Abstract
Background:
Intake of polyunsaturated fatty acids (PUFAs) may impact mortality following breast cancer (BC); however, epidemiological studies have relied on self-reported assessment of PUFA intake. Herein, we examined the associations between red blood cell (RBC) PUFAs and mortality.
Methods:
This nested case-control study included 1,104 women from, the Women’s Healthy Eating and Living Study, a multi-site randomized controlled trial. Cases (n=290) were women who died from 1995–2006. Matched controls (n=814) were women who were alive at the end of follow-up. PUFAs were measured in baseline RBC samples and included four ω-3 and seven ω-6 PUFAs. We examined each PUFA individually and Principal Components Factor Analysis (PCFA)-derived scores in association with all-cause mortality (ACM) and BC-specific mortality (BCM) using conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs).
Results:
In fully-adjusted models, ACM ORs were elevated among women with PUFAs >median (versus ≤median) for α-linolenic acid (ALA, OR=1.63, 95%CI=1.18–2.24) and for linolenic acid (LA, OR=1.56, 95%CI=1.16–2.09), and BCM ORs were elevated for ALA (OR=1.83, 95%CI=1.27–2.63), LA (OR=1.70, 95%CI=1.23–2.37), and γ-linolenic acid (GLA, OR=1.50; 95%CI=1.04–2.16). PCFA-Factor 1 [arachidonic acid/adrenic acid/docosapentaenoic acid] scores >median (versus ≤median) were associated with lower odds of ACM (OR=0.71; 95%CI=0.52–0.97) and BCM (OR=0.69; 95%CI=0.49–0.97), and PCFA-Factor 4 [ALA/GLA] scores >median (versus ≤median) were associated with increased odds of BCM (OR=1.47; 95%CI=1.04–2.09).
Conclusions:
RBC ALA, LA, and GLA may be prognostic indicators among BC survivors.
Impact:
These results are important for understanding the associations between a biomarker of PUFA intake and mortality among BC survivors.
Keywords: breast cancer, mortality, red blood cell, polyunsaturated fatty acids, PUFAs, omega fatty acids
INTRODUCTION
Women diagnosed with breast cancer are often motivated to make lifestyle and behavioral changes including dietary changes to reduce their risk of recurrence and to improve their quality of life (1). To support them in making healthier dietary choices, breast cancer survivors are advised to achieve a healthful dietary pattern that is high in vegetables, fruits, whole grains, and legumes (2). Indeed, women diagnosed with breast cancer have been observed to increase their fruit and vegetable intake and to reduce their intake of sugar, red meat, and fat after diagnosis (3). The benefits or risks associated with the intake of unsaturated fat in breast cancer survival, however, are unclear.
Polyunsaturated fatty acids (PUFAs) include the ω-3 fats such as α-linolenic acid (ALA) found in green leafy vegetables, vegetable oils, and seeds and nuts (4) and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) found in cold water fatty fish (5), and the omega ω-6 fats such as linoleic acid (LA) found in vegetable oils including sunflower, corn, and canola oils; and arachidonic acid (AA) found in poultry, meat, eggs, and dairy products (5). PUFAs are essential for normal growth and development and have been shown to play an important role in the prevention of cardiovascular disease (6), cancer, including breast cancer (7,8), and mortality (9). With respect to cancer, marine ω-3 fatty acids have been shown to inhibit carcinogenesis in in vitro and in vivo animal studies through anti-inflammatory, pro-apoptotic, anti-proliferative, and anti-angiogenic mechanisms (10,11), while ω-6 fatty acids promote carcinogenesis (12).
Early epidemiological studies reported increases in mortality in association with higher self-reported intake of polyunsaturated fat (13–15), while more recent studies including a study in the Women’s Healthy Eating and Living (WHEL) Study, on which this study is based, reported reduced mortality risk (16–18). While self-reported assessment of dietary intake is relatively inexpensive and easily employed in large epidemiologic studies, it is subject to recall bias and measurement error thus potentially biasing risk estimates, though bias would be non-differential with respect to the outcomes of interest in a prospective study, underscoring the need for incorporating objective methods of dietary assessment when feasible.
In this study, we examined the associations between red blood cell (RBC) measures of ω-3 and ω-6 PUFAs and all-cause and breast cancer-specific mortality in a sample of women with breast cancer who participated in the WHEL Study (19). We hypothesized that higher versus lower RBC composition of ω-3 PUFAs and lower versus higher RBC composition of ω-6 PUFAs would be inversely associated with mortality risk.
MATERIALS AND METHODS
Study Population
The Women’s Healthy Eating and Living (WHEL) Study was a multi-site randomized trial investigating the effect of a high-vegetable and low-fat diet on breast cancer recurrence and mortality (clinicaltrials.gov, NCT00003787) (20). From 1995–2000, 3,088 women ages 18–70 years with recently diagnosed Stage I, II or IIIA invasive breast cancer were enrolled across seven study sites. Women were excluded from participating in the WHEL Study if they were currently undergoing or planned to undergo chemotherapy; had evidence of recurrent disease or had been diagnosed with a new breast cancer since completion of initial local treatment; or had any other cancer diagnosis in the past 10 years. At enrollment, participants were randomly assigned to a diet intervention designed to encourage women to meet daily dietary targets on vegetables, fruits, fiber, and fat intake or to a comparison group that was provided with print materials from the US Department of Agriculture and the National Cancer Institute describing a diet with a recommended daily intake of five servings of vegetables and fruit, more than 20 grams of fiber, and less than 30% total energy intake from fat. Among women in the intervention group, fish intake was neither explicitly encouraged nor discouraged. Questionnaires eliciting information on medical history, lifestyle behaviors, family history, and diet were completed at enrollment and again at 6-month intervals. WHEL Study participants were also asked to attend clinic visits during which blood samples were collected for laboratory analyses and stored at ‑80ºC. The WHEL Study intervention was not shown to reduce mortality over the study follow-up period (19).
Nested Case-Control Study Design
In the WHEL Study, any breast cancer event or death reported during the semiannual telephone interview prompted a confirmatory interview and collection of medical records or death certificates. Mortality endpoints for women who were lost to follow-up were ascertained from the National Death Index (RRID:SCR_016369). For this nested case-control study, we defined cases as WHEL Study women who died from any cause during the median 7.1-year follow-up (range=0.00–11.21 years) period through June 1, 2006, and who had blood samples available for laboratory analyses (n=290). Of these, 234 had died from breast cancer. Cases were matched (using the SAS GMATCH macro, RRID:SCR_025263) with up to 3 controls (n=814) on age at diagnosis (+/− 5 years); study site (sites A-G); tumor stage (Stage I, II or IIIA); years randomized after diagnosis (+/− 3 years); and intervention group (intervention or control). Most cases (93%) were matched to 3 controls, 5% were matched to 2 matched controls, and 2% were matched to 1 control.
All procedures performed in the WHEL Study involving human participants were in accordance with the ethical standards of the Institutional Review Board (IRB) of the University of California, San Diego (UCSD) and were in compliance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all subjects involved in the study. The present study was considered non-human subjects research by the IRB of San Diego State University.
Laboratory Measures
RBC fatty acid composition was measured at OmegaQuant Laboratories (Sioux Falls, SD, USA) using archived samples of washed RBCs collected at the baseline WHEL Study clinic visit. Fatty acid composition was measured via gas chromatography (GC) with flame ionization detection. GC was carried out using a GC2010 Gas Chromatograph (Shimadzu Corporation, Columbia, MD) equipped with a SP2560, 100-m fused silica capillary column (0.25 mm internal diameter, 0.2 um film thickness; Supelco, Bellefonte, PA). Fatty acids were identified by comparison with a standard mixture of those characteristic of RBCs (GLC OQ-A, NuCheck Prep, Elysian, MN, USA), which was also used to determine individual fatty acid calibration curves. RBC fatty acid composition including the levels of four ω-3 fatty acids [ALA (C18:3n3); EPA (C20:5n3); docosapentaenoic acid (C22:5n3); and DHA (C22:6n3)] and seven ω-6 fatty acids [LA (C18:2n6); γ-linolenic acid (GLA, C18:3n6); eicosadienoic acid (C20:2n6); eicosatrienoic acid (C20:3n6); AA (C22:4n6); adrenic acid (C22:4n6); and docosapentaenoic acid (C22:5n6)] was reported as a percent of total fatty acids. Coefficients of variation (CVs) ranged from 1.36% for adrenic acid to 13.78% for docosapentaenoic acid, with the exception of GLA, which had a CV of 48.49%, reflective of the very low levels of GLA in the RBC samples.
Covariates
Potential confounders of the associations between PUFAs and all-cause and breast cancer-specific mortality were determined a priori from the breast cancer literature (21) and using directed acyclic graphs (DAGs) (22). Covariates included age at diagnosis (continuous in years); body mass index [BMI, continuous in kilograms (kg) /meters (m)2] computed from measured height and weight; total energy intake (continuous in kcal) derived from a food frequency questionnaire; physical activity (continuous in metabolic equivalent task, METS, per week) assessed using a 9-item measure adapted from the Women’s Health Initiative (23); race/ethnicity (non-Hispanic White; non-Hispanic Black; Hispanic; Asian or Pacific Islander; or other race/ethnicity); education (high school graduate or less; post high school or some college education, college graduate, or post college); smoking status (current, former, or never smoker); and history of hormone replacement therapy (HRT) use (yes or no). Additional covariates derived from the medical records included tumor grade (I, II, or III); tumor hormone receptor status [estrogen receptor (ER) or progesterone receptor (PR) positive], and receipt of radiation therapy (yes or no) or chemotherapy (yes or no).
Statistical Analysis
Baseline demographic characteristics were summarized using means and standard deviations (SDs) or frequencies. We used boxplots to compare the unadjusted levels of fatty acids between cases and controls. We used conditional multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between fatty acids and all-cause and breast cancer-specific mortality, adjusting for age at diagnosis, time from diagnosis to randomization, BMI, physical activity, total calorie intake, race/ethnicity, education, smoking status, history of HRT use, grade, ER/PR status, and receipt of radiation therapy or chemotherapy. In addition to examining each fatty acid individually, we also examined the total of the four ω-3 PUFAS, the ω-3 index (DHA+EPA), the total of the seven ω-6 PUFAs, and the ratio of LA:ALA. Fatty acids were examined as categorical variables (categorized at the median) and as continuous variables (per standard deviation, SD, increase). In sensitivity analyses, we excluded deaths that occurred within the first year of follow-up (n cases=82), as these deaths may be unrelated to baseline PUFA exposures. Last, given the high correlations previously reported between the RBC fatty acids (24), we examined Spearman correlations (and included the Nutrition Data System software estimates of intake of ALA, EPA, and DHA (g) from 24-hour dietary recalls (17)) and used principal components factor analysis (PCFA) with varimax rotation, dimensionality-reduction methods used to reduce the number of variables (in this case fatty acids) into fewer uncorrelated factors, each of which represents a group of correlated fatty acids.
Statistical analyses were performed using SAS Version 9.4 (SAS Institute Inc., Cary, North Carolina, USA).
Data availability
The WHEL Study data are archived and available online as part of the UC San Diego Library Digital Collections: https://library.ucsd.edu/dc/object/bb2493244b.
RESULTS
Baseline characteristics of the 290 cases and 814 controls included in this ancillary study are reported in Table 1. Cases had a mean age of 51.7±9.98 years at diagnosis, a mean BMI of 28.2±6.6 kg/m2, a mean energy intake of 1,688.4±441.5 kcal/day. Consistent with the overall WHEL Study sample, 82.4% of cases were non-Hispanic White and 43.5% had a college education. Compared to controls, cases had a higher BMI, were more likely to be current smokers, and were more likely to have used HRT. Cases were also less likely than controls to have been diagnosed with ER or PR positive tumors and had higher grade tumors. Overall, there were few differences in the unadjusted RBC PUFA levels between cases and controls (Figure 1).
Table 1.
Baseline characteristics of the Women’s Healthy Eating and Living (WHEL) Study cohort and the nested case-control ancillary study sample.
| Characteristic | WHEL Study | Controls | Cases |
|---|---|---|---|
|
|
|
|
|
| n (%) | n (%) | n (%) | |
|
| |||
| n | 3,088 | 814 | 290 |
| Mean age at diagnosisa ± SD (years) | 50.7 ± 8.9 | 51.6 ± 9.3 | 51.7 ± 9.98 |
| Mean age at randomizationa ± SD (years) | 52.7 ± 9.0 | 53.4 ± 9.5 | 53.4 ± 10.2 |
| Mean time from diagnosis to randomization ± SD (years) | 2.0 ± 1.0 | 1.8 ± 0.9 | 1.8 ± 1.0 |
| Race/Ethnicity | |||
| Non-Hispanic White | 2,634 (85.3) | 685 (84.2) | 239 (82.4) |
| Other | 454 (14.7) | 129 (15.8) | 51 (17.6) |
| Non-Hispanic Black | 118 (3.8) | 25 (3.1) | 15 (5.2) |
| Hispanic | 165 (5.3) | 53 (6.5) | 17 (5.9) |
| Asian or Pacific Islander | 119 (3.9) | 37 (4.5) | 12 (4.1) |
| Other race | 52 (1.7) | 14 (1.7) | 7 (2.4) |
| Education | |||
| High School Graduate or less | 379 (12.3) | 118 (14.5) | 46 (15.9) |
| Post High School or Some College | 1,035 (33.5) | 273 (33.5) | 118 (40.7) |
| College Graduate | 881 (28.5) | 208 (25.6) | 64 (22.1) |
| Post College | 793 (25.7) | 215 (26.4) | 62 (21.4) |
| Mean BMI ± SD (kg/m2) | 27.3 ± 6.1 | 25.9 ± 5.8 | 28.2 ± 6.6 |
| Smoking status | |||
| Never smoker | 1,643 (53.7) | 443 (54.6) | 138 (48.4) |
| Current or former smoker | 1,414 (46.3) | 368 (45.4) | 147 (51.6) |
| Former | 1,276 (41.7) | 339 (41.8) | 130 (45.6) |
| Current smoker | 138 (4.5) | 29 (3.6) | 17 (6.0) |
| Missing | 31 | 3 | 5 |
| Mean Physical Activity ± SD (METS/week) | 867.6 ± 879.3 | 839.4 ± 855.3 | 692.8 ± 722.8 |
| Missing | 103 | 21 | 10 |
| Menopausal Status | |||
| Premenopausal | 350 (11.4) | 74 (9.1) | 35 (12.1) |
| Perimenopausal | 285 (9.2) | 82 (10.1) | 21 (7.2) |
| Postmenopausal | 2,448 (79.4) | 657 (80.8) | 234 (80.7) |
| History of HRT use | |||
| No | 1,619 (52.9) | 430 (53.3) | 137 (47.7) |
| Yes | 1440 (47.1) | 376 (46.7) | 150 (52.3) |
| Missing | 29 | 8 | 3 |
| Tumor stagea | |||
| I | 1,191 (38.6) | 182 (22.4) | 61 (21.0) |
| II/IIIA | 1,743 (56.4) | 632 (77.6) | 229 (79.0) |
| ER/PR status | |||
| ER or PR positive | 2,422 (79.4) | 634 (78.7) | 212 (73.6) |
| ER and PR negative | 627 (20.6) | 172 (21.3) | 76 (26.4) |
| Missing | 39 | 8 | 2 |
| Grade | |||
| I | 484 (15.7) | 113 (13.9) | 18 (6.2) |
| II | 1,240 (40.2) | 331 (40.7) | 111 (38.3) |
| III | 1,108 (35.9) | 299 (36.7) | 140 (48.3) |
| Not applicable or not available | 256 (8.3) | 71 (8.7) | 21 (7.2) |
| Radiation | |||
| No | 1,185 (38.4) | 312 (38.3) | 110 (37.9) |
| Yes | 1,899 (61.6) | 502 (61.7) | 180 (62.1) |
| Unknown | 4 | 0 | 0 |
| Chemotherapy | |||
| No | 929 (30.1) | 212 (26.0) | 63 (21.7) |
| Yes | 2,157 (69.9) | 602 (74.0) | 227 (78.3) |
| Unknown | 2 | 0 | 0 |
| Mean energy intake ± SD (kcal/day) | 1,717.6 ± 407.6 | 1,692.9 ± 402.0 | 1,688.4 ± 441.5 |
| Missing | 7 | 2 | 1 |
Women’s Healthy Eating and Living (WHEL) Study participants were enrolled into a randomized trial investigating the effect of a high-vegetable and low-fat diet on breast cancer recurrence and mortality between 1995 and 2000 and followed through June 1, 2006. This nested case control study included 290 cases (women who died from any cause over the mean 7.3 year follow-up) and 814 controls (women who were alive at the end of the study follow-up) matched to cases on age at diagnosis (+/- 5 years); study site (sites A-G); tumor stage (Stage I, II or IIIA); years randomized after diagnosis (+/- 3 years); and intervention group (intervention or control).
BMI, body mass index; ER, estrogen receptor; HRT, hormone replacement therapy; METs, metabolic equivalent tasks; PR, progesterone receptor.
Case-control matching factors.
Figure 1.
Distributions of ω-3 and ω-6 polyunsaturated fatty acid composition in red blood cell samples by case-control status among the nested case-control study sample of women from the Women’s Healthy Eating and Living (WHEL) Study (n=1,104). Red horizontal line indicates the median polyunsaturated fatty acid percentage.
Results of the conditional logistic regression analyses examining the associations between the RBC PUFAs and all-cause and breast cancer-specific mortality are reported in Table 2. In fully-adjusted models, all-cause mortality ORs (and 95% CIs) among women with PUFA levels above the median compared to below the median were 1.63 (95%CI=1.18, 2.24) for ALA; 1.56 (95%CI=1.16, 2.09) for LA; and 1.28 (95%CI=0.92, 1.77) for GLA, with evidence of linear dose-response relationships (i.e., per SD increase). Estimates were larger in magnitude when we considered breast cancer-specific mortality as the outcome for ALA (OR=1.83, 95%CI=1.27, 2.63); LA (OR=1.70, 95%CI=1.23, 2.37); and GLA (OR=1.50, 95%CI=1.04, 2.16). In fully-adjusted models, a ratio of LA:ALA (C18:2n6:18:3n3) above the median versus below the median was associated with an all-cause mortality OR of 0.70 (95%CI=0.51, 0.0.98) and with a breast cancer-specific mortality OR of 0.63 (95%CI=0.43, 0.91). Results and conclusions were similar in sensitivity analyses in which we excluded deaths that occurred within the first year of diagnosis (Supplementary Table S1).
Table 2.
Conditional logistic regression odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between all-cause and breast cancer-specific mortality and red blood cell (RBC) polyunsaturated fatty acids (PUFAs) composition in the WHEL Study, 1995–2006.
| All-Cause Mortality | Breast Cancer-Specific Mortality | |||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| PUFAs | n Cases/n Controls | OR (95%CI)a | OR (95% CI)b | n Cases/n Controls | OR (95%CI)a | OR (95% CI)b |
|
| ||||||
| ω-3 PUFAs | ||||||
| ω-3 totalc | ||||||
| 0.32–7.75% | 161/392 | 1.00 (Ref.) | 1.00 (Ref.) | 125/317 | 1.00 (Ref.) | 1.00 (Ref.) |
| 7.76–17.31% | 129/422 | 0.75 (0.57, 0.98) | 0.85 (0.62, 1.16) | 109/332 | 0.82 (0.61, 1.12) | 0.94 (0.66, 1.33) |
| Per SD | 0.92 (0.80, 1.06) | 0.99 (0.85, 1.17) | 0.96 (0.82, 1.11) | 1.04 (0.87, 1.25) | ||
| ω-3 index d | ||||||
| 0.18–5.13% | 159/394 | 1.00 (Ref.) | 1.00 (Ref.) | 124/320 | 1.00 (Ref.) | 1.00 (Ref.) |
| 5.14-12.68% | 131/422 | 0.76 (0.57, 1.01) | 0.86 (0.62, 1.18) | 110/329 | 0.84 (0.62, 1.14) | 0.96 (0.67, 1.37) |
| Per SD | 0.91 (0.79, 1.06) | 0.99 (0.84, 1.17) | 0.94 (0.80, 1.10) | 1.03 (0.86, 1.24) | ||
| C18:3n3 (ALA) | ||||||
| 0.01-0.13% | 129/422 | 1.00 (Ref.) | 1.00 (Ref.) | 101/344 | 1.00 (Ref.) | 1.00 (Ref.) |
| 0.14-0.90% | 161/392 | 1.40 (1.05, 1.87) | 1.63 (1.18, 2.24) | 133/305 | 1.57 (1.14, 2.17) | 1.83 (1.27, 2.63) |
| Per SD | 1.13 (0.99, 1.29) | 1.22 (1.05, 1.41) | 1.16 (1.01, 1.33) | 1.23 (1.06, 1.44) | ||
| C20:5n3 (EPA) | ||||||
| 0.04-0.50% | 147/405 | 1.00 (Ref.) | 1.00 (Ref.) | 113/328 | 1.00 (Ref.) | 1.00 (Ref.) |
| 0.51-4.11% | 143/409 | 0.99 (0.76, 1.30) | 1.09 (0.81, 1.48) | 121/321 | 1.11 (0.82, 1.49) | 1.17 (0.84, 1.64) |
| Per SD | 0.97 (0.84, 1.11) | 1.03 (0.88, 1.21) | 1.02 (0.88, 1.18) | 1.07 (0.90, 1.27) | ||
| C22:5n3 | ||||||
| 0.12-2.50% | 141/411 | 1.00 (Ref.) | 1.00 (Ref.) | 109/331 | 1.00 (Ref.) | 1.00 (Ref.) |
| 2.51-4.55% | 149/403 | 1.13 (0.85, 1.48) | 1.14 (0.83, 1.55) | 125/318 | 1.23 (0.91, 1.67) | 1.20 (0.85, 1.71) |
| Per SD | 0.95 (0.83, 1.08) | 0.98 (0.84, 1.14) | 1.01 (0.87, 1.17) | 1.03 (0.87, 1.21) | ||
| C22:6n3 (DHA) | ||||||
| 0.15-4.65% | 157/396 | 1.00 (Ref.) | 1.00 (Ref.) | 122/320 | 1.00 (Ref.) | 1.00 (Ref.) |
| 4.66-9.96% | 133/418 | 0.78 (0.59, 1.04) | 0.90 (0.65, 1.24) | 112/329 | 0.86 (0.63, 1.17) | 1.02 (0.71, 1.46) |
| Per SD | 0.91 (0.79, 1.05) | 0.98 (0.84, 1.16) | 0.92 (0.79, 1.08) | 1.02 (0.85, 1.22) | ||
| ω-6 PUFAs | ||||||
| ω-6 totale | ||||||
| 6.44-33.68% | 150/402 | 1.00 (Ref.) | 1.00 (Ref.) | 121/315 | 1.00 (Ref.) | 1.00 (Ref.) |
| 33.69-40.91% | 140/412 | 0.90 (0.68, 1.19) | 0.93 (0.68, 1.26) | 113/334 | 0.89 (0.65, 1.21) | 0.96 (0.68, 1.36) |
| Per SD | 1.07 (0.93, 1.24) | 1.08 (0.93, 1.26) | 1.07 (0.92, 1.25) | 1.10 (0.93, 1.29) | ||
| C18:2n6 (LA) | ||||||
| 4.36-10.79% | 122/429 | 1.00 (Ref.) | 1.00 (Ref.) | 99/353 | 1.00 (Ref.) | 1.00 (Ref.) |
| 10.80-21.33% | 168/385 | 1.55 (1.18, 2.05) | 1.56 (1.16, 2.09) | 135/296 | 1.62 (1.19, 2.2) | 1.70 (1.23, 2.37) |
| Per SD | 1.20 (1.04, 1.38) | 1.25 (1.08, 1.45) | 1.21 (1.04, 1.41) | 1.25 (1.07, 1.47) | ||
| C18:3n6 (GLA) | ||||||
| 0.00-0.07% | 132/419 | 1.00 (Ref.) | 1.00 (Ref.) | 108/334 | 1.00 (Ref.) | 1.00 (Ref.) |
| 0.08-0.29% | 158/395 | 1.27 (0.94, 1.71) | 1.28 (0.92, 1.77) | 126/305 | 1.43 (1.03, 1.99) | 1.50 (1.04, 2.16) |
| Per SD | 1.15 (0.99, 1.32) | 1.18 (1.00, 1.39) | 1.16 (0.99, 1.36) | 1.22 (1.01, 1.46) | ||
| C20:2n6 | ||||||
| 0.11-0.29% | 145/407 | 1.00 (Ref.) | 1.00 (Ref.) | 118/323 | 1.00 (Ref.) | 1.00 (Ref.) |
| 0.30-0.56% | 145/407 | 1.01 (0.77, 1.34) | 1.09 (0.81, 1.48) | 116/326 | 0.98 (0.72, 1.33) | 1.05 (0.76, 1.46) |
| Per SD | 0.94 (0.82, 1.08) | 0.98 (0.84, 1.15) | 0.93 (0.79, 1.08) | 0.97 (0.82, 1.14) | ||
| C20:3n6 | ||||||
| 0.19-1.84% | 137/414 | 1.00 (Ref.) | 1.00 (Ref.) | 108/336 | 1.00 (Ref.) | 1.00 (Ref.) |
| 1.85-3.59 | 153/400 | 1.17 (0.89, 1.54) | 1.16 (0.86, 1.57) | 126/313 | 1.23 (0.91, 1.67) | 1.18 (0.85, 1.65) |
| Per SD | 1.12 (0.98, 1.28) | 1.08 (0.93, 1.25) | 1.11 (0.95, 1.28) | 1.04 (0.88, 1.22) | ||
| C20:4n6 (AA) | ||||||
| 1.32-15.73% | 154/399 | 1.00 (Ref.) | 1.00 (Ref.) | 124/319 | 1.00 (Ref.) | 1.00 (Ref.) |
| 15.74-22.31 | 136/415 | 0.86 (0.65, 1.14) | 0.88 (0.65, 1.19) | 110/330 | 0.86 (0.63, 1.17) | 0.89 (0.63, 1.24) |
| Per SD | 0.95 (0.83, 1.09) | 0.97 (0.84, 1.12) | 0.97 (0.84, 1.12) | 1.01 (0.86, 1.18) | ||
| C22:4n6 | ||||||
| 0.07-3.82% | 148/405 | 1.00 (Ref.) | 1.00 (Ref.) | 125/314 | 1.00 (Ref.) | 1.00 (Ref.) |
| 3.83-6.29% | 142/409 | 0.96 (0.73, 1.26) | 0.89 (0.65, 1.20) | 109/335 | 0.83 (0.62, 1.12) | 0.76 (0.55, 1.07) |
| Per SD | 0.99 (0.87, 1.14) | 0.95 (0.82, 1.11) | 0.96 (0.83, 1.11) | 0.94 (0.79, 1.11) | ||
| C22:5n6 | ||||||
| 0.03-0.76% | 148/404 | 1.00 (Ref.) | 1.00 (Ref.) | 122/320 | 1.00 (Ref.) | 1.00 (Ref.) |
| 0.77%-1.82% | 142/410 | 0.94 (0.72, 1.24) | 0.92 (0.68, 1.24) | 112/329 | 0.90 (0.67, 1.22) | 0.97 (0.70, 1.35) |
| Per SD | 0.98 (0.86, 1.13) | 0.99 (0.85, 1.15) | 0.92 (0.79, 1.07) | 0.95 (0.80, 1.12) | ||
| C18:2n6:C18:3n3 (LA:ALA) | ||||||
| 12.8-81.2 | 152/400 | 1.00 (Ref.) | 1.00 (Ref.) | 123/319 | 1.00 (Ref.) | 1.00 (Ref.) |
| 81.3-469.4 | 138/414 | 0.83 (0.61, 1.12) | 0.70 (0.51, 0.98) | 111/330 | 0.71 (0.51, 0.99) | 0.63 (0.43, 0.91) |
| Per SD | 0.90 (0.76, 1.05) | 0.83 (0.69, 0.99) | 0.85 (0.72, 1.01) | 0.80 (0.66, 0.97) | ||
Women’s Healthy Eating and Living (WHEL) Study participants were enrolled into a randomized trial investigating the effect of a high-vegetable and low-fat diet on breast cancer recurrence and mortality between 1995 and 2000 and followed through June 1, 2006. This nested case control study included 290 cases (women who died from any cause over the mean 7.3 year follow-up) and 814 controls (women who were alive at the end of the study follow-up) matched to cases on age at diagnosis (+/- 5 years); study site (sites A-G); tumor stage (Stage I, II or IIIA); years randomized after diagnosis (+/- 3 years); and intervention group (intervention or control).
AA, arachidonic acid; ALA, α-linolenic acid; CI, confidence interval; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; GLA, γ-linolenic acid; LA, linoleic acid; OR, odds ratio; PUFA, polyunsaturated fatty acid; RBC, red blood cell; SD, standard deviation
Model is adjusted for age at diagnosis (continuous, years).
Model is adjusted for age at diagnosis (continuous, years), time from diagnosis to randomization (continuous, years) body mass index (continuous, kg/m2), physical activity (continuous, METs per week), total calorie intake (continuous, kcal), race/ethnicity (non-Hispanic White vs. other race/ethnicity), education (high school graduate or less, post high school or some college, or college graduate vs. post college), smoking status (current or former smoker vs. never smoker), history of hormone replacement therapy use (yes vs. no), grade (I, II, or NA vs. III), estrogen/progesterone receptor status (ER or PR positive vs. ER and PR negative), and receipt of radiation therapy (yes vs. no) or chemotherapy (yes vs. no).
ΣC18:3n3, C20:5n3, C22:5n3, and C22:6n3.
ΣC20:5n3 and C22:6n3.
ΣC18:2n6, C18:3n6, C20:2n6, C20:3n6, C20:4n6, C22:4n6, and C22:5n6.
As shown in Figure 2, there were a number of high pair-wise correlations observed between the PUFAs including the largest positive correlations between C20:5n3 and C22:5n3 (ρ=0.7); C20:5n3 and C22:6n3 (ρ=0.6); and C22:4n6 and C22:5n6 (ρ=0.6) and the largest negative correlations between C20:5n3 and C22:5n6 (ρ=−0.6); C20:5n3 and C22:4n6 (ρ=−0.5); and C22:6n3 and C22:4n6 (ρ=−0.5). From the PFCA, we retained 4 factors using a cutoff eigenvalue score greater than 1.0, which accounted for 71.4% of the shared variance. As shown in Table 3, PUFAs with PCFA-Factor 1 loadings >0.50 included C20:4n6, C22:4n6, and C22:5n6; PUFAs with PCFA-Factor 2 loadings >0.50 included C20:5n3, C22:5n3, and C22:6n3; PUFAs with PCFA-Factor 3 loadings >0.50 included C18:2n6, C20:2n6, and C20:3n6; and PUFAs with PCFA-Factor 4 loadings >0.50 included C18:3n3 and C18:3n6. In fully-adjusted conditional logistic regression models examining each of the factors categorized at the median and per SD increase (Table 4), PCFA-Factor 1 scores >median versus <median were inversely associated with all-cause (OR=0.71; 95%CI=0.52, 0.97) and breast cancer-specific (OR=0.69, 95%CI=0.49, 0.97) mortality; and PCFA-Factor 4 scores >median versus <median were positively associated with all-cause (OR=1.30; 95%CI=0.95, 1.79) and breast cancer-specific (OR=1.47, 95%CI=1.10, 1.54) mortality. Additionally, a one-SD increase in PCFA-Factor 4 scores was associated with a 26% increase (OR=1.26, 95%CI=1.08, 1.47) in the odds of all-cause mortality and with a 30% increase (OR=1.30, 95%CI=1.10, 1.54) in the odds of breast cancer-specific mortality.
Figure 2.
Spearman correlations between ω-3 and ω-6 polyunsaturated fatty acids in red blood cell samples among the nested case-control study sample of women from the Women’s Healthy Eating and Living (WHEL) Study (n=1,104). *Nutrition Data System Software (NDS) estimates of intake (g) from 24-hour dietary recalls.
Table 3.
Principal Components Factor Analysis of red blood cell (RBC) ω-3 and ω-6 polyunsaturated fatty acids (PUFAs) in the WHEL Study, 1995–2006.
| Factor Loadingsa | ||||
|---|---|---|---|---|
|
|
||||
| 1 | 2 | 3 | 4 | |
|
| ||||
| ω-3 PUFAs | ||||
| C18:3n3 | -0.32 | 0.31 | 0.46 | 0.50 |
| C20:5n3 | -0.37 | 0.84 | -0.12 | 0.06 |
| C22:5n3 | 0.14 | 0.86 | 0.01 | 0.14 |
| C22:6n3 | -0.12 | 0.78 | -0.06 | -0.31 |
| ω-6 PUFAs | ||||
| C18:2n6 | -0.14 | -0.11 | 0.66 | 0.41 |
| C18:3n6 | 0.12 | -0.08 | -0.05 | 0.81 |
| C20:2n6 | -0.08 | -0.06 | 0.77 | -0.25 |
| C20:3n6 | 0.21 | -0.03 | 0.64 | 0.04 |
| C20:4n6 | 0.86 | 0.26 | -0.13 | 0.11 |
| C22:4n6 | 0.85 | -0.21 | 0.03 | 0.07 |
| C22:5n6 | 0.82 | -0.29 | 0.11 | -0.12 |
| Eigenvalues | 3.01 | 2.03 | 1.68 | 1.13 |
| Percent Variance | 27.39 | 18.47 | 15.30 | 10.26 |
Women’s Healthy Eating and Living (WHEL) Study participants were enrolled into a randomized trial investigating the effect of a high-vegetable and low-fat diet on breast cancer recurrence and mortality between 1995 and 2000 and followed through June 1, 2006. This nested case control study included 290 cases (women who died from any cause over the mean 7.3 year follow-up) and 814 controls (women who were alive at the end of the study follow-up) matched to cases on age at diagnosis (+/- 5 years); study site (sites A-G); tumor stage (Stage I, II or IIIA); years randomized after diagnosis (+/- 3 years); and intervention group (intervention or control).
PUFA, polyunsaturated fatty acid; RBC, red blood cell
Extraction method: Principal Components; Rotation method: Varimax; Factor scores were saved with the regression method.
Table 4.
Conditional logistic regression odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between red blood cell (RBC) polyunsaturated fatty acids (PUFA) principal components factor analysis-derived scores and all-cause and breast cancer-specific mortality in the WHEL Study, 1995–2006.
|
All-Cause Mortality
(n cases=290; n controls=814) |
Breast Cancer-Specific Mortality
(n cases=234; n controls = 649) |
|||
|---|---|---|---|---|
|
|
|
|||
| PUFA | OR (95%CI)a | OR (95% CI)b | OR (95%CI)a | OR (95% CI)b |
|
| ||||
| Factor 1 c | ||||
| ≤Median | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) |
| >Median | 0.81 (0.61, 1.07) | 0.71 (0.52, 0.97) | 0.74 (0.55, 1.01) | 0.69 (0.49, 0.97) |
| Per SD | 0.96 (0.84, 1.10) | 0.95 (0.82, 1.10) | 0.94 (0.82, 1.08) | 0.95 (0.81, 1.11) |
| Factor 2 d | ||||
| ≤Median | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) |
| >Median | 0.82 (0.62, 1.08) | 0.90 (0.66, 1.23) | 0.91 (0.68, 1.24) | 0.98 (0.69, 1.39) |
| Per SD | 0.94 (0.82, 1.07) | 1.00 (0.86, 1.17) | 0.99 (0.86, 1.15) | 1.06 (0.89, 1.25) |
| Factor 3 e | ||||
| ≤Median | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) |
| >Median | 1.08 (0.82, 1.43) | 1.20 (0.89, 1.63) | 1.06 (0.78, 1.44) | 1.19 (0.86, 1.66) |
| Per SD | 1.08 (0.95, 1.25) | 1.13 (0.97, 1.31) | 1.08 (0.93, 1.26) | 1.11 (0.94, 1.31) |
| Factor 4 f | ||||
| ≤Median | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) |
| >Median | 1.33 (0.99, 1.77) | 1.30 (0.95, 1.79) | 1.45 (1.06, 2.00) | 1.47 (1.04, 2.09) |
| Per SD | 1.22 (1.06, 1.40) | 1.26 (1.08, 1.47) | 1.26 (1.09, 1.46) | 1.30 (1.10, 1.54) |
Women’s Healthy Eating and Living (WHEL) Study participants were enrolled into a randomized trial investigating the effect of a high-vegetable and low-fat diet on breast cancer recurrence and mortality between 1995 and 2000 and followed through June 1, 2006. This nested case control study included 290 cases (women who died from any cause over the mean 7.3 year follow-up) and 814 controls (women who were alive at the end of the study follow-up) matched to cases on age at diagnosis (+/- 5 years); study site (sites A-G); tumor stage (Stage I, II or IIIA); years randomized after diagnosis (+/- 3 years); and intervention group (intervention or control).
CI, confidence interval; OR, odds ratio; PUFA, polyunsaturated fatty acid; RBC, red blood cell; WHEL, Women’s Healthy Eating and Living Study
Model is adjusted for age at diagnosis (continuous, years).
Model is adjusted for age at diagnosis (continuous, years), time from diagnosis to randomization (continuous, years) body mass index (continuous, kg/m2), physical activity (continuous, METs per week), total calorie intake (continuous, kcal), race/ethnicity (non-Hispanic White vs. other race/ethnicity), education (high school graduate or less, post high school or some college, or college graduate vs. post college), smoking status (current or former smoker vs. never smoker), history of hormone replacement therapy use (yes vs. no), grade (I, II, or NA vs. III), estrogen/progesterone receptor status (ER or PR positive vs. ER and PR negative), and receipt of radiation therapy (yes vs. no) or chemotherapy (yes vs. no).
Factor 1 loadings ≥0.50: C20:4n6, C22:4n6, and C22:5n6.
Factor 2 loadings ≥0.50: C20:5n3, C22:5n3, and C22:6n3.
Factor 3 loadings ≥0.50: C18:2n6, C20:2n6, and C20:3n6.
Factor 4 loadings ≥0.50: C18:3n3 and C18:3n6.
DISCUSSION
In this study, we examined the associations between RBC measures of ω-3 and ω-6 PUFAs and all-cause and breast cancer-specific mortality among a sample of breast cancer survivors from the WHEL Study. The ω-3 fatty acid ALA and the ω-6 fatty acids LA and GLA were positively associated with risk of all-cause and breast cancer-specific mortality. Additionally, higher RBC ratios of LA to ALA were inversely associated with risk of all-cause and breast cancer-specific mortality. When we considered the high correlations between the RBC PUFAs, the long-chain ω-6 fatty acids were inversely associated with mortality risk while ALA and GLA were positively associated with mortality risk. Our results for GLA, however, should be interpreted with caution given the relatively low levels and thus high CV for this fatty acid. Nonetheless, these results suggest that RBC polyunsaturated fatty acids may be a prognostic indicator among women with breast cancer.
Some of our results are consistent with several (13,14), but not all (15,16) of the published epidemiologic studies on the associations between PUFAs and breast cancer mortality. An early study by Nomura and colleagues examined the effect of dietary fat on breast cancer survival among 161 non-Hispanic White and 182 Japanese women in Hawaii (13). In that study, non-Hispanic White women with high total intake of polyunsaturated fat estimated from a diet history interview were reported to have a 72% increase in the risk of mortality compared to women with low intake of polyunsaturated fat (13). In another study of 412 Australian women by Rohan and colleagues, the highest versus the lowest quintile of total polyunsaturated fat intake estimated using a FFQ was associated with a 57% increase in the risk of mortality (14). In more recent studies including in the WHEL Study, however, intake of ω-3 fatty acids was reported to be associated with a reduced risk of mortality. In the WHEL Study, the highest versus lowest tertile of EPA+DHA derived from 24-hour recalls was associated with a mortality HR of 0.59, and in the Long Island Breast Cancer Study Project, the highest versus lowest quartile of DHA estimated using an FFQ was associated with a HR of 0.86. In this study, we did not observe a lower mortality risk among women with higher levels of EPA or DHA, and observed an increase in mortality risk in association with ALA. Differences in these findings may be due to the assessment of polyunsaturated fats. Whereas previous studies have relied on self-reported assessments of dietary intake, to our knowledge, this is the first study to consider a biomarker of PUFA intake (i.e., RBC fatty acid composition) in association with breast cancer mortality.
The major pathways by which fatty acids act in the body are through the synthesis of eicosanoids including thromboxanes, prostaglandins, prostacyclins, and leukotrienes from 20-carbon PUFAs (5). Eicosanoids derived from the ω-6 PUFA AA generally promote inflammation, while those derived from the ω-3 PUFA EPA are less inflammatory or anti-inflammatory (25). Additionally, since ALA and LA are metabolized to their corresponding products via common enzyme systems, increased intake of ALA and in particular relative to LA intake, can counter the inflammatory effects of the ω-6-derived eicosanoids through displacement of the ω-6 fatty acids from the elongase and desaturase enzymes, competitive inhibition of the cyclooxygenase (COX) and lipoxygenase (LOX) enzymes, and counteraction of the effects of the eicosanoids (5). In vitro and animal studies provide a strong body of evidence establishing ω-3 PUFAs as having anti-inflammatory and anti-carcinogenic effects thus suggesting a role of ω-3 PUFAs as chemopreventive agents (11). It is therefore conceivable that ω-3 PUFAs could act through these same mechanisms to improve the prognosis of breast cancer survivors. In this study, however, we did not observe decreases in mortality risk in association with the ω-3 PUFAs, and contrary to our hypothesis of increases in risk of all-cause and breast cancer-specific mortality in association with increasing levels of LA relative to ALA, we observed reduced risks of mortality. Similar contrary results were reported in a previous study of RBC PUFAs and the risk of sudden cardiac arrest (26) that reported higher RBC ALA levels associated with increased risk of sudden cardiac arrest (26). The authors hypothesized that high levels of RBC membrane ALA may be a marker of poor conversion to EPA (26), which could explain our findings reported here including our results examining the ratio of LA to ALA. Another potential explanation is that PUFAs induce a cytotoxic environment via lipid peroxidation. While this cytotoxic environment could be beneficial in inhibiting tumor progression (27), for breast cancer survivors this oxidative stress could increase the risk of mortality (28). Additional studies are needed to clarify these associations.
This main strength of this study is the use of a valid and reliable biomarker of PUFA intake (29,30), which eliminates the potential recall bias and measurement error of self-reported dietary intake assessments. Compared to other biomarkers, the RBC marker has lower within-subject variability, is resistant to artificial elevations, has a longer biological half-life, is not sensitive to fasting status, and is stable when samples are appropriately stored (30). Although RBC PUFAs may be susceptible to degradation through lipid peroxidation, the probability of which increases with increasing number of double bonds in fatty acids and with higher temperatures at which samples are stored, this degradation would result in non-differential exposure misclassification with respect to the outcome (i.e., mortality), which would likely bias results towards the null. While adipose tissue may be the preferred medium, obtaining these samples is highly invasive and therefore not feasible for many epidemiological studies. Thus, RBC PUFA quantification is the next best alternative. Importantly, however, we do not call for a replacement of self-reported dietary intake records, which among other advantages can provide specific information on the food sources and can be used to assess a respondent’s typical long-term diet pattern, with a biomarker. On the contrary, the objective PUFA amounts from the RBC biomarker in conjunction with a dietary recall or food frequency questionnaire may provide the most valid estimates of intake. As such, future studies, should consider leveraging the strengths of both assessments to minimize bias. Another strength of our study is the study design. We used a nested case-control study design which maximizes power while preserving biological specimens and reducing costs. However, several limitations should be noted. First, we relied on a single measurement of RBC fatty acids, and therefore we are not able to account for dietary changes in fatty acid intake over time. Additionally, as we report in this study, there were high correlations between some of the RBC PUFAs. We were a priori interested in accounting for these high correlations, given that any single PUFA-mortality association may be confounded by other highly correlated PUFAs, and used principal components analysis and to reduce the dimensionality of the large number of covariates; however, the final factor loadings were not associated with any clear dietary pattern and were difficult to interpret. Second, the follow-up period of the WHEL study is relatively short. It is unclear whether these associations would persist among long-term breast cancer survivors. Third, the WHEL Study sample is relatively homogenous with respect to characteristics such as race, ethnicity, and socioeconomic status. Therefore, these results may not be generalizable to other groups of breast cancer survivors. Fourth, multiple comparisons could have resulted in spurious associations; however, adjustment of multiple comparisons, which reduces Type I errors at the expense of Type II errors, in hypothesis driven epidemiologic research is not recommended (31). Last, as with other observational studies, these results may be confounded by unmeasured variables. For example, it may be important to account for exposure to persistent organic pollutants for which fish intake is a major exposure pathway (32) and which have been found to be associated with mortality among breast cancer survivors (33–35). Future studies should examine these associations in more diverse samples, and with consideration to potential effect measure modification by important breast cancer prognostic factors such as adiposity.
Conclusions
Higher RBC levels of the ω-3 PUFA ALA and the ω-6 PUFAs LA and GLA were associated with increases in the risk of mortality among women with breast cancer. However, given that our results reported here are in contrast to the current scientific evidence which suggests potential benefits of consuming ω-3 PUFA-rich foods and in particular relative to the amounts of ω-6 PUFA-rich foods consumed, additional epidemiological studies replicating our findings and those that combine biomarker data with self-reported dietary intake data and laboratory studies that elucidate the biological mechanisms by which PUFAs may impact mortality among breast cancer survivors will be needed before these findings can be used to provide meaningful care to patients. Additional studies of unsaturated fat and breast cancer mortality will also be important for tailoring dietary recommendations for breast cancer survivors who comprise more than 3.8 million women in the US (36).
Supplementary Material
ACKNOWLEDGEMENTS
H Parada Jr was supported by the National Cancer Institute (K01 CA234317), the SDSU/UCSD Comprehensive Cancer Center Partnership (U54 CA132384 & U54 CA132379), and the Alzheimer’s Disease Resource Center for Advancing Minority Aging Research at the University of California San Diego (P30 AG059299). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the views of the NIH.
Footnotes
Competing interests. The authors declare they have no conflict of interest.
Ethics approval and consent to participate. This study was determined to be non-human subjects research by the San Diego State University Institutional Review Board.
Type of manuscript: Original Research Article
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The WHEL Study data are archived and available online as part of the UC San Diego Library Digital Collections: https://library.ucsd.edu/dc/object/bb2493244b.