# Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study

Andrei-emil Constantinescu, Caroline J Bull, Nicholas Jones, Ruth Mitchell, Kimberley Burrows, Niki Dimou, Stéphane Bézieau, Hermann Brenner, Daniel D Buchanan, Mauro d'Amato, et al.

► To cite this version:

Andrei-emil Constantinescu, Caroline J Bull, Nicholas Jones, Ruth Mitchell, Kimberley Burrows, et al. Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study. International Journal of Cancer, 2023, 154, pp.94 - 103. 10.1002/ijc.34691. hal-05116664

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Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

Cancer Epidemiology

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INTERNATIONAL
JOURNAL of CANCER

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# Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study

Andrei-Emil Constantinescu<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID | Caroline J. Bull<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID | Nicholas Jones<fnref n="4" /> |
Ruth Mitchell<fnref n="1" />,<fnref n="2" /> | Kimberley Burrows<fnref n="1" />,<fnref n="2" /> | Niki Dimou<fnref n="5" /> | Stéphane Bézieau<fnref n="6" /> |
Hermann Brenner<fnref n="7" />,<fnref n="8" />,<fnref n="9" /> | Daniel D. Buchanan<fnref n="10" />,<fnref n="11" />,<fnref n="12" /> ID | Mauro D'Amato<fnref n="13" />,<fnref n="14" />,<fnref n="15" /> |
Mark A. Jenkins<fnref n="16" /> | Victor Moreno<fnref n="17" />,<fnref n="18" />,<fnref n="19" />,<fnref n="20" /> ID | Rish K. Pai<fnref n="21" /> | Caroline Y. Um<fnref n="22" /> |
Emily White<fnref n="23" />,<fnref n="24" /> | Neil Murphy<fnref n="5" /> ID | Marc Gunter<fnref n="5" /> | Nicholas J. Timpson<fnref n="1" />,<fnref n="2" /> |
Jeroen R. Huyghe<fnref n="24" /> | Emma E. Vincent<fnref n="1" />,<fnref n="2" />,<fnref n="3" />

**Abbreviations:** BMI, body mass index; CI, confidence interval; CRC, colorectal cancer; EDN, eosinophil-derived neurotoxin; GWAS, genome-wide association study; IVW, inverse-variance weighted; LD, linkage disequilibrium; MR, Mendelian randomisation; MV, multivariable; NK, natural kills; OR, odds ratio; PVE, proportion of variance explained; RCT, randomised controlled trial; RR, risk ratio; SNP, single-nucleotide polymorphism; TIL, tumour-infiltrating lymphocyte; UKBB, UK Biobank; WBC, white blood cell.

Jeroen R. Huyghe and Emma E. Vincent contributed equally to this study.

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.

© 2023 The Authors. *International Journal of Cancer* published by John Wiley & Sons Ltd on behalf of UICC.

<footnote n="1">¹MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK</footnote>

<footnote n="2">²Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK</footnote>

<footnote n="3">³School of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK</footnote>

<footnote n="4">⁴Institute of Life Science, Swansea University Medical School, Swansea, UK</footnote>

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

<footnote n="6">⁶Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France</footnote>

<footnote n="7">⁷Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany</footnote>

<footnote n="8">⁸Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany</footnote>

<footnote n="9">⁹German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany</footnote>

<footnote n="10">¹⁰Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia</footnote>

<footnote n="11">¹¹University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia</footnote>

<footnote n="12">¹²Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia</footnote>

<footnote n="13">¹³Department of Medicine and Surgery, LUM University, Casamassima, Italy</footnote>

<footnote n="14">¹⁴Gastrointestinal Genetics Lab, CIC bioGUNE - BRTA, Derio, Spain</footnote>

<footnote n="15">¹⁵Ikerbasque, Basque Foundation for Science, Bilbao, Spain</footnote>

<footnote n="16">¹⁶Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia</footnote>

<footnote n="17">¹⁷Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain</footnote>

<footnote n="18">¹⁸CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain</footnote>

<footnote n="19">¹⁹Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain</footnote>

<footnote n="20">²⁰ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain</footnote>

<footnote n="21">²¹Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA</footnote>

<footnote n="22">²²Department of Population Science, American Cancer Society, Atlanta, Georgia, USA</footnote>

<footnote n="23">²³Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, USA</footnote>

<footnote n="24">²⁴Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA</footnote>
**Correspondence**

Andrei-Emil Constantinescu, Oakfield House,
Oakfield Grove, Bristol, UK.

Email: andrei constantinescu@bristol.ac.uk

**Funding information**

Cancer Research UK, Grant/Award Number: C18281/A29019; Diabetes UK, Grant/Award Number: 17/0005587; Medical Research Council, Grant/Award Numbers: MC_UU_00011/1, MC_UU_00011/4, MR/ N0137941/1; National Cancer Institute, Grant/Award Number: R21CA230486; NIHR Bristol Biomedical Research Centre, Grant/Award Number: BRC-1215-2001; Wellcome Trust, Grant/Award Numbers: 202802/Z/16/Z, 204813/Z/16/Z, 217065/ Z/19/Z; World Cancer Research Fund International, Grant/Award Number: IIG_2019_2009

**Abstract**

Observational studies have suggested a protective role for eosinophils in colorectal cancer (CRC) development and implicated neutrophils, but the causal relationships remain unclear. Here, we aimed to estimate the causal effect of circulating white blood cell (WBC) counts ($N = ~550 000$) for basophils, eosinophils, monocytes, lymphocytes and neutrophils on CRC risk ($N = 52 775$ cases and 45 940 controls) using Mendelian randomisation (MR). For comparison, we also examined this relationship using individual-level data from UK Biobank (4043 incident CRC cases and 332 773 controls) in a longitudinal cohort analysis. The inverse-variance weighted (IVW) MR analysis suggested a protective effect of increased basophil count and eosinophil count on CRC risk [OR per 1-SD increase: 0.88, 95% CI: 0.78-0.99, $P = .04$; OR: 0.93, 95% CI: 0.88-0.98, $P = .01$]. The protective effect of eosinophils remained [OR per 1-SD increase: 0.88, 95% CI: 0.80-0.97, $P = .01$] following adjustments for all other WBC subtypes, to account for genetic correlation between the traits, using multivariable MR. A protective effect of increased lymphocyte count on CRC risk was also found [OR: 0.84, 95% CI: 0.76-0.93, $P = 6.70e-4$] following adjustment. Consistent with MR results, a protective effect for eosinophils in the cohort analysis in the fully adjusted model [RR per 1-SD increase: 0.96, 95% CI: 0.93-0.99, $P = .02$] and following adjustment for the other WBC subtypes [RR: 0.96, 95% CI: 0.93-0.99, $P = .001$] was observed. Our study implicates peripheral blood immune cells, in particular eosinophils and lymphocytes, in CRC development, highlighting a need for mechanistic studies to interrogate these relationships.

**KEYWORDS**

colorectal cancer, eosinophils, Mendelian randomisation, UK biobank, White blood cell count

**What's new?**

Alterations to circulating white blood cell counts have been associated with colorectal cancer, but whether these changes have causal effects remains unexplored. Using multivariable Mendelian randomisation, the authors assessed the independent causal effects of white blood cell counts by cell subtype by adjusting for shared genetic architecture. In parallel, they ran the largest longitudinal cohort study to explore causal effects between white blood cell counts and colorectal cancer using UK Biobank data. The evidence suggests that elevated eosinophil and lymphocyte counts may have a protective effect against colorectal cancer, adding new insights into colorectal cancer pathogenesis.

# 1 | INTRODUCTION

Colorectal cancer (CRC) accounts for over 10% of all worldwide cancer cases and is the second leading cause of cancer-related deaths globally.¹⁻³ Overall, the number of CRC cases is rising⁴ and is alarmingly on the rise in younger people (aged <50 years).⁵⁻⁷ Given current challenges, and the estimation that 50% of CRC cases may be preventable,⁸ a focus on identifying novel risk factors, and subsequent prophylactic and treatment options are warranted to limit the future healthcare burden.

White blood cells (WBCs) are commonly measured in routine blood tests and are divided into five subtypes: basophils, eosinophils,

lymphocytes, monocytes and neutrophils.⁹ Alterations to circulating WBC counts have been found to play a role in disease risk, severity and progression, CRC development and mortality.¹⁰⁻¹³ For example, higher circulating counts of basophils and eosinophils, cells with a role in IgE-mediated immunity, have been associated with reduced CRC risk and increased survival.¹⁴⁻¹⁷ Higher absolute lymphocyte count [such as T, B and natural killer (NK) cells] has also been associated with better overall survival of CRC.¹⁸,¹⁹ By contrast, a high absolute monocyte count was found to be associated with worse CRC survival,¹⁸,¹⁹ consistent with their potential role in tumour progression and metastasis.²⁰ Finally, neutrophils, a critical part of the innate immune system,²¹ have also been associated with poor CRC overall survival.¹⁸,¹⁹
Traditional (ie, cross-sectional, case-control or cohort) studies account for the majority of epidemiologic analyses undertaken on CRC, which can suffer from certain limitations, such as confounding and reverse causation which can bias effect estimates.²² Mendelian randomisation (MR) is a method in genetic epidemiology which could overcome these issues by using single-nucleotide polymorphisms (SNPs) to proxy for an exposure of interest to estimate the effect of an exposure on an outcome.²³ It operates akin to a randomised control trial (RCT), as alleles are randomly assigned at birth.²³ We have used MR to estimate the total effect of each WBC trait on CRC,²⁴ and, to account for correlation between WBC traits, multivariable (MV) MR to estimate the direct effect of these exposures on CRC by adjusting for their shared genetic architecture.²⁴

In our study, we aimed to investigate the relationship between circulating WBC subtypes and CRC incidence. First, an MR analysis was undertaken using the most comprehensive genome-wide association study (GWAS) for both WBC subtype counts and incident CRC available. Second, for comparison, we ran the largest longitudinal cohort study between WBCs and CRC to date using UK Biobank. Together these analyses allow us to compare between genetically proxied and observational estimates to thoroughly explore the relationship between circulating WBC counts and CRC development.

## 2 | METHODS

### 2.1 | Study design

We aimed to investigate the relationship between circulating WBCs and CRC using a genetic epidemiologic and complementary observational approach. First, MR analyses were undertaken to estimate the effect of

WBC subtype counts on CRC. We then performed a multivariable MR (MVMR) analysis where the direct effect of each WBC subtype count was estimated by adding all five WBC subtypes into the model (Figure 1A). STROBE-MR guidelines were followed (STROBE-MR Supplement).²⁵ Here, units were interpreted as odds ratio (OR) for CRC per a normalised SD (1-SD) increase in WBC count. Next, a prospective longitudinal cohort study was undertaken using UK Biobank individual-level data. Here, subtype-specific WBC counts were studied individually, and then each was adjusted for each of the other traits (Figure 1B). STROBE guidelines were followed (STROBE Cohort Supplement). Here, units were interpreted as risk ratio (RR) for CRC per a normalised SD (1-SD) increase in WBC count.

### 2.2 | WBC count GWAS data

Summary statistics for WBC subtype counts were obtained from the 'Blood Cell Consortium' (BCX) meta-analysis, the largest study being UKBB (N = ~562 243).²⁶ Genetic sex, age, age², study-specific covariates and PCs 1 to 10 were used as covariates. A brief description of each study included in the meta-analysis is available in Table S1. Only variants which did not display heterogenous effects across studies were selected. Specific details on QC steps and association testing are available in the source manuscripts. Summary statistics for WBC counts were downloaded from: http://www.mhi-humangenetics.org/en/resources/.

### 2.3 | CRC GWAS data

GWAS summary statistics for overall CRC risk and anatomical subsite were taken from the most comprehensive meta-analyses to date²⁷:

<figure><img src="image_4.png" /><figcaption>**FIGURE 1** Study design. We triangulated findings from two study designs: a Mendelian randomisation analysis (A) and a longitudinal cohort analysis (B) to estimate the causal effect of WBC on CRC risk.</figcaption></figure>
colon cancer (split into proximal and distal colon cancer) and rectal
cancer. Sixty-six studies, which include those of the Genetics and
Epidemiology of Colorectal Cancer Consortium (GECCO), Colorectal
Cancer Transdisciplinary Study (CORECT) and Colon Cancer Family
Registry (CCFR) consortia were meta-analysed.²⁷ The final sample
was predominantly of European ancestry with 5.36% of East Asian
ancestry. These were included due to their similar genetic architecture
with regard to CRC risk.²⁸ Genetic sex, age, study-specific variables
and PCs were used as covariates. An overview of all consortia
included in the CRC meta-analysis is available in Table S2, and a
breakdown of the sample sizes for each set of CRC summary statistics
is presented in Table S3.

2.4 | Genetic data processing

To select valid MR instruments, summary statistics for exposures
were processed using the ‘TwoSampleMR’ R package.²⁹,³⁰ The pres-
ence of correlated instruments, that is, those in linkage disequilibrium
(LD), can bias MR estimates.²⁹,³⁰ Therefore, the exposure SNPs were
clumped (r² = 0.001, window = 10 Mb, P-value threshold = 5e-8)
using the 1000 Genomes European dataset³¹ as a reference panel.
Following this step, the exposure and outcome datasets were ‘harmo-
nised’, that is, had their effect alleles placed on the same reference
strand.³² SNPs with incorrect but unambiguous strand references
were corrected, while those with ambiguous strand references were
removed.

2.5 | MR analysis of WBC counts on CRC risk

Primary MR analyses were undertaken using the inverse-variance
weighted (IVW) method, which is the multiplicative random-effects
meta-analysis of the estimated effect of all exposure SNPs on the
CRC outcome.³³ Conditional F-statistics were calculated to detect
weak instrument bias for each exposure SNP as previously
described.³⁴ Several sensitivity analyses were undertaken to compare
with the main IVW estimates. The presence of vertical pleiotropy, that
is, when a trait is downstream of the genetic variant but on the same
biological pathway as the exposure,³⁵ was measured using Cochran's
Q heterogeneity test. Horizontal pleiotropy, when some or all instru-
ments for a trait act through a different pathway to the exposure,³⁵ can
violate one of the main MR assumptions. A number of sensitivity MR
analyses were undertaken to identify horizontal pleiotropy:
MR-Egger (where the regression intercept is not constrained to zero),³⁶
Weighted median (the median of all SNP ratio estimates, where each
ratio is weighted by the inverse of the variance),³⁷ Weighted modes
(assumes that the most frequent estimate in a set of instruments is
zero)³⁸ and MR-PRESSO (detects individual SNPs contributing to hori-
zontal pleiotropy).³⁹ The direction of the causal relationship between
WBC traits and CRC risk was tested using the MR Steiger method,
which uses Steiger's test to test the difference between the Pearson
correlations of genetic variants with both the exposure and outcome.³⁰

2.6 | Multivariable MR analysis of WBC counts on CRC risk

The IVW method was used for the MVMR analysis. First, a pair-wise
analysis between all five WBC subtype counts was undertaken, where
the proportion of variance explained (PVE) for SNPs used to instru-
ment a WBC trait was estimated in the other four WBC subtypes
using the previously described methodology.³⁴ The direct effect of
each WBC subtype was estimated by adding all five WBC subtypes
into the MVMR model. Bias arising from weak instruments was also
determined here. This was undertaken using the methodology
described by Sanderson et al, where a generalised version of
Cochran's Q was employed to evaluate instrument strength.⁴⁰ Stan-
dard Cochran's Q statistic was calculated to detect the presence of
heterogeneity. For those traits with an F-statistic <10, a follow-up
MVMR analysis was done accounting for the presence of weak
instruments.

2.7 | UK Biobank phenotypic data

Between the years 2007 and 2010, UKBB participants visited assess-
ment centres (N = 22) throughout the UK.⁴¹ The individuals had their
health records linked, their genomes sequenced and underwent
multiple evaluations, such as self-report questionnaires and medical
examinations.⁴¹ The latter includes the analysis of blood cell samples
using Beckman Coulter LH750 instruments designed for high through-
put screening.⁴² Total WBC count and WBC subtype percentage (%)
were measured, with absolute WBC subtype count derived as ‘WBC
subtype %/100 × total WBC’ and expressed as 10⁹ cells/L.⁴² The
blood sampling date variable was split into year, month, day and
minutes (passed since the start of the day of the appointment visit).
Additional variables were gathered including recruitment centre, sam-
pling device ID, age, genetic sex, principal components 1 to 10, body
mass index (BMI), Townsend deprivation index, smoking and alcohol
drinker status (self-report questionnaire—UKBB codes 20116 and
20117). CRC cases were identified through hospital inpatient records
coded to the 10th version of the International Classification of Disease
(ICD-10). UK Biobank data was granted under application code 81499.

2.8 | Filtering and selection criteria

The UKBB dataset underwent a series of steps prior to further ana-
lyses. Withdrawn participants and those of non-European ancestry
were excluded. Viable controls and incident CRC cases were defined
using methodology previously described by Burrows et al⁴³ (Table S4).
Here, we defined incident CRC cases as those diagnosed at least
1 year after blood sampling. Participants with no WBC measurement
data or sampling date were removed, as were those who were known
to be pregnant, have chronic conditions (eg, HIV, blood cancers, thal-
assaemia) or undergoing erythropoietin treatment, as in Astle et al⁴⁴
and Chen et al,²⁶ given the effects of these traits on WBC
measurements. Those with acute conditions (eg, upper respiratory infections) diagnosed less than 3 months prior to blood sampling were excluded. Finally, missing values in ‘Townsend Deprivation Index’, ‘Body mass index’, ‘Smoking status’ and ‘Alcohol drinker status’ variables were removed.

## 2.9 | Cohort study between WBC count and CRC

We conducted a cohort analysis between circulating WBCs and incident CRC. WBC count values were log-transformed, after which they were adjusted for the following covariates: assessment centre, sex, age, age², PCs 1 to 10, as in Chen et al's GWAS.²⁶ The resulting residuals were rank-inverse normal transformed and then used in a logistic regression on CRC incidence. This main observational analysis was termed ‘Model 1’, a minimally adjusted model. An additional fully adjusted analysis was undertaken, termed ‘Model 2’, where BMI, Townsend DI, smoker status and alcohol drinker status were added as additional covariates. Following this, another pair of analyses was run, where all five WBC subtype counts were added together into the model to reduce potential bias due to their correlated values. Analyses, where each WBC trait was studied individually, were termed as ‘univariable’, while those where they were added together were termed as ‘multivariable’.

## 2.10 | Working environment

All analyses were performed with R version 4.1.2 (Bird Hippie) in a Linux environment supported by the University of Bristol's Advanced Computing Research Centre (ACRC). Genetic data preparation, as well as the MR analyses, were undertaken with the ‘TwoSampleMR’ R package.²⁹,³⁰ The MVMR analyses were undertaken with the ‘MVMR’ R package.⁴⁵

## 2.11 | Patient and public involvement

Patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.

# 3 | RESULTS

## 3.1 | Effect of WBC count on CRC

Before performing the MR analysis, the average F-statistic for each WBC trait was estimated to detect the presence of weak instrument bias, which is generally indicated by an average F-statistic <10.⁴⁶ For overall CRC, these were 64.48 (basophil count, 171 SNPs), 124.72 (eosinophil count, 396 SNPs), 105.85 (lymphocyte count, 444 SNPs), 147.44 (monocyte count, 477 SNPs)

and 98.84 (neutrophil count, 387 SNPs), indicating strong MR instruments (Table S5).

The IVW method showed evidence of a protective effect of basophil count (1-SD increase in WBC count OR: 0.88, 95% CI: 0.78-0.99, P-value: .037; Figure 2, Figure S1) and eosinophil count for CRC risk (OR: 0.93, 95% CI: 0.88-0.98, P-value: .012; Figure 2, Figure S1). All results, including those for sex- and site-specific CRC, are available in Table S6.

Next, the presence of vertical and horizontal pleiotropy in the MR analyses was analysed. Cochran's heterogeneity test indicated the presence of heterogeneity in all but one WBC trait-CRC pair (basophil count-male CRC, *P*<sub>HET</sub> = .104; Table S7). Following this, the MR-Egger test for horizontal pleiotropy was performed. Here, evidence for this type of pleiotropy was identified for eosinophil count and overall (*P*<sub>PLT</sub> = .015) CRC risk, suggesting a possible bias of MR estimates (Table S7). Although the MR-PRESSO method identified the presence of SNP horizontal-pleiotropic outliers, there was little evidence that the removal of these outliers contributed to a notable shift in the point estimates (Table S8).

## 3.2 | Multivariable MR of WBC count on CRC

A pair-wise analysis of the proportion of variance explained (PVE) for SNPs instrumenting each WBC trait indicated a low PVE for each of the other WBC traits, with the exception of basophil count (2.44% vs 2.39% when instrumenting neutrophil count; Table S9). The overall results indicated that statistical power should not suffer to a large degree by adding all five WBC subtype counts into the MVMR analysis. Here, the MVMR IVW method estimated a protective effect of eosinophil count (OR: 0.88, 95% CI: 0.80-0.97, P-value: .011) and lymphocyte count (OR: 0.84, 95% CI: 0.76-0.93, P-value: .0007) on CRC risk (Figure 2). Site-specific CRC estimates are available in Table S10.

Sensitivity analyses were undertaken to assess heterogeneity and the presence of weak instruments in the MVMR analysis. There was evidence of heterogeneity in all WBC trait-CRC pairs (Table S11). Conditional F-statistics showed evidence of weak instruments (F < 10) for basophil count (Table S11). Based on these results, an additional MVMR analysis was run adjusting for weak instruments for basophil count (Table S11).

## 3.3 | Cohort observational analysis between WBC count and CRC

In total, 336 816 UKBB participants remained after passing the filtering and selection criteria (Figure S2). Of these, 332 773 were controls and 4043 were incident CRC cases. When split by genetic sex, there were 154 629 male and 178 144 female controls and 2316 male and 1727 female cases. Those with CRC were more likely to be male (57% vs 46%), had a higher average age (60.7 vs 55.8 years), had slightly higher BMI (28.0 vs 27.4 kg/m²) and were more likely to have
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** The relationship between WBC count and CRC risk based on UV, MV two-sample MR and cohort observational analyses. Each WBC trait is presented on the X-axis. The estimated effect is presented on the Y-axis. Point estimates were filled where the *P*-value was less than .05. Results are interpreted as ORs (95% CI) for CRC risk per 1-SD normalised increment in WBC count. ¹Chen et al²⁶; ²Huyghe et al²⁷ (https://doi.org/10.1136/gutjnl-2020-321534); ³UK Biobank.⁴⁷</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<table><caption>TABLE 1 Baseline characteristics of UK Biobank study sample.</caption><thead><tr><th>Characteristic</th><th>Control,<br>N = 332773<sup>a</sup></th><th>Case,<br>N = 4043<sup>a</sup></th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td></tr><tr><td>Female</td><td>178 144/332 773 (54%)</td><td>1727/4043 (43%)</td></tr><tr><td>Male</td><td>154 629/332 773 (46%)</td><td>2316/4043 (57%)</td></tr><tr><td>Age (years)</td><td>55.8 (8.1)</td><td>60.7 (6.6)</td></tr><tr><td>Body mass index</td><td>27.4 (4.7)</td><td>28.0 (4.7)</td></tr><tr><td>Smoking status</td><td></td><td></td></tr><tr><td>Never</td><td>183 987/332 773 (55%)</td><td>1861/4043 (46%)</td></tr><tr><td>Previous</td><td>114 349/332 773 (34%)</td><td>1790/4043 (44%)</td></tr><tr><td>Current</td><td>34 437/332 773 (10%)</td><td>392/4043 (9.7%)</td></tr><tr><td>Alcohol drinker status</td><td></td><td></td></tr><tr><td>Never</td><td>10 655/332 773 (3.2%)</td><td>122/4043 (3.0%)</td></tr><tr><td>Previous</td><td>10 982/332 773 (3.3%)</td><td>140/4043 (3.5%)</td></tr><tr><td>Current</td><td>311 136/332 773 (93%)</td><td>3781/4043 (94%)</td></tr><tr><td>Townsend deprivation index</td><td>-1.4 (3.0)</td><td>-1.6 (3.0)</td></tr><tr><td>Basophil count</td><td>0.0 (0.1)</td><td>0.0 (0.1)</td></tr><tr><td>Eosinophil count</td><td>0.2 (0.1)</td><td>0.2 (0.1)</td></tr><tr><td>Lymphocyte count</td><td>1.9 (0.6)</td><td>1.9 (0.6)</td></tr><tr><td>Monocyte count</td><td>0.5 (0.2)</td><td>0.5 (0.2)</td></tr><tr><td>Neutrophil count</td><td>4.2 (1.4)</td><td>4.3 (1.4)</td></tr><tr><td>Overall WBC count</td><td>6.9 (1.7)</td><td>7.0 (1.8)</td></tr></tbody></table>

been cigarette smokers in the past (46% vs 55% never smokers and 44% vs 34% previous smokers; Table 1).

As a percentage of the total WBC count based on the median values, basophils accounted for 0.3%, eosinophils 2.11%, lymphocytes 28.16%, monocytes 6.78% and neutrophils 60.39% (Table S12). A pair-wise correlation matrix between each WBC subtype demonstrated correlation coefficients equal to or below 0.3 (Figure S3). Batch variables (eg, blood sample device and sampling date), Townsend DI, and alcohol drinker status explained some of the variances in WBC count (0%-0.66%). Depending on the WBC subtype, genetic sex explained between 0.23% and 2.93% of the variance, BMI explained between 0.14% and 2.64%, and smoking status explained between 0.44% and 3.85% (Figure S4).

In the main analysis ('Model 1'—the minimally adjusted model), basophil count was positively associated with CRC risk (RR: 1.06, 95% CI: 1.02-1.09, P-value: .0005), as was monocyte (RR: 1.05, 95% CI: 1.02-1.08, P-value: .003) and neutrophil count (RR: 1.09, 95% CI: 1.06-1.13, P-value: 1.94E-08; Figure 2, Table S13). In 'Model 2', eosinophil count was negatively associated with CRC risk (overall CRC RR: 0.96, 95% CI: 0.93-0.99, P-value: .022; Table S13).

Observational associations were re-computed by adding all five WBC subtype counts together, largely showing agreement with the main analysis (Table S14).

4 | DISCUSSION

In our study, we aimed to estimate the effects of five circulating WBC subtypes on CRC risk by using a combined genetic epidemiologic and longitudinal cohort framework. Through the aid of MVMR, we were able to assess the independent causal effect of WBC counts by adjusting for their shared genetic architecture. Taken together, the evidence across analyses suggests a potential protective effect of increased circulating eosinophil and lymphocyte count on CRC risk.

Consistent with our study, Prizment et al found that eosinophil count (tertiles Q3 and Q2 vs Q1) was negatively associated with odds of developing colon, but not of rectal cancer.¹⁶ Similar results have been reported for other cancers; Wong et al reported a negative trend between increasing eosinophil count quartiles and lung adenocarcinoma odds in a UKBB study,⁴⁷ while a similar study looking at prostate cancer showed a negative association between eosinophil count quintiles Q3-5, as well as a per 1-SD increase in the trait (HR 0.96 vs OR 0.93 for CRC in our analysis).⁴⁸

Eosinophils have a well-established role in allergic diseases, including asthma and allergic rhinitis.⁴⁹ Indeed, MR analyses have also reported that eosinophil count levels affect the risk of developing allergic disease⁴⁴,⁵⁰ and a recent systematic review investigating the relationship between allergies and cancer reported evidence for a reduced risk of CRC in those with allergic diseases.⁵¹

Here, our results suggest that the immune response through eosinophils provides protection against tumour development. Indeed, in several neoplasias, including CRC, eosinophils have been found to play an anti-tumourigenic role and are a source of anti-tumourigenic molecules, such as eosinophil-derived neurotoxin (EDN).⁵²,⁵³ Experimental studies have also found a tumour-protective effect of IgE.⁵⁴ Increased eosinophil recruitment to the CRC tumour site has also been associated with better survival, even when adjusting for the effects of CD8⁺ T-cells,¹¹ and eosinophil-specific granule secretion of granzyme A has been linked with the killing of CRC cells.⁵⁵

In addition to eosinophil count, we also found a protective effect of lymphocyte count on CRC risk. While this was not apparent in the MR analysis, the MVMR estimates indicated lower ORs for CRC across all anatomical subsites (proximal colon cancer trended towards protective) with increased lymphocyte counts. The multivariable fully adjusted 'Model 2' in the cohort analysis also indicated that there could be a negative association with CRC risk. It is not surprising that we found a protective effect of higher circulating levels of lymphocytes with CRC odds given their established role in combatting tumour development.⁵⁶ Tumour-infiltrating lymphocytes (TILs) like CD8⁺ T-cells help antagonise tumour growth through direct action and recruitment of other immune cells.⁵⁶ High levels of TILs were previously associated with better CRC overall survival and disease-free survival.¹⁹,⁵⁶ In support of our findings, two observational studies found higher lymphocyte counts compared to cases vs controls a year to 6 months prior to CRC diagnosis.⁵⁷,⁵⁸ However, these results could indicate production and recruitment of lymphocytes to the site of precancerous or undetected tumours rather than a causal effect.

<footnote>a/n/N (%); Mean (SD).</footnote>
## 4.1 | Limitations

There are several limitations to our study. With regard to the cohort analysis, only baseline blood measurements were available. This assumes that WBC counts were constant and did not allow us to establish a relationship between a trend in WBC count and its relationship with CRC odds. Nevertheless, baseline WBC count measurements have previously been shown to be associated with disease risk,⁴⁷,⁴⁸,⁵⁹,⁶⁰ making their study in relation to disease development a worthwhile endeavour. Also, in the cohort analysis, incident CRC cases were defined as those diagnosed at least 1 year after blood sampling, in order to not diminish the number of cases to a large degree. However, as CRC develops over a long period, our cohort analysis may have not excluded all participants with undiagnosed CRC.

With regards to the MR analysis, the genetic instruments used here proxied for lifetime variation of WBC count. Therefore, the MR analysis cannot be used to infer how large changes over a short time-span might affect CRC development. Regarding the MVMR method, caution should be applied when investigating traits with very weak instruments, as it cannot reliably adjust for those traits.⁴⁵ This was the case for basophil count, as the F-statistic was estimated to be between 4.7 and 4.8 (Table S12). Therefore, despite pointing to an increased detrimental effect compared to the main MVMR analysis, ORs derived from the weak-MVMR analysis should be interpreted with this in mind.

## 5 | CONCLUSION

In summary, the results generated here provide evidence for a protective causal effect of elevated levels of circulating eosinophil and lymphocyte counts on CRC risk. Going forward, additional research is needed to disentangle the biological mechanisms and pinpoint-specific pathways through which eosinophils and lymphocytes might protect against CRC development.

### AUTHOR CONTRIBUTIONS

Andrei-Emil Constantinescu, Caroline J. Bull, Jeroen R. Huyghe and Emma E. Vincent conceived the idea for the paper. Andrei-Emil Constantinescu conducted the analysis. All authors contributed to the interpretation of the findings. Andrei-Emil Constantinescu, Caroline J. Bull and Emma E. Vincent wrote the manuscript. All authors critically revised the paper for intellectual content and approved the final version of the manuscript. The work reported in the paper has been performed by the authors, unless clearly specified in the text.

### FUNDING INFORMATION

Andrei-Emil Constantinescu acknowledges funding from grant MR/N0137941/1 for the GW4 BIOMED MRC DTP, awarded to the Universities of Bath, Bristol, Cardiff and Exeter from the Medical Research Council (MRC)/UKRI. Nicholas J. Timpson is the PI of the

Avon Longitudinal Study of Parents and Children (Medical Research Council & Wellcome Trust 217065/Z/19/Z) and is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215-2001). Nicholas J. Timpson acknowledges funding from the Wellcome Trust (202802/Z/16/Z). Emma E. Vincent, Caroline J. Bull, Niki Dimou and Nicholas J. Timpson acknowledge funding by the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019). Nicholas J. Timpson, Emma E. Vincent, Caroline J. Bull and Ruth Mitchell work in a unit funded by the UK Medical Research Council (MC_UU_00011/1 and MC_UU_00011/4) and the University of Bristol. Emma E. Vincent and Caroline J. Bull are supported by Diabetes UK (17/0005587) and the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant program (IIG_2019_2009). Jeroen R. Huyghe acknowledges funding by the National Cancer Institute at the U.S. National Institutes of Health (R21CA230486). This work was also supported by the Elizabeth Blackwell Institute for Health Research, the University of Bristol and the Wellcome Trust Institutional Strategic Support Fund (ISSF 204813/Z/16/Z). The funders of the study had no role in the study design, data collection, data analysis, data interpretation or writing of the report.

### CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

### DATA AVAILABILITY STATEMENT

Summary statistics for WBC counts were downloaded from the following website: http://www.mhi-humangenetics.org/en/resources/. The summary-level GWAS data on outcomes used in our study were made available following an application to the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): https://www.fredhutch.org/en/research/divisions/public-health-sciences-division/research/cancer-prevention/genetics-epidemiology-colorectal-cancer-consortium-gecco.html. Scripts associated with our study are available on GitHub: https://github.com/andrewcon/wbc-crc. Further information is available from the corresponding author upon request.

### ETHICS STATEMENT

All participants provided written informed consent, and each study was approved by the relevant research ethics committee or institutional review board. UK Biobank received ethical approval from the NHS National Research Ethics Service North West (11/NW/0382; 16/NW/0274) and was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before enrolment in the study.

### ORCID

Andrei-Emil Constantinescu ID https://orcid.org/0000-0002-7974-5245

Caroline J. Bull ID https://orcid.org/0000-0002-2176-5120

Daniel D. Buchanan ID https://orcid.org/0000-0003-2225-6675

Victor Moreno ID https://orcid.org/0000-0002-2818-5487

Neil Murphy ID https://orcid.org/0000-0003-3347-8249
TWITTER

Andrei-Emil Constantinescu

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

_andreicon

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## 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:** Constantinescu A-E, Bull CJ, Jones N, et al. Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study. *Int J Cancer*. 2024; 154(1):94-103. doi:10.1002/ijc.34691