugene Jansen • Sabina Rinaldi • Isabelle Romieu • Pietro Ferrari • Neil Murphy • Marc J. Gunter • Elio Riboli • Sabine Westhpal • Kim Overvad • Anne Tjønneland • Jytte Halkjær • Marie-Christine Boutron-Ruault • Laure Dossus • Antoine Racine • Antonia Trichopoulou • Christina Bamia • Philippos Orfanos • Claudia Agnoli • Domenico Palli • Salvatore Panico • Rosario Tumino • Paolo Vineis • Petra H. Peeters • Eric J. Duell • Esther Molina-Montes • J. Ramo´n Quiro´s • Miren Dorronsoro • Maria-Dolores Chirlaque • Aurelio Barricarte • Ingrid Ljuslinder • Richard Palmqvist • Ruth C. Travis • Kay-Tee Khaw • Nicholas Wareham • Tobias Pischon • Heiner Boeing

•

Received: 23 December 2013 / Accepted: 4 April 2014 / Published online: 4 May 2014 Ó Springer Science+Business Media Dordrecht 2014

Abstract A number of biomarkers of inflammatory and metabolic pathways are individually related to higher risk of colorectal cancer (CRC); however, the association between biomarker patterns and CRC incidence has not

Electronic supplementary material The online version of this article (doi:10.1007/s10654-014-9901-8) contains supplementary material, which is available to authorized users. K. Aleksandrova (&)
H. Boeing Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert Allee 114-116, 14558 Nuthetal, Germany e-mail: [email protected] M. Jenab
V. Fedirko
S. Rinaldi
I. Romieu
P. Ferrari International Agency for Research on Cancer (IARC-WHO), Lyon, France H. B. Bueno-de-Mesquita
F. J. B. van Duijnhoven
E. Jansen National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands H. B. Bueno-de-Mesquita Department of Gastroenterology and Hepatology, University Medical Center, Utrecht, The Netherlands

been previously evaluated. Our study investigates the association of biomarker patterns with CRC in a prospective nested case–control study within the European Prospective Investigation into Cancer and Nutrition (EPIC). During median follow-up time of 7.0 (3.7–9.4) years, 1,260 incident CRC cases occurred and were matched to 1,260 controls using risk-set sampling. Pre-diagnostic

V. Fedirko Winship Cancer Institute, Emory University, Atlanta, GA, USA R. Kaaks
A. Lukanova Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg, Germany A. Lukanova Department of Medical Biosciences/Pathology, University of Umea˚, Umea˚, Sweden F. J. B. van Duijnhoven Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands N. Murphy
M. J. Gunter
E. Riboli
P. Vineis
P. H. Peeters Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

V. Fedirko Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

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measurements of C-peptide, glycated hemoglobin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), reactive oxygen metabolites (ROM), insulin-like growth factor 1, adiponectin, leptin and soluble leptin receptor (sOB-R) were used to derive biomarker patterns from principal component analysis (PCA). The relation with CRC incidence was assessed using conditional logistic regression models. We identified four biomarker patterns ‘HDL-C/Adiponectin fractions’, ‘ROM/CRP’, ‘TG/C-peptide’ and ‘leptin/sOB-R’ to explain 60 % of the overall biomarker variance. In multivariable-adjusted logistic regression, the ‘HDL-C/Adiponectin fractions’, ‘ROM/CRP’ and ‘leptin/sOB-R’ patterns were associated with CRC risk [for the highest quartile vs the lowest, incidence rate ratio (IRR) = 0.69, 95 % CI 0.51–0.93, P-trend = 0.01; IRR = 1.70, 95 % CI 1.30–2.23, P-trend = 0.002; and IRR = 0.79, 95 % CI 0.58–1.07; P-trend = 0.05, respectively]. In contrast, the ‘TG/C-peptide’ pattern was not associated with CRC risk (IRR = 0.75, 95 % CI 0.56–1.00, P-trend = 0.24). After cases within the first 2 follow-up years were excluded, the ‘ROM/CRP’ pattern was no longer associated with CRC risk, suggesting potential influence of preclinical disease on these associations. By application of PCA, the study identified ‘HDL-C/Adiponectin fractions’, ‘ROM/CRP’ and ‘leptin/sOB-R’ as biomarker patterns representing potentially important pathways for CRC development. Keywords Colorectal cancer
Biomarker patterns
Inflammatory and metabolic pathways
Principal component analysis
European Prospective Investigation into Cancer and Nutrition (EPIC)

S. Westhpal Institute of Clinical Chemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

K. Aleksandrova et al.

Introduction Globally, colorectal cancer (CRC) represents the third most commonly diagnosed cancer in men and the second cancer in women, with over 1.2 million new cases and 608,700 deaths in 2008 [1]. The processes involved in the pathogenesis of CRC are complex and only partially understood, but current research suggests body fatness and its associated metabolic dysregulations to play a major role [2]. Body fatness acts on multiple metabolic processes that cover different but also highly interrelated and possibly overlapping pathways which may be relevant for cancer, including hyperinsulinemia, hyperlipidemia, oxidative stress and inflammation [3] (Fig. 1). To date, a number of circulating biomarkers of these metabolic processes were shown to be individually associated with CRC risk in large population studies [4, 5]. For example, a number of studies reported on a higher risk of CRC with higher concentrations of C-peptide—a marker of hyperinsulinemia [6–8], glycated hemoglobin (Hba1c)—a marker of hyperglycemia [9, 10], C-reactive protein (CRP)—a marker of chronic low grade inflammation [11, 12], reactive oxygen metabolites (ROM) reflecting oxidative stress [13], and lower levels of high-density lipoprotein cholesterol (HDL-C)—a marker of dyslipidemia [14–16]. A variety of cytokines and proteins abundantly secreted in the adipose tissue may also influence CRC development through direct effects in promoting or inhibiting angiogenesis and tumor growth [17–19], but also by indirectly reflecting inflammatory and metabolic response in human organism [20]. Previous studies suggested an inverse association between high levels of adiponectin and soluble leptin receptor (sOB-R) [21–25] in CRC development. Experimental data has shown that the high-molecular-weight (HMW) and non-HMW adiponectin A. Trichopoulou Hellenic Health Foundation, Athens, Greece

K. Overvad Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark

A. Trichopoulou
C. Bamia
P. Orfanos WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece

A. Tjønneland
J. Halkjær Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark

C. Agnoli Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy

M.-C. Boutron-Ruault
L. Dossus
A. Racine Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team, 94805 Villejuif, France

D. Palli Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), Florence, Italy

M.-C. Boutron-Ruault
L. Dossus
A. Racine UMRS 1018, Univ Paris Sud, 94805 Villejuif, France M.-C. Boutron-Ruault
L. Dossus
A. Racine IGR, 94805 Villejuif, France

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S. Panico Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy

Biomarker patterns of inflammatory and metabolic pathways

263

combinations of the original variables [32]. Thus, it makes it possible to determine distinct constructs in a set of biomarkers that may point to common etiological pathways. Modeling of different biomarkers for identifying etiological pathways was first reported by Edwards et al. [33] in an effort to disentangle the underlying structure of metabolic syndrome. In cancer epidemiology, biomarker patterns were recently investigated in relation to endometrial cancer [34]. The objective of this study is to identify biomarker patterns of inflammatory and metabolic pathways and to explore potential associations with risk of CRC using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

fractions have different biological activities, such that the HMW form is more closely related to insulin sensitivity, whereas complexes with lower molecular weight are having stronger anti-inflammatory potential, therefore it may be important to consider adiponectin fractions as separate factors [26]. Insulin may also be involved in CRC carcinogenesis through insulin-like growth factor 1 (IGF-1) acting as potent mitogen [27–29]. Although knowledge obtained in previous studies has contributed to understanding the individual roles of inflammatory and metabolic biomarkers in CRC risk, the ‘single biomarker’ approach used in these studies has not taken into account the complex synergistic interactions among these biomarkers (Fig. 1). The high level of intercorrelation among some biomarkers makes it difficult to disentangle underlying etiological pathways; therefore it is unclear whether these factors act independently or through overlapping mechanisms. A biomarker pattern approach may be able to capture the ‘full picture’ on complex biomarker-cancer relation, in a way that studies on single biomarkers e.g. using traditional multiple regression cannot. It may also help in providing clues about pathways through which some biomarkers may influence disease risk. Therefore, it might be preferable to consider complex pathways rather than the effects of single biomarkers alone. Principal component analysis (PCA) has proven to be a complementary approach that might aid in the interpretation of the underlying biological and statistical structure of many inter-correlated variables [30, 31]. This relatively simple statistical method works by reducing a number of inter-correlated variables into a smaller number of new variables, called principal components, which are linear

EPIC is a large prospective cohort study with approximately 520,000 participants, aged 25–70 years at enrollment during the period from 1992 through 2000 and recruited from 23 centers in 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) [35]. Participants gave written informed consent, underwent anthropometric measurements, and completed questionnaires on socio-demographic and lifestyle characteristics, including detailed dietary assessment [35]. Usual food intakes were measured by using country-specific validated dietary questionnaires, and individual nutrient intakes were derived from foods included in the dietary questionnaires

R. Tumino Cancer Registry and Histopathology Unit, ‘‘M.P.Arezzo’’ Hospital, Ragusa, Italy

E. Molina-Montes Instituto de Investigacio´n Biosanitaria de Granada (Granada.bs), Granada, Spain

P. Vineis HuGeF Foundation, Turin, Italy

J. R. Quiro´s Public Health Directorate, Asturias, Spain

P. H. Peeters Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands

M. Dorronsoro Epidemiology and Health Information, Public Health Division of Gipuzkoa, Basque Regional Health Department, San Sebastian, Spain

E. J. Duell Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Bellvitge Biomedical Research Institute (IDIBELL), Catalan Institute of Oncology (ICO), Barcelona, Spain E. Molina-Montes Escuela Andaluza de Salud Pu´blica, Granada, Spain E. Molina-Montes
M.-D. Chirlaque CIBER Epidemiology and Public Health CIBERESP, Spain, http://www.ciberesp.es/

Materials and methods Study population

M.-D. Chirlaque Department of Epidemiology, Murcia Regional Health Authority, Murcia, Spain A. Barricarte Navarre Public Health Institute, Pamplona, Spain I. Ljuslinder Department of Radiation Sciences, Umea˚ University Hospital, Umea˚, Sweden

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Fig. 1 Excess body weight and adiposity in relation to colorectal cancer: putative underlying mechanisms. This scheme outlines the putative mechanisms that link adipose tissue in excess body weight with the molecular mechanisms relevant for carcinogenesis. Obesity may drive colorectal carcinogenesis directly via several interrelated mechanisms including abnormal adipokine production, chronic lowgrade inflammation, insulin resistance, oxidative stress, dyslipidemia and hyperglycemia. These processes may contribute to cancer

progression both by inhibiting apoptosis and stimulating cell proliferation and angiogenesis. Diet and physical inactivity are closely linked to excess body weight and may modify the effect of excess adiposity on carcinogenesis. IGF-1 insulin-like growth factor 1, CRP C-reactive protein, sOB-R soluble leptin receptor, HDL-cholesterol high-density-lipoprotein cholesterol, ROS formation reactive oxygen species formation

through the standardized EPIC Nutrient Database [36]. All dietary variables used in the present study were calibrated by using an additive calibration method as previously

described [37]. Blood samples were collected at study baseline from about 65 % of the women and 93 % of the men using standardized protocols [35]. The participants’ body weight and waist circumference were mostly measured with the exception of EPIC-Oxford center where weight and waist circumference were derived from prediction equations based on self-reports of all participants and measurements in a subsample of the Oxford cohort [38].

R. Palmqvist Department of Medical Biosciences, Pathology, Umea˚ University, Umea˚, Sweden R. C. Travis Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK K.-T. Khaw Clinical Gerontology Unit, Addenbrooke’s Hospital, University of Cambridge School of Clinical Medicine, Cambridge, UK N. Wareham MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine , Cambridge, UK T. Pischon Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine (MDC), Berlin-Buch, Germany

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Cohort follow-up and case ascertainment Incident cancer cases were identified through record linkage with regional cancer registries at all study centers except those in Germany, France, Greece, and Naples (Italy), where follow-up was based on a combination of methods, including health insurance records, cancer and pathology registries, and active follow-up of study subjects and their next of kin. Closure dates for the present study were defined as the latest date of complete follow-up for both cancer incidence and vital status. Closure dates ranged

Biomarker patterns of inflammatory and metabolic pathways

from December 1999 to June 2003 for study centers using registry data and from June 2000 to December 2002 for study centers using active follow-up procedures. For the present study, colon cancers were defined as tumors in the cecum, appendix, ascending colon, hepatic flexure, transverse colon, splenic flexure, and descending and sigmoid colon (C18.0–C18.7, according to the 10th Revision of the International Statistical Classification of Diseases, Injury and Causes of Death), as well as tumors that were overlapping or unspecified (C18.8 and C18.9). While rectum cancers were defined as those occurring at the rectosigmoid junction (ICD-10 code C19) or in the rectum (ICD-10 code C20). Nested case–control study A total of 1,260 incident cases of CRC cancer (794 colon cancer and 466 rectal cancer) were included in the present analyses as follows, according to tumor site (colon/rectum): 28/8 from France, 105/42 from Italy, 79/42 from Spain, 151/64 from the United Kingdom, 93/48 from the Netherlands, 13/14 from Greece94/55 from Germany, 41/25 from Umea—Sweden, and 190/168 from Denmark. An incidence density sampling protocol was used such that for each case one control subject was chosen at random among appropriate risk sets consisting of all cohort members alive and free of cancer (except non-melanoma skin cancer) at the time of diagnosis of the index case. Matching characteristics were study center at the time of enrollment, sex, age at blood collection (6-month to 2-year intervals), time of blood collection (2- to 4-h intervals), and fasting status (\3, 3–6, or [6 h, to account for differences in analyte values by fasting status). Women were also matched on menopausal status (premenopausal, perimenopausal, postmenopausal, or surgically postmenopausal). Premenopausal women were matched on phase of the menstrual cycle at blood collection (early follicular, late follicular, ovulatory, early luteal, midluteal, or late luteal), and postmenopausal women were matched on current use of hormone replacement therapy (yes/no). These latter matching criteria among women were included because a separate study on the association between endogenous hormones and CRC risk was planned using the same matched case–control sets [6]. Selection of biomarkers The biomarkers included in the biomarker pattern analysis were chosen based on the following criteria: (1) representing biologically plausible inflammatory and metabolic pathways for CRC development; (2) shown in epidemiological studies to be individually related to CRC risk; (3) previously investigated within the EPIC study. Therefore,

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the final list of biomarkers included: the adiponectin fractions—HMW adiponectin and non-HMW adiponectin, HDL-C, ROM, CRP, Triglycerides (TG), C-peptide, leptin, sOB-R, Hba1c and IGF1. Total adiponectin was not included in the biomarker pattern analysis due to its strong correlation with its fractions—HMW and non-HMW adiponectin. Exclusion criteria and management of missing data Excluded from the analysis were the cases and matched controls from the EPIC centers from Norway and the Malmo¨ center in Sweden for which no blood samples were available for the current study. Data for the measurements of biomarkers was missing in cases and controls as follows: IGF1 (177/425); ROM (8/12); TG (35/25); HDL-C (15/10); C-peptide (216/449), Hba1c (273/503); CRP (81/93), total adiponectin (20/16), HMW-adiponectin (26/28), leptin (53/ 46), sOB-R (74/59), waist circumference (66/66). Overall, participants with missing information on biomarkers tended to be slightly more educated, whereas no substantial differences were seen in the other characteristics. In order to account for missing biomarker data, we applied multiple imputation technique [39]. The multiple imputation was performed with the following steps: [1] analyze each completed dataset separately, [2] extract the point estimate and standard error from each analysis; [3] combine the multiple sets of point estimates and standard errors to obtain a single point estimate, standard error, and the associated confidence interval. We applied the SAS procedures ‘PROC MI’ which adopts regression methods and propensity scores for imputation and ‘PROC MIANALYZE’ which combines estimates output from various complete-data procedures. All variables in the regression model (exposure, outcome and covariates) were included in the ‘PROC MI’ procedure. Ten duplicate datasets were sampled from their predictive distribution based on the observed data with the missing values replaced by imputed values. The procedure was following the assumption that values are missing at random and that the pattern of missing data was arbitrary [39, 40]. In a sensitivity analysis, the main associations were compared with estimates from a complete-case analysis (562 case-sets) in order to test whether missing observations of the covariates may have influenced the effect estimates. Laboratory procedures Blood samples were processed, aliquotted into heat-sealed straws, and stored in liquid nitrogen freezers (-196 °C) [35]. Storage protocols differed in Denmark and Sweden, where tubes were stored in the vapour phase of liquid

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nitrogen (-150 °C) or in -80 °C freezers, respectively. The blood collection and processing protocols are described in detail elsewhere [41]. Approval was obtained from the ethics review board of the International Agency for Research on Cancer, Lyon, France and the local EPIC centers review boards. The measurement of all biomarkers included in the analysis has been described elsewhere (i.e. C-peptide [6], Hba1c [9], TG, HDL-C [14], CRP [11], IGF1 [27], ROM [13], adiponectin, HMW-adiponectin [24], leptin and sOB-R [21]). Statistical analysis Descriptive statistics (means, SDs, and frequencies) were computed for variables by case–control status and colon or rectal cancer anatomical sub-sites to describe the demographic and dietary characteristics of the study population. Baseline characteristics were compared between cases and matched controls using Student’s paired t test and Wilcoxon’s signed rank test for continuous variables, McNemar’s test for dichotomous variables and Bowker’s test of symmetry for categorical variables. Biomarker patterns were identified using PCA, first in all study participants, and consequently separately among men and women. Briefly, the principal axis method was used to extract the components based on log-transformed values of all biomarkers, and this was followed by a varimax (orthogonal) rotation. The analysis was conducted using the ‘PROC FACTOR’ procedure in SAS. To determine the number of principal components to retain, we considered eigenvalues[1, the scree plot, and the interpretability of the principal components. Markers with |loadings| greater than ±0.55 were used to interpret the principal components and these were labeled according to the biomarkers with highest loadings within each component. After evaluation of the final principal component solutions in terms of interpretability of their structure, four uncorrelated principal components were retained. The first component represents a linear combination of the variables accounting for the maximum of the data variance, the second component accounts for the next largest amount of the remaining variance, etc. We confirmed that when the analysis was done separately for men and women, similar biomarker patterns were extracted for each sex, though explaining different proportions of total variation. For each component (biomarker pattern), a principal component score was calculated by summing concentrations of the biomarkers weighted by their principal component loadings. Principal component scores were categorized into quartiles based on the distribution in the control population. The associations between biomarker patterns and risk of colon and rectal cancer was analyzed using multivariable conditional logistic regression, taking into account matching factors with additional adjustment for a priori chosen covariates known to be

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associated with CRC risk and metabolic biomarkers [11, 13, 21, 24, 42–44]. Incidence rate ratios (IRRs) as derived from the risk set sampling design [45] and 95 % confidence intervals (CI-s) were computed. The list of covariates included smoking status, education, physical activity, alcohol consumption, fiber, red and processed meat, fruit and vegetables, fish and shellfish, and waist circumference as a proxy measure for adiposity. Adjustment for other anthropometric measures, such as body mass index (BMI) did not provide additional information beyond waist circumference, therefore we present a model only with waist circumference. Tests for trend (two sided) across quartiles were based on the median principal component scores within quartiles modeled as a continuous variable. We have repeated these analyses for colon and rectal cancer and in men and women (principal component scores were categorized into quartiles based on the distribution for men and women separately). In addition, in a subset of participants with available information on tumor stage (1,129 cases and 1,129 controls) we stratified the analyses according to Stage I localized and Stage II localized with invasion (n = 449) versus Stage III metastatic regional and Stage IV metastatic distal (n = 468). Further, we examined whether associations differ by cancer site (colon or rectum), sex, and length of follow-up (continuously) using interaction terms (principal component score variable multiplied by stratum variable). Statistical interaction on multiplicative scale was tested based on Wald test. Finally, we repeated the main multivariable analyses after excluding participants with diabetes (93 case-sets), cases that occurred during the first 2 years of follow-up (n = 318) and non-fasting participants (550 case-sets). All analyses were performed using SAS ver. 9.2, with graphical interface SAS Enterprise ver. 4.3 (SAS Institute, Inc.). Statistical tests were two-sided, and P \ 0.05 was considered statistically significant.

Results The median (interquartile range) follow-up time of study participants was 7.0 (3.7–9.4) years. The baseline characteristics of incident colon and rectal cancer cases and their corresponding controls are presented in Table 1. Overall, cases of colon cancer had higher BMI and waist circumference, and tended to be physically inactive; whereas the cases of rectal cancer had higher alcohol consumption, particularly in men. Both colon and rectal cancer cases had higher concentrations of ROM and lower concentrations of adiponectin, non-HMW adiponectin and sOB-R compared to controls. Colon cancer cases also had higher concentrations of CRP and lower concentrations of HDL-C. With the exception of IGF-1, all biomarkers were correlated with both waist circumference and

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Table 1 Baseline characteristicsa of colon and rectal cancer cases and controls, the European Prospective Investigation into Cancer and Nutrition Cohort (1992–2003) Characteristics

Colon cancer Cases (N = 794)

Rectal cancer Controls (N = 794)

Cases (N = 466)

Controls (N = 466)

Cases (N = 794)

Controls (N = 794)

Women (%)

53

53

45.7

45.7

Age (years, mean ± SD)

58.6 ± 7.3

58.6 ± 7.3

0.64

58.0 ± 6.8

58.0 ± 6.8

0.42

University degree (%)

16.8

17.4

0.43

19.5

18.9

0.34

Physically inactive (%)

15.2

11.6

0.06

14.6

13.3

0.65

Smokers (%)

23.7

21.5

0.54

28.1

28.8

0.78

Alcohol intake in men (g/day)

14.6 (5.3–36.2)

13.5 (5.6–33.2)

0.32

19.0 (7.8–47.0)

14.3 (5.1–36.1)

0.005

Alcohol intake in women (g/day)

2.7 (0.3–12.0)

3.6 (0.4–11.7)

0.06

4.0 (0.7–12.6)

3.8 (0.7–12.3)

0.45

Dietary factors Fibre [g/day, median (IQR)]

22.0 (17.0–27.5)

22.2 (18.0–27.3)

0.15

21.6(17.4–27.2)

22.4 (17.7–27.1)

0.24

Fruits and vegetables [g/day, median (IQR)]

363.0 (234.9–529.1)

387.7 (247.4–553.3)

0.13

352.4 (284.5–574.7)

361.2 (239.6–521.9)

0.16

Red and processed meat [g/day, median (IQR)]

79.0 (50.8–115.2)

79.7 (49.5–112.3)

0.65

87.4 (59.4–128.3)

83.2 (52.4–121.7)

0.04

Body mass index (kg/m2, mean ± SD)

26.8 ± 4.4

26.3 ± 3.9

0.05

26.6 ± 4.1

26.4 ± 3.9

0.65

Waist circumference (cm, mean ± SD)

90.7 ± 13.1

88.5 ± 12.2

\0.0001

90.4 ± 12.9

89.7 ± 12.9

0.34

ROM [U/ml, median (IQR)]

400.0 (350.0–450.0)

382.0 (334.0–428.0)

0.01

389.0 (345.0–433.0)

378.0 (328.0–425.0)

0.03

TG [mmol/l, median (IQR)]

1.4 (1.0–2.2)

1.4 (1.0–2.0)

0.88

1.5 (1.0–2.2)

1.5 (1.0–2.2)

0.38

Anthropometric factors

Biomarkers

HDL-C [mmol/l, median (IQR)]

1.3 (1.0–1.7)

1.4 (1.2–1.7)

\0.001

1.4 (1.2–1.7)

1.4 (1.1–1.7)

0.55

C-peptide in non-fasting participants [ng/mL, median (IQR)]

5.3 (3.5–7.7)

5.2 (3.4–7.2)

0.23

4.0 (2.8–6.2)

3.9 (2.6–5.9)

0.34

C-peptide in fasting participants [ng/mL, median (IQR)]

3.4 (2.6–4.4)

3.1 (2.4–4.4)

0.08

3.1 (2.4–4.4)

3.2 (2.2–4.2)

0.54

Hba1c [%, median (IQR)]

5.8 (5.5–6.1)

5.7 (5.4–6.1)

0.06

5.7 (5.5–6.1)

5.7 (5.4–6.1)

0.65

IGF-1 [ng/mL, median (IQR)]

213.4 (167.4–256.3)

206.3 (170.7–261.2)

0.16

212.5 (168.2–263.1)

209.6 (170.3–257.2)

0.09

CRP [mg/L, median (IQR)]

3.0 (1.2–5.6)

2.3 (1.0–4.8)

0.01

2.4 (0.9–2.3)

2.3 (0.9–4.2)

0.56

Total adiponectin [lg/mL, median (IQR)]

6.7 (4.8–9.2)

6.8 (5.0–9.3)

0.05

6.5 (4.6–8.9)

6.8 (4.9–9.2)

0.02

HMW adiponectin [lg/mL, median (IQR)]

3.5 (2.2–5.2)

3.4 (2.2–5.3)

0.72

3.3 (2.1–4.9)

3.4 (2.1–5.1)

0.45

Non-HMW adiponectin [lg/mL, median (IQR)]

3.2 (2.5–4.0)

3.4 (2.6–4.2)

\0.0001

3.0 (2.3–3.9)

3.4 (2.6–4.1)

\0.0001

Leptin [ng/mL, median (IQR)]

9.5 (4.8–18.3)

8.9 (4.1–18.1)

sOB-R [ng/mL, median (IQR)]

20.2 (16.0–24.1)

21.4 (17.4–26.5)

0.07 \0.0001

8.0 (3.6–15.5)

7.6 (3.6–15.0)

20.3 (16.5–25.9)

21.2 (17.0–25.3)

0.29 \0.0001

Number non-fasting participants: 1100 (550 case-sets) P-values for the difference between cases and controls were determined by Student’s paired t test for variables expressed as means; by Wilcoxon’s signed rank test for variables expressed as medians, by Mc Nemar’s test and Bowker’s test of symmetry for variables expressed as percentages N number, SD standard deviation, IQR interquartile range, Hba1c glycated hemoglobin, IGF-1 insulin growth factor 1, HDL-C high-density-lipoprotein cholesterol, TG triglycerides, CRP C-reactive protein, ROM reactive oxygen metabolites, HMW adiponectin high-molecular-weight adiponectin, sOBR soluble leptin receptor a

Sex and age at recruitment were among the matching criteria

BMI having correlation coefficients in the range of -0.36 for sOB-R to 0.31 for CRP. The correlations among the different biomarkers ranged from weak to moderate strength (Table 2).

In PCA, we identified 4 main biomarker patterns with eigenvalues greater than 1, that collectively explained nearly two-thirds (60.0 %) of the total variance in the biomarker data.

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Table 2 Correlationsa among metabolic biomarkers in colorectal cancer cases and matched controls, the European Prospective Investigation into Cancer and Nutrition Cohort (1992–2003)

ROM TG HDL-C C-peptide

ROM

TG

HDL-C

C-peptide

Hba1c

IGF-1

1.00

0.05

0.07

1.00

-0.37 1.00

-0.22 1.00

0

0.18

-0.16

0.35

0.14

0.33

0.19

-0.05

0.05

-0.24

-0.14 0.16

-0.09 -0.04

-0.09 0.18

0.49 -0.19

0.39 -0.15

1.00

-0.10

0.20

-0.11

-0.10

0.09

0.03

1.00

-0.15

-0.14

-0.15

-0.07

-0.11

1.00

-0.08

-0.08

0.14

-0.07

1.00

0.62

0.06

0.17

1.00

0.05

0.14

1.00

-0.29

Hba1c IGF-1 CRP HMW adiponectin Non-HMW adiponectin

CRP

HMW adiponectin

Non-HMW adiponectin

Leptin

sOB-R

0.17

0.25

-0.03

-0.19

0.05

-0.09

0.03 0.11

0.15 -0.18

Leptin sOB-R

1.00

Hba1c glycated hemoglobin, IGF-1 insulin growth factor 1, TG triglycerides, HDL-C high-density-lipoprotein cholesterol, CRP C-reactive protein, ROM reactive oxygen metabolites, HMW adiponectin high-molecular-weight adiponectin, sOB-R soluble leptin receptor a

Correlation matrix of the biomarker measurements in PCA. Pearson correlation coefficients rounded to the nearest integer

The first pattern was characterized by positive loadings for adiponectin fractions—HMW adiponectin and non-HMW adiponectin—and HDL-C, and explained 22.8 % of the total variance in biomarker data (Table 3). The second pattern was characterized by positive loadings for ROM, CRP, and Hba1c and negative loading for IGF1, altogether explaining 16.4 % of the total variance. The third pattern was characterized by positive loadings of TG and C-peptide, and a negative loading for HDL-cholesterol, explaining 11.2 % of the total variance. The fourth pattern was characterized by a positive loading for sOB-R and a negative loading for leptin, explaining 9.5 % of the total variance. Biomarker patterns were named according to the biomarkers which had the highest loading on each of the 4 biomarker patterns: [1] ‘HDL-C/Adiponectin fractions’; [2] ‘ROM/CRP’; [3] ‘TG/C-peptide’; [4] ‘leptin/sOB-R’ (Fig. 2). In multivariable-adjusted logistic regression, ‘HDL-C/ Adiponectin fractions’ and ‘leptin/sOB-R’ biomarker patterns (components 1 and 4) were inversely associated with CRC risk, for the highest quartile versus the lowest, IRR = 0.69, 95 % CI 0.51–0.93, P-trend = 0.01, and IRR = 0.79, 95 % CI 0.58–0.1.07; P-trend = 0.05, respectively; whereas the ‘ROM/CRP’ pattern was positively associated with CRC risk, IRR = 1.70, 95 % CI 1.30–2.23, P-trend = 0.002 (Table 4). The TG/C-peptide pattern was not associated with CRC risk, IRR = 0.85, 95 % CI 0.65–1.12, P-trend = 0.28. When the analyses were repeated by colon and rectal cancer, the ‘HDLC/Adiponectin fractions’, ‘ROM/CRP’ and ‘leptin/sOB-R’ patterns were associated with colon cancer, IRR = 0.68, 95 % CI 0.47–0.99, P-trend = 0.04, IRR = 1.60, 95 % CI 1.11–2.30, P-trend = 0.01, and IRR = 0.73, 95 % CI 0.50–1.00; P-trend = 0.05, respectively; whereas no associations were observed for rectal cancer (Table 5).

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In analyses by sex, the ‘HDL-C/Adiponectin fractions’ pattern explained the greatest proportion of variation in biomarkers for both sexes—22.3 % in men and 24.4 % in women. The proportion explained by the rest of the patterns differed by sex, such that in men ‘ROM/CRP’, ‘leptin/sOB-R’ and ‘TG/C-peptide’ explained 15.4, 11.6 and 9.6 % of the proportion, while in women, ‘TG/C-peptide’, ‘ROM/CRP’ and ‘leptin/sOB-R’ patterns explained 14.4, 10.5 and 9.5 % (Supplementary Table 1). The ‘HDL-C/ adiponectin’ pattern tended to be more strongly associated with rectal cancer risk in men compared with women; whereas the ‘ROM/CRP’ pattern was related to colon cancer risk in both men and women (Supplementary Table 2 and 3). Overall, results for the associations of biomarker patterns and CRC risk were not statistically different by sex (Pfor difference [0.05). In sensitivity analyses, after we excluded cases with cancer diagnosed in the first 2 years of study follow-up, the associations remained unaltered, with the exception of those for the ‘ROM/CRP’ pattern which were no longer statistically significant (for the highest quartile vs the lowest, IRR = 1.15, 95 % CI 0.77–1.74, P-trend = 0.73). In contrast, when we restricted the analysis only to cases diagnosed within the first 2 years of study follow-up, the ‘ROM/CRP’ pattern was strongly significantly related to CRC risk (IRR = 3.88, 95 % CI 1.63–9.22, P-trend = 0.008). Similar results were observed when the sensitivity analyses were run separately in men and in women. We did not observe substantial differences in the main associations according to tumor stage and grade (Supplementary Table 4). No differences in the associations were seen also when participants with prevalent diabetes or non-fasting participants were excluded from the

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Table 3 Principal component analysisa of metabolic biomarkers among colorectal cancer cases and matched controls, the European Prospective Investigation into Cancer and Nutrition Cohort (1992–2003) Biomarker

Component 1 (HDL-C/ Adiponectin fractions)

Component 2 (ROM/CRP)

Component 3 (TG/C-peptide)

Component 4 (leptin/sOB-R)

Hi2b

HDL-C

64*

-1

-40

0

0.56

HMW adiponectin

84*

0

-16

1

0.73

Non-HMW adiponectin ROM

83* 15

-7 74*

-4 -9

1 -19

0.69 0.62

CRP

-15

75*

0

-10

0.59

TG

-18

2

78*

-1

0.63 0.56

C-peptide

-10

4

71*

-22

Leptin

17

28

10

-72*

0.63

sOB-R

20

8

-9

80*

0.70

Hba1c

-12

51

33

16

0.41

IGF-1

-39

-38

-31

-23

0.44

Total variance explained (%)

22.8

16.4

11.2

9.5

Cumulative total variance (%)

22.8

39.0

50.5

60.0

HDL-C high-density-lipoprotein cholesterol, HMW adiponectin high-molecular-weight adiponectin, ROM reactive oxygen metabolites, CRP high-sensitivity C-reactive protein, sOB-R soluble leptin receptor, TG triglycerides, Hba1c glycated hemoglobin, IGF-1 insulin growth factor-1 a

The principal axis method was used to extract the components, and this was followed by a varimax (orthogonal) rotation. Printed values are multiplied by 100 and rounded to the nearest integer. Values with principal component loadings greater than ±55 are flagged by an ‘*’

b

Communality estimates (proportion of variance of the specific biomarker explained by the four components) appear in column headed Hi2

Fig. 2 Biomarker patterns derived by PCA in the European Prospective Investigation into Cancer and Nutrition Cohort (1992–2003). HDL-C highdensity-lipoprotein cholesterol, HMW-adiponectin highmolecular-weight adiponectin, CRP high-sensitivity C-reactive protein, TG triglycerides, ROM reactive oxygen metabolites, sOB-R soluble leptin receptor, Hba1c glycated hemoglobin, IGF-1 insulin growth factor 1

Component 1 (HDL-C/Adiponectin fractions) Component 3 (TG/C-peptide)

Component 2 (ROM/CRP)

Component 4 (leptin/sOB-R)

HDL-C 100 IGF-1

80

HMW-adiponectin

60 40 20 0

Hba1c

Non-HMW adiponectin

-20 -40 -60 -80 sOB-R

ROM

Leptin

CRP

C-peptide

TG

123

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Table 4 Incidence rate ratios and 95 % confidence intervals of biomarker patternsa with colorectal cancer (n = 2,520), the European Prospective Investigation into Cancer and Nutrition (1992–2003) Biomarker pattern

Q1

Q2

Q3

Q4

P for trendd

Component 1 (HDL-C/Adiponectin fractions) Cases/Controls

370/315

316/315

295/316

279/315

Crude modelb

[1]

0.81 (0.63–1.03)

0.73 (0.56–0.93)

0.66 (0.49–0.88)

0.004

Multivariable modelc

[1]

0.84 (0.65–1.08)

0.76 (0.58–0.98)

0.69 (0.51–0.93)

0.01

258/315

309/315

300/315

392/315

[1] [1]

1.18 (0.82–1.71) 1.17 (0.88–1.54)

1.31 (0.91–1.87) 1.30 (1.00–1.69)

1.82 (1.29–2.28) 1.70 (1.30–2.23)

Cases/Controls

345/315

284/315

317/315

314/315

Crude modelb

[1]

0.82 (0.63–1.07)

0.81 (0.70–1.18)

0.90 (0.68–1.18)

0.66

[1]

0.81 (0.62–1.06)

0.83 (0.64–1.09)

0.75 (0.56–1.00)

0.24

Component 2 (ROM/CRP) Cases/Controls b

Crude model Multivariable modelc

0.0002 0.002

Component 3 (TG/C-peptide)

Multivariable model

c

Component 4 (leptin/sOB-R) Cases/Controls

351/315

366/315

278/315

265/315

Crude modelb

[1]

0.99 (0.78–1.26)

0.75 (0.56–0.99)

0.70 (0.53–0.82)

0.0003

Multivariable modelc

[1]

1.05 (0.81–1.33)

0.83 (0.60–1.14)

0.79 (0.58–1.07)

0.05

All P-values are two-sided HDL-C high-density-lipoprotein cholesterol, CRP high-sensitivity C-reactive protein, TG triglycerides, ROM reactive oxygen metabolites, sOBR soluble leptin receptor a

Biomarkers found to load on the first component were HMW and non-HMW adiponectin and HDL-cholesterol; biomarkers mostly loading on the second component were ROM and CRP, biomarkers mostly loading the third component were TG and C-peptide; and biomarkers loading on the fourth component were leptin and sOB-R

b

Crude model is taking into account matching factors: age, sex, study centre, follow-up time since blood collection, time of the day at blood collection and fasting status; Women were further matched by menopausal status, phase of menstrual cycle at blood collection and postmenopausal women were matched by HRT use

c

Multivariable model was based on the crude model with additional adjustment for education (no school degree or primary school, technical or professional school, secondary school, university degree, or unknown), smoking status (never, past, current, or unknown), physical activity (inactive, moderately inactive, moderately active, active, or missing), alcohol intake (continuous), fruits and vegetables (g/day), fish and shellfish intake (g/day), red and processed meat (g/day), fibre intake (g/day) and waist circumference (cm)

d

P-value for trend (two sided) across quartiles is based on the median principal component score within quartiles as a continuous variable. Wald v2 test was employed to evaluate the significance of the linear trend

analysis. When we performed the analysis only among participants who had biomarker information available for all case–control sets (562 case-sets), the main results did not essentially change (data not shown).

Discussion In this large prospective study, four distinct biomarker patterns described as ‘HDL-C/Adiponectin fractions’, ‘ROM/ CRP’, ‘TG/C-peptide’ and ‘leptin/sOB-R’ explained 60 % of the overall biomarker variance in the data. The ‘HDL-C/ Adiponectin fractions’, and ‘leptin/sOB-R’ biomarker patterns were inversely associated with CRC risk; whereas the ‘ROM/CRP’ biomarker pattern was positively associated with CRC risk. However, after cases diagnosed within the first 2 years of study follow-up were excluded from the analysis ‘ROM/CRP’ pattern was no longer significantly

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associated with risk of CRC, suggesting potential influence of preclinical disease on these associations. Although associations on the individual biomarkers used in this analysis and CRC risk have been previously reported [6, 9, 11, 13, 14, 21, 24, 27] (Supplementary Table 5), no attempt has been made to address the complex interrelations among these biomarkers. To our knowledge, this is the first study to explore biomarker patterns of inflammatory and metabolic pathways in relation to CRC risk. Application of PCA allows reducing a number of interrelated biomarkers to newly defined biomarker patterns [32]. Because this statistical technique creates constructs that are uncorrelated to each other, the four biomarker patterns identified in our data may be interpreted as representing potentially distinct pathophysiological pathways. The biomarker pattern that explained the highest proportion of biomarker variation in our data was composed

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271

Table 5 Incidence rate ratios and 95 % confidence intervals of biomarker patternsa with colon and rectal cancers (n = 2,520), the European Prospective Investigation into Cancer and Nutrition (1992–2003) Biomarker pattern

Q1

Q2

Q3

Q4

P for trendd

Colon cancer (n = 1,588) Component 1 (HDL-C/Adiponectin fractions) Cases/controls

230/192

198/200

193/200

173/202

Crude modelb

[1]

0.79 (0.58–1.07)

0.73 (0.53–1.00)

0.62 (0.44–0.89)

0.01

Multivariable modelc

[1]

0.84 (0.61–1.15)

0.77 (0.56–1.07)

0.68 (0.47–0.99)

0.04

Cases/controls Crude modelb

151/190 [1]

174/192 1.18 (0.81–1.71)

196/202 1.31 (0.91–1.87)

273/210 1.82 (1.23–2.58)

0.0007

Multivariable modelc

[1]

1.14 (0.77–1.67)

1.22 (0.84–1.76)

1.60 (1.11–2.30)

0.01

221/206

177/204

204/196

192/188

[1]

0.81 (0.58–1.13)

0.96 (0.71–1.32)

0.94 (0.67–1.32)

0.98

[1]

0.79 (0.56–1.12)

0.85 (0.60–1.12)

0.75 (0.52–1.07)

0.16

Component 2 (ROM/CRP)

Component 3 (TG/C-peptide) Cases/controls Crude model

b

Multivariable modelc Component 4 (leptin/sOB-R) Cases/controls

228/213

246/184

173/198

147/199

Crude modelb

[1]

1.18 (0.87–1.58)

0.75 (0.53–1.07)

0.63 (0.45–0.88)

0.004

Multivariable modelc

[1]

1.31 (0.95–1.78)

0.88 (0.60–1.29)

0.73 (0.50–1.00)

0.05

Rectal cancer (n = 932) Component 1 (HDL-C/Adiponectin fractions) Cases/controls

141/123

118/115

102/116

105/112

Crude modelb

[1]

0.85 (0.55–1.30)

0.72 (0.47–1.08)

0.72 (0.47–1.18)

0.15

Multivariable modelc Component 2 (ROM/CRP)

[1]

0.84 (0.54–1.32)

0.70 (0.45–1.10)

0.70 (0.41–1.19)

0.15

Cases/controls

107/125

116/123

119/112

124/106

Crude modelb

[1]

1.14 (0.72–1.81)

1.29 (0.83–2.02)

1.47 (0.95–2.27)

0.06

Multivariable modelc

[1]

1.06 (0.65–1.73)

1.21 (0.76–1.93)

1.43 (0.89–2.30)

0.10

Cases/controls

124/109

107/111

113/119

122/127

Crude modelb

[1]

0.84 (0.53–1.33)

0.81 (0.53–1.25)

0.82 (0.53–1.28)

0.43

Multivariable modelc

[1]

0.85 (0.53–1.38)

0.82 (0.52–1.22)

0.75 (0.46–1.21)

0.24

Component 3 (TG/C-peptide)

Component 4 (leptin/sOB-R) Cases/controls

123/101

120/131

105/117

118/117

Crude modelb

[1]

0.72 (0.47–1.07)

0.70 (0.45–1.07)

0.79 (0.52–1.20)

0.26

[1]

0.66 (0.43–1.02)

0.68 (0.42–1.11)

0.73 (0.47–1.28)

0.36

Multivariable model

c

All P-values are two-sided HDL-C high-density-lipoprotein cholesterol, CRP C-reactive protein, TG triglycerides, ROM reactive oxygen metabolites, sOB-R soluble leptin receptor a

Biomarkers found to load on the first component were HMW and non-HMW adiponectin and HDL-cholesterol; biomarkers mostly loading on the second component were ROM and CRP, biomarkers mostly loading the third component were TG and C-peptide; and biomarkers loading on the fourth components were leptin and sOB-R b Crude model is taking into account matching factors: age, sex, study centre, follow-up time since blood collection, time of the day at blood collection and fasting status; Women were further matched by menopausal status, phase of menstrual cycle at blood collection and postmenopausal women were matched by HRT use c

Multivariable model was based on the crude model with additional adjustment for education (no school degree or primary school, technical or professional school, secondary school, university degree, or unknown), smoking status (never, past, current, or unknown), physical activity (inactive, moderately inactive, moderately active, active, or missing), alcohol intake (continuous), fruits and vegetables (g/day), fish and shellfish intake (g/day), red and processed meat (g/day), fibre intake (g/day) and waist circumference (cm)

d

P-value for trend (two sided) across quartiles is based on the median principal component score within quartiles as a continuous variable. Wald v2 test was employed to evaluate the significance of the linear trend

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by the adiponectin fractions (HMW adiponectin and nonHMW adiponectin) and HDL-C. This is a biologically plausible pathway, because low levels of adiponectin are associated with low levels of HDL-C independently from obesity and other common metabolic risk factors [46–48]. In addition, adiponectin seems to be specifically related to HDL-C, but not to other lipid traits [49, 50] possibly through environmental rather than genetic pathways [50]. A number of epidemiological studies suggested that adiponectin is inversely associated with CRC possibly through direct or indirect pathophysiological pathways [22, 24, 25, 51]. In vitro, adiponectin was shown to directly control malignant potential—cell proliferation, adhesion, invasion and colony formation—and to regulate metabolic, inflammatory and cell cycle signalling pathways [52]. Low HDL-C was associated with incident cancer in systemic analyses of lipid-altering trials [15], but also in big cohort studies such as the Framingham Heart Study and EPIC [14, 16]. The role of HDL-C in relation to cancer risk, however, remains unclear. On one side, HDL-C may promote carcinogenesis through its effects on smoking, obesity, or hyperinsulinemia, while on the other side low HDL-C levels may themselves manifest from active neoplastic processes (reverse causality). Our current data suggest HDL-C and adiponectin to share a common pathway in CRC development independent of lifestyle factors or subclinical malignancy. Of note, this shared pathway was not evident in our previous analyses on adiponectin and CRC [24], where adjustment for HDL-C did not attenuate the associations between adiponectin with CRC risk; however, adjustment in multivariable analyses may not remove all the confounding effects because biomarkers may interact with each other [31]. This may be an example to illustrate that traditional modeling of individual risk factors may not sufficiently capture the effect of inter-related factors and that PCA may be useful to uncover pathways through which biomarkers may influence disease risk. The second most important biomarker pattern identified in our data included ROM—a biomarker of oxidative stress, and CRP—a biomarker of chronic low-grade inflammation. Excessive and uncontrollable production of reactive oxygen species results in persistent injury of cells in the tissue and consequently chronic inflammation [53]. In turn, inflammatory cells produce soluble mediators, which act by further recruiting inflammatory cells to the site of injury and producing more reactive species. This sustained inflammatory/ oxidative environment leads to an enhanced production of hydroperoxides in a vicious circle, which can damage healthy cells and over a long time may lead to carcinogenesis [54]. Previous evidence has implicated continued oxidative stress and chronic inflammation in CRC development [11–13, 55]. In our data, the ‘ROM/CRP’ pattern was associated with risk of CRC; however this association was no longer apparent

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when the cancer cases occurring in the first 2 years of study follow-up were excluded from the analysis. These results are partly in conformity with our previous observation that prediagnostic serum ROM levels were associated with increased risk of CRC only in subjects with relatively short follow-up time [13], potentially explained by elevated production of reactive oxygen species in preclinical tumors [56]. The next pattern with relevance for CRC risk was composed by leptin and its soluble receptor (sOB-R) which are strongly albeit inversely related to each other. The circulating soluble form of the leptin receptor is the main leptinbinding protein and determinant of free leptin index, the presumed biologically active form of leptin, regulated by gender, adiposity, and hormone levels [57]. sOB-R may also represent activities of the short form of leptin receptor (OBRs) shown to mediate the effects of insulin sensitivity and other peripheral effects that might be relevant for CRC. In particular, insulin resistance and abdominal obesity were associated with low sOB-R concentration and low boundfree ratio of leptin independent of fat mass [58]. Previously in EPIC we reported strong inverse association between circulating sOB-R and CRC risk, independent of obesity measures, leptin concentrations, and other metabolic biomarkers [21]. Although the in vitro carcinogenic effects of leptin signaling through sOB-R in CRC cell lines are well characterized, the in vivo effects are quite complex and can be affected by multiple factors, such as energy balance, inflammation and insulin signaling. Finally, our data also revealed ‘TG/C-peptide’ as a distinct biomarker pattern. Previous studies have shown that TG and C-peptide are highly correlated [59]. People with insulin resistance have a characteristic dyslipidemia that has, as its central feature, hypertriglyceridemia [60]. Indeed, insulinresistant individuals who are not diabetic have lipid profiles that are nearly identical to those seen in the large majority of subjects with type 2 diabetes [61]. The major sources of TG in the liver, such as uptake of fatty acids released by lipolysis of adipose tissue TG, are abnormally increased in insulin resistance. Treatment of the dyslipidemia in insulin resistant individuals and patients with type 2 diabetes has been successful in reducing cardiovascular disease [62]. However, our findings did not suggest an association between ‘TG/C-peptide’ biomarker pattern and CRC risk. These results are in line with previous findings showing no relation between elevated levels of TG and CRC risk [14, 63]. When interpreting these data, it should be also noted that only a subset of subjects was fasting at the time of blood draw and this may have affected the results for TG and C-peptide which levels are dependent of fasting [6, 14]. Nevertheless, we restricted our analysis only to fasting participants and could not detect significant changes in the results. When we repeated the analyses separately for colon and rectal cancer, ‘ROM/CRP’ and ‘leptin/sOB-R’ patterns were associated with colon cancer; whereas no associations were

Biomarker patterns of inflammatory and metabolic pathways

observed for rectal cancer. Different associations for the biomarker patterns were suggested to exist also by sex. However, the observed differences by colon and rectal cancer site and sex were not statistically different and having the smaller number of cases in stratified analyses these results should be cautiously interpreted. Further analyses based on large number of CRC cases are needed in order to allow sufficient precision of estimates for more meaningful interpretations in subgroup analyses. We adopted PCA as a method of identifying patterns among a set of correlated biomarkers known to be individually related to higher CRC risk. Main advantage of this technique is that it allows us to reveal hidden, simplified structures that often underlie data. However, PCA has been also criticized for its subjectivity regarding the choice and interpretation of the components and difficulty in replicating the results in other populations. Although statistically PCA identifies uncorrelated constructs of variables we cannot preclude that the physiological pathways are not interrelated. For example, excess adiposity is known to be causally related to lower levels of adiponectin and lower HDL-C and is also a well-established cause of insulin resistance. Nevertheless, the 4 components identified did show a very clear pattern with respect to known physiological relationships between the individual biomarkers. Thus, the identified biomarker patterns represent synthetic description of interrelated effects along biologically plausible physiological axes. In turn, this allowed us to explore which of these major axes may independently contribute to CRC risk. Strengths of our study include its prospective design, the long follow-up time with a sizable number of incident cancers of the colorectum, the measured rather than selfreported anthropometry (from most of the EPIC centers), exploration of a wide range of biomarkers with different actions, including the novel factors HMW adiponectin, non-HMW adiponectin and sOB-R, and availability of detailed information on a number of dietary and lifestyle factors, that allowed maximum control for potential confounding effects. Some limitations of the current study should also be considered. Although we used a large set of biomarkers, still this list may not sufficiently capture the whole spectrum of potential biological pathways. This is especially true for the inflammation pathway, where only CRP was available, but not other potentially important biomarkers such as circulating cytokines. As a consequence, the PCA was limited by availability of previously measured biomarkers. In our study data was missing on a number of biomarkers. Missing data can lead to underestimation of the covariances in PCA and therefore underestimation of the loadings. Therefore, we imputed the missing values using multiple imputation technique. This method results in valid statistical inferences that properly reflect the uncertainty due to missing

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values. To control our results, we compared participants with and without data on key variables with respect to population characteristics and repeated main analyses in a subset of participants with available data on all biomarker measurements. These analyses revealed results comparable to those derived from the multiple imputation procedure. We used single assessments of biomarker concentrations at baseline that may be susceptible to short-term variation, which could lead to ‘‘bias toward the null’’. However, previously, most of the biomarkers indicated high reliability of single measurements over time [64]. Finally, despite clustering of multiple biomarkers by PCA proved useful in exploring interactions between different biomarkers, it also largely reflected existing associations between individual biomarkers and CRC in EPIC. Therefore originality of findings might be limited to identifying common pathways among the biomarkers in relation to CRC. In conclusion, the biomarker patterns ‘HDL-C/Adiponectin fractions’, ‘leptin/sOB-R’ and ‘ROM/CRP’ are associated with CRC risk. The association for the ‘ROM/CRP’ pattern was present in the first 2 years of study follow-up potentially reflecting development of precancerous or cancerous processes. PCA seems a suitable statistical technique for identification of biologically plausible biomarker patterns in relation to cancer. Nevertheless, further studies are needed, with a larger set of biomarkers and of sufficient size for performing subgroup analyses in order to better characterize potential pathways involved in CRC pathogenesis. Acknowledgments This work has been supported by World Cancer Research Fund International and Wereld Kanker Onderzoek Fonds (WCRF NL). The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue contre le Cancer, Institut Gustave Roussy, Mutuelle Ge´ne´rale de l’Education Nationale, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), Regional Governments of Andalucı´a, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Ska˚ne and Va¨sterbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (United Kingdom). The funding sources had no influence on the design of the study; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the paper for publication. The authors thank all EPIC participants and staff for their outstanding contribution to the study.

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