Background:

African Americans have the highest incidence of pancreatic cancer of any racial/ethnic group in the United States. There is evidence that consumption of red or processed meat and foods containing saturated fats may increase the risk of pancreatic cancer, but there is limited evidence in African Americans.

Methods:

Utilizing the Black Women's Health Study (1995–2018), we prospectively investigated the associations of red and processed meat and saturated fats with incidence of pancreatic adenocarcinoma (n = 168). A food frequency questionnaire was completed by 52,706 participants in 1995 and 2001. Multivariable-adjusted HRs and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. We observed interactions with age (Pinteraction = 0.01). Thus, results were stratified at age 50 (<50, ≥50).

Results:

Based on 148 cases among women aged ≥50 years, total red meat intake was associated with a 65% increased pancreatic cancer risk (HRQ4 vs. Q1 = 1.65; 95% CI, 0.98–2.78; Ptrend = 0.05), primarily due to unprocessed red meat. There was also a nonsignificant association between total saturated fat and pancreatic cancer (HRQ4 vs. Q1 = 1.85; 95% CI, 0.92–3.72; Ptrend = 0.08). Red meat and saturated fat intakes were not associated with pancreatic cancer risk in younger women, and there was no association with processed meat in either age group.

Conclusions:

Red meat—specifically, unprocessed red meat—and saturated fat intakes were associated with an increased risk of pancreatic cancer in African-American women aged 50 and older, but not among younger women.

Impact:

The accumulating evidence—including now in African-American women—suggests that diet, a modifiable factor, plays a role in the etiology of pancreatic cancer, suggesting opportunities for prevention.

African Americans have the highest pancreatic cancer incidence and mortality rate of any racial/ethnic group in the United States, with 5-year survival of only 9.0% (1). As most pancreatic cancers present at an advanced stage with poor survival, etiologic research on pancreatic cancer is limited. Thus, there are few established modifiable risk factors for pancreatic cancer; they include cigarette smoking, high alcohol intake, and metabolic factors (type 2 diabetes and obesity). Due to the rarity of pancreatic cancer (11.7/100,000 persons), research on this disease in prospective cohorts has been limited to date, and there have been few African Americans in those studies.

A number of mechanisms linking red or processed meat to pancreatic carcinogenesis have been postulated, including saturated fat content (2–4), by-products of cooking methods (i.e., polycyclic aromatic hydrocarbons, heterocyclic amines, N-nitroso compounds, and advanced glycation endproducts; ref. 5), and increased insulin resistance (6). However, the epidemiologic literature has been inconsistent, which led the World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) Continuous Update Project to conclude that there was limited evidence suggesting consumption of red or processed meat and foods containing saturated fatty acids increased the risk of pancreatic cancer (7). Further, the WCRF/AICR Continuous Update Project reported evidence of modification by sex for red and processed meat intake, with men having a significant 21% to 43% increased risk of pancreatic cancer and women a nonsignificant 6% to 9% increased risk (7). Among the prospective cohorts, only one examined these possible associations in African Americans and reported that red meat consumption was associated with increased risk (8). These results were not stratified by sex, and processed meat or saturated fats were not examined. Until recently, African Americans have consumed the highest quantities of processed meat of any racial/ethnic group in the United States (9, 10). Thus, the relation between intake of red and processed meat and pancreatic cancer risk is of particular concern in this population. In the present study, we prospectively assessed the association of total red meat, unprocessed red meat, processed meat, and saturated fatty acids with pancreatic risk among African American women.

Study population

The Black Women's Health Study (BWHS) is an ongoing prospective cohort study, which was designed to assess risk factors for disease outcomes in African-American women (11). In 1995, 59,000 women ages 21 to 69 years were recruited by mailing questionnaires, largely to subscribers of Essence magazine. At study baseline, participants completed a self-administered questionnaire on demographics, medical history, lifestyle factors, and diet. Follow-up has been ongoing for over 20 years, and participants complete a questionnaire every 2 years either online or by mail. Follow-up is complete for 85% of potential person-years. The Institutional Review Board of Boston University (Boston, MA) approved the BWHS protocol and reviews the study annually.

Outcome

Cases for this analysis were women diagnosed with primary pancreatic adenocarcinoma (consistent with the International Classification of Diseases 10 topography codes C25.0–25.9 and morphology code 8140) from July 1, 1997 (2 years after the baseline questionnaire) through December 31, 2018. Cases were ascertained by self-report on follow-up questionnaires and linkage with cancer registries in 24 states (covering 95% of participants) and the National Death Index (NDI). The majority of pancreatic cancer cases were identified by cancer registries or NDI, as many cases were presumably too ill or had died prior to reporting the disease. Self-reported cancers were confirmed by review of hospital and cancer registry data. Four self-reported cases without available cancer registry data were also included. Participants who reported prevalent pancreatic cancer at baseline in 1995 or a diagnosis within the first 2 years were excluded (n = 5). To date, BWHS has identified 191 incident pancreatic cancer cases.

Exposure

Dietary data were collected in 1995 using the National Cancer Institute-Block short-form food frequency questionnaire (FFQ; 68 line items) and again in 2001 (85 line items; refs. 12, 13), modified slightly to include food items commonly eaten by African Americans but not previously included on the Block FFQ. The BWHS FFQ has been validated using three 24-hour recalls as criterion instruments (14). The energy-adjusted and deattenuated Pearson correlation coefficients between the FFQ and 24-hour recalls for protein and saturated fat were 0.78 and 0.63, respectively. Study participants were asked to report usual dietary intake over the past year. Of the 59,000 BWHS participants at baseline, women were excluded due to incomplete or implausible FFQs (n = 6,110, including 23 pancreatic cancer cases), prevalent pancreatic cancer or diagnosis within the first 2 years (n = 5), nonadenocarcinoma pancreatic cancer (n = 5), and death within the first 2 years (n = 174). Complete (i.e., <10 blank items) and plausible (i.e., intake ≥500 kcal/day and ≤3,800 kcal/day) FFQs were obtained for over 89% of participants (n = 52,706) in 1995. Of eligible participants that responded to the 2001 questionnaire (n = 43,114), complete and plausible FFQs were obtained for over 76% of participants (n = 33,151).

For each food, a common portion size was specified and the participant was asked to report how often she had consumed the food in the past year and the portion size of the food. The portion sizes used were small, medium, and large; in 2001, super-size was also included. A small serving was defined as half or less of the medium serving, a large serving was one and a half times the medium serving, and a super-size was twice the medium serving size. The responses for frequency of consumption ranged from “never or <1 per month” to “2 or more per day.” To calculate grams per day of red or processed meat participants consumed, serving size–adjusted frequency of intake was multiplied by the number of grams in a medium serving. One serving was estimated as 85 g, or approximately three ounces. To calculate saturated fat intake, the serving size–adjusted grams per day for each specific food was multiplied by its saturated fat content (in grams) per 100 g fresh weight edible portion of food, using DIETCALC software, version 1.4.1 (National Cancer Institute, Bethesda, MD).

Intake of processed meat included bacon, sausage, hot dogs, and lunchmeats (e.g., turkey, ham, bologna, and salami). Total red meat intake included processed and unprocessed red meat—beef (e.g., hamburgers, steak, roast, and stew) and pork (e.g., chops, roasts, and dinner ham). Total dietary saturated fat intake included butyric acid (4:0), caproic acid (6:0), caprylic acid (8:0), capric acid (10:0), lauric acid (12:0), myristic acid (14:0), palmitic acid (16:0), and stearic acid (18:0).

Meat and saturated fat intake were categorized into quartiles of grams per day, based on the distribution of intake among the complete analytic study population. Tests of linear trend were performed based on the quartile-specific medians of meat or fat intake. We calculated HRs for every 100 g per day of total and unprocessed red meat intake, 50 g per day of processed meat intake, and 10 g per day of total saturated fat intake (7). We also examined modeling total red meat intake using restricted cubic splines, to allow for a nonlinear relationship (15). However, there was a lack of evidence of nonlinearity (P ≥ 0.05). Dietary intake was examined using the cumulative average approach; thus, beginning in 2001, dietary data were averaged over 1995 and 2001 FFQ reports. This method reduces measurement error and provides a statistically more powerful test of diet–disease associations (16). For participants that did not complete the 2001 FFQ, the 1995 dietary values were carried forward.

Statistical analysis

Cox proportional hazards models, with follow-up time as the underlying time metric, were used to estimate HRs, as an approximation of the incidence ratio, and 95% confidence intervals (CI) for the associations between total red meat, unprocessed red meat, processed meat, and saturated fat intake with incidence of pancreatic cancer. Follow-up began at time of baseline questionnaire and continued until diagnosis of pancreatic cancer, death, or the end of study follow-up, whichever occurred first. Analyses were lagged by excluding the first 2 years of follow-up as women diagnosed with pancreatic cancer during that period may have already altered their dietary habits due to underlying disease. The proportional hazards assumption was tested using an interaction term between meat and saturated fat intakes (defined as quartiles) and age. We observed interaction between total red meat and age (whether continuous or dichotomized at age 50, P < 0.05). Thus, we stratified the analyses by age (<50, ≥50), based on prior literature suggesting etiologic differences in earlier versus later-onset pancreatic cancer (17–19). Women <50 years at baseline contributed person-time to the <50 years of age stratum until they reached age 50, at which time they contributed person-time to the ≥50 years of age stratum. The sample size of women <50 years of age was quite limited (n = 20 cases; ref. 20); thus, results for women <50 years of age are presented in Supplementary Tables S1 and S2.

All models were stratified by continuous age and time-period (2-year questionnaire cycle), assuming that the baseline hazard differed by age and period of follow-up; total energy intake (kcal/day) was included as an a priori confounder. Additional potential covariates included body mass index (BMI; <25, 25–29.9, ≥30 kg/m2), history of type 2 diabetes, alcohol intake (never, past, current), cigarette smoking (never, current, former; pack-years), vigorous physical activity (none, <5 hours/week, ≥5 hours/week), and education (≤12, 13–15, 16, ≥17 years). If the log(HR) changed by ≥10% due to variable elimination, the variable was considered a confounder and remained in the model (21); only cigarette smoking met this criterion and was included in final models. Models were adjusted for total energy intake utilizing the standard multivariate approach for ease of interpretation. In a sensitivity analysis that utilized the residual and nutrient density models, results did not differ (16).

Effect measure modification by BMI, diabetes, alcohol intake, cigarette smoking, and vigorous physical activity was assessed using likelihood ratio tests comparing regression models with and without a multiplicative term (22). We found no evidence of effect measure modification (P ≥ 0.05). All P values are two-sided. Statistical analyses were conducted using SAS version 9.4 (SAS Institute).

After exclusions, the analysis included 168 incident pancreatic cancer cases, with an average of 13.0 years of follow-up. Women with the highest intake of total red meat (Quartile 4) consumed an average of 91.0 g/day, whereas women with the lowest intake (Quartile 1) consumed an average of 9.0 g/day (Table 1). Compared with women consuming the lowest amount, women consuming the highest amount of total red meat were more likely at baseline to be younger, have a higher BMI, smoke cigarettes, drink alcohol, have lower educational attainment, be inactive, have a history of type 2 diabetes, and have higher total energy intake.

Table 1.

Baseline characteristicsa of study participants by quartiles of total red meat consumption, BWHS.

Total red meat quartiles (g/day)
0–16.80 (n = 13,175)16.81–32.38 (n = 13,179)32.39–55.84 (n = 13,176)55.84–516.59 (n = 13,176)
Age, years (mean ± SD) 40.3 ± 11.0 39.6 ± 10.8 38.6 ± 10.5 37.0 ± 10.2 
BMI, kg/m2 (mean ± SD) 26.6 ± 5.6 27.6 ± 6.3 28.3 ± 6.7 29.4 ± 7.6 
Smoking status (%) 
 Current 10 14 17 21 
 Past 21 20 20 19 
 Never 69 66 64 60 
Smoking,b pack-years (mean ± SD) 3.4 ± 8.2 4.0 ± 9.1 4.5 ± 9.6 5.2 ± 10.4 
Alcohol intake (%) 
 Never 61 58 54 51 
 Past 14 14 15 15 
 Current 24 28 31 34 
 Missing 
Education, years (%) 
 ≤12 13 17 20 23 
 13–15 32 35 37 40 
 16 27 26 23 22 
 ≥17 28 23 20 16 
Vigorous activity, hours/week (%) 
 None 24 30 34 39 
 <5 53 53 51 48 
 ≥5 19 13 11 10 
 Missing 
Ever diagnosed with diabetes (%) 
 Yes 
 No 97 96 96 94 
Energy, kcal/day (mean ± SD) 1,146 ± 495 1,262 ± 531 1,478 ± 563 2,009 ± 670 
Total red meat intake, g/day (mean ± SD) 9.0 ± 4.8 24.4 ± 4.5 42.8 ± 6.7 91.0 ± 37.1 
Unprocessed red meat intake, g/day (mean ± SD) 7.1 ± 4.3 18.6 ± 5.6 32.3 ± 9.0 69.7 ± 34.0 
Processed meat intake, g/day (mean ± SD) 2.1 ± 2.5 6.4 ± 5.2 11.9 ± 8.6 23.9 ± 20.2 
Total saturated fat, g/day (mean ± SD) 11.8 ± 6.4 14.2 ± 6.8 18.0 ± 7.5 26.8 ± 10.0 
 Butyric acid (4:0) 0.2 ± 0.2 0.2 ± 0.2 0.3 ± 0.2 0.4 ± 0.3 
 Caproic acid (6:0) 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 0.2 ± 0.1 
 Caprylic acid (8:0) 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 
 Capric acid (10:0) 0.2 ± 0.1 0.2 ± 0.2 0.2 ± 0.2 0.3 ± 0.2 
 Lauric acid (12:0) 0.3 ± 0.2 0.3 ± 0.3 0.4 ± 0.3 0.6 ± 0.3 
 Myristic acid (14:0) 0.9 ± 0.7 1.1 ± 0.7 1.3 ± 0.8 1.9 ± 0.9 
 Palmitic acid (16:0) 6.7 ± 3.4 8.0 ± 3.5 10.1 ± 3.9 15.2 ± 5.3 
 Stearic acid (18:0) 3.0 ± 1.6 3.7 ± 1.7 4.8 ± 1.9 7.2 ± 2.6 
Total red meat quartiles (g/day)
0–16.80 (n = 13,175)16.81–32.38 (n = 13,179)32.39–55.84 (n = 13,176)55.84–516.59 (n = 13,176)
Age, years (mean ± SD) 40.3 ± 11.0 39.6 ± 10.8 38.6 ± 10.5 37.0 ± 10.2 
BMI, kg/m2 (mean ± SD) 26.6 ± 5.6 27.6 ± 6.3 28.3 ± 6.7 29.4 ± 7.6 
Smoking status (%) 
 Current 10 14 17 21 
 Past 21 20 20 19 
 Never 69 66 64 60 
Smoking,b pack-years (mean ± SD) 3.4 ± 8.2 4.0 ± 9.1 4.5 ± 9.6 5.2 ± 10.4 
Alcohol intake (%) 
 Never 61 58 54 51 
 Past 14 14 15 15 
 Current 24 28 31 34 
 Missing 
Education, years (%) 
 ≤12 13 17 20 23 
 13–15 32 35 37 40 
 16 27 26 23 22 
 ≥17 28 23 20 16 
Vigorous activity, hours/week (%) 
 None 24 30 34 39 
 <5 53 53 51 48 
 ≥5 19 13 11 10 
 Missing 
Ever diagnosed with diabetes (%) 
 Yes 
 No 97 96 96 94 
Energy, kcal/day (mean ± SD) 1,146 ± 495 1,262 ± 531 1,478 ± 563 2,009 ± 670 
Total red meat intake, g/day (mean ± SD) 9.0 ± 4.8 24.4 ± 4.5 42.8 ± 6.7 91.0 ± 37.1 
Unprocessed red meat intake, g/day (mean ± SD) 7.1 ± 4.3 18.6 ± 5.6 32.3 ± 9.0 69.7 ± 34.0 
Processed meat intake, g/day (mean ± SD) 2.1 ± 2.5 6.4 ± 5.2 11.9 ± 8.6 23.9 ± 20.2 
Total saturated fat, g/day (mean ± SD) 11.8 ± 6.4 14.2 ± 6.8 18.0 ± 7.5 26.8 ± 10.0 
 Butyric acid (4:0) 0.2 ± 0.2 0.2 ± 0.2 0.3 ± 0.2 0.4 ± 0.3 
 Caproic acid (6:0) 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 0.2 ± 0.1 
 Caprylic acid (8:0) 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 0.1 ± 0.1 
 Capric acid (10:0) 0.2 ± 0.1 0.2 ± 0.2 0.2 ± 0.2 0.3 ± 0.2 
 Lauric acid (12:0) 0.3 ± 0.2 0.3 ± 0.3 0.4 ± 0.3 0.6 ± 0.3 
 Myristic acid (14:0) 0.9 ± 0.7 1.1 ± 0.7 1.3 ± 0.8 1.9 ± 0.9 
 Palmitic acid (16:0) 6.7 ± 3.4 8.0 ± 3.5 10.1 ± 3.9 15.2 ± 5.3 
 Stearic acid (18:0) 3.0 ± 1.6 3.7 ± 1.7 4.8 ± 1.9 7.2 ± 2.6 

aValues are standardized to the age distribution of the study population.

bAmong current and past smokers.

Overall, there was no association between total red meat intake and pancreatic cancer risk (HRQ4 vs. Q1 = 1.19; 95% CI, 0.73–1.92; Ptrend = 0.3; Table 2). Among women aged ≥50 years, an age group that included 88% of the cases, the highest intake of total red meat was associated with a 65% increased risk of pancreatic cancer (HRQ4 vs. Q1 = 1.65; 95% CI, 0.98–2.78; Ptrend = 0.05). For every 100 g increase of total red meat intake, risk of pancreatic cancer increased by 49% (HR = 1.49; 95% CI; 0.88–2.53). In terms of servings, consuming more than three servings of total red meat per week was associated with a 36% increased risk of pancreatic cancer (HR = 1.36; 95% CI, 0.83–2.23), compared with less than once a week. Among women <50 years of age, based on 20 cases, there was no association between total red meat intake and pancreatic cancer (HRQ3-4 vs. Q1-2 = 0.48; 95% CI, 0.18–1.28; Ptrend = 0.1; Supplementary Table S1).

Table 2.

Adjusteda HRs and 95% CIs for associations of red and processed meat intake with risk of pancreatic cancer overall and among women aged ≥50 years.

All agesAge ≥50 years
IntakeNumber of casesPerson-yearsHR (95% CI)Number of casesPerson-yearsHR (95% CI)
Total red meat, g/day 
 Quartile 1 44 254,838 1.00 34 134,494 1.00 
 Quartile 2 37 255,015 0.85 (0.55–1.31) 36 126,295 1.10 (0.69–1.76) 
 Quartile 3 42 254,154 1.03 (0.67–1.60) 37 116,015 1.27 (0.79–2.05) 
 Quartile 4 45 253,078 1.19 (0.73–1.92) 41 100,147 1.65 (0.98–2.78) 
Ptrendb   0.3   0.05 
 Continuous (per 100 g/day)   1.06 (0.63–1.79)   1.49 (0.88–2.53) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces)     
  <1/week 33 161,537 1.00 24 85,053 1.00 
  ≥1/week–≤3/week 52 393,753 0.65 (0.42–1.01) 49 196,307 0.87 (0.53–1.42) 
  >3/week 83 461,794 0.97 (0.63–1.51) 75 195,592 1.36 (0.83–2.23) 
Unprocessed red meat, g/day       
 Quartile 1 48 254,552 1.00 38 134,834 1.00 
 Quartile 2 35 254,457 0.74 (0.48–1.14) 34 126,099 0.93 (0.59–1.49) 
 Quartile 3 43 254,606 0.96 (0.63–1.46) 37 115,828 1.12 (0.70–1.78) 
 Quartile 4 42 253,470 1.01 (0.63–1.61) 39 100,191 1.38 (0.83–2.28) 
Ptrendb   0.7   0.6 
 Continuous (per 100 g/day)   1.20 (0.66–2.19)   1.67 (0.92–3.02) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces)     
  <1/week 46 245,854 1.00 36 129,528 1.00 
  ≥1/week–≤3/week 70 457,038 0.83 (0.57–1.21) 65 221,284 1.03 (0.68–1.55) 
  >3/week 52 314,192 0.99 (0.64–1.55) 47 126,140 1.31 (0.81–2.13) 
Processed meat, g/day       
 Quartile 1 46 254,552 1.00 39 126,862 1.00 
 Quartile 2 45 254,774 0.95 (0.63–1.44) 40 124,611 1.02 (0.66–1.59) 
 Quartile 3 39 254,650 0.83 (0.54–1.28) 32 118,041 0.85 (0.53–1.36) 
 Quartile 4 38 253,109 0.79 (0.49–1.25) 37 107,437 1.01 (0.62–1.64) 
Ptrendb   0.3   1.0 
 Continuous (per 50 g/day)   0.74 (0.39–1.44)   0.99 (0.52–1.90) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces) 
  <1/week 107 646,682 1.00 92 310,085 1.00 
  ≥1/week–≤3/week 55 315,504 0.99 (0.70–1.39) 50 144,780 1.09 (0.76–1.56) 
  >3/week 54,899 0.59 (0.25–1.38) 22,087 0.77 (0.32–1.82) 
All agesAge ≥50 years
IntakeNumber of casesPerson-yearsHR (95% CI)Number of casesPerson-yearsHR (95% CI)
Total red meat, g/day 
 Quartile 1 44 254,838 1.00 34 134,494 1.00 
 Quartile 2 37 255,015 0.85 (0.55–1.31) 36 126,295 1.10 (0.69–1.76) 
 Quartile 3 42 254,154 1.03 (0.67–1.60) 37 116,015 1.27 (0.79–2.05) 
 Quartile 4 45 253,078 1.19 (0.73–1.92) 41 100,147 1.65 (0.98–2.78) 
Ptrendb   0.3   0.05 
 Continuous (per 100 g/day)   1.06 (0.63–1.79)   1.49 (0.88–2.53) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces)     
  <1/week 33 161,537 1.00 24 85,053 1.00 
  ≥1/week–≤3/week 52 393,753 0.65 (0.42–1.01) 49 196,307 0.87 (0.53–1.42) 
  >3/week 83 461,794 0.97 (0.63–1.51) 75 195,592 1.36 (0.83–2.23) 
Unprocessed red meat, g/day       
 Quartile 1 48 254,552 1.00 38 134,834 1.00 
 Quartile 2 35 254,457 0.74 (0.48–1.14) 34 126,099 0.93 (0.59–1.49) 
 Quartile 3 43 254,606 0.96 (0.63–1.46) 37 115,828 1.12 (0.70–1.78) 
 Quartile 4 42 253,470 1.01 (0.63–1.61) 39 100,191 1.38 (0.83–2.28) 
Ptrendb   0.7   0.6 
 Continuous (per 100 g/day)   1.20 (0.66–2.19)   1.67 (0.92–3.02) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces)     
  <1/week 46 245,854 1.00 36 129,528 1.00 
  ≥1/week–≤3/week 70 457,038 0.83 (0.57–1.21) 65 221,284 1.03 (0.68–1.55) 
  >3/week 52 314,192 0.99 (0.64–1.55) 47 126,140 1.31 (0.81–2.13) 
Processed meat, g/day       
 Quartile 1 46 254,552 1.00 39 126,862 1.00 
 Quartile 2 45 254,774 0.95 (0.63–1.44) 40 124,611 1.02 (0.66–1.59) 
 Quartile 3 39 254,650 0.83 (0.54–1.28) 32 118,041 0.85 (0.53–1.36) 
 Quartile 4 38 253,109 0.79 (0.49–1.25) 37 107,437 1.01 (0.62–1.64) 
Ptrendb   0.3   1.0 
 Continuous (per 50 g/day)   0.74 (0.39–1.44)   0.99 (0.52–1.90) 
 Frequency, servings per week (1 serving = 85 g ≈ 3 ounces) 
  <1/week 107 646,682 1.00 92 310,085 1.00 
  ≥1/week–≤3/week 55 315,504 0.99 (0.70–1.39) 50 144,780 1.09 (0.76–1.56) 
  >3/week 54,899 0.59 (0.25–1.38) 22,087 0.77 (0.32–1.82) 

aAdjusted for age (continuous), cigarette smoking (never, former, current; pack-years), and total energy intake (kcals).

bTests for linear trend were calculated by assigning the median of each quartile as scores.

Results were similar for unprocessed red meat as for total red meat (Table 2 and Supplementary Table S1). For women 50 years and older, the highest intake of unprocessed red meat was associated with a 38% increased risk of pancreatic cancer (HRQ4 vs. Q1 = 1.38; 95% CI, 0.83–2.28), but there was little evidence of an association at lower levels of intake (Ptrend = 0.6). For every 100 g increase of unprocessed red meat intake, risk of pancreatic cancer increased by 67% (HR = 1.67; 95% CI, 0.92–3.02).

No association was observed between processed meat intake and risk of pancreatic cancer in the full cohort (HRQ4 vs. Q1 = 0.79; 95% CI, 0.49–1.25; Ptrend = 0.3; Table 2), and there was no evidence of interaction with age (Pinteraction = 0.2; Supplementary Table S1).

The HR for highest versus lowest quartile of total saturated fat intake in the overall sample was 1.56 (95% CI, 0.82–2.99, Ptrend = 0.2; Table 3). There was not a statistically significant interaction by age (Pinteraction = 0.4), but HRs were higher among women ≥50 years of age (HRQ4 vs. Q1 = 1.85; 95% CI, 0.92–3.72; Ptrend = 0.08) than among women <50 years (HRQ3-4 vs. Q1-2 = 0.61; 95% CI, 0.18–2.06; Ptrend = 0.4). For saturated fats that have primary food sources of red meat (i.e., myristic, palmitic, and stearic acids), there was consistent, but not significant, increased pancreatic cancer risk (e.g., palmitic acid HRQ4 vs. Q1 = 1.54; 95% CI, 0.74–3.21; Ptrend = 0.3). There was little to no association between saturated fats and pancreatic cancer for women <50 years of age (Supplementary Table S2).

Table 3.

Adjusteda HRs and 95% CIs for associations of saturated fatty acid intake with risk of pancreatic cancer overall and among women aged ≥50 years.

All agesAge ≥50 years
Intake (g/day)Number of casesPerson-yearsHR (95% CI)Number of casesPerson-yearsHR (95% CI)
Total saturated fat 
 Quartile 1 39 253,786 1.00 33 133,634 1.00 
 Quartile 2 39 254,511 1.07 (0.67–1.69) 36 125,652 1.22 (0.74–1.99) 
 Quartile 3 42 253,859 1.23 (0.75–2.04) 39 115,595 1.49 (0.87–2.54) 
 Quartile 4 48 253,489 1.56 (0.82–2.99) 40 101,650 1.85 (0.92–3.72) 
Ptrendb   0.2   0.08 
 Continuous (per 10 g/day)   0.93 (0.69–1.25)   1.04 (0.76–1.42) 
Fatty acid 4:0 (butyric acid) 
 Quartile 1 42 247,181 1.00 37 137,178 1.00 
 Quartile 2 34 254,551 0.91 (0.57–1.44) 31 122,764 0.97 (0.60–1.58) 
 Quartile 3 51 256,278 1.39 (0.90–2.15) 48 114,813 1.57 (1.00–2.47) 
 Quartile 4 40 250,292 1.13 (0.68–1.89) 31 99,485 1.09 (0.63–1.90) 
Ptrendb   0.4   0.6 
 Continuous (per 1 g/day)   1.21 (0.52–2.81)   1.24 (0.50–3.07) 
Fatty acid 6:0 (caproic acid) 
 Quartile 1 37 246,535 1.00 33 133,757 1.00 
 Quartile 2 43 256,730 1.27 (0.81–1.98) 38 124,606 1.30 (0.81–2.08) 
 Quartile 3 42 253,682 1.30 (0.82–2.07) 38 113,100 1.41 (0.87–2.29) 
 Quartile 4 45 249,610 1.40 (0.85–2.31) 38 101,951 1.46 (0.85–2.49) 
Ptrendb   0.3   0.2 
 Continuous (per 1 g/day)   1.66 (0.36–7.64)   2.10 (0.41–10.69) 
Fatty acid 8:0 (caprylic acid) 
 Quartile 1 46 262,815 1.00 41 143,567 1.00 
 Quartile 2 31 236,417 0.88 (0.56–1.40) 27 110,470 0.90 (0.55–1.47) 
 Quartile 3 47 260,409 1.18 (0.77–1.81) 45 119,214 1.32 (0.84–2.06) 
 Quartile 4 44 250,820 1.13 (0.69–1.86) 35 101,567 1.09 (0.64–1.87) 
Ptrendb   0.5   0.6 
 Continuous (per 1 g/day)   1.50 (0.16–14.11)   1.80 (0.16–19.90) 
Fatty acid 10:0 (capric acid) 
 Quartile 1 44 250,201 1.00 38 136,345 1.00 
 Quartile 2 33 251,086 0.86 (0.54–1.35) 31 120,626 0.97 (0.60–1.57) 
 Quartile 3 50 257,858 1.25 (0.81–1.92) 46 116,336 1.41 (0.89–2.23) 
 Quartile 4 40 250,508 1.00 (0.59–1.67) 32 101,133 1.02 (0.58–1.79) 
Ptrendb   0.8   0.8 
 Continuous (per 1 g/day)   1.18 (0.40–3.49)   1.35 (0.42–4.28) 
Fatty acid 12:0 (lauric acid) 
 Quartile 1 38 252,089 1.00 34 138,978 1.00 
 Quartile 2 43 249,861 1.34 (0.86–2.08) 37 119,700 1.33 (0.83–2.15) 
 Quartile 3 44 258,676 1.33 (0.84–2.13) 43 115,631 1.55 (0.96–2.51) 
 Quartile 4 43 249,811 1.34 (0.78–2.29) 34 100,311 1.30 (0.73–2.33) 
Ptrendb   0.4   0.5 
 Continuous (per 1 g/day)   0.88 (0.44–1.77)   0.96 (0.46–2.03) 
Fatty acid 14:0 (myristic acid) 
 Quartile 1 40 250,653 1.00 34 137,943 1.00 
 Quartile 2 41 253,663 1.15 (0.74–1.80) 37 124,127 1.28 (0.79–2.06) 
 Quartile 3 41 254,591 1.19 (0.74–1.92) 40 114,411 1.48 (0.90–2.44) 
 Quartile 4 46 252,700 1.43 (0.82–2.49) 37 98,715 1.56 (0.86–2.83) 
Ptrendb   0.2   0.2 
 Continuous (per 1 g/day)   1.07 (0.83–1.37)   1.13 (0.87–1.47) 
Fatty acid 16:0 (palmitic acid) 
 Quartile 1 38 253,826 1.00 32 132,820 1.00 
 Quartile 2 41 254,648 1.11 (0.70–1.76) 38 125,199 1.26 (0.77–2.07) 
 Quartile 3 43 254,164 1.23 (0.74–2.04) 41 116,351 1.48 (0.86–2.55) 
 Quartile 4 46 253,565 1.40 (0.71–2.76) 37 102,327 1.54 (0.74–3.21) 
Ptrendb   0.3   0.3 
 Continuous (per 1 g/day)   0.98 (0.93–1.04)   1.00 (0.94–1.07) 
Fatty acid 18:0 (stearic acid) 
 Quartile 1 42 254,106 1.00 35 133,107 1.00 
 Quartile 2 39 253,998 0.97 (0.61–1.52) 37 124,865 1.15 (0.71–1.86) 
 Quartile 3 39 254,435 1.00 (0.61–1.66) 36 115,677 1.22 (0.71–2.09) 
 Quartile 4 47 253,341 1.26 (0.66–2.42) 39 102,924 1.52 (0.76–3.06) 
Ptrendb   0.4   0.2 
 Continuous (per 1 g/day)   0.95 (0.85–1.06)   1.00 (0.88–1.12) 
All agesAge ≥50 years
Intake (g/day)Number of casesPerson-yearsHR (95% CI)Number of casesPerson-yearsHR (95% CI)
Total saturated fat 
 Quartile 1 39 253,786 1.00 33 133,634 1.00 
 Quartile 2 39 254,511 1.07 (0.67–1.69) 36 125,652 1.22 (0.74–1.99) 
 Quartile 3 42 253,859 1.23 (0.75–2.04) 39 115,595 1.49 (0.87–2.54) 
 Quartile 4 48 253,489 1.56 (0.82–2.99) 40 101,650 1.85 (0.92–3.72) 
Ptrendb   0.2   0.08 
 Continuous (per 10 g/day)   0.93 (0.69–1.25)   1.04 (0.76–1.42) 
Fatty acid 4:0 (butyric acid) 
 Quartile 1 42 247,181 1.00 37 137,178 1.00 
 Quartile 2 34 254,551 0.91 (0.57–1.44) 31 122,764 0.97 (0.60–1.58) 
 Quartile 3 51 256,278 1.39 (0.90–2.15) 48 114,813 1.57 (1.00–2.47) 
 Quartile 4 40 250,292 1.13 (0.68–1.89) 31 99,485 1.09 (0.63–1.90) 
Ptrendb   0.4   0.6 
 Continuous (per 1 g/day)   1.21 (0.52–2.81)   1.24 (0.50–3.07) 
Fatty acid 6:0 (caproic acid) 
 Quartile 1 37 246,535 1.00 33 133,757 1.00 
 Quartile 2 43 256,730 1.27 (0.81–1.98) 38 124,606 1.30 (0.81–2.08) 
 Quartile 3 42 253,682 1.30 (0.82–2.07) 38 113,100 1.41 (0.87–2.29) 
 Quartile 4 45 249,610 1.40 (0.85–2.31) 38 101,951 1.46 (0.85–2.49) 
Ptrendb   0.3   0.2 
 Continuous (per 1 g/day)   1.66 (0.36–7.64)   2.10 (0.41–10.69) 
Fatty acid 8:0 (caprylic acid) 
 Quartile 1 46 262,815 1.00 41 143,567 1.00 
 Quartile 2 31 236,417 0.88 (0.56–1.40) 27 110,470 0.90 (0.55–1.47) 
 Quartile 3 47 260,409 1.18 (0.77–1.81) 45 119,214 1.32 (0.84–2.06) 
 Quartile 4 44 250,820 1.13 (0.69–1.86) 35 101,567 1.09 (0.64–1.87) 
Ptrendb   0.5   0.6 
 Continuous (per 1 g/day)   1.50 (0.16–14.11)   1.80 (0.16–19.90) 
Fatty acid 10:0 (capric acid) 
 Quartile 1 44 250,201 1.00 38 136,345 1.00 
 Quartile 2 33 251,086 0.86 (0.54–1.35) 31 120,626 0.97 (0.60–1.57) 
 Quartile 3 50 257,858 1.25 (0.81–1.92) 46 116,336 1.41 (0.89–2.23) 
 Quartile 4 40 250,508 1.00 (0.59–1.67) 32 101,133 1.02 (0.58–1.79) 
Ptrendb   0.8   0.8 
 Continuous (per 1 g/day)   1.18 (0.40–3.49)   1.35 (0.42–4.28) 
Fatty acid 12:0 (lauric acid) 
 Quartile 1 38 252,089 1.00 34 138,978 1.00 
 Quartile 2 43 249,861 1.34 (0.86–2.08) 37 119,700 1.33 (0.83–2.15) 
 Quartile 3 44 258,676 1.33 (0.84–2.13) 43 115,631 1.55 (0.96–2.51) 
 Quartile 4 43 249,811 1.34 (0.78–2.29) 34 100,311 1.30 (0.73–2.33) 
Ptrendb   0.4   0.5 
 Continuous (per 1 g/day)   0.88 (0.44–1.77)   0.96 (0.46–2.03) 
Fatty acid 14:0 (myristic acid) 
 Quartile 1 40 250,653 1.00 34 137,943 1.00 
 Quartile 2 41 253,663 1.15 (0.74–1.80) 37 124,127 1.28 (0.79–2.06) 
 Quartile 3 41 254,591 1.19 (0.74–1.92) 40 114,411 1.48 (0.90–2.44) 
 Quartile 4 46 252,700 1.43 (0.82–2.49) 37 98,715 1.56 (0.86–2.83) 
Ptrendb   0.2   0.2 
 Continuous (per 1 g/day)   1.07 (0.83–1.37)   1.13 (0.87–1.47) 
Fatty acid 16:0 (palmitic acid) 
 Quartile 1 38 253,826 1.00 32 132,820 1.00 
 Quartile 2 41 254,648 1.11 (0.70–1.76) 38 125,199 1.26 (0.77–2.07) 
 Quartile 3 43 254,164 1.23 (0.74–2.04) 41 116,351 1.48 (0.86–2.55) 
 Quartile 4 46 253,565 1.40 (0.71–2.76) 37 102,327 1.54 (0.74–3.21) 
Ptrendb   0.3   0.3 
 Continuous (per 1 g/day)   0.98 (0.93–1.04)   1.00 (0.94–1.07) 
Fatty acid 18:0 (stearic acid) 
 Quartile 1 42 254,106 1.00 35 133,107 1.00 
 Quartile 2 39 253,998 0.97 (0.61–1.52) 37 124,865 1.15 (0.71–1.86) 
 Quartile 3 39 254,435 1.00 (0.61–1.66) 36 115,677 1.22 (0.71–2.09) 
 Quartile 4 47 253,341 1.26 (0.66–2.42) 39 102,924 1.52 (0.76–3.06) 
Ptrendb   0.4   0.2 
 Continuous (per 1 g/day)   0.95 (0.85–1.06)   1.00 (0.88–1.12) 

aAdjusted for age (continuous), cigarette smoking (never, former, current; pack-years), and total energy intake (kcals).

bTests for linear trend were calculated by assigning the median of each quartile as scores.

In this large prospective study of African-American women, the highest consumption of total red meat intake compared with lowest was associated with a 65% increased risk of pancreatic cancer among women aged 50 and older. Little to no association was observed with processed meat. Total saturated fat was associated with increased risk of pancreatic cancer in the women aged 50 and older, but the findings were not statistically significant.

Our findings are consistent with the WCRF/AICR Continuous Update Project, which found that red meat was associated with an increased risk of pancreatic cancer (7). Specifically, the WCRF recommends limiting red meat consumption to no more than 350 to 500 g per week and eating a minimal amount of processed meats for cancer prevention. In our study, women in the highest quartile of total red meat consumption consumed a median of 558 g per week in 1995. Of note, most of the literature to date indicates that the increased risk associated with red meat is primarily confined to men. In the Continuous Update Project, red meat (per 100 g/day) was associated with a 19% increased risk of pancreatic cancer (43% in men, 6% in women; ref. 2). Only four studies in the Continuous Update Project examined the association between red meat and pancreatic cancer in women (23–26), and none of the studies stratified by race or ethnicity. Another recent study, based in the Cancer Prevention Study-II Nutrition Cohort, found no association between red or processed meat and pancreatic cancer risk in men or women (27). In our study, we report that total red meat consumption per 100 g in women aged 50 or older was associated with a 49% increased risk of pancreatic cancer, with unprocessed red meat associated with a 67% increased risk.

Since the Continuous Update Project, one study has examined the association of red meat and pancreatic cancer by race—the Multiethnic Cohort Study (8). This study reported little to no association between red meat and pancreatic cancer among European Americans, Native Hawaiians, or Japanese Americans, but an increased risk among African Americans and Latino Americans. For African Americans—men and women together—the highest consumption of red meat intake compared with lowest was associated with a 48% increased risk of pancreatic cancer (8), which is similar to findings in our study of African-American women aged 50 and older.

None of the prior cohort studies have published on the association at ages <50 years, as most cohort studies begin recruitment at or near age 50. A consortium of case–control studies examined risk factors, albeit not diet, for pancreatic cancer diagnosed before age 45 and reported some differences, primarily for alcohol (17). Thus, it is plausible that there may be different associations between diet and pancreatic cancer by age. However, the small number of early-onset pancreatic cancer cases in the BWHS (age <50 years n = 20) limits interpretation of results in that age group.

A number of mechanisms have been proposed linking red and processed meat intake to pancreatic cancer, including mutagens which are by-products of cooking methods (i.e., polycyclic aromatic hydrocarbons, heterocyclic amines, N-nitroso compounds, and advanced glycation endproducts; ref. 5), heme iron intake (28), increased insulin resistance (6), and saturated fat intake (2–4). The majority of studies to date examining mutagens formed in meats, due to grilling, barbequing, or cooking at high temperatures, have reported that increased mutagenic activity is associated with an increased pancreatic cancer risk (29–31). N-nitroso compounds are known to be a potent carcinogen in experimental models (32, 33), and exposure through tobacco smoking is an established pancreatic cancer risk factor (28). Apart from tobacco smoking, humans are exposed to N-nitroso compounds via diet. Specifically, N-nitrosamines form in meat which has been preserved with nitrate (e.g., cured, smoked, or pickled) or dried at high temperatures. In addition, N-nitroso compounds can be formed endogenously by nitrate in the stomach and amides ingested from meat (34, 35). Both the ingested or endogenously produced N-nitroso compounds can reach the pancreas through the blood stream (36). Heme iron has been hypothesized to increase pancreatic cancer risk through promoting oxidative stress or catalyzing formation of N-nitroso (37). However, epidemiologic studies to date of dietary heme iron and pancreatic cancer risk have been largely null (25, 38–46). Red and processed meat (6) and saturated fat (47–49) are associated with increasing insulin resistance, which can lead to hyperinsulinemia and subsequent type 2 diabetes. Pre-existing type 2 diabetes has consistently been associated with an increased risk of pancreatic cancer (50).

In the current study, processed meat and saturated fats were not significantly associated with increased pancreatic cancer risk, although HRs for saturated fats were of a similar magnitude to those for red meat. The Continuous Update Project deemed there to be limited suggestive evidence that processed meat and saturated fat intake increase pancreatic cancer risk (7). Processed meat (per 50 g/day) was associated with a 17% increased risk (21% in men, 9% in women), but only four studies reported on the association in women (23–25, 51). Further, the majority of studies examining saturated fat intake did not stratify by sex (7). The one study of this association in women (the Nurses' Health Study) observed no association between saturated fat and pancreatic cancer risk (25).

In animal models, diets rich in saturated fats promote pancreatic carcinogenesis through augmented lipid metabolism (52, 53). Saturated fat may promote pancreatic cancer through other mechanisms as well. For instance, palmitic acid reduces proliferation and induces apoptosis of β-cells, which could lead to a reduction in β-cell mass and decreased functional activity (54). Decreased β-cell mass and function can lead to type 2 diabetes (55), which is an established risk factor for pancreatic cancer. High levels of dietary fat can also cause pancreatic hypertrophy or hyperplasia, which may increase the vulnerability of the pancreas to other carcinogenic insults (56, 57). Bile acids are also hypothesized to promote pancreatic cancer, as bile acids stimulate the tumor promoter cyclooxygenase-2 in cell lines (4, 58–61). Primary bile acids are derived from cholesterol, synthesized in the liver, and stored in the gallbladder. After food ingestion, they are moved into the gut to facilitate lipid absorption (62, 63). However, higher fat intake may induce bile acid reflux into the pancreatic duct, potentially leading to cancer (58).

In our study, we examined saturated fats, as beef and processed meat are primary contributors to saturated fat intake in the United States. Approximately 9% of total saturated fat in a typical U.S. diet is derived from beef, whereas 7% is from processed meat (64). The main dietary sources of butyric, caproic, caprylic, capric, and lauric acids are dairy products (e.g., milk, cheese, butter), whereas the main dietary sources of myristic, palmitic, and stearic acids are red and processed meats. A nonsignificant increased risk of pancreatic cancer was associated with total saturated fat. Increasing levels of intake of individual fatty acids derived primarily from red and processed meats were associated with increased, but not statistically significant risks in women aged 50 and older. Conversely, among the fatty acids derived from dairy products, there was evidence of an association with pancreatic cancer risk for caproic acid only.

Strengths of the current study include the prospective design; thus, error in reporting of dietary data is unlikely to be associated with disease outcome. As pancreatic cancer can be rapidly fatal and thus potentially susceptible to reverse causation, we excluded cases occurring within 2 years of completing the baseline FFQ about usual diet over the past year. However, whether such time period—versus cumulative lifetime exposure—reflects intakes during the time relevant to pancreatic cancer development is unknown. The cases were identified through self-report and through repeated linkage with cancer registries and the NDI, and over 97% were confirmed through hospital, registry, and death records. Finally, this study was able to assess a wide range of potential confounders.

Limitations of the current study include potential measurement error and generalizability. FFQs have known measurement errors, but they are nevertheless useful for ranking individuals' dietary intake relative to one another, which was our primary objective (16). The BWHS FFQ was validated with criterion instruments (i.e., 3-day food records and three 24-hour recalls) and showed high correlations (14), which are within the range of acceptable validity and comparable with other dietary validation studies (65–68). The BWHS population is more highly educated than the general U.S. population. However, the estimated fat intake from the FFQs are consistent estimates from nationally representative African-American adult populations (69). Thus, these results are likely generalizable to other African-American women.

In conclusion, higher consumption of red meat—specifically, unprocessed red meat—was associated with an increased risk of pancreatic cancer among African-American women aged 50 or older; based on small numbers, there was no association at younger ages. There was a possible association between saturated fat and increased risk of pancreatic cancer in the women aged 50 and older, but the findings were not statistically significant. There is no evidence of an association between processed meat and pancreatic cancer risk. Further research is needed to elucidate the association between red meat—and possibly saturated fat—and pancreatic cancer risk in African-American populations, particularly examining these associations by age.

No potential conflicts of interest were disclosed.

Conception and design: J.L. Petrick, J.R. Palmer

Development of methodology: J.L. Petrick, J.R. Palmer

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): T.N. Bethea, L. Rosenberg, J.R. Palmer

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.L. Petrick, N. Castro-Webb, H. Gerlovin, E.A. Ruiz-Narváez

Writing, review, and/or revision of the manuscript: J.L. Petrick, N. Castro-Webb, H. Gerlovin, T.N. Bethea, S. Li, E.A. Ruiz-Narváez, L. Rosenberg, J.R. Palmer

Pathology data were obtained from the following state cancer registries: AZ, CA, CO, CT, DE, DC, FL, GA, IL, IN, KY, LA, MD, MA, MI, NJ, NY, NC, OK, PA, SC, TN, TX, and VA, and results reported do not necessarily represent their views.

This research was funded by NIH grants U01 CA164974 (L. Rosenberg and J.R. Palmer), U01 CA187508 (J.R. Palmer), and R01 CA058420 (L. Rosenberg), and the Karin Grunebaum Cancer Research Foundation (J.R. Palmer).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Surveillanc
e, Epidemiology, and End Results (SEER) Program
(www.seer.cancer.gov)
SEER*Stat Database: Incidence - SEER 18 Regs Research Data, Nov 2018 Sub (2000-2015) <Katrina/Rita Population Adjustment>- Linked To County Attributes - Total U.S., 1969-2017 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released April 2019, based on the November 2018 submission
.
2.
Woutersen
RA
,
Appel
MJ
,
van Garderen-Hoetmer
A
,
Wijnands
MV
. 
Dietary fat and carcinogenesis
.
Mutat Res
1999
;
443
:
111
27
.
3.
Sanchez
GV
,
Weinstein
SJ
,
Stolzenberg-Solomon
RZ
. 
Is dietary fat, vitamin D, or folate associated with pancreatic cancer?
Mol Carcinog
2012
;
51
:
119
27
.
4.
Tucker
ON
,
Dannenberg
AJ
,
Yang
EK
,
Fahey
TJ
 3rd
. 
Bile acids induce cyclooxygenase-2 expression in human pancreatic cancer cell lines
.
Carcinogenesis
2004
;
25
:
419
23
.
5.
Chiang
VS
,
Quek
SY.
The relationship of red meat with cancer: effects of thermal processing and related physiological mechanisms
.
Crit Rev Food Sci Nutr
2017
;
57
:
1153
73
.
6.
Kim
Y
,
Keogh
J
,
Clifton
P
. 
A review of potential metabolic etiologies of the observed association between red meat consumption and development of type 2 diabetes mellitus
.
Metabolism
2015
;
64
:
768
79
.
7.
World
Cancer Research Fund/American Institute for Cancer Research
. 
Continuous Update Project Expert Report 2018. Diet, nutrition, physical activity and pancreatic cancer
.
London; Arlington (VA):
World Research Cancer Fund
;
American Institute for Cancer Research; 2018. Available from:
dietandcancerreport.org.
8.
Huang
BZ
,
Stram
DO
,
Le Marchand
L
,
Haiman
CA
,
Wilkens
LR
,
Pandol
SJ
, et al
Interethnic differences in pancreatic cancer incidence and risk factors: The Multiethnic Cohort
.
Cancer Med
2019
;
8
:
3592
603
.
9.
Daniel
CR
,
Cross
AJ
,
Koebnick
C
,
Sinha
R
. 
Trends in meat consumption in the USA
.
Public Health Nutr
2011
;
14
:
575
83
.
10.
Zeng
L
,
Ruan
M
,
Liu
J
,
Wilde
P
,
Naumova
EN
,
Mozaffarian
D
, et al
Trends in processed meat, unprocessed red meat, poultry, and fish consumption in the United States, 1999–2016
.
J Acad Nutr Diet
2019
;
119
:
1085
–98 e12.
11.
Rosenberg
L
,
Adams-Campbell
L
,
Palmer
JR
. 
The Black Women's Health Study: a follow-up study for causes and preventions of illness
.
J Am Med Womens Assoc (1972)
1995
;
50
:
56
8
.
12.
Block
G
,
Hartman
AM
,
Naughton
D
. 
A reduced dietary questionnaire: development and validation
.
Epidemiology
1990
;
1
:
58
64
.
13.
Nomura
SJ
,
Dash
C
,
Rosenberg
L
,
Yu
J
,
Palmer
JR
,
Adams-Campbell
LL
. 
Is adherence to diet, physical activity, and body weight cancer prevention recommendations associated with colorectal cancer incidence in African American women?
Cancer Causes Control
2016
;
27
:
869
79
.
14.
Kumanyika
SK
,
Mauger
D
,
Mitchell
DC
,
Phillips
B
,
Smiciklas-Wright
H
,
Palmer
JR
. 
Relative validity of food frequency questionnaire nutrient estimates in the black women's health study
.
Ann Epidemiol
2003
;
13
:
111
8
.
15.
Durrleman
S
,
Simon
R
. 
Flexible regression models with cubic splines
.
Stat Med
1989
;
8
:
551
61
.
16.
Willett
W
.
Nutritional epidemiology
.
3rd ed.
New York
:
Oxford University Press
; 
2013
.
17.
McWilliams
RR
,
Maisonneuve
P
,
Bamlet
WR
,
Petersen
GM
,
Li
D
,
Risch
HA
, et al
Risk factors for early-onset and very-early-onset pancreatic adenocarcinoma: a pancreatic cancer case-control consortium (PanC4) analysis
.
Pancreas
2016
;
45
:
311
6
.
18.
Ansari
D
,
Althini
C
,
Ohlsson
H
,
Andersson
R
. 
Early-onset pancreatic cancer: a population-based study using the SEER registry
.
Langenbecks Arch Surg
2019
;
404
:
565
71
.
19.
Piciucchi
M
,
Capurso
G
,
Valente
R
,
Larghi
A
,
Archibugi
L
,
Signoretti
M
, et al
Early onset pancreatic cancer: risk factors, presentation and outcome
.
Pancreatology
2015
;
15
:
151
5
.
20.
Surveillance, Epidemiology, and End Results (SEER) Program
(www.seer.cancer.gov)
SEER*Stat Database: National Program of Cancer Registries (NPCR) and SEER Incidence - U.S. Cancer Statistics Public Use Database, Nov 2018 Sub (2001-2016), National Cancer Institute, DCCPS, Surveillance Research Program, released April 2019, based on the November 2018 submission
.
21.
Rothman
KJ
,
Greenland
S
,
Lash
TL
.
Modern epidemiology
.
3rd ed
.
Philadelphia
:
Wolters Kluwer Health/Lippincott Williams & Wilkins
; 
2008
.
22.
Kleinbaum
DG
,
Klein
M
,
Pryor
ER
.
Logistic regression: a self-learning text
.
2nd ed
.
New York
:
Springer
; 
2002
.
23.
Cross
AJ
,
Leitzmann
MF
,
Gail
MH
,
Hollenbeck
AR
,
Schatzkin
A
,
Sinha
R
. 
A prospective study of red and processed meat intake in relation to cancer risk
.
PLoS Med
2007
;
4
:
e325
.
24.
Larsson
SC
,
Hakanson
N
,
Permert
J
,
Wolk
A
. 
Meat, fish, poultry and egg consumption in relation to risk of pancreatic cancer: a prospective study
.
Int J Cancer
2006
;
118
:
2866
70
.
25.
Michaud
DS
,
Giovannucci
E
,
Willett
WC
,
Colditz
GA
,
Fuchs
CS
. 
Dietary meat, dairy products, fat, and cholesterol and pancreatic cancer risk in a prospective study
.
Am J Epidemiol
2003
;
157
:
1115
25
.
26.
Inoue-Choi
M
,
Flood
A
,
Robien
K
,
Anderson
K
. 
Nutrients, food groups, dietary patterns, and risk of pancreatic cancer in postmenopausal women
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
711
4
.
27.
McCullough
ML
,
Jacobs
EJ
,
Shah
R
,
Campbell
PT
,
Wang
Y
,
Hartman
TJ
, et al
Meat consumption and pancreatic cancer risk among men and women in the cancer prevention study-II nutrition cohort
.
Cancer Cause Control
2018
;
29
:
125
33
.
28.
Risch
HA.
Etiology of pancreatic cancer, with a hypothesis concerning the role of N-nitroso compounds and excess gastric acidity
.
J Natl Cancer Inst
2003
;
95
:
948
60
.
29.
Anderson
KE
,
Mongin
SJ
,
Sinha
R
,
Stolzenberg-Solomon
R
,
Gross
MD
,
Ziegler
RG
, et al
Pancreatic cancer risk: associations with meat-derived carcinogen intake in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort
.
Mol Carcinog
2012
;
51
:
128
37
.
30.
Li
D
,
Day
RS
,
Bondy
ML
,
Sinha
R
,
Nguyen
NT
,
Evans
DB
, et al
Dietary mutagen exposure and risk of pancreatic cancer
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
655
61
.
31.
Stolzenberg-Solomon
RZ
,
Cross
AJ
,
Silverman
DT
,
Schairer
C
,
Thompson
FE
,
Kipnis
V
, et al
Meat and meat-mutagen intake and pancreatic cancer risk in the NIH-AARP cohort
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2664
75
.
32.
Kokkinakis
DM
,
Reddy
MK
,
Norgle
JR
,
Baskaran
K
. 
Metabolism and activation of pancreas specific nitrosamines by pancreatic ductal cells in culture
.
Carcinogenesis
1993
;
14
:
1705
9
.
33.
Parsa
I
,
Marsh
WH
,
Sutton
AL
. 
An in vitro model of human pancreas carcinogenesis: effects of nitroso compounds
.
Cancer
1981
;
47
:
1543
51
.
34.
Oostindjer
M
,
Alexander
J
,
Amdam
GV
,
Andersen
G
,
Bryan
NS
,
Chen
D
, et al
The role of red and processed meat in colorectal cancer development: a perspective
.
Meat Sci
2014
;
97
:
583
96
.
35.
Lewin
MH
,
Bailey
N
,
Bandaletova
T
,
Bowman
R
,
Cross
AJ
,
Pollock
J
, et al
Red meat enhances the colonic formation of the DNA adduct O6-carboxymethyl guanine: implications for colorectal cancer risk
.
Cancer Res
2006
;
66
:
1859
65
.
36.
Reber
H
,
editor
.
Pancreatic cancer: pathogenesis, diagnosis, and treatment
.
Totowa (NJ)
:
Humana Press
; 
1998
.
37.
Cross
AJ
,
Pollock
JRA
,
Bingham
SA
. 
Haem, not protein or inorganic iron, is responsible for endogenous intestinal N-nitrosation arising from red meat
.
Cancer Res
2003
;
63
:
2358
60
.
38.
Baghurst
PA
,
Mcmichael
AJ
,
Slavotinek
AH
,
Baghurst
KI
,
Boyle
P
,
Walker
AM
. 
A case-control study of diet and cancer of the pancreas
.
Am J Epidemiol
1991
;
134
:
167
79
.
39.
Lin
YS
,
Tamakoshi
A
,
Hayakawa
T
,
Naruse
S
,
Kitagawa
M
,
Ohno
Y
. 
Nutritional factors and risk of pancreatic cancer: a population-based case-control study based on direct interview in Japan
.
J Gastroenterol
2005
;
40
:
297
301
.
40.
Olsen
GW
,
Mandel
JS
,
Gibson
RW
,
Wattenberg
LW
,
Schuman
LM
. 
Nutrients and pancreatic-cancer - a population-based case-control study
.
Cancer Cause Control
1991
;
2
:
291
7
.
41.
Silverman
DT
,
Swanson
CA
,
Gridley
G
,
Wacholder
S
,
Greenberg
RS
,
Brown
LM
, et al
Dietary and nutritional factors and pancreatic cancer: a case-control study based on direct interviews
.
Jnci-J Natl Cancer I
1998
;
90
:
1710
9
.
42.
Ghadirian
P
,
Simard
A
,
Baillargeon
J
,
Maisonneuve
P
,
Boyle
P
. 
Nutritional factors and pancreatic-cancer in the Francophone community in Montreal, Canada
.
Int J Cancer
1991
;
47
:
1
6
.
43.
Bravi
F
,
Polesel
J
,
Bosetti
C
,
Talamini
R
,
Negri
E
,
Dal Maso
L
, et al
Dietary intake of selected micronutrients and the risk of pancreatic cancer: an Italian case-control study
.
Ann Oncol
2011
;
22
:
202
6
.
44.
Kesavan
Y
,
Giovannucci
E
,
Fuchs
CS
,
Michaud
DS
. 
A prospective study of magnesium and iron intake and pancreatic cancer in men
.
Am J Epidemiol
2010
;
171
:
233
41
.
45.
Molina-Montes
E
,
Wark
PA
,
Sanchez
MJ
,
Norat
T
,
Jakszyn
P
,
Lujan-Barroso
L
, et al
Dietary intake of iron, heme-iron and magnesium and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort
.
Int J Cancer
2012
;
131
:
E1134
47
.
46.
Taunk
P
,
Hecht
E
,
Stolzenberg-Solomon
R
. 
Are meat and heme iron intake associated with pancreatic cancer? Results from the NIH-AARP Diet and Health cohort
.
Int J Cancer
2016
;
138
:
2172
89
.
47.
Vessby
B
. 
Dietary fat and insulin action in humans
.
Br J Nutr
2000
;
83
:
S91
6
.
48.
Lopez
S
,
Bermudez
B
,
Pacheco
YM
,
Villar
J
,
Abia
R
,
Muriana
FJ
. 
Distinctive postprandial modulation of beta cell function and insulin sensitivity by dietary fats: monounsaturated compared with saturated fatty acids
.
Am J Clin Nutr
2008
;
88
:
638
44
.
49.
Vessby
B
,
Uusitupa
M
,
Hermansen
K
,
Riccardi
G
,
Rivellese
AA
,
Tapsell
LC
, et al
Substituting dietary saturated for monounsaturated fat impairs insulin sensitivity in healthy men and women: the KANWU study
.
Diabetologia
2001
;
44
:
312
9
.
50.
Li
D
. 
Diabetes and pancreatic cancer
.
Mol Carcinog
2012
;
51
:
64
74
.
51.
Lin
Y
,
Kikuchi
S
,
Tamakoshi
A
,
Yagyu
K
,
Obata
Y
,
Inaba
Y
, et al
Dietary habits and pancreatic cancer risk in a cohort of middle-aged and elderly Japanese
.
Nutr Cancer
2006
;
56
:
40
9
.
52.
Appel
MJ
,
nan Garderen-Hoetmer
A
,
Woutersen
RA
. 
Azaserine-induced pancreatic carcinogenesis in rats: promotion by a diet rich in saturated fat and inhibition by a standard laboratory chow
.
Cancer Lett
1990
;
55
:
239
48
.
53.
Yu
M
,
Liu
H
,
Duan
Y
,
Zhang
D
,
Li
S
,
Wang
F
. 
Four types of fatty acids exert differential impact on pancreatic cancer growth
.
Cancer Lett
2015
;
360
:
187
94
.
54.
Maedler
K
,
Spinas
GA
,
Dyntar
D
,
Moritz
W
,
Kaiser
N
,
Donath
MY
. 
Distinct effects of saturated and monounsaturated fatty acids on beta-cell turnover and function
.
Diabetes
2001
;
50
:
69
76
.
55.
Meier
JJ
,
Bonadonna
RC.
Role of reduced beta-cell mass versus impaired beta-cell function in the pathogenesis of type 2 diabetes
.
Diabetes Care
2013
;
36
:
S113
9
.
56.
Roebuck
BD
,
Yager
JD
 Jr.
,
Longnecker
DS
. 
Dietary modulation of azaserine-induced pancreatic carcinogenesis in the rat
.
Cancer Res
1981
;
41
:
888
93
.
57.
Birt
DF
,
Julius
AD
,
Dwork
E
,
Hanna
T
,
White
LT
,
Pour
PM
. 
Comparison of the effects of dietary beef tallow and corn oil on pancreatic carcinogenesis in the hamster model
.
Carcinogenesis
1990
;
11
:
745
8
.
58.
Feng
HY
,
Chen
YC
. 
Role of bile acids in carcinogenesis of pancreatic cancer: an old topic with new perspective
.
World J Gastroenterol
2016
;
22
:
7463
77
.
59.
Joshi
S
,
Cruz
E
,
Rachagani
S
,
Guha
S
,
Brand
RE
,
Ponnusamy
MP
, et al
Bile acids-mediated overexpression of MUC4 via FAK-dependent c-Jun activation in pancreatic cancer
.
Mol Oncol
2016
;
10
:
1063
77
.
60.
Lu
Y
,
Onda
M
,
Uchida
E
,
Yamamura
S
,
Yanagi
K
,
Matsushita
A
, et al
The cytotoxic effects of bile acids in crude bile on human pancreatic cancer cell lines
.
Surg Today
2000
;
30
:
903
9
.
61.
Wu
Z
,
Lu
Y
,
Wang
B
,
Liu
C
,
Wang
ZR
. 
Effects of bile acids on proliferation and ultrastructural alteration of pancreatic cancer cell lines
.
World J Gastroenterol
2003
;
9
:
2759
63
.
62.
Jia
W
,
Xie
G
,
Jia
W
. 
Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis
.
Nat Rev Gastroenterol Hepatol
2017
.
63.
Nguyen
TT
,
Ung
TT
,
Kim
NH
,
Jung
YD
. 
Role of bile acids in colon carcinogenesis
.
World J Clin Cases
2018
;
6
:
577
88
.
64.
Huth
PJ
,
Fulgoni
VL
,
Keast
DR
,
Park
K
,
Auestad
N
. 
Major food sources of calories, added sugars, and saturated fat and their contribution to essential nutrient intakes in the U.S. diet: data from the National Health and Nutrition Examination Survey (2003–2006)
.
Nutr J
2013
;
12
:
116
.
65.
Mayer-Davis
EJ
,
Vitolins
MZ
,
Carmichael
SL
,
Hemphill
S
,
Tsaroucha
G
,
Rushing
J
, et al
Validity and reproducibility of a food frequency interview in a multi-cultural epidemiologic study
.
Ann Epidemiol
1999
;
9
:
314
24
.
66.
Stram
DO
,
Hankin
JH
,
Wilkens
LR
,
Pike
MC
,
Monroe
KR
,
Park
S
, et al
Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles
.
Am J Epidemiol
2000
;
151
:
358
70
.
67.
Flagg
EW
,
Coates
RJ
,
Calle
EE
,
Potischman
N
,
Thun
MJ
. 
Validation of the American Cancer Society Cancer Prevention Study II Nutrition Survey Cohort food frequency questionnaire
.
Epidemiology
2000
;
11
:
462
8
.
68.
Yanek
LR
,
Moy
TF
,
Becker
DM
. 
Comparison of food frequency and dietary recall methods in African-American women
.
J Am Diet Assoc
2001
;
101
:
1361
4
.
69.
Wright
JD
,
Wang
CY
. 
Trends in intake of energy and macronutrients in adults from 1999-2000 through 2007-2008
.
NCHS Data Brief
2010
:
1
8
.