278
Research paper
Total fat consumption and pancreatic cancer risk: a meta-analysis of epidemiologic studies Qi-Wei Shen and Qi-Yuan Yao Fat consumption has been hypothesized to influence pancreatic cancer risk, but the results of epidemiologic studies have been controversial. We conducted a systematic review and meta-analysis of case–control and cohort studies to investigate this issue. Relevant published studies were identified by searching MEDLINE (PubMed) through February 2014. Two authors (Q.-W.S. and Q.-Y.Y.) independently assessed eligibility and the extracted data. Study-specific relative risks (RRs) were pooled using a random-effects model. We also carried out heterogeneity and publication bias analyses. Six cohort and 13 case–control studies with 6159 pancreatic cancer cases and 1 068 476 noncases were included in this metaanalysis. The summary RR for pancreatic cancer for the highest versus lowest intake was 1.04 [95% confidence interval (CI) = 0.90–1.20, I2 = 57.3%, P for heterogeneity = 0.001] for total fat. In addition, when separately analyzed by study design, case–control (RR = 1.03, 95% CI = 0.83–1.27, I2 = 55.8%, P for heterogeneity = 0.007) and cohort studies (RR = 1.05, 95% CI = 0.85–1.29, I2 = 66.7%, P for heterogeneity = 0.010) yielded similar results. Furthermore, no statistically
Introduction It is estimated that ∼ 300 000 new pancreatic cancer cases were diagnosed in 2012 worldwide (Ferlay et al., 2012). Because of the few early symptoms and poor response to therapeutic modalities (Li et al., 2004), pancreatic cancer has a high fatality rate, with 5-year survival rate less than 6% and survival on average only 6 months after diagnosis (Aune et al., 2012). Because of the poor prognosis and the minimal impact of conventional treatment methods (Li et al., 2004), it is extremely important to identify modifiable risk factors that may lead to the primary prevention of pancreatic cancer through alteration of these factors. However, except for cigarette smoking, diabetes mellitus (DM), and obesity, other modifiable risk factors of this disease are not well established (Anderson et al., 2006; Raimondi et al., 2009). In recent times, extensive research has focused on the role of diet, which might be a modifiable factor in the development of pancreatic cancer (Johnson and de Mejia, 2011). Various dietary factors have been investigated as potential risk factors in pancreatic cancer. In particular, total fat consumption was hypothesized to be associated with elevated pancreatic cancer risk in experimental studies (Birt et al., 1989; Smith et al., 1990;
significant associations were observed in the subgroup analyses on the basis of fat source, geographic location, whether using energy-adjusted models, and whether adjusted for several potential confounders and important risk factors. There was no evidence of publication bias or significant heterogeneity between subgroups on metaregression analyses. The results of this meta-analysis do not support an independent association between diets high in total fat and pancreatic cancer risk. European Journal of Cancer Prevention 24:278–285 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. European Journal of Cancer Prevention 2015, 24:278–285 Keywords: epidemiologic studies, fat, meta-analysis, pancreatic neoplasms Department of General Surgery, Center for Obesity and Metabolic Surgery, Huashan Hospital, Fudan University, Shanghai, China Correspondence to Qi-Yuan Yao, Department of General Surgery, Center for Obesity and Metabolic Surgery, Huashan Hospital, Fudan University, No. 12, Urumqi Zhong Road, Shanghai 200040, People’s Republic of China Tel: + 86 21 52887055; fax: + 86 21 62489191; e-mail: [email protected] Received 16 April 2014 Accepted 30 June 2014
Andren-Sandberg et al., 1999; Woutersen et al., 1999). In addition, an ecological study found statistically significant positive associations between animal fat consumption and pancreatic cancer after adjusting for the population’s smoking, energy, meat and vegetable consumption, and alcohol intake (Zhang et al., 2005). However, evidence from epidemiologic studies was still controversial (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Michaud et al., 2003; Lin et al., 2005; Nothlings et al., 2005; Chan et al., 2007; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Zhang et al., 2009; Lucenteforte et al., 2010; Arem et al., 2013; Jansen et al., 2014). Although the second report from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR), 2007 and several previous reviews have focused on this issue (Woutersen et al., 1999; Anderson et al., 2006), only a few additional epidemiologic studies [especially large prospectively designed studies (Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Arem et al., 2013)] have been published so far; thus, we carried out this systematic review and meta-analysis with the aim to clarify whether total fat intake is associated with pancreatic cancer risk.
0959-8278 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
DOI: 10.1097/CEJ.0000000000000073
Total fat consumption and pancreatic cancer Shen and Yao 279
Materials and methods Search strategy and study selection
To identify epidemiologic studies on total fat consumption and pancreatic cancer risk, we conducted a literature search in MEDLINE (PubMed) for articles published in any language from database initiation until February 2014. The following search terms were used: ‘diet’ or ‘dietary’ or ‘fat’ and ‘pancreatic’ or ‘pancreas’ and ‘cancer’ or ‘neoplasm’. In addition, we searched the reference lists of retrieved articles to identify further studies. We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for conducting and reporting meta-analyses (Moher et al., 2009). The following criteria were applied for literature selection: (a) the study had a case–control, nested case–control, case–cohort, or cohort design; (b) the study evaluated the relationship between total fat intake and incidence of pancreatic cancer; (c) investigators provided the following data: the number of cases, the total number of patients, and odds ratio (OR), relative risks (RRs), or hazard ratios (HRs) with 95% confidence intervals (CIs) for categories of total fat intake; and (d) the study was carried out in humans. If multiple articles were on the same study population, the one with most complete design or larger sample size was finally selected. Data extraction
Two reviewers (Q.-W.S. and Q.-Y.Y.) independently extracted and tabulated all data and any discrepancies were resolved by discussion. The following data were extracted from each publication: first author, year of publication, study design and region, study sample size (number of cases and controls or cohort size), exposure and outcome assessment including total fat intake categories, study-specific adjusted estimates with their 95% CIs for the highest versus lowest total fat intake, and factors matched by or adjusted for in the design or data analysis. Data synthesis and analysis
As the incidence of pancreatic cancer is low, the OR from case–control studies is approximated to the RR. In addition, we included the RR or hazard ratio based on the Cox proportional hazard model in the statistical analysis. Thus, we report all effect sizes as RR for simplicity. Between-study heterogeneity was assessed by the I2 statistic (I2 < 30%, no between-study heterogeneity or marginal between-study heterogeneity; I2 = 30–75%, mild heterogeneity; I2 > 75%, notable heterogeneity) (Higgins and Thompson, 2002). We combined the RRs from each study by the method of DerSimonian and Laird (1986) using the assumptions of a random-effects model, which takes into account both within-study and between-study variability. Ji et al. (1995) reported results separately for male and female patients and we pooled the results using a fixed-effects model to obtain an overall
combined estimate before combining with the rest of the studies. A P value less than 0.05 for meta-regression was considered representative of significant statistical difference between subgroups. In addition, publication bias was evaluated using Egger’s (Egger et al., 1997) and Begg’s methods (Begg and Mazumdar, 1994) and funnel plots. Finally, sensitivity analyses were executed by deleting each study in turn to reflect the influence of individual data sets on the overall estimate. Statistical analyses were performed with Stata (version 11; StataCorp, College Station, Texas, USA). P-values were two-sided and P less than 0.05 was considered statistically significant.
Results Literature search and study characteristics
The literature searches yielded 2384 studies. Initial screening of abstracts and full reviews of original articles were performed; 39 studies were included after first screening. Of these, 20 were excluded because of insufficient data on total fat consumption or duplication. By our criteria, 19 studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Michaud et al., 2003; Lin et al., 2005; Nothlings et al., 2005; Chan et al., 2007; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Zhang et al., 2009; Lucenteforte et al., 2010; Arem et al., 2013; Jansen et al., 2014) were finally eligible for inclusion in the meta-analysis (Fig. 1). Characteristics of the 19 included studies are shown in Table 1. All included studies, which included 6159 cases and 1 068 476 noncases, were published between 1990 and 2014, consisting of six cohort studies (Michaud et al., 2003; Nothlings et al., 2005; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Arem et al., 2013) and 13 case–control studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Lin et al., 2005; Chan et al., 2007; Zhang et al., 2009; Lucenteforte et al., 2010; Jansen et al., 2014). Among these studies, 10 were carried out in North America (Farrow and Davis, 1990; Olsen et al., 1991; Ghadirian et al., 1995; Michaud et al., 2003; Nothlings et al., 2005; Chan et al., 2007; Thiebaut et al., 2009; Zhang et al., 2009; Arem et al., 2013; Jansen et al., 2014), six in Europe (Bueno et al., 1990; Zatonski et al., 1991; Kalapothaki et al., 1993; Heinen et al., 2009; Meinhold et al., 2009; Lucenteforte et al., 2010), two in Asia (Ji et al., 1995; Lin et al., 2005), and one in Australia (Baghurst et al., 1991). Sample sizes of cohort studies ranged from 27 035 (Michaud et al., 2003) to 525 473 (Thiebaut et al., 2009), and the number of pancreatic cancer cases varied from 104 (Baghurst et al., 1991) to 1337 (Thiebaut et al., 2009). Among the case–control studies, control individuals were drawn from the general
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280 European Journal of Cancer Prevention 2015, Vol 24 No 4
Fig. 1 Records identified through PubMed search (n = 2384) Records were excluded based on screening of titles (n = 1975) or abstracts using general criteria (n = 370) Records obtained from title/abstract screening (n = 39)
Records obtained from full-text screening (n = 19)
Records were excluded because of: (a) No usable risk estimates (n = 13); (b) Duplicate reports on the same study populations (n = 6); (c) Collaborative study based on 5 previously published case−control studies (n = 1)
No additional records obtained from checking reference lists of retrieved articles
Studies included in meta-analysis (n = 19) Case−control studies (n = 13) Cohort studies (n = 6)
References searched and selection of studies in the meta-analysis.
population in 11 studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Lin et al., 2005; Chan et al., 2007; Zhang et al., 2009) and from hospitals in two studies (Lucenteforte et al., 2010; Jansen et al., 2014). All studies adjusted for age and smoking, and a majority of studies also used an energy-density or an energy-residual model to adjust for total energy intake (n = 14) or adjusted for the history of DM (n = 11). Fewer studies controlled for BMI (n = 7) or alcohol drinking (n = 4). Total fat consumption
Nineteen studies examined the association between consumption of total fat and risk for pancreatic cancer. The overall RR indicated no statistically significant association between total fat consumption and pancreatic cancer risk (RR = 1.04; 95% CI = 0.90–1.20) (Fig. 2). There was statistically significant heterogeneity among these studies (P for heterogeneity = 0.001; I2 = 57.3%). We found no evidence of publication bias with Egger’s test (P for bias = 0.686) or with Begg’s test (P for bias = 0.944). In a sensitivity analysis in which we removed one study at a time and analyzed the rest, the RRs ranged from 1.00 (95% CI = 0.87–1.15; P for heterogeneity = 0.011, I2 = 48.4%) after excluding the study by Chan et al. (2007) to 1.08 (95% CI = 0.94–1.23; P for heterogeneity = 0.007, I2 = 51.1%) after excluding the study by Ji et al. (1995). Moreover, we carried out the analysis without using the
fixed-effects model to pool the risk estimates for the different sexes (Ji et al., 1995); the results were similar in general (RR = 1.03; 95% CI = 0.90–1.18; P for heterogeneity = 0.002, I2 = 55.2%). Subgroup analysis
When stratified by fat source, we yielded nonsignificant results both for vegetable fat (RR = 0.95, 95% CI: = 0.78–1.15, P for heterogeneity = 0.519, I2 = 0%) and animal fat (RR = 1.21, 95% CI = 0.79–1.87, P for heterogeneity = 0.004, I2 = 77.5%). Although the results of analyses stratified by other study characteristics or after adjustment for confounders (e.g. BMI, DM, and alcohol drinking) were consistent in showing no association between intake of total fat and pancreatic cancer risk, we observed little or no heterogeneity in studies carried out in Asia, unadjusted for BMI, and adjusted for alcohol drinking (Table 2). In addition, in meta-regression analyses, there was no evidence of heterogeneity between the subgroups with and without adjustment for these confounders or important risk factors (Table 2).
Discussion This meta-analysis showed no statistically significant association between total fat consumption and pancreatic cancer. In addition, there was no evidence of an association in the stratified analyses on the basis of fat source, study design, geographic location, validated FFQ, whether using energy-adjusted models, and whether adjusted for confounders or important risk factors (Table 2). The incidence of pancreatic cancer varies globally. The most recent report from the International Agency for Research on Cancer demonstrated that the agestandardized incidence rate of pancreatic cancer in Asian countries (3.2 cases/100 000) was significantly lower than those in North America (7.4 cases/100 000) and Europe (6.8 cases/100 000) (Ferlay et al., 2012). One of the explanations for the difference may be attributed to different dietary patterns between Caucasians and Asians (Nkondjock et al., 2005; Chan et al., 2013). Compared with Asians, Caucasians tend to have a diet that is rich in meat and/or fat. Although we did not find any significant association between dietary fat and pancreatic cancer risk in Caucasians in this meta-analysis, the point estimates for Caucasians were higher than those for Asians (Table 2). By comparison, Lucenteforte et al. (2010) reported a median intake of 79.2 g/day total fat in 652 controls. A study in Japan (Lin et al., 2005) estimated the lower limit of the highest tertile of total fat intake to be 58.6 g/day among 218 controls. Apparently, there is a difference in total fat intake between these two populations, which might partly support the explanation. When this meta-analysis was stratified by the use of energy-adjusted models to adjust for the total energy intake, the results of meta-regression provided no evidence that the aforementioned variable was the source of
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USA Japan China
Canada Greece
Poland
Chan et al. (2007), PC-CS Lin et al. (2005), PC-CS Ji et al. (1995), PC-CS
Ghadirian et al. (1995), PC-CS Kalapothaki et al. (1993), PC-CS
Zatonski et al. (1991), PC-CS
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. F
1976–1998
1993–2001
1993–2009 1995–2003 1985–2004 1986–1999
1982–1986
1980–1983 1984–1987 1984–1988
1985–1988
1984–1988 1991–1992
1995–1999 2000–2002 1990–1993
1994–1998
1991–2008
2004–2009
Study period
178
482
411 1337 305 350
148
212 104 164
110
179 181
532 109 451
186
326
384
Number of cases
88 802
190 545
111 416 525 473 27 035 120 852
188
220 253 480
195
239 181
1701 218 1552
554
652
983
Number of controls/ size of cohort
FFQ
FFQ
DHQ FFQ/DHQ FFQ FFQ
DHQ
FFQ FFQ FFQ
DHQ
FFQ FFQ
Validated FFQ Validated FFQ FFQ
Validated FFQ
Validated FFQ
Validated FFQ
Dietary assessment
Age, sex, calories, DM, BMI, and smoking status Age, sex, total energy intake, smoking history, BMI, and DM Age, BMI, number of cigarettes/day, years of smoking, total calories, and DM Age, sex, energy, smoking, DM, history of hypertension, BMI, and vegetable and fruit intake Age, sex, time on study, ethnicity, DM, familial history of pancreatic cancer, smoking status, and energy intake Age, pack-years of smoking, BMI, DM, caloric intake, height, PA, menopausal status, and glycemic load intake
Age, smoking, education, and calorie-adjusted calcium intake, calorie-adjusted protein intake
Age, sex, cigarette smoking, usual adult BMI, DM, energy intake, number of drinks of alcohol/week, and daily servings of total fruit and vegetable consumption Age, sex, center and year of interview, education, tobacco smoking, DM, and total energy intake Age, sex, race, education, cigarette smoking, and alcohol intake, PA, fiber, and vegetable and fruit intake Age, sex, BMI, race, education, smoking, DM, and energy intake (residual model) Age, sex, energy intake, and smoking Age, income, smoking, green tea drinking (female only), response status, total calories Age, sex, lifetime cigarette consumption, response status, and total energy intake Age, sex, hospital, past residence, years of schooling, cigarette smoking, DM, energy intake Age, sex, area of residence, cigarette lifetime consumption, and other sources of calories Age, total energy, cigarette usage, alcohol consumption, DM, and educational level Age, sex, total energy, and alcohol and tobacco usage Age, total smoking, and response status
Matched/adjusted factors
CS, cohort study; DHQ, dietary history questionnaire; DM, diabetes mellitus; F, female; FFQ, food frequency questionnaire; HC-CS, hospital-based case–control study; M, male; PA, physical activity; PC-CS, population-based case–control study.
USA
Michaud et al. (2003), CS
M/F M/F M M/F
USA USA Finland The Netherlands M/F
M/F
USA
USA
M M/F M/F
M/F
M/F
USA Australia The Netherlands
Nothlings et al. (2005), CS
Olsen et al. (1991), PC-CS Baghurst et al. (1991), PC-CS Bueno de Mesquita et al. (1990), PC-CS Farrow and Davis (1990), PC-CS Cohort studies Arem et al. (2013), CS Thiebaut et al. (2009), CS Meinhold et al. (2009), CS Heinen et al. (2009), CS
M/F
USA M/F M/F M/F
M/F
M/F
Italy
USA
Sex
Lucenteforte et al. (2010), HC-CS Zhang et al. (2009), PC-CS
Case–control studies Jansen et al. (2014), HC-CS
Country
Characteristics of studies included in the meta-analysis
References
Table 1
Total fat consumption and pancreatic cancer Shen and Yao 281
282 European Journal of Cancer Prevention 2015, Vol 24 No 4
Fig. 2
References
RR (95% CI)
Case−control studies Jansen et al. (2014)
1.08 (0.71 − 1.64)
Lucenteforte et al. (2010)
0.84 (0.51 − 1.38)
Zhang et al. (2009)
2.16 (0.91 − 5.27)
Chan et al. (2007)
1.60 (1.20 − 2.10)
Lin et al. (2005)
0.97 (0.51 − 1.82)
Ji et al. (1995)
0.65 (0.46 − 0.94)
Ghadirian et al. (1995)
2.24 (0.74 − 6.73)
Kalapothaki et al. (1993)
1.03 (0.87 − 1.22)
Zatonski et al. (1991)
0.29 (0.08 − 1.05)
Olsen et al. (1991)
1.10 (0.60 − 2.00)
Baghurst et al. (1991)
1.25 (0.61 − 2.54)
Bueno de Mesquita et al. (1990)
0.49 (0.17 − 1.47)
Farrow and Davis (1990)
0.90 (0.50 − 1.80)
Subtotal (I
2 = 55.8%,
P = 0.007)
1.03 (0.83 − 1.27)
Cohort studies Arem et al. (2013)
0.70 (0.51 − 0.95)
Thiebaut et al. (2009)
1.23 (1.03 − 1.46)
Meinhold et al. (2009)
1.52 (1.04 − 2.22)
Heinen et al. (2009)
0.95 (0.67 − 1.34)
Nothlings et al. (2005)
0.95 (0.78 − 1.15)
Michaud et al. (2003) Subtotal (I2 = 66.7%, P = 0.010)
1.24 (0.70 − 2.20)
Overall (I2 = 57.3%, P = 0.001)
1.04 (0.90 − 1.20)
1.05 (0.85 − 1.29)
Note: Weights are from random effects analysis 0.5
1 Relative risk
2
Forest plot (random effects model) of total fat consumption and pancreatic cancer risk by study design. Squares indicate study-specific RRs (size of the square reflects the study-specific statistical weight); horizontal lines indicate 95% CIs; diamond indicates the summary RR estimate with its 95% CI. CI, confidence interval; RR, relative risk.
heterogeneity (Table 2). Recently, four common models have been used to adjust for the total energy intake in the field of nutrient epidemiology (Willett et al., 1997). Compared with the risk estimates of the other three models, the results of the standard multivariate model might differ when analyzing the total fat intake, one of the three contributors to the energy source, which was highly associated with total energy intake (Brown et al., 1994; Willett et al., 1997). In addition, the residual and nutrient density models generally have more power to detect associations when the exposure variable is categorized (Brown et al., 1994; Willett et al., 1997). Thus, control for confounding by total energy in the literature
dealing with exposures that are highly correlated with total energy remains an important analytical issue for future studies. When analysis was stratified by different fat sources, although there was no statistically significant result, the point estimate for animal fat was slightly higher than that of vegetable fat (1.21 vs. 0.95). As limited studies were included, whether the associations were different between these two sources of fat needs further investigation. Although the exact biologic mechanisms underlying the association between total fat consumption and increased
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Total fat consumption and pancreatic cancer Shen and Yao 283
Table 2
Summary risk estimates of the association between total fat intake and pancreatic cancer risk P for heterogeneity
Subgroups
Number of studies
Total fat intake 19 Fat source Vegetable fat 4 Animal fat 4 Study design Cohort studies 13 Case–control studies 6 Geographic location North America 10 Europe 6 Asia 2 Energy-adjusted modelsa Yes 14 No 5 Validated FFQ Yes 10 No 9 Adjustment for confounders or risk factors BMI Yes 7 No 12 Diabetes mellitus Yes 11 No 8 Alcohol drinking Yes 4 No 15
2
Summary RR (95% CI)
I (%)
1.04 (0.90–1.20)
57.3
Within each subgroup 0.001
0.95 (0.78–1.15) 1.21 (0.79–1.87)
0 77.5
0.519 0.004
1.05 (0.85–1.29) 1.03 (0.83–1.27)
66.7 55.8
0.010 0.007
1.13 (0.93–1.37) 0.98 (0.76–1.26) 0.73 (0.51–1.03)
61.1 52.1 13.5
0.006 0.064 0.282
1.06 (0.93–1.21) 1.04 (0.57–1.91)
50.8 70.9
0.015 0.008
1.16 (0.97–1.39) 0.90 (0.71–1.13)
44.5 66.2
0.063 0.003
1.15 (0.92–1.43) 0.94 (0.80–1.11)
68.4 33.9
0.004 0.119
1.08 (0.94–1.25) 0.93 (0.64–1.36)
58.3 51.6
0.008 0.044
1.20 (0.90–1.61) 1.01 (0.86–1.18)
0 64.4
0.561
Research paper
Total fat consumption and pancreatic cancer risk: a meta-analysis of epidemiologic studies Qi-Wei Shen and Qi-Yuan Yao Fat consumption has been hypothesized to influence pancreatic cancer risk, but the results of epidemiologic studies have been controversial. We conducted a systematic review and meta-analysis of case–control and cohort studies to investigate this issue. Relevant published studies were identified by searching MEDLINE (PubMed) through February 2014. Two authors (Q.-W.S. and Q.-Y.Y.) independently assessed eligibility and the extracted data. Study-specific relative risks (RRs) were pooled using a random-effects model. We also carried out heterogeneity and publication bias analyses. Six cohort and 13 case–control studies with 6159 pancreatic cancer cases and 1 068 476 noncases were included in this metaanalysis. The summary RR for pancreatic cancer for the highest versus lowest intake was 1.04 [95% confidence interval (CI) = 0.90–1.20, I2 = 57.3%, P for heterogeneity = 0.001] for total fat. In addition, when separately analyzed by study design, case–control (RR = 1.03, 95% CI = 0.83–1.27, I2 = 55.8%, P for heterogeneity = 0.007) and cohort studies (RR = 1.05, 95% CI = 0.85–1.29, I2 = 66.7%, P for heterogeneity = 0.010) yielded similar results. Furthermore, no statistically
Introduction It is estimated that ∼ 300 000 new pancreatic cancer cases were diagnosed in 2012 worldwide (Ferlay et al., 2012). Because of the few early symptoms and poor response to therapeutic modalities (Li et al., 2004), pancreatic cancer has a high fatality rate, with 5-year survival rate less than 6% and survival on average only 6 months after diagnosis (Aune et al., 2012). Because of the poor prognosis and the minimal impact of conventional treatment methods (Li et al., 2004), it is extremely important to identify modifiable risk factors that may lead to the primary prevention of pancreatic cancer through alteration of these factors. However, except for cigarette smoking, diabetes mellitus (DM), and obesity, other modifiable risk factors of this disease are not well established (Anderson et al., 2006; Raimondi et al., 2009). In recent times, extensive research has focused on the role of diet, which might be a modifiable factor in the development of pancreatic cancer (Johnson and de Mejia, 2011). Various dietary factors have been investigated as potential risk factors in pancreatic cancer. In particular, total fat consumption was hypothesized to be associated with elevated pancreatic cancer risk in experimental studies (Birt et al., 1989; Smith et al., 1990;
significant associations were observed in the subgroup analyses on the basis of fat source, geographic location, whether using energy-adjusted models, and whether adjusted for several potential confounders and important risk factors. There was no evidence of publication bias or significant heterogeneity between subgroups on metaregression analyses. The results of this meta-analysis do not support an independent association between diets high in total fat and pancreatic cancer risk. European Journal of Cancer Prevention 24:278–285 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. European Journal of Cancer Prevention 2015, 24:278–285 Keywords: epidemiologic studies, fat, meta-analysis, pancreatic neoplasms Department of General Surgery, Center for Obesity and Metabolic Surgery, Huashan Hospital, Fudan University, Shanghai, China Correspondence to Qi-Yuan Yao, Department of General Surgery, Center for Obesity and Metabolic Surgery, Huashan Hospital, Fudan University, No. 12, Urumqi Zhong Road, Shanghai 200040, People’s Republic of China Tel: + 86 21 52887055; fax: + 86 21 62489191; e-mail: [email protected] Received 16 April 2014 Accepted 30 June 2014
Andren-Sandberg et al., 1999; Woutersen et al., 1999). In addition, an ecological study found statistically significant positive associations between animal fat consumption and pancreatic cancer after adjusting for the population’s smoking, energy, meat and vegetable consumption, and alcohol intake (Zhang et al., 2005). However, evidence from epidemiologic studies was still controversial (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Michaud et al., 2003; Lin et al., 2005; Nothlings et al., 2005; Chan et al., 2007; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Zhang et al., 2009; Lucenteforte et al., 2010; Arem et al., 2013; Jansen et al., 2014). Although the second report from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR), 2007 and several previous reviews have focused on this issue (Woutersen et al., 1999; Anderson et al., 2006), only a few additional epidemiologic studies [especially large prospectively designed studies (Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Arem et al., 2013)] have been published so far; thus, we carried out this systematic review and meta-analysis with the aim to clarify whether total fat intake is associated with pancreatic cancer risk.
0959-8278 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
DOI: 10.1097/CEJ.0000000000000073
Total fat consumption and pancreatic cancer Shen and Yao 279
Materials and methods Search strategy and study selection
To identify epidemiologic studies on total fat consumption and pancreatic cancer risk, we conducted a literature search in MEDLINE (PubMed) for articles published in any language from database initiation until February 2014. The following search terms were used: ‘diet’ or ‘dietary’ or ‘fat’ and ‘pancreatic’ or ‘pancreas’ and ‘cancer’ or ‘neoplasm’. In addition, we searched the reference lists of retrieved articles to identify further studies. We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for conducting and reporting meta-analyses (Moher et al., 2009). The following criteria were applied for literature selection: (a) the study had a case–control, nested case–control, case–cohort, or cohort design; (b) the study evaluated the relationship between total fat intake and incidence of pancreatic cancer; (c) investigators provided the following data: the number of cases, the total number of patients, and odds ratio (OR), relative risks (RRs), or hazard ratios (HRs) with 95% confidence intervals (CIs) for categories of total fat intake; and (d) the study was carried out in humans. If multiple articles were on the same study population, the one with most complete design or larger sample size was finally selected. Data extraction
Two reviewers (Q.-W.S. and Q.-Y.Y.) independently extracted and tabulated all data and any discrepancies were resolved by discussion. The following data were extracted from each publication: first author, year of publication, study design and region, study sample size (number of cases and controls or cohort size), exposure and outcome assessment including total fat intake categories, study-specific adjusted estimates with their 95% CIs for the highest versus lowest total fat intake, and factors matched by or adjusted for in the design or data analysis. Data synthesis and analysis
As the incidence of pancreatic cancer is low, the OR from case–control studies is approximated to the RR. In addition, we included the RR or hazard ratio based on the Cox proportional hazard model in the statistical analysis. Thus, we report all effect sizes as RR for simplicity. Between-study heterogeneity was assessed by the I2 statistic (I2 < 30%, no between-study heterogeneity or marginal between-study heterogeneity; I2 = 30–75%, mild heterogeneity; I2 > 75%, notable heterogeneity) (Higgins and Thompson, 2002). We combined the RRs from each study by the method of DerSimonian and Laird (1986) using the assumptions of a random-effects model, which takes into account both within-study and between-study variability. Ji et al. (1995) reported results separately for male and female patients and we pooled the results using a fixed-effects model to obtain an overall
combined estimate before combining with the rest of the studies. A P value less than 0.05 for meta-regression was considered representative of significant statistical difference between subgroups. In addition, publication bias was evaluated using Egger’s (Egger et al., 1997) and Begg’s methods (Begg and Mazumdar, 1994) and funnel plots. Finally, sensitivity analyses were executed by deleting each study in turn to reflect the influence of individual data sets on the overall estimate. Statistical analyses were performed with Stata (version 11; StataCorp, College Station, Texas, USA). P-values were two-sided and P less than 0.05 was considered statistically significant.
Results Literature search and study characteristics
The literature searches yielded 2384 studies. Initial screening of abstracts and full reviews of original articles were performed; 39 studies were included after first screening. Of these, 20 were excluded because of insufficient data on total fat consumption or duplication. By our criteria, 19 studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Michaud et al., 2003; Lin et al., 2005; Nothlings et al., 2005; Chan et al., 2007; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Zhang et al., 2009; Lucenteforte et al., 2010; Arem et al., 2013; Jansen et al., 2014) were finally eligible for inclusion in the meta-analysis (Fig. 1). Characteristics of the 19 included studies are shown in Table 1. All included studies, which included 6159 cases and 1 068 476 noncases, were published between 1990 and 2014, consisting of six cohort studies (Michaud et al., 2003; Nothlings et al., 2005; Heinen et al., 2009; Meinhold et al., 2009; Thiebaut et al., 2009; Arem et al., 2013) and 13 case–control studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Lin et al., 2005; Chan et al., 2007; Zhang et al., 2009; Lucenteforte et al., 2010; Jansen et al., 2014). Among these studies, 10 were carried out in North America (Farrow and Davis, 1990; Olsen et al., 1991; Ghadirian et al., 1995; Michaud et al., 2003; Nothlings et al., 2005; Chan et al., 2007; Thiebaut et al., 2009; Zhang et al., 2009; Arem et al., 2013; Jansen et al., 2014), six in Europe (Bueno et al., 1990; Zatonski et al., 1991; Kalapothaki et al., 1993; Heinen et al., 2009; Meinhold et al., 2009; Lucenteforte et al., 2010), two in Asia (Ji et al., 1995; Lin et al., 2005), and one in Australia (Baghurst et al., 1991). Sample sizes of cohort studies ranged from 27 035 (Michaud et al., 2003) to 525 473 (Thiebaut et al., 2009), and the number of pancreatic cancer cases varied from 104 (Baghurst et al., 1991) to 1337 (Thiebaut et al., 2009). Among the case–control studies, control individuals were drawn from the general
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280 European Journal of Cancer Prevention 2015, Vol 24 No 4
Fig. 1 Records identified through PubMed search (n = 2384) Records were excluded based on screening of titles (n = 1975) or abstracts using general criteria (n = 370) Records obtained from title/abstract screening (n = 39)
Records obtained from full-text screening (n = 19)
Records were excluded because of: (a) No usable risk estimates (n = 13); (b) Duplicate reports on the same study populations (n = 6); (c) Collaborative study based on 5 previously published case−control studies (n = 1)
No additional records obtained from checking reference lists of retrieved articles
Studies included in meta-analysis (n = 19) Case−control studies (n = 13) Cohort studies (n = 6)
References searched and selection of studies in the meta-analysis.
population in 11 studies (Bueno et al., 1990; Farrow and Davis, 1990; Baghurst et al., 1991; Olsen et al., 1991; Zatonski et al., 1991; Kalapothaki et al., 1993; Ghadirian et al., 1995; Ji et al., 1995; Lin et al., 2005; Chan et al., 2007; Zhang et al., 2009) and from hospitals in two studies (Lucenteforte et al., 2010; Jansen et al., 2014). All studies adjusted for age and smoking, and a majority of studies also used an energy-density or an energy-residual model to adjust for total energy intake (n = 14) or adjusted for the history of DM (n = 11). Fewer studies controlled for BMI (n = 7) or alcohol drinking (n = 4). Total fat consumption
Nineteen studies examined the association between consumption of total fat and risk for pancreatic cancer. The overall RR indicated no statistically significant association between total fat consumption and pancreatic cancer risk (RR = 1.04; 95% CI = 0.90–1.20) (Fig. 2). There was statistically significant heterogeneity among these studies (P for heterogeneity = 0.001; I2 = 57.3%). We found no evidence of publication bias with Egger’s test (P for bias = 0.686) or with Begg’s test (P for bias = 0.944). In a sensitivity analysis in which we removed one study at a time and analyzed the rest, the RRs ranged from 1.00 (95% CI = 0.87–1.15; P for heterogeneity = 0.011, I2 = 48.4%) after excluding the study by Chan et al. (2007) to 1.08 (95% CI = 0.94–1.23; P for heterogeneity = 0.007, I2 = 51.1%) after excluding the study by Ji et al. (1995). Moreover, we carried out the analysis without using the
fixed-effects model to pool the risk estimates for the different sexes (Ji et al., 1995); the results were similar in general (RR = 1.03; 95% CI = 0.90–1.18; P for heterogeneity = 0.002, I2 = 55.2%). Subgroup analysis
When stratified by fat source, we yielded nonsignificant results both for vegetable fat (RR = 0.95, 95% CI: = 0.78–1.15, P for heterogeneity = 0.519, I2 = 0%) and animal fat (RR = 1.21, 95% CI = 0.79–1.87, P for heterogeneity = 0.004, I2 = 77.5%). Although the results of analyses stratified by other study characteristics or after adjustment for confounders (e.g. BMI, DM, and alcohol drinking) were consistent in showing no association between intake of total fat and pancreatic cancer risk, we observed little or no heterogeneity in studies carried out in Asia, unadjusted for BMI, and adjusted for alcohol drinking (Table 2). In addition, in meta-regression analyses, there was no evidence of heterogeneity between the subgroups with and without adjustment for these confounders or important risk factors (Table 2).
Discussion This meta-analysis showed no statistically significant association between total fat consumption and pancreatic cancer. In addition, there was no evidence of an association in the stratified analyses on the basis of fat source, study design, geographic location, validated FFQ, whether using energy-adjusted models, and whether adjusted for confounders or important risk factors (Table 2). The incidence of pancreatic cancer varies globally. The most recent report from the International Agency for Research on Cancer demonstrated that the agestandardized incidence rate of pancreatic cancer in Asian countries (3.2 cases/100 000) was significantly lower than those in North America (7.4 cases/100 000) and Europe (6.8 cases/100 000) (Ferlay et al., 2012). One of the explanations for the difference may be attributed to different dietary patterns between Caucasians and Asians (Nkondjock et al., 2005; Chan et al., 2013). Compared with Asians, Caucasians tend to have a diet that is rich in meat and/or fat. Although we did not find any significant association between dietary fat and pancreatic cancer risk in Caucasians in this meta-analysis, the point estimates for Caucasians were higher than those for Asians (Table 2). By comparison, Lucenteforte et al. (2010) reported a median intake of 79.2 g/day total fat in 652 controls. A study in Japan (Lin et al., 2005) estimated the lower limit of the highest tertile of total fat intake to be 58.6 g/day among 218 controls. Apparently, there is a difference in total fat intake between these two populations, which might partly support the explanation. When this meta-analysis was stratified by the use of energy-adjusted models to adjust for the total energy intake, the results of meta-regression provided no evidence that the aforementioned variable was the source of
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USA Japan China
Canada Greece
Poland
Chan et al. (2007), PC-CS Lin et al. (2005), PC-CS Ji et al. (1995), PC-CS
Ghadirian et al. (1995), PC-CS Kalapothaki et al. (1993), PC-CS
Zatonski et al. (1991), PC-CS
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited. F
1976–1998
1993–2001
1993–2009 1995–2003 1985–2004 1986–1999
1982–1986
1980–1983 1984–1987 1984–1988
1985–1988
1984–1988 1991–1992
1995–1999 2000–2002 1990–1993
1994–1998
1991–2008
2004–2009
Study period
178
482
411 1337 305 350
148
212 104 164
110
179 181
532 109 451
186
326
384
Number of cases
88 802
190 545
111 416 525 473 27 035 120 852
188
220 253 480
195
239 181
1701 218 1552
554
652
983
Number of controls/ size of cohort
FFQ
FFQ
DHQ FFQ/DHQ FFQ FFQ
DHQ
FFQ FFQ FFQ
DHQ
FFQ FFQ
Validated FFQ Validated FFQ FFQ
Validated FFQ
Validated FFQ
Validated FFQ
Dietary assessment
Age, sex, calories, DM, BMI, and smoking status Age, sex, total energy intake, smoking history, BMI, and DM Age, BMI, number of cigarettes/day, years of smoking, total calories, and DM Age, sex, energy, smoking, DM, history of hypertension, BMI, and vegetable and fruit intake Age, sex, time on study, ethnicity, DM, familial history of pancreatic cancer, smoking status, and energy intake Age, pack-years of smoking, BMI, DM, caloric intake, height, PA, menopausal status, and glycemic load intake
Age, smoking, education, and calorie-adjusted calcium intake, calorie-adjusted protein intake
Age, sex, cigarette smoking, usual adult BMI, DM, energy intake, number of drinks of alcohol/week, and daily servings of total fruit and vegetable consumption Age, sex, center and year of interview, education, tobacco smoking, DM, and total energy intake Age, sex, race, education, cigarette smoking, and alcohol intake, PA, fiber, and vegetable and fruit intake Age, sex, BMI, race, education, smoking, DM, and energy intake (residual model) Age, sex, energy intake, and smoking Age, income, smoking, green tea drinking (female only), response status, total calories Age, sex, lifetime cigarette consumption, response status, and total energy intake Age, sex, hospital, past residence, years of schooling, cigarette smoking, DM, energy intake Age, sex, area of residence, cigarette lifetime consumption, and other sources of calories Age, total energy, cigarette usage, alcohol consumption, DM, and educational level Age, sex, total energy, and alcohol and tobacco usage Age, total smoking, and response status
Matched/adjusted factors
CS, cohort study; DHQ, dietary history questionnaire; DM, diabetes mellitus; F, female; FFQ, food frequency questionnaire; HC-CS, hospital-based case–control study; M, male; PA, physical activity; PC-CS, population-based case–control study.
USA
Michaud et al. (2003), CS
M/F M/F M M/F
USA USA Finland The Netherlands M/F
M/F
USA
USA
M M/F M/F
M/F
M/F
USA Australia The Netherlands
Nothlings et al. (2005), CS
Olsen et al. (1991), PC-CS Baghurst et al. (1991), PC-CS Bueno de Mesquita et al. (1990), PC-CS Farrow and Davis (1990), PC-CS Cohort studies Arem et al. (2013), CS Thiebaut et al. (2009), CS Meinhold et al. (2009), CS Heinen et al. (2009), CS
M/F
USA M/F M/F M/F
M/F
M/F
Italy
USA
Sex
Lucenteforte et al. (2010), HC-CS Zhang et al. (2009), PC-CS
Case–control studies Jansen et al. (2014), HC-CS
Country
Characteristics of studies included in the meta-analysis
References
Table 1
Total fat consumption and pancreatic cancer Shen and Yao 281
282 European Journal of Cancer Prevention 2015, Vol 24 No 4
Fig. 2
References
RR (95% CI)
Case−control studies Jansen et al. (2014)
1.08 (0.71 − 1.64)
Lucenteforte et al. (2010)
0.84 (0.51 − 1.38)
Zhang et al. (2009)
2.16 (0.91 − 5.27)
Chan et al. (2007)
1.60 (1.20 − 2.10)
Lin et al. (2005)
0.97 (0.51 − 1.82)
Ji et al. (1995)
0.65 (0.46 − 0.94)
Ghadirian et al. (1995)
2.24 (0.74 − 6.73)
Kalapothaki et al. (1993)
1.03 (0.87 − 1.22)
Zatonski et al. (1991)
0.29 (0.08 − 1.05)
Olsen et al. (1991)
1.10 (0.60 − 2.00)
Baghurst et al. (1991)
1.25 (0.61 − 2.54)
Bueno de Mesquita et al. (1990)
0.49 (0.17 − 1.47)
Farrow and Davis (1990)
0.90 (0.50 − 1.80)
Subtotal (I
2 = 55.8%,
P = 0.007)
1.03 (0.83 − 1.27)
Cohort studies Arem et al. (2013)
0.70 (0.51 − 0.95)
Thiebaut et al. (2009)
1.23 (1.03 − 1.46)
Meinhold et al. (2009)
1.52 (1.04 − 2.22)
Heinen et al. (2009)
0.95 (0.67 − 1.34)
Nothlings et al. (2005)
0.95 (0.78 − 1.15)
Michaud et al. (2003) Subtotal (I2 = 66.7%, P = 0.010)
1.24 (0.70 − 2.20)
Overall (I2 = 57.3%, P = 0.001)
1.04 (0.90 − 1.20)
1.05 (0.85 − 1.29)
Note: Weights are from random effects analysis 0.5
1 Relative risk
2
Forest plot (random effects model) of total fat consumption and pancreatic cancer risk by study design. Squares indicate study-specific RRs (size of the square reflects the study-specific statistical weight); horizontal lines indicate 95% CIs; diamond indicates the summary RR estimate with its 95% CI. CI, confidence interval; RR, relative risk.
heterogeneity (Table 2). Recently, four common models have been used to adjust for the total energy intake in the field of nutrient epidemiology (Willett et al., 1997). Compared with the risk estimates of the other three models, the results of the standard multivariate model might differ when analyzing the total fat intake, one of the three contributors to the energy source, which was highly associated with total energy intake (Brown et al., 1994; Willett et al., 1997). In addition, the residual and nutrient density models generally have more power to detect associations when the exposure variable is categorized (Brown et al., 1994; Willett et al., 1997). Thus, control for confounding by total energy in the literature
dealing with exposures that are highly correlated with total energy remains an important analytical issue for future studies. When analysis was stratified by different fat sources, although there was no statistically significant result, the point estimate for animal fat was slightly higher than that of vegetable fat (1.21 vs. 0.95). As limited studies were included, whether the associations were different between these two sources of fat needs further investigation. Although the exact biologic mechanisms underlying the association between total fat consumption and increased
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Total fat consumption and pancreatic cancer Shen and Yao 283
Table 2
Summary risk estimates of the association between total fat intake and pancreatic cancer risk P for heterogeneity
Subgroups
Number of studies
Total fat intake 19 Fat source Vegetable fat 4 Animal fat 4 Study design Cohort studies 13 Case–control studies 6 Geographic location North America 10 Europe 6 Asia 2 Energy-adjusted modelsa Yes 14 No 5 Validated FFQ Yes 10 No 9 Adjustment for confounders or risk factors BMI Yes 7 No 12 Diabetes mellitus Yes 11 No 8 Alcohol drinking Yes 4 No 15
2
Summary RR (95% CI)
I (%)
1.04 (0.90–1.20)
57.3
Within each subgroup 0.001
0.95 (0.78–1.15) 1.21 (0.79–1.87)
0 77.5
0.519 0.004
1.05 (0.85–1.29) 1.03 (0.83–1.27)
66.7 55.8
0.010 0.007
1.13 (0.93–1.37) 0.98 (0.76–1.26) 0.73 (0.51–1.03)
61.1 52.1 13.5
0.006 0.064 0.282
1.06 (0.93–1.21) 1.04 (0.57–1.91)
50.8 70.9
0.015 0.008
1.16 (0.97–1.39) 0.90 (0.71–1.13)
44.5 66.2
0.063 0.003
1.15 (0.92–1.43) 0.94 (0.80–1.11)
68.4 33.9
0.004 0.119
1.08 (0.94–1.25) 0.93 (0.64–1.36)
58.3 51.6
0.008 0.044
1.20 (0.90–1.61) 1.01 (0.86–1.18)
0 64.4
0.561
