IJC
International Journal of Cancer
A prospective evaluation of serum methionine-related
metabolites in relation to pancreatic cancer risk in two
prospective cohort studies
Joyce Y. Huang1,2, Hung N. Luu 1,2, Lesley M. Butler1,2, Øivind Midttun3, Arve Ulvik4, Renwei Wang1, Aizhen Jin5,
Yu-Tang Gao6, Yuting Tan6, Per M. Ueland3,7, Woon-Puay Koh5,8 and Jian-Min Yuan 1,2
1
Division of Cancer Control and Population Science, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
3
Bevital A/S, Bergen, Norway
4
Department of Clinical Science, University of Bergen, Bergen, Norway
5
Health Service and Systems Research, Duke-NUS Medical School Singapore, Singapore, Singapore
6
Department of Epidemiology, Shanghai Cancer Institute/Shanghai Jiaotong University, Shanghai, China
7
Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway
8
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
Deficiencies in methyl donor status may render DNA methylation changes and DNA damage, leading to carcinogenesis.
Epidemiological studies reported that higher dietary intake of choline is associated with lower risk of pancreatic cancer, but no
study has examined the association of serum choline and its metabolites with risk of pancreatic cancer. Two parallel
case–control studies, one nested within the Shanghai Cohort Study (129 cases and 258 controls) and the other within the
Singapore Chinese Health Study (58 cases and 104 controls), were conducted to evaluate the associations of baseline serum
concentrations of choline, betaine, methionine, total methyl donors (i.e., sum of choline, betaine and methionine),
dimethylglycine and trimethylamine N-oxide (TMAO) with pancreatic cancer risk. In the Shanghai cohort, odds ratios and 95%
confidence intervals of pancreatic cancer for the highest quartile of choline, betaine, methionine, total methyl donors and
TMAO were 0.27 (0.11–0.69), 0.57 (0.31–1.05), 0.50 (0.26–0.96), 0.37 (0.19–0.73) and 2.81 (1.37–5.76), respectively,
compared to the lowest quartile. The corresponding figures in the Singapore cohort were 0.85 (0.23–3.17), 0.50 (0.17–1.45),
0.17 (0.04–0.68), 0.33 (0.10–1.16) and 1.42 (0.50–4.04). The inverse associations of methionine and total methyl donors
including choline, betaine and methionine with pancreatic cancer risk in both cohorts support that DNA repair and methylation
play an important role against the development of pancreatic cancer. In the Shanghai cohort, TMAO, a gut microbiota-derived
metabolite of dietary phosphatidylcholine, may contribute to higher risk of pancreatic cancer, suggesting a modifying role of
gut microbiota in the dietary choline-pancreatic cancer risk association.
Introduction
Worldwide pancreatic cancer is the 12th most common
cancer in men and 11th most common cancer in women with
an estimated number of new cases of 460,000 in 2018.1 Pancreatic cancer is the third leading cause of cancer death in the
US with an estimated 55,440 deaths due to pancreatic cancer
in 2018,2 with only 8% of patients survive 5 years after diagnosis.3 Established risk factors for pancreatic cancer include
chronic pancreatitis, obesity and type 2 diabetes.4 Collectively,
these risk factors are attributable to less than half of pancreatic
cancer burden in the US.5 The underlying causes are still controversial and unknown for majority of pancreatic cancer
Additional Supporting Information may be found in the online version of this article.
Key words: pancreatic cancer, risk factors, choline, betaine, methionine, trimethylamine N-oxide, DNA methylation, microbiota
Abbreviations: CI: confidence interval; CV: coefficient of variation; DMG: dimethylglycine; eGFR: estimated glomerular filtration rate; ICD:
International Classification of Diseases-Oncology; LC–MS/MS: liquid chromatography–tandem mass spectrometry; MAPK: mitogen-activated
protein kinase; NF-kB: nuclear factor kappa-light-chain-enhancer of activated B cells or nuclear factor kappa B; NHANES: US National Health
and Nutrition Examination Survey; OR: odds ratio; SAM: S-adenosylmethionine; SCH: Shanghai Cohort Study; SCHS: Singapore Chinese
Health Study; SD: standard deviation; sTNF-R p55: human soluble TNF receptor p55; sTNF-R p75: human soluble TNF receptor p75; TLR:
Toll-like receptor; TMA: trimethylamine; TMAO: trimethylamine N-oxide; TNF-alpha: tumor necrosis factor-alpha
DOI: 10.1002/ijc.32994
History: Received 30 May 2019; Accepted 11 Mar 2020; Online 29 Mar 2020
Correspondence to: Hung N. Luu, E-mail: luuh@upmc.edu
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
Cancer Epidemiology
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Choline metabolites and pancreatic cancer risk
Cancer Epidemiology
What’s new?
Over half of all pancreatic cancers aren’t associated with known risk factors. In this prospective study, the authors examined
serum levels of three nutrients (choline, methionine, and betaine) that have been associated with reduced oxidative DNA damage
and epigenetic changes such as methylation. They found that, as predicted, higher serum levels of these nutrients were
correlated with lower pancreatic cancer risk. They also found that certain compounds associated with gut microbiota increased
this risk. These results identify novel etiological factors that may guide future prevention strategies for pancreatic cancer.
cases. Therefore, it is an urgent need to identify novel etiological factors that would help the development of primary prevention strategies for pancreatic cancer.
Choline is involved in lipid transportation, methylation
reaction and synthesis of neurotransmitters (i.e., glycine or
glutamine)6 and shapes the composition of gut microbiota.
Abnormalities of choline metabolism were reported to be
associated with oncogenesis and tumor progression.6 In rats,
choline deficiency altered composition of mitochondria membranes and induced excessive production of reactive oxygen
species,7 which can induce oxidative DNA damage, in turn
promote carcinogenesis.8 Choline may also influence DNA
epigenetic change by its one-carbon metabolism to produce
methionine and dimethylglycine (DMG).9 Methionine is the
precursor of S-adenosylmethionine (SAM), an important
methyl donor in histone methylation.10 Deficiency in methyl
donors may result in global DNA hypomethylation, leading to
genetic instability11 and loss of heterozygosity.12 In addition,
hypomethylation was reported to induce endogenous retroviral elements, leading to the activation of proto-oncogenes,
such as c-myc.12
Dietary restriction in choline and/or methionine was found
to alter genome methylation pattern and enhanced incidence
of carcinogen-induced pancreatic cancer.13 Our group recently
found a novel inverse association between intake of dietary
choline and pancreatic cancer risk in a prospective cohort of
Singapore Chinese men and women.14 In a similar cohort
study of a Swedish population, higher dietary intake of methionine was associated with lower risk of pancreatic cancer.15
Besides betaine, choline can be metabolized to trimethylamine
(TMA) in the gut.16 The metabolism is catalyzed by the bacteria
enzyme choline TMA-lyase.17 Two different phyla Firmicutes
and Proteobacteria and eight genera (i.e., Anaerococcus
hydrogenalis, Clostridium asparagiforme, Clostridium hathewayi,
Clostridium sporogenes, Escherichia fergusonii, Proteus penneri,
Providencia rettgeri, and Edwardsiella tarda) were reported to be
involved in this metabolism process.16 TMA, after absorbed and
transported to the liver, is converted to trimethylamine N-oxide
(TMAO) by the liver enzyme flavin monooxygenase (FMO).18
TMAO has been shown to disturb cholesterol transport and bile
acid synthesis and promote atherosclerosis.19 Prior studies have
showed that trimethylamine N-oxide (TMAO) was associated
with the risk of cardiovascular events,20 type 2 diabetes,21 and
colorectal cancer.22 Similar to other gastrointestinal cancers,23 the
risk of pancreatic cancer has been found to be associated with
oral and intestinal microbiota such as Neisseria elongate, Streptococcus mitis, and Granulicatella adiacens.24 The exact mechanism
for microbiota promoting pancreatic cancer development in
humans, however, remains unknown.
To the best of our knowledge, no epidemiologic study has
investigated the associations for serum levels of choline and
its metabolites including betaine, DMG and TMAO with risk
of pancreatic cancer. The objective of our study was to comprehensively examine these associations in two prospective
cohorts of more than 80,000 participants.
Methods
Study population
Subjects were drawn from two population-based cohorts—the
Shanghai Cohort Study and the Singapore Chinese Health
Study.25,26 All study participants from both cohorts provided
written informed consent. Both cohort studies have been continuously approved by the Institutional Review Boards of the
respective institutions—the Shanghai Cancer Institute, the
National University of Singapore and the University of
Pittsburgh.
The Shanghai Cohort Study25 consisted of 18,244 male residents in four communities in Shanghai, China (representing
80% of eligible subjects), 45–64 years of age at enrollment during January 1986–September 1989. In addition to in-person
interviews for information on use of tobacco and alcohol, usual
diet and medical history, a 10-ml nonfasting blood sample and a
single-void urine specimen were collected from each participant
at baseline. Serum and urine samples were stored at −72 C until
analysis.
The Singapore Chinese Health Study26 enrolled 63,257 Chinese men and women 45–74 years of age at enrollment during
April 1993–December 1998, in Singapore. At baseline each participant was interviewed in person by a trained interviewer
using a structured questionnaire that requested information on
demographics, lifetime use of tobacco, current consumption of
alcoholic beverages, current physical activity, menstrual and
reproductive histories (women only), occupational exposure,
medical history, family history of cancer and dietary habits during the past 12 months using a validated27 semi-quantitative
food frequency questionnaire. A nonfasting blood (20 ml) and
single-void urine specimens were requested from a random 3%
sample of cohort participants during April 1994–December
1999. Beginning in January 2000, request for biospecimens was
extended to all surviving cohort participants. Overall,
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
Huang et al.
Alcohol consumption definition
We used a structured questionnaire for collection of information on subject’s alcohol consumption for both cohorts. In
Singapore Chinese Health Study,28 participant was asked
drinking frequency during the past year of four types of alcoholic beverages (i.e., beer, wine, Western and Chinese hard
liquors), using eight predefined categories response: never or
hardly, once a month, 2–3 times a month, once a week, 2–3
times a week, 4–6 times a week, once a day and ≥2 times a
day. The portion size was defined as: (i) Beer: 1 small bottle
(375 ml), 2 small bottles or 1 large bottle (750 ml), 2 large
bottles or ≥3 large bottles; (ii) Wine: 1 glass (118 ml), 2, 3 or
≥4 glasses; (iii) hard liquor or Chinese rice wine: 1 shot
(30 ml), 2, 3 or ≥4 shots. In Shanghai Cohort Study,29 participant was asked if he had ever alcoholic beverages at least once
a week for ≥6 months continuously. If the answer was “yes”,
he was asked to provide information about age he began
drinking regularly and typical amount of beer, wine, or spirits
consumed per week, separately. One drink was defined as
360 g of beer (12.6 g ethanol), 103 g of wine (12.3 g ethanol)
or 30 g of spirits (12.9 g ethanol).28,29
Identification of pancreatic cancer cases
The incident cancer cases and deaths among participants of
the Shanghai Cohort Study were identified through annual
contacts with surviving study participants or next of kin for
deceased participants, and through record linkage analyses
with the databases of the population-based Shanghai Cancer
Registry and the Shanghai Municipal Vital Statistics Office.
The incident cancer cases and deaths among participants of
the Singapore Chinese Health Study were identified via linkage analyses with the databases of the nationwide Singapore
Cancer Registry and the Birth and Death Registry. Pancreatic
cancer was defined as cancer cases with the International
Classification of Diseases-Oncology, 9th edition (ICD-9) code
157 and ICD 10th edition (ICD-10) 2nd revision code C25.
The follow-up for cancer incidence and death is virtually complete. To date, 612 (3.4%) participants of the Shanghai Cohort
Study and 56 (<0.1%) participants of the Singapore Chinese
Health Study were lost in annual follow-ups.
Nested case–control studies
The present study included two nested case–control studies.
We identified 129 incident pancreatic cancer cases among
participants of the Shanghai Cohort Study by December
31, 2009. For each case, two controls were randomly chosen
among the cohort participants who were free of cancer and
alive during the time from blood draw to pancreatic cancer
diagnosis of the index case. The selected controls were
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
individually matched to the index case by age (2 years), date
of blood draw (1 month) and residence location at study
enrollment.
For the Singapore Chinese Health Study, we identified
58 incident pancreatic cancer cases among participants who
provided a prediagnostic blood sample as of December 2013.
Similarly, we randomly selected up to two control subjects for
each cancer case among all eligible participants who provided
a baseline blood sample and were alive and free of cancer during the time from blood collection to cancer diagnosis of the
index case. The controls were individually matched to the
index case by gender, dialect group (Hokkien, Cantonese), age
at enrollment (3 years), date of baseline interview (2 year)
and date of biospecimen collection (6 months).
To be consistent and comparable for biomarkers in serum
for the nested case–control study from two different cohorts,
we restricted the study subjects who provided baseline serum
samples only. We excluded 12 control subjects from the Singapore cohort whose blood samples provided plasma but not
serum sample. Thus, we included 104 control subjects of the
Singapore cohort. The present study included a total of 187 incident pancreatic cancer cases and 362 individually matched
controls.
Assessment of serum biomarkers
Serum specimens of cases and their matched controls were
processed, aliquoted, shipped in frozen state and assayed
together at Bevital A/S (www.bevital.no), Bergen, Norway.
The serum samples of each matched case–control set (1 case
and up to 2 controls) were placed next to each other in random order and tested in the same batch. The case/control
status of all test samples was blind to laboratory personnel.
We used liquid chromatography–tandem mass spectrometry
(LC–MS/MS) to quantify serum choline, betaine, methionine,
DMG, TMAO, creatinine and cotinine,30 metabolites of
choline,31 and serum pyridoxal 50 -phosphate.32 We additionally measured methionine sulfoxide, an oxidative metabolite
of methionine formed during prolonged storage of serum
samples.30 Serum creatinine was used for the calculation of
glomerular filtration rate (eGFR), an indicator of renal function.33 Serum cotinine is a metabolite of nicotine, an indicator
of recent exposure to tobacco smoking and use of other
nicotine-containing products.34 For quality control purpose,
14 duplicated samples derived from a pool of serum samples
collected from cohort participants at the same time period of
the study sample collection were dispersed in seven batches of
test sample (two per batch). The within-batch coefficients of
variation (CV) for all biomarkers tested ranged from 3.07%
and 4.90% while the between-batch CV ranged from 2.29% to
11.24% (Supporting Information Table S1).
Statistical analysis
In the current analysis, the sum of methionine and methionine sulfoxide for each sample was used to represent total
Cancer Epidemiology
biospecimens were obtained from 32,535 participants, representing approximately 60% of eligible subjects by April 2005.
Blood components (i.e., plasma, serum, buffy coat and red
blood cells) were separated and stored at −80 C until analysis.
3
Cancer Epidemiology
4
amount of methionine. Total methyl donors were the sum of
choline, betaine and methionine. We used natural logarithmic
transformation of original values for statistical analysis to
reduce their skewness and improve normal distribution. The
analysis of covariance (ANCOVA) was used to examine the
difference in concentrations among controls according to
demographic characteristics and lifestyles, as well as between
cases and controls.
Conditional logistic regression method was used to examine
the associations for serum concentrations of individual choline
metabolites and total methyl donors with pancreatic cancer
risk. Study subjects were divided into quartiles according to the
distribution of individual biomarkers among control subjects of
each cohort. The magnitude of the association between levels of
serum biomarkers and pancreatic cancer risk was evaluated
using odds ratios (ORs) and their 95% confidence intervals
(CIs). Ordinal values of quartile (i.e., 1, 2, 3 and 4) for each of
the studied biomarkers were used for linear trend test in the
biomarker-pancreatic cancer risk association.
Multivariable conditional logistic regression models for pancreatic cancer risk included the following covariates as potential
confounders: body mass index (BMI) categories (<18.5,
18.5–<23 or ≥23 kg/m2), level of education (no formal schooling, primary school, secondary school or above), smoking status (never, former or current smokers), serum cotinine
(nmol/l), alcohol consumption (number of drinks per week),
history of physician-diagnosed diabetes (yes, no), estimated
glomerular filtration rate (eGFR),32 and serum pyridoxal
50 -phosphate (nmol/l).32 The analyses were conducted separately for the two cohorts and combined.
Stratified analysis by smoking status (never or ever
smokers) and alcohol intake (nondrinkers or drinkers) were
performed on the two cohorts combined. Potential effect
modification of these risk factors on the biomarker-pancreatic
cancer risk associations was assessed by adding to the model a
product term of the biomarker of interest and alcohol intake
(or smoking). We also conducted a sensitivity analysis by
excluding all pancreatic cancer cases diagnosed within the first
2 years after blood draw (n = 13 incident cases which were
excluded) and their matched controls. Statistical analyses were
carried out using SAS software version 9.3 (SAS Institute,
Cary, NC). All p values reported are two-sided. p ≤ 0.05 was
considered being statistically significant.
Data availability
De-identified data relevant to the report can be shared and is
available upon request through the University of Pittsburgh
for researchers who meet the criteria for access to confidential
data. Data is accessible to the corresponding author and also
is available from the University of Pittsburgh Institutional
Data Access/Ethics Committee with the following contact
information: 3500 Fifth Avenue, Hieber Building Main Office,
Suite 106 Pittsburgh, PA 15213. Tel.: +1-412-383-1480. Fax:
+1-412-383-1508. E-mail: ude.ttip@briksa.
Choline metabolites and pancreatic cancer risk
Results
The mean (standard deviation [SD]) age at blood draw for study
participants of the Shanghai and Singapore cohorts was 56.4
(5.5) and 64.4 (7.3) years, respectively. The average (range) time
from blood draw to cancer diagnosis was 12.5 years (3 months23.2 years) for cases of the Shanghai cohort, and 6.8 years
(5 months–13.0 years) for cases of the Singapore cohort.
The characteristics of pancreatic cancer cases and matched
controls of the Shanghai and Singapore cohorts were
described previously.32 There was a higher proportion of current smokers in pancreatic cancer cases than controls of the
Shanghai cohort only at baseline (68.2% vs. 50.0%, p = 0.003)
whereas no statistically significant differences between cases
and controls in both Shanghai and Singapore cohorts, respectively, were observed for the distributions of age, sex, alcohol
intake, history of diabetes, use of multivitamins and eGFR
(Supporting Information Table S2).
Serum concentrations of choline, betaine, methionine and
DMG were moderately correlated with each other. The
Spearman correlation coefficient ranged from 0.202 (between
choline and DMG) to 0.323 (between betaine and DMG).
TMAO was positively associated with choline, betaine, methionine and DMG whereas eGFR was inversely associated with
serum concentrations of all biomarkers except for betaine
(Supporting Information Table S3).
We examined the potential effect of demographic and
lifestyle factors on serum concentrations of all measured biomarkers among control subjects of the Shanghai and Singapore
cohorts, separately. Age was positively associated with serum
concentration of choline in both cohorts. Women had significantly lower serum levels of choline, DMG, betaine and methionine. BMI and alcohol consumption were inversely related to
TMAO in the Singapore cohort. Former smokers had elevated
level of choline in the Shanghai cohort whereas higher number
of cigarettes was positively associated with choline and methionine in the Singapore cohort. Diabetic patients had significantly
lower level of betaine in the Singapore cohort. Higher eGFR
was associated with significantly lower serum concentrations of
choline, DMG and TMAO in both controls of Shanghai and
Singapore cohorts (Supporting Information Table S4).
Pancreatic cancer cases had significantly lower concentrations of choline and methionine in the Shanghai cohort, lower
methionine and total methyl donors in Singapore cohort and
lower levels of betaine, choline, methionine and total methyl
donors in both cohorts combined, than their respective control subjects. Between the two cohorts, circulating levels of
choline, betaine and DMG were significantly higher whereas
total methyl donors and TMAO were significantly lower in
both cases and controls of the Shanghai cohort than their
counterparts of the Singapore cohort (all p values < 0.05;
Table 1). Comparing with control subjects, pancreatic cancer
cases had significantly lower levels of choline in the Shanghai
cohort, methionine in both Shanghai and Singapore cohorts
and total methyl donors in the Singapore cohort. When both
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
Huang et al.
5
Table 1. Geometric means of serum concentrations of betaine, choline, methionine, total methyl donors, dimethylglycine (DMG) and TMAO
among pancreatic cancer cases and control subjects (both Cohorts), the Shanghai Cohort Study and the Singapore Chinese Health Study
Combined Cohorts
Biomarkers,
μmol/l
Controls,
n = 362
Shanghai Cohort
Cases,
n = 187
Singapore Cohort
p-value
Controls,
n = 258
Cases,
n = 129
p-value1
Controls,
n = 104
Cases,
n = 58
p-value1
Choline
19.0
18.0
0.05
22.2
20.5
0.03
15.8
15.9
0.91
Betaine
50.2
48.0
0.03
60.0
57.6
0.12
48.6
46.1
0.19
Methionine
33.3
31.2
0.002
33.6
31.6
0.01
32.4
30.0
0.04
Total methyl
donors2
108.6
103.2
0.003
98.0
93.2
0.11
117.6
111.8
0.01
DMG
5.9
5.9
0.65
6.2
6.1
0.39
5.4
5.5
0.71
TMAO
3.7
4.1
0.16
3.1
3.6
0.12
4. 6
4.6
0.99
p-Value to compare geometric means adjusted for age and gender.
Total methyl donors: the sum of betaine, choline and methionine.
Abbreviations: DMG, dimethylglycine; eGFR, estimated glomerular filtration rate; TMAO, trimethylamine N-oxide.
1
cohorts combined, cases had significantly lower serum levels
of choline, betaine, methionine and total methyl donors.
While the difference in serum betaine concentration between
cases and controls was not statistically significant for the two
cohorts separately, such a difference in both cohorts combined
reached statistical significance (p = 0.03; Table 1).
Given the difference in the serum concentrations of biomarkers between the Shanghai and Singapore cohorts, we
used cohort-specific cut-off values of each biomarkers for
grouping of study subjects into quartiles (Supporting Information Table S5). compared to the lowest quartile, higher quartiles of choline (the Shanghai cohort and the two cohorts
combined), betaine (the two cohorts combined), methionine
(two cohorts separately and combined) and total methyl
donors (the Shanghai cohort and the two cohorts combined)
were associated with significantly lower risk of pancreatic cancer, whereas higher TMAO was associated with higher risk of
pancreatic cancer in the Shanghai cohort and in the two
cohorts combined (Table 2). DMG was not associated with
risk of pancreatic cancer in either of the two cohorts
separately or combined (Table 2).
The inverse association between choline and pancreatic cancer risk was present in alcohol drinkers, but not in nondrinkers
(Table 3). The ORs (95% CIs) for pancreatic cancer in the
highest versus lowest quartile of choline was 0.26 (0.09–0.72) in
alcohol drinkers (ptrend = 0.01) and 0.80 (0.39–1.64) in nondrinkers. The heterogeneity in the choline-pancreatic cancer
risk association was statistically significant between alcohol
drinkers and nondrinkers (pinteraction = 0.03). In contrast, the
inverse association was presented in nondrinkers for betaine
(ORQ4 vs. Q1 = 0.50, 95% CI: 0.26–0.96, ptrend = 0.02). The
methionine and total methyl donors were inversely associated
with risk of pancreatic cancer in both alcohol drinkers and
nondrinkers. Higher quartiles of TMAO were associated with
elevated risk of pancreatic cancer in both alcohol drinkers and
nondrinkers although the linear trend test in nondrinkers was
not statistically significant (ptrend = 0.19).
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
The risk estimates for choline metabolites in relation to
pancreatic cancer risk were similar for never smokers as those
in ever smokers (all pinteraction > 0.05; Supporting Information
Table S6). Higher levels of choline and methionine were significantly associated with lower risk of pancreatic cancer in
ever smokers only whereas the inverse association between
total methyl donors and risk of pancreatic cancer was present
in both never smokers and ever smokers. The multivariableadjusted ORs (95% CIs) for the highest versus lowest quartile
of total methyl donors in never and ever smokers were 0.30
(0.11–0.83) and 0.41 (0.20–0.81), respectively (both
ptrend < 0.05). On the other hand, higher levels of TMAO were
associated with elevated risk of pancreatic cancer in both
never and ever smokers, although none of the linear trend test
was statistically significant (Supporting Information Table S6).
We also examined the associations between the biomarkers
measured and pancreatic cancer risk by gender and did not find
discernable difference in the association between male and female
study participants in the Singapore cohort (all pheterogeneity > 0.05;
all participants were male in the Shanghai cohort). For example, the ORs (95% CIs) of pancreatic cancer for the highest versus lowest quartile of choline, betaine, methionine and total
methyl donors among men were 2.05 (0.48–8.85), 0.33
(0.11–0.98), 0.25 (0.07–0.94) and 0.31 (0.09–1.11), respectively.
The corresponding figures among women were 0.49
(0.08–2.97), 0.51 (0.09–2.83), 1.16 (0.24–5.69) and no risk estimate obtained for total methyl donors. The large variation in
risk estimates by gender was due to small sample size, especially for women.
The sensitivity analysis after excluding incident cases of
pancreatic cancer diagnosed within the first 2 years after
blood draw and their matched controls provided the similar
results as those observed in the entire dataset. Compared to
the lowest quartiles, ORs (95% CIs) of pancreatic cancer in
the highest quartiles of choline, methionine and total methyl
donors, and TMAO were 0.38 (0.18–0.80), 0.41 (0.23–0.73),
0.38 (0.21–0.70), and 2.36 (1.26–4.36), respectively (all
Cancer Epidemiology
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Choline metabolites and pancreatic cancer risk
Table 2. Associations between serum concentrations of serum choline, betaine, methionine, total methyl donors, DMG and TMAO and
pancreatic cancer risk in pooled analysis of both cohorts
Combined cohorts
Controls/cases
OR (95% CI)1
Shanghai cohort OR
(95% CI)1
Singapore cohort OR
(95% CI)1
1.00
Choline
Q1
91/61
1.00
1.00
Q2
90/43
0.65 (0.39–1.08)
0.58 (0.31–1.06)
0.88 (0.29–2.65)
Q3
91/47
0.70 (0.40–1.23)
0.42 (0.21–0.83)
2.22 (0.68–7.24)
Q4
90/36
0.42 (0.20–0.85)
0.27 (0.11–0.69)
0.85 (0.23–3.17)
0.03
0.003
0.98
0.98 (0.95–1.01)
0.97 (0.94–1.01)
1.00 (0.90–1.13)
ptrend
Continuous scale (nmol/l)
Cancer Epidemiology
Betaine
Q1
91/67
1.00
1.00
1.00
Q2
90/41
0.54 (0.32–0.93)
0.42 (0.21–0.82)
0.94 (0.36–2.48)
Q3
91/37
0.51 (0.30–0.87)
0.59 (0.32–1.11)
0.29 (0.09–0.95)
Q4
90/42
0.59 (0.35–0.98)
0.57 (0.31–1.05)
0.50 (0.17–1.45)
0.04
0.11
0.12
0.99 (0.98–1.00)
0.99 (0.98–1.01)
0.98 (0.95–1.01)
ptrend
Continuous scale (nmol/l)
Methionine
Q1
91/72
1.00
1.00
1.00
Q2
90/33
0.48 (0.28–0.79)
0.58 (0.32–1.04)
0.33 (0.11–0.98)
Q3
91/50
0.66 (0.41–1.08)
0.59 (0.32–1.07)
0.77 (0.29–2.10)
Q4
90/32
0.40 (0.23–0.70)
0.50 (0.26–0.96)
0.17 (0.04–0.68)
0.004
0.03
0.05
0.96 (0.94–0.99)
0.97 (0.94–1.00)
0.94 (0.89–0.99)
1.00
1.00
1.00
ptrend
Continuous scale (nmol/l)
Total methyl donors
Q1
91/74
Q2
90/41
0.48 (0.28–0.81)
0.56 (0.30–1.02)
0.28 (0.08–0.93)
Q3
91/38
0.44 (0.26–0.74)
0.37 (0.19–0.71)
0.69 (0.25–1.87)
Q4
90/34
0.38 (0.21–0.68)
0.37 (0.19–0.73)
0.33 (0.10–1.16)
0.001
0.18
0.99 (0.98–1.00)
0.99 (0.98–1.00)
0.98 (0.95–1.00)
ptrend
<0.001
Continuous scale (nmol/l)
DMG
Q1
91/53
1.00
1.00
1.00
Q2
90/41
0.75 (0.43–1.29)
0.81 (0.42–1.56)
0.63 (0.22–1.78)
Q3
91/44
0.74 (0.43–1.26)
0.86 (0.46–1.61)
0.50 (0.17–1.46)
Q4
90/49
0.93 (0.54–1.60)
0.96 (0.50–1.82)
0.94 (0.31–2.88)
0.76
0.94
0.69
0.99 (0.92–1.07)
0.97 (0.88–1.07)
1.02 (0.92–1.13)
ptrend
Continuous scale (nmol/l)
TMAO
Q1
91/31
1.00
1.00
1.00
Q2
90/52
1.92 (1.09–3.39)
2.00 (0.98–4.08)
1.52 (0.58–4.00)
Q3
91/45
1.46 (0.83–2.59)
1.60 (0.79–3.25)
1.09 (0.40–2.93)
Q4
90/59
2.36 (1.30–4.26)
2.81 (1.37–5.76)
1.42 (0.50–4.04)
0.02
0.01
0.71
1.01 (0.99–1.04)
1.01 (0.99–1.04)
0.99 (0.91–1.08)
ptrend
Continuous scale (nmol/L)
Abbreviations: DMG, dimethylglycine; TMAO, trimethylamine N-oxide.
1
Odds ratios were calculated from conditional logistic regression models; adjusted for level of education (no formal schooling, primary school and secondary school or above), body mass index (<18.5, 18.5–<23.0, ≥23.0 kg/m2), smoking status (never, former and current smokers), serum cotinine concentration (nmol/l), number of alcoholic drinkers per week (continuous), history of diabetes (no, yes), serum pyridoxal 50 -phosphate concentration
(nmol/L) and estimated glomerular filtration rate ((ml/min/1.73 m2). For p < 0.05, the values are in bold.
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
Huang et al.
7
Table 3. Associations between serum concentrations of choline, betaine, methionine, total methyl donors, DMG and TMAO and pancreatic
cancer risk, stratified by alcohol drinking status
Nondrinkers
Drinkers
1
Controls/Cases
OR (95% CI)1
Controls/Cases
OR (95% CI)
Q1
64/32
1.0
27/29
1.0
Q2
56/30
0.93 (0.49–1.75)
34/13
0.29 (0.11–0.73)
Q3
59/35
1.07 (0.57–2.02)
32/12
0.38 (0.15–0.97)
Q4
53/24
0.80 (0.39–1.64)
37/12
0.26 (0.09–0.72)
Choline
ptrend
pinteraction
0.01
0.69
0.033
Betaine
61/45
1.0
30/22
Q2
58/31
0.67 (0.36–1.23)
32/10
0.41 (0.15–1.10)
Q3
55/19
0.43 (0.22–0.85)
36/18
0.83 (0.34–1.98)
Q4
58/26
0.50 (0.26–0.96)
32/16
0.85 (0.34–2.09)
ptrend
pinteraction
0.02
1.0
0.97
0.269
Methionine
Q1
63/41
1.0
28/31
1.0
Q2
53/25
0.73 (0.38–1.38)
37/8
0.17 (0.06–0.46)
Q3
64/38
0.82 (0.46–1.48)
27/12
0.36 (0.14–0.96)
Q4
52/17
0.44 (0.22–0.90)
38/15
0.38 (0.15–0.94)
ptrend
pinteraction
0.05
0.05
0.664
Total methyl donors
Q1
55/30
1.0
36/44
1.0
0.47 (0.24–0.93)
Q2
47/16
0.45 (0.20–1.00)
43/25
Q3
39/15
0.68 (0.29–1.56)
52/23
0.36 (0.18–0.70)
Q4
35/8
0.30 (0.11–0.83)
55/26
0.41 (0.20–0.81)
ptrend
0.04
0.005
pinteraction
DMG
Q1
56/32
1.0
35/21
1.0
Q2
58/30
0.87 (0.46–1.65)
32/11
0.45 (0.17–1.19)
Q3
56/32
0.87 (0.45–1.67)
35/12
0.51 (0.20–1.34)
Q4
62/27
0.67 (0.34–1.35)
28/22
1.80 (0.71–4.55)
ptrend
pinteraction
0.29
0.29
0.145
TMAO
Q1
63/18
Ref.
28/13
Ref.
Q2
55/40
2.52 (1.26–5.02)
35/15
0.52 (0.19–1.46)
Q3
52/31
2.17 (1.07–4.42)
39/14
0.70 (0.25–1.92)
Q4
62/32
1.89 (0.93–3.85)
28/27
2.53 (0.94–6.85)
ptrend
pinteraction
0.19
0.03
0.546
Abbreviations: DMG, dimethylglycine; TMAO, trimethylamine N-oxide.
1
Odds ratios were calculated from conditional logistic regression models adjusted for level of education (no formal schooling, primary school and secondary
school or above), body mass index (<18.5, 18.5–<23.0, ≥23.0 kg/m2), smoking status (never, former and current smokers), serum cotinine concentration
(nmol/l), number of alcoholic drinkers per week (continuous), history of diabetes (no, yes), serum pyridoxal 50 -phosphate concentration (nmol/l) and estimated glomerular filtration rate ((ml/min/1.73 m2). For p < 0.05, the values are in bold.
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
Cancer Epidemiology
Q1
8
Choline metabolites and pancreatic cancer risk
ptrend < 0.05). The inverse association between betaine and
pancreatic cancer risk was not linear, but the risk estimates
were significantly below one; the ORs (95% CIs) of pancreatic
cancer for the second, third and fourth quartile of betaine
were 0.57 (0.32–0.99), 0.49 (0.28–0.87) and 0.63 (0.37–1.06),
respectively, compared to the lowest quartile (ptrend = 0.07).
The null association between DMG and pancreatic cancer risk
remained in this subset analysis (ptrend = 0.69; Supporting
Information Table S7).
Cancer Epidemiology
Discussion
To the best of our knowledge, this is the first study investigating the associations between serum concentrations of individual and total methyl donors including choline and its
metabolites with pancreatic cancer risk. We found that highest
quartiles of choline and total methyl donors in Shanghai
cohort and of methionine and total methyl donors in both the
Shanghai and Singapore cohorts in serum samples collected
on average about 10 years prior to cancer diagnosis were
associated with a statistically significant 40–70% lower risk of
pancreatic cancer than the lowest quartile. We also found a
significant interaction between choline and alcohol intake on
pancreatic cancer risk. Our study also showed a significant
association between high risk of pancreatic cancer and high
concentration of TMAO in the Shanghai cohort, a gut
microbiota-derived metabolite from choline and L-carnitine,
which is abundant in red meat, and a mediator in chronic kidney disease patients.35
Our finding on the inverse association between serum
methionine and pancreatic cancer risk is consistent with the
results from a prior study in a Swedish population,15 suggesting
the association between methionine and pancreatic cancer risk
is robust across different study populations.
Choline is obtained mainly from food sources such as animal liver, eggs, fish and milk36 or through the de novo synthesis
of phosphatidylethanolamine N-methyltransferase (PEMT)related pathway. Choline is required for synthesizing phospholipids, particularly phosphatidylcholine and sphingomyelin,
which are important components of cell membranes.9 Experimental studies in rats showed that choline deficiency altered
the composition of mitochondrial membranes, impaired mitochondria function and induced overproduction of reactive
oxygen species.37 Furthermore, rats deprived of dietary choline
had accumulated oxidized purines and oxidative DNA
damage.38 In mammals, including humans, prolonged dietary
choline deficiency can lead to cell death and functional disorders in the pancreas, liver, muscle and lymphocytes.39
Choline also plays a significant role in one-carbon metabolism (Fig. 1). Choline is converted to betaine, a reaction that is
catalyzed by the choline dehydrogenase.10 Betaine donates a
methyl group to the re-methylation of homocysteine, forming
methionine and DMG.9 Methionine is the precursor of SAM,
a key methyl donor in methylation of DNA and histones.9
DNA methylation and histone modification lead to aberrant
Figure 1. Schematic diagram of choline metabolism pathway.
gene expression and increased cell growth and survival, potentially contributing to the development of pancreatic cancer.40
In rodents, a diet depleted with choline and methionine
resulted in decreased concentration of SAMs in tissues,41 leads
to hypomethylation and increased expression of oncogenes,
including c-Ha-ras, c-Ki-ras and c-fos,42 and increases the
incidence of carcinogen-induced pancreatic carcinomas.43
These experimental data suggest choline may play an important role in the development pancreatic cancer in humans.
Alcohol intake has significant impact on the metabolism and
uptake of choline.44 Our study demonstrated a statistically significant interaction between serum choline and alcohol drinking
on pancreatic cancer risk. The disturbing effect of alcohol on
absorption and transport of nutrients involved in one-carbon
metabolism in the pancreas has been well documented.45
Chronic ingestion of alcohol increased requirement of choline,10
which may explain why the inverse association between serum
choline and pancreatic cancer risk was stronger for alcohol
drinkers than nondrinkers in our study. The similar inverse
association between total methyl donors and pancreatic cancer
in both alcohol nondrinkers and drinkers could be explained by
the fact that betaine and methionine may compensate the
decreased level of choline. In animal studies, betaine could partially replace dietary methionine46 and betaine supplementation
can reduce the dietary requirement for choline.47 The lack of
linear dose–response relationship between total methyl donors
and pancreatic cancer risk among nondrinkers might be due to
the small sample size. Future studies with larger samples are,
therefore, warranted to replicate our findings.
The apparent inverse association between choline concentration and pancreatic cancer risk in Shanghai cohort might
be explained by the fact that the Shanghai cohort included
only men, younger age at enrollment and consumed more
alcohol than the subjects of the Singapore cohort. Furthermore, the inverse association of choline with pancreatic cancer
risk was seen only in drinkers and not in nondrinkers
(Table 3). Our ability to detect the association between the
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
biomarkers studied and risk of pancreatic cancer among
women was limited due to a small sample size (23 cases and
41 controls). These differences in the exposure to risk factors
for pancreatic cancer and their impact on the circulating levels
of choline may explain the more apparent choline-pancreatic
cancer risk association in the Shanghai cohort than the
Singapore cohort.
Our findings of an inverse association for methionine with
lower risk of pancreatic cancer in both the Shanghai and the
Singapore cohorts are inconsistent with those of a similarly
designed case–control study involved 170 incident pancreatic
cancer cases and 340 controls from a Japanese population.48
In that study, Nakagawa et al.48 reported a statistically nonsignificant reduced risk of pancreatic cancer (OR = 0.77, 95% CI:
0.17–3.44) for the highest versus the lowest quartile of serum
methionine. Differences between our analysis and Nakagawa
et al.48 may due to the difference in study populations and
assay platform. We used a validated liquid chromatography–
tandem mass spectrometry (LC–MS/MS) assay31 in our study
whereas the gas chromatography/MS/MS in the Japanese.48
The mean concentrations of methionine among controls of
the Shanghai and Singapore cohorts were 33.6 and 32.4 μmol/l,
respectively, compared to the corresponding figure at
27.2 μmol/l, which was approximately 20% lower in healthy
individuals of the Japanese study.48 The difference in the
method and study population may explain the different results
between ours and the Japanese study.
Our study demonstrated a positive association between
TMAO and pancreatic cancer risk in Shanghai cohort only.
TMAO can be generated via TMA from choline and L-carnitine, nutrients that are abundant in red meat,20 or provided by
bacteria in the human oral cavity and gut.49 No previous epidemiological study has investigated the association between
TMAO and pancreatic cancer risk. However, a matched case–
control study (n = 835 pairs) within the Women’s Health Initiative Observational Study22 found an increased risk of rectal
cancer at high plasma TMAO. Although mechanistic studies
directly linking TMAO to pancreatic cancer is lacking, TMAO
may contribute to pancreatic cancer development through several plausible mechanisms. Recent experimental evidence suggests that trimethylamine (TMA)-producing microbiota in the
gut could reduce the bioavailability of choline.16 Alternatively,
TMAO could contribute to low-grade chronic inflammation
that is known to promote pancreatic cancer.50 In human study,
plasma concentration of TMAO was positively associated with
pro-inflammatory biomarkers, including TNF-alpha, sTNF-R
p55 and sTNF-R p75.51 Microbial dysbiosis plays a role in the
development of gastrointestinal cancers, including pancreatic
cancer.52 Accordingly Toll-like receptors (TLRs), which recognize compounds derived from microbes, are upregulated in
human pancreatic cancers.53 In mice studies, TLR activation
promotes inflammation and accelerates carcinogenesis in the
pancreas by stimulating the NF-kB and MAPK pathways.50,53
Additionally, epidemiological studies suggest periodontal
Int. J. Cancer: 00, 00–00 (2020) © 2020 UICC
9
pathogens, in particular P. gingivalis, to be associated with
increased risk of pancreatic cancer.54 Experimental studies in
mice showed that oral administration of P. gingivalis leads to an
increase in Bacteroidetes, a TMA-producing bacteria phylum in
the gut, and to increased expression of genes related to proinflammatory cytokines.55 Mechanistic studies are warranted to
reveal the exact mechanisms by which TMAO or the TMAOproducing bacteria influences pancreatic cancer development.
In the current study, lower eGFR, a test to measure kidney
function, was significantly associated with high serum DMG
and TMAO in both cohorts. This observation is consistent
with a recent experimental study by Johnson et al.56 in which
they showed that mice fed a diet supplemented with 0.2% adenine to induced chronic kidney disease, resulting in increased
serum TMAO concentration and that accumulation of circulating TMAO was accompanied by a decrease in renal clearance. Thus, it is important for the adjustment for kidney
function in any study that examines the association for serum
TMAO with disease risk. In the present study, we adjusted for
eGFR in the statistical analysis, thus our results were less likely
to be confounded by decreased renal function.
TMAO has a very long stability. Many dietary sources
including fish and seafood contain TMAO. Dietary TMAO contributes to the increase of serum TMAO concentration.57,58 In
humans, long-term dietary L-carnitine can alter the production
of TMAO from dietary precursors via the gut microbial composition; studies have shown that meat-eaters had higher concentration of circulating TMAO than vegetarians.19 TMAO could
be a link underlying the association between red meat intake
and pancreatic cancer.59 Interventions modulating gut microbiota, such as probiotics, may have beneficial effect on choline
metabolism by shifting the bacterial production of TMAO from
choline to the synthesis of membrane phospholipids and
one-carbon groups by the host and thereby have a potential as
primary prevention strategy against pancreatic cancer.
In the current study, we found that DMG is not associated
with pancreatic cancer risk. One possible reason for this null
association is that DMG is not a methyl donor, thus would
not alter gene expression through epigenetic changes.9
The strengths of our study include prospective study
design, long-term follow-up, inclusion of two independent
cohorts and comprehensive assessment of methionine and
their related metabolites using a validated state-of-the-art
LC–MS/MC assay.31 In addition, we adjusted for many potential confounders, especially eGFR, an important determinant
of circulating concentration of choline and TMAO.60 Serum
concentrations of choline reflected both dietary intake and de
novo synthesis of choline. No previous epidemiological study
has assessed the associations for serum concentrations of various choline metabolites and total methyl donors with risk of
pancreatic cancer. The main limitation of our study is modest
sample size, especially for women. However, given a plausible
biological mechanism for the role of one-carbon metabolism
in the development of pancreatic cancer, our sample size with
Cancer Epidemiology
Huang et al.
Cancer Epidemiology
10
Choline metabolites and pancreatic cancer risk
187 cases and 362 controls would have at least 80% power to
detect a minimal OR of 2 or 0.5 for the two extreme quartiles
of biomarker concentrations (https://dceg.cancer.gov/tools/
design/power). Thus, the present study provided a reasonable
effect size of the biomarkers studied on the risk of developing
pancreatic cancer.
Our finding on serum choline (in the Shanghai cohort alone
and both cohorts combined), betaine (two cohorts combined),
methionine (both the Shanghai and Singapore separately and
combined) and TMAO (the Shanghai cohort alone and the two
cohorts combined) in relation to pancreatic cancer risk has several implications and warrants future mechanistic studies. The
adequate intake of choline is recommended at 550 mg for men
and 425 mg for women.36 According to the US National Health
and Nutrition Examination Survey (NHANES) 2007–2008, the
mean daily intake of choline was at 332 mg in men and
294 mg in women, 30–40% below the recommended daily
intake levels, respectively. Foods high in choline include beef
liver, eggs, fish and milk.36 Strict vegetarians and vegans may
be at greater risk for inadequate dietary intake of choline. Studies on the interaction between gene and nutritional status of
choline may provide an evidence-based approach for the development of personalized nutrition strategy. On the other hand,
epidemiological studies in general support a positive association
between intake of red meat and pancreatic cancer risk,61,62
although the mechanisms remain unclear.
In summary, our study found novel inverse associations
between individual and total methyl donors and risk of pancreatic cancer, supporting a protective role of metabolites in
the choline pathway against the development of pancreatic
cancer in humans. Our study also showed an interaction effect
between choline and alcohol drinking on pancreatic cancer
risk. A statistically significant positive association between
TMAO and pancreatic cancer suggests that gut microbiota
may play a contributing role in the development of pancreatic
cancer through their function on TMAO production.
Acknowledgements
We thank Xue-Li Wang of the Shanghai Cancer Institute for assistance with
data collection and management and the staff of the Shanghai Cancer
Registry for their assistance in verifying cancer diagnoses in study participants. We thank the Singapore Cancer Registry for the identification of
incident cancer cases among participants of the Singapore Chinese Health
Study. We also thank Siew-Hong Low of the National University of Singapore for supervising the field work of the Singapore Chinese Health Study.
The Shanghai Cohort Study and the Singapore Chinese Health Study were
supported by the National Institutes of Health (NIH) of the United States
(grants # R01 CA144034 and UM1 CA182876). W-P Koh is supported by
the National Medical Research Council, Singapore (NMRC/CSA/0055/2013).
HN Luu is partially supported by the University of Pittsburgh Medical
Center Hillman Cancer Center start-up grant.
Conflict of interest
Dr L.M.B. is currently a full-time employee of F. Hoffmann–La Roche LTD.
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