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Glycemic index, glycemic load, dietary carbohydrate, and dietary fiber intake and risk of liver and biliary tract cancers in Western Europeans

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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 methodsThe 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.ResultsHigher 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.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.
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Annals of Oncology 24: 543553, 2013
doi:10.1093/annonc/mds434
Published online 2 November 2012
Glycemic index, glycemic load, dietary carbohydrate,
and dietary ber 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.ArezzoHospital, 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, Nufeld
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 quantied by glycemic index (GI) and glycemic load
(GL), and dietary ber may inuence 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-specic, validated dietary questionnaires. Hazard ratios and 95% condence intervals were
estimated from proportional hazard models. HBV/HCV status was measured in a nested casecontrol 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 (1.171.74) per 50 g/day, total
starch = 0.70 (0.550.90) per 50 g/day, and total dietary ber = 0.70 (0.520.93) per 10 g/day. The ndings for dietary
ber were conrmed among HBV/HCV-free participants [0.48 (0.231.01)]. Similar associations were observed for IBD
[dietary ber = 0.59 (0.370.99) 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 ber and lower consumption of total sugars are
associated with lower HCC risk. In addition, high dietary ber intake could be associated with lower IBD cancer risk.
Key words: biliary tract neoplasms, dietary carbohydrate, dietary ber, 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, reecting the prevalence of
two established risk factorshepatitis B/C (HBV/HCV) and
aatoxin exposure. Recent data show that PLC rates are rapidly
increasing in traditionally lower-risk industrialized countries
[24] 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
difcult 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 [1520] by increasing chronic and local inammation
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
ber may prevent development of HCC, IBD, and BTC by
benecially inuencing glycemic control, lipid proles, 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 [2430]. 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 ber) 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-specic 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-specic food composition tables. The denitions 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% stratied random sample (N= 36 900) for calibration [36,37].
A GI database was assembled from the published GI values [3840],
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
reects 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 reects 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 dened as tumor in the liver (C22.0 as per the 10th Revision of
the International Statistical Classication of Diseases, Injury, and Causes of
Death [42]). IBD carcinoma was dened as tumor in the IBDs (C22.1).
BTC was dened as tumor in the gallbladder (C23.9), Ampulla of Vater
(C24.1), and biliary tract (C24.0, C24.8 and C24.9). Cholangiocarcinoma
was dened 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 casecontrol 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 reect 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% condence intervals (95% CI)
for GI, GL, total carbohydrate, and total sugar, starch, and dietary ber 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 dened as the subjects 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-specic 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-specic 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 stratied 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 xed-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 t with calibrated/predicted values on a continuous scale.
The standard error of the calibrated coefcient was estimated by bootstrap
sampling with 1000 repetitions to take into consideration the uncertainty
related to measurement error correction [46].
effect modication
Effect modication 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 signicance of interactions was assessed using likelihood ratio
tests based on the models with and without the interaction terms.
nested casecontrol 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 signicant. 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 specic 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
1.163.03; P
trend
= 0.008). Conversely, an inverse HCC risk was
observed for higher intakes of total starch (HR = 0.59, 95% CI
0.350.99, P
trend
= 0.014) and dietary ber (HR = 0.51, 95% CI
0.310.83, P
trend
= 0.013). Further adjustment for dietary ber
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 0.601.31, HR
Q3
= 0.64,
95% CI 0.391.04, HR
Q4
= 0.70, 95% CI 0.401.23, P
trend
=
0.110) and per 50 g/day (HR = 0.77, 95% CI 0.591.02).
The calibrated continuous models results suggested possibly
stronger associations between these dietary exposures and HCC
risk (HR = 1.45, 95% CI 1.012.09 per 50 g/day of sugar; HR =
0.71, 95% CI 0.431.16 per 50 g/day of starch; HR = 0.65, 95%
CI 0.420.96 per 10 g/day of ber). 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 ber intakes, by subcohort EPIC cohort study, 19922010
Country Cohort
size
Total no.
of PY
Mean (5th95th percentiles) No. of cancer cases Mean (5th95th 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
ber (g/day)
France 67 382 704 125 52.7 (44.265.3) 10.5 (4.112.0) 3 5 5 3 5 5 127 (62209) 55.8 (47.262.7) 122 (51214) 103 (50170) 22.6 (12.535.1)
Italy 44 528 515 974 50.5 (37.863.2) 11.6 (9.114.2) 29 4 11 8 10 3 149 (72249) 56.5 (50.363.1) 161 (66288) 100 (47173) 22.3 (11.936.1)
Spain 39 995 493 614 49.2 (36.862.9) 12.3 (9.514.5) 9 3 13 4 6 1 122 (61200) 55.9 (47.962.9) 128 (54223) 89 (41151) 24.6 (13.039.5)
UK general
population
29 503 354 318 57.6 (43.673.4) 12.0 (10.114.6) 17 13 1 6 1 14 131 (71210) 56.1 (51.360.9) 104 (54169) 127 (62217) 22.2 (11.635.8)
UK health
conscious
45 880 510 590 43.9 (23.870.7) 11.1 (9.213.4) 1 2 5 2 4 3 130 (73204) 55.5 (50.660.5) 111 (57178) 122 (61204) 26.0 (13.143.1)
The Netherlands 36 501 443 852 49.0 (25.666.2) 12.2 (10.114.6) 4 1 7 7 8 1 132 (73216) 57.2 (51.163.0) 113 (58193) 116 (57195) 23.0 (13.534.6)
Greece 26 018 251 170 53.1 (33.072.4) 9.7 (3.613.5) 16 7 2 2 7 4 106 (59167) 55.0 (49.460.5) 94 (49158) 84 (38144) 21.8 (12.634.0)
Germany 48 569 495 614 50.6 (36.763.6) 10.2 (5.512.7) 37 13 11 2 21 10 124 (65204) 54.0 (48.958.7) 112 (57183) 107 (43207) 21.6 (12.034.0)
Sweden 48 672 669 944 52.0 (30.268.8) 13.8 (7.616.8) 29 7 24 6 14 5 136 (73221) 57.1 (51.462.6) 139 (75233) 99 (44173) 19.9 (10.132.9)
Denmark 54 989 625 098 56.7 (50.764.2) 11.4 (7.613.2) 44 10 7 11 19 11 130 (73204) 55.3 (49.860.5) 117 (62187) 103 (47187) 25.0 (13.239.7)
Norway 35 169 351 086 48.1 (41.654.9) 10.0 (10.010.1) 2 1 1 3 0 1 112 (63164) 58.1 (52.863.0) 108 (58159) 76 (36126) 20.6 (11.330.9)
Total 47 7206 541 5385 51.2 (33.466.3) 11.4 (6.914.8) 191 66 87 54 95 58 128 (67210) 56.0 (49.762.1) 121 (57211) 103 (46183) 22.8 (12.136.7)
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, fth 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, 19922010
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, 115 cigarettes/day 23 (12.0) 6 (9.1) 26 (11) 55 258 (11.6)
Current smoker, 1625 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 1120 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 ber (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 specic.
d
Total energy consumption was strongly correlated with total dietary GL (Spearmans partial correlation coefcient, 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 ber (ρ= 0.33); and GI with total sugar (ρ=0.27), total starch (ρ= 0.52), and total ber (ρ=0.03).
All correlation coefcients were statistically signicant (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% condence intervals for hepatocellular carcinoma and BTC, by quartiles of GI and energy-adjusted GL, total carbohydrate,
and other carbohydrate components, EPIC cohort study, 19922010
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 (0.581.28) 0.95 (0.641.42) 47 0.77 (0.531.13) 0.78 (0.531.15)
Quartile 3 1 366 382 42 0.83 (0.551.25) 0.90 (0.591.36) 73 1.26 (0.891.80) 1.29 (0.911.84)
Quartile 4 1 368 837 48 1.11 (0.731.69) 1.09 (0.711.66) 54 1.04 (0.701.53) 1.06 (0.711.57)
P
trend d
0.779 0.832 0.340 0.295
Uncalibrated, per 5 units/day 0.97 (0.781.20) 0.98 (0.801.21) 1.05 (0.871.27) 1.06 (0.881.28)
Calibrated, per 5 units/day 0.97 (0.611.55) 1.04 (0.711.51) 1.28 (0.841.96) 1.23 (0.851.79)
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 (0.621.39) 1.15 (0.761.74) 56 0.99 (0.671.46) 0.99 (0.671.48)
Quartile 3 1 369 788 41 0.78 (0.501.21) 1.03 (0.641.64) 68 1.18 (0.801.73) 1.20 (0.801.79)
Quartile 4 1 371 051 46 0.86 (0.541.37) 1.19 (0.721.97) 59 1.06 (0.691.61) 1.08 (0.691.69)
P
trend d
0.381 0.639 0.596 0.545
Uncalibrated, per 50 units/day 0.88 (0.651.20) 1.12 (0.811.56) 0.93 (0.691.25) 0.92 (0.671.27)
Calibrated, per 50 units/day 0.71 (0.391.28) 1.19 (0.642.21) 0.91 (0.511.61) 0.97 (0.501.87)
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 (0.441.01) 0.84 (0.551.29) 55 0.89 (0.601.30) 0.87 (0.591.30)
Quartile 3 1 373 975 42 0.67 (0.441.03) 0.92 (0.581.46) 65 0.97 (0.661.43) 0.96 (0.641.44)
Quartile 4 1 361 653 49 0.75 (0.481.18) 1.06 (0.641.75) 60 0.93 (0.611.41) 0.92 (0.591.44)
P
trend d
0.220 0.769 0.872 0.861
Uncalibrated, per 100 g/day 0.86 (0.581.27) 1.25 (0.811.93) 0.88 (0.601.28) 0.84 (0.551.28)
Calibrated, per 100 g/day 0.68 (0.351.32) 1.24 (0.572.69) 0.76 (0.401.45) 0.80 (0.371.75)
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 (0.771.82) 1.46 (0.942.27) 49 0.66 (0.450.96) 0.66 (0.450.97)
Quartile 3 1 364 409 54 1.36 (0.882.10) 1.77 (1.122.78) 65 0.83 (0.571.20) 0.83 (0.571.22)
Quartile 4 1 358 794 50 1.42 (0.902.24) 1.88 (1.163.03) 59 0.79 (0.531.16) 0.78 (0.521.18)
P
trend d
0.110 0.008 0.448 0.472
Uncalibrated, per 50 g/day 1.31 (1.061.61) 1.43 (1.171.74) 0.89 (0.711.11) 0.88 (0.701.11)
Calibrated, per 50 g/day 1.48 (1.032.14) 1.45 (1.012.09) 0.86 (0.581.27) 0.90 (0.601.33)
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 (0.511.07) 0.84 (0.571.23) 51 0.82 (0.561.21) 0.81 (0.551.20)
Quartile 3 1 372 299 34 0.47 (0.300.74) 0.56 (0.360.90) 60 0.98 (0.671.45) 0.98 (0.661.45)
Quartile 4 1 383 868 39 0.49 (0.300.80) 0.59 (0.350.99) 66 1.16 (0.761.75) 1.14 (0.751.75)
P
trend d
0.001 0.014 0.395 0.429
Uncalibrated, per 50 g/day 0.62 (0.490.78) 0.70 (0.550.90) 1.03 (0.821.29) 1.03 (0.811.29)
Calibrated, per 50 g/day 0.35 (0.210.58) 0.71 (0.431.16) 1.06 (0.631.78) 1.11 (0.671.86)
Total ber
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 (0.400.86) 0.70 (0.471.04) 59 0.94 (0.651.35) 0.93 (0.641.34)
Quartile 3 1 343 820 50 0.63 (0.430.92) 0.75 (0.501.13) 60 0.91 (0.631.32) 0.88 (0.601.29)
Quartile 4 1 364 708 29 0.39 (0.250.63) 0.51 (0.310.83) 56 0.86 (0.581.28) 0.83 (0.551.26)
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 signicantly 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 modications
The ndings did not change considerably for any of the cancer
sites after exclusion of the rst 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 (0.440.76) 0.70 (0.520.93) 0.92 (0.721.16) 0.89 (0.691.14)
Calibrated, per 10 g/day 0.43 (0.280.66) 0.65 (0.420.96) 0.79 (0.531.15) 0.74 (0.491.10)
a
All dietary variables, except for glycemic index, were energy adjusted by residual method. Quartile cut points were based on studywide energy-adjusted sex-
specic nutrient intake distributions. Medians of sex-specic 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 ber (men), Q1= 16.5, Q2 = 21.7, Q3 = 25.7, Q4 = 33.3 g/day; total dietary
ber (women), Q1 = 15.6, Q2 = 19.9, Q3 = 23.2, Q4 = 29.8 g/day.
b
Stratied by age (1-year intervals), sex, and center and adjusted for total energy intake (continuous).
c
Additionally adjusted for sex-specic 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, 1524 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, condence 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% condence 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, 19922010
Dietary variables
b
Intrahepatic bile duct
cancer (N= 66)
Cholangiocarcinoma
(N= 58)
Gallbladder
cancer (N= 87)
Glycemic index, per 5 units/day 1.05 (0.731.52) 1.04 (0.701.54) 1.08 (0.801.47)
Glycemic load, per 50 units/day 0.89 (0.501.56) 0.83 (0.451.51) 0.97 (0.571.67)
Total carbohydrate, per 100 g/day 0.81 (0.391.68) 0.70 (0.331.50) 1.02 (0.502.07)
Total sugar, per 50 g/day 1.12 (0.771.63) 0.93 (0.621.41) 0.95 (0.641.41)
Total starch, per 50 g/day 0.75 (0.481.17) 0.87 (0.541.41) 1.16 (0.801.69)
Total ber, per 10 g/day 0.59 (0.370.95) 0.67 (0.411.09) 1.09 (0.731.63)
a
Stratied by age (1-year intervals), sex, and center and adjusted for total energy intake (continuous), for sex-specic 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, 1524, 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 signicant 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
ber from cereals and cereal products was statistically
signicantly inversely associated with HCC risk (HR = 0.78,
95% CI 0.640.96 per 5 g/day; P
trend
= 0.012), after mutual
adjustment for ber from other food sources. Fiber from
vegetable (HR = 0.79, 95% CI 0.551.15 per 5 g/day; P
trend
=
0.424) or other sources (HR = 0.90, 95% CI 0.751.08 per 5
g/day; P
trend
= 0.221), but not from fruits (HR = 1.06, 95% CI
0.831.35 per 5 g/day; P
trend
= 0.854), were also inversely, but
statistically nonsignicantly, associated with HCC risk.
Additionally, sugar from nonalcoholic beverages (HR = 1.11,
95% CI 1.041.19 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 0.280.79; P
trend
= 0.006) were
statistically signicantly associated with lower HCC risk.
A similar association, but weaker, was observed for IBD (for
high versus low quartile, HR = 0.80, 95% CI 0.361.77; P
trend
=
0.435), but not BTC ( for high versus low quartile, HR = 1.03,
95% CI 0.651.64; P
trend
= 0.680).
nested casecontrol 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 ber intake, was
associated with lower HCC risk (for high versus low quartile,
RR = 0.26, 95% CI 0.080.80, P
trend
= 0.022; per 10 g/day, RR =
0.48, 95% CI 0.231.01), but only weakly and statistically
nonsignicantly with BTC risk (for high versus low quartile,
RR = 0.83, 95% CI 0.411.67; P
trend
=0.420; per 10 g/day, RR =
0.84, 95% CI 0.531.33). Consideration of all nested case
control subjects but with adjustment for HBV/HCV status
resulted in similar ndings (data not shown).
discussion
In this large prospective study, a higher intake of total dietary
ber 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 signicant only for dietary sugar and
ber with HCC. Consideration of food sources of dietary ber
showed that cereal ber and cereal products were statistically
signicantly associated with lower HCC risk. No statistically
signicant effect modications of the dietary exposures were
observed for either cancer site. In a nested casecontrol subset,
restriction of analyses to participants without HBV/HCV
infections showed a statistically signicant inverse association
between dietary ber 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 casecontrol settings [2426,48] 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 GLliver cancer association and, similarly to our
ndings, 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 casecontrol
Table 5. Incidence rate ratios and 95% condence 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 casecontrol study, 19922006
Dietary variables
b
Hepatocellular
carcinoma
(Ca = 84/Co = 162)
Biliary tract cancer
(Ca = 124/Co = 241)
Glycemic index, per 5 units/day 1.08 (0.651.80) 1.08 (0.751.55)
Glycemic load, per 50 units/day 0.87 (0.322.35) 1.30 (0.632.71)
Total carbohydrate,
per 100 g/day
0.70 (0.192.61) 1.31 (0.543.19)
Total sugar, per 50 g/day 1.40 (0.752.61) 1.32 (0.881.97)
Total starch, per 50 g/day 0.50 (0.231.08) 0.86 (0.541.39)
Total ber, per 10 g/day 0.48 (0.231.01) 0.84 (0.531.33)
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, 36, 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-specic 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, 1524, 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, condence 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
signicant. 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 [27,5052]. In our study, no signicant
associations were observed for BTC.
Limited epidemiologic evidence supports the hypothesis that
dietary ber and its main sources (cereals, vegetables, and
fruits) reduce the risk of HCC, IBD, and BTC [28,29,5355].
Our study has suggested a possible inverse association between
total dietary ber consumption and HCC and IBD cancer risk,
which was further conrmed among HBV/HCV-negative
participants. Also, a potential benecial effect of total dietary
ber on BTC risk, though not statistically signicant, was
suggested. In general, our data support the World Cancer
Research Fund conclusion about possible benecial role of
cereals consumption in liver carcinogenesis [17], which could
be in large part due to their high-ber content.
The potential mechanisms by which diets high in ber could
lower HCC, IBD, and BTC risk may relate to reduction in
subjective appetite and energy intake, maintenance of normal
body weight [23], or benecial effects on postprandial glucose
level and blood lipid prole [22]. Fibers 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 ber 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 rst primary tumors.
This study is the rst to incorporate biomarkers of HBV/HCV
infection into the analysis of prospective cohort, thus
conrming the ndings 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 ber, and other dietary
exposures, might be susceptible to confounding since high
intake of ber in general reects 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 aatoxins, which is uncommon
in Western Europe [56]. Finally, the small sample size for some
cancer sites (e.g. cholangiocarcinoma), particularly within a
nested casecontrol subset, did not allow performing some
multivariable analyses and stratication 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 ndings
have shown that high dietary ber 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 signicant.
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
ndings and approval of the nal version for publication.
Reagents for the hepatitis infection determinations were kindly
provided by Abbott Diagnostics Division, Lyon, France. The
funding sources had no inuence 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 (LInstitut National du Cancer; INCA) (grant number
2009-139). The coordination of EPIC is nancially 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 lEducation 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 Scientic 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 conicts of interest.
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Annals of Oncology original articles
Volume 24 | No. 2 | February 2013 doi:10.1093/annonc/mds434 | 
... [7] In western developed countries such as Europe and the United States, the more common reason is chronic hepatitis C virus(HCV) infection or alcoholic cirrhosis caused by heavy drinking. [4] HCC incidence has increased in developed countries in recent years, [8][9][10][11]partly due to lifestyle related obesity, insulin resistance, metabolic and hormonal changes which ultimately lead to type 2 diabetes (T2D) and/or non-alcoholic fatty liver disease (NAFLD). [12][13][14]Nonalcoholic steatohepatitis (NASH) can lead to the development of HCC in the absence of cirrhosis. ...
... After searching PubMed, Embase and Web of Science, 518 articles were identi ed. According to the inclusion criteria established in advance, we reviewed and nally included 7 studies, [11,28,[33][34][35][36][37] including 4 cohort studies (7 cohorts) and 3 case-control studies. Three studies were from North-America, 3 from West-Europe, and 1 from Asia. ...
... Four studies [11,28,[33][34][35][36][37](5 cohorts, 1 case-control study) with approximately 1,062,000 participants were included to assess the association between GI and the risk of HCC. Comparing the highest with the lowest categories, GI was not signi cantly associated with HCC risk (pooled RR 1.11, 95%CI 0.80-1.53), ...
Preprint
Full-text available
Background Glycemic index (GI), glycemic load (GL), and carbohydrates have been shown to be associated with a variety of cancers, but their correlation with hepatocellular carcinoma (HCC) remains controversial. The purpose of our study was to investigate the correlation of GI, GL and carbohydrate with risk of HCC. Methods Systematic searches were conducted in PubMed, Embase and Web of Science until November 2020. According to the size of heterogeneity, the random effect model or the fixed effect model was performed to calculate the pooled relative risks (RRs) and 95% confidence intervals (CIs) for the correlation of GI, GL, and carbohydrates with the risk of HCC. Results Seven cohort studies involving 1,193,523 participants and 1,004 cases, and 3 case-control studies involving 827 cases and 5,502 controls were eventually included. The pooled results showed no significant correlation of GI (RR=1.11, 95%CI 0.80-1.53, I²= 62.2%), GL (RR=1.09, 95%CI 0.76-1.55, I2 = 66%), and carbohydrate (RR=1.09, 95%CI 0.84-1.32, I²=0%) with the risk of HCC in general population. Subgroup analysis revealed that in hepatitis B virus (HBV) or/and hepatitis C virus (HCV)-positive group, GI was not correlated with the risk of HCC (RR=0.65, 95%CI 0.32-1.32, p=0.475, I²=0.0%), while GL was significantly correlated with the risk of HCC (RR=1.52, 95%CI 1.04-2.23, p=0.016, I²=70.9%). In contrast, in HBV and HCV-negative group, both GI (RR=1.23, 95%CI 0.88-1.70, p=0.222, I²=33.6%) and GL (RR=1.17, 95% CI 0.83-1.64, p=0.648, I²=0%) were not correlated with the risk of HCC. Conclusion A high GL diet is correlated with a higher risk of HCC in people with hepatitis virus. A low GL diet may be recommended for patients with viral hepatitis to reduce the risk of HCC.
... A recent systematic review suggested a protective role for whole grain intake in HCC risk 23 . So far, two prospective cohort studies have reported inverse associations between dietary fiber intake and liver cancer risk 24,25 , and one reported an inverse association for whole grain intake 25 . However, these two studies included a limited number of cases. ...
... reported significant associations for total fiber (HR Q4 vs. Q1 = 0.51, 95% CI: 0.31-0.83) and fiber from cereals, but not from vegetables, fruits, and other sources 24 . The Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) (n = 141 HCC cases) reported a suggestive association between fiber from cereals (HR T3 vs. ...
... In this current study, no association was found for fiber from fruits, and an inverse association was found for whole grains and total fiber, which were consistent with the observation from the EPIC 24 and NHS/HPFS studies 25 . Moreover, inverse associations were observed for fiber from vegetables and grains in this study, adding to the knowledge that fiber from different food sources might have different associations with liver cancer. ...
Article
Full-text available
The relationship between dietary factors and liver disease remains poorly understood. This study evaluated the associations of whole grain and dietary fiber intake with liver cancer risk and chronic liver disease mortality. The National Institutes of Health–American Association of Retired Persons Diet and Health Study cohort recruited 485, 717 retired U.S. participants in 1995–1996. Follow-up through 2011 identified 940 incident liver cancer cases and 993 deaths from chronic liver disease. Compared with the lowest, the highest quintile of whole grain intake was associated with lower liver cancer risk (Hazard ratio [HR]Q5 vs. Q1 = 0.78, 95% confidence interval [CI]: 0.63–0.96) and chronic liver disease mortality (HRQ5 vs. Q1 = 0.44, 95% CI: 0.35–0.55) in multivariable Cox models. Dietary fiber was also associated with lower liver cancer risk (HRQ5 vs. Q1 = 0.69, 95% CI: 0.53–0.90) and chronic liver disease mortality (HRQ5 vs. Q1 = 0.37, 95% CI: 0.29–0.48). Fiber from vegetables, beans and grains showed potential protective effect. Here, we show that higher intake of whole grain and dietary fiber are associated with lower risk of liver cancer and liver disease mortality. Higher intake of dietary fiber and whole grains are associated with reduced risk of various diseases including some cancers. Here, the authors estimate reductions in liver cancer of 22% and 31% and chronic liver disease mortality of 56% and 63% associated with increased whole grain and dietary fiber intake, respectively.
... The type and quantity of dietary carbohydrates, as quantified by the glycemic index (GI) and glycemic load (GL), and dietary fiber may influence the risk of liver cancer [26]. Among 477,206 participants of the European Prospective Investigation into Cancer and Nutrition cohort (EPIC), higher dietary GI, GL, or an increased intake of total carbohydrate was not associated with liver cancer risk. ...
... However, the risk for HCC was increased by 43% per 50 g/day of total sugar, and in contrast, reduced by 30% per 10 g/day of total dietary fiber. The findings for dietary fiber were also confirmed among hepatitis B virus (HBV)/HCV-free participants [26]. ...
Article
Full-text available
The increasing burden of hepatocellular carcinoma (HCC) emphasizes the unmet need for primary prevention. Lifestyle measures appear to be important modifiable risk factors for HCC regardless of its etiology. Lifestyle patterns, as a whole and each component separately, are related to HCC risk. Dietary composition is important beyond obesity. Consumption of n-3 polyunsaturated fatty acids, as well as fish and poultry, are inversely associated with HCC, while red meat, saturated fat, and cholesterol are related to increased risk. Sugar consumption is associated with HCC risk, while fiber and vegetable intake is protective. Data from multiple studies clearly show a beneficial effect for physical activity in reducing the risk of HCC. However, the duration, mode and intensity of physical activity needed are yet to be determined. There is evidence that smoking can lead to liver fibrosis and liver cancer and has a synergistic effect with alcohol drinking. On the other hand, an excessive amount of alcohol by itself has been associated with increased risk of HCC directly (carcinogenic effect) or indirectly (liver fibrosis and cirrhosis progression. Large-scale intervention studies testing the effect of comprehensive lifestyle interventions on HCC prevention among diverse cohorts of liver disease patients are greatly warranted.
... FODMAP intake and cancer risk 1063 As reviewed by Makarem et al. (2) in 2018, total sugar intake was associated with higher risk of endometrial cancer (53) and hepatocellular carcinoma (54) in the European Prospective Investigation into Cancer and Nutrition cohort. Sweet food intakes were also associated with increased endometrial cancer in a Swedish cohort (55). ...
Article
Introduction et but de l’étude Les oligosaccharides, disaccharides, monosaccharides et polyols fermentescibles (FODMAPs) ont été impliqués dans l’étiologie des troubles gastro-intestinaux. Compte tenu de leur potentiel pro-inflammatoire et de leurs interactions avec le microbiote intestinal, leur contribution au développement d’autres maladies chroniques telles que les cancers a été postulée. Cependant, aucune étude épidémiologique n’a jusqu’à présent examiné cette hypothèse. Notre objectif était d’étudier les associations entre l’apport en FODMAPs (total et par type) et le risque de cancer (au global et par localisation : sein, prostate et colorectal) dans une vaste cohorte prospective. Matériel et méthodes Un total de 104 909 adultes français de la cohorte prospective NutriNet-Santé (2009–2020) ont été inclus dans nos analyses (âge moyen : 42,1 ± 14,5). Les apports en FODMAPs ont été obtenus à partir d’enregistrements alimentaires de 24 h répétés, liés à une table de composition alimentaire détaillée. Les associations entre les FODMAPs et les risques de cancer ont été évaluées par des modèles de Cox ajustés sur un large éventail de variables liées au mode de vie, sociodémographiques et anthropométriques. Résultats et analyse statistique La consommation totale de FODMAPs était associée à une augmentation du risque de cancer au global (n = 3374 cas incidents, HRQ5 vs. Q1 = 1,21, intervalle de confiance à 95 % : 1,02–1,44, P de tendance = 0,04). Les oligosaccharides semblaient être plus particulièrement associés au risque de cancer : une tendance était observée pour le cancer au global (HRQ5 vs. Q1 = 1,10 ; IC95 % : 0,97–1,25 ; p = 0,04) et le cancer colorectal (n = 272, HRQ5 vs. Q1 = 1,78 ; IC95 % : 1,13–2,79 ; p = 0,02). Les associations sont restées stables au cours des analyses de sensibilité. Conclusion Cette étude prospective à grande échelle suggère une association entre apports en FODMAPs et risque de cancer. Davantage d’études épidémiologiques et expérimentales sont nécessaires pour confirmer ces résultats et fournir des données sur les potentiels mécanismes sous-jacents.
... In the same cohort, it has been reported that total fish assumption defended against liver carcinoma [132]. As well as dietary fibers down-modulate the susceptibility to develop HCC [133]. Conversely, the impact of meat consumption in HCC onset is still debate [134]. ...
Article
Full-text available
Nonalcoholic fatty liver disease (NAFLD) is the leading contributor to the global burden of chronic liver diseases. The phenotypic umbrella of NAFLD spans from simple and reversible steatosis to nonalcoholic steatohepatitis (NASH), which may worsen into cirrhosis and hepatocellular carcinoma (HCC). Notwithstanding, HCC may develop also in the absence of advanced fibrosis, causing a delayed time in diagnosis as a consequence of the lack of HCC screening in these patients. The precise event cascade that may precipitate NASH into HCC is intricate and it entails diverse triggers, encompassing exaggerated immune response, endoplasmic reticulum (ER) and oxidative stress, organelle derangement and DNA aberrancies. All these events may be accelerated by both genetic and environmental factors. On one side, common and rare inherited variations that affect hepatic lipid remodeling, immune microenvironment and cell survival may boost the switching from steatohepatitis to liver cancer, on the other, diet-induced dysbiosis as well as nutritional and behavioral habits may furtherly precipitate tumor onset. Therefore, dietary and lifestyle interventions aimed to restore patients’ health contribute to counteract NASH progression towards HCC. Even more, the combination of therapeutic strategies with dietary advice may maximize benefits, with the pursuit to improve liver function and prolong survival.
... Moreover, the European Prospective Investigation into Cancer and Nutrition study, which examined a cohort of 477,000 individuals, showed a positive correlation between total sugar intake and total soft drink consumption (sugar-and artificially sweetened beverages) and HCC risk (9,10). Although fructose consumption, especially from liquid sources, is a direct dietary factor associated with NAFLD (11), the role of individual sugars as dietary risk factors for HCC is less clear. ...
Article
Full-text available
Background: Non-alcoholic fatty liver disease (NAFLD) has increased over the last decades and may evolve into hepatocellular carcinoma (HCC). As HCC is challenging to treat, knowledge on the modifiable risk factors for NAFLD/HCC (e.g. hyper caloric diets rich in fructose) is essential. Objective and design: We used a model of diethyl nitrosamine-induced hepatocarcinogenesis to investigate the liver cancer-promoting effects of a diet supplemented with 10% liquid fructose, administered to male and female rats for 11 months. A subset of the fructose-supplemented rats received resveratrol (RVT) in the last 4 months of treatment. Results and discussion: Rat livers showed no de visu or histological evidence of liver tumorigenesis. However, we observed metabolic abnormalities that could be related to cancer development mainly in the female fructose-supplemented rats, such as increases in weight, adiposity and hepatic triglyceride levels, as well as hyperglycaemia, hyperuricemia, hyperleptinemia and a reduced insulin sensitivity index, which were partially reversed by RVT. Therefore, we performed a targeted analysis of 84 cancer-related genes in the female liver samples, which revealed expression changes associated with cancer-related pathways. Analysis of individual genes indicated that some changes increased the risk of hepatocarcinogenesis (Sfrp2, Ccl5, Socs3, and Gstp1), while others exerted a protective/preventive effect (Bcl2 and Cdh1). Conclusion: Our data clearly demonstrate that chronic fructose supplementation, as the sole dietary intervention, does not cause HCC development in rats.
... Although several different food components have been assessed with regard to HCC development, few studies have investigated the impact of a dietary fat and/or fatty acids on the risk of HCC; and overall, results for dietary risk factors have been inconsistent. [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] The large, prospective National Institutes of Health-American Association of Retired Persons (NIH-AARP) Diet and Health study showed a positive association between saturated fat intake and liver cancer. 19 Another US cohort, the Nurses' Health Study and the Health Professionals Follow-up Study, reported an inverse association of monounsaturated fatty acids (MUFAs), n-3 polyunsaturated fatty acids (PUFAs), and n-6 PUFA with risk of HCC. 31 However, these two US studies lacked information on HBV and HCV infection status, strong potential confounders; and two other studies conducted in Europe reported mixed results. ...
Article
Full-text available
Background and Aims The role of dietary fat consumption in the etiology of hepatocellular carcinoma (HCC) remains unclear. We investigated the associations of total fat and fatty acids with risk of HCC among US adults in a hospital-based case–control study. Methods We analyzed data from 641 cases and 1034 controls recruited at MD Anderson Cancer Center during 2001–2018. Cases were new patients with a pathologically or radiologically confirmed diagnosis of HCC; controls were cancer-free spouses of patients with cancers other than gastrointestinal, lung, liver, or head and neck. Cases and controls were frequency-matched by age and sex. Dietary intake was assessed using a validated food frequency questionnaire. Odds ratios (ORs) and corresponding confidence intervals (CIs) were computed using unconditional logistic regression with adjustment for major HCC risk factors, including hepatitis B virus and hepatitis C virus infection. Results Monounsaturated fatty acid (MUFA) intake was inversely associated with HCC risk (highest vs. lowest tertile: OR, 0.49; 95% CI, 0.33–0.72). Total polyunsaturated fatty acid (PUFA) intake was directly associated with HCC risk (highest vs. lowest tertile: OR, 1.82; 95% CI, 1.23–2.70). Omega-6 PUFA was directly associated with HCC risk (highest vs. lowest tertile: OR 2.29; 95% CI, 1.52–3.44). Long-chain omega-3 PUFA (eicosapentaenoic acid and docosahexaenoic acid) intake was also inversely associated with HCC risk (highest vs. lowest tertile: OR, 0.50; 95% CI, 0.33–0.70). No association was observed for saturated fat and HCC risk. Conclusion Our findings support a direct association of omega-6 PUFA intake with HCC and an inverse association of MUFA and long-chain omega-3 PUFA intake with HCC.
Article
Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population worldwide, and its prevalence is anticipated to increase globally. While most NAFLD patients are asymptomatic, NAFLD may progress to fibrosis, cirrhosis, cardiovascular disease, and diabetes. Research reports, with daunting results, show the challenge that NAFLD's burden causes to global population health. The current process for identifying fibrosis risk levels is inefficient, expensive, does not cover all potential populations, and does not identify the risk in time. Instead of invasive liver biopsies, we implemented a non-invasive fibrosis assessment process calculated from clinical data (accessed via EMRs/EHRs). We stratified patients' risks for fibrosis from 2007 to 2017 by modeling the risk in 5,579 individuals. The process involved time-series machine learning models (Hidden Markov Models and Group-Based Trajectory Models) profiled fibrosis risk by modeling patients’ latent medical status resulted in three groups. The high-risk group had abnormal lab test values and a higher prevalence of chronic conditions. This study can help overcome the inefficient, traditional process of detecting fibrosis via biopsies (that are also medically unfeasible due to their invasive nature, the medical resources involved, and costs) at early stages. Thus longitudinal risk assessment may be used to make population-specific medical recommendations targeting early detection of high risk patients, to avoid the development of fibrosis disease and its complications as well as decrease healthcare costs.
Article
Full-text available
The concurrence of diabetes (predominantly type II) and hepatic cancer with the growing burden of evidence have generated global attention, posing a challenge in defining the possible association or molecular link between these diseases. Hence, the complex pathophysiological relationship is challenging to elucidate; still, multiple meta-analyses of epidemiological reports suggest that the prevalence of diabetes significantly increases the risk of developing hepatic cancer. This association has hypothesized several pathophysiological mechanisms, such as hyperglycemia, hyperinsulinemia, insulin resistance, obesity, and enhanced inflammatory processes. The treatment with metformin, a well-known biguanide class of anti-diabetic drugs, is associated with a lower incidence of hepatic cancer. This review addresses and summarizes the current understanding of the potential biological link/mechanism between diabetes and hepatic cancer incidence and prognosis. Lastly, we have also outlined the possible role of metformin in reducing the overall hepatic cancer risk.
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General obesity has been positively associated with risk of liver and probably with biliary tract cancer, but little is known about abdominal obesity or weight gain during adulthood. We used multivariable Cox proportional hazard models to investigate associations between weight, body mass index, waist and hip circumference, waist-to-hip and waist-to-height ratio (WHtR), weight change during adulthood and risk of hepatocellular carcinoma (HCC), intrahepatic (IBDC) and extrahepatic bile duct system cancer [EBDSC including gallbladder cancer (GBC)] among 359,525 men and women in the European Prospective Investigation into Cancer and Nutrition study. Hepatitis B and C virus status was measured in a nested case-control subset. During a mean follow-up of 8.6 years, 177 cases of HCC, 58 cases of IBDC and 210 cases of EBDSC, including 76 cases of GBC, occurred. All anthropometric measures were positively associated with risk of HCC and GBC. WHtR showed the strongest association with HCC [relative risk (RR) comparing extreme tertiles 3.51, 95% confidence interval (95% CI): 2.09-5.87; p(trend) < 0.0001] and with GBC (RR: 1.56, 95% CI: 1.12-2.16 for an increment of one unit in WHtR). Weight gain during adulthood was also positively associated with HCC when comparing extreme tertiles (RR: 2.48, 95% CI: 1.49-4.13; <0.001). No statistically significant association was observed between obesity and risk of IBDC and EBDSC. Our results provide evidence of an association between obesity, particularly abdominal obesity, and risk of HCC and GBC. Our findings support public health recommendations to reduce the prevalence of obesity and weight gain in adulthood for HCC and GBC prevention in Western populations.
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To describe dietary glycaemic index (GI) and glycaemic load (GL) values in the population participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study according to food groups, nutrients and lifestyle characteristics. Single 24-h dietary recalls (24-HDRs) from 33 566 subjects were used to calculate dietary GI and GL, and an ad hoc database was created as the main reference source. Mean GI and GL intakes were adjusted for age, total energy intake, height and weight, and were weighted by season and day of recall. GI was the lowest in Spain and Germany, and highest in the Netherlands, United Kingdom and Denmark for both genders. In men, GL was the lowest in Spain and Germany and highest in Italy, whereas in women, it was the lowest in Spain and Greece and highest in the UK health-conscious cohort. Bread was the largest contributor to GL in all centres (15-45%), but it also showed the largest inter-individual variation. GL, but not GI, tended to be lower in the highest body mass index category in both genders. GI was positively correlated with starch and intakes of bread and potatoes, whereas it was correlated negatively with intakes of sugar, fruit and dairy products. GL was positively correlated with all carbohydrate components and intakes of cereals, whereas it was negatively correlated with fat and alcohol and with intakes of wine, with large variations across countries. GI means varied modestly across countries and genders, whereas GL means varied more, but it may possibly act as a surrogate of carbohydrate intake.
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The glycemic index was proposed in 1981 as an alternative system for classifying carbohydrate-containing food. Since then, several hundred scientific articles and numerous popular diet books have been published on the topic. However, the clinical significance of the glycemic index remains the subject of debate. The purpose of this review is to examine the physiological effects of the glycemic index and the relevance of these effects in preventing and treating obesity, diabetes, and cardiovascular disease.
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Background. Gallbladder cancer has an unusual geographic and demographic distribution, suggesting many possible etiologies.Methods. A case-control study was undertaken at four hospitals in La Paz, Bolivia, and at one hospital in Mexico City, Mexico. Eighty-four patients with newly diagnosed, histologically confirmed gallbladder cancer were compared with 126 control subjects without stones and with 264 control subjects with cholelithiasis or choledocholithiasis without cancer. All study subjects underwent abdominal surgery. Study subjects were interviewed regarding demographic characteristics, medical history, family history, diet, and exposure to agents presumed to be risk factors for biliary cancer.Results. Virtually all subjects in Mexico were judged to be mestizos (i.e., persons of mixed ancestry). In contrast, race was a very strong risk factor for gallbladder cancer in Bolivia. Relative to mestizos who spoke neither language, the odds ratio (95% confidence interval [CI]) for cases versus control subjects without stones for those who spoke Aymara well was 15.9 (CI, 1.9–179), whereas it was 1.4 (CI, 0.2–8.2) for those who spoke Quechua well. An increased risk was also noted for elevated maximum body mass index (P = 0.03), family history of gallstones (odds ratio [OR] = 3.6 [CI, 1.3–11.4]), and physician-diagnosed typhoid (OR = 12.7 [CI, 1.5-598]). An increased risk was also seen with elevated maximum body mass index; compared with those with a body mass index less than 24 kg/m2, those with an index of 24–25 kg/m2, 26–28 kg/m2, and greater than 28 kg/m2 had odds ratios of 1.6 (CI, 0.4–7.6), 1.3 (CI, 0.3–5.6), and 2.6 (CI, 0.5–18.6), respectively (asymptotic test for trend, P = 0.03). Finally, a number of associations were noted with certain dietary and cooking habits.Conclusions. Patients with gallbladder cancer differed from control subjects in race, body mass, physician-diagnosed typhoid, and certain dietary patterns. These findings may provide useful clues to the pathogenesis of gallbladder cancer, but given the number of analyses performed, additional cases need to be studied. Cancer 1995:76:1747–56.
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