# Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study

Thu Thi Pham, Katharina Nimptsch, Krasimira Aleksandrova, Mazda Jenab, Veronika Fedirko, Kana Wu, Anne Kirstine Eriksen, Anne Tjønneland, Gianluca Severi, Joseph Rothwell, et al.

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## To cite this version:

Thu Thi Pham, Katharina Nimptsch, Krasimira Aleksandrova, Mazda Jenab, Veronika Fedirko, et al. Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study. International Journal of Cancer, 2024, 154 (9), pp.1596-1606. 10.1002/ijc.34830. hal-04566143

HAL Id: hal-04566143

https://hal.science/hal-04566143

Submitted on 4 Jun 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study

Thu Thi Pham, Katharina Nimptsch, Krasimira Aleksandrova, Mazda Jenab, Veronika Fedirko, Kana Wu, Anne Kirstine Eriksen, Anne Tjønneland, Gianluca Severi, Joseph Rothwell, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Thu Thi Pham, Katharina Nimptsch, Krasimira Aleksandrova, Mazda Jenab, Veronika Fedirko, et al. Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study. International Journal of Cancer, 2024, 154 (9), pp.1596-1606. 10.1002/ijc.34830. hal-04566143

HAL Id: hal-04566143

https://hal.science/hal-04566143v1

Submitted on 4 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
RESEARCH ARTICLE

Cancer Epidemiology

<figure><img src="image_3.png" /></figure>

INTERNATIONAL
JOURNAL of CANCER

<figure><img src="image_4.png" /></figure>

# Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study

Thu Thi Pham¹,² ID

Katharina Nimptsch¹ ID

Krasimira Aleksandrova³,⁴

Mazda Jenab⁵

Veronika Fedirko⁶,⁷ ID

Kana Wu⁸

Anne Kirstine Eriksen⁹

Anne Tjønneland⁹,¹⁰

Gianluca Severi¹¹ ID

Joseph Rothwell¹¹ | Rudolf Kaaks¹²

Verena Katzke¹²

Alberto Catalano¹³

Claudia Agnoli¹⁴

Giovanna Masala¹⁵

Maria Santucci De Magistris¹⁶

Rosario Tumino¹⁷

Roel Vermeulen¹⁸

Amaia Aizpurua¹⁹,²⁰

Camino Trobajo-Sanmartín²¹,²²,²³

María-Dolores Chirlaque²²,²⁴

María-Jose Sánchez²²,²⁵,²⁶,²⁷

Sai San Moon Lu²⁸

Amanda J. Cross²⁹

Sofia Christakoudi²⁹,³⁰ ID

Elisabete Weiderpass³¹ ID

Tobias Pischon¹,²,³²,³³ ID

Correspondence

Tobias Pischon, Molecular Epidemiology
Research Group, Max-Delbrueck-Center for
Molecular Medicine in the Helmholtz
Association (MDC), 13125 Berlin, Germany.

Email: tobias.pischon@mdc-berlin.de

Funding information

International Agency for Research on Cancer (IARC); Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London; Danish Cancer Society (Denmark); Ligue contre le Cancer; Institut Gustave Roussy; Mutuelle Générale de l'Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM, France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and the Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); the Italian Association for Research on Cancer (AIRC); the National Research Council (Italy); the Dutch Ministry of Public Health, Welfare and Sports (VWS); the Netherlands Cancer

Abstract

Resistin is a protein involved in inflammation and angiogenesis processes and may play a role in the progression of colorectal cancer (CRC). However, it remains unclear whether resistin is associated with increased mortality after CRC diagnosis. We examined pre-diagnostic serum resistin concentrations in relation to CRC-specific and all-cause mortality among 1343 incident CRC cases from the European Prospective Investigation into Cancer and Nutrition cohort. For CRC-specific mortality as the primary outcome, hazard ratios (HRs) and 95% confidence intervals (95% CI) were estimated from competing risk analyses based on cause-specific Cox proportional hazards models and further in sensitivity analyses using Fine–Gray proportional subdistribution hazards models. For all-cause mortality as the secondary outcome, Cox proportional hazards models were used. Subgroup analyses were performed by sex, tumor subsite, tumor stage, body mass index and time to CRC diagnosis. Resistin was measured on a median of 4.8 years before CRC diagnosis. During a median follow-up of 8.2 years, 474 deaths from CRC and 147 deaths from other causes were observed. Resistin concentrations were not associated with

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policies or views of the International Agency for Research on Cancer/ World Health Organization.

For affiliations refer to page 1604

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. *International Journal of Cancer* published by John Wiley & Sons Ltd on behalf of UICC.
RESEARCH ARTICLE

Cancer Epidemiology

<figure><img src="image_3.png" /></figure>

INTERNATIONAL
JOURNAL of CANCER

<figure><img src="image_4.png" /></figure>

# Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study

Thu Thi Pham<sup>1,2 ID</sup>

Katharina Nimptsch<sup>1 ID</sup>

Krasimira Aleksandrova<sup>3,4</sup>

Mazda Jenab<sup>5</sup>

Veronika Fedirko<sup>6,7 ID</sup>

Kana Wu<sup>8</sup>

Anne Kirstine Eriksen<sup>9</sup>

Anne Tjønneland<sup>9,10</sup>

Gianluca Severi<sup>11 ID</sup>

Joseph Rothwell<sup>11</sup> | Rudolf Kaaks<sup>12</sup>

Verena Katzke<sup>12</sup>

Alberto Catalano<sup>13</sup>

Claudia Agnoli<sup>14</sup>

Giovanna Masala<sup>15</sup>

Maria Santucci De Magistris<sup>16</sup>

Rosario Tumino<sup>17</sup>

Roel Vermeulen<sup>18</sup>

Amaia Aizpurua<sup>19,20</sup>

Camino Trobajo-Sanmartín<sup>21,22,23</sup>

María-Dolores Chirlaque<sup>22,24</sup>

María-Jose Sánchez<sup>22,25,26,27</sup>

Sai San Moon Lu<sup>28</sup>

Amanda J. Cross<sup>29</sup>

Sofia Christakoudi<sup>29,30 ID</sup>

Elisabete Weiderpass<sup>31 ID</sup>

Tobias Pischon<sup>1,2,32,33 ID</sup>

Correspondence

Tobias Pischon, Molecular Epidemiology
Research Group, Max-Delbrueck-Center for
Molecular Medicine in the Helmholtz
Association (MDC), 13125 Berlin, Germany.

Email: tobias.pischon@mdc-berlin.de

Funding information

International Agency for Research on Cancer (IARC); Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London; Danish Cancer Society (Denmark); Ligue contre le Cancer; Institut Gustave Roussy; Mutuelle Générale de l'Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM, France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and the Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); the Italian Association for Research on Cancer (AIRC); the National Research Council (Italy); the Dutch Ministry of Public Health, Welfare and Sports (VWS); the Netherlands Cancer

Abstract

Resistin is a protein involved in inflammation and angiogenesis processes and may play a role in the progression of colorectal cancer (CRC). However, it remains unclear whether resistin is associated with increased mortality after CRC diagnosis. We examined pre-diagnostic serum resistin concentrations in relation to CRC-specific and all-cause mortality among 1343 incident CRC cases from the European Prospective Investigation into Cancer and Nutrition cohort. For CRC-specific mortality as the primary outcome, hazard ratios (HRs) and 95% confidence intervals (95% CI) were estimated from competing risk analyses based on cause-specific Cox proportional hazards models and further in sensitivity analyses using Fine–Gray proportional subdistribution hazards models. For all-cause mortality as the secondary outcome, Cox proportional hazards models were used. Subgroup analyses were performed by sex, tumor subsite, tumor stage, body mass index and time to CRC diagnosis. Resistin was measured on a median of 4.8 years before CRC diagnosis. During a median follow-up of 8.2 years, 474 deaths from CRC and 147 deaths from other causes were observed. Resistin concentrations were not associated with

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policies or views of the International Agency for Research on Cancer/ World Health Organization.

For affiliations refer to page 1604

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2024 The Authors. *International Journal of Cancer* published by John Wiley & Sons Ltd on behalf of UICC.
Registry (NKR); LK Research Funds; Dutch
Prevention Funds; Dutch ZON (Zorg
Onderzoek Nederland); the World Cancer
Research Fund (WCRF); Statistics Netherlands
(The Netherlands); ERC-2009-AdG 232997;
Nordforsk; Nordic Centre of Excellence
programme on Food, Nutrition and Health
(Norway); Health Research Fund (FIS) -
Instituto de Salud Carlos III (ISCIII); Regional
Governments of Andalucía, Asturias, Basque
Country, Murcia and Navarra, Grant/Award
Number: ISCIII RETIC (RD06/0020); the
Swedish Cancer Society; the Swedish Scientific
Council; the Regional Government of Skåne
and Västerbotten (Sweden); Cancer Research
UK; Medical Research Council; CPRIT Rising
Stars Award, Grant/Award Number:
RR200056

CRC-specific mortality (HRQ4vsQ1 = 0.95, 95% CI: 0.73-1.23; P<sub>trend</sub> = .97; and HR<sub>per doubling of resistin concentration</sub> = 1.00; 95% CI: 0.84-1.19; P = .98) or all-cause mor-
tality. Results from competing risk (sensitivity) analysis were similar. No associations were
found in any subgroup analyses. These findings suggest no association between pre-
diagnostic circulating resistin concentrations and CRC-specific or all-cause mortality
among persons with CRC, and the potential insignificance of resistin in CRC progression.

KEYWORDS

colorectal cancer, EPIC, mortality, pre-diagnostic, resistin, survival

What's new?

Previous studies suggested that obesity and inflammation are related to cancer progression, and resistin was proposed as an obesity biomarker involved in inflammatory processes in humans. However, it remained unclear whether resistin is associated with increased mortality after colorectal cancer diagnosis. In this study the authors examined the novel association between pre-diagnostic level of resistin and mortality in 1343 incident colorectal cancer patients. Resistin concentrations measured 4.8 years before diagnosis were not associated with mortality in individuals with CRC. These findings do not support the hypothesis that resistin may contribute to cancer progression.

1 | INTRODUCTION

Resistin is a protein produced by adipocytes discovered in rodent
models and is consequently considered as an adipokine.¹ However
subsequent human studies have shown that it is primarily produced
and expressed by mononuclear cells (monocytes, macrophages and
lymphocytes).² In rodents, resistin is related to obesity and insulin
resistance,¹ whereas, in humans, resistin is purportedly involved in
inflammation by creating a vicious circle,³⁻⁶ and implicated in
angiogenesis,⁷ which both may be relevant for cancer promotion and
progression.⁸⁻¹⁰ For example, resistin induces pro-inflammatory cyto-
kines in human peripheral blood mononuclear cells⁵,⁶; while, vice
versa, stimulation of human macrophages with tumor necrosis factor-
alpha significantly induced resistin protein expression¹¹; furthermore,
resistin could induce its own expression and secretion in macrophages
suggesting the possibility of a positive feedback loop on resistin.⁶

Colorectal cancer (CRC) was the second most common cause of
cancer death in 2020, with an estimated 1.8 million deaths worldwide.¹²
The course of CRC is determined by complex systemic processes and
accompanied by systemic inflammation, which is one of the common
factors contributing to CRC deaths.¹⁰ Obesity is an established risk fac-
tor for CRC, and studies suggest that pre-diagnostic obesity is also
related to decreased survival in persons with CRC.¹³⁻¹⁵ Obesity is also
characterized by chronic low-grade systemic inflammation (CLGSI),¹⁵
and by the infiltration of inflammatory cells into adipose tissues.⁸ While
some studies found higher resistin concentrations in patients with CRC
as compared to controls,¹⁶ findings from prospective studies, including
our recent study nested in the European Prospective Investigation into
Cancer and Nutrition (EPIC) study, suggest that pre-diagnostic resistin
concentrations are not associated with risk of CRC.¹⁷,¹⁸ In a recent
Mendelian randomization study with substantial statistical power to

detect even weak associations, we found no association between
genetically predicted levels of resistin and CRC risk.¹⁹ However, this
does not preclude the possibility that resistin may have an effect on
CRC cancer progression and mortality, because inflammation³⁻⁶ and
angiogenesis,⁷ in which resistin is involved, may contribute to advanced
stages of the disease.²⁰,²¹ Indeed, a previous study reported that CRC
patients whose tumor tissue specimens were strongly positive for resis-
tin expression had slightly lower relapse-free survival and overall sur-
vival times compared to those without positive resistin expression, but
these differences were not statistically significant.²²

To our knowledge, the association between pre-diagnostic circulat-
ing resistin concentrations and mortality among CRC patients has not
been investigated so far. However, as a consequence of the vicious cir-
cle of inflammation, resistin concentrations measured after CRC diagno-
sis may be difficult to interpret as they may be affected by the presence
of tumors or by cancer treatment. In contrast, given the long-term
reliability of circulating resistin concentrations in humans,²³,²⁴ pre-
diagnostic measurements may better reflect sustained resistin concen-
trations in a steady-state condition of inflammation. Therefore, we
investigated whether higher pre-diagnostic resistin concentrations are
associated with higher CRC-specific or all-cause mortality among
individuals with CRC in the EPIC study.

2 | METHODS

2.1 | Study population and baseline data collection

The study population was derived from EPIC participants with first-
incident CRC. EPIC is a large multicenter cohort study that included
519,978 participants enrolled between 1992 and 2000 in 23 centers
Registry (NKR); LK Research Funds; Dutch
Prevention Funds; Dutch ZON (Zorg
Onderzoek Nederland); the World Cancer
Research Fund (WCRF); Statistics Netherlands
(The Netherlands); ERC-2009-AdG 232997;
Nordforsk; Nordic Centre of Excellence
programme on Food, Nutrition and Health
(Norway); Health Research Fund (FIS) -
Instituto de Salud Carlos III (ISCIII); Regional
Governments of Andalucía, Asturias, Basque
Country, Murcia and Navarra, Grant/Award
Number: ISCIII RETIC (RD06/0020); the
Swedish Cancer Society; the Swedish Scientific
Council; the Regional Government of Skåne
and Västerbotten (Sweden); Cancer Research
UK; Medical Research Council; CPRIT Rising
Stars Award, Grant/Award Number:
RR200056

CRC-specific mortality (HR<sub>Q4vsQ1</sub> = 0.95, 95% CI: 0.73-1.23; P<sub>trend</sub> = .97; and HR<sub>per doubling of resistin concentration</sub> = 1.00; 95% CI: 0.84-1.19; P = .98) or all-cause mortality. Results from competing risk (sensitivity) analysis were similar. No associations were found in any subgroup analyses. These findings suggest no association between pre-diagnostic circulating resistin concentrations and CRC-specific or all-cause mortality among persons with CRC, and the potential insignificance of resistin in CRC progression.

KEYWORDS

colorectal cancer, EPIC, mortality, pre-diagnostic, resistin, survival

What's new?

Previous studies suggested that obesity and inflammation are related to cancer progression, and resistin was proposed as an obesity biomarker involved in inflammatory processes in humans. However, it remained unclear whether resistin is associated with increased mortality after colorectal cancer diagnosis. In this study the authors examined the novel association between pre-diagnostic level of resistin and mortality in 1343 incident colorectal cancer patients. Resistin concentrations measured 4.8 years before diagnosis were not associated with mortality in individuals with CRC. These findings do not support the hypothesis that resistin may contribute to cancer progression.

**1** | INTRODUCTION

Resistin is a protein produced by adipocytes discovered in rodent models and is consequently considered as an adipokine.¹ However subsequent human studies have shown that it is primarily produced and expressed by mononuclear cells (monocytes, macrophages and lymphocytes).² In rodents, resistin is related to obesity and insulin resistance,¹ whereas, in humans, resistin is purportedly involved in inflammation by creating a vicious circle,³⁻⁶ and implicated in angiogenesis,⁷ which both may be relevant for cancer promotion and progression.⁸⁻¹⁰ For example, resistin induces pro-inflammatory cytokines in human peripheral blood mononuclear cells⁵,⁶; while, vice versa, stimulation of human macrophages with tumor necrosis factor-alpha significantly induced resistin protein expression¹¹; furthermore, resistin could induce its own expression and secretion in macrophages suggesting the possibility of a positive feedback loop on resistin.⁶

Colorectal cancer (CRC) was the second most common cause of
cancer death in 2020, with an estimated 1.8 million deaths worldwide.¹²
The course of CRC is determined by complex systemic processes and
accompanied by systemic inflammation, which is one of the common
factors contributing to CRC deaths.¹⁰ Obesity is an established risk fac-
tor for CRC, and studies suggest that pre-diagnostic obesity is also
related to decreased survival in persons with CRC.¹³⁻¹⁵ Obesity is also
characterized by chronic low-grade systemic inflammation (CLGSI),¹⁵
and by the infiltration of inflammatory cells into adipose tissues.⁸ While
some studies found higher resistin concentrations in patients with CRC
as compared to controls,¹⁶ findings from prospective studies, including
our recent study nested in the European Prospective Investigation into
Cancer and Nutrition (EPIC) study, suggest that pre-diagnostic resistin
concentrations are not associated with risk of CRC.¹⁷,¹⁸ In a recent
Mendelian randomization study with substantial statistical power to

detect even weak associations, we found no association between
genetically predicted levels of resistin and CRC risk.¹⁹ However, this
does not preclude the possibility that resistin may have an effect on
CRC cancer progression and mortality, because inflammation³⁻⁶ and
angiogenesis,⁷ in which resistin is involved, may contribute to advanced
stages of the disease.²⁰,²¹ Indeed, a previous study reported that CRC
patients whose tumor tissue specimens were strongly positive for resis-
tin expression had slightly lower relapse-free survival and overall sur-
vival times compared to those without positive resistin expression, but
these differences were not statistically significant.²²

To our knowledge, the association between pre-diagnostic circulat-
ing resistin concentrations and mortality among CRC patients has not
been investigated so far. However, as a consequence of the vicious cir-
cle of inflammation, resistin concentrations measured after CRC diagno-
sis may be difficult to interpret as they may be affected by the presence
of tumors or by cancer treatment. In contrast, given the long-term
reliability of circulating resistin concentrations in humans,²³,²⁴ pre-
diagnostic measurements may better reflect sustained resistin concen-
trations in a steady-state condition of inflammation. Therefore, we
investigated whether higher pre-diagnostic resistin concentrations are
associated with higher CRC-specific or all-cause mortality among
individuals with CRC in the EPIC study.

**2** | METHODS

**2.1** | Study population and baseline data collection

The study population was derived from EPIC participants with first-
incident CRC. EPIC is a large multicenter cohort study that included
519,978 participants enrolled between 1992 and 2000 in 23 centers
in 10 European countries (France, Greece, Germany, Italy, Spain, Denmark, Sweden, Norway, the Netherlands and the United Kingdom). Details of the EPIC study can be found elsewhere.²⁵ Briefly, at baseline, standardized lifestyle, dietary questionnaires and anthropometric data were collected from all participants. Blood samples were collected from 80% of the participants following a strict protocol, aliquoted into plastic straws and stored both locally at each study center and centrally at the International Agency for Research on Cancer (IARC) in the vapor phase of liquid nitrogen (at -196°C). Exceptions from this procedure were Sweden and Denmark, where blood samples were aliquoted into tubes. Since the central EPIC repository at IARC was not suitable for tube storage, these samples were only stored locally in freezers at -70°C (Sweden) and in nitrogen vapor at -150°C (Denmark).

## 2.2 | Cancer incidence follow-up

Follow-up for cancer incidence was based on a passive follow-up procedure utilizing regional or national population-based cancer registries (Italy, Spain, Denmark, Sweden, Norway, the Netherlands and the United Kingdom) or through an active follow-up procedure which combined several methods, including health insurance records, cancer and pathology registries and self-reporting from study subjects or their next-of-kin which were later verified through pathology reports and physicians (Germany, Greece and France). The active follow-up procedure has been described elsewhere.²⁶

## 2.3 | Case ascertainment and selection for the current analysis

CRC cases were defined based on the International Classification of Diseases, 10th Revision (ICD-10).²⁷ Participants who were free of cancer at recruitment but later developed colon (C18.0-C18.7) or rectal cancer (C19-C20) with the availability of blood samples were included in the analysis. The current study included 1343 incident CRC cases to examine the association between pre-diagnostic resistin concentrations and mortality after CRC. The closure dates for complete follow-up for cancer incidence were between 2002 and 2012. Due to administrative issues, data from Greece and Norway were not included in the current study.

## 2.4 | Vital status follow-up

Information on vital status was also collected by a passive follow-up procedure through regional/national mortality registries in all countries except Germany and France, where data are collected by an active follow-up procedure as used for cancer incidence follow-up. The closure dates for vital status follow-up, which were either the date of death or the last date of contact, whichever occurred first, were ranged between 2009 and 2015 depending on the local country

and study center. The current study outcomes were CRC-specific mortality and all-cause mortality and they were determined by the underlying cause of death using the ICD-10.

## 2.5 | Resistin concentration measurements and exposure assessments

Resistin concentrations in serum were measured one time in baseline samples using Human Resistin enzyme-linked immunosorbent assay (ELISA) (BioVendor Laboratory Medicine, Inc; Brno, Czech Republic) according to the manufacturer's protocols. Samples were analyzed in 39-well microtiter plates. Mean interassay coefficients of variation (CV) (representing resistin levels' variance between plates/assays) for all quality controls were estimated by BioVendor and reported as 7.4% for the quality control high concentrations and 6.6% for the quality control low concentrations. The inter-assay CVs for the pooled sera used as the internal quality controls were <10.4%. The means of the intra-assay CVs (representing resistin levels' variance within an assay) were all <2.5%.

A study using three measurements of resistin per individual over 3-4 years revealed a high intraclass correlation coefficient (ICC) of 0.95, suggesting a high stability of resistin levels within a person.²⁴ Thus, the use of one measurement of resistin per individual is supported in epidemiological studies.²³,²⁴

## 2.6 | Baseline variables and prognostic factors

Data on prognostic factors were extracted from the medical records, including age at diagnosis, tumor subsite and tumor stage. Due to the utilization of different stage classifications among EPIC centers (including TNM staging, Dukes classification or the “localized/ metastatic/metastatic regional/ metastatic distant” categories), a harmonization procedure had been previously carried out to obtain a broad category for tumor stage (I, II, III and IV).²⁷ Data were collected from baseline questionnaires for age at recruitment, sex, body mass index (BMI), waist circumference, physical activity index, highest education level and smoking status. Moreover, all diet exposure, including consumed amounts of alcohol, dairy, fruit, red meat, processed meat, vegetables and dietary fiber was assessed by food frequency questionnaires or diet history logs.

Overall, information on tumor stage was missing in 13.3% of the participants. In the centers in France, Ragusa (Italy), Granada (Spain), Heidelberg (Germany) and Umeå (Sweden), data on tumor stage were completed for all participants. In contrast, these data were missing for all participants at the Oxford center (United Kingdom). In the other centers, the proportion of missing information on tumor stage ranged from 1.3% (Potsdam, Germany) to 30.8% (Varese, Italy). Information on waist circumference was missing in 5.6% of participants with all the missing data occurring in Umeå (Sweden).
in 10 European countries (France, Greece, Germany, Italy, Spain, Denmark, Sweden, Norway, the Netherlands and the United Kingdom). Details of the EPIC study can be found elsewhere.²⁵ Briefly, at baseline, standardized lifestyle, dietary questionnaires and anthropometric data were collected from all participants. Blood samples were collected from 80% of the participants following a strict protocol, aliquoted into plastic straws and stored both locally at each study center and centrally at the International Agency for Research on Cancer (IARC) in the vapor phase of liquid nitrogen (at -196°C). Exceptions from this procedure were Sweden and Denmark, where blood samples were aliquoted into tubes. Since the central EPIC repository at IARC was not suitable for tube storage, these samples were only stored locally in freezers at -70°C (Sweden) and in nitrogen vapor at -150°C (Denmark).

## 2.2 | Cancer incidence follow-up

Follow-up for cancer incidence was based on a passive follow-up procedure utilizing regional or national population-based cancer registries (Italy, Spain, Denmark, Sweden, Norway, the Netherlands and the United Kingdom) or through an active follow-up procedure which combined several methods, including health insurance records, cancer and pathology registries and self-reporting from study subjects or their next-of-kin which were later verified through pathology reports and physicians (Germany, Greece and France). The active follow-up procedure has been described elsewhere.²⁶

## 2.3 | Case ascertainment and selection for the current analysis

CRC cases were defined based on the International Classification of Diseases, 10th Revision (ICD-10).²⁷ Participants who were free of cancer at recruitment but later developed colon (C18.0-C18.7) or rectal cancer (C19-C20) with the availability of blood samples were included in the analysis. The current study included 1343 incident CRC cases to examine the association between pre-diagnostic resistin concentrations and mortality after CRC. The closure dates for complete follow-up for cancer incidence were between 2002 and 2012. Due to administrative issues, data from Greece and Norway were not included in the current study.

## 2.4 | Vital status follow-up

Information on vital status was also collected by a passive follow-up procedure through regional/national mortality registries in all countries except Germany and France, where data are collected by an active follow-up procedure as used for cancer incidence follow-up. The closure dates for vital status follow-up, which were either the date of death or the last date of contact, whichever occurred first, were ranged between 2009 and 2015 depending on the local country

and study center. The current study outcomes were CRC-specific mortality and all-cause mortality and they were determined by the underlying cause of death using the ICD-10.

## 2.5 | Resistin concentration measurements and exposure assessments

Resistin concentrations in serum were measured one time in baseline samples using Human Resistin enzyme-linked immunosorbent assay (ELISA) (BioVendor Laboratory Medicine, Inc; Brno, Czech Republic) according to the manufacturer's protocols. Samples were analyzed in 39-well microtiter plates. Mean interassay coefficients of variation (CV) (representing resistin levels' variance between plates/assays) for all quality controls were estimated by BioVendor and reported as 7.4% for the quality control high concentrations and 6.6% for the quality control low concentrations. The inter-assay CVs for the pooled sera used as the internal quality controls were <10.4%. The means of the intra-assay CVs (representing resistin levels' variance within an assay) were all <2.5%.

A study using three measurements of resistin per individual over 3-4 years revealed a high intraclass correlation coefficient (ICC) of 0.95, suggesting a high stability of resistin levels within a person.²⁴ Thus, the use of one measurement of resistin per individual is supported in epidemiological studies.²³,²⁴

## 2.6 | Baseline variables and prognostic factors

Data on prognostic factors were extracted from the medical records, including age at diagnosis, tumor subsite and tumor stage. Due to the utilization of different stage classifications among EPIC centers (including TNM staging, Dukes classification or the "localized/ metastatic/metastatic regional/ metastatic distant" categories), a harmonization procedure had been previously carried out to obtain a broad category for tumor stage (I, II, III and IV).²⁷ Data were collected from baseline questionnaires for age at recruitment, sex, body mass index (BMI), waist circumference, physical activity index, highest education level and smoking status. Moreover, all diet exposure, including consumed amounts of alcohol, dairy, fruit, red meat, processed meat, vegetables and dietary fiber was assessed by food frequency questionnaires or diet history logs.

Overall, information on tumor stage was missing in 13.3% of the participants. In the centers in France, Ragusa (Italy), Granada (Spain), Heidelberg (Germany) and Umeå (Sweden), data on tumor stage were completed for all participants. In contrast, these data were missing for all participants at the Oxford center (United Kingdom). In the other centers, the proportion of missing information on tumor stage ranged from 1.3% (Potsdam, Germany) to 30.8% (Varese, Italy). Information on waist circumference was missing in 5.6% of participants with all the missing data occurring in Umeå (Sweden).
## 2.7 | Statistical analysis

Study population characteristics were tabulated across quartiles of resistin concentrations (≤3.54; 3.55–4.37; 4.38–5.45; >5.45 ng/mL). A cumulative incidence function (CIF) of CRC-specific mortality was plotted using a Fine–Gray model,²⁸ with time from CRC diagnosis to death or last contact as the time metric. Gray's test was used to test for incidence function changes over the quantiles of resistin.²⁸

The associations between pre-diagnostic resistin concentrations and CRC-specific (primary endpoint) and all-cause mortality (secondary endpoint) were examined using Cox proportional hazards regression models with PROC PHREG in SAS. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the primary endpoint were estimated from competing risk analyses based on cause-specific Cox proportional hazards models,²⁹ with non-CRC death serving as the competing event²⁹; while HRs (95% CIs) for the secondary endpoint were derived from multivariable-adjusted Cox proportional hazards models. We fitted three models with time from the date of CRC diagnosis to date of death or date of last contact as the underlying time variable, stratified by country and adjusted for: age at CRC diagnosis (continuous), sex (men, women) (model 1); additionally adjusted for tumor subsite (colon or rectum), year of diagnosis (model 2); and additionally adjusted for baseline BMI (continuous) and residuals from linear models regressing waist circumference on BMI and height (residuals<sub>(WC~BMI+Height)</sub>) to avoid multicollinearity (model 3). Model 3 was used as the final model in further analyses. Because data were missing for one covariate (residuals<sub>(WC~BMI+Height)</sub>), which were assumed to be missing at random, missing data were imputed based on multiple imputation using chained equations to generate 20 imputed datasets using PROC MI in SAS. The results of the analyses of imputations were then combined using PROC MIANALYZE.

We also estimated risk of CRC-specific mortality according to resistin concentration using a restricted cubic spline cause-specific Cox proportional hazards regression analysis with knots at the 5th, 35th, 65th and 95th percentiles of the resistin distribution. To assess any deviation from linearity, we tested the significance of the spline term using likelihood ratio tests. No deviation from linearity was detected with *P* = .89 for the null hypothesis of linearity. However, due to non-normal distribution and the comparability of results, resistin concentrations were analyzed in quartiles as well as a continuous variable by base-2-log-transformed resistin, which corresponds to a doubling of resistin on its original scale. Adding non-linear terms to log-transformed resistin concentrations did not significantly improve the model (*P* = .81), suggesting that log-transformed resistin sufficiently captures the hazard function for risk of mortality after CRC. We found no violation of the Cox proportional hazards assumption (see the Supporting Information Methods section S1 for details).

The associations between resistin and mortality were further examined in subgroup analyses by sex (men, women), tumor subsite (colon, rectum), BMI (<30, ≥30 kg/m²), tumor stage (I, II, III,

IV) and time from resistin measurement to CRC diagnosis (≤2 years, >2 to ≤8 years and >8 years). In these subgroup analyses, missing data for tumor stage were imputed by multiple imputation and HRs for each subgroup were estimated from model 3, excluding the subgroup-determining variable itself. Wald Chi-Squared tests were used to test interaction terms (and calculate *P*-values) between log-transformed resistin and covariates. In sensitivity analyses, we (a) re-estimated HRs and 95% CI for the primary endpoint again using the Fine-Gray proportional subdistribution hazards models as another competing risk model, (b) fitted all the models again after excluding extreme resistin levels (defined as concentrations of 1.5 times the interquartile range below the 25th percentile and above the 75th percentile),³⁰ (c) performed complete-case analyses and (d) additionally adjusted for tumor stage.

With a standard deviation of 0.52 for log-transformed resistin concentrations,¹⁷ a sample size of 1343 participants, CRC-specific mortality of 0.35, a 5% probability of type I error and 80% power, the minimum detectable HR<sub>per doubling of resistin levels</sub> is 1.28, which represents the smallest effect size in the hazard ratio that our study can statistically distinguish from the null hypothesis. Further information on the rationales of model covariate selection, the implementation of multiple imputation and assessment of the proportional hazard assumption is provided in Data S1. All statistical tests were two-sided, and *P*-values <.05 were considered statistically significant. All analyses were performed by using SAS enterprise guide, version 8.3 (SAS Institute, Inc., Cary, North Carolina).

## 3 | RESULTS

Resistin was measured at a median of 4.8 years (25th–75th percentile, 2.6–7.0) before CRC diagnosis. Median follow-up time, defined as time from CRC diagnosis to the end of follow-up for vital status, was 8.2 years (25th–75th percentile, 2.1–12.2). Among 1343 CRC cases, 474 deaths from CRC and 147 deaths from other causes were observed. CRC cases in the upper compared to the lower quartiles of resistin levels were more likely to be women, have higher age at diagnosis, consume more dairy and fruit, less alcohol and processed meat (Table 1).

The unadjusted cumulative incidence functions of CRC-specific mortality over time were not significantly different among quartile groups at all time points (*P* = .98 from Gray's test [Figure S1]). Circulating resistin concentrations were not significantly associated with CRC mortality in the cause-specific Cox proportional hazards models. In the fully adjusted model (model 3): HR<sub>Q2vsQ1</sub>: 0.98, 95% CI: 0.76–1.27; HR<sub>Q3vsQ1</sub>: 0.96, 95% CI: 0.74–1.24; HR<sub>Q4vsQ1</sub>: 0.95, 95% CI: 0.73–1.23; *P*<sub>trend</sub> = .97; and HR<sub>per doubling of resistin concentrations</sub>, 1.00; 95% CI: 0.84–1.19; *P* = .98 (Table 2). No substantial difference in HRs was observed among the three models. We also found no association between resistin concentrations and all-cause mortality (HR<sub>Q4vsQ1</sub>: 0.99, 95% CI: 0.79–1.24; *P*<sub>trend</sub> = .87; Table 2). There was
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

## 2.7 | Statistical analysis

Study population characteristics were tabulated across quartiles of resistin concentrations (≤3.54; 3.55–4.37; 4.38–5.45; >5.45 ng/mL). A cumulative incidence function (CIF) of CRC-specific mortality was plotted using a Fine–Gray model,²⁸ with time from CRC diagnosis to death or last contact as the time metric. Gray's test was used to test for incidence function changes over the quantiles of resistin.²⁸

The associations between pre-diagnostic resistin concentrations and CRC-specific (primary endpoint) and all-cause mortality (secondary endpoint) were examined using Cox proportional hazards regression models with PROC PHREG in SAS. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the primary endpoint were estimated from competing risk analyses based on cause-specific Cox proportional hazards models,²⁹ with non-CRC death serving as the competing event²⁹; while HRs (95% CIs) for the secondary endpoint were derived from multivariable-adjusted Cox proportional hazards models. We fitted three models with time from the date of CRC diagnosis to date of death or date of last contact as the underlying time variable, stratified by country and adjusted for: age at CRC diagnosis (continuous), sex (men, women) (model 1); additionally adjusted for tumor subsite (colon or rectum), year of diagnosis (model 2); and additionally adjusted for baseline BMI (continuous) and residuals from linear models regressing waist circumference on BMI and height (residuals<sub>(WC~BMI+Height)</sub>) to avoid multicollinearity (model 3). Model 3 was used as the final model in further analyses. Because data were missing for one covariate (residuals<sub>(WC~BMI+Height)</sub>), which were assumed to be missing at random, missing data were imputed based on multiple imputation using chained equations to generate 20 imputed datasets using PROC MI in SAS. The results of the analyses of imputations were then combined using PROC MIANALYZE.

We also estimated risk of CRC-specific mortality according to resistin concentration using a restricted cubic spline cause-specific Cox proportional hazards regression analysis with knots at the 5th, 35th, 65th and 95th percentiles of the resistin distribution. To assess any deviation from linearity, we tested the significance of the spline term using likelihood ratio tests. No deviation from linearity was detected with *P* = .89 for the null hypothesis of linearity. However, due to non-normal distribution and the comparability of results, resistin concentrations were analyzed in quartiles as well as a continuous variable by base-2-log-transformed resistin, which corresponds to a doubling of resistin on its original scale. Adding non-linear terms to log-transformed resistin concentrations did not significantly improve the model (*P* = .81), suggesting that log-transformed resistin sufficiently captures the hazard function for risk of mortality after CRC. We found no violation of the Cox proportional hazards assumption (see the Supporting Information Methods section S1 for details).

The associations between resistin and mortality were further examined in subgroup analyses by sex (men, women), tumor subsite (colon, rectum), BMI (<30, ≥30 kg/m²), tumor stage (I, II, III,

IV) and time from resistin measurement to CRC diagnosis (≤2 years, >2 to ≤8 years and >8 years). In these subgroup analyses, missing data for tumor stage were imputed by multiple imputation and HRs for each subgroup were estimated from model 3, excluding the subgroup-determining variable itself. Wald Chi-Squared tests were used to test interaction terms (and calculate *P*-values) between log-transformed resistin and covariates. In sensitivity analyses, we (a) re-estimated HRs and 95% CI for the primary endpoint again using the Fine-Gray proportional subdistribution hazards models as another competing risk model, (b) fitted all the models again after excluding extreme resistin levels (defined as concentrations of 1.5 times the interquartile range below the 25th percentile and above the 75th percentile),³⁰ (c) performed complete-case analyses and (d) additionally adjusted for tumor stage.

With a standard deviation of 0.52 for log-transformed resistin concentrations,¹⁷ a sample size of 1343 participants, CRC-specific mortality of 0.35, a 5% probability of type I error and 80% power, the minimum detectable HR<sub>per doubling of resistin levels</sub> is 1.28, which represents the smallest effect size in the hazard ratio that our study can statistically distinguish from the null hypothesis. Further information on the rationales of model covariate selection, the implementation of multiple imputation and assessment of the proportional hazard assumption is provided in Data S1. All statistical tests were two-sided, and *P*-values <.05 were considered statistically significant. All analyses were performed by using SAS enterprise guide, version 8.3 (SAS Institute, Inc., Cary, North Carolina).

## 3 | RESULTS

Resistin was measured at a median of 4.8 years (25th–75th percentile, 2.6–7.0) before CRC diagnosis. Median follow-up time, defined as time from CRC diagnosis to the end of follow-up for vital status, was 8.2 years (25th–75th percentile, 2.1–12.2). Among 1343 CRC cases, 474 deaths from CRC and 147 deaths from other causes were observed. CRC cases in the upper compared to the lower quartiles of resistin levels were more likely to be women, have higher age at diagnosis, consume more dairy and fruit, less alcohol and processed meat (Table 1).

The unadjusted cumulative incidence functions of CRC-specific mortality over time were not significantly different among quartile groups at all time points (*P* = .98 from Gray's test [Figure S1]). Circulating resistin concentrations were not significantly associated with CRC mortality in the cause-specific Cox proportional hazards models. In the fully adjusted model (model 3): HR<sub>Q2vsQ1</sub>: 0.98, 95% CI: 0.76–1.27; HR<sub>Q3vsQ1</sub>: 0.96, 95% CI: 0.74–1.24; HR<sub>Q4vsQ1</sub>: 0.95, 95% CI: 0.73–1.23; *P*<sub>trend</sub> = .97; and HR<sub>per doubling of resistin concentrations</sub>, 1.00; 95% CI: 0.84–1.19; *P* = .98 (Table 2). No substantial difference in HRs was observed among the three models. We also found no association between resistin concentrations and all-cause mortality (HR<sub>Q4vsQ1</sub>: 0.99, 95% CI: 0.79–1.24; *P*<sub>trend</sub> = .87; Table 2). There was
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<table><caption>TABLE 1 Baseline characteristics according to quartiles of pre-diagnostic circulating resistin concentrations among CRC cases (N = 1343) in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Serum resistin concentrations (ng/mL)</th></tr><tr><th>Quartile 1</th><th>Quartile 2</th><th>Quartile 3</th><th>Quartile 4</th></tr><tr><th>≤3.54<br>N = 338</th><th>3.55-4.37<br>N = 335</th><th>4.38-5.45<br>N = 336</th><th>>5.45<br>N = 334</th></tr></thead><tbody><tr><td>Age at CRC diagnosis, years, mean (SD)</td><td>56.8 (6.5)</td><td>58.3 (6.6)</td><td>58.2 (7.1)</td><td>59.1 (7.4)</td></tr><tr><td>Women, %</td><td>46.6</td><td>51.0</td><td>54.3</td><td>56.3</td></tr><tr><td>Tumor stage, n (%)<sup>a</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>I</td><td>95 (28.1)</td><td>98 (29.3)</td><td>70 (20.8)</td><td>70 (21.0)</td></tr><tr><td>II</td><td>67 (19.8)</td><td>60 (17.9)</td><td>68 (20.2)</td><td>73 (21.9)</td></tr><tr><td>III</td><td>104 (30.8)</td><td>98 (29.3)</td><td>113 (33.6)</td><td>102 (30.5)</td></tr><tr><td>IV</td><td>36 (10.7)</td><td>36 (10.7)</td><td>39 (11.6)</td><td>35 (10.5)</td></tr><tr><td>Unknown</td><td>36 (10.7)</td><td>43 (12.8)</td><td>46 (13.7)</td><td>54 (16.2)</td></tr><tr><td>Tumor subsite, colon, n (%)</td><td>204 (60.4)</td><td>218 (65.1)</td><td>208 (61.9)</td><td>224 (67.1)</td></tr><tr><td>BMI, kg/m<sup>2</sup>, mean (SD)</td><td>26.6 (3.8)</td><td>26.7 (3.9)</td><td>26.9 (4.5)</td><td>27 (4.2)</td></tr><tr><td>Waist circumference, cm, mean (SD)<sup>b,c</sup></td><td>90.6 (12.6)</td><td>89.9 (12.6)</td><td>90.9 (13.5)</td><td>90.5 (12.4)</td></tr><tr><td>Diabetes at baseline, n (%)</td><td>30 (8.9)</td><td>27 (8.1)</td><td>28 (8.3)</td><td>24 (7.2)</td></tr><tr><td>Physically moderately active or active, sex-specific n (%)</td><td>185 (54.7)</td><td>160 (47.8)</td><td>170 (50.6)</td><td>159 (47.6)</td></tr><tr><td>University degree and higher education level, n (%)</td><td>51 (15.1)</td><td>69 (20.6)</td><td>63 (18.8)</td><td>53 (15.9)</td></tr><tr><td>Current smoking, n (%)</td><td>89 (26.3)</td><td>86 (25.7)</td><td>84 (25.0)</td><td>86 (25.7)</td></tr><tr><td>Alcohol consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>13.5 (2.6-37.1)</td><td>9.1 (1.6-24.3)</td><td>9.5 (1.8-23.7)</td><td>5.1 (0.8-15.7)</td></tr><tr><td>Dairy consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>247.6 (120.4-432.6)</td><td>289.2 (155.6-462.8)</td><td>284.3 (158.1-451.9)</td><td>314.5 (171.7-473.1)</td></tr><tr><td>Energy intake, kcal/d, median (P25-P75)<sup>b,d</sup></td><td>2108 (1683-2521)</td><td>2108 (1683-2496)</td><td>2072 (1699-2480)</td><td>2055 (1694-2452)</td></tr><tr><td>Fish and shellfish consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>29.7 (15.1-54.8)</td><td>27.7 (14.1-47.5)</td><td>27.8 (16.2-49.7)</td><td>25.4 (14.6-44.3)</td></tr><tr><td>Fruit consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>158.5 (80.6-271.4)</td><td>187.7 (117.4-288.2)</td><td>186.8 (93.9-288.1)</td><td>196.8 (100.9-313.7)</td></tr><tr><td>Red meat consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>53.8 (27.2-85.4)</td><td>44.4 (24.8-71.9)</td><td>48.3 (24.2-73.6)</td><td>47.1 (23.2-75.0)</td></tr><tr><td>Processed meat, g/d, median (P25-P75)<sup>b,d</sup></td><td>26.5 (13.6-51.2)</td><td>26.1 (15.7-42.1)</td><td>25.2 (13.1-44.8)</td><td>25.0 (13.4-43.5)</td></tr><tr><td>Vegetable consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>161.8 (104.8-244.6)</td><td>148.7 (98.5-225.0)</td><td>160.3 (111.4-227.4)</td><td>149.2 (99.3-235.8)</td></tr><tr><td>Dietary fiber intake, g/d, median (P25-P75)<sup>b,d</sup></td><td>21.6 (17.2-27.2)</td><td>21.9 (16.8-27.2)</td><td>22.0 (18.1-27.2)</td><td>22.7 (18.0-27.9)</td></tr></tbody></table>

Abbreviations: BMI, body mass index; CRC, colorectal cancer; P25, the 25th percentile; P75, the 75th percentile; SD, standard deviation.

<sup>a</sup>Missing rates of tumor stage in EPIC study centers range from 1.3% to 30.8%. No information is available about tumor stage for participants in the Oxford center (the United Kingdom).

<sup>b</sup>Among users only.

<sup>c</sup>Data on waist circumference were missing in 5.58% of the participants.

<sup>d</sup>Data on all other dietary variables were missing in 0.30% (4/1343) of the participants.

no significant association between resistin concentrations and CRC-specific mortality in subgroup analyses stratified by sex, tumor subsite, tumor stage, BMI and time to CRC diagnosis in cause-specific Cox hazards models (Figure 1).

The main results were not substantially different when using the Fine-Gray proportional subdistribution hazards models as the competing risk model (Table S1 and Figure S2A) when excluding extreme resistin levels (Table S2) or in complete-case analyses (Table S3). However, there was a statistically significant interaction between resistin and tumor stage in relation to CRC mortality in complete-case analyses with cause-specific Cox proportional hazards models (P = .02) (Figure S2B) (HRs (95% CI)

of CRC mortality for a doubling of resistin concentrations in persons with stage I, II, III and IV tumors were 0.63, 95% CI: 0.33-1.18; 0.83, 95% CI: 0.48-1.41; 0.91, 95% CI: 0.66-1.24 and 1.52, 95% CI: 1.05-2.19, respectively; P<sub>Interaction</sub> = .02). This significant interaction was not observed in the complete-case analyses with Fine-Gray proportional subdistribution hazards models (Figure S2C).

Further adjustment of our main results for tumor stage did not alter the results in both imputation and complete case analysis (cause-specific hazards models, HR<sub>Q4vsQ1</sub>: 0.93 (0.71-1.22); P-trend = .82; and HR<sub>per doubling of resistin concentrations</sub>, 0.98 (0.81-1.19); P = .85 (data not shown)).
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<table><caption>TABLE 1 Baseline characteristics according to quartiles of pre-diagnostic circulating resistin concentrations among CRC cases (N = 1343) in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Serum resistin concentrations (ng/mL)</th></tr><tr><th>Quartile 1</th><th>Quartile 2</th><th>Quartile 3</th><th>Quartile 4</th></tr><tr><th>≤3.54<br>N = 338</th><th>3.55-4.37<br>N = 335</th><th>4.38-5.45<br>N = 336</th><th>>5.45<br>N = 334</th></tr></thead><tbody><tr><td>Age at CRC diagnosis, years, mean (SD)</td><td>56.8 (6.5)</td><td>58.3 (6.6)</td><td>58.2 (7.1)</td><td>59.1 (7.4)</td></tr><tr><td>Women, %</td><td>46.6</td><td>51.0</td><td>54.3</td><td>56.3</td></tr><tr><td>Tumor stage, n (%)<sup>a</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>I</td><td>95 (28.1)</td><td>98 (29.3)</td><td>70 (20.8)</td><td>70 (21.0)</td></tr><tr><td>II</td><td>67 (19.8)</td><td>60 (17.9)</td><td>68 (20.2)</td><td>73 (21.9)</td></tr><tr><td>III</td><td>104 (30.8)</td><td>98 (29.3)</td><td>113 (33.6)</td><td>102 (30.5)</td></tr><tr><td>IV</td><td>36 (10.7)</td><td>36 (10.7)</td><td>39 (11.6)</td><td>35 (10.5)</td></tr><tr><td>Unknown</td><td>36 (10.7)</td><td>43 (12.8)</td><td>46 (13.7)</td><td>54 (16.2)</td></tr><tr><td>Tumor subsite, colon, n (%)</td><td>204 (60.4)</td><td>218 (65.1)</td><td>208 (61.9)</td><td>224 (67.1)</td></tr><tr><td>BMI, kg/m<sup>2</sup>, mean (SD)</td><td>26.6 (3.8)</td><td>26.7 (3.9)</td><td>26.9 (4.5)</td><td>27 (4.2)</td></tr><tr><td>Waist circumference, cm, mean (SD)<sup>b,c</sup></td><td>90.6 (12.6)</td><td>89.9 (12.6)</td><td>90.9 (13.5)</td><td>90.5 (12.4)</td></tr><tr><td>Diabetes at baseline, n (%)</td><td>30 (8.9)</td><td>27 (8.1)</td><td>28 (8.3)</td><td>24 (7.2)</td></tr><tr><td>Physically moderately active or active, sex-specific n (%)</td><td>185 (54.7)</td><td>160 (47.8)</td><td>170 (50.6)</td><td>159 (47.6)</td></tr><tr><td>University degree and higher education level, n (%)</td><td>51 (15.1)</td><td>69 (20.6)</td><td>63 (18.8)</td><td>53 (15.9)</td></tr><tr><td>Current smoking, n (%)</td><td>89 (26.3)</td><td>86 (25.7)</td><td>84 (25.0)</td><td>86 (25.7)</td></tr><tr><td>Alcohol consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>13.5 (2.6-37.1)</td><td>9.1 (1.6-24.3)</td><td>9.5 (1.8-23.7)</td><td>5.1 (0.8-15.7)</td></tr><tr><td>Dairy consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>247.6 (120.4-432.6)</td><td>289.2 (155.6-462.8)</td><td>284.3 (158.1-451.9)</td><td>314.5 (171.7-473.1)</td></tr><tr><td>Energy intake, kcal/d, median (P25-P75)<sup>b,d</sup></td><td>2108 (1683-2521)</td><td>2108 (1683-2496)</td><td>2072 (1699-2480)</td><td>2055 (1694-2452)</td></tr><tr><td>Fish and shellfish consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>29.7 (15.1-54.8)</td><td>27.7 (14.1-47.5)</td><td>27.8 (16.2-49.7)</td><td>25.4 (14.6-44.3)</td></tr><tr><td>Fruit consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>158.5 (80.6-271.4)</td><td>187.7 (117.4-288.2)</td><td>186.8 (93.9-288.1)</td><td>196.8 (100.9-313.7)</td></tr><tr><td>Red meat consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>53.8 (27.2-85.4)</td><td>44.4 (24.8-71.9)</td><td>48.3 (24.2-73.6)</td><td>47.1 (23.2-75.0)</td></tr><tr><td>Processed meat, g/d, median (P25-P75)<sup>b,d</sup></td><td>26.5 (13.6-51.2)</td><td>26.1 (15.7-42.1)</td><td>25.2 (13.1-44.8)</td><td>25.0 (13.4-43.5)</td></tr><tr><td>Vegetable consumption, g/d, median (P25-P75)<sup>b,d</sup></td><td>161.8 (104.8-244.6)</td><td>148.7 (98.5-225.0)</td><td>160.3 (111.4-227.4)</td><td>149.2 (99.3-235.8)</td></tr><tr><td>Dietary fiber intake, g/d, median (P25-P75)<sup>b,d</sup></td><td>21.6 (17.2-27.2)</td><td>21.9 (16.8-27.2)</td><td>22.0 (18.1-27.2)</td><td>22.7 (18.0-27.9)</td></tr></tbody></table>

Abbreviations: BMI, body mass index; CRC, colorectal cancer; P25, the 25th percentile; P75, the 75th percentile; SD, standard deviation.

<sup>a</sup>Missing rates of tumor stage in EPIC study centers range from 1.3% to 30.8%. No information is available about tumor stage for participants in the Oxford center (the United Kingdom).

<sup>b</sup>Among users only.

<sup>c</sup>Data on waist circumference were missing in 5.58% of the participants.

<sup>d</sup>Data on all other dietary variables were missing in 0.30% (4/1343) of the participants.

no significant association between resistin concentrations and CRC-specific mortality in subgroup analyses stratified by sex, tumor subsite, tumor stage, BMI and time to CRC diagnosis in cause-specific Cox hazards models (Figure 1).

The main results were not substantially different when using the Fine-Gray proportional subdistribution hazards models as the competing risk model (Table S1 and Figure S2A) when excluding extreme resistin levels (Table S2) or in complete-case analyses (Table S3). However, there was a statistically significant interaction between resistin and tumor stage in relation to CRC mortality in complete-case analyses with cause-specific Cox proportional hazards models (P = .02) (Figure S2B) (HRs (95% CI)

of CRC mortality for a doubling of resistin concentrations in persons with stage I, II, III and IV tumors were 0.63, 95% CI: 0.33-1.18; 0.83, 95% CI: 0.48-1.41; 0.91, 95% CI: 0.66-1.24 and 1.52, 95% CI: 1.05-2.19, respectively; P<sub>Interaction</sub> = .02). This significant interaction was not observed in the complete-case analyses with Fine-Gray proportional subdistribution hazards models (Figure S2C).

Further adjustment of our main results for tumor stage did not alter the results in both imputation and complete case analysis (cause-specific hazards models, HR<sub>Q4vsQ1</sub>: 0.93 (0.71-1.22); P-trend = .82; and HR<sub>per doubling of resistin concentrations</sub>, 0.98 (0.81-1.19); P = .85 (data not shown)).
<table><caption>TABLE 2 Hazard ratios and 95% confidence intervals for CRC mortality and all-cause mortality according to pre-diagnostic circulating resistin concentrations.</caption><thead><tr><th rowspan="2">Resistin category</th><th rowspan="2">Resistin quartile ranges, ng/mL</th><th rowspan="2">No. of participants<sup>a</sup></th><th rowspan="2">Number of events</th><th rowspan="2">Number of competing events</th><th colspan="2">Model 1<sup>b</sup></th><th colspan="2">Model 2<sup>c</sup></th><th colspan="2">Model 3<sup>d</sup></th></tr><tr><th>Alive</th><th>HR (95% CI)</th><th>P-value<sup>f</sup></th><th>HR (95% CI)</th><th>P-value<sup>f</sup></th><th>HR (95% CI)<sup>e</sup></th><th>P-value<sup>e,f</sup></th></tr></thead><tbody><tr><td colspan="11">CRC-specific mortality (Competing risk analysis with cause-specific hazard model)</td></tr><tr><td>Quartile 1</td><td>&lt;3.54</td><td>338</td><td>119</td><td>31</td><td>188</td><td>Ref</td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Quartile 2</td><td>3.55-4.37</td><td>335</td><td>120</td><td>27</td><td>188</td><td>1.02 (0.79-1.32)</td><td>.99</td><td>1.00 (0.77-1.29)</td><td>.98</td><td>0.98 (0.76-1.27)</td></tr><tr><td>Quartile 3</td><td>4.38-5.45</td><td>336</td><td>118</td><td>44</td><td>174</td><td>0.99 (0.77-1.28)</td><td></td><td>0.97 (0.75-1.26)</td><td></td><td>0.96 (0.74-1.24)</td><td>.97</td></tr><tr><td>Quartile 4</td><td>&gt;5.45</td><td>334</td><td>117</td><td>45</td><td>172</td><td>0.98 (0.75-1.27)</td><td></td><td>0.96 (0.74-1.24)</td><td></td><td>0.95 (0.73-1.23)</td><td></td></tr><tr><td>Per doubling of resistin concentrations<sup>g</sup></td><td></td><td>1343</td><td>474</td><td>147</td><td>722</td><td>1.02 (0.85-1.21)</td><td>.84</td><td>1.01 (0.84-1.20)</td><td>.94</td><td>1.00 (0.84-1.19)</td><td>.98</td></tr><tr><td colspan="11">Overall mortality (Cox proportional hazards model)</td></tr><tr><td>Quartile 1</td><td>&lt;3.54</td><td>338</td><td>150</td><td>—</td><td>188</td><td>Ref</td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Quartile 2</td><td>3.55-4.37</td><td>335</td><td>147</td><td>—</td><td>188</td><td>0.97 (0.77-1.22)</td><td>.85</td><td>0.95 (0.76-1.20)</td><td>.87</td><td>0.94 (0.74-1.18)</td></tr><tr><td>Quartile 3</td><td>4.38-5.45</td><td>336</td><td>162</td><td>—</td><td>174</td><td>1.07 (0.86-1.34)</td><td></td><td>1.05 (0.84-1.32)</td><td></td><td>1.03 (0.82-1.29)</td><td>.87</td></tr><tr><td>Quartile 4</td><td>&gt;5.45</td><td>334</td><td>162</td><td>—</td><td>172</td><td>1.02 (0.81-1.28)</td><td></td><td>1.00 (0.80-1.26)</td><td></td><td>0.99 (0.79-1.24)</td><td></td></tr><tr><td>Per doubling of resistin concentrations<sup>g</sup></td><td></td><td>1343</td><td>621</td><td></td><td>722</td><td>1.04 (0.89-1.21)</td><td>.64</td><td>1.03 (0.88-1.20)</td><td>.74</td><td>1.01 (0.87-1.18)</td><td>.87</td></tr></tbody></table>

Abbreviations: BMI, body mass index; CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio.

<sup>a</sup>Data of a covariate (residual, WC~BMI~+Height) were missing in 75/1343 CRC patients and were imputed using the multiple imputation method.

<sup>b</sup>Model 1: Cause-specific Cox hazard model, or Cox proportional hazards model with time from CRC diagnosis to death or last contact (years) as the underlying time variable, stratified by country and adjusted for age at CRC diagnosis (continuous) and sex (male, female).

<sup>c</sup>Model 2: Model 1 with additional adjustment for year of CRC diagnosis (continuous) and tumor subsite (colon or rectum).

<sup>d</sup>Model 3: Model 2 with additional adjustment for BMI (kg/m²) and residual, WC~BMI~+Height, in a linear model with BMI.

<sup>e</sup>HRs and P-values from model 3 were estimated for each of the 20 imputed datasets, and combined into a pooled HR and pooled P-value.

<sup>f</sup>Where resistin was used as a categorical variable, p values were estimated from the test for trend across the quartiles of resistin.

<sup>g</sup>Models with continuous log-transformed resistin concentrations by log 2.
<table><caption>TABLE 2 Hazard ratios and 95% confidence intervals for CRC mortality and all-cause mortality according to pre-diagnostic circulating resistin concentrations.</caption><thead><tr><th rowspan="2">Resistin category</th><th rowspan="2">Resistin quartile ranges, ng/mL</th><th rowspan="2">No. of participants<sup>a</sup></th><th rowspan="2">Number of events</th><th rowspan="2">Number of competing events</th><th colspan="2">Model 1<sup>b</sup></th><th colspan="2">Model 2<sup>c</sup></th><th colspan="2">Model 3<sup>d</sup></th></tr><tr><th>Alive</th><th>HR (95% CI)</th><th>P-value<sup>f</sup></th><th>HR (95% CI)</th><th>P-value<sup>f</sup></th><th>HR (95% CI)<sup>e</sup></th><th>P-value<sup>e,f</sup></th></tr></thead><tbody><tr><td colspan="11">CRC-specific mortality (Competing risk analysis with cause-specific hazard model)</td></tr><tr><td>Quartile 1</td><td>&lt;3.54</td><td>338</td><td>119</td><td>31</td><td>188</td><td>Ref</td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Quartile 2</td><td>3.55-4.37</td><td>335</td><td>120</td><td>27</td><td>188</td><td>1.02 (0.79-1.32)</td><td>.99</td><td>1.00 (0.77-1.29)</td><td>.98</td><td>0.98 (0.76-1.27)</td></tr><tr><td>Quartile 3</td><td>4.38-5.45</td><td>336</td><td>118</td><td>44</td><td>174</td><td>0.99 (0.77-1.28)</td><td></td><td>0.97 (0.75-1.26)</td><td></td><td>0.96 (0.74-1.24)</td><td>.97</td></tr><tr><td>Quartile 4</td><td>&gt;5.45</td><td>334</td><td>117</td><td>45</td><td>172</td><td>0.98 (0.75-1.27)</td><td></td><td>0.96 (0.74-1.24)</td><td></td><td>0.95 (0.73-1.23)</td><td></td></tr><tr><td>Per doubling of resistin concentrations<sup>g</sup></td><td></td><td>1343</td><td>474</td><td>147</td><td>722</td><td>1.02 (0.85-1.21)</td><td>.84</td><td>1.01 (0.84-1.20)</td><td>.94</td><td>1.00 (0.84-1.19)</td><td>.98</td></tr><tr><td colspan="11">Overall mortality (Cox proportional hazards model)</td></tr><tr><td>Quartile 1</td><td>&lt;3.54</td><td>338</td><td>150</td><td>—</td><td>188</td><td>Ref</td><td></td><td>Ref</td><td></td><td></td></tr><tr><td>Quartile 2</td><td>3.55-4.37</td><td>335</td><td>147</td><td>—</td><td>188</td><td>0.97 (0.77-1.22)</td><td>.85</td><td>0.95 (0.76-1.20)</td><td>.87</td><td>0.94 (0.74-1.18)</td></tr><tr><td>Quartile 3</td><td>4.38-5.45</td><td>336</td><td>162</td><td>—</td><td>174</td><td>1.07 (0.86-1.34)</td><td></td><td>1.05 (0.84-1.32)</td><td></td><td>1.03 (0.82-1.29)</td><td>.87</td></tr><tr><td>Quartile 4</td><td>&gt;5.45</td><td>334</td><td>162</td><td>—</td><td>172</td><td>1.02 (0.81-1.28)</td><td></td><td>1.00 (0.80-1.26)</td><td></td><td>0.99 (0.79-1.24)</td><td></td></tr><tr><td>Per doubling of resistin concentrations<sup>g</sup></td><td></td><td>1343</td><td>621</td><td></td><td>722</td><td>1.04 (0.89-1.21)</td><td>.64</td><td>1.03 (0.88-1.20)</td><td>.74</td><td>1.01 (0.87-1.18)</td><td>.87</td></tr></tbody></table>

Abbreviations: BMI, body mass index; CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio.

<sup>a</sup>Data of a covariate (residual, WC~BMI~+Height) were missing in 75/1343 CRC patients and were imputed using the multiple imputation method.

<sup>b</sup>Model 1: Cause-specific Cox hazard model, or Cox proportional hazards model with time from CRC diagnosis to death or last contact (years) as the underlying time variable, stratified by country and adjusted for age at CRC diagnosis (continuous) and sex (male, female).

<sup>c</sup>Model 2: Model 1 with additional adjustment for year of CRC diagnosis (continuous) and tumor subsite (colon or rectum).

<sup>d</sup>Model 3: Model 2 with additional adjustment for BMI (kg/m²) and residual, WC~BMI~+Height, in a linear model with BMI.

<sup>e</sup>HRs and P-values from model 3 were estimated for each of the 20 imputed datasets, and combined into a pooled HR and pooled P-value.

<sup>f</sup>Where resistin was used as a categorical variable, p values were estimated from the test for trend across the quartiles of resistin.

<sup>g</sup>Models with continuous log-transformed resistin concentrations by log 2.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

Association between circulating resistin concentrations and CRC-specific mortality

<figure><img src="image_9.png" /></figure>

**FIGURE 1** Association between resistin concentrations and CRC mortality in cause-specific Cox hazards models in subgroup analyses. BMI, body mass index; CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio. Missing data of residual(WC~BMI+Height) (75/1343), and stage (254/1343) were assumed to be missing at random and were imputed using multiple imputation. The imputation model contained the variables included in the analysis model and auxiliary variables (all baseline lifestyle and dietary variables as in Table 1). Hazard ratios and 95% CIs refer to a doubling in resistin concentrations and were derived from cause-specific Cox hazards models (model 3) with time from CRC diagnosis to death or last contact (years) as the underlying time variable, stratified by country and adjusted for age at CRC diagnosis (continuous), sex (male, female), year of CRC diagnosis (continuous), tumor subsite (colon or rectum), BMI (kg/m²) and residual(WC~BMI+Height). In each subgroup analysis, the subgroup-determining variable itself was excluded from the models. Hazard ratios and 95% CIs were estimated for each of the 20 imputed datasets, and combined into pooled values. P-values for the interaction of each variable with log-transformed resistin were estimated using Wald Chi-squared tests and presented as the median of the P-values from the 20 imputed data analyses.

4 | DISCUSSION

In our study, we found that pre-diagnostic resistin concentrations were not associated with CRC-specific or all-cause mortality risk among individuals with incident CRC. No association was found when performing subgroup analysis by sex, tumor subsite, tumor stage, BMI and time to CRC diagnosis.

To our knowledge, our study is the first to investigate the association between pre-diagnostic resistin concentrations and mortality in CRC patients. Therefore, we cannot compare our results directly to those of other published studies. The REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study investigated the association between baseline resistin concentrations and cancer mortality among community-dwelling adults in the United States and found no significant relationship with mortality due to obesity-related cancers (defined as breast, colorectal, kidney, pancreas, stomach, endometrial and esophagus cancer) or total cancer mortality.³¹ However, in subgroup analyses, the study found a statistically significant association between higher resistin concentrations and risk of cancer mortality in Black (but not in White) participants.³¹ In another study of 599 elderly Finnish patients with hypertension, resistin

concentrations at recruitment were significantly associated with all-cause mortality (HR<sub>One standard deviation increment in log-resistin</sub>, 5.48, 95% CI: 1.10–27.25).³² However, the findings of these studies are more difficult to interpret because resistin has been associated with cardiovascular disease (CVD) mortality in high-CVD-risk populations³³; thus, any relationship of resistin with total mortality may be driven by risk of CVD mortality. Although case-control studies suggest that persons with CRC have higher resistin concentrations than controls,¹⁶ we have previously found in a prospective study that in persons free of cancer at baseline resistin is not significantly associated with risk of incident CRC.¹⁷ Findings from that prospective study are consistent with a previous case-cohort study within the Women's Health Initiative study,¹⁸ and our recent Mendelian Randomization study.¹⁹ Together with the results from the present analysis, evidence suggests that circulating resistin concentrations are neither related to risk of CRC nor mortality among persons with CRC.

In our study, we used the multiple imputation approach to impute the missing data in residuals(WC~BMI+Height) in all models and the missing data of tumor stage in subgroup analysis. Complete-case analyses were used for sensitivity analyses, however, they may not provide an unbiased estimate for the association since participants with missing
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

Association between circulating resistin concentrations and CRC-specific mortality

<table><thead><tr><th rowspan="2">Sub-group</th><th rowspan="2">Categories</th><th rowspan="2">Total no. of participants</th><th rowspan="2">Number of events</th><th rowspan="2">Number of competing events</th><th rowspan="2">Alive</th><th rowspan="2">Hazard Ratio</th><th rowspan="2">HR (95% CI)</th><th rowspan="2">P value per doubling resistin</th><th rowspan="2">P interaction with resistin</th></tr><tr></tr></thead><tbody><tr><td>All</td><td>All participants</td><td>1343</td><td>474</td><td>147</td><td>722</td><td></td><td>1.00 (0.84-1.19)</td><td>.98</td><td></td></tr><tr><td rowspan="2">Sex</td><td>Women</td><td>695</td><td>243</td><td>66</td><td>386</td><td></td><td>0.92 (0.72-1.18)</td><td>.52</td><td>.36</td></tr><tr><td>Men</td><td>648</td><td>231</td><td>81</td><td>336</td><td></td><td>1.10 (0.85-1.43)</td><td>.46</td><td></td></tr><tr><td rowspan="2">Tumor subsite</td><td>Colon</td><td>854</td><td>306</td><td>99</td><td>449</td><td></td><td>1.05 (0.85-1.31)</td><td>.64</td><td>.17</td></tr><tr><td>Rectal</td><td>489</td><td>168</td><td>48</td><td>273</td><td></td><td>0.86 (0.63-1.17)</td><td>.33</td><td></td></tr><tr><td rowspan="4">Tumor stage</td><td>I</td><td>390</td><td>70</td><td>30</td><td>290</td><td></td><td>0.70 (0.42-1.16)</td><td>.16</td><td>.12</td></tr><tr><td>II</td><td>310</td><td>61</td><td>39</td><td>210</td><td></td><td>0.85 (0.50-1.45)</td><td>.55</td><td></td></tr><tr><td>III</td><td>479</td><td>218</td><td>58</td><td>204</td><td></td><td>0.96 (0.71-1.29)</td><td>.79</td><td></td></tr><tr><td>IV</td><td>164</td><td>126</td><td>20</td><td>18</td><td></td><td>1.39 (0.96-2.00)</td><td>.08</td><td></td></tr><tr><td rowspan="2">BMI</td><td>BMI ≥ 30</td><td>1099</td><td>378</td><td>109</td><td>612</td><td></td><td>0.96 (0.79-1.17)</td><td>.69</td><td>.57</td></tr><tr><td>BMI < 30</td><td>244</td><td>96</td><td>38</td><td>110</td><td></td><td>1.16 (0.78-1.72)</td><td>.48</td><td></td></tr><tr><td rowspan="3">Time to CRC diagnosis</td><td>Longer than 8 years</td><td>195</td><td>64</td><td>18</td><td>113</td><td></td><td>0.95 (0.60-1.51)</td><td>.82</td><td>.99</td></tr><tr><td>2-8 years</td><td>913</td><td>317</td><td>104</td><td>492</td><td></td><td>1.05 (0.85-1.31)</td><td>.63</td><td></td></tr><tr><td>Within 2 years</td><td>235</td><td>93</td><td>25</td><td>117</td><td></td><td>0.87 (0.54-1.42)</td><td>.59</td><td></td></tr></tbody></table>

**FIGURE 1** Association between resistin concentrations and CRC mortality in cause-specific Cox hazards models in subgroup analyses. BMI, body mass index; CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio. Missing data of residual(WC~BMI+Height) (75/1343), and stage (254/1343) were assumed to be missing at random and were imputed using multiple imputation. The imputation model contained the variables included in the analysis model and auxiliary variables (all baseline lifestyle and dietary variables as in Table 1). Hazard ratios and 95% CIs refer to a doubling in resistin concentrations and were derived from cause-specific Cox hazards models (model 3) with time from CRC diagnosis to death or last contact (years) as the underlying time variable, stratified by country and adjusted for age at CRC diagnosis (continuous), sex (male, female), year of CRC diagnosis (continuous), tumor subsite (colon or rectum), BMI (kg/m²) and residual(WC~BMI+Height). In each subgroup analysis, the subgroup-determining variable itself was excluded from the models. Hazard ratios and 95% CIs were estimated for each of the 20 imputed datasets, and combined into pooled values. P-values for the interaction of each variable with log-transformed resistin were estimated using Wald Chi-squared tests and presented as the median of the P-values from the 20 imputed data analyses.

4 | DISCUSSION

In our study, we found that pre-diagnostic resistin concentrations were not associated with CRC-specific or all-cause mortality risk among individuals with incident CRC. No association was found when performing subgroup analysis by sex, tumor subsite, tumor stage, BMI and time to CRC diagnosis.

To our knowledge, our study is the first to investigate the association between pre-diagnostic resistin concentrations and mortality in CRC patients. Therefore, we cannot compare our results directly to those of other published studies. The REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study investigated the association between baseline resistin concentrations and cancer mortality among community-dwelling adults in the United States and found no significant relationship with mortality due to obesity-related cancers (defined as breast, colorectal, kidney, pancreas, stomach, endometrial and esophagus cancer) or total cancer mortality.31 However, in subgroup analyses, the study found a statistically significant association between higher resistin concentrations and risk of cancer mortality in Black (but not in White) participants.31 In another study of 599 elderly Finnish patients with hypertension, resistin

concentrations at recruitment were significantly associated with all-cause mortality (HR<sub>One standard deviation increment in log-resistin</sub>, 5.48, 95% CI: 1.10-27.25).32 However, the findings of these studies are more difficult to interpret because resistin has been associated with cardiovascular disease (CVD) mortality in high-CVD-risk populations33; thus, any relationship of resistin with total mortality may be driven by risk of CVD mortality. Although case-control studies suggest that persons with CRC have higher resistin concentrations than controls,16 we have previously found in a prospective study that in persons free of cancer at baseline resistin is not significantly associated with risk of incident CRC.17 Findings from that prospective study are consistent with a previous case-cohort study within the Women's Health Initiative study,18 and our recent Mendelian Randomization study.19 Together with the results from the present analysis, evidence suggests that circulating resistin concentrations are neither related to risk of CRC nor mortality among persons with CRC.

In our study, we used the multiple imputation approach to impute the missing data in residuals(WC~BMI+Height) in all models and the missing data of tumor stage in subgroup analysis. Complete-case analyses were used for sensitivity analyses, however, they may not provide an unbiased estimate for the association since participants with missing
data for cancer stage also have higher levels of resistin than those with complete stage data. Furthermore, with the exception of a marginally significant association between resistin and CRC-specific mortality among people with stage IV CRC at diagnosis using a cause-specific hazard model, the main results of the complete-case analyses did not differ from analyses of the multiple-imputation data. The results were not replicated using sub-distribution hazard as an alternative competing risk model, suggesting that the marginally significant association may be due to chance. Nevertheless, the sample sizes of the subgroups were not large enough to ensure a reasonably precise point estimate. Future large sample studies should be implemented to substantiate the findings.

Measurements of resistin concentrations after CRC diagnosis ("post-diagnosis") could be influenced by systemic inflammatory reactions during the expansion of the tumor or by treatment (eg, surgery, chemotherapy and radiotherapy), leading to higher circulating resistin concentrations compared to healthy individuals as observed in case-control studies.¹⁶ However, it is difficult to determine whether this increase is due to the expansion of the tumor or to the effects of the treatment. Ideally, to determine whether resistin is influenced by tumors, resistin should be measured at the time of CRC diagnosis, before any treatment or prescription is given to the patients. While this setting is difficult to protocol in clinical practice, a relatively large number of CRC patients were needed to estimate the effect, indicating the challenges in real-world implementation. Nevertheless, future studies employing this setting will provide more insights into the relationship being studied. In contrast, as the first study to investigate this relationship, the current study measured resistin at a median of 4.8 years before CRC diagnosis to avoid the aforementioned ambiguity. This can be considered a strength of our study. As such, resistin concentrations are not likely to be influenced by the tumor but rather reflect "steady-state conditions" of inflammation. This is supported by studies showing that resistin levels are stable within individuals over 3 years.²⁴ Although we cannot rule out that our analysis included persons with existing but not yet diagnosed CRC at baseline, in the subgroup analysis by time from baseline to CRC diagnosis (≤2 years, >2 to ≤8 years and >8 years), we found no difference in the association between resistin and CRC-specific mortality between these three groups. High resistin levels in the "steady-state" conditions could be caused by CLGSI, which many conditions may have triggered. Under CLGSI, C-reactive protein (CRP), an inflammatory marker, was not significantly associated with CRC mortality, as previously reported.³⁴ Of the conditions that trigger CLGSI, obesity is typical, as inflammation is initially induced in white adipose tissue and extends to other tissues and circulation, resulting in CLGSI.³⁵ Pre-diagnostic obesity was reported as a significant factor in the risk of death in patients with non-metastatic CRC.¹⁴,³⁶ However, in a previous study using EPIC data, we found no significant correlation between pre-diagnostic resistin and BMI (correlation coefficient = -0.02, P = .52),¹⁷ but a weakly significant positive correlation with CRP concentrations in control participants (correlation coefficient = 0.12, P < .01).¹⁷ The findings were consistent with several population-based studies that showed a significant but comparatively weak positive association

between resistin and BMI in humans (modest absolute value of correlation coefficients).³⁷⁻³⁹ The null or least significant correlation between resistin and BMI as well as CRP might partially help explain the lack of association between resistin and CRC mortality in our study. Of note, CLGSI is not only caused by obesity, but also by other triggers such as chronic infections, physical inactivity, microbiome dysbiosis, westernized diet, social isolation, psychological stress, disrupted sleep and circadian rhythm disruption and xenobiotic exposure including tobacco smoking.⁴⁰ All inflammatory conditions are major determinants of circulating resistin concentrations.⁸ Thus, many non-cancerous conditions could induce the "steady state conditions" of resistin, such as atherosclerosis,⁸ non-alcoholic steatohepatitis,⁸ inflammatory bowel disease,⁴¹ low-grade and high-grade dysplasia adenoma.⁴²

Our study has several strengths, including the prospective design, population data with a large sample size, pre-diagnostic exposure data, detailed outcome data extracted from medical records and a long follow-up period, which is reasonable for follow-up of CRC incidence and mortality after CRC. Further, we adjusted for several variables that may act as confounders or competing exposures. However, there were several limitations in our study. First, we acknowledge that the generalizability of the findings to different populations or ethnicities other than European is limited by the use of data from European countries only. Second, we had only a single measurement of resistin concentration available for each participant, and long-term storage of biosamples at -196°C may affect the stability of resistin, and biological fluctuations over time may dilute biomarker-disease associations. However, previous studies suggest that resistin concentrations are stable even when stored for long periods (roughly 3 years), thus, supporting the use of baseline resistin concentrations in long-term follow-up population studies.²³,²⁴ In line with this, we found no correlation between resistin concentrations and storage time in our dataset (data not shown). Third, in our study, we relied on a single measurement of resistin while resistin levels may have changed in individuals whose resistin was measured closer to their CRC diagnosis. We do not have data for within-person variation of resistin concentrations in our study. However, we checked the scatter plot of resistin concentrations and time to CRC diagnosis which revealed no pattern deviating from a zero-slope line (Figure S3), suggesting that there is no significant change in resistin levels when measured further or closer to the time of CRC diagnosis. However, future studies with repeated resistin measurements are necessary to confirm this assumption. Fourth, we lacked data on other comorbidities that may be related to inflammation and to mortality, such as cardiovascular disease, inflammatory bowel disease and asthma, which might affect inflammatory pathways. Nevertheless, given that we found no significant association of resistin with all-cause mortality, which was driven by risk of CVD mortality, it is unlikely that non-adjustment for these potential confounders may have affected our results because such confounding, if anything, would be expected to bias relative risk estimates away from the null. Further, additional adjustments for all inflammatory and metabolic biomarkers (total cholesterol, triglycerides, low-density lipoprotein cholesterol, C-peptide, HbA1c and CRP) did not
data for cancer stage also have higher levels of resistin than those with complete stage data. Furthermore, with the exception of a marginally significant association between resistin and CRC-specific mortality among people with stage IV CRC at diagnosis using a cause-specific hazard model, the main results of the complete-case analyses did not differ from analyses of the multiple-imputation data. The results were not replicated using sub-distribution hazard as an alternative competing risk model, suggesting that the marginally significant association may be due to chance. Nevertheless, the sample sizes of the subgroups were not large enough to ensure a reasonably precise point estimate. Future large sample studies should be implemented to substantiate the findings.

Measurements of resistin concentrations after CRC diagnosis ("post-diagnosis") could be influenced by systemic inflammatory reactions during the expansion of the tumor or by treatment (eg, surgery, chemotherapy and radiotherapy), leading to higher circulating resistin concentrations compared to healthy individuals as observed in case-control studies.¹⁶ However, it is difficult to determine whether this increase is due to the expansion of the tumor or to the effects of the treatment. Ideally, to determine whether resistin is influenced by tumors, resistin should be measured at the time of CRC diagnosis, before any treatment or prescription is given to the patients. While this setting is difficult to protocol in clinical practice, a relatively large number of CRC patients were needed to estimate the effect, indicating the challenges in real-world implementation. Nevertheless, future studies employing this setting will provide more insights into the relationship being studied. In contrast, as the first study to investigate this relationship, the current study measured resistin at a median of 4.8 years before CRC diagnosis to avoid the aforementioned ambiguity. This can be considered a strength of our study. As such, resistin concentrations are not likely to be influenced by the tumor but rather reflect "steady-state conditions" of inflammation. This is supported by studies showing that resistin levels are stable within individuals over 3 years.²⁴ Although we cannot rule out that our analysis included persons with existing but not yet diagnosed CRC at baseline, in the subgroup analysis by time from baseline to CRC diagnosis (≤2 years, >2 to ≤8 years and >8 years), we found no difference in the association between resistin and CRC-specific mortality between these three groups. High resistin levels in the "steady-state" conditions could be caused by CLGSI, which many conditions may have triggered. Under CLGSI, C-reactive protein (CRP), an inflammatory marker, was not significantly associated with CRC mortality, as previously reported.³⁴ Of the conditions that trigger CLGSI, obesity is typical, as inflammation is initially induced in white adipose tissue and extends to other tissues and circulation, resulting in CLGSI.³⁵ Pre-diagnostic obesity was reported as a significant factor in the risk of death in patients with non-metastatic CRC.¹⁴,³⁶ However, in a previous study using EPIC data, we found no significant correlation between pre-diagnostic resistin and BMI (correlation coefficient = -0.02, P = .52),¹⁷ but a weakly significant positive correlation with CRP concentrations in control participants (correlation coefficient = 0.12, P < .01).¹⁷ The findings were consistent with several population-based studies that showed a significant but comparatively weak positive association

between resistin and BMI in humans (modest absolute value of correlation coefficients).³⁷⁻³⁹ The null or least significant correlation between resistin and BMI as well as CRP might partially help explain the lack of association between resistin and CRC mortality in our study. Of note, CLGSI is not only caused by obesity, but also by other triggers such as chronic infections, physical inactivity, microbiome dysbiosis, westernized diet, social isolation, psychological stress, disrupted sleep and circadian rhythm disruption and xenobiotic exposure including tobacco smoking.⁴⁰ All inflammatory conditions are major determinants of circulating resistin concentrations.⁸ Thus, many non-cancerous conditions could induce the "steady state conditions" of resistin, such as atherosclerosis,⁸ non-alcoholic steatohepatitis,⁸ inflammatory bowel disease,⁴¹ low-grade and high-grade dysplasia adenoma.⁴²

Our study has several strengths, including the prospective design, population data with a large sample size, pre-diagnostic exposure data, detailed outcome data extracted from medical records and a long follow-up period, which is reasonable for follow-up of CRC incidence and mortality after CRC. Further, we adjusted for several variables that may act as confounders or competing exposures. However, there were several limitations in our study. First, we acknowledge that the generalizability of the findings to different populations or ethnicities other than European is limited by the use of data from European countries only. Second, we had only a single measurement of resistin concentration available for each participant, and long-term storage of biosamples at -196°C may affect the stability of resistin, and biological fluctuations over time may dilute biomarker-disease associations. However, previous studies suggest that resistin concentrations are stable even when stored for long periods (roughly 3 years), thus, supporting the use of baseline resistin concentrations in long-term follow-up population studies.²³,²⁴ In line with this, we found no correlation between resistin concentrations and storage time in our dataset (data not shown). Third, in our study, we relied on a single measurement of resistin while resistin levels may have changed in individuals whose resistin was measured closer to their CRC diagnosis. We do not have data for within-person variation of resistin concentrations in our study. However, we checked the scatter plot of resistin concentrations and time to CRC diagnosis which revealed no pattern deviating from a zero-slope line (Figure S3), suggesting that there is no significant change in resistin levels when measured further or closer to the time of CRC diagnosis. However, future studies with repeated resistin measurements are necessary to confirm this assumption. Fourth, we lacked data on other comorbidities that may be related to inflammation and to mortality, such as cardiovascular disease, inflammatory bowel disease and asthma, which might affect inflammatory pathways. Nevertheless, given that we found no significant association of resistin with all-cause mortality, which was driven by risk of CVD mortality, it is unlikely that non-adjustment for these potential confounders may have affected our results because such confounding, if anything, would be expected to bias relative risk estimates away from the null. Further, additional adjustments for all inflammatory and metabolic biomarkers (total cholesterol, triglycerides, low-density lipoprotein cholesterol, C-peptide, HbA1c and CRP) did not
substantially change the results (data not shown). Of note, there
were 32%-40% missing data for these biomarkers in our dataset,
thus, HRs estimated adjusted for all variables may have insufficient
statistical power.

In conclusion, data from this prospective study suggest that pre-
diagnostic resistin concentrations, measured around 5 years before
diagnosis and assumed to be stable in the human body, are not associ-
ated with CRC survival. As the current study is the first prospective
study on this topic, it warrants confirmation by large sample size pro-
spective studies.

AUTHOR CONTRIBUTIONS

The work reported in the article has been performed by the authors,
unless clearly specified in the text. **Thu Thi Pham:** conducted the
research, analyzed data, wrote the article and had primary responsibil-
ity for the final content; **Katharina Nimptsch:** designed the project, val-
idated data analyses and contributed to article writing; **Krasimira
Aleksandrova:** were part of the writing group and helped with the pro-
ject design, data analysis and article writing; **Mazda Jenab:** were part
of the writing group and helped with the project design, data analysis
and article writing; **Veronika Fedirko:** were part of the writing group
and helped with the project design, data analysis and article writing;
**Kana Wu:** were part of the writing group and helped with the project
design, data analysis and article writing; **Anne Kirstine Eriksen:** contrib-
uted data and edited the article; **Anne Tjønneland:** contributed data
and edited the article; **Gianluca Severi:** contributed data and edited the
article; **Joseph Rothwell:** contributed data and edited the article;
**Rudolf Kaaks:** contributed data and edited the article; **Verena Katzke:**
contributed data and edited the article; **Alberto Catalano:** contributed
data and edited the article; **Claudia Agnoli:** contributed data and edited
the article; **Giovanna Masala:** contributed data and edited the article;
**Maria Santucci De Magistris:** contributed data and edited the article;
**Rosario Tumino:** contributed data and edited the article; **Roel Vermeu-
len:** contributed data and edited the article; **Amaia Aizpurua:** contrib-
uted data and edited the article; **Camino Trobajo-Sanmartín:** contributed data and edited the article; **María-Dolores Chirlaque:** con-
tributed data and edited the article; **María-Jose Sánchez:** contributed
data and edited the article; **Sai San Moon Lu:** contributed data and edi-
ted the article; **Amanda J. Cross:** contributed data and edited the arti-
cle; **Sofia Christakoudi:** contributed data and edited the article;
**Elisabete Weiderpass:** contributed data and edited the article; and
**Tobias Pischon:** designed and supervised the project and contributed
to interpretation of data and writing of the article. All authors reviewed
the article. The author(s) read and approved the final article.

AFFILIATIONS

<footnote n="1">¹Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany</footnote>

<footnote n="2">²Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Hum-boldt-Universität zu Berlin, Berlin, Germany</footnote>

<footnote n="3">³Department of Epidemiological Methods and Etiological Research,
Leibniz Institute for Prevention Research and Epidemiology—BIPS,
Bremen, Germany</footnote>

<footnote n="4">⁴Faculty of Human and Health Sciences, University of Bremen,
Bremen, Germany</footnote>

<footnote n="5">⁵Nutrition and Metabolism Branch, International Agency for Research
on Cancer (IARC-WHO), World Health Organization, Lyon, France</footnote>

<footnote n="6">⁶Department of Epidemiology, University of Texas M.D. Anderson
Cancer Center, Houston, Texas, USA</footnote>

<footnote n="7">⁷Department of Epidemiology, Rollins School of Public Health, Emory
University, Atlanta, Georgia, USA</footnote>

<footnote n="8">⁸Department of Nutrition, Harvard T.H. Chan School of Public Health,
Boston, Massachusetts, USA</footnote>

<footnote n="9">⁹Danish Cancer Society Research Center, Copenhagen, Denmark</footnote>

<footnote n="10">¹⁰Department of Public Health, University of Copenhagen,
Copenhagen, Denmark</footnote>

<footnote n="11">¹¹Université Paris-Saclay, UVSQ, Inserm “Exposome and Heredity”
Team, Villejuif, France</footnote>

<footnote n="12">¹²Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany</footnote>

<footnote n="13">¹³Centre for Biostatistics, Epidemiology, and Public Health,
Department of Clinical and Biological Sciences, University of Turin,
Orbassano, Italy</footnote>

<footnote n="14">¹⁴Epidemiology and Prevention Unit, Department of Epidemiology
and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori,
Milan, Italy</footnote>

<footnote n="15">¹⁵Institute for Cancer Research, Prevention and Clinical Network
(ISPRO), Florence, Italy</footnote>

<footnote n="16">¹⁶Azienda Ospedaliera Universitaria (AOU) Federico II, Naples, Italy</footnote>

<footnote n="17">¹⁷Hyblean Association for Epidemiological Research, AIRE ONLUS,
Ragusa, Italy</footnote>

<footnote n="18">¹⁸Institute of Risk Assessment Sciences, Utrecht University, Utrecht,
The Netherlands</footnote>

<footnote n="19">¹⁹Ministry of Health of the Basque Government, Sub Directorate for
Public Health and Addictions of Gipuzkoa, San Sebastian, Spain</footnote>

<footnote n="20">²⁰Biodonostia Health Research Institute, Epidemiology of Chronic and
Communicable Diseases Group, San Sebastián, Spain</footnote>

<footnote n="21">²¹Instituto de Salud Pública y Laboral de Navarra, Pamplona, Spain</footnote>

<footnote n="22">²²Centro de Investigación Biomédica en Red de Epidemiología y Salud
Pública (CIBERESP), Madrid, Spain</footnote>

<footnote n="23">²³Navarre Institute for Health Research (IdiSNA), Pamplona, Spain</footnote>

<footnote n="24">²⁴Department of Epidemiology, Regional Health Council, IMIB-
Arrixaca, Murcia University, Murcia, Spain</footnote>

<footnote n="25">²⁵Escuela Andaluza de Salud Pública (EASP), Granada, Spain</footnote>

<footnote n="26">²⁶Instituto de Investigación Biosanitaria ibs.GRANADA, Granada,
Spain</footnote>

<footnote n="27">²⁷Department of Preventive Medicine and Public Health, University
of Granada, Granada, Spain</footnote>

<footnote n="28">²⁸Department of Radiation Sciences, Oncology Unit, Umeå University,
Umeå, Sweden</footnote>

<footnote n="29">²⁹Department of Epidemiology and Biostatistics, School of Public
Health, Imperial College London, London, UK</footnote>
substantially change the results (data not shown). Of note, there
were 32%-40% missing data for these biomarkers in our dataset,
thus, HRs estimated adjusted for all variables may have insufficient
statistical power.

In conclusion, data from this prospective study suggest that pre-
diagnostic resistin concentrations, measured around 5 years before
diagnosis and assumed to be stable in the human body, are not associ-
ated with CRC survival. As the current study is the first prospective
study on this topic, it warrants confirmation by large sample size pro-
spective studies.

AUTHOR CONTRIBUTIONS

The work reported in the article has been performed by the authors,
unless clearly specified in the text. **Thu Thi Pham:** conducted the
research, analyzed data, wrote the article and had primary responsibil-
ity for the final content; **Katharina Nimptsch:** designed the project, val-
idated data analyses and contributed to article writing; **Krasimira
Aleksandrova:** were part of the writing group and helped with the pro-
ject design, data analysis and article writing; **Mazda Jenab:** were part
of the writing group and helped with the project design, data analysis
and article writing; **Veronika Fedirko:** were part of the writing group
and helped with the project design, data analysis and article writing;
**Kana Wu:** were part of the writing group and helped with the project
design, data analysis and article writing; **Anne Kirstine Eriksen:** contrib-
uted data and edited the article; **Anne Tjønneland:** contributed data
and edited the article; **Gianluca Severi:** contributed data and edited the
article; **Joseph Rothwell:** contributed data and edited the article;
**Rudolf Kaaks:** contributed data and edited the article; **Verena Katzke:**
contributed data and edited the article; **Alberto Catalano:** contributed
data and edited the article; **Claudia Agnoli:** contributed data and edited
the article; **Giovanna Masala:** contributed data and edited the article;
**Maria Santucci De Magistris:** contributed data and edited the article;
**Rosario Tumino:** contributed data and edited the article; **Roel Vermeu-
len:** contributed data and edited the article; **Amaia Aizpurua:** contrib-
uted data and edited the article; **Camino Trobajo-Sanmartín:** contributed data and edited the article; **María-Dolores Chirlaque:** con-
tributed data and edited the article; **María-Jose Sánchez:** contributed
data and edited the article; **Sai San Moon Lu:** contributed data and edi-
ted the article; **Amanda J. Cross:** contributed data and edited the arti-
cle; **Sofia Christakoudi:** contributed data and edited the article;
**Elisabete Weiderpass:** contributed data and edited the article; and
**Tobias Pischon:** designed and supervised the project and contributed
to interpretation of data and writing of the article. All authors reviewed
the article. The author(s) read and approved the final article.

AFFILIATIONS

<footnote n="1">¹Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany</footnote>

<footnote n="2">²Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Hum-boldt-Universität zu Berlin, Berlin, Germany</footnote>

<footnote n="3">³Department of Epidemiological Methods and Etiological Research,
Leibniz Institute for Prevention Research and Epidemiology—BIPS,
Bremen, Germany</footnote>

<footnote n="4">⁴Faculty of Human and Health Sciences, University of Bremen,
Bremen, Germany</footnote>

<footnote n="5">⁵Nutrition and Metabolism Branch, International Agency for Research
on Cancer (IARC-WHO), World Health Organization, Lyon, France</footnote>

<footnote n="6">⁶Department of Epidemiology, University of Texas M.D. Anderson
Cancer Center, Houston, Texas, USA</footnote>

<footnote n="7">⁷Department of Epidemiology, Rollins School of Public Health, Emory
University, Atlanta, Georgia, USA</footnote>

<footnote n="8">⁸Department of Nutrition, Harvard T.H. Chan School of Public Health,
Boston, Massachusetts, USA</footnote>

<footnote n="9">⁹Danish Cancer Society Research Center, Copenhagen, Denmark</footnote>

<footnote n="10">¹⁰Department of Public Health, University of Copenhagen,
Copenhagen, Denmark</footnote>

<footnote n="11">¹¹Université Paris-Saclay, UVSQ, Inserm "Exposome and Heredity"
Team, Villejuif, France</footnote>

<footnote n="12">¹²Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany</footnote>

<footnote n="13">¹³Centre for Biostatistics, Epidemiology, and Public Health,
Department of Clinical and Biological Sciences, University of Turin,
Orbassano, Italy</footnote>

<footnote n="14">¹⁴Epidemiology and Prevention Unit, Department of Epidemiology
and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori,
Milan, Italy</footnote>

<footnote n="15">¹⁵Institute for Cancer Research, Prevention and Clinical Network
(ISPRO), Florence, Italy</footnote>

<footnote n="16">¹⁶Azienda Ospedaliera Universitaria (AOU) Federico II, Naples, Italy</footnote>

<footnote n="17">¹⁷Hyblean Association for Epidemiological Research, AIRE ONLUS,
Ragusa, Italy</footnote>

<footnote n="18">¹⁸Institute of Risk Assessment Sciences, Utrecht University, Utrecht,
The Netherlands</footnote>

<footnote n="19">¹⁹Ministry of Health of the Basque Government, Sub Directorate for
Public Health and Addictions of Gipuzkoa, San Sebastian, Spain</footnote>

<footnote n="20">²⁰Biodonostia Health Research Institute, Epidemiology of Chronic and
Communicable Diseases Group, San Sebastián, Spain</footnote>

<footnote n="21">²¹Instituto de Salud Pública y Laboral de Navarra, Pamplona, Spain</footnote>

<footnote n="22">²²Centro de Investigación Biomédica en Red de Epidemiología y Salud
Pública (CIBERESP), Madrid, Spain</footnote>

<footnote n="23">²³Navarre Institute for Health Research (IdiSNA), Pamplona, Spain</footnote>

<footnote n="24">²⁴Department of Epidemiology, Regional Health Council, IMIB-
Arrixaca, Murcia University, Murcia, Spain</footnote>

<footnote n="25">²⁵Escuela Andaluza de Salud Pública (EASP), Granada, Spain</footnote>

<footnote n="26">²⁶Instituto de Investigación Biosanitaria ibs.GRANADA, Granada,
Spain</footnote>

<footnote n="27">²⁷Department of Preventive Medicine and Public Health, University
of Granada, Granada, Spain</footnote>

<footnote n="28">²⁸Department of Radiation Sciences, Oncology Unit, Umeå University,
Umeå, Sweden</footnote>

<footnote n="29">²⁹Department of Epidemiology and Biostatistics, School of Public
Health, Imperial College London, London, UK</footnote>
³⁰Department of Inflammation Biology, School of Immunology and
Microbial Sciences, King's College London, London, UK

³¹International Agency for Research on Cancer, World Health
Organization, Lyon, France

³²Biobank Technology Platform, Max-Delbrueck-Center for Molecular
Medicine in the Helmholtz Association (MDC), Berlin, Germany

³³Core Facility Biobank, Berlin Institute of Health, Charité-
Universitätsmedizin Berlin, Berlin, Germany

## ACKNOWLEDGEMENTS

The authors thank all participants in the EPIC study for their out-
standing cooperation. We acknowledge the use of data and biolog-
ical samples from the following EPIC sub-cohorts: EPIC-Denmark
Aarhus Centre, PI Kim Overvad; EPIC-Germany Postdam,
Pl. Matthias Schulze; EPIC - The Netherlands - Bilthoven center,
Pl. Monique Verschuren; EPIC-Spain Asturias center, PI Ramón
Quirós; EPIC-UK Norfolk center, PI Nick Wareham; EPIC-UK
Oxford center, Pl. Tim Key. We thank the National Institute for
Public Health and the Environment (RIVM), Bilthoven, the Nether-
lands, for their contribution and ongoing support to the EPIC
Study. Open Access funding enabled and organized by Pro-
jekt DEAL.

## FUNDING INFORMATION

The coordination of EPIC is financially supported by the Interna-
tional Agency for Research on Cancer (IARC) and by the Depart-
ment of Epidemiology and Biostatistics, School of Public Health,
Imperial College London, which has additional infrastructure sup-
port provided by the NIHR Imperial Biomedical Research Centre
(BRC). The national cohorts are supported by the Danish Cancer
Society (Denmark); Ligue contre le Cancer, Institut Gustave
Roussy, Mutuelle Générale de l'Education Nationale, Institut
National de la Santé et de la Recherche Médicale (INSERM,
France); Deutsche Krebshilfe, Deutsches Krebsforschungszen-
trum and the Federal Ministry of Education and Research
(Germany); the Hellenic Health Foundation (Greece); the Italian
Association for Research on Cancer (AIRC) and the National
Research Council (Italy); the Dutch Ministry of Public Health,
Welfare and Sports (VWS), the Netherlands Cancer Registry
(NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON
(Zorg Onderzoek Nederland), the World Cancer Research Fund
(WCRF) and Statistics Netherlands (The Netherlands); ERC-
2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence
programme on Food, Nutrition and Health (Norway); Health
Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII),
Regional Governments of Andalucía, Asturias, Basque Country,
Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); the
Swedish Cancer Society, the Swedish Scientific Council and the
Regional Government of Skåne and Västerbotten (Sweden); Can-
cer Research UK, Medical Research Council (United Kingdom).

V. Fedirko is a Cancer Prevention and Research Institute of Texas
(CPRIT) Scholar of Cancer Research and is supported by the CPRIT
Rising Stars Award (Grant ID RR200056).

## CONFLICT OF INTEREST STATEMENT

Kana Wu is currently employed and is a shareholder of Vertex Phar-
maceuticals. This research was not funded by this commercial entity.
The authors declare no other conflicts of interest.

## DATA AVAILABILITY STATEMENT

EPIC data are accessible by written request to and approval of the
EPIC Steering Committee. Instructions and further information on
the EPIC data access policy can be found here https://epic.iarc.fr/
access/index.php. Further information and aggregated data are
available from the corresponding author upon reasonable request.

## ETHICS STATEMENT

Informed consent was obtained from all participants involved in the
study. The EPIC study was conducted according to the guidelines of
the Declaration of Helsinki, and approved by the Ethical Review Board
of the International Agency for Research on Cancer (IARC) and the
ethical committees of the participating centers.

## ORCID

Thu Thi Pham ID https://orcid.org/0000-0002-2129-0911

Katharina Nimptsch ID https://orcid.org/0000-0001-7877-205X

Veronika Fedirko ID https://orcid.org/0000-0002-7805-9913

Gianluca Severi ID https://orcid.org/0000-0001-7157-419X

Sofia Christakoudi ID https://orcid.org/0000-0001-9219-4436

Elisabete Weiderpass ID https://orcid.org/0000-0003-2237-0128

Tobias Pischon ID https://orcid.org/0000-0003-1568-767X

## REFERENCES

1. Steppan CM, Bailey ST, Bhat S, et al. The hormone resistin links obe-sity to diabetes. *Nature*. 2001;409(6818):307-312.

2. Patel L, Buckels AC, Kinghorn IJ, et al. Resistin is expressed in human macrophages and directly regulated by PPARγ activators. *Biochem Biophys Res Commun*. 2003;300(2):472-476.

3. Lehrke M, Reilly MP, Millington SC, Iqbal N, Rader DJ, Lazar MA. An inflammatory cascade leading to hyperresistinemia in humans. *PLoS Med*. 2004;1(2):e45.

4. Anderson PD, Mehta NN, Wolfe ML, et al. Innate immunity modulates adipokines in humans. *J Clin Endocrinol Metabol*. 2007;92(6):2272-2279. doi:10.1210/jc.2006-2545

5. Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. *J Immunol*. 2005; 174(9):5789-5795. doi:10.4049/jimmunol.174.9.5789

6. Zuniga MC, Raghuraman G, Hitchner E, Weyand C, Robinson W, Zhou W. PKC-epsilon and TLR4 synergistically regulate resistin-mediated inflammation in human macrophages. *Atherosclerosis*. 2017;259:51-59. doi:10.1016/j.atherosclerosis.2017.02.021

7. Mu H, Ohashi R, Yan S, et al. Adipokine resistin promotes in vitro angiogenesis of human endothelial cells. *Cardiovasc Res*. 2006;70(1):146-157.

8. Codoñer-Franch P, Alonso-Iglesias E. Resistin: insulin resistance to malignancy. *Clin Chim Acta*. 2015;438:46-54.

9. Riondino S, Roselli M, Palmirotta R, Della-Morte D, Ferroni P, Guadagni F. Obesity and colorectal cancer: role of adipokines in tumor initiation and progression. *World J Gastroenterol*. 2014;20(18):5177-5190.

10. Tuomisto AE, Mäkinen MJ, Väyrynen JP. Systemic inflammation in colorectal cancer: underlying factors, effects, and prognostic
³⁰Department of Inflammation Biology, School of Immunology and
Microbial Sciences, King's College London, London, UK

³¹International Agency for Research on Cancer, World Health
Organization, Lyon, France

³²Biobank Technology Platform, Max-Delbrueck-Center for Molecular
Medicine in the Helmholtz Association (MDC), Berlin, Germany

³³Core Facility Biobank, Berlin Institute of Health, Charité-
Universitätsmedizin Berlin, Berlin, Germany

## ACKNOWLEDGEMENTS

The authors thank all participants in the EPIC study for their out-
standing cooperation. We acknowledge the use of data and biolog-
ical samples from the following EPIC sub-cohorts: EPIC-Denmark
Aarhus Centre, PI Kim Overvad; EPIC-Germany Postdam,
Pl. Matthias Schulze; EPIC - The Netherlands - Bilthoven center,
Pl. Monique Verschuren; EPIC-Spain Asturias center, PI Ramón
Quirós; EPIC-UK Norfolk center, PI Nick Wareham; EPIC-UK
Oxford center, Pl. Tim Key. We thank the National Institute for
Public Health and the Environment (RIVM), Bilthoven, the Nether-
lands, for their contribution and ongoing support to the EPIC
Study. Open Access funding enabled and organized by Pro-
jekt DEAL.

## FUNDING INFORMATION

The coordination of EPIC is financially supported by the Interna-
tional Agency for Research on Cancer (IARC) and by the Depart-
ment of Epidemiology and Biostatistics, School of Public Health,
Imperial College London, which has additional infrastructure sup-
port provided by the NIHR Imperial Biomedical Research Centre
(BRC). The national cohorts are supported by the Danish Cancer
Society (Denmark); Ligue contre le Cancer, Institut Gustave
Roussy, Mutuelle Générale de l'Education Nationale, Institut
National de la Santé et de la Recherche Médicale (INSERM,
France); Deutsche Krebshilfe, Deutsches Krebsforschungszen-
trum and the Federal Ministry of Education and Research
(Germany); the Hellenic Health Foundation (Greece); the Italian
Association for Research on Cancer (AIRC) and the National
Research Council (Italy); the Dutch Ministry of Public Health,
Welfare and Sports (VWS), the Netherlands Cancer Registry
(NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON
(Zorg Onderzoek Nederland), the World Cancer Research Fund
(WCRF) and Statistics Netherlands (The Netherlands); ERC-
2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence
programme on Food, Nutrition and Health (Norway); Health
Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII),
Regional Governments of Andalucía, Asturias, Basque Country,
Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); the
Swedish Cancer Society, the Swedish Scientific Council and the
Regional Government of Skåne and Västerbotten (Sweden); Can-
cer Research UK, Medical Research Council (United Kingdom).

V. Fedirko is a Cancer Prevention and Research Institute of Texas
(CPRIT) Scholar of Cancer Research and is supported by the CPRIT
Rising Stars Award (Grant ID RR200056).

## CONFLICT OF INTEREST STATEMENT

Kana Wu is currently employed and is a shareholder of Vertex Phar-
maceuticals. This research was not funded by this commercial entity.
The authors declare no other conflicts of interest.

## DATA AVAILABILITY STATEMENT

EPIC data are accessible by written request to and approval of the
EPIC Steering Committee. Instructions and further information on
the EPIC data access policy can be found here https://epic.iarc.fr/
access/index.php. Further information and aggregated data are
available from the corresponding author upon reasonable request.

## ETHICS STATEMENT

Informed consent was obtained from all participants involved in the
study. The EPIC study was conducted according to the guidelines of
the Declaration of Helsinki, and approved by the Ethical Review Board
of the International Agency for Research on Cancer (IARC) and the
ethical committees of the participating centers.

## ORCID

Thu Thi Pham ID https://orcid.org/0000-0002-2129-0911

Katharina Nimptsch ID https://orcid.org/0000-0001-7877-205X

Veronika Fedirko ID https://orcid.org/0000-0002-7805-9913

Gianluca Severi ID https://orcid.org/0000-0001-7157-419X

Sofia Christakoudi ID https://orcid.org/0000-0001-9219-4436

Elisabete Weiderpass ID https://orcid.org/0000-0003-2237-0128

Tobias Pischon ID https://orcid.org/0000-0003-1568-767X

## REFERENCES

1. Steppan CM, Bailey ST, Bhat S, et al. The hormone resistin links obe-sity to diabetes. *Nature*. 2001;409(6818):307-312.

2. Patel L, Buckels AC, Kinghorn IJ, et al. Resistin is expressed in human macrophages and directly regulated by PPARγ activators. *Biochem Biophys Res Commun*. 2003;300(2):472-476.

3. Lehrke M, Reilly MP, Millington SC, Iqbal N, Rader DJ, Lazar MA. An inflammatory cascade leading to hyperresistinemia in humans. *PLoS Med*. 2004;1(2):e45.

4. Anderson PD, Mehta NN, Wolfe ML, et al. Innate immunity modulates adipokines in humans. *J Clin Endocrinol Metabol*. 2007;92(6):2272-2279. doi:10.1210/jc.2006-2545

5. Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. *J Immunol*. 2005;174(9):5789-5795. doi:10.4049/jimmunol.174.9.5789

6. Zuniga MC, Raghuraman G, Hitchner E, Weyand C, Robinson W, Zhou W. PKC-epsilon and TLR4 synergistically regulate resistin-mediated inflammation in human macrophages. *Atherosclerosis*. 2017;259:51-59. doi:10.1016/j.atherosclerosis.2017.02.021

7. Mu H, Ohashi R, Yan S, et al. Adipokine resistin promotes in vitro angiogenesis of human endothelial cells. *Cardiovasc Res*. 2006;70(1):146-157.

8. Codoñer-Franch P, Alonso-Iglesias E. Resistin: insulin resistance to malignancy. *Clin Chim Acta*. 2015;438:46-54.

9. Riondino S, Roselli M, Palmirotta R, Della-Morte D, Ferroni P, Guadagni F. Obesity and colorectal cancer: role of adipokines in tumor initiation and progression. *World J Gastroenterol*. 2014;20(18):5177-5190.

10. Tuomisto AE, Mäkinen MJ, Väyrynen JP. Systemic inflammation in colorectal cancer: underlying factors, effects, and prognostic
significance. *World J Gastroenterol.* 2019;25(31):4383-4404. doi:10.3748/wj.v25.i31.4383

11. Shyu KG, Chua SK, Wang BW, Kuan P. Mechanism of inhibitory effect of atorvastatin on resistin expression induced by tumor necrosis factor-alpha in macrophages. *J Biomed Sci.* 2009;16(1):50. doi:10.1186/1423-0127-16-50

12. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. *CA Cancer J Clin.* 2021;71(3):209-249. doi:10.3322/caac.21660

13. Fedirko V, Romieu I, Aleksandrova K, et al. Pre-diagnostic anthropometry and survival after colorectal cancer diagnosis in Western European populations. *Int J Cancer.* 2014;135(8):1949-1960.

14. Lee J, Meyerhardt JA, Giovannucci E, Jeon JY. Association between body mass index and prognosis of colorectal cancer: a meta-analysis of prospective cohort studies. *PLoS One.* 2015;10(3):e0120706. doi:10.1371/journal.pone.0120706

15. Nimptsch K, Pischon T. Obesity biomarkers, metabolism and risk of cancer: an epidemiological perspective. *Recent Results Cancer Res.* 2016;208:199-217. doi:10.1007/978-3-319-42542-9_11

16. Yang G, Fan W, Luo B, et al. Circulating resistin levels and risk of colorectal cancer: a meta-analysis. *Biomed Res Int.* 2016;2016:1-11.

17. Pham T-T, Nimptsch K, Aleksandrova K, et al. Pre-diagnostic circulating resistin concentrations are not associated with colorectal cancer risk in the European prospective investigation into cancer and nutrition study. *Cancer.* 2022;14(22):5499.

18. Ho GY, Wang T, Gunter MJ, et al. Adipokines linking obesity with colorectal cancer risk in postmenopausal women. *Cancer Res.* 2012; 72(12):3029-3037.

19. Pham TT, Nimptsch K, Papadimitriou N, et al. Genetically determined circulating resistin concentrations and risk of colorectal cancer: a two-sample mendelian randomization study. *J Cancer Res Clin Oncol.* 2023; 149(16):14889-14900. doi:10.1007/s00432-023-05193-0

20. Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. *Nat Rev Gastroenterol Hepatol.* 2019;16(12):713-732. doi:10.1038/s41575-019-0189-8

21. Nguyen LH, Goel A, Chung DC. Pathways of colorectal carcinogenesis. *Gastroenterology.* 2020;158(2):291-302. doi:10.1053/j.gastro.2019.08.059

22. Wang C-Q, Wang Y, Huang B-F, et al. High expression of both resistin and fascin-1 predicts a poor prognosis in patients with colorectal cancer. *Biomed Res Int.* 2020;2020:8753175. doi:10.1155/2020/8753175

23. Weikert C, Westphal S, Luley C, Willich SN, Boeing H, Pischon T. Within-subject variation of plasma resistin levels over a 1-year period. *Clin Chem Lab Med.* 2007;45(7):899-902.

24. Kaplan RC, Ho GY, Xue X, et al. Within-individual stability of obesity-related biomarkers among women. *Cancer Epidemiol Biomarkers Prev.* 2007;16(6):1291-1293.

25. Riboli E, Hunt K, Slimani N, et al. European prospective investigation into cancer and nutrition (EPIC): study populations and data collection. *Public Health Nutr.* 2002;5(6b):1113-1124.

26. Bergmann M, Bussas U, Boeing H. Follow-up procedures in EPIC-Germany - data quality aspects. *Ann Nutr Metab.* 1999;43(4): 225-234.

27. Fedirko V, Riboli E, Tjønneland A, et al. Prediagnostic 25-hydroxyvitamin D, VDR and CASR polymorphisms, and survival in patients with colorectal cancer in western European populations. *Cancer Epidemiol Biomarkers Prev.* 2012;21(4):582-593.

28. Fine JP, Gray RJ. A proportional hazards models for the subdistribution of a competing risk. *J Am Stat Assoc.* 1999;94(446):496-509. doi:10.1080/01621459.1999.10474144

29. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. *Circulation.* 2016;133(6):601-609. doi:10.1161/circulationaha.115.017719

30. Shannon D. Box-and-whisker plots with the SAS system®. *Pharm Stat.* 2003;2(4):291-294.

31. Akinyemiju T, Moore JX, Judd SE, et al. Pre-diagnostic biomarkers of metabolic dysregulation and cancer mortality. *Oncotarget.* 2018;9(22): 16099-16109.

32. Parkkila K, Kiviniemi A, Tulppo M, Perkiömäki J, Kesäniemi YA, Ukkola O. Resistin is a risk factor for all-cause mortality in elderly Finnish population: a prospective study in the OPERA cohort. *PLoS One.* 2021;16(3):e0248015.

33. Fontana A, Spadaro S, Copetti M, et al. Association between resistin levels and all-cause and cardiovascular mortality: a new study and a systematic review and meta-analysis. *PLoS One.* 2015;10(3): e0120419. doi:10.1371/journal.pone.0120419

34. Nimptsch K, Aleksandrova K, Fedirko V, et al. Pre-diagnostic C-reactive protein concentrations, CRP genetic variation and mortality among individuals with colorectal cancer in Western European populations. *BMC Cancer.* 2022;22:695. doi:10.1186/s12885-022-09778-9

35. Zatterale F, Longo M, Naderi J, et al. Chronic adipose tissue inflammation linking obesity to insulin resistance and type 2 diabetes. *Front Physiol.* 2020;10:1607.

36. Campbell PT, Newton CC, Dehal AN, Jacobs EJ, Patel AV, Gapstur SM. Impact of body mass index on survival after colorectal cancer diagnosis: the cancer prevention study-ll nutrition cohort. *J Clin Oncol.* 2012;30(1):42-52. doi:10.1200/jco.2011.38.0287

37. Fargnoli JL, Fung TT, Olenczuk DM, Chamberland JP, Hu FB, Mantzoros CS. Adherence to healthy eating patterns is associated with higher circulating total and high-molecular-weight adiponectin and lower resistin concentrations in women from the Nurses' Health Study. *Am J Clin Nutr.* 2008;88(5):1213-1224. doi:10.3945/ajcn.2008.26480

38. Qi Q, Wang J, Li H, et al. Associations of resistin with inflammatory and fibrinolytic markers, insulin resistance, and metabolic syndrome in middle-aged and older Chinese. *Eur J Endocrinol.* 2008;159(5):585-593. doi:10.1530/eje-08-0427

39. Rallidis LS, Katsimardos A, Kosmas N, et al. Differential prognostic value of resistin for cardiac death in patients with coronary artery disease according to the presence of metabolic syndrome. *Heart Vessels.* 2022;37(5):713-719. doi:10.1007/s00380-021-01964-2

40. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. *Nat Med.* 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0

41. Konrad A, Lehrke M, Schachinger V, et al. Resistin is an inflammatory marker of inflammatory bowel disease in humans. *Eur J Gastroenterol Hepatol.* 2007;19(12):1070-1074.

42. Kumor A, Daniel P, Pietruczuk M, Małecka-Panas E. Serum leptin, adiponectin, and resistin concentration in colorectal adenoma and carcinoma (CC) patients. *Int J Colorectal Dis.* 2009;24(3):275-281.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Pham TT, Nimptsch K, Aleksandrova K, et al. Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study. *Int J Cancer.* 2024;154(9):1596-1606. doi:10.1002/ijc.34830
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

significance. World J Gastroenterol. 2019;25(31):4383-4404. doi:10.3748/wj.v25.i31.4383

11. Shyu KG, Chua SK, Wang BW, Kuan P. Mechanism of inhibitory effect of atorvastatin on resistin expression induced by tumor necrosis factor-alpha in macrophages. *J Biomed Sci*. 2009;16(1):50. doi:10.1186/1423-0127-16-50

12. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. *CA Cancer J Clin*. 2021;71(3):209-249. doi:10.3322/caac.21660

13. Fedirko V, Romieu I, Aleksandrova K, et al. Pre-diagnostic anthropometry and survival after colorectal cancer diagnosis in Western European populations. *Int J Cancer*. 2014;135(8):1949-1960.

14. Lee J, Meyerhardt JA, Giovannucci E, Jeon JY. Association between body mass index and prognosis of colorectal cancer: a meta-analysis of prospective cohort studies. *PLoS One*. 2015;10(3):e0120706. doi:10.1371/journal.pone.0120706

15. Nimptsch K, Pischon T. Obesity biomarkers, metabolism and risk of cancer: an epidemiological perspective. *Recent Results Cancer Res*. 2016;208:199-217. doi:10.1007/978-3-319-42542-9_11

16. Yang G, Fan W, Luo B, et al. Circulating resistin levels and risk of colorectal cancer: a meta-analysis. *Biomed Res Int*. 2016;2016:1-11.

17. Pham T-T, Nimptsch K, Aleksandrova K, et al. Pre-diagnostic circulating resistin concentrations are not associated with colorectal cancer risk in the European prospective investigation into cancer and nutrition study. *Cancer*. 2022;14(22):5499.

18. Ho GY, Wang T, Gunter MJ, et al. Adipokines linking obesity with colorectal cancer risk in postmenopausal women. *Cancer Res*. 2012; 72(12):3029-3037.

19. Pham TT, Nimptsch K, Papadimitriou N, et al. Genetically determined circulating resistin concentrations and risk of colorectal cancer: a two-sample mendelian randomization study. *J Cancer Res Clin Oncol*. 2023; 149(16):14889-14900. doi:10.1007/s00432-023-05193-0

20. Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. *Nat Rev Gastroenterol Hepatol*. 2019;16(12):713-732. doi:10.1038/s41575-019-0189-8

21. Nguyen LH, Goel A, Chung DC. Pathways of colorectal carcinogenesis. *Gastroenterology*. 2020;158(2):291-302. doi:10.1053/j.gastro.2019.08.059

22. Wang C-Q, Wang Y, Huang B-F, et al. High expression of both resistin and fascin-1 predicts a poor prognosis in patients with colorectal cancer. *Biomed Res Int*. 2020;2020:8753175. doi:10.1155/2020/8753175

23. Weikert C, Westphal S, Luley C, Willich SN, Boeing H, Pischon T. Within-subject variation of plasma resistin levels over a 1-year period. *Clin Chem Lab Med*. 2007;45(7):899-902.

24. Kaplan RC, Ho GY, Xue X, et al. Within-individual stability of obesity-related biomarkers among women. *Cancer Epidemiol Biomarkers Prev*. 2007;16(6):1291-1293.

25. Riboli E, Hunt K, Slimani N, et al. European prospective investigation into cancer and nutrition (EPIC): study populations and data collection. *Public Health Nutr*. 2002;5(6b):1113-1124.

26. Bergmann M, Bussas U, Boeing H. Follow-up procedures in EPIC-Germany - data quality aspects. *Ann Nutr Metab*. 1999;43(4): 225-234.

27. Fedirko V, Riboli E, Tjønneland A, et al. Prediagnostic 25-hydroxyvitamin D, VDR and CASR polymorphisms, and survival in patients with colorectal cancer in western European populations. *Cancer Epidemiol Biomarkers Prev*. 2012;21(4):582-593.

28. Fine JP, Gray RJ. A proportional hazards models for the subdistribution of a competing risk. *J Am Stat Assoc*. 1999;94(446):496-509. doi:10.1080/01621459.1999.10474144

29. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. *Circulation*. 2016;133(6):601-609. doi:10.1161/circulationaha.115.017719

30. Shannon D. Box-and-whisker plots with the SAS system®. *Pharm Stat*. 2003;2(4):291-294.

31. Akinyemiju T, Moore JX, Judd SE, et al. Pre-diagnostic biomarkers of metabolic dysregulation and cancer mortality. *Oncotarget*. 2018;9(22): 16099-16109.

32. Parkkila K, Kiviniemi A, Tulppo M, Perkiömäki J, Kesäniemi YA, Ukkola O. Resistin is a risk factor for all-cause mortality in elderly Finnish population: a prospective study in the OPERA cohort. *PLoS One*. 2021;16(3):e0248015.

33. Fontana A, Spadaro S, Copetti M, et al. Association between resistin levels and all-cause and cardiovascular mortality: a new study and a systematic review and meta-analysis. *PLoS One*. 2015;10(3): e0120419. doi:10.1371/journal.pone.0120419

34. Nimptsch K, Aleksandrova K, Fedirko V, et al. Pre-diagnostic C-reactive protein concentrations, CRP genetic variation and mortality among individuals with colorectal cancer in Western European populations. *BMC Cancer*. 2022;22:695. doi:10.1186/s12885-022-09778-9

35. Zatterale F, Longo M, Naderi J, et al. Chronic adipose tissue inflammation linking obesity to insulin resistance and type 2 diabetes. *Front Physiol*. 2020;10:1607.

36. Campbell PT, Newton CC, Dehal AN, Jacobs EJ, Patel AV, Gapstur SM. Impact of body mass index on survival after colorectal cancer diagnosis: the cancer prevention study-II nutrition cohort. *J Clin Oncol*. 2012;30(1):42-52. doi:10.1200/jco.2011.38.0287

37. Fargnoli JL, Fung TT, Olenczuk DM, Chamberland JP, Hu FB, Mantzoros CS. Adherence to healthy eating patterns is associated with higher circulating total and high-molecular-weight adiponectin and lower resistin concentrations in women from the Nurses' Health Study. *Am J Clin Nutr*. 2008;88(5):1213-1224. doi:10.3945/ajcn.2008.26480

38. Qi Q, Wang J, Li H, et al. Associations of resistin with inflammatory and fibrinolytic markers, insulin resistance, and metabolic syndrome in middle-aged and older Chinese. *Eur J Endocrinol*. 2008;159(5):585-593. doi:10.1530/eje-08-0427

39. Rallidis LS, Katsimardos A, Kosmas N, et al. Differential prognostic value of resistin for cardiac death in patients with coronary artery disease according to the presence of metabolic syndrome. *Heart Vessels*. 2022;37(5):713-719. doi:10.1007/s00380-021-01964-2

40. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. *Nat Med*. 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0

41. Konrad A, Lehrke M, Schachinger V, et al. Resistin is an inflammatory marker of inflammatory bowel disease in humans. *Eur J Gastroenterol Hepatol*. 2007;19(12):1070-1074.

42. Kumor A, Daniel P, Pietruczuk M, Małecka-Panas E. Serum leptin, adiponectin, and resistin concentration in colorectal adenoma and carcinoma (CC) patients. *Int J Colorectal Dis*. 2009;24(3):275-281.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Pham TT, Nimptsch K, Aleksandrova K, et al. Pre-diagnostic circulating resistin concentrations and mortality among individuals with colorectal cancer: Results from the European Prospective Investigation into Cancer and Nutrition study. *Int J Cancer*. 2024;154(9):1596-1606. doi:10.1002/ijc.34830