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RESEARCH ARTICLE
Cancer Epidemiology
Association between serum urea concentrations and the risk
of colorectal cancer, particularly in individuals with type
2 diabetes: A cohort study
Peipei Gao
1,2
| Zhendong Mei
1
| Zhenqiu Liu
1,2
| Dongliang Zhu
2,3
|
Huangbo Yuan
1,2
| Renjia Zhao
1,2
| Kelin Xu
2,4
| Tiejun Zhang
2,3
|
Yanfeng Jiang
1,2
| Chen Suo
2,3
| Xingdong Chen
1,2,5,6
1
State Key Laboratory of Genetic Engineering, Human Phenome Institute, Fudan University, Shanghai, China
2
Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
3
Department of Epidemiology and Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
4
Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
5
National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
6
Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China
Correspondence
Xingdong Chen, State Key Laboratory of
Genetic Engineering, Human Phenome
Institute, Fudan University, Shanghai, 200438,
China.
Email: xingdongchen@fudan.edu.cn
Chen Suo, Department of Epidemiology and
Ministry of Education Key Laboratory of Public
Health Safety, School of Public Health, Fudan
University, Shanghai, China.
Email: suochen@fudan.edu.cn
Funding information
National Key Research and Development
Program of China, Grant/Award Numbers:
2022YFC3400700, 2019YFC1315804;
National Natural Science Foundation of China,
Grant/Award Numbers: 82073637, 82122060,
91846302; National Science & Technology
Fundamental Resources Investigation Program
of China, Grant/Award Number:
2019FY101103; Natural Science Foundation
of Shanghai, China, Grant/Award Number:
22ZR1405300; The Innovation Grant from
Science and Technology Commission of
Shanghai Municipality, China, Grant/Award
Number: 20ZR1405600; Three-Year Action
Plan for Strengthening Public Health System in
Shanghai, Grant/Award Number: GWV-
10.2-YQ32
Abstract
Dysregulation of the urea cycle (UC) has been detected in colorectal cancer (CRC).
However, the impact of the UC's end product, urea, on CRC development remains
unclear. We investigated the association between serum urea and CRC risk based on
the data of 348 872 participants cancer-free at recruitment from the UK Biobank.
Multivariable Cox proportional hazards models were fitted to conduct risk estimates.
Stratification analyses based on sex, diet pattern, metabolic factors (including body
mass index [BMI], the estimated glomerular filtration rate [eGFR] and type 2 diabetes
[T2D]) and genetic profiles (the polygenic risk score [PRS] of CRC) were conducted to
find potential modifiers. During an average of 9.0 years of follow-up, we identified
3408 (1.0%) CRC incident cases. Serum urea showed a nonlinear relationship with
CRC risk (P-nonlinear: .035). Lower serum urea levels were associated with a higher
CRC risk, with a fully-adjusted hazard ratio (HR) of 1.26 (95% confidence interval [CI]:
1.13-1.41) in the first quartile (Q1) of urea, compared to the Q4. This association was
largely consistent across subgroups of sex, protein diet, BMI, eGFR and CRC-PRSs
(P-interaction >.05); however, it was stronger in the T2D, with an interaction
between urea and T2D on both additive (synergy index: 3.32, [95% CI: 1.24-8.88])
and multiplicative scales (P-interaction: .019). Lower serum urea concentrations were
associated with an increased risk of CRC, with a more pronounced effect observed in
Abbreviations: ARG, arginase; ASL, argininosuccinate lyase; ASS1, argininosuccinate synthase 1; BMI, body mass index; CI, confidence interval; CKD-EPI, Chronic Kidney Disease Epidemiology
Collaboration; CPS1, carbamoyl phosphate synthase 1; CRC, colorectal cancer; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HCC, hepatocellular carcinoma; HDL-C,
high-density lipoprotein cholesterol; HR, hazard ratio; ICD, International Classification of Diseases; IPAQ, International Physical Activity Questionnaire; LDL, low-density lipoprotein direct; NAGS,
N-acetylglutamate synthase; ORNT1, ornithine transporter 1; OTC, ornithine transcarbamylase; PRS, polygenic risk score; Q, quartile; SLC25A13, solute carrier family 25 member 13; T2D, type
2 diabetes; TC, total cholesterol; TG, triglyceride; UC, urea cycle.
Received: 25 April 2023 Revised: 14 August 2023 Accepted: 18 August 2023
DOI: 10.1002/ijc.34719
Int. J. Cancer. 2024;154:297–306. wileyonlinelibrary.com/journal/ijc ©2023 UICC. 297
individuals with T2D. Maintaining stable levels of serum urea has important implica-
tions for CRC prevention, particularly in individuals with T2D.
KEYWORDS
colorectal cancer, polygenic risk scores, serum urea, type 2 diabetes
What's new?
Dysregulation of the urea cycle has been detected in colorectal cancer, but the role of urea in
colorectal cancer development remains unclear. This large-scale cohort study revealed an associ-
ation between reduced serum urea levels and elevated colorectal cancer risk. Stratified analysis
suggested that protein diet, body mass index, estimated glomerular filtration rate and polygenic
risk score may not influence this relationship, which was however more pronounced in partici-
pants with type 2 diabetes. The findings highlight the potential importance of maintaining stable
serum urea levels for colorectal cancer prevention, especially in populations with type
2 diabetes.
1|INTRODUCTION
Colorectal cancer (CRC) is responsible for nearly 10% of all diagnosed
cancers and cancer-related deaths worldwide each year.
1
Symptoms
of CRC often present only in advanced stages, leading to a poor prog-
nosis.
2
Therefore, it is essential to identify potential biomarkers for
early detection and diagnosis of CRC to reduce the incidence and
mortality rates.
2
Cancer cells are known to reprogram metabolic pathways to opti-
mize the utilization of carbon and nitrogen for tumor growth and pro-
liferation.
3
In this context, alterations occur during the urea cycle
(UC), which typically removes excess nitrogen and ammonia produced
by nitrogen-containing compounds and protein breakdown.
3,4
Serum
urea is the end product of protein metabolism from the UC, and
emerging cancer-related studies have turned their attention to the
roles of urea in cancer development.
4-6
A previous research has sug-
gested that urea may serve as a serum biomarker for differentiating
hepatocellular carcinoma (HCC) from other liver diseases,
6
and
decreased serum urea levels have been observed in patients with
advanced oral cancer.
7
Furthermore, alterations in the expression of
UC-related genes or enzymes, such as arginase (ARG) and arginino-
succinate synthase 1 (ASS1),
8,9
have been demonstrated in CRC. A
recent study has also indicated that UC activation triggered by host-
microbiota maladaptation plays a significant role in driving colorectal
tumorigenesis.
10
Despite these findings, no current reports have
established a link between serum urea levels and the risk of develop-
ing CRC. The potential of serum urea as a biomarker for CRC thus
warrants further investigation.
Serum urea concentrations differ across different populations,
especially in patients with type 2 diabetes (T2D), which is a common
disease and expected to double in prevalence by 2030.
11,12
This
underscores the importance of examining the association between
urea and CRC risk in populations with T2D. Urea can affect beta-cell
glycolysis, insulin secretion and glucose tolerance, indicating its poten-
tial role in T2D.
11,13
Previous studies have found correlations
between serum urea and severe diabetic retinopathy,
14
as well as
higher levels of salivary urea in patients with diabetes compared to
healthy controls.
15
Intriguingly, CRC and T2D share some common
risk factors, such as obesity and physical inactivity.
16
A burgeoning
number of studies have reported a higher incidence of CRC among
patients with T2D,
16-18
and those with preexisting diabetes and CRC
have lower overall survival rates than individuals without diabetes
have.
19,20
In addition, the influence of other risk factors on the associa-
tion of urea with CRC, including diet, genetic profiles and other meta-
bolic factors (i.e., obesity, renal function), are not fully understood.
2
Collectively, research is necessary to explore the potential effects of
these factors on the association between urea and CRC, which holds
significance in contributing to the prevention and management of CRC.
In the current study, we examined the association between serum
urea concentrations and the risk of CRC in the UK Biobank study. We
analyzed available dietary, metabolic and genetic data to explore
potential effect modifiers.
2|METHODS
2.1 |Study population
The UK Biobank is a large population-based prospective cohort study
designed for exploring the potential determinants of a wide range of
common genetic and environmental diseases in individuals aged 40 to
70 years.
21
More than 500 000 participants from 22 assessment cen-
ters across England, Scotland and Wales were recruited and selected
between 2006 and 2010. The data collected at the assessment center
included phenotypic and genotypic information. Participants' socio-
economic and demographic data, including lifestyle, dietary habits,
medications and medical history, were collected using touch-screen
questionnaires. Physical measurements were taken to collect anthro-
pometric data. Biological samples of blood and urine were obtained
during the baseline period.
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Data from 500 938 participants were potentially available for the
current analyses. After excluding participants with a previous diagno-
sis of cancer at baseline (n =28 550, except non-melanoma skin can-
cer: C44), incomplete information on serum urea (n =29 415),
covariates (n =88 074) or pre-existing type 1 diabetes (6027), data
from 348 872 participants were included in the study (Figure S1).
2.2 |Assessment of serum urea
Blood samples were collected from consenting participants after an
overnight fast or a fast for at least 8 hours during recruitment. Standard
hematological tests were performed on fresh whole blood within
24 hours of the blood draw. The serum concentration of urea (mmol/L)
was measured using the enzymatic method (Beckman Coulter AU5800),
with an analytical range of the assays between 0.8 and 50 mmol/L. The
coefficient of variation for serum urea ranged from 2.29% to 3.04%,
and the score for external quality assurance was 100%. Full details of
participants' assay performance were published previously.
22
2.3 |Ascertainment of outcomes
Since recruitment, the incidence of cancer and mortality of the partici-
pants have been followed through links with the Health and Social
Care Information Centre (England and Wales), the Scottish Cancer
Registry (Scotland) and the UK Office of National Statistics. Person-
time was calculated from the day of the baseline assessment to the
day of a CRC diagnosis, death, loss to follow-up or February 14, 2018,
whichever event occurred first. Cases of CRC were identified based
on the International Classification of Diseases, 10th Revision (ICD-10)
codes C18, C19, C20 and C21, and the ninth Revision (ICD-9) codes
153 and 154 (Table S1). The prevalence of T2D at baseline assess-
ment was determined based on information from self-report records,
medications and medical histories (Table S1).
23
2.4 |Calculation of the polygenic risk scores
for CRC
We calculated the polygenic risk score (PRS) for CRC to test its poten-
tial influence on the association between serum urea and CRC risk.
The CRC-PRS was derived based on 87 single nucleotide polymor-
phisms previously associated with CRC at the genome-wide signifi-
cance level (P<510
8
, Table S2) for independence (r
2
< .01), from
a combined meta-analysis (58 131 CRC and 67 347 controls).
24
Details on genotyping and imputation have been described else-
where.
25
The PRSs of individuals were calculated by summing the
weighted dosages of each selected genetic variant.
26
The CRC-PRSs
analysis was limited to the white participants who made up 94.0% of
the whole population. After excluding individuals with sex discordance
or genetic relatedness to others (kinship coefficient ≥0.08), 306 857
participants remained in the current analysis.
2.5 |Covariates
Data on participants' demographic characteristics including age, sex,
ethnic background, college or university degree, Townsend depriva-
tion index, alcohol drinking, smoking status and sleep duration were
obtained during the baseline assessment. Ethnic background was
grouped as White European and Other. Smoking status was classified
as never, previous and current smoker. Alcohol drinking frequency
was classified as never, less than daily and daily or almost daily. Body
mass index (BMI) was calculated as weight (kg) divided by the square of
the person's height in meters, and categorized as underweight/normal
(<25 kg/m
2
), overweight (25-29.9 kg/m
2
)andobese(≥30 kg/m
2
). Physi-
cal activity was assessed using the short-form of the International Phys-
ical Activity Questionnaire (IPAQ), with regular physical activity defined
as at least 150 minutes/week of moderate activity, 75 minutes/week of
vigorous activity or 150 minutes/week of a combination of moderate
and vigorous activity. A protein diet pattern was defined as having
an adequate intake of at least two of the four dietary protein compo-
nents, including unprocessed meat, processed meat, fish and dairy
(Table S3).
23,27
The Townsend deprivation index is a comprehensive
measurement of deprivation, with lower scores indicating a higher level
of socioeconomic status.
28
The duration of T2D refers to the number of
years between the first occurrence of T2D and the baseline assessment
for participants diagnosed with T2D. Information about medication his-
tory was collected via questionnaires or verbal interviews at the base-
line assessment, and medications for diabetes were grouped as insulin,
only oral drugs or neither. The prevalence of hypertension and hyperlip-
idemia were obtained through self-reports and hospital inpatient
records. Data on serum levels of triglycerides (TGs), total cholesterol
(TC), creatinine and cystatin C were obtained during health examina-
tions. The estimated glomerular filtration rate (eGFR, an indicator of kid-
ney metabolic function) was calculated using the 2021 Chronic Kidney
Disease Epidemiology Collaboration (CKD-EPI) equation, which incor-
porates information on both creatinine and cystatin C.
29
2.6 |Statistical analysis
Baseline characteristics are reported as number (%) for categorical
variables and mean ± SD (SD) for continuous variables. The relation-
ship between continuous urea levels and CRC risk was examined
using a restricted cubic spline method, with the first quartile (Q1) of
serum urea as the reference. The Wald test was used to examine the
data for linearity. Hazard ratios (HRs) and 95% confidence intervals
(CIs) were estimated using multivariable Cox proportional hazards
regression models to analyze the relationship between urea categories
and CRC risk. Model 1 was adjusted for age (continuous) and sex
(male, female). Model 2 was further adjusted for ethnic background
(White European, other), college or university degree (no, yes), Town-
send deprivation index (continuous), alcohol drinking (never, less often
than daily, daily or almost daily drinking), smoking status (never, previ-
ous and current), BMI (underweight/normal, overweight, obese), pro-
tein diet (no, yes), regular physical activity (no, yes) and sleep duration
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(<7, 7-8, >8 hours/day) based on Model 1. Model 3 was further
adjusted for T2D (no, yes), hyperlipidemia (no, yes), hypertension (no,
yes), TGs (continuous), TC (continuous), glucose (continuous) and
eGFR (continuous) based on Model 2. Considering the possible impact
of metabolic conditions on the effect of serum urea,
11,14,30
we con-
ducted stratification analyses based on metabolic status, including
BMI, eGFR (a biomarker of renal metabolic function)
29
and T2D, to
investigate the potential effect modification. Additionally, we also per-
formed analyses stratified by sex and CRC-PRS to examine the influ-
ence of sex and genetic profiles. In the analysis stratified by T2D
status, information on the duration of T2D (<1, 1-5, >5 years, catego-
rized as <1 year for participants without T2D) and medications for
diabetes (neither, only oral drugs, insulin) were further adjusted.
For risk estimates stratified by CRC-PRSs, the genotyping arrays and
the first 10 genetic principal components were further adjusted. Inter-
action effects were examined by calculating the synergy index on the
additive scale and P-value on the multiplicative scale.
31
Sensitivity analyses were performed to assess the robustness of
the current results. First, the tests of relationships between serum
urea and CRC risk were further adjusted for glycated hemoglobin
(HbA1c), high-density lipoprotein cholesterol (HDL-C) and low-density
lipoprotein direct (LDL) to exclude potential confounders. Second,
participants with less than 2 years of follow-up were excluded from
the analyses to minimize the possibility of reverse causality in the
observed associations. Third, the association was additionally strati-
fied by age, smoking and alcohol-drinking status to identify potential
effect modifiers. Finally, we used random sampling to select individ-
uals without T2D who were frequency-matched to individuals with
T2D based on age and sex, which yielded ratios of 1:5 and 1:7,
respectively. We then examined the association of urea with CRC risk
in the matched non-T2D group and the interaction between serum
urea and T2D status when individuals without T2D were randomly
selected using the frequency-matching method.
All statistical analyses were performed using the R program
(Version 4.2.2). Reported P-values were two-tailed, and P< .05 was
considered statistically significant.
3|RESULTS
3.1 |Baseline characteristics
During a mean follow-up period of 9.0 years, 3048 incident cases of
CRC were identified among the 348 872 participants with a mean age
of 56.1 years. The distribution of baseline characteristics according to
the quartiles of serum urea levels is shown in Table 1. Participants
with lower serum urea concentrations tended to be females, current
smokers and nondrinkers, and were more likely to have a lower BMI,
engage in less physical activity, consume less protein in their diets,
have lower levels of glucose, HbA1c, TGs, TC and eGFR and higher
levels of HDL-C and eGFR. Notably, individuals with T2D at baseline
assessment had higher serum urea concentrations compared to those
without it.
3.2 |Serum urea and the risk of CRC
A nonlinear relationship between serum urea and CRC was observed
(Figure 1,Pfor nonlinear: .035). The restricted cubic spline analysis
revealed a negative correlation between serum urea and CRC risk
within the first quartile (Q1) of urea, with an HR per SD of 0.76 (95%
CI, 0.65-0.90) when compared to Q1 of urea. This negative associa-
tion almost disappeared between Q2 and Q3. Consequently, serum
urea levels were categorized into three groups: (1) less than Q1
(<4.48 mmol/L), (2) Q2 and Q3 (4.48-6.12 mmol/L) and (3) Q4
(>6.12 mmol/L). As shown in Table 2, urea levels of Q1 had a fully
adjusted HR of 1.26 (95% CI, 1.13-1.41) for CRC risk when compared
to the Q4 category.
3.3 |Subgroup analyses
To identify potential modifiers, we examined the relationship between
continuous urea levels and CRC risk within various subgroups, includ-
ing sex, protein diet and metabolic status (including BMI, eGFR
[a biomarker of renal function] and T2D status). Our results indicated
that the inverse relationship between lower urea levels and higher
CRC risk was consistent across the protein diet (Figure 2B), BMI
(Figure 2C) and eGFR (Figure 2D) subgroups. However, this relation-
ship was more pronounced in males (Figure 2A) and individuals with
T2D (Figure 2E).
We further assessed the association between urea categories and
CRC risk within these subgroups, as well as the interaction effects on
additive and multiplicative scales. We found that lower urea levels sig-
nificantly interact with T2D to increase CRC risk (P-interaction: .019;
Figure 3, Figure S2E and Table S4). Using participants without T2D
and with urea levels in the Q4 category as the reference group, the
fully adjusted HRs were 1.50 (95% CI: 1.07-2.12) for urea levels
between Q2 and Q3 with T2D, and 2.40 (95% CI: 1.61-3.59) for urea
levels less than Q1 with T2D (Figure 3). The synergy index values for
serum urea and T2D were 1.74 (95% CI: 0.47-6.48) and 3.32 (95% CI:
1.24-8.88), suggesting a positive interaction. This implies that the
combined effect of serum urea and T2D on the additive scale was
greater than the sum of their individual effects. Moreover, when mea-
sured on a multiplicative scale, the P-interaction of .019 further con-
firmed a positive interaction, indicating that the joint effect of lower
urea levels and T2D on CRC risk exceeded the product of their indi-
vidual effects. The association between urea categories and CRC risk
remained generally unchangeable across other subgroups, including
sex, protein diet, BMI and the eGFR (all P-values for interaction >.05;
Figure S2A-D and Table S4).
We also calculated the PRS for CRC and performed stratified
analyses by the CRC-PRS to explore the potential impact of genetic
profiles. The computed PRS was positively associated with CRC risk,
with high CRC-PRS resulting in a 1.58-fold and 2.35-fold risk for CRC
in tertiles 2 and 3, respectively (Table S5). We observed no evidence
of interaction between serum urea and CRC-PRSs on the risk of CRC
(P-interaction =.439, Figure 2F, Figure S2F and Table S4).
300 GAO ET AL.
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TABLE 1 Characteristics of 348 872 participants according to the quartiles of serum urea concentrations.
Variables
Serum urea concentration (mmol/L)
Q4 (>6.12) Q3 (5.26-6.12) Q2 (4.48-5.25) Q1 (<4.48)
Incident cases of CRC (person-years) 870 (769477) 784 (778213) 727 (781896) 667 (774879)
Age (years), mean (SD) 58.8 (7.42) 57.0 (7.81) 55.5 (8.02) 53.1 (8.13)
Sex
Male 49 803 (57.4%) 44 920 (51.4%) 40 464 (46.2%) 32 302 (37.1%)
Female 36 906 (42.6%) 42 456 (48.6%) 47 182 (53.8%) 54 839 (62.9%)
White European 83 625 (96.4%) 83 734 (95.8%) 83 154 (94.9%) 80 025 (91.8%)
College or university degree 27 127 (31.3%) 30 118 (34.5%) 32 131 (36.7%) 34 062 (39.1%)
Townsend deprivation index 1.56 (2.95) 1.56 (2.95) 1.47 (2.98) 1.07 (3.18)
BMI
Underweight/normal (<25 kg/m
2
) 24 490 (28.2%) 28 032 (32.1%) 30 737 (35.1%) 35 453 (40.7%)
Overweight (25-29.9 kg/m
2
) 39 695 (45.8%) 39 289 (45.0%) 37 587 (42.9%) 33 832 (38.8%)
Obese (≥30 kg/m
2
) 22 524 (26.0%) 20 055 (23.0%) 19 322 (22.0%) 17 856 (20.5%)
Smoking status
Never 46 575 (53.7%) 48 682 (55.7%) 49 265 (56.2%) 47 569 (54.6%)
Previous 33 321 (38.4%) 30 976 (35.5%) 29 622 (33.8%) 27 246 (31.3%)
Current 6813 (7.9%) 7718 (8.8%) 8759 (10.0%) 12 326 (14.1%)
Alcohol drinking frequency
Never 6080 (7.0%) 5629 (6.4%) 5883 (6.7%) 7517 (8.6%)
Less often than daily 62 181 (71.7%) 62 600 (71.6%) 62 911 (71.8%) 61 380 (70.4%)
Daily or almost daily 18 448 (21.3%) 19 147 (21.9%) 18 852 (21.5%) 18 244 (20.9%)
Protein diet 59 201 (68.3%) 56 749 (64.9%) 53 627 (61.2%) 46 464 (53.3%)
Regular physical activity 70 938 (81.8%) 71 191 (81.5%) 70 572 (80.5%) 68 485 (78.6%)
Sleep duration, hours/day
<7 20 879 (24.1%) 20 842 (23.9%) 20 603 (23.5%) 21 483 (24.7%)
7-8 58 771 (67.8%) 60 390 (69.1%) 61 105 (69.7%) 59 516 (68.3%)
>8 7059 (8.1%) 6144 (7.0%) 5938 (6.8%) 6142 (7.0%)
Hypertension 34 379 (39.6%) 27 645 (31.6%) 24 783 (28.3%) 21 953 (25.2%)
Hyperlipidemia 17 461 (20.1%) 13 509 (15.5%) 11 682 (13.3%) 9659 (11.1%)
T2D 4936 (5.7%) 3230 (3.7%) 2803 (3.2%) 2567 (2.9%)
Duration of T2D, years
<1 83 986 (96.9%) 85 658 (98.0%) 86 133 (98.3%) 85 715 (98.4%)
1-5 1640 (1.9%) 1115 (1.3%) 973 (1.1%) 912 (0%)
>5 1083 (1.2%) 603 (0.7%) 540 (0.6%) 514 (0.6%)
Medications for diabetes
Neither 83 251 (96.0%) 85 199 (97.5%) 85 723 (97.8%) 85 354 (97.9%)
Only oral drugs 2783 (3.2%) 1812 (2.1%) 1597 (1.8%) 1493 (1.7%)
Insulin 675 (0.8%) 365 (0.4%) 326 (0.4%) 294 (0.3%)
Glucose, mmol/L (mean, SD) 5.14 (1.15) 5.06 (1.01) 5.03 (0.98) 4.98 (0.89)
HbA1c, mmol/mol 36.3 (5.96) 35.7 (5.38) 35.3 (5.33) 34.9 (5.41)
HDL-C, mmol/L 1.42 (0.37) 1.45 (0.37) 1.46 (0.38) 1.48 (0.39)
LDL, mmol/L 3.55 (0.88) 3.60 (0.86) 3.59 (0.85) 3.51 (0.84)
TGs, mmol/L 1.86 (1.07) 1.78 (1.02) 1.71 (0.999) 1.58 (0.95)
TC, mmol/L 5.68 (1.17) 5.74 (1.13) 5.73 (1.11) 5.63 (1.10)
eGFR, mL/min/1.73 m
2
88.0 (15.8) 94.4 (12.9) 97.9 (12.3) 102.0 (12.1)
Abbreviations: BMI, body mass index; CRC, colorectal cancer; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; LDL, low-density
lipoprotein direct; T2D, type 2 diabetes; TC, total cholesterol; TGs, triglycerides.
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3.4 |Sensitivity analyses
The results of our sensitivity analyses did not demonstrate notable
changes when risk estimates were further adjusted for HbA1c, HDL-C
and LDL (Table S6), when participants with less than 2 years of
follow-up were excluded (Table S7), or when participants without
T2D were randomly selected using the frequency-match method
(Table S8 and S9). The risk estimates stratified by age, smoking or
drinking status yielded results that were almost consistent across the
corresponding strata (All Pvalues for interaction >.05, Table S10).
4|DISCUSSION
To the best of our knowledge, there has been no previous large-scale
cohort study examining the association between baseline serum urea
and the risk of developing CRC. In the current study, we demon-
strated the inverse association between low serum urea levels and
CRC risk, which was further strengthened in individuals with T2D.
Studies evaluating the association between serum urea and can-
cer risk remain limited. In a retrospective study with 145 patients with
oral cancer and 80 controls, serum urea levels were reduced in the
patients with oral cancer.
7
Similarly, lower plasma urea levels were
found in pediatric patients with cancer compared to healthy controls.
4
These findings concur with those of the present study. However, a
two-sample Mendelian Randomization analysis indicated a positive
association between urea and renal cell carcinoma, but no association
with breast or prostate cancer.
32
Moreover, increased serum urea
levels were detected in patients with HCC.
6
The heterogeneity across
different analyses concerning the relationship between urea and can-
cer risk may be attributed to several factors, such as variations in the
types of cancers or study samples, different analytical methods or lim-
ited sample sizes.
Serum urea is the end product of protein metabolism from the UC,
which involves one cofactor-producing enzyme (N-acetylglutamate
synthase [NAGS]), two transporters (ornithine transporter 1 [ORNT1]
and citrin [asolute carrier family 25 member 13, SLC25A13]) and five
catalytic enzymes (ARG, ASS1, carbamoyl phosphate synthase 1 [CPS1],
ornithine transcarbamylase [OTC] and argininosuccinate lyase [ASL]).
33
In recent years, it has become increasingly evident that alterations in
the expression of the UC genes, enzymes and metabolites play an active
FIGURE 1 Multivariable-adjusted hazard ratios (HRs) for
continuous urea and the risk of colorectal cancer (CRC). Estimates
were calculated to compare the first quartile (Q1) of urea levels
(4.48 mmol/L) using a restricted cubic spline analysis with five knots
(at the fifth, 25th, 50th, 75th and 95th centiles) to fit the models. The
hazard ratios (HRs) and 95% confidence intervals (CIs) are presented
using colored lines with light shading. HRs were adjusted for age
(continuous), sex (male, female), ethnic background (White European,
other), college or university degree (no, yes), Townsend deprivation
index (continuous), alcohol drinking (never, less often than daily, daily
or almost daily drinking), smoking status (never, previous and current),
BMI (underweight/normal, overweight, obese), protein diet (no, yes),
regular physical activity (no, yes), sleep duration (<7, 7-8, >8 hours/
day), hyperlipidemia (no, yes), hypertension (no, yes), T2D (no, yes),
TGs (continuous), TC (continuous), glucose (continuous) and eGFR
(continuous). The histograms show the frequency density of
continuous urea. BMI, body mass index; eGFR, estimated glomerular
filtration rate; T2D, type 2 diabetes; TC, total cholesterol; TGs,
triglycerides.
TABLE 2 Hazard ratios (95%
confidence intervals) for serum urea and
the risk of colorectal cancer (CRC).
Population and
Models
Serum urea concentration (mmol/L)
P-trendQ4 Q2-Q3 Q1
Full cohort
Model 1 Reference 1.05 (0.97-1.15) 1.22 (1.10-1.35) <.01
Model 2 Reference 1.06 (0.98-1.15) 1.23 (1.11-1.37) <.01
Model 3 Reference 1.08 (0.99-1.18) 1.26 (1.13-1.41) <.01
Note: Model 1: adjusted for age (continuous) and sex (male, female). Model 2: further adjusted for ethnic
background (White European, other), college or university degree (no, yes), Townsend deprivation index
(continuous), alcohol drinking (never, less often than daily, daily or almost daily drinking), smoking status
(never, previous and current), BMI (underweight/normal, overweight, obese), protein diet (no, yes),
regular physical activity (no, yes) and sleep duration (<7, 7-8, >8 hours/day) based on Model 1. Model 3:
further adjusted for hyperlipidemia (no, yes), hypertension (no, yes), T2D (no, yes), TGs (continuous), TC
(continuous), glucose (continuous) and eGFR (continuous) based on Model 2.
Abbreviations: eGFR, estimated glomerular filtration rate; T2D, type 2 diabetes; TC, total cholesterol;
TGs, triglycerides.
302 GAO ET AL.
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role in tumorigenesis,
3,34
including their promotion of anabolic path-
ways, impact on the selection of specific tumor mutagenic profiles, and
influence on the immune response.
4
However, some genes involved in
the UC have been indicated to be upregulated in certain cancers but
downregulated in others, such as the upregulated ARG1
9
and ASS1
8
in
CRC, and upregulated CPS1
35
in lung cancer, as well as the downregu-
lated ASL
36
in CRC and ARG1,
37
OTC,
38
ASL
39
and ASS1
40
in hepatic
cancer. In these cases, dysregulation of the UC is thought to facilitate
cancer development and progression by altering the availability of UC-
related metabolites.
4,35
These alterations may have different effects on
different types of cancers, leading to varying outcomes.
It is worth noting that the association between serum urea and
CRC risk was found to be stronger in participants with prevalent T2D
than in those without it. As the prevalence of diabetes is expected to
double by 2050,
12
and digestive cancers (including colorectal, pancre-
atic, liver and gallbladder cancer) have become the leading contributor
to diabetes-related deaths,
41
it is essential to understand the role of
T2D in the relationship between urea and CRC. Previous studies have
reported elevated urea levels in individuals with T2D,
14,15
which is
consistent with the findings of our study. Urea can impair insulin
secretion by directly affecting pancreatic βcells,
13
which may contrib-
ute to the observed interactive effects of urea and T2D. It is also
important to consider potential confounding factors, such as changes
in diet, metabolic status or genetic profiles, although stratified analysis
results suggest that protein diet, BMI, eGFR (an indicator of kidney
metabolic function) and CRC-PRSs may not influence the relationship
between urea and CRC. Further research is needed to investigate the
mechanism of the interaction between serum urea and T2D status on
CRC risk. Although the specific roles of T2D in the relationship of urea
with CRC are not fully understood, this analysis provides new insights
into the prevention and management of CRC, in particular, for individ-
uals with T2D.
The study has several strengths, including its large sample
size, multivariable adjustments and leveraging of extensive data (i.e.,
FIGURE 2 Association between continuous serum urea and the risk of colorectal cancer by (A) sex, (B) protein diet pattern, (C) BMI,
(D) eGFR, (E) T2D status at baseline assessment and (F) CRC-PRSs. Estimates were calculated to compare the first quartile (Q1) of urea levels
(4.48 mmol/L) using a restricted cubic spline analysis with five knots (at the 5th, 25th, 50th, 75th and 95th centiles) to fit the models. The hazard
ratios (HRs) and 95% confidence intervals (CIs) are presented using colored lines with light shading. HRs were adjusted for age (continuous), sex
(male, female), ethnic background (White European, other), college or university degree (no, yes), Townsend deprivation index (continuous),
alcohol drinking (never, less often than daily, daily or almost daily drinking), smoking status (never, previous and current), BMI (underweight/
normal, overweight, obese), protein diet (no, yes), regular physical activity (no, yes), sleep duration (<7, 7-8, >8 hours/day), hyperlipidemia (no,
yes), hypertension (no, yes), T2D (no, yes), TGs (continuous), TC (continuous), glucose (continuous) and eGFR (continuous), where appropriate.
HRs were further adjusted for the genotyping arrays and the first 10 genetic principal components in the analysis stratified by CRC-PRSs. HRs
were further adjusted for the duration of T2D (<1, 1-5, >5 years) and medications for diabetes (neither, only oral drugs, insulin) in the analysis
stratified by T2D status. The histograms show the frequency density of continuous urea. BMI, body mass index; CRC, colorectal cancer; eGFR,
estimated glomerular filtration rate; PRS, polygenic risk score; T2D, type 2 diabetes; TC, total cholesterol; TGs, triglycerides.
GAO ET AL.303
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demographic, anthropometric, biochemical and genetic characteris-
tics). Furthermore, the rich data resources available enabled the
detailed analysis of the relationship between serum levels and the risk
of CRC, and potential modifiers including sex, diet, metabolic status
and genetic profiles. There are a few limitations that warrant mention.
First, the primarily Caucasian study population limits the generalizabil-
ity of the findings to other ethnic populations. Second, the study did
not capture changes in serum urea levels during follow-up, which may
lead to nondifferential misclassification bias. However, we included
participants with and without T2D, who have different dietary and
lifestyle habits as well as varying levels of urea. Yet we observed a
negative correlation between lower urea levels and a higher CRC risk,
with this association being more pronounced in individuals with T2D.
Further studies are needed to explore additional potential effect
modifications.
In summary, individuals with low levels of serum urea are at an
elevated risk for developing CRC, and lower urea levels interact with
T2D to increase the risk of CRC. The findings of our study highlight
the potential importance of maintaining serum urea levels for the pre-
vention of CRC, especially in individuals with T2D.
AUTHOR CONTRIBUTIONS
All authors contributed to the conception and design of the study;
Peipei Gao, Zhendong Mei, Zhenqiu Liu, Renjia Zhao, Chen Suo and
Xingdong Chen acquired the data; Peipei Gao, Zhendong Mei, Zhen-
qiu Liu, Dongliang Zhu and Chen Suo analyzed the data; Zhendong
Mei, Huangbo Yuan, Renjia Zhao and Yanfeng Jiang contributed to
the interpretation of data; Kelin Xu, Tiejun Zhang, Yanfeng Jiang,
Chen Suo and Xingdong Chen supported the sensitivity analyses and
their interpretation; Peipei Gao drafted the manuscript; all authors
contributed to the advanced draft of the manuscript; all authors read
and approved the final manuscript. The work reported in the paper
has been performed by the authors, unless clearly specified in
the text.
ACKNOWLEDGEMENTS
We sincerely appreciate the great works of UK Biobank collaborators.
FUNDING INFORMATION
This study was funded by the National Key Research and Develop-
ment Program of China (2022YFC3400700, 2019YFC1315804),
National Science & Technology Fundamental Resources Investiga-
tion Program of China (grant number: 2019FY101103), the
National Natural Science Foundation of China (82073637,
82122060, 91846302), the Natural Science Foundation of Shang-
hai, China (grant number: 22ZR1405300), the Three-Year Action
Plan for Strengthening Public Health System in Shanghai (grant
number: GWV-10.2-YQ32); and an Innovation Grant from Science
and Technology Commission of Shanghai Municipality, China
(grant number: 20ZR1405600). The funders had no role in the
study design, data collection, data analysis, interpretation or
writing of the manuscript.
T2D
No
Yes
Serum urea
Q4
Q2−Q3
Q1
Q4
Q2−Q3
Q1
CRC (N, %)
800 (28.2)
1,418 (50.0)
617 (21.8)
70 (32.9)
93 (43.7)
50 (23.5)
Non−CRC
80,973 (24.4)
167,571 (50.4)
83,957 (25.3)
4,866 (36.5)
5,940 (44.6)
2,517 (18.9)
HR (95% CI)
Reference
1.07 (0.97−1.17)
1.20 (1.07−1.35)
1.22 (0.85−1.76)
1.50 (1.07−2.12)
2.40 (1.61−3.59)
P−trend
< 0.01
< 0.01
0.7 1 1.5 2 2.5 3
FIGURE 3 Interaction between serum urea and type 2 diabetes (T2D) status on the risk of colorectal cancer (CRC). Synergy index values on
the additive scale were 1.74 (95% CI: 0.47-6.48) and 3.32 (95% CI: 1.24-8.88) for the serum urea groups and T2D status, respectively. The
P-value for interaction on the multiplicative scale was 0.019 for the serum urea groups and T2D status. Hazard ratios (HRs) were adjusted for age
(continuous), sex (male, female), ethnic background (White European, other), college or university degree (no, yes), Townsend deprivation index
(continuous), alcohol drinking (never, less often than daily, daily or almost daily drinking), smoking status (never, previous and current), BMI
(underweight/normal, overweight, obese), protein diet (no, yes), regular physical activity (no, yes), sleep duration (<7, 7-8, >8 hours/day),
hyperlipidemia (no, yes), hypertension (no, yes), TGs (continuous), TC (continuous), glucose (continuous) and eGFR (continuous). BMI, body mass
index; eGFR, estimated glomerular filtration rate; TC, total cholesterol; TGs, triglycerides.
304 GAO ET AL.
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CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
This work has been conducted using the UK Biobank Resource. The
UK Biobank is an open-access resource and bona fide researchers can
apply to use the UK Biobank dataset by registering and applying at
http://ukbiobank.ac.uk/register-apply/. Further information is avail-
able from the corresponding author upon request.
ETHICS STATEMENT
The study was approved by the North West Multicenter Research
Ethics Committee, the Patient Information Advisory Group in England
and Wales and the Community Health Index Advisory Group in Scot-
land. All participants in the surveys have given informed consent.
ORCID
Zhenqiu Liu https://orcid.org/0000-0002-5244-6894
Chen Suo https://orcid.org/0000-0002-5274-4584
Xingdong Chen https://orcid.org/0000-0003-3763-160X
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How to cite this article: Gao P, Mei Z, Liu Z, et al. Association
between serum urea concentrations and the risk of colorectal
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cohort study. Int J Cancer. 2024;154(2):297‐306. doi:10.1002/
ijc.34719
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