Abstract
Background:
The objective of this study was to evaluate the role of low-carbohydrate diets after breast cancer diagnosis in relation to breast cancer–specific and all-cause mortality.
Methods:
For 9621 women with stage I–III breast cancer from two ongoing cohort studies, the Nurses’ Health Study and Nurses’ Health Study II, overall low-carbohydrate, animal-rich low-carbohydrate, and plant-rich low-carbohydrate diet scores were calculated by using food frequency questionnaires collected after breast cancer diagnosis.
Results:
Participants were followed up for a median 12.4 years after breast cancer diagnosis. We documented 1269 deaths due to breast cancer and 3850 all-cause deaths. With the use of Cox proportional hazards regression and after controlling for potential confounding variables, we observed a significantly lower risk of overall mortality among women with breast cancer who had greater adherence to overall low-carbohydrate diets (hazard ratio for quintile 5 vs. quintile 1 [HRQ5vsQ1], 0.82; 95% CI, 0.74–0.91; ptrend = .0001) and plant-rich low-carbohydrate diets (HRQ5vsQ1, 0.73; 95% CI, 0.66–0.82; ptrend <.0001) after breast cancer diagnosis but not animal-rich low-carbohydrate diets (HRQ5vsQ1, 0.93; 95% CI, 0.84–1.04; ptrend = .23). However, greater adherence to overall, animal-rich, or plant-rich low-carbohydrate diets was not significantly associated with a lower risk of breast cancer–specific mortality.
Conclusions:
This study showed that greater adherence to low-carbohydrate diets, especially plant-rich low-carbohydrate diets, was associated with better overall survival but not breast cancer–specific survival among women with stage I–III breast cancer.
Keywords: animal-rich low-carbohydrate diet, breast cancer, low-carbohydrate diet, plant-rich low-carbohydrate diet, survival
INTRODUCTION
Following breast cancer diagnosis, nutrition therapy may offer a viable and efficient way to improve survivorship. High intake of carbohydrate may play an important role by providing necessary fuel for tumor growth and proliferation.1 According to our previous analyses that used data from the Nurses’ Health Study (NHS) and the Nurses’ Health Study II (NHSII), diets with a high glycemic load after breast cancer diagnosis were related to a higher risk of breast cancer–specific and all-cause mortality.2 In addition, we found a higher risk of breast cancer–specific and all-cause mortality in relation to high consumption of fruit juice and sugar-sweetened beverages as well as total carbohydrate after breast cancer diagnosis.2–4 According to our findings, dietary carbohydrate intake influenced the risk of mortality in women with breast cancer disproportionately depending on the source and type of carbohydrate; whereas carbohydrate from vegetables was associated with a lower risk of all-cause mortality, carbohydrate from fruit juice was linked with a higher risk of breast cancer–specific and all-cause mortality.5
However, it is unclear what role protein and fat intake plays in breast cancer survival in relation to carbohydrate consumption. Therefore, we specifically hypothesized that decreasing carbohydrate intake and increasing protein and fat intake might improve survival rates of women with breast cancer. Furthermore, we hypothesized that decreasing carbohydrate intake and increasing plant protein and fat may affect breast cancer survival differently from decreasing carbohydrate intake and increasing animal protein and fat. In this regard, we evaluated the associations of postdiagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores with breast cancer–specific and all-cause mortality by using the combined NHS and NHSII data.
MATERIALS AND METHODS
Study population
The data we used for this study were from two ongoing cohort studies in the United States: the NHS, which enrolled 121,700 female nurses aged 30–55 years in 1976, and the NHSII, which enrolled 116,429 female nurses aged 25–42 years in 1989. When we followed up with the NHS from 1980 to 2010 and the NHSII from 1991 to 2015, women with invasive breast cancer were identified. In the group of women diagnosed with invasive breast cancer who reported their dietary intake at least 12 months after diagnosis, we excluded participants if they had reported implausible total energy intake after diagnosis (<600 or >3500 kcal/day), left blank more than 70 food items on the food frequency questionnaire (FFQ), or reported another cancer diagnosis (except nonmelanoma skin cancer) before breast cancer and those who had initially been diagnosed with stage IV disease or not been given information about the stage of their disease at diagnosis. As a result, we analyzed the data of 9621 women with stage I–III breast cancer.
Completing the questionnaire was considered to imply informed consent when the study protocol was approved in 1976 (NHS) and 1989 (NHSII) by the institutional review boards of Brigham and Women’s Hospital (Boston, Massachusetts) and Harvard T. H. Chan School of Public Health (Boston, Massachusetts) and those of participating registries as required. The research was conducted in compliance with established ethical guidelines (Declaration of Helsinki).
Assessment of dietary intake
Data on postdiagnostic dietary intake of women were collected from all validated semiquantitative FFQs that women filled out at least after 12 months from diagnosis. These FFQs were administered to women in 1980, 1984, and 1986 and every 4 years thereafter in the NHS and in 1991 and every 4 years thereafter in the NHSII (questionnaires are available at http://www.nurseshealthstudy.org/participants/questionnaires). To obtain the average daily intake of each nutrient, we multiplied the frequency of consumption by the food’s nutritional content and then summed it across all foods. Available carbohydrate (total carbohydrate minus dietary fiber), protein, and fat intake as well as protein and fat intake from animal and plant sources were calculated as the percentage of total energy intake. The method to derive overall, animal-rich, and plant-rich low-carbohydrate diet scores has previously been detailed.6 Briefly, to calculate the overall low-carbohydrate diet scores, the percentages of energy intake from available carbohydrate, protein, and fat were categorized into 11 equal groups. In terms of carbohydrate intake, categories were ranked from 10 (lowest intake) to 0 (highest intake), whereas in terms of total protein intake and total fat intake, those categories were ranked from 0 (lowest intake) to 10 (highest intake). To derive the overall low-carbohydrate diet score, ranks were summed, which ranged from 0 (lowest protein intake, lowest fat intake, and highest carbohydrate intake) to 30 (highest protein intake, highest fat intake, and lowest carbohydrate intake). Based on the percentages of energy intake from available carbohydrate, animal protein, and animal fat, the animal-rich low-carbohydrate diet score was similarly calculated. The plant-rich low-carbohydrate diet score was calculated from the percentages of energy intake from available carbohydrate, plant protein, and plant fat instead of total protein and total fat. The first postdiagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores were obtained from the first FFQ completed 12 months or more after diagnosis to avoid assessment during active treatment. To reduce within-person variation and evaluate dietary intake over time after diagnosis, cumulative averages of postdiagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores were calculated. On the basis of the last FFQ reported before breast cancer diagnosis, we calculated prediagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores to estimate their roles in breast cancer survival.
Ascertainment of breast cancer and death
The study physicians confirmed the self-reported breast cancers on the biennial questionnaires by reviewing medical records and pathology reports. Additionally, we collected diagnostic information from the medical records and pathology reports including the stage of breast cancer at the time of diagnosis, the status of estrogen and progesterone receptors (ER/PR) in breast tumors, and other relevant data. After obtaining death notifications from family members, the postal service, or a search of the National Death Index, the study physicians determined the underlying cause of death by reviewing the death certificate and medical records.
The breast tumors of approximately 70% of the women with breast cancer were collected. The expression of ER and PR in the breast tumors was measured by immunohistochemistry with the use of tissue microarrays (TMAs).7–9 If TMAs were not available, the ER and PR status was obtained from medical records. Insulin receptor (IR) expression (cytoplasmic and membranous) was measured with Definiens image analysis software (Tissue Studio, Definiens AG, Munich, Germany) in 2501 breast tumors in the NHS.10
Covariates
Body mass index (BMI), smoking status, alcohol consumption, physical activity, and aspirin use were collected via biennial questionnaires that the women completed at least 12 months after breast cancer diagnosis. Because reverse causation may occur, we calculated the cumulative averages of postdiagnostic BMI and physical activity based on 4-year lagged values as well as the changes in BMI between prediagnosis (collected from the last questionnaire before diagnosis) and postdiagnosis (4-year lagged cumulative averages of postdiagnostic BMI). From biennial questionnaires, we also collected prediagnostic information about menopausal status, age at menopause, postmenopausal hormone use, and oral contraceptive use. In addition, we obtained information about the age at diagnosis, the stage of breast cancer, and self-reported treatment including radiation therapy, chemotherapy, and hormonal treatment by reviewing medical records and supplemental questionnaires.
Statistical analysis
We evaluated breast cancer–specific and all-cause mortality as end points in the combined data from the NHS and NHSII. Follow-up was based on the return date of the first FFQ after diagnosis until death or the end of the study period (December 2019 in the NHS and NHSII), whichever came first.
Participants with breast cancer were categorized into quintiles on the basis of their prediagnostic, first postdiagnostic, and cumulative average of postdiagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) by using Cox proportional hazards regression models. Model 1 was stratified by cohort and adjusted for age at diagnosis and calendar year of diagnosis. In the analysis of breast cancer–specific mortality, follow-up was censored with death from other causes. We have not done any model selection, and in model 2, in addition to stratifying by cohort and adjusting for age at diagnosis and calendar year of diagnosis, we adjusted for time between diagnosis and first FFQ after diagnosis, calendar year at the start of follow-up of each 2-year questionnaire cycle, and potential predictors of breast cancer survival including prediagnostic BMI, changes in BMI from prediagnosis to postdiagnosis, postdiagnostic smoking, postdiagnostic physical activity, postdiagnostic aspirin use, postdiagnostic alcohol consumption, postdiagnostic total energy intake, stage of disease, ER/PR status, treatment, race, prediagnostic oral contraceptive use, menopausal status, age at menopause, and postmenopausal hormone use. Our analysis included missing indicator variables for postdiagnostic smoking status, BMI before diagnosis, changes in BMI from prediagnosis to postdiagnosis, postdiagnostic aspirin use, postdiagnostic physical activity, ER/PR status, hormonal treatment, radiotherapy, and chemotherapy, as well as menopausal status, age at menopause, and postmenopausal hormone therapy before diagnosis. Additionally, we controlled for the postdiagnostic modified alternate healthy eating index (AHEI)11 (excluding alcohol scores), dietary glycemic index (GI), or prediagnostic low-carbohydrate diet scores to handle the confounding effect of other dietary factors.
In stratified analyses, we assessed whether IR status (IR positive/negative), ER status (ER positive/negative), postdiagnostic BMI (<25/≥25 kg/m2), and history of type 2 diabetes (yes/no) might affect the associations between low-carbohydrate diet scores and survival.
Furthermore, we estimated the effect of replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from total protein, animal protein, plant protein, total fat, animal fat, plant fat, saturated fatty acids, monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) as well as replacing 3% of energy intake from available carbohydrate with an equivalent energy intake from red meat protein, processed meat protein, poultry protein, fish protein, egg protein, and dairy protein. When estimating the effect of substituting one type of fat or protein for carbohydrate, we included energy intake from carbohydrate, protein, fat, and alcohol as well as total energy intake simultaneously as continuous variables in the multivariate-adjusted model. The HRs and 95% CIs for the substitution effects were derived from the difference between the regression coefficients, variances, and covariances.12
All analyses were performed with SAS software version 9.4 (SAS Institute, Cary, North Carolina) with a two-sided p value of <0.05.
RESULTS
A cohort of 9621 eligible women diagnosed with stage I, II, or III breast cancer was followed for a median of 12.4 years; 1269 women died from breast cancer and 3850 died from any causes. Participants’ characteristics are shown in Table 1 based on the quintiles of the overall low-carbohydrate diet score. Compared with women with lower scores, those with higher scores on the overall low-carbohydrate diet consumed more total protein and total fat and less fiber, fruits, whole grains, refined grains, fruit juice, sugar-sweetened beverages, and added sugar after diagnosis. They also smoked more and took more aspirin after diagnosis, and used oral contraceptives more frequently before diagnosis. However, they had higher BMI and lower physical activity levels after diagnosis.
TABLE 1.
Age-standardized characteristics of 9621 women with breast cancer in the Nurses’ Health Study and Nurses’ Health Study II after breast cancer diagnosis, according to quintiles of overall low-carbohydrate diet score.
| Overall low-carbohydrate diet score | |||||
|---|---|---|---|---|---|
| Characteristic | Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 |
| No. | 1746 | 2101 | 1887 | 1798 | 2089 |
| Mean | |||||
| Alcohol consumption, g/day | 3.9 | 6.0 | 6.6 | 6.9 | 5.5 |
| Total fiber intake, g/day | 22.8 | 21.7 | 20.9 | 19.8 | 18.4 |
| Available carbohydrate intake, % of energy/day | 56.8 | 50.2 | 46.1 | 42.4 | 36.8 |
| Total protein intake, % of energy/day | 14.8 | 17.1 | 17.8 | 18.2 | 20.2 |
| Animal protein intake, % of energy/day | 8.9 | 11.2 | 12.0 | 12.7 | 14.9 |
| Plant protein intake, % of energy/day | 6.0 | 5.9 | 5.8 | 5.6 | 5.3 |
| Total fat intake, % of energy/day | 24.4 | 27.5 | 30.6 | 33.8 | 38.0 |
| Animal fat intake, % of energy/day | 10.2 | 12.4 | 14.1 | 15.7 | 19.0 |
| Plant fat intake, % of energy/day | 14.1 | 15.1 | 16.6 | 18.1 | 19.0 |
| Saturated fat intake, % of energy/day | 7.9 | 9.0 | 10.0 | 10.9 | 12.3 |
| Monounsaturated fat intake, % of energy/day | 9.0 | 10.3 | 11.6 | 13.1 | 15.1 |
| Polyunsaturated fat intake, % of energy/day | 5.2 | 5.7 | 6.2 | 6.7 | 7.3 |
| Total energy intake, kcal/day | 1753 | 1725 | 1740 | 1724 | 1692 |
| Total fruit intake, servings/day | 2.2 | 1.9 | 1.7 | 1.5 | 1.3 |
| Total vegetable intake, servings/day | 3.3 | 3.3 | 3.3 | 3.3 | 3.4 |
| Whole grain intake, servings/day | 1.2 | 1.2 | 1.1 | 1.0 | 0.8 |
| Refined grain intake, servings/day | 2.0 | 1.9 | 1.8 | 1.7 | 1.5 |
| Legume intake, servings/day | 0.2 | 0.2 | 0.2 | 0.1 | 0.1 |
| Fruit juice consumption, servings/day | 1.0 | 0.8 | 0.6 | 0.5 | 0.4 |
| Sugar-sweetened beverage consumption, servings/day | 0.6 | 0.3 | 0.2 | 0.1 | 0.1 |
| Added sugar intake, g/day | 62.4 | 45.4 | 40.2 | 35.6 | 28.6 |
| Age at diagnosis, years | 59.3 | 59.1 | 58.5 | 58.4 | 57.6 |
| BMI, kg/m2 | 25.5 | 26.2 | 26.7 | 26.9 | 27.6 |
| Physical activity, MET hours/week | 19.5 | 19.0 | 18.4 | 18.3 | 16.9 |
| % | |||||
| Race, non-Hispanic White | 95 | 96 | 96 | 97 | 97 |
| Current smokers | 8 | 8 | 9 | 10 | 11 |
| Ever used oral contraceptives | 55 | 60 | 59 | 62 | 62 |
| Ever used menopausal hormone | 45 | 47 | 47 | 51 | 47 |
| Current use of aspirin | 41 | 44 | 45 | 43 | 46 |
| Premenopausal at diagnosis | 26 | 26 | 26 | 25 | 26 |
| Stage of breast cancer | |||||
| I | 59 | 60 | 60 | 60 | 61 |
| II | 32 | 30 | 29 | 30 | 29 |
| III | 9 | 10 | 11 | 10 | 10 |
| Estrogen receptor status | |||||
| Positive | 77 | 77 | 77 | 77 | 78 |
| Negative | 17 | 17 | 17 | 17 | 17 |
| Missing | 6 | 6 | 6 | 6 | 5 |
| Treatment | |||||
| Radiotherapy | 56 | 59 | 59 | 60 | 57 |
| Chemotherapy | 46 | 48 | 49 | 48 | 49 |
| Hormonal treatment | 69 | 72 | 71 | 74 | 71 |
Abbreviations: BMI, body mass index; MET, metabolic equivalent of task.
After adjusting for potential confounding variables, a higher postdiagnostic overall low-carbohydrate diet score was significantly associated with a lower risk of all-cause mortality: HR for quintile 5 vs. quintile 1 (HRQ5vsQ1), 0.82; 95% CI, 0.74–0.91; ptrend = .0001 (Table 2). However, the association with breast cancer–specific mortality was somewhat significant (HRQ5vsQ1, 0.80; 95% CI, 0.67–0.96; ptrend = .07). Similar associations were observed after additionally controlling for postdiagnostic AHEI or postdiagnostic dietary GI (Table S1). A postdiagnostic overall low-carbohydrate diet score was significantly associated with a lower risk of breast cancer–specific mortality after additionally adjusting for a prediagnostic overall low-carbohydrate diet score (Table S1).
TABLE 2.
Postdiagnostic cumulative average of low-carbohydrate diet scores in relation to mortality after breast cancer diagnosis (n = 9621 women; 3850 deaths including 1269 deaths from breast cancer): Nurses’ Health Study and Nurses’ Health Study II.
| Breast cancer-specific mortality | All-cause mortality | ||||||
|---|---|---|---|---|---|---|---|
| Quintile | Median score | No. of deaths | Model 1 HR (95% CI) | Model 2 HR (95% CI) | No. of deaths | Model 1 HR (95% CI) | Model 2 HR (95% CI) |
| Overall low-carbohydrate diet | |||||||
| 1 | 6.3 | 311 | 1 | 1 | 962 | 1 | 1 |
| 2 | 11.4 | 255 | 0.80 (0.68–0.95) | 0.93 (0.78–1.10) | 843 | 0.91 (0.83–0.99) | 0.92 (0.83–1.01) |
| 3 | 15.0 | 228 | 0.81 (0.68–0.96) | 0.95 (0.79–1.13) | 734 | 0.93 (0.85–1.03) | 0.92 (0.83–1.01) |
| 4 | 18.0 | 268 | 0.87 (0.74–1.03) | 1.02 (0.86–1.20) | 709 | 0.86 (0.78–0.95) | 0.87 (0.79–0.96) |
| 5 | 23.0 | 207 | 0.75 (0.63–0.89) | 0.80 (0.67–0.96) | 602 | 0.83 (0.75–0.92) | 0.82 (0.74–0.91) |
| p trend | .006 | .07 | .0003 | .0001 | |||
| Animal-rich low-carbohydrate diet | |||||||
| 1 | 6.0 | 287 | 1 | 1 | 832 | 1 | 1 |
| 2 | 11.0 | 238 | 0.84 (0.71–1.00) | 0.92 (0.77–1.10) | 783 | 1.01 (0.91–1.11) | 0.97 (0.88–1.08) |
| 3 | 14.5 | 240 | 0.84 (0.70–0.99) | 0.87 (0.73–1.04) | 789 | 1.02 (0.93–1.13) | 0.96 (0.87–1.06) |
| 4 | 18.3 | 260 | 0.92 (0.78–1.09) | 0.99 (0.83–1.18) | 784 | 1.07 (0.97–1.18) | 0.97 (0.88–1.07) |
| 5 | 23.5 | 244 | 0.90 (0.76–1.07) | 0.89 (0.74–1.06) | 662 | 0.96 (0.87–1.07) | 0.93 (0.84–1.04) |
| p trend | .46 | .36 | .89 | .23 | |||
| Plant-rich low-carbohydrate diet | |||||||
| 1 | 8.0 | 299 | 1 | 1 | 959 | 1 | 1 |
| 2 | 12.0 | 245 | 0.78 (0.66–0.93) | 0.89 (0.75–1.05) | 846 | 0.89 (0.81–0.97) | 0.88 (0.80–0.97) |
| 3 | 15.0 | 271 | 0.86 (0.73–1.01) | 0.97 (0.82–1.15) | 792 | 0.87 (0.79–0.96) | 0.91 (0.82–1.00) |
| 4 | 18.0 | 228 | 0.76 (0.63–0.90) | 0.94 (0.79–1.12) | 693 | 0.82 (0.74–0.90) | 0.86 (0.78–0.95) |
| 5 | 22.0 | 226 | 0.76 (0.64–0.90) | 0.86 (0.72–1.03) | 560 | 0.67 (0.61–0.75) | 0.73 (0.66–0.82) |
| p trend | .002 | .19 | <.0001 | <.0001 | |||
Note: Model 1 was stratified by cohort and adjusted for age at diagnosis (year) and calendar year of diagnosis. Model 2 was stratified by cohort and adjusted for age at diagnosis (year), calendar year of diagnosis, time between diagnosis and first FFQ (year), calendar year at the start of follow-up of each 2-year questionnaire cycle, prediagnostic BMI (<18.5, 18.5 to <25, 25.0 to <30, 30 to <35, ≥35 kg/m2, missing), BMI change after diagnosis (no change [≥−0.5 to ≤0.5 kg/m2], decrease [<−0.5 kg/m2], increase [>0.5–2 kg/m2], increase [>2 kg/m2], missing), postdiagnostic smoking (never, past, current 1–14 cigarettes/day, current 15–24 cigarettes/day, current ≥25 cigarettes/day, missing), postdiagnostic physical activity (<5, 5 to <11.5, 11.5 to <22, ≥22 MET h/week, missing), postdiagnostic aspirin use (never, past, current, missing), postdiagnostic alcohol consumption (<0.15, 0.15 to <2.0, 2.0 to <7.5, ≥7.5 g/day), postdiagnostic total energy intake (quintiles, kcal/day), stage of disease (I, II, III), ER/PR status (ER/PR positive, ER positive and PR negative, ER/PR negative, missing), radiotherapy (yes, no, missing), chemotherapy (yes, no, missing), hormonal treatment (yes, no, missing), race (non-Hispanic White, other), oral contraceptive use (ever, never), and prediagnostic menopausal status, age at menopause, and postmenopausal hormone use (premenopausal; postmenopausal, age at menopause < 50 years, and never postmenopausal hormone use; postmenopausal, age at menopause < 50 years, and past postmenopausal hormone use; postmenopausal, age at menopause < 50 years, and current postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and never postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and past postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and current postmenopausal hormone use; missing).
Abbreviations: BMI, body mass index; ER, estrogen receptor; FFQ, food frequency questionnaire; MET, metabolic equivalent of task; PR, progesterone receptor.
Although a higher postdiagnostic animal-rich low-carbohydrate diet score was not associated with a lower risk of either breast cancer–specific mortality or all-cause mortality (Table 2), a higher postdiagnostic plant-rich low-carbohydrate diet score was significantly associated with a lower risk of all-cause mortality (HRQ5vsQ1, 0.73; 95% CI, 0.66–0.82; ptrend < .0001) (Table 2). A postdiagnostic plant-rich low-carbohydrate diet score was significantly associated with a lower risk of breast cancer–specific mortality after additionally adjusting for a prediagnostic plant-rich low-carbohydrate diet score. The association of a postdiagnostic plant-rich low-carbohydrate diet score and all-cause mortality remained significant after additionally controlling for postdiagnostic AHEI, postdiagnostic dietary GI, or a prediagnostic plant-rich low-carbohydrate diet score (Table S1).
In the analysis of postdiagnostic overall, animal-rich, and plant-rich low-carbohydrate diet scores in relation to breast cancer–specific and all-cause mortality stratified by IR status, no significant interactions were observed (Table 3). In addition, no significant interaction in associations was observed by ER status (Table 3).
TABLE 3.
Postdiagnostic cumulative average of low-carbohydrate diet scores in relation to breast cancer–specific mortality after breast cancer diagnosis, stratified by insulin receptor status (n = 2501 women, n = 444 breast cancer deaths) and estrogen receptor status (n = 9052 women, n = 1169 breast cancer deaths): Nurses’ Health Study and Nurses’ Health Study II.
| IR status | ER status | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Quintile | Median score | No. of deaths | IR positive HR (95% CI) | No. of deaths | IR negative HR (95% CI) | No. of deaths | ER positive HR (95% CI) | No. of deaths | ER negative HR (95% CI) |
| Overall low-carbohydrate diet | |||||||||
| 1 | 6.3 | 42 | 1 | 63 | 1 | 215 | 1 | 61 | 1 |
| 2 | 11.4 | 49 | 1.42 (0.92–2.19) | 40 | 0.70 (0.47–1.06) | 191 | 0.95 (0.78–1.16) | 47 | 0.90 (0.60–1.33) |
| 3 | 15.0 | 32 | 1.18 (0.73–1.92) | 39 | 0.69 (0.45–1.05) | 170 | 0.97 (0.79–1.20) | 39 | 0.95 (0.62–1.45) |
| 4 | 18.0 | 41 | 1.25 (0.79–1.98) | 53 | 0.86 (0.59–1.27) | 205 | 1.03 (0.85–1.25) | 45 | 0.94 (0.63–1.41) |
| 5 | 23.0 | 39 | 1.09 (0.68–1.74) | 46 | 0.83 (0.55–1.24) | 152 | 0.78 (0.63–0.97) | 44 | 1.00 (0.66–1.51) |
| p trend | .84 | .51 | .08 | .99 | |||||
| p interaction | .46 | .71 | |||||||
| Animal-rich low-carbohydrate diet | |||||||||
| 1 | 6.0 | 34 | 1 | 65 | 1 | 199 | 1 | 50 | 1 |
| 2 | 11.0 | 46 | 1.59 (0.99–2.55) | 32 | 0.55 (0.35–0.86) | 177 | 0.94 (0.77–1.16) | 45 | 1.44 (0.94–2.20) |
| 3 | 14.5 | 41 | 1.30 (0.80–2.12) | 39 | 0.73 (0.48–1.10) | 179 | 0.87 (0.71–1.08) | 42 | 1.08 (0.70–1.67) |
| 4 | 18.3 | 43 | 1.52 (0.94–2.46) | 51 | 0.83 (0.56–1.23) | 192 | 1.00 (0.81–1.22) | 53 | 1.63 (1.07–2.48) |
| 5 | 23.5 | 39 | 1.38 (0.84–2.28) | 54 | 0.81 (0.55–1.20) | 186 | 0.89 (0.73–1.10) | 46 | 1.14 (0.74–1.75) |
| p trend | .29 | .67 | .45 | .41 | |||||
| p interaction | .25 | .42 | |||||||
| Plant-rich low-carbohydrate diet | |||||||||
| 1 | 8.0 | 42 | 1 | 45 | 1 | 216 | 1 | 59 | 1 |
| 2 | 12.0 | 36 | 1.00 (0.62–1.59) | 51 | 1.03 (0.68–1.56) | 184 | 0.87 (0.71–1.06) | 45 | 1.08 (0.72–1.62) |
| 3 | 15.0 | 48 | 1.27 (0.83–1.97) | 51 | 1.02 (0.67–1.55) | 201 | 1.00 (0.82–1.21) | 43 | 0.88 (0.58–1.33) |
| 4 | 18.0 | 39 | 1.07 (0.68–1.69) | 45 | 1.00 (0.65–1.54) | 170 | 0.96 (0.78–1.17) | 46 | 0.95 (0.63–1.43) |
| 5 | 22.0 | 38 | 0.82 (0.51–1.32) | 49 | 1.10 (0.72–1.68) | 162 | 0.80 (0.65–0.98) | 43 | 1.07 (0.71–1.62) |
| p trend | .51 | .73 | .11 | .97 | |||||
| p interaction | .59 | .93 | |||||||
Note: Models were stratified by cohort and adjusted for age at diagnosis (year), calendar year of diagnosis, time between diagnosis and first FFQ (year), calendar year at the start of follow-up of each 2-year questionnaire cycle, prediagnostic BMI (<18.5, 18.5 to <25, 25.0 to <30, 30 to <35, ≥35 kg/m2, missing), BMI change after diagnosis (no change [≥−0.5 to ≤0.5 kg/m2], decrease [<−0.5 kg/m2], increase [>0.5–2 kg/m2], increase [>2 kg/m2], missing), postdiagnostic smoking (never, past, current 1–14 cigarettes/day, current 15–24 cigarettes/day, current ≥25 cigarettes/day, missing), postdiagnostic physical activity (<5, 5 to <11.5, 11.5 to <22, ≥22 MET h/week, missing), postdiagnostic aspirin use (never, past, current, missing), postdiagnostic alcohol consumption (<0.15, 0.15 to <2.0, 2.0 to <7.5, ≥7.5 g/day), postdiagnostic total energy intake (quintiles, kcal/day), stage of disease (I, II, III), ER/PR status (ER/PR positive, ER positive and PR negative, ER/PR negative, missing), radiotherapy (yes, no, missing), chemotherapy (yes, no, missing), hormonal treatment (yes, no, missing), race (non-Hispanic White, other), oral contraceptive use (ever, never), and prediagnostic menopausal status, age at menopause, and postmenopausal hormone use (premenopausal; postmenopausal, age at menopause < 50 years, and never postmenopausal hormone use; postmenopausal, age at menopause < 50 years, and past postmenopausal hormone use; postmenopausal, age at menopause < 50 years, and current postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and never postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and past postmenopausal hormone use; postmenopausal, age at menopause ≥ 50 years, and current postmenopausal hormone use; missing). In the ER status analysis, we did not adjust for ER/PR status.
Abbreviations: BMI, body mass index; ER, estrogen receptor; FFQ, food frequency questionnaire; IR, insulin receptor; MET, metabolic equivalent of task; PR, progesterone receptor.
We did not observe any significant interaction by obesity status (Table S2). When we looked at the associations based on a history of type 2 diabetes, a higher postdiagnostic overall low-carbohydrate diet score was significantly associated with a lower risk of all-cause mortality among women without a history of diabetes (HRQ5vsQ1, 0.78; 95% CI, 0.69–0.88; ptrend < .0001; p for interaction = .03) and a higher animal-rich low-carbohydrate diet score was significantly associated with a lower risk of all-cause mortality among women without a history of diabetes (HRQ5vsQ1, 0.87; 95% CI, 0.77–0.98; ptrend = .04; p for interaction = .04) (Table S3).
In substitution analyses, replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from total protein was associated with a 13% lower risk of breast cancer–specific mortality (HR, 0.87; 95% CI, 0.79–0.96) and an 8% lower risk of all-cause mortality (HR, 0.92; 95% CI, 0.86–0.98). Likewise, replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from animal protein was associated with a 16% lower risk of breast cancer–specific mortality (HR, 0.84; 95% CI, 0.75–0.93) and a 13% lower risk of all-cause mortality (HR, 0.87; 95% CI, 0.81–0.93). However, replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from plant protein was just significantly associated with an 18% lower risk of all-cause mortality (HR, 0.82; 95% CI, 0.68–0.998). Replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from total fat and plant fat was associated with a 5% decreased risk (HR, 0.95; 95% CI, 0.93–0.98) and a 9% decreased risk (HR, 0.91; 95% CI, 0.88–0.94) of all-cause mortality, respectively (Figure 1). In addition, replacing 5% of energy intake from available carbohydrate with an equivalent energy intake from MUFAs was associated with an 11% lower risk of all-cause mortality (HR, 0.89; 95% CI, 0.82–0.96) and replacing with an equivalent energy intake from PUFAs was associated with a 13% lower risk of all-cause mortality (HR, 0.87; 95% CI, 0.77–0.98). We also evaluated the risk of breast cancer–specific and all-cause mortality if women replaced 3% of energy intake from available carbohydrate with an equivalent energy intake from animal protein based on the source. We observed a 17% lower risk of breast cancer–specific mortality (HR, 0.83; 95% CI, 0.70–0.97) and a 15% lower risk of all-cause mortality (HR, 0.85; 95% CI, 0.77–0.95) if women replaced 3% of energy intake from available carbohydrate with an equivalent energy intake from fish protein, whereas replacing with energy intake from egg protein was associated with a 65% higher risk of breast cancer–specific mortality (HR, 1.65; 95% CI, 1.11–2.46). Replacing 3% of energy intake from available carbohydrate with an equivalent energy intake from red meat protein (HR, 0.93; 95% CI, 0.86–0.999), poultry protein (HR, 0.90; 95% CI, 0.84–0.95) or dairy protein (HR, 0.94; 95% CI, 0.89–0.99) was associated with a lower risk of all-cause mortality (Figure 1).
FIGURE 1.
Replacing available carbohydrate intake with equivalent energy from protein and fat intake after breast cancer diagnosis in relation to mortality: Nurses’ Health Study and Nurses’ Health Study II.
We also examined the associations from the first FFQ after diagnosis. For the first FFQ after diagnosis, a lower risk of all-cause mortality was observed with a higher score of plant-rich low-carbohydrate diet (Table S4). We did not observe any significant associations between prediagnostic overall, animal-rich, or plant-rich low-carbohydrate diet scores from the last FFQ before diagnosis and breast cancer–specific or all-cause mortality (Table S5).
DISCUSSION
We found that among women with stage I–III breast cancer, higher postdiagnostic scores for overall and plant-rich low-carbohydrate diets were significantly associated with a lower risk of all-cause mortality when controlling for other potential risk factors. Stratified analysis suggests a possible role of a history of diabetes in association with overall as well as animal-rich low-carbohydrate diet scores and risk of all-cause mortality. In substitution analyses, we observed that replacing energy intake from available carbohydrate with an equivalent energy intake from total protein, animal protein, or plant protein was associated with a lower risk of breast cancer–specific and all-cause mortality, although it was not significant for plant protein and breast cancer–specific mortality. With regard to animal protein, a lower risk of breast cancer–specific mortality was observed with replacing energy intake from available carbohydrate with an equivalent energy intake from fish protein, whereas a lower risk of all-cause mortality was observed with replacing energy intake from available carbohydrate with an equivalent energy intake from red meat protein, poultry protein, fish protein, and dairy protein. In contrast, replacing energy intake from available carbohydrate with an equivalent energy intake from egg protein was associated with a higher risk of breast cancer–specific mortality. Considering fat intake, a lower risk of all-cause mortality was observed with replacing energy intake from available carbohydrate with an equivalent energy intake from total fat, plant fat, MUFAs, or PUFAs.
A limited number of studies have examined the effects of carbohydrate intake as a prognostic factor for cancer. A high-carbohydrate diet may contribute to the development and prognosis of cancer. In addition to supplying tumor cells with fuel, high dietary carbohydrate increases the release of insulin, which aids in breast cancer cell proliferation13 and boosts glucose uptake into the cells. We have recently shown that the amount of carbohydrate consumed and the source of carbohydrate after breast cancer diagnosis may influence the risk of mortality.2,5 Similarly, high total carbohydrate intake was associated with a poorer prognosis in individuals with stage III colon cancer,14 and following a plant-rich low-carbohydrate diet had a favorable effect on mortality risk in individuals with stage I–III colorectal cancer.6 Following a breast cancer diagnosis, this study suggests that decreasing carbohydrate intake and increasing protein and fat consumption, especially plant-based ones, may improve overall survival. Note that this study does not recommend a diet that is very low in carbohydrates. According to Table 1, on average, for women in the highest quintile of overall low-carbohydrate diet score, 36.8% of their energy came from available carbohydrate, 20.2% from total protein, and 38.0% from total fat, whereas 56.8% of energy came from available carbohydrate, 14.8% from total protein, and 24.4% from total fat for women in the first quintile.
Although adherence to an animal-rich low-carbohydrate diet was not associated with better survival after breast cancer, replacing carbohydrate intake with some types of animal protein seems to be beneficial for breast cancer survivors. In our previous study,2 we found that a high intake of animal protein was associated with a lower risk of breast cancer–specific mortality. In the current study, among animal protein, we also found that substituting fish protein for carbohydrate intake may lead to a lower risk of breast cancer–specific mortality and substituting fish protein, red meat protein, poultry protein, or dairy protein for carbohydrate intake may lead to a lower risk of all-cause mortality. High consumption of fish was associated with a lower risk of overall mortality among 1463 women with breast cancer in a population-based follow-up study conducted in Long Island, New York.15 According to a meta-analysis of observational studies, consuming fish was associated with a reduced risk of mortality in individuals with ovarian cancer as well as cancer patients in general. In addition, intake of marine omega-3 PUFAs appears to have protective effects on overall cancer survival.16 It is believed that omega-3 PUFAs in fish can inhibit tumor growth and progression17–19; however, more studies are needed to understand the underlying mechanisms.
We also observed that replacing carbohydrate intake with egg protein was associated with a higher risk of breast cancer–specific mortality. Egg consumption was associated with a higher risk of cancer mortality in a meta-analysis of 13 prospective studies among healthy populations.20 Eggs are rich in cholesterol. Cholesterol functions as an agonist of the estrogen-related receptor alpha (ERRα) and stimulates the activation of multiple ERRα metabolic target genes, leading to increased cellular proliferation and migration.21 ERRα immunoreactivity has been associated with a poorer breast cancer prognosis.22 Further studies are needed to verify this result.
Because most women with breast cancer die from other causes, it is also important to understand how diet affects their overall survival. When carbohydrate intake was replaced with PUFAs or MUFAs, the risk of all-cause mortality was decreased. These findings are consistent with other studies that have shown that healthy populations with higher consumption of MUFAs and PUFAs have a lower risk of overall mortality.23,24
Better survival was observed among cancer patients with high IR expression.25,26 A high-carbohydrate diet can lead to decreased IR expression,25 so we hypothesized that the association between a low-carbohydrate diet score and breast cancer-specific mortality may vary based on the IR status of tumor. However, we did not find any significant interactions. As mentioned in Table S3, overall and animal-rich low-carbohydrate diet scores were associated with a lower risk of all-cause mortality among women without a history of diabetes but we did not observe any heterogeneity based on IR status of tumor. One reason might be related to a smaller number of breast cancer participants with data on IR status (n = 2501) compared with breast cancer participants with data on diabetes status (n = 9621).
The current study has a number of strengths, including data collected from a prospective diet and lifestyle survey before and after breast cancer diagnosis, which gave us the unique opportunity to analyze the relationship between low-carbohydrate diet scores and breast cancer survival after adjusting for other factors. These analyses also pointed out that repeated assessments of diet after diagnosis are more effective than one diet assessment. On the basis of the fact that dietary behavior is complex and can vary widely over time, by measuring and monitoring dietary intake over time via multiple assessments, we can gain a more accurate and comprehensive picture of an individual’s dietary habits. Exploring the associations based on hormone status, using standard medical record review, and following up for several decades are other noteworthy aspects of the present study.
However, our study has some limitations. The study controlled for several breast cancer risk factors, but there is still some possibility of residual confounding. Given that non-Hispanic White and health professionals constituted the majority of the participants, we cannot be certain that the current results are generalizable to other sociodemographic groups. The FFQ was used to estimate dietary intake; therefore, under- and overreporting of food groups cannot be ruled out as a measurement error. Type I error may occur as a result of multiple comparisons. However, the primary hypothesis involved the associations of overall, animal-rich, or plant-rich low-carbohydrate diets with breast cancer–specific and all-cause mortality and further analyses were directed into better understanding these associations. Despite these limitations, our results are scientifically compelling and closely align with previous work.
In summary, overall mortality was lower among women with breast cancer who adhered to overall and plant-rich low-carbohydrate diets after diagnosis. The findings of this study suggest that breast cancer survivors benefit from replacing carbohydrate with protein. We found that replacing carbohydrate with either animal protein or plant protein was associated with a lower risk of breast cancer–specific and all-cause mortality, although it was not significant for plant protein and breast cancer–specific mortality. Even though an animal-rich low-carbohydrate diet has not been linked to improved survival in women with breast cancer, consuming fish, red meat, poultry, and dairy protein instead of carbohydrate may give women with breast cancer a better survival benefit. Additionally, lowering carbohydrate consumption and increasing fat consumption, especially plant fat and fat rich in PUFAs and MUFAs, may decrease overall mortality risk among breast cancer survivors. The findings of our study and other evidence2–4 suggest that breast cancer survivors could benefit from limiting intake of carbohydrates, especially from fruit juice, sugar-sweetened beverages, and added sugar, and increasing the amount of protein and fat, in particular from plant sources. Larger prospective studies and more experimental data are needed to confirm the findings and to clarify the mechanisms.
Supplementary Material
ACKNOWLEDGMENTS
The study was supported by National Institutes of Health grants (U01 CA176726 and UM1 CA186107) and University of Toronto. The study sponsors were not involved in the study design and collection, analysis, and interpretation of the data, the writing of the article, or the decision to submit it for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors were independent from study sponsors. The authors would like to acknowledge the contribution to this study from central cancer registries supported by the Centers for Disease Control and Prevention’s National Program of Cancer Registries and/or the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. Central registries may also be supported by state agencies, universities, and cancer centers. Participating central cancer registries include the following: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, and Wyoming.
CONFLICT OF INTEREST STATEMENT
Maryam S. Farvid is a founder of the Institute for Cancer Prevention and Healing and the Data Statistics Group. Michelle D. Holmes reports a grant from FHI Solutions, nonfinancial support from Bayer AG (Bayer supplies aspirin and placebo for the Aspirin After Breast Cancer trial), and personal fees from Arla Foods (participation in a systematic review of dietary intake in Nigerian children for this company) outside the submitted work as well as being a consultant for Bayer. The other authors declare no conflicts of interest.
Funding information
National Institutes of Health, Grant/Award Numbers: U01 CA176726, UM1 CA186107; University of Toronto
Footnotes
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this article.
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