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Annals of Oncology 24: 543–553, 2013
doi:10.1093/annonc/mds434
Published online 2 November 2012
Glycemic index, glycemic load, dietary carbohydrate,
and dietary fiber intake and risk of liver and biliary
tract cancers in Western Europeans
V. Fedirko1*, A. Lukanova2, C. Bamia3, A. Trichopolou3,4, E. Trepo5, U. Nöthlings6, S. Schlesinger6,
K. Aleksandrova7, P. Boffetta8, A. Tjønneland9, N. F. Johnsen9, K. Overvad10, G. Fagherazzi11,12,
A. Racine11,12, M. C. Boutron-Ruault11,12,V.Grote
2, R. Kaaks2, H. Boeing7, A. Naska3,
G. Adarakis4, E. Valanou4, D. Palli13, S. Sieri14, R. Tumino15, P. Vineis16,17, S. Panico18,
H. B(as). Bueno-de-Mesquita19,20, P. D. Siersema20, P. H. Peeters21,16, E. Weiderpass22,23,24,25,
G. Skeie22, D. Engeset22, J. R. Quirós26, R. Zamora-Ros27, M. J. Sánchez28,29, P. Amiano30,29,
J. M. Huerta31,29, A. Barricarte32,29, D. Johansen33, B. Lindkvist34, M. Sund35, M. Werner36,
F. Crowe37, K. T. Khaw38, P. Ferrari1, I. Romieu1, S. C. Chuang16, E. Riboli16 & M. Jenab1
1
Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France;
2
Division of Cancer
Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany;
3
WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene,
Epidemiology, Medical Statistics, University of Athens Medical School, Athens;
4
Hellenic Health Foundation, Athens, Greece;
5
Centre de Bioloqie Republique, Lyon,
France;
6
Section of Epidemiology, Institute for Experimental Medicine, Christian-Albrechts University of Kiel, Kiel;
7
Department of Epidemiology, German Institute of Human
Nutrition Potsdam-Rehbruecke, Nuthetal, Germany;
8
Institute for Translational Epidemiology, Mount Sinai School of Medicine, The Tisch Cancer Institute, New York, USA;
9
Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen;
10
Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark;
11
Centre for Research in Epidemiology and Population Health, Inserm (Institut National de la Santé et de la Recherche Médicale), Institut Gustave Roussy Villejuif;
12
Paris
South University, UMRS 1018 Villejuif, France;
13
Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute - ISPO, Florence;
14
Nutritional
Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan;
15
Cancer Registry and Histopathology Unit, “Civile M.P.Arezzo”Hospital, Ragusa, Italy;
16
School of Public Health, Imperial College, London, UK;
17
HuGeF Foundation, Turin;
18
Department of Clinical and Experimental Medicine, Federico II University, Naples,
Italy;
19
Centre for Nutrition and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven;
20
Department of Gastroenterology and Hepatology,
University Medical Centre Utrecht (UMCU), Utrecht;
21
Department of Epidemiology Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht,
the Netherlands;
22
Department of Community Medicine, University of Tromsø, Tromsø;
23
Cancer Registry of Norway, Oslo, Norway;
24
Department of Medical
Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;
25
Samfundet Folkhälsan, Genetic Epidemiology Group, Folkhälsan Research Center, University of
Helsinki, Helsinki, Finland;
26
Public Health Directorate, Health and Health Care Services Council, Asturias;
27
Unit of Nutrition, Environment and Cancer, Catalan Institute of
Oncology (ICO-IDIBELL), Barcelona;
28
Andalusian School of Public Health, Granada;
29
Consortium for Biomedical Research in Epidemiology and Public Health (CIBER
Epidemiología y Salud Pública-CIBERESP) Granada;
30
Public Health Division of Gipuzkoa, BIODonostia Research Institute, Department ofHealth of the regional
Government of the Basque Country, San Sebastian;
31
Department of Epidemiology, Murcia Regional Health Council, Murcia;
32
Navarre Public Health Institute, Pamplona,
Spain;
33
Skånes Universitetssjukhus, Malmö;
34
Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg;
35
Department of Surgical and
Perioperative Sciences, Umea University;
36
Department of Public Health and Clinical Medicine, Umea University, Sweden;
37
Cancer Epidemiology Unit, Nuffield
Department of Clinical Medicine, University of Oxford, Oxford;
38
Clinical Gerontology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
Received 13 June 2012; revised 20 July 2012; accepted 24 July 2012
Background: The type and quantity of dietary carbohydrate as quantified by glycemic index (GI) and glycemic load
(GL), and dietary fiber may influence the risk of liver and biliary tract cancers, but convincing evidence is lacking.
Patients and methods: The association between dietary GI/GL and carbohydrate intake with hepatocellular
carcinoma (HCC; N= 191), intrahepatic bile duct (IBD; N= 66), and biliary tract (N= 236) cancer risk was investigated in
477 206 participants of the European Prospective Investigation into Cancer and Nutrition cohort. Dietary intake was
assessed by country-specific, validated dietary questionnaires. Hazard ratios and 95% confidence intervals were
estimated from proportional hazard models. HBV/HCV status was measured in a nested case–control subset.
Results: Higher dietary GI, GL, or increased intake of total carbohydrate was not associated with liver or biliary tract
cancer risk. For HCC, divergent risk estimates were observed for total sugar = 1.43 (1.17–1.74) per 50 g/day, total
starch = 0.70 (0.55–0.90) per 50 g/day, and total dietary fiber = 0.70 (0.52–0.93) per 10 g/day. The findings for dietary
fiber were confirmed among HBV/HCV-free participants [0.48 (0.23–1.01)]. Similar associations were observed for IBD
[dietary fiber = 0.59 (0.37–0.99) per 10 g/day], but not biliary tract cancer.
*Correspondence to: Dr V. Fedirko, International Agency for Research on Cancer
(IARC-WHO), 150 Cours Albert Thomas, Lyon, France 69372. Tel: +33-4-72-73-8032;
Fax: +33-4-72-73-8361; E-mail: fedirkov@iarc.fr
Annals of Oncology original articles
© The Author 2012. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Conclusions: Findings suggest that higher consumption of dietary fiber and lower consumption of total sugars are
associated with lower HCC risk. In addition, high dietary fiber intake could be associated with lower IBD cancer risk.
Key words: biliary tract neoplasms, dietary carbohydrate, dietary fiber, glycemic index, hepatocellular carcinoma,
liver neoplasms
introduction
Primary liver cancer (PLC; ranked sixth in incidence
worldwide), a cancer grouping composed of hepatocellular
(HCC) and intrahepatic bile duct (IBD) carcinomas, is highly
malignant, usually diagnosed at late stages and often has very
poor prognosis with limited treatment options [1]. The global
geographic incidence trends are highest in developing regions
and lowest in developed countries, reflecting the prevalence of
two established risk factors—hepatitis B/C (HBV/HCV) and
aflatoxin exposure. Recent data show that PLC rates are rapidly
increasing in traditionally lower-risk industrialized countries
[2–4] likely due to obesity, insulin resistance, metabolic, and
hormonal changes which accompany the Western lifestyle and
eventually lead to type 2 diabetes (T2D) and/or nonalcoholic
fatty liver disease (NAFLD), a hepatic manifestation of the
metabolic syndrome [5]. Biliary tract cancers (BTC; including
cancers of the gallbladder, Ampulla of Vater and extrahepatic
bile ducts) are another important grouping of tumors which,
similar to PLC, have poorly understood etiology and are
difficult to detect early and to treat [6,7].
The type and amount of dietary carbohydrate are the main
determinants of postprandial glucose and insulin responses [8].
Therefore, dietary glycemic index (GI) [9] and glycemic load
(GL), measures of the glucose and insulin responses to
different dietary carbohydrates, may play a role in liver
carcinogenesis by increasing blood glucose, triglyceride, and
cholesterol levels, insulin demand, and bioavailability of
insulin-like growth factor-1 resulting in growth promotion and
inhibition of apoptosis [10,11]. This hypothesis is
strengthened by the fact that diets with a high GI or GL are
associated with an increased risk of obesity, T2D, gallbladder
disease, hyperlipidemia [12], liver steatosis [13], and NAFLD
[14], all of which may enhance susceptibility to HCC, IBD, and
BTC [15–20] by increasing chronic and local inflammation
and altering insulin and IGF signaling [21]. The liver is
exposed to high concentrations of insulin because it is
transported from the pancreas via the portal vein to the liver.
Thus, deregulation of insulin-related pathways may promote
liver or bile tract carcinogenesis. On the other hand, dietary
fiber may prevent development of HCC, IBD, and BTC by
beneficially influencing glycemic control, lipid profiles, and
body weight [22,23].
The association between GI, GL, and dietary carbohydrate
in relation to HCC, IBD, and BTC risk has been investigated
in only a few studies [24–30]. However, prospective evidence
is limited to only one recent study reporting a null
association for GI, and an inverse association for GL [30].
We, therefore, investigated the association between GI, GL,
and dietary carbohydrate (including total sugar, starch, and
dietary fiber) with HCC, IBD, and BTC risk in the European
Prospective Investigation into Cancer and Nutrition (EPIC)
study.
materials and methods
study design
EPIC is a multicenter prospective cohort study designed to investigate the
association between environmental factors and incidence of chronic
diseases. The rationale, study design, and methods of recruitment are
detailed elsewhere [31], including baseline assessment of lifestyle factors
(physical activity [32], alcohol drinking and smoking [31],
anthropometrics [33], and diet [34]), which were collected from ∼520 000
individuals enroled between 1992 and 2000 in 23 centers throughout 10
European countries [31].
A total of 477 206 participants were included in the present analysis
after an exclusion of 23 818 with prevalent cancer other than
nonmelanoma skin cancer, 4380 with incomplete follow-up data or
missing information on date of diagnosis, 6192 with missing dietary
information, 60 with missing lifestyle information, and 9596 who were in
the top or bottom 1% of the distribution of the ratio of reported total
energy intake to estimated energy requirement, and 78 with metastasis in
the liver or ineligible histology code.
All cohort members provided written informed consent. Ethical
approval was obtained from the International Agency for Research on
Cancer Ethics Review Committee and EPIC centers.
dietary measurement
Diet during the previous 12 months from recruitment into the study was
assessed with validated country-specific dietary questionnaires (DQ)
designed to ensure high compliance and better measures of local dietary
habits [34]. Dietary intakes (in grams per day) of total carbohydrate and its
components were estimated from the dietary instruments by using
standardized country-specific food composition tables. The definitions of
all nutrients including carbohydrate and the methods used to determine
their values and standardize them across centers have been described
elsewhere [35]. In order to improve comparability of dietary data across
centers and to partially correct for dietary measurement error, a single
standardized, computer-assisted 24-h dietary recall was obtained from an
8% stratified random sample (N= 36 900) for calibration [36,37].
A GI database was assembled from the published GI values [38–40],
which were assigned in a standardized manner to carbohydrate-providing
food items as described elsewhere [41] and in the Supplementary data,
available at Annals of Oncology online. The overall dietary GL, which
reflects the quantity and quality of carbohydrate in the diet, was calculated
by multiplying the digestible carbohydrate content of a given food item by
the quantity of that food item consumed per day and its GI value, and then
summing the values for all food items. The overall GI, which reflects the
average quality of carbohydrate consumed, was calculated by dividing the
total GL by the total digestible daily carbohydrate consumption.
follow-up for cancer incidence and mortality
Vital status follow-up (98.5% complete) was collected by record linkage
with regional and/or national mortality registries in all countries except
Germany and Greece, where follow-up was based on active follow-up
through study subjects or their next-of-kin. Cancer incidence was
determined through record linkage with regional cancer registries
(Denmark/Italy/Netherlands/Norway/Spain/Sweden/UK; complete up to
original articles Annals of Oncology
| Fedirko et al. Volume 24 | No. 2 | February 2013
December 2006) or via the use of health insurance records, contacts with
cancer and pathology registries, and/or active follow-up (France/Germany/
Greece; complete up to June 2010).
case ascertainment
HCC was defined as tumor in the liver (C22.0 as per the 10th Revision of
the International Statistical Classification of Diseases, Injury, and Causes of
Death [42]). IBD carcinoma was defined as tumor in the IBDs (C22.1).
BTC was defined as tumor in the gallbladder (C23.9), Ampulla of Vater
(C24.1), and biliary tract (C24.0, C24.8 and C24.9). Cholangiocarcinoma
was defined as tumor in the intra/extrahepatic bile ducts with morphology
code ‘8160/3’. A total of 191 HCC, 66 IBD, and 236 biliary tract
(gallbladder = 87, Ampulla of Vater = 54, and biliary tract = 95) cancer
cases were included in the present analyses. Fifty-eight
cholangiocarcinomas (intrahepatic = 48 and extrahepatic = 10) were also
analyzed.
HBV and HCV seropositivity was measured in the nested within the
EPIC cohort case–control study [including 290 cases (HCC = 122, IBD = 35
and BTC = 133) and 577 controls], the design of which has been previously
described [43] and is detailed in the Supplementary data, available at
Annals of Oncology online.
statistical analyses
The residual method was used to adjust for total energy by computing the
residuals from a linear regression of dietary exposures of interest (all except
GI since GI values reflect the physiological response to the consumption of
the food item, but not its quantity) on total energy consumption with
additional adjustment for center [44].
Cox proportional hazards models were used to calculate hazard ratio
(HR) as estimates of relative risks and 95% confidence intervals (95% CI)
for GI, GL, total carbohydrate, and total sugar, starch, and dietary fiber in
relation to HCC, IBD, BTC, gallbladder, and cholangiocarcinoma risk.
There was no violation of the proportional hazards assumption as checked
by Schoenfeld residuals. Age was used as the underlying time variable, with
entry and exit time defined as the subject’s age at recruitment and age of
censoring or cancer diagnosis, respectively. Dietary exposures of interest
were included in models as continuous and as categorical variables, with
quartile cut points based on sex-specific studywide energy adjusted
(nonenergy adjusted for GI) all-cohort distributions. Results for IBD,
cholangiocarcinoma, and gallbladder cancers are presented only for
continuous dietary exposures due to low case numbers. To test dose
responses, trend variables were assigned the sex-specific median values for
overall quartiles of dietary exposures of interest. Heterogeneity of effects by
sex and cancer subsites was assessed by χ
2
statistic.
Crude Cox models were stratified by study center to control for
differences in follow-up procedures and questionnaire design, by age at
recruitment (in 1-year categories), and by sex to allow for different baseline
hazard rates, and adjusted for total energy intake. Multivariable models
included the variables listed in Table 3.
calibration
Nutrient intakes and total energy intake were calibrated by utilizing a
multivariable fixed-effects linear model in which 24-h recall values were
regressed on the main DQ values for the calibration subsample of the EPIC
cohort [45]. Individual predicted values for each dietary exposure of
interest were computed from the calibration models. For all models, Cox
regressions were fit with calibrated/predicted values on a continuous scale.
The standard error of the calibrated coefficient was estimated by bootstrap
sampling with 1000 repetitions to take into consideration the uncertainty
related to measurement error correction [46].
effect modification
Effect modification on the multiplicative scale for potential effect modifying
variables (including sex, body mass index, self-reported diabetes, smoking,
baseline alcohol intake, and total dietary fat consumption) was tested by
including the interaction terms formed by the product of modifying
variable categories and the value of categories of nutrient intake. The
statistical significance of interactions was assessed using likelihood ratio
tests based on the models with and without the interaction terms.
nested case–control subset
Two conditional logistic models, with matching factors only and with
adjustment for the same confounders as described above, were used to
assess the strengths of association (incidence rate ratio, RR as estimated by
odds ratio [47]; with 95% CI and tests for trend) among all and HBV/HCV
negative individuals.
All statistical tests were two-sided, and Pvalues < 0.05 were considered
statistically significant. All statistical analyses were conducted using SAS
version 9.2 software (SAS Institute, Inc., NC).
results
cohort study
A total of 5 415 385 person years of follow-up (mean = 11.4/
maximum = 14.8 years) were contributed by 142 194 men and
335 012 women between 1992 and 2010. During this period,
191 HCC, 66 IBD, and 236 BTC cases were diagnosed
(Table 1). The participants who developed HCC were more
likely to be men, older, obese, current smokers, and to have
higher baseline alcohol intake and diabetes (only for HCC)
compared with participants who did not develop cancer. The
participants who developed BTC were, at baseline, more likely
to be women, be older, and have self-reported gallstones
(Table 2).
HCC, IBD, and cholangiocarcinoma
GI, GL, and total carbohydrate were not associated with HCC
risk (Table 3). Of the specific carbohydrate that was examined
in relation to HCC, a positive association was observed for
total sugar (for high versus low quartile, HR = 1.88, 95% CI
1.16–3.03; P
trend
= 0.008). Conversely, an inverse HCC risk was
observed for higher intakes of total starch (HR = 0.59, 95% CI
0.35–0.99, P
trend
= 0.014) and dietary fiber (HR = 0.51, 95% CI
0.31–0.83, P
trend
= 0.013). Further adjustment for dietary fiber
made no material difference in risk estimates for GI, GL, total
carbohydrate, and sugar; however, for total starch,
multivariable-adjusted risk estimates were slightly attenuated
across quartiles (HR
Q2
= 0.88, 95% CI 0.60–1.31, HR
Q3
= 0.64,
95% CI 0.39–1.04, HR
Q4
= 0.70, 95% CI 0.40–1.23, P
trend
=
0.110) and per 50 g/day (HR = 0.77, 95% CI 0.59–1.02).
The calibrated continuous models results suggested possibly
stronger associations between these dietary exposures and HCC
risk (HR = 1.45, 95% CI 1.01–2.09 per 50 g/day of sugar; HR =
0.71, 95% CI 0.43–1.16 per 50 g/day of starch; HR = 0.65, 95%
CI 0.42–0.96 per 10 g/day of fiber). Sex did not modify any of
the associations (all Pvalues for heterogeneity > 0.10). The
results for IBD and cholangiocarcinoma are presented in
Table 4.
Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 |
Table 1. Size of the EPIC cohort, numbers of cancer cases, and distribution of dietary glycemic load, total carbohydrate, starch, sugar and dietary fiber intakes, by subcohort EPIC cohort study, 1992–2010
Country Cohort
size
Total no.
of PY
Mean (5th–95th percentiles) No. of cancer cases Mean (5th–95th percentiles) among all cohort participants
Age at
recruitment, years
No. of years
of follow-up
HCC IBD GB Amp V Other
BTC
a
CCA Glycemic load
(unit/day)
Glycemic index
(unit/day)
Total starch
(g/day)
Total sugar
(g/day)
Total dietary
fiber (g/day)
France 67 382 704 125 52.7 (44.2–65.3) 10.5 (4.1–12.0) 3 5 5 3 5 5 127 (62–209) 55.8 (47.2–62.7) 122 (51–214) 103 (50–170) 22.6 (12.5–35.1)
Italy 44 528 515 974 50.5 (37.8–63.2) 11.6 (9.1–14.2) 29 4 11 8 10 3 149 (72–249) 56.5 (50.3–63.1) 161 (66–288) 100 (47–173) 22.3 (11.9–36.1)
Spain 39 995 493 614 49.2 (36.8–62.9) 12.3 (9.5–14.5) 9 3 13 4 6 1 122 (61–200) 55.9 (47.9–62.9) 128 (54–223) 89 (41–151) 24.6 (13.0–39.5)
UK general
population
29 503 354 318 57.6 (43.6–73.4) 12.0 (10.1–14.6) 17 13 1 6 1 14 131 (71–210) 56.1 (51.3–60.9) 104 (54–169) 127 (62–217) 22.2 (11.6–35.8)
UK health
conscious
45 880 510 590 43.9 (23.8–70.7) 11.1 (9.2–13.4) 1 2 5 2 4 3 130 (73–204) 55.5 (50.6–60.5) 111 (57–178) 122 (61–204) 26.0 (13.1–43.1)
The Netherlands 36 501 443 852 49.0 (25.6–66.2) 12.2 (10.1–14.6) 4 1 7 7 8 1 132 (73–216) 57.2 (51.1–63.0) 113 (58–193) 116 (57–195) 23.0 (13.5–34.6)
Greece 26 018 251 170 53.1 (33.0–72.4) 9.7 (3.6–13.5) 16 7 2 2 7 4 106 (59–167) 55.0 (49.4–60.5) 94 (49–158) 84 (38–144) 21.8 (12.6–34.0)
Germany 48 569 495 614 50.6 (36.7–63.6) 10.2 (5.5–12.7) 37 13 11 2 21 10 124 (65–204) 54.0 (48.9–58.7) 112 (57–183) 107 (43–207) 21.6 (12.0–34.0)
Sweden 48 672 669 944 52.0 (30.2–68.8) 13.8 (7.6–16.8) 29 7 24 6 14 5 136 (73–221) 57.1 (51.4–62.6) 139 (75–233) 99 (44–173) 19.9 (10.1–32.9)
Denmark 54 989 625 098 56.7 (50.7–64.2) 11.4 (7.6–13.2) 44 10 7 11 19 11 130 (73–204) 55.3 (49.8–60.5) 117 (62–187) 103 (47–187) 25.0 (13.2–39.7)
Norway 35 169 351 086 48.1 (41.6–54.9) 10.0 (10.0–10.1) 2 1 1 3 0 1 112 (63–164) 58.1 (52.8–63.0) 108 (58–159) 76 (36–126) 20.6 (11.3–30.9)
Total 47 7206 541 5385 51.2 (33.4–66.3) 11.4 (6.9–14.8) 191 66 87 54 95 58 128 (67–210) 56.0 (49.7–62.1) 121 (57–211) 103 (46–183) 22.8 (12.1–36.7)
a
Other BTC include biliary tract cancers, excluding cancers in the Ampulla of Vater and gallbladder.
PY, person-years; HCC, hepatocellular carcinoma; IBD, intrahepatic bile duct cancer; BTC, biliary tract cancer; GB, gallbladder cancer; Amp V, Ampulla of Vater; CCA, cholangiocarcinoma; SD, standard
deviation; p5, fifth percentile; p95, 95th percentile.
original articles Annals of Oncology
| Fedirko et al. Volume 24 | No. 2 | February 2013
Table 2. Selected baseline demographic and lifestyle characteristics of cancer cases and noncases, EPIC cohort study, 1992–2010
Baseline characteristics Hepatocellular
carcinoma (N= 191)
Intrahepatic bile duct
cancer (N= 66)
Biliary tract
cancer (N= 236)
Noncases
(N= 476 713)
Men (N, %) 127 (66.5) 33 (50.0) 89 (37.7) 141 945 (29.8)
Women (N, %) 64 (33.5) 33 (50.0) 147 (62.3) 334 768 (70.2)
Age at recruitment (years) 59.6 (6.9) 59.6 (7.7) 58.1 (8.1) 51.2 (9.9)
Smoking status and intensity (N,%)
Never smoker 53 (27.8) 28 (42.4) 110 (46.6) 205 157 (43.0)
Current smoker, occasional 14 (7.3) 3 (4.6) 11 (4.7) 40 046 (8.4)
Current smoker, 1–15 cigarettes/day 23 (12.0) 6 (9.1) 26 (11) 55 258 (11.6)
Current smoker, 16–25 cigarettes/day 24 (12.6) 4 (6.1) 17 (7.2) 29 822 (6.3)
Current smoker, >25 cigarettes/day 14 (7.3) 1 (1.5) 5 (2.1) 8647 (1.8)
Former smoker, quit ≤10 years ago 17 (8.9) 3 (4.6) 15 (6.4) 45 552 (9.6)
Former smoker, quit 11–20 years ago 18 (9.4) 9 (13.6) 29 (12.3) 38 923 (8.2)
Former smoker, quit >20 years ago 24 (12.6) 8 (12.1) 15 (6.4) 37 566 (7.9)
No. with diabetes (N,%)
a
22 (11.5) 2 (3.0) 16 (6.8) 12 478 (2.6)
No. with gallstones (N,%)
b
21 (11.0) 15 (22.7) 30 (12.7) 24 473 (5.1)
Anthropometric factors (mean, SD)
Height (cm) 168.4 (10.1) 166.4 (9.8) 166.3 (9.2) 166 (8.9)
Weight (kg) 79.7 (17.2) 75.1 (15.1) 73.6 (14) 70.2 (13.7)
Body mass index (kg/m
2
) 28.0 (4.8) 27.0 (4.2) 26.6 (4.5) 25.4 (4.3)
Waist-to-hip ratio 0.94 (0.1) 0.90 (0.1) 0.87 (0.1) 0.84 (0.1)
Total physical activity (N,%)
c
Inactive 18 (9.4) 8 (12.1) 29 (12.3) 71 709 (15)
Moderately inactive 68 (35.6) 20 (30.3) 76 (32.2) 142 918 (30)
Moderately active 78 (40.8) 28 (42.4) 92 (39.0) 156 660 (32.9)
Active 18 (9.4) 5 (7.6) 22 (9.3) 39 198 (8.2)
Education (N,%)
None/primary 88 (46.1) 31 (47) 99 (42.0) 142 818 (30.0)
Technical/professional 53 (27.8) 14 (21.2) 50 (21.2) 106 176 (22.3)
Secondary 12 (6.3) 5 (7.6) 38 (16.1) 97 407 (20.4)
University or higher 34 (17.8) 11 (16.7) 41 (17.4) 113 406 (23.8)
Lifetime pattern of alcohol intake (N,%)
Never drinkers 8 (4.2) 3 (4.6) 12 (5.1) 28 136 (5.9)
Former light drinkers 12 (6.3) 6 (9.1) 9 (3.8) 15 030 (3.2)
Former heavy drinkers 10 (5.2) 2 (3) 3 (1.3) 1979 (0.4)
Light drinkers 23 (12.0) 10 (15.2) 39 (16.5) 87806 (18.4)
Never heavy drinkers 63 (33.0) 25 (37.9) 94 (39.8) 184 436 (38.7)
Periodically heavy drinkers 32 (16.8) 9 (13.6) 17 (7.2) 42 408 (8.9)
Always heavy drinkers 6 (3.1) 1 (1.5) 2 (0.9) 2968 (0.6)
Daily dietary intake (mean, SD)
Total energy (kcal)
d
2180.4 (689.2) 2166.6 (664.8) 2051.4 (623.5) 2074 (619.2)
Glycemic index 56.0 (4.0) 55.9 (2.9) 56.0 (3.8) 56.0 (3.9)
Glycemic load (unit) 131.1 (48.1) 131.7 (45.6) 125.9 (43.3) 128.2 (44.6)
Total carbohydrate (g) 233.1 (80.8) 234.4 (77.2) 223.6 (72.4) 228 (74.4)
Total starch (g) 117.6 (45.7) 115.2 (46.6) 120.1 (48.7) 120.9 (49.0)
Total sugar (g) 108.6 (51.5) 113.4 (46.8) 99.4 (41.3) 102.9 (43.8)
Total dietary fiber (g) 21.1 (8.0) 21.4 (6.6) 22.1 (8.0) 22.8 (7.7)
Alcohol (g) 20.8 (31.1) 13.9 (18.5) 12.3 (17.1) 11.9 (17.1)
Missing values were not excluded from percentage calculations; therefore, the sum of percent across subgroups may not add up to 100%. The number of
noncases includes only cohort subjects without liver cancer.
Categorical variables are presented as numbers and percentages, continuous variables are presented as mean and standard deviations, adjusted for age and
center except for age at recruitment, which was adjusted for center only.
a
Self-reported data. Number of participants with missing data on diabetes status: HCC = 17, IBD = 13, EBD = 15, noncases = 39 143.
b
Self-reported data. Number of participants with missing data on gallstones status: HCC = 17, IBD = 18, EBD = 77, noncases = 146 938.
c
Total physical activity categories were sex specific.
d
Total energy consumption was strongly correlated with total dietary GL (Spearman’s partial correlation coefficient, ρ= 0.81), dietary carbohydrate intake
(ρ= 0.81), but weakly with overall GI (ρ= 0.10), after adjustment for study center, sex, and age. After additional adjustment for total energy, total dietary
carbohydrate was strongly correlated with GL (ρ= 0.94), weakly with GI (ρ= 0.19), and inversely with total fats (ρ=–0.60); GI with GL (ρ= 0.48); GL with
total sugar (ρ= 0.42), total starch (ρ= 0.68), and total fiber (ρ= 0.33); and GI with total sugar (ρ=−0.27), total starch (ρ= 0.52), and total fiber (ρ=−0.03).
All correlation coefficients were statistically significant (P< 0.0001).
Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 |
Table 3. Hazard ratios and 95% confidence intervals for hepatocellular carcinoma and BTC, by quartiles of GI and energy-adjusted GL, total carbohydrate,
and other carbohydrate components, EPIC cohort study, 1992–2010
Dietary variables
a
No. of
person-years
Hepatocellular carcinoma Biliary tract cancer
No. of
cases
Crude
b
HR (95%CI)
Multivariable
c
HR (95%CI)
No. of
cases
Crude
b
HR (95%CI)
Multivariable
c
HR (95%CI)
Glycemic index
Quartile 1 1329 767 55 1.00 (ref.) 1.00 (ref.) 62 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 350 399 46 0.86 (0.58–1.28) 0.95 (0.64–1.42) 47 0.77 (0.53–1.13) 0.78 (0.53–1.15)
Quartile 3 1 366 382 42 0.83 (0.55–1.25) 0.90 (0.59–1.36) 73 1.26 (0.89–1.80) 1.29 (0.91–1.84)
Quartile 4 1 368 837 48 1.11 (0.73–1.69) 1.09 (0.71–1.66) 54 1.04 (0.70–1.53) 1.06 (0.71–1.57)
P
trend d
0.779 0.832 0.340 0.295
Uncalibrated, per 5 units/day 0.97 (0.78–1.20) 0.98 (0.80–1.21) 1.05 (0.87–1.27) 1.06 (0.88–1.28)
Calibrated, per 5 units/day 0.97 (0.61–1.55) 1.04 (0.71–1.51) 1.28 (0.84–1.96) 1.23 (0.85–1.79)
Glycemic load
Quartile 1 1319 793 53 1.00 (ref.) 1.00 (ref.) 53 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 3547 53 51 0.93 (0.62–1.39) 1.15 (0.76–1.74) 56 0.99 (0.67–1.46) 0.99 (0.67–1.48)
Quartile 3 1 369 788 41 0.78 (0.50–1.21) 1.03 (0.64–1.64) 68 1.18 (0.80–1.73) 1.20 (0.80–1.79)
Quartile 4 1 371 051 46 0.86 (0.54–1.37) 1.19 (0.72–1.97) 59 1.06 (0.69–1.61) 1.08 (0.69–1.69)
P
trend d
0.381 0.639 0.596 0.545
Uncalibrated, per 50 units/day 0.88 (0.65–1.20) 1.12 (0.81–1.56) 0.93 (0.69–1.25) 0.92 (0.67–1.27)
Calibrated, per 50 units/day 0.71 (0.39–1.28) 1.19 (0.64–2.21) 0.91 (0.51–1.61) 0.97 (0.50–1.87)
Total carbohydrate
Quartile 1 1318 461 58 1.00 (ref.) 1.00 (ref.) 56 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 361 296 42 0.67 (0.44–1.01) 0.84 (0.55–1.29) 55 0.89 (0.60–1.30) 0.87 (0.59–1.30)
Quartile 3 1 373 975 42 0.67 (0.44–1.03) 0.92 (0.58–1.46) 65 0.97 (0.66–1.43) 0.96 (0.64–1.44)
Quartile 4 1 361 653 49 0.75 (0.48–1.18) 1.06 (0.64–1.75) 60 0.93 (0.61–1.41) 0.92 (0.59–1.44)
P
trend d
0.220 0.769 0.872 0.861
Uncalibrated, per 100 g/day 0.86 (0.58–1.27) 1.25 (0.81–1.93) 0.88 (0.60–1.28) 0.84 (0.55–1.28)
Calibrated, per 100 g/day 0.68 (0.35–1.32) 1.24 (0.57–2.69) 0.76 (0.40–1.45) 0.80 (0.37–1.75)
Total sugar
Quartile 1 1338 111 37 1.00 (ref.) 1.00 (ref.) 63 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 354 070 50 1.18 (0.77–1.82) 1.46 (0.94–2.27) 49 0.66 (0.45–0.96) 0.66 (0.45–0.97)
Quartile 3 1 364 409 54 1.36 (0.88–2.10) 1.77 (1.12–2.78) 65 0.83 (0.57–1.20) 0.83 (0.57–1.22)
Quartile 4 1 358 794 50 1.42 (0.90–2.24) 1.88 (1.16–3.03) 59 0.79 (0.53–1.16) 0.78 (0.52–1.18)
P
trend d
0.110 0.008 0.448 0.472
Uncalibrated, per 50 g/day 1.31 (1.06–1.61) 1.43 (1.17–1.74) 0.89 (0.71–1.11) 0.88 (0.70–1.11)
Calibrated, per 50 g/day 1.48 (1.03–2.14) 1.45 (1.01–2.09) 0.86 (0.58–1.27) 0.90 (0.60–1.33)
Total starch
Quartile 1 1315 988 66 1.00 (ref.) 1.00 (ref.) 59 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 343 230 52 0.74 (0.51–1.07) 0.84 (0.57–1.23) 51 0.82 (0.56–1.21) 0.81 (0.55–1.20)
Quartile 3 1 372 299 34 0.47 (0.30–0.74) 0.56 (0.36–0.90) 60 0.98 (0.67–1.45) 0.98 (0.66–1.45)
Quartile 4 1 383 868 39 0.49 (0.30–0.80) 0.59 (0.35–0.99) 66 1.16 (0.76–1.75) 1.14 (0.75–1.75)
P
trend d
0.001 0.014 0.395 0.429
Uncalibrated, per 50 g/day 0.62 (0.49–0.78) 0.70 (0.55–0.90) 1.03 (0.82–1.29) 1.03 (0.81–1.29)
Calibrated, per 50 g/day 0.35 (0.21–0.58) 0.71 (0.43–1.16) 1.06 (0.63–1.78) 1.11 (0.67–1.86)
Total fiber
Quartile 1 1369 061 68 1.00 (ref.) 1.00 (ref.) 61 1.00 (ref.) 1.00 (ref.)
Quartile 2 1 337 796 44 0.59 (0.40–0.86) 0.70 (0.47–1.04) 59 0.94 (0.65–1.35) 0.93 (0.64–1.34)
Quartile 3 1 343 820 50 0.63 (0.43–0.92) 0.75 (0.50–1.13) 60 0.91 (0.63–1.32) 0.88 (0.60–1.29)
Quartile 4 1 364 708 29 0.39 (0.25–0.63) 0.51 (0.31–0.83) 56 0.86 (0.58–1.28) 0.83 (0.55–1.26)
Continued
original articles Annals of Oncology
| Fedirko et al. Volume 24 | No. 2 | February 2013
biliary tract cancers
None of the dietary exposure variables of interest were
statistically significantly associated with BTC risk (Table 3). Sex
did not modify any of the associations (all Pvalues for
heterogeneity > 0.10). The results did not differ by subsite
(gallbladder versus other BTC; all Pvalues for heterogeneity
>0.30). Findings for continuous dietary exposures in relation to
gallbladder cancer are presented in Table 4.
sensitivity analyses and effect modifications
The findings did not change considerably for any of the cancer
sites after exclusion of the first 3 and 6 years of follow-up.
Table 3.. Continued
Dietary variables
a
No. of
person-years
Hepatocellular carcinoma Biliary tract cancer
No. of
cases
Crude
b
HR (95%CI)
Multivariable
c
HR (95%CI)
No. of
cases
Crude
b
HR (95%CI)
Multivariable
c
HR (95%CI)
P
trend d
<0.001 0.013 0.461 0.369
Uncalibrated, per 10 g/day 0.58 (0.44–0.76) 0.70 (0.52–0.93) 0.92 (0.72–1.16) 0.89 (0.69–1.14)
Calibrated, per 10 g/day 0.43 (0.28–0.66) 0.65 (0.42–0.96) 0.79 (0.53–1.15) 0.74 (0.49–1.10)
a
All dietary variables, except for glycemic index, were energy adjusted by residual method. Quartile cut points were based on studywide energy-adjusted sex-
specific nutrient intake distributions. Medians of sex-specific quartiles of energy adjusted by residual method (except GI) nutrients were: GI (men), Q1 = 52.2,
Q2 = 55.6, Q3 = 57.8, Q4 = 61.2 units/day; GI (women), Q1 = 50.7, Q2 = 54.6, Q3 = 57.0, Q4 = 60.5 units/day; GL (men), Q1 = 111.0, Q2 = 136.8, Q3 = 154.0,
Q4 = 185.8 units/day; GL (women), Q1 = 92.2, Q2 = 112.2, Q3 = 125.8, Q4 = 151.1 units/day; total carbohydrate (men), Q1 = 201.7, Q2 = 243.0, Q3 = 270.2, Q4 =
317.3 g/day; total carbohydrate (women), Q1 = 170.3, Q2 = 202.5, Q3 = 225.0, Q4 = 263.0 g/day; total sugar (men), Q1 = 68.1, Q2 = 97.0, Q3 = 119.1, Q4 = 159.9
g/day; total sugar (women), Q1 = 63.6, Q2 = 87.7, Q3 = 106.5, Q4= 140.0 g/day; total starch (men), Q1= 93.9, Q2 = 125.5, Q3 = 150.5, Q4 = 195.8 g/day; total
starch (women), Q1 = 76.9, Q2 = 100.8, Q3 = 117.9, Q4 = 153.3 g/day; total dietary fiber (men), Q1= 16.5, Q2 = 21.7, Q3 = 25.7, Q4 = 33.3 g/day; total dietary
fiber (women), Q1 = 15.6, Q2 = 19.9, Q3 = 23.2, Q4 = 29.8 g/day.
b
Stratified by age (1-year intervals), sex, and center and adjusted for total energy intake (continuous).
c
Additionally adjusted for sex-specific physical activity level (inactive, moderately inactive, moderately active, active, and missing), education (none/primary
school, technical/professional school, secondary school, university degree, and unknown), body mass index (kg/m
2
; continuous), smoking status and intensity
(never, former <10 and ≥10 years, current (<15, 15–24 and ≥25 cigarettes/day, other than cigarettes, and unknown), self-reported diabetes status (yes, no, and
unknown), baseline alcohol intake (g/day; continuous), and lifetime alcohol intake pattern (never drinkers, former light drinker, former heavy drinkers, light
drinkers, never heavy drinkers, periodically heavy drinkers, always heavy drinkers, and unknown). Other potential confounders examined, but not included in
the model since their inclusion did not change the effect estimates by more than 10% were waist-to-hip ratio, total dietary fat, intake of meat, fruits and
vegetables, and coffee consumption; for BTC and gallbladder cancer, self-reported history of gallstones.
EPIC, European Prospective Investigation into Cancer and Nutrition; HR, hazard ratio; CI, confidence interval; BTC, biliary tract cancer, GI, glycemic index; GL,
glycemic load.
d
P-value for trend test.
Table 4. Multivariable-adjusted
a
hazard ratios and 95% confidence intervals for intrahepatic bile duct, cholangiocarcinoma, and gallbladder cancers by
increase in intake of GI and energy-adjusted GL, total carbohydrate, and other carbohydrate components, EPIC cohort study, 1992–2010
Dietary variables
b
Intrahepatic bile duct
cancer (N= 66)
Cholangiocarcinoma
(N= 58)
Gallbladder
cancer (N= 87)
Glycemic index, per 5 units/day 1.05 (0.73–1.52) 1.04 (0.70–1.54) 1.08 (0.80–1.47)
Glycemic load, per 50 units/day 0.89 (0.50–1.56) 0.83 (0.45–1.51) 0.97 (0.57–1.67)
Total carbohydrate, per 100 g/day 0.81 (0.39–1.68) 0.70 (0.33–1.50) 1.02 (0.50–2.07)
Total sugar, per 50 g/day 1.12 (0.77–1.63) 0.93 (0.62–1.41) 0.95 (0.64–1.41)
Total starch, per 50 g/day 0.75 (0.48–1.17) 0.87 (0.54–1.41) 1.16 (0.80–1.69)
Total fiber, per 10 g/day 0.59 (0.37–0.95) 0.67 (0.41–1.09) 1.09 (0.73–1.63)
a
Stratified by age (1-year intervals), sex, and center and adjusted for total energy intake (continuous), for sex-specific physical activity level (inactive,
moderately inactive, moderately active, active, and missing), education (none/primary school, technical/professional school, secondary school, university
degree, and unknown), body mass index (kg/m
2
; continuous), smoking status and intensity (never, former <10 and ≥10 years, current (<15, 15–24, and ≥25
cigarettes/day, other than cigarettes, and unknown), self-reported diabetes status (yes, no, and unknown), baseline alcohol intake (g/day; continuous), and
lifetime alcohol intake pattern (never drinkers, former light drinker, former heavy drinkers, light drinkers, never heavy drinkers, periodically heavy drinkers,
always heavy drinkers, and unknown).
b
All dietary variables, except for glycemic index, were energy-adjusted by residual method.
Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 |
The results for HCC did not change substantially after
excluding persons with self-reported diabetes; and for BTC
cancer, after excluding persons with self-reported gallstones.
We did not observe any statistically significant multiplicative
interactions (data not shown).
by food source and groups
The results of analyses by food source (Supplementary Tables
S1 and S2, available at Annals of Oncology online) showed that
fiber from cereals and cereal products was statistically
significantly inversely associated with HCC risk (HR = 0.78,
95% CI 0.64–0.96 per 5 g/day; P
trend
= 0.012), after mutual
adjustment for fiber from other food sources. Fiber from
vegetable (HR = 0.79, 95% CI 0.55–1.15 per 5 g/day; P
trend
=
0.424) or other sources (HR = 0.90, 95% CI 0.75–1.08 per 5
g/day; P
trend
= 0.221), but not from fruits (HR = 1.06, 95% CI
0.83–1.35 per 5 g/day; P
trend
= 0.854), were also inversely, but
statistically nonsignificantly, associated with HCC risk.
Additionally, sugar from nonalcoholic beverages (HR = 1.11,
95% CI 1.04–1.19 per 10 g/day; P
trend
= 0.011) were associated
with a high risk for HCC. Similar associations were observed
for IBD, but not BTC (data not shown). In analyses by food
groups, cereal and cereal products (for high versus low
quartile, HR = 0.47, 95% CI 0.28–0.79; P
trend
= 0.006) were
statistically significantly associated with lower HCC risk.
A similar association, but weaker, was observed for IBD (for
high versus low quartile, HR = 0.80, 95% CI 0.36–1.77; P
trend
=
0.435), but not BTC ( for high versus low quartile, HR = 1.03,
95% CI 0.65–1.64; P
trend
= 0.680).
nested case–control subset
Cancer cases were diagnosed, on average, 5 years (standard
deviation = 2.9) after blood collection. Thirty-one percent, 3%,
and 5% of HCC, IBD, and BTC cases, respectively, had either
an HBV or HCV infection, or both. The corresponding
percents for matched controls were 4%, 6%, and 6%
(Supplementary Table S3, available at Annals of Oncology
online).
In multivariable adjusted analyses limited to HBV and HCV
negative participants (Table 5), dietary GI and GL, total
carbohydrate, starch, and sugar were not associated with risk of
HCC and BTC. Whereas higher total fiber intake, was
associated with lower HCC risk (for high versus low quartile,
RR = 0.26, 95% CI 0.08–0.80, P
trend
= 0.022; per 10 g/day, RR =
0.48, 95% CI 0.23–1.01), but only weakly and statistically
nonsignificantly with BTC risk (for high versus low quartile,
RR = 0.83, 95% CI 0.41–1.67; P
trend
=0.420; per 10 g/day, RR =
0.84, 95% CI 0.53–1.33). Consideration of all nested case–
control subjects but with adjustment for HBV/HCV status
resulted in similar findings (data not shown).
discussion
In this large prospective study, a higher intake of total dietary
fiber and a lower intake of dietary sugar were associated with
decreased risk of HCC and possibly of IBD, but not BTC risk.
Calibration of nutrient intakes to account for potential
measurement error strengthened the associations, but they
remained statistically significant only for dietary sugar and
fiber with HCC. Consideration of food sources of dietary fiber
showed that cereal fiber and cereal products were statistically
significantly associated with lower HCC risk. No statistically
significant effect modifications of the dietary exposures were
observed for either cancer site. In a nested case–control subset,
restriction of analyses to participants without HBV/HCV
infections showed a statistically significant inverse association
between dietary fiber and HCC risk.
The role of dietary GI, GL, and total dietary carbohydrate in
liver carcinogenesis has been little studied, with most of the
evidence coming from case–control settings [24–26,48] with
retrospective evaluation of diet, which is particularly
problematic among individuals with HCV/HBV infections
since they are more likely to change their diets before cancer
diagnosis. The only prospective evidence to date originates
from the NIH-AARP Diet and Health Study and suggests an
inverse GL–liver cancer association and, similarly to our
findings, the null results for GI [30]. No prospective
epidemiologic studies have investigated the association between
dietary GI and/or GL and BTC risk, and only few case–control
Table 5. Incidence rate ratios and 95% confidence intervals for HCC and
BTC, by quartiles of GI and energy-adjusted GL, total carbohydrate, and
other carbohydrate components among HBV and HCV free individuals,
within the EPIC nested case–control study, 1992–2006
Dietary variables
b
Hepatocellular
carcinoma
(Ca = 84/Co = 162)
Biliary tract cancer
(Ca = 124/Co = 241)
Glycemic index, per 5 units/day 1.08 (0.65–1.80) 1.08 (0.75–1.55)
Glycemic load, per 50 units/day 0.87 (0.32–2.35) 1.30 (0.63–2.71)
Total carbohydrate,
per 100 g/day
0.70 (0.19–2.61) 1.31 (0.54–3.19)
Total sugar, per 50 g/day 1.40 (0.75–2.61) 1.32 (0.88–1.97)
Total starch, per 50 g/day 0.50 (0.23–1.08) 0.86 (0.54–1.39)
Total fiber, per 10 g/day 0.48 (0.23–1.01) 0.84 (0.53–1.33)
a
All dietary variables, except for glycemic index, were energy-adjusted by
residual method.
b
Conditional logistic model, matching factors were age at blood collection
(±1 year), sex, study center, time of the day at blood collection (±3 h
interval), and fasting status at blood collection (<3, 3–6, and >6 h); among
women, additionally by menopausal status (pre-, peri-, and postmenopausal),
and hormone replacement therapy use at time of blood collection (yes/no),
and adjusted for total energy intake.
c
Additionally adjusted for sex-specific physical activity level (inactive,
moderately inactive, moderately active, active, and missing), education
(none/primary school, technical/professional school, secondary school,
university degree, and unknown), body mass index (kg/m
2
; continuous),
smoking status and intensity (never, former <10 and ≥10 years, current
(<15, 15–24, and ≥25 cigarettes/day, other than cigarettes, and unknown),
self-reported diabetes status (yes, no, and unknown), baseline alcohol
intake (g/day; continuous), and lifetime alcohol intake pattern (never
drinkers, former light drinker, former heavy drinkers, light drinkers, never
heavy drinkers, periodically heavy drinkers, always heavy drinkers, and
unknown).
EPIC, European Prospective Investigation into Cancer and Nutrition; Ca,
cases; Co, controls; OR, odds ratio; CI, confidence interval; HCC,
hepatocellular carcinoma; BTC, biliary tract cancers; GI, glycemic index;
GL, glycemic load.
original articles Annals of Oncology
| Fedirko et al. Volume 24 | No. 2 | February 2013
studies have reported on carbohydrate intake with inconsistent
results [28,29]. Despite a biologically plausible link of HCC,
IBD, and BTC with high-GL and high-GI diets, our study
shows null results for these cancers.
No published studies have reported on the association
between dietary sugar and starch and HCC and/or IBD risk.
Our results suggest a positive association for dietary sugar with
HCC risk. In HBV/HCV-negative participants, these
associations were in similar directions but no longer
significant. A positive association observed for HCC could be
in part explained by the increased fructose consumption,
which may underlie the development of NAFLD [49]. The
previous epidemiologic evidence for an association of total
dietary sugar with BTC is inconclusive and derived from case–
control studies [27,50–52]. In our study, no significant
associations were observed for BTC.
Limited epidemiologic evidence supports the hypothesis that
dietary fiber and its main sources (cereals, vegetables, and
fruits) reduce the risk of HCC, IBD, and BTC [28,29,53–55].
Our study has suggested a possible inverse association between
total dietary fiber consumption and HCC and IBD cancer risk,
which was further confirmed among HBV/HCV-negative
participants. Also, a potential beneficial effect of total dietary
fiber on BTC risk, though not statistically significant, was
suggested. In general, our data support the World Cancer
Research Fund conclusion about possible beneficial role of
cereals consumption in liver carcinogenesis [17], which could
be in large part due to their high-fiber content.
The potential mechanisms by which diets high in fiber could
lower HCC, IBD, and BTC risk may relate to reduction in
subjective appetite and energy intake, maintenance of normal
body weight [23], or beneficial effects on postprandial glucose
level and blood lipid profile [22]. Fiber’s hypocholesterolemic
action is mediated by a lower absorption of intestinal bile acid
in the colon resulting in higher fecal bile acid loss and de novo
synthesis of bile acids from cholesterol in the liver, and hence
reduced blood total and low-density lipoprotein cholesterol
concentrations, which might be involved in
hepatocarcinogenesis. Therefore, the protective effects of
dietary fiber in liver and biliary tract carcinogenesis are
biologically plausible and require further study.
The major advantages of this study are its prospective
design, which eliminates differential recall of diet between
cancer cases and noncases, large size, and careful selection of
cancer cases based on tumor morphology, histology, and
behavior to ensure the inclusion of only first primary tumors.
This study is the first to incorporate biomarkers of HBV/HCV
infection into the analysis of prospective cohort, thus
confirming the findings in a hepatitis-free population.
Limitations are the following: (i) diet was assessed only at
baseline and may not have accounted for potential dietary
changes during follow-up and may not have included a period
of exposure relevant to cancer initiation; (ii) dietary
measurement errors may have occurred, but these were
addressed to some extent by the application of the calibration
method; (iii) since measurement errors of Food Frequency
Questionnaire and 24-h recall are likely correlated, the effect
estimates observed in our study could possibly underestimate
the true associations; (iv) the reference GI values were obtained
mainly from Australian, British, and US foods for a limited
number of food items, therefore a potential variation in
processing and cooking methods [38], as well as food choices
and dietary practices in different European countries may not
have been fully accounted for. Dietary fiber, and other dietary
exposures, might be susceptible to confounding since high
intake of fiber in general reflects a healthier lifestyle such as
being physically active, lower alcohol consumption, and not
smoking. In our models, we have adjusted for other
determinants of healthy lifestyle; however, the presence of
possible residual confounding may not be ruled out, especially
for such risk factors as self-reported history of diabetes and
gallstones. No data were available on sclerosing cholangitis, a
risk factor for IBD and BTC, on incidence of T2D and
gallstones, and on exposure to aflatoxins, which is uncommon
in Western Europe [56]. Finally, the small sample size for some
cancer sites (e.g. cholangiocarcinoma), particularly within a
nested case–control subset, did not allow performing some
multivariable analyses and stratification by potential effect
modifying factors.
In conclusion, this large and comprehensive study has
shown no association of overall GI, total GL, and total dietary
carbohydrate with HCC, IBD, and BTC risk. The results also
have suggested a possible positive association for dietary sugar
with HCC, but not IBD or BTC risk. In addition, our findings
have shown that high dietary fiber intake is associated with
lower HCC and IBD risk among all and HBV/HCV-free
participants, whereas the inverse association for BTC was not
statistically significant.
acknowledgements
The authors thank C. Biessy and B. Hemon for their assistance
in database preparation.
ER is the overall coordinator of the EPIC study. All authors
contributed to recruitment, data collection/acquisition,
biological sample collection, follow-up and/or management of
the EPIC cohort, as well as the interpretation of the present
findings and approval of the final version for publication.
Reagents for the hepatitis infection determinations were kindly
provided by Abbott Diagnostics Division, Lyon, France. The
funding sources had no influence on the design of the study; the
collection, analysis, and interpretation of data; the writing of the
report; or the decision to submit the paper for publication.
funding
This work was supported by the French National Cancer
Institute (L’Institut National du Cancer; INCA) (grant number
2009-139). The coordination of EPIC is financially supported
by the European Commission (DG-SANCO); and the
International Agency for Research on Cancer. The national
cohorts are supported by Danish Cancer Society (Denmark);
Ligue Contre le Cancer; Institut Gustave Roussy; Mutuelle
Générale de l’Education Nationale; and Institut National de la
Santé et de la Recherche Médicale (INSERM) (France);
Deutsche Krebshilfe, Deutsches Krebsforschungszentrum
Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 |
(DKFZ); and Federal Ministry of Education and Research
(Germany); Stavros Niarchos Foundation; Hellenic Health
Foundation; and Ministry of Health and Social Solidarity
(Greece); Italian Association for Research on Cancer (AIRC);
National Research Council; and AIRE-ONLUS Ragusa, AVIS
Ragusa, Sicilian Government (Italy); Dutch Ministry of Public
Health, Welfare and Sports (VWS); Netherlands Cancer
Registry (NKR); LK Research Funds; Dutch Prevention Funds;
Dutch ZON (Zorg Onderzoek Nederland); World Cancer
Research Fund (WCRF); and Statistics Netherlands (the
Netherlands); European Research Council (ERC) (grant
number ERC-2009-AdG 232997) and Nordforsk; and Nordic
Center of Excellence Programme on Food, Nutrition and
Health (Norway); Health Research Fund (FIS); Regional
Governments of Andalucía, Asturias, Basque Country, Murcia
(No. 6236) and Navarra; and ISCIII RETIC (RD06/0020)
(Spain); Swedish Cancer Society; Swedish Scientific Council;
and Regional Government of Skåne and Västerbotten
(Sweden); Cancer Research UK; Medical Research Council;
Stroke Association; British Heart Foundation; Department of
Health; Food Standards Agency; and Wellcome Trust (UK).
disclosure
The authors have declared no conflicts of interest.
references
1. Jemal A, Bray F, Center MM et al. Global cancer statistics. CA Cancer J Clin
2011; 61: 69–90.
2. Siegel R, Ward E, Brawley O et al. Cancer statistics, 2011: the impact of
eliminating socioeconomic and racial disparities on premature cancer deaths. CA
Cancer J Clin 2011; 61: 212–236.
3. Altekruse SF, McGlynn KA, Reichman ME. Hepatocellular carcinoma incidence,
mortality, and survival trends in the United States from 1975 to 2005. J Clin
Oncol 2009; 27: 1485–1491.
4. Center MM, Jemal A. International trends in liver cancer incidence rates. Cancer
Epidemiol Biomarkers Prev 2011; 20: 2362–2368.
5. Vanni E, Bugianesi E, Kotronen A et al. From the metabolic syndrome to NAFLD or
vice versa? Dig Liver Dis 2010; 42: 320–330.
6. de Groen PC, Gores GJ, LaRusso NF et al. Biliary tract cancers. N Engl J Med
1999; 341: 1368–1378.
7. Seyama Y, Makuuchi M. Current surgical treatment for bile duct cancer. World J
Gastroenterol 2007; 13: 1505–1515.
8. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity,
diabetes, and cardiovascular disease. JAMA 2002; 287: 2414–2423.
9. Jenkins DJ, Wolever TM, Taylor RH et al. Glycemic index of foods: a physiological
basis for carbohydrate exchange. Am J Clin Nutr 1981; 34: 362–366.
10. Augustin LS, Franceschi S, Jenkins DJ et al. Glycemic index in chronic disease:
a review. Eur J Clin Nutr 2002; 56: 1049–1071.
11. Giovannucci E, Harlan DM, Archer MC et al. Diabetes and cancer: a consensus
report. CA Cancer J Clin 2010; 60: 207–221.
12. Barclay AW, Petocz P, McMillan-Price J et al. Glycemic index, glycemic load, and
chronic disease risk—a meta-analysis of observational studies. Am J Clin Nutr
2008; 87: 627–637.
13. Valtuena S, Pellegrini N, Ardigo D et al. Dietary glycemic index and liver
steatosis. Am J Clin Nutr 2006; 84: 136–142; quiz 268–139.
14. Le KA, Bortolotti M. Role of dietary carbohydrates and macronutrients in the
pathogenesis of nonalcoholic fatty liver disease. Curr Opin Clin Nutr Metab Care
2008; 11: 477–482.
15. Saunders D, Seidel D, Allison M et al. Systematic review: the association
between obesity and hepatocellular carcinoma—epidemiological evidence.
Aliment pharmacol Ther 2010; 31: 1051–1063.
16. Larsson SC, Wolk A. Overweight, obesity and risk of liver cancer: a meta-analysis
of cohort studies. Br J Cancer 2007; 97: 1005–1008.
17. WCRF/AICR. World Cancer Research Fund/American Institute for Cancer
Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer:
A Global Perspective. Washington, DC: AICR 2007.
18. Schlesinger S, Aleksandrova K, Pischon T et al. Abdominal obesity, weight gain
during adulthood and risk of liver and biliary tract cancer in a European cohort.
Int J Cancer 2012; doi: 10.1002/ijc.27645.
19. Ren HB, Yu T, Liu C et al. Diabetes mellitus and increased risk of biliary tract
cancer: systematic review and meta-analysis. Cancer Causes Control 2011; 22:
837–847.
20. El-Serag HB, Hampel H, Javadi F. The association between diabetes and
hepatocellular carcinoma: a systematic review of epidemiologic evidence. Clin
Gastroenterol Hepatol 2006; 4: 369–380.
21. Kant P, Hull MA. Excess body weight and obesity—the link with gastrointestinal
and hepatobiliary cancer. Nat Rev Gastroenterol Hepatol 2011; 8: 224–
238.
22. Babio N, Balanza R, Basulto J et al. Dietary fibre: influence on body weight,
glycemic control and plasma cholesterol profile. Nutr Hosp 2010; 25: 327–
340.
23. Wanders AJ, van den Borne JJ, de Graaf C et al. Effects of dietary fibre on
subjective appetite, energy intake and body weight: a systematic review of
randomized controlled trials. Obesity Rev 2011; 12: 724–739.
24. Rossi M, Lipworth L, Maso LD et al. Dietary glycemic load and hepatocellular
carcinoma with or without chronic hepatitis infection. Ann Oncol 2009; 20:
1736–1740.
25. Lagiou P, Rossi M, Tzonou A et al. Glycemic load in relation to hepatocellular
carcinoma among patients with chronic hepatitis infection. Ann Oncol 2009; 20:
1741–1745.
26. Kuper H, Tzonou A, Lagiou P et al. Diet and hepatocellular carcinoma: a case-
control study in Greece. Nutr Cancer 2000; 38: 6–12.
27. Moerman CJ, Bueno de Mesquita HB, Runia S. Dietary sugar intake in the
aetiology of biliary tract cancer. Int J Epidemiol 1993; 22: 207–214.
28. Zatonski WA, Lowenfels AB, Boyle P et al. Epidemiologic aspects of gallbladder
cancer: a case-control study of the SEARCH Program of the International Agency
for Research on Cancer. J Natl Cancer Inst 1997; 89: 1132–1138.
29. Zatonski WA, La Vecchia C, Przewozniak K et al. Risk factors for gallbladder
cancer: a Polish case-control study. Int J Cancer 1992; 51: 707–711.
30. George SM, Mayne ST, Leitzmann MF et al. Dietary glycemic index, glycemic
load, and risk of cancer: a prospective cohort study. Am J Epidemiol 2009; 169:
462–472.
31. Riboli E, Hunt KJ, Slimani N et al. European Prospective Investigation into Cancer
and Nutrition (EPIC): study populations and data collection. Public Health Nutr
2002; 5: 1113–1124.
32. Friedenreich C, Cust A, Lahmann PH et al. Physical activity and risk of
endometrial cancer: the European prospective investigation into cancer and
nutrition. Int J Cancer 2007; 121: 347–355.
33. Haftenberger M, Lahmann PH, Panico S et al. Overweight, obesity and fat
distribution in 50- to 64-year-old participants in the European Prospective
Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 2002; 5:
1147–1162.
34. Margetts BM, Pietinen P. European Prospective Investigation into Cancer and
Nutrition: validity studies on dietary assessment methods. Int J Epidemiol 1997;
26: S1–S5.
35. Slimani N, Deharveng G, Unwin I et al. The EPIC nutrient database project
(ENDB): a first attempt to standardize nutrient databases across the 10 European
countries participating in the EPIC study. Eur J Clin Nutr 2007; 61: 1037–1056.
36. Slimani N, Kaaks R, Ferrari P et al. European Prospective Investigation into
Cancer and Nutrition (EPIC) calibration study: rationale, design and population
characteristics. Public Health Nutr 2002; 5: 1125–1145.
37. Ferrari P, Kaaks R, Fahey MT et al. Within- and between-cohort variation in
measured macronutrient intakes, taking account of measurement errors, in the
original articles Annals of Oncology
| Fedirko et al. Volume 24 | No. 2 | February 2013
European Prospective Investigation into Cancer and Nutrition study. Am J
Epidemiol 2004; 160: 814–822.
38. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic
index and glycemic load values: 2002. Am J Clin Nutr 2002; 76:
5–56.
39. Henry CJ, Lightowler HJ, Strik CM et al. Glycaemic index and glycaemic load
values of commercially available products in the UK. Br J Nutr 2005; 94:
922–930.
40. Human Nutrition Unit SoMaMB, University of Sydney. The Official Website of the
Glycemic Index and GI Database. University of Sydney: Sydney 2006; http://www.
glycemicindex.com (15 December 2009 date last accessed).
41. van Bakel MM, Kaaks R, Feskens EJ et al. Dietary glycaemic index and
glycaemic load in the European Prospective Investigation into Cancer and
Nutrition. Eur J Clin Nutr 2009; 63(Suppl 4): S188–S205.
42. World Health Organization. International Statistical Classification of Diseases and
Related Health Problems. Geneva: World Health Organization 1992.
43. Trichopoulos D, Bamia C, Lagiou P et al. Hepatocellular carcinoma risk factors
and disease burden in a European cohort: a nested case-control study. J Natl
Cancer Inst 2011; 103: 1686–1695.
44. Willett W. Nutritional Epidemiology. New York: Oxford University Press 1998.
45. Ferrari P, Day NE, Boshuizen HC et al. The evaluation of the diet/disease relation
in the EPIC study: considerations for the calibration and the disease models. Int J
Epidemiol 2008; 37: 368–378.
46. Rosner B, Gore R. Measurement error correction in nutritional epidemiology
based on individual foods, with application to the relation of diet to breast
cancer. Am J Epidemiol 2001; 154: 827–835.
47. Knol MJ, Vandenbroucke JP, Scott P et al. What do case-control studies
estimate? Survey of methods and assumptions in published case-control
research. Am J Epidemiol 2008; 168: 1073–1081.
48. Zhang MD. [A population-based case control study of primary liver cancer in
Fusui]. Zhonghua Liu Xing Bing Xue Za Zhi 1993; 14: 14–18.
49. Lim JS, Mietus-Snyder M, Valente A et al. The role of fructose in the
pathogenesis of NAFLD and the metabolic syndrome. Nat Rev Gastroenterol
Hepatol 2010; 7: 251–264.
50. Strom BL, Soloway RD, Rios-Dalenz JL et al. Risk factors for gallbladder cancer.
An international collaborative case-control study. Cancer 1995; 76: 1747–1756.
51. Moerman CJ, Bueno de Mesquita HB, Smeets FW et al. Lifestyle factors
including diet and cancer of the gallbladder and bile duct: a population-
based case-control study in The Netherlands. Eur J Cancer Prev 1997; 6:
139–142.
52. Moerman CJ, Bueno de Mesquita HB, Smeets FW et al. Consumption of foods
and micronutrients and the risk of cancer of the biliary tract. Prev Med 1995;
24: 591–602.
53. Yu MW, Hsieh HH, Pan WH et al. Vegetable consumption, serum retinol level,
and risk of hepatocellular carcinoma. Cancer Res 1995; 55: 1301–1305.
54. Talamini R, Polesel J, Montella M et al. Food groups and risk of hepatocellular
carcinoma: a multicenter case-control study in Italy. Int J Cancer 2006; 119:
2916–2921.
55. George SM, Park Y, Leitzmann MF et al. Fruit and vegetable intake and risk of
cancer: a prospective cohort study. Am J Clin Nutr 2009; 89: 347–353.
56. Liu Y, Wu F. Global burden of aflatoxin-induced hepatocellular carcinoma: a risk
assessment. Environ Health Perspect 2010; 118: 818–824.
Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 |
