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Keywords:

  • alcohol;
  • beer;
  • breast cancer;
  • energy adjustment;
  • fat intake;
  • prospective study;
  • spirits;
  • wine

Abstract

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Associations between intakes of relative fat, total alcohol and alcoholic beverages and risk of breast cancer were examined in a subsample of 11,726 postmenopausal women from the MDC cohort. The MDC conducted baseline examinations from 1991 to 1996; the end of follow-up was 31 December 2001. Data were obtained by an interview-based diet history method, a structured questionnaire, anthropometric measurements and national and regional cancer registries. During 89,602 person-years of follow-up, 342 incident cases were documented. Cox regression analysis examined breast cancer risks adjusted for potential confounders. Two energy-adjustment approaches (i.e., adjusting for total energy vs. adjusting for nonalcohol energy) were used. High total alcohol intake was associated with a nonsignificantly elevated risk. High wine intake was associated with a significantly elevated breast cancer risk (relative risk = 2.12, 95% CI 1.24–3.60). There were significant trends of increased breast cancer risk across quintiles of relative fat intake. Mutual adjustment did not affect risk estimates for total alcohol or relative fat intakes. The specific energy-adjustment approach did not influence associations differentially. © 2004 Wiley-Liss, Inc.

Although all studies do not report an association between alcohol intake and breast cancer risk,1 there is accumulated evidence for a positive association from both cohort and case-control studies.2, 3, 4, 5, 6 A large collaborative reanalysis of individual data from 53 different epidemiologic studies found a dose–response effect of alcohol.7 However, because of reporting errors of alcohol intake, uncertainty remains about the quantitative effects of intake of a fixed amount of alcohol. Furthermore, firm conclusions about the risk of breast cancer at low intake levels of alcohol cannot be drawn because of the likelihood of measurement errors, particularly the tendency of underestimation.

Some of the earlier studies found a dose–response effect,2, 3, 4, 5, 8 while others found an effect at high intake levels only.6, 9, 10, 11 Several studies have examined the separate effects of beer, wine and spirits; but the type of alcoholic beverage does not appear to differentially influence the risk of breast cancer.1, 3, 5

One meta-analysis2 addressed several issues concerning the disparity in outcome from different studies. Risk estimates were higher in hospital-based case-control studies compared to cohort or community-based case-control studies; cohort studies with a follow-up of <10 years had higher estimates compared to cohort studies with longer follow-up. No differences were found depending on publication year, menopausal status and location of study (United States vs. outside United States). However, Longnecker3 found that studies with the strongest alcohol–breast cancer association came from countries with high per capita alcohol consumption, i.e., Western European countries. Studies indicate that different cultures could imply differences in alcohol drinking patterns, type of alcoholic beverage used and the association between alcohol intake and potential dietary (e.g., fruit and vegetable intakes) and nondietary confounders.12, 13

Only a limited number of studies have examined alcohol intake together with other dietary factors. The association between alcohol intake and breast cancer was independent of dietary habits in general in some studies.3, 6 However, other studies found that high folate consumption might reduce breast cancer risk at high alcohol intake levels.14, 15 Yet another study indicated higher risks in strata of higher absolute fat intake.9 The case of adjusting for fat intake is of special analytic interest since both alcohol and fat potentially influence breast cancer risk, though the role of fat is more controversial16 than that of alcohol. The common approach in analytic projects is to examine total fat intake as relative intake based on TE. Since alcohol, like fat, is an energy source, this definition leads to an inherent inverse correlation between fat and alcohol intake. However, this is true only if alcohol and fat intakes are independent variables. If this is not the case and, e.g., a positive correlation exists, the inverse correlation between relative intakes will be influenced and weakened. Furthermore, current nutrient recommendations concerning macronutrient composition of the diet express macronutrient intakes as percentages of NAE, not of TE.17 It is thus of interest to evaluate the influence of the 2 definitions of energy when estimating breast cancer risk associated with alcohol, with simultaneous adjustment for fat.

Our aims were to examine (i) if breast cancer risk in postmenopausal women is associated with intakes of total alcohol, specific alcoholic beverages or total fat; (ii) the genuine effects of total alcohol and fat intakes when adjusted for each other; and (iii) if the specific energy-adjustment approach influences these associations.

MATERIAL AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

MDC

The MDC study is a prospective cohort study in Malmö, a city in the south of Sweden with approximately 250,000 inhabitants. The MDC source population was, in 1991, defined as all persons living in the city of Malmö and born during 1926–1945. However, in May 1995, the cohort was extended to include all women born during1923–1950 and all men born during 1923–1945. With this extension, 74,138 persons constituted the source population. Inadequate Swedish language skills and mental incapacity were the only exclusion criteria. When baseline examinations closed in October 1996, 28,098 participants had completed all parts. Details of recruitment and the cohort are described elsewhere.18 The ethics committee at Lund University has approved the MDC study protocol. Participants signed informed consent.

Participants visited the MDC screening center twice. During the first visit, they were informed about the study and instructed how to register meals in the menu book and how to fill in the diet questionnaire. Project nurses took blood samples, blood pressure and anthropometric measurements. Participants completed all questionnaires at home. During the second visit, approximately 2 weeks after the first, the socioeconomic questionnaire was checked for completeness and the dietary interview conducted.

Study population

We used an age-based definition of menopausal status.19 The questionnaire item “What year did your menses cease?” was answered by all participants, but it was unclear to the women how to relate the answer to surgery (hysterectomy or oophorectomy) or hormone therapy use. Further, the computerized information on hysterectomy and oophorectomy from Malmö University Hospital does not cover the entire relevant time period for the whole MDC cohort. Natural menopause was determined in a group of women (n = 2,898) in the MDC cohort without surgery and without hormone therapy. The median natural menopause age was 50.0 years.

Eligible participants were thus women 50 years or older at baseline examination. All prevalent cancer cases, except cervical cancer in situ and nonmalignant melanoma skin cancer, were excluded. In total, 11,726 postmenopausal women were included in this subsample. The average follow-up time was 7.6 years. Women born during 1923–1925 and 1946 have shorter follow-up due to the MDC study design.

Case definition and ascertainment

The National Swedish Cancer Registry provided data until December 1999. Additional information until end of follow up (31 December 2001) was obtained from the Southern Swedish Regional Tumor Registry. Cases were women diagnosed during follow-up with invasive breast cancer or breast cancer in situ. Information on vital status was obtained from the National Tax Board, which provides up-to-date information on vital status for all Swedish residents. Cases contributed person-time from date of enrollment until time of diagnosis. Noncases contributed person-time from date of enrollment until death (470 women), migration from Sweden (57 women) or end of follow-up (31 December 2001), whichever was the first. In total, 342 incident (312 invasive and 30 in situ) breast cancer cases were documented during 89,602 person-years of follow-up.

Dietary data

The MDC uses an interview-based, modified diet history method. It combines (i) a 7-day menu book for registration of lunch and dinner meals, including cold beverages and alcohol, and (ii) a questionnaire for assessment of meal patterns, consumption frequencies and portion sizes of regularly eaten foods (i.e., sandwiches, cakes and cookies, fruit, breakfast cereals, milk and yoghurt, coffee and tea, sweets, snacks). Drugs, natural remedies and nutrient supplements were recorded in the menu book. During the interview, participants were asked complementary questions on their usual meal pattern, cooking methods and details of food choices. The participant at home used a booklet with 48 black-and-white photographs to estimate portion sizes. Usual portion sizes of foods and dishes listed in the menu book were estimated during the interview from a more extensive book with black-and-white photographs. Diet interviewers coded and entered the information from the menu book during the interview, using interactive software (Kostsvar; Aivo, Stockholm, Sweden).

Mean daily intake of foods was calculated based on frequency and portion size estimates from the questionnaire and menu book. The food intake was converted to energy and nutrient intakes using the MDC nutrient database, where the majority of the nutrient information comes from the Food Composition Database, Version PC-K0572-93, from the National Food Administration (Uppsala, Sweden).

The relative validity of the MDC method was evaluated in a sample of Malmö residents, 105 women and 101 men, 50–69 years old, using weighed food records, 3 days every second month during a year (i.e., 18 daily food records), as the reference method. Pearson correlation coefficients between the reference method and the MDC method administrated after the 12-month reference period were 0.69 for energy-adjusted fat intake, 0.78 for energy-adjusted total alcohol,20 0.77 for beer, 0.65 for wine and 0.71 for spirits.21 Reproducibility for total alcohol and alcoholic beverages was high.22

Variables

Daily intakes of TE (kcal/day), NAE (kcal/day), fat (g/day), E% from fat, total alcohol (g/day), E% from alcohol, beer (i.e., beer with 1.8, 2.8 or 4.5% alcohol and cider with 0.8% alcohol; cl/day), wine (aggregated code including white, red and fortified wines; cl/day), spirits (cl/day), the sum of folic acid (μg/day) from diet and dietary supplements and the sum of all fruits and vegetables (g/day) were calculated from the dietary information. The contribution of alcohol from each beverage group, beer-alcohol, wine-alcohol and spirits-alcohol, was calculated as the percentage contribution of the total amount of alcohol ingested from all alcoholic beverages.

Two relative fat intake variables were defined: FATTE and FATNAE. For each definition, 5 exposure categories were created based on the quintile ranking of participants on fat residuals.

Information on total alcohol consumption was converted into a 4-category variable. Women reporting 0 consumption in the menu book and no consumption of any type of alcohol during the previous year in the questionnaire were categorized as total abstainers. The other women were categorized according to an assumption of biologic risk;23 this definition has previously been used in several projects from the MDC cohort.24 Category ranges were <15 g alcohol/day (low), 15–30 g (medium) and >30 g (high).

Intakes of beer, wine and spirits were categorized into 4-category variables based on the ranking of absolute intakes of each beverage: abstainers (0 consumption in menu book and no consumption of any type of alcohol during the last year in the questionnaire); low consumers (intakes below or equal to the median); medium consumers (intakes above median but below or equal to the 97.5th percentile); high consumers (intakes above the 97.5th percentile).

In September 1994, the processing of dietary data was slightly altered.25 Method version (indicating data collection before or after 1 September 1994), diet interviewer and season of diet interview were used to control for undue variation in dietary data collection over time.

Age at baseline was obtained from the 10-digit personal identification number.

Information on reproduction, socioeconomic factors and lifestyle factors was collected by a structured multiple-choice questionnaire.

Past change of dietary habits was based on the questionnaire item “Have you substantially changed your dietary habits because of illness or other reason?”

Age at menarche was used as a continuous variable. Age at birth of first child was divided into 4 categories (≤24 years, >24–30 years, >30 years and “no children”), missing information (2.0%) was recoded as “no children”. Current use of hormone therapy (dichotomous variable, yes/no) was based on the questionnaire item “Which medications do you use on a regular basis?” in combination with information on drug use from the 7-day menu book.26

Participants were divided into 4 categories according to highest level of education: ≤8 years, 9–10 years, 11–12 years and college education/university degree.

Participants indicated the number of minutes per week spent on 17 different physical activities, separately for the 4 seasons.27 Based on this information, an overall leisure time physical activity score was created.28 The score was divided into quintiles and further categorized as low (quintile 1), moderate (quintiles 2–4) or high (quintile 5). Participants were categorized according to smoking habits as current smokers (including irregular smokers), ex-smokers or never-smokers.

Standing height was measured with a fixed stadiometer calibrated in centimeters. Weight was measured to the nearest 0.1 kg using a balance-beam scale with subjects wearing light clothing and no shoes. Waist was measured midway between the lowest rib margin and iliac crest.

To evaluate low energy reporting, which is a major concern in dietary assessment,29, 30, 31, 32 the ratio between reported total EI and BMR was calculated. BMR was calculated using the equation recommended by the WHO, based on age, sex, weight and height.33

Statistical methods

All dietary variables were log-transformed to normalize distributions. The SPSS (Chicago, IL) statistical computer package (version 10.0) was used for statistical analyses.

Both fat and alcohol contribute to total EI. Different models for energy adjustment are discussed in the literature.34, 35, 36, 37 We compared 2 different energy-adjustment models, adjusting for TE or NAE:

  • equation image(1)
  • equation image(2)

In model (1), β1t symbolizes the change in IRRt with increased alcohol intake, while FATTE and TE are constant, i.e., a substitution of alcohol energy for carbohydrate or protein energy. β2t symbolizes an increase in risk with increased FATTE when alcohol and TE intakes are constant, i.e., a substitution of fat energy for carbohydrate or protein energy. In model (2), β1n symbolizes the change in IRRn with increased alcohol intake, while FATNAE and NAE are constant, i.e., adding alcohol energy. β2n symbolizes an increase in risk with increased FATNAE when alcohol and NAE are constant, i.e., a substitution of fat energy for carbohydrate or protein energy.

The bivariate relation between disease status and exposure variables and covariates was examined with Student's t-test and χ2 test. Bivariate relations between categories of FATTE; FATNAE; wine, beer, spirits and total alcohol intakes; and covariates were examined with ANOVA and χ2 test. The examined variables were diet interviewer, season of diet interview, past food habit change, method version, age at baseline, height, waist, current hormone use, age at birth of first child, age at menarche, leisure time physical activity, smoking habits, educational level, EI:BMR, E% fat, E% alcohol, beer-alcohol, wine-alcohol, spirits-alcohol, folic acid and total fruit and vegetables.

Cox regression examined the associations between intakes of beer, wine, spirits, total alcohol and relative fat and breast cancer incidence. Low consumers of total alcohol and alcoholic beverages were chosen as the reference category since abstainers probably are heterogeneous and may not represent the lowest risk. Further, the abstainer categories had fewer person-years of follow-up. First, the influences of beer, wine, spirits, total alcohol and relative fat intakes on breast cancer risk were examined in separate models while controlling for diet interviewer, season of diet interview, method version, age at baseline, past food habit change and energy. In a second step, the multivariate model was extended to include both known nondietary risk factors38 and potential confounders, i.e., height, waist, current hormone use, age at birth of first child, age at menarche, leisure time physical activity, smoking habits and educational status. The full models were also repeated while adjusting for folic acid intake and total fruit and vegetable intake.

In addition, the genuine effects of total alcohol and relative fat intakes on breast cancer risk were examined in mutually adjusted models, both the basic and the full models.

Finally, the full models were repeated with exclusion of women younger than 51 years at baseline (n = 719, including 27 cases), cases diagnosed with incident breast cancer within 1 year from baseline examination (n = 31) or cases with noninvasive breast cancer (n = 30).

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Cases were younger at baseline (a consequence of the MDC study design and shorter follow-up among many older women). In addition, cases were taller, were more often current users of hormone therapy and had more often medium level of physical activity and high wine intake (Table I). Height, waist and current hormone use were significant risk factors for breast cancer in all multivariate models (data not shown).

Table I. Baseline Characteristics, Means (SD) or Proportions (%) by Breast Cancer Incidence Status in the MDC Cohort 1991–2001
 Noncases (n = 11,328)Cases (n = 342)p1
  • 1

    Two-sided p for t-test when comparing means and 2-sided p for χ2 when comparing proportions.

  • 2

    Energy from fat as percent of TE.

  • 3

    Energy from alcohol as percent of TE.

Age (years)60.4 (6.5)59.4 (5.8)<0.01
Height (cm)163.0 (5.9)164.2 (6.0)<0.01
Waist (cm)78.5 (10.6)79.0 (10.5)0.42
Age at menarche (years)13.7 (1.5)13.6 (1.4)0.54
Total vegetable and fruit (g/day)453 (189)452 (173)0.93
Folic acid (μg/day)231 (72)234 (73)0.38
EI:BMR1.45 (0.39)1.46 (0.38)0.90
E% fat237.4 (5.9)37.9 (5.8)0.20
E% alcohol31.45 (1.7)1.48 (1.8)0.78
Current hormone therapy (%)   
 Yes20.131.9<0.01
 No79.968.1 
Age at first child (%)   
 ≤24 years44.342.10.74
 >24 to ≤30 years32.432.2 
 >30 years9.010.2 
 No children (includes missing)14.215.5 
Leisure time physical activity (%)   
 Low20.115.10.01
 Medium59.867.8 
 High20.117.1 
Smoking status (%)   
 Smoker25.926.60.28
 Ex-smoker26.529.8 
 Never-smoker47.643.6 
Education level (%)   
 ≤8 years45.841.10.28
 9–10 years30.433.4 
 11–12 years12.912.6 
 University degree10.912.9 
Beer intake (%)   
 Abstainers9.06.70.40
 ≤5.6 cl/day45.244.4 
 >5.6 to ≤36.1 cl/day43.545.9 
 >36.1 cl/day2.32.9 
Wine intake (%)   
 Abstainers12.412.60.01
 ≤2.9 cl/day44.644.4 
 >2.9 to ≤20.8 cl/day40.938.3 
 ≥20.8 cl/day2.14.7 
Spirits intake (%)   
 Abstainers12.912.60.28
 0 cl/day62.767.0 
 >0 to ≤2.5 cl/day22.117.8 
 >2.5 cl/day2.32.6 
Total alcohol intake (%)   
 Abstainers8.76.70.20
 ≤15 g/day77.178.7 
 >15 to ≤30 g/day12.311.4 
 >30 g/day1.93.2 
FATTE (%)   
 Quintile 120.018.40.12
 Quintile 220.115.2 
 Quintile 319.923.5 
 Quintile 420.021.1 
 Quintile 519.922.2 
FATNAE (%)   
 Quintile 120.117.80.63
 Quintile 220.118.1 
 Quintile 320.020.5 
 Quintile 419.921.6 
 Quintile 519.921.9 

Covariates differed across total alcohol exposure categories. For instance, high consumers of alcohol were younger, were taller, ate less fruit, had higher EI:BMR ratios (i.e., were less likely to report low energy), more often used hormone therapy, were younger at birth of the first child, had higher leisure time physical activity, were more often smokers and had longer education compared to those in the other alcohol intake categories (Table II). Differences across categories of relative fat intake were small and similar for both definitions (FATTE and FATNAE). Briefly, a larger proportion of low fat consumers were never-smokers, had high leisure time physical activity and had changed dietary habits in the past. There were only small or no differences in the proportions of current hormone therapy use, educational level or age at birth of the first child. Mean differences were small or nonexistent for age at baseline, waist, height, age at menarche and EI:BMR ratio. Low fat consumers had higher intakes of total fruit and vegetables and of folate. There were no differences in alcohol source across quintiles of FATTE; however, wine and spirits contributed more alcohol and beer less alcohol in FATNAE quintiles 4 and 5 (data not shown).

Table II. Baseline Characteristics, Means (SD) or Proportions (%), Across Categories of Total Alcohol Intake in Women with no Diagnosed Breast Cancer in the MDC Cohort 1991–2001
 Total abstainers (n = 987)≤15 g/day (n = 8,772)15 to ≤30 g/day (n = 1,405)>30 g/day (n = 220)p1
  • 1

    F-test (ANOVA) when comparing means and 2-sided p for χ2 when comparing proportions.

  • 2

    Energy from fat as percentage of TE.

  • 3

    Energy from alcohol as percentage of TE.

Age (years)62.2 (6.3)60.5 (6.5)58.6 (6.4)57.6 (6.3)<0.001
Height (cm)161.0 (6.1)163.0 (5.9)164.6 (5.68)165.0 (5.9)<0.001
Waist (cm)81.9 (12.5)78.4 (10.5)76.6 (9.4)79.3 (10.2)<0.001
Age at menarche (years)13.8 (1.6)13.7 (1.5)13.6 (1.4)13.6 (1.7)0.009
Total fruit and vegetables (g/day)463 (214)458 (190)443 (183)405 (195)<0.001
Folic acid (μg/day)223 (79)232 (72)234 (71)223 (74)<0.001
EI:BMR1.38 (0.44)1.44 (0.38)1.54 (0.37)1.55 (0.34)<0.001
E% fat237.0 (6.9)37.5 (5.8)37.5 (5.4)35.4 (5.5)<0.001
E% alcohol31.0 (1.0)4.0 (1.3)7.6 (2.4)<0.001
Beer-alcohol as percentage of total alcohol intake47.0 (40.1)18.2 (19.0)17.4 (20.8)<0.001
Wine-alcohol as percentage of total alcohol intake43.8 (40.0)67.6 (25.2)63.9 (25.9)<0.001
Spirits-alcohol as percentage of total alcohol intake9.2 (20.6)14.2 (18.2)18.7 (19.3)<0.001
Current hormone therapy? (%)    <0.001
 Yes12.519.130.029.1 
 No87.580.970.069.1 
Changed dietary habits? (%)    <0.001
 Yes35.726.417.724.5 
 No64.373.682.375.5 
Age at first child (%)    <0.001
 ≤24 years50.244.140.948.6 
 >24 to ≤30 years24.733.133.331.8 
 >30 years9.79.09.07.7 
 No children (includes missing)15.413.816.811.8 
Leisure time physical activity (%)    <0.001
 Low29.719.615.920.1 
 Medium53.160.562.456.8 
 High17.219.921.723.1 
Smoking (%)    <0.001
 Yes (includes irregular)26.724.631.042.3 
 Ex-smoker17.026.233.832.7 
 Never-smoker56.349.235.225.0 
Education (%)    <0.001
 ≤8 years67.647.126.717.7 
 9–10 years19.330.635.838.6 
 11–128.512.219.222.3 
 University degree4.610.118.321.4 

Cross-classification of the total alcohol and relative fat intake categories showed different patterns for FATTE and FATNAE, respectively (Table III). Medium and high consumers of total alcohol were more often classified in the lower FATTE quintiles but in the highest FATNAE quintile. Women classified as low alcohol consumers were evenly distributed across quintiles of both relative fat intake definitions.

Table III. Cross-Classification (Number and Proportions) of Alcohol Intake Categories Across Quintiles of Relative Fat Intake in Women with no Diagnosed Breast Cancer in the MDC Cohort 1991–2001
Relative fat intake categoriesFat intake, mean (SD)Total alcohol intake categories (g/day)p2
GramsE%1Abstainers≤15>15 to ≤30>30
  • 1

    Energy from fat as percent of TE.

  • 2

    Two-sided p value for χ2.

FATTE      <0.001
 Quintile 165 (20)29.6 (3.2)247 (25.0)1 677 (19.1)287 (20.4)71 (32.3) 
 Quintile 278 (23)34.6 (1.8)178 (18.0)1 777 (20.3)289 (20.6)49 (22.3) 
 Quintile 384 (25)37.5 (1.8)150 (15.2)1 752 (20.0)312 (22.2)53 (24.1) 
 Quintile 490 (27)40.3 (2.0)170 (17.2)1 770 (20.2)300 (21.4)33 (15.0) 
 Quintile 5100 (31)45.4 (3.5)242 (24.6)1 796 (20.4)217 (15.4)14 (6.4) 
FATNAE      <0.001
 Quintile 165 (21)29.7 (3.2)294 (29.8)1 793 (20.4)174 (12.4)23 (10.5) 
 Quintile 277 (23)34.6 (2.0)192 (19.5)1 860 (21.2)210 (14.9)21 (9.5) 
 Quintile 384 (25)37.5 (2.2)160 (16.2)1 771 (20.2)294 (20.9)51 (23.2) 
 Quintile 491 (26)40.4 (2.2)165 (16.7)1 733 (19.8)325 (23.1)48 (21.8) 
 Quintile 5100 (31)45.1 (3.9)176 (17.8)1 615 (18.4)402 (28.6)77 (35.0) 

High beer intakes were associated with a nonsignificantly elevated risk of breast cancer. High intakes of wine (>20.8 cl/day) significantly increased the risk for breast cancer compared to low intakes. Medium intakes of spirits (>0 to ≤2.5 cl/day) were associated with decreased risk of breast cancer. Both associations remained significant after adjustment for established risk factors (Table IV). Nonsignificantly elevated risks were found for high (>30 g/day) intake of total alcohol, both with and without adjustment for established risk factors (Tables V, VI). Results did not change substantially when we adjusted for folic acid or total fruit and vegetable intakes or excluded women younger than 51 years at baseline, cases diagnosed within 1 year from baseline or cases with in situ breast cancer (data not shown).

Table IV. IRR of Breast Cancer According to Intake Categories of Beer, Wine and Spirits in the MDC Cohort 1991–2001
Alcoholic beverage intake categoriesTotal alcohol intake (g/day)1Basic model2Full model3
Person-yearsCasesIRR295% CIPerson-yearsCasesIRR95% CI
  • 1

    Crude median intakes.

  • 2

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, TE, change of dietary habits.

  • 3

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, TE, change of dietary habits, height, waist, current hormone use, age at birth of first child, age at menarche, leisure time physical activity, smoking habits, educational level.

Beer (cl/day)         
 Abstainers0.07,783230.820.53–1.287,464220.920.58–1.44
 ≤5.63.040,3631521 39,2381421 
 >5.6 to ≤36.17.439,0911571.080.86–1.3538,1231551.110.88–1.40
 >36.118.31,980101.430.75–2.721,950101.440.75–2.75
Wine (cl/day)         
 Abstainers0.010,815431.090.78–1.5410,345421.210.86–1.72
 ≤2.91.739,9081521 38,8381441 
 >2.9 to ≤20.810.836,6791310.930.74–1.1835,8281270.880.69–1.13
 >20.831.61,815162.281.36–3.841,764162.111.24–3.60
Spirits (cl/day)         
 Abstainers0.011,173431.000.72–1.3910,713421.130.80–1.59
 03.956,0532291 54,6162181 
 >0 to ≤2.511.219,967610.740.56–0.9919,471600.730.55–0.98
 >2.523.22,02491.060.54–2.071,97591.050.54–2.07
Table V. IRR of Breast Cancer According to Intake Categories of Total Alcohol and Fatte in the MDC Cohort 1991–2001
 Basic model1Full model2
Person-yearsCasesIRR95% CIWith adjustment for FATTEPerson-yearsCasesIRR95% CIWith adjustment for FATTE
IRR95% CIIRR95% CI
Total alcohol (g/day)            
 Abstainers7,550230.820.53–1.260.820.54–1.267,231220.890.57–1.390.910.58–1.41
 ≤1569,0352691 1 67,2442571 1 
 >15 to ≤3010,953390.910.65–1.280.920.66–1.2910,663390.880.62–1.240.880.63–1.25
 >301,678111.730.94–3.181.800.98–3.321,637111.680.91–3.121.770.95–3.28
     With adjustment for total alcohol    With adjustment for total alcohol
  • 1

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, TE, change of dietary habits.

  • 2

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, TE, change of dietary habits, height, waist, current hormone use, age at birth of first child, age at menarche, leisure time physical activity, smoking habits, educational level.

  • 3

    Two-sided p for trend.

FATTE            
 Quintile 118,210631 1 17,745611 1 
 Quintile 218,045520.840.58–1.220.840.58–1.2217,654480.800.55–1.180.810.55–1.18
 Quintile 317,764791.340.96–1.871.340.96–1.8817,315781.360.97–1.911.360.97–1.92
 Quintile 417,745721.230.87–1.721.240.90–1.7417,242701.250.88–1.771.260.89–1.79
 Quintile 517,452761.350.96–1.891.370.97–1.9216,818721.340.94–1.901.360.96–1.94
   p for trend = 0.0183p for trend = 0.0153  p for trend = 0.0183p for trend = 0.0163
Table VI. IRR of Breast Cancer According to Intake Categories of Total Alcohol and Fatnae in the MDC cohort 1991–2001
 Basic model1Full model2
Person-yearsCasesIRR95% CIWith adjustment for FATNAEPerson-yearsCasesIRR95% CIWith adjustment for FATNAE
IRR95% CIIRR95% CI
Total alcohol (g/day)            
 Abstainers7,550230.820.53–1.250.830.54–1.287,231220.890.57–1.390.920.59–1.43
 ≤1569,0352691 1 67,2442571 1 
 >15 to ≤3010,953390.940.65–1.280.880.63–1.2410,663390.880.62–1.240.850.60–1.20
 >301,678111.750.95–3.211.660.90–3.051,637111.690.91–3.121.620.87–3.00
     With adjustment for total alcohol    With adjustment for total alcohol
  • 1

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, NAE, change of dietary habits.

  • 2

    Adjusted for diet interviewer, method version, season of diet interview, age at baseline, NAE, change of dietary habits, height, waist, current hormone use, age at first child, age at menarche, leisure time physical activity, smoking habits, educational level.

  • 3

    Two-sided p for trend.

FATNAE            
 Quintile 118,219611 1 17,774581 1 
 Quintile 218,016621.040.73–1.481.030.72–1.4717,570591.050.73–1.511.050.73–1.51
 Quintile 317,765701.230.87–1.741.210.86–1.7217,236671.240.87–1.771.230.86–1.76
 Quintile 417,725741.310.93–1.841.300.92–1.8317,280741.380.97–1.961.380.97–1.96
 Quintile 517,492751.360.97–1.921.350.95–1.9016,915711.350.94–1.931.350.94–1.93
   p for trend = 0.0353p for trend = 0.0473  p for trend = 0.0433p for trend = 0.0503

Significant positive trends were found across quintiles of both relative fat intake variables when examined in separate models (Tables V, VI). Adjustment for total alcohol did not affect trends across FATTE quintiles (Table V), but the p values for trends across FATNAE quintiles became higher (Table VI). In the mutually adjusted models, the risk associated with high alcohol intakes was somewhat higher in the model testing substitution of alcohol energy for energy from carbohydrate or protein (FATTE model, Table V) compared to the models without adjustment for relative fat. However, the risk estimates in the model testing adding alcohol energy (FATNAE model, Table VI) were somewhat lower compared to the models without relative fat adjustment. Excluding women younger than 51 years at baseline or cases diagnosed within 1 year from baseline did not change results appreciably (data not shown). However, when in situ cancer cases were excluded, the p value for trend across fat quintiles became lower for FATTE (p = 0.004) and for FATNAE (p = 0.015). In addition, risk estimates were significantly increased in FATTE quintile 5 (IRR = 1.57, 95% CI 1.08–2.28) and in FATNAE quintiles 4 (IRR = 1.49, 95% CI 1.03–2.16) and 5 (IRR = 1.51, 95% CI 1.03–2.20).

Additional ad hoc analyses were performed with the model estimating the genuine risk of wine. Adjusting for total alcohol, beer or spirit intakes (continuous) in separate models did not affect risk estimates for wine. Adjustment for total alcohol intake categories did not affect risk estimates for wine (IRR = 2.38, 95% CI 1.14–5.06), but the nonsignificant elevated risk among high consumers of total alcohol did not remain (IRR = 0.92, 95% CI 0.40–2.15).

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

High consumption of total alcohol was associated with a nonsignificantly elevated risk of breast cancer. High intake of wine was associated with significantly increased breast cancer risk. We found a significant positive trend in risk across total fat intake quintiles. In the mutually adjusted models, the risk associated with substitution of alcohol energy (FATTE) had somewhat higher estimates compared to the model adding alcohol energy (FATNAE). Mutual adjustment did not affect the significant trend across fat quintiles in the FATTE model, while the p value for trend increased in the FATNAE model.

Alcohol

Observational epidemiologic studies in general show an association between alcohol intake and breast cancer risk.5, 6, 39 Also, there are several hypotheses concerning the biologic effect of alcohol on breast cancer development: an influence on estrogen levels through decreased steroid metabolism,40 an influence on the production of reactive oxygen species via alcohol metabolism41 and inhibition of DNA repair and greater metastatic potential of breast cancer cells.42 It has also been suggested that the alcohol dehydrogenase-3 genotype may modify the alcohol effect.43

Our results support a threshold effect; we have no indication of elevated risk for reported total alcohol consumption <30 g/day, corresponding to approximately 2 glasses (30 cl) of wine daily. However, due to misreporting of alcohol, the absolute level of alcohol intake required for an effect remains unclear. Self-reports of alcohol intake must be used with caution when trying to establish a level where alcohol exerts a biologic effect. In our study, intakes of alcoholic beverages were recorded in the menu book during 7 consecutive days. The relative validity and reproducibility of the MDC diet history for total alcohol and alcoholic beverages are high.20, 21, 22 Nevertheless, the reported absolute intakes of total alcohol were, in women, substantially lower than with the reference method.20 Food record data on alcohol intake has been found to better predict the association between alcohol intake and estradiol levels compared to self-reported usual consumption.44 Information on alcohol habits is sensitive, prone to reporting bias; it is possible that women in this cohort reported wine more accurately than beer and spirits. This could mean higher misclassification of women into total alcohol intake categories compared to wine intake categories, which might explain the nonsignificant association between total alcohol and breast cancer risk. In this population, the correlation between total alcohol and alcohol from wine is high (0.88) compared to the correlations with alcohol from beer (0.48) or alcohol from spirits (0.55). In addition, as shown in Table IV, median intakes of total alcohol differed between the different alcoholic beverages. “High wine consumption” was the only category with a median total alcohol intake >30 g/day. Thus, because wine is the major source of alcohol in this population, it is difficult to separate the effect of wine from that of total alcohol. In the model including both wine and total alcohol intake categories, only the elevated risk associated with high wine intake remained.

It is difficult to elucidate the genuine effect of alcohol from a web of surrounding confounders (Table II) in observational epidemiologic studies. We adjusted for known risk factors of breast cancer and selected nutritional variables, but there might be factors not accounted for, e.g., psychologic and personality factors45 or other nutritional factors, that influence results. In addition, there is a risk of residual confounding. For instance, the influence of higher vs. lower education, which is interrelated with a number of factors, including social class, is difficult to capture with one question. Thus, even when models are adjusted for education, a residual effect might remain.

Total fat

Few associations are so extensively studied in epidemiology as the one between total fat and breast cancer risk. Measurement errors, different energy-adjustment models and the diversity and range of fat intakes might be factors contributing to the conflicting results observed in epidemiologic studies.16, 46, 47, 48, 49, 50, 51, 52, 53, 54

One plausible role of dietary fat is via an influence on the levels of bioavailable estrogen. A meta-analysis of 13 intervention studies showed that a reduction of fat intake significantly reduced serum estradiol levels.55 The authors concluded that reducing fat intake below 20% of energy might influence breast cancer risk. However, they also pointed out that it is difficult to elucidate the genuine effect of fat per se. Weight loss and increased fiber intake could have influenced the results in the intervention studies.

One hypothesis suggests overeating of total, especially saturated, fat as the starting point of a series of events finally leading to a cycle of insulin resistance, increased plasma levels of insulin, decreased levels of sex hormone–binding globulin, increased levels of free estrogen and increased ovarian androgen production. This leads to a hormonal–metabolic profile that is associated with an increased risk of breast cancer.56

In animal studies, the type and amount of fat are important determinants of the effect of fat. The promoting effect appears to be dependent on the ratio of n-6 to n-3 fatty acids in the diet. Eicosapentaenoic acid (n-3) especially lacks tumor-promoting effects in animal models. The mechanism is believed to depend on the eicosanoids formed. Eicosapentaenoic acid inhibits eicosanoid production from the n-6 family by competing for desaturates.57 Besides the influence on eicosanoid production, other mechanisms are also proposed for the effect of fatty acids, e.g., DNA damage caused by reactive oxygen species from polyunsaturated fatty acids, interaction between fatty acids and genomic DNA leading to alterations in gene expression, influences on hormone levels, structural and functional changes in cell membranes influencing hormone and growth factor receptors and effects on immune function.57 Only a few epidemiologic studies have examined the effect of different fatty acids. Observations in the MDC cohort58 suggest that high intakes of n-6 fatty acids increase the risk of postmenopausal breast cancer when mutually adjusted for other types of fat. A few other epidemiologic studies have reported similar findings.52, 59, 60

Contrary to many prospective studies, our results support an effect of total fat independent of EI. Interestingly, the association became stronger when in situ cancer cases were excluded. We used relative fat intake, FATTE and FATNAE, as fat exposure variables. Relative fat intake is a complex variable, including aspects of the total diet and with varying associations with other nutrients and food groups. For instance, plant foods show strong inverse associations to relative fat intake in the MDC population.61 Food sources of fat and fatty acid composition differ across populations.62, 63, 64 Thus, total fat intake may not be regarded as the same exposure in different populations, which makes comparisons across studies difficult. The high validity20 in the MDC study could contribute to the ability to detect associations not found in other studies.58

Energy adjustment: variables and models

The bivariate association between relative fat and total alcohol intake differed depending on the energy definition used for the calculation of relative fat (Table III). The FATNAE model could be interpreted as the association between food energy and alcohol: e.g., subjects who consume a great deal of energy from fatty foods also drink a great deal of alcohol. The FATTE model illustrates the association between fat energy and alcohol energy relative to total EI. The crude correlation between absolute fat and alcohol intake is low (r = 0.073). The FATTE model introduces an inverse association between fat and alcohol by the way relative fat is expressed.

In contrast, outcomes of the multivariate analysis differed only slightly for the 2 relative fat definitions. Risk estimates in multivariate models had similar directions, though the p for trend was lower for FATTE compared to FATNAE. This indicates that these variables may be interchangeable when estimating risks. In addition, both models test substitution of fat energy for carbohydrate or protein energy; thus, the research question is the same. The risk estimates for total alcohol, however, represent 2 different hypotheses: exchanging alcohol when TE is constant or adding alcohol energy (i.e., increasing TE). The FATTE model could be regarded as a question of the ethanol effect per se. The FATNAE model, however, estimates an effect of both increasing ethanol and increasing energy. In this population, the first model yielded somewhat higher risk estimates. The differences in estimates are, however, small between the models. One reason could be the low mean reported total alcohol intake (1.5 E%). The outcomes of the 2 models, i.e., TE vs. NAE, might be different in populations with higher alcohol intakes.

Bivariate analyses indicated a lower EI:BMR ratio among alcohol abstainers and low consumers. If participants with “true” high total alcohol intakes misreported their alcohol intakes and were classified as abstainers or as having low total alcohol intakes, the risk associated with high consumption of total alcohol might be underestimated. In contrast, the differences in EI:BMR ratios across FATTE and FATNAE quintiles were small, mean ratios ranging 1.48–1.52. Thus, it is less likely that the observed positive trend in breast cancer risk across fat intake quintiles is influenced by differential underreporting of energy among low and high fat consumers.

To conclude, our results support the hypothesis of a threshold effect of alcohol intake on breast cancer risk. A nonsignificantly elevated breast cancer risk was found among women reporting high (>30 g/day) total alcohol intake. The major source of alcohol was wine, and high consumption of wine was significantly associated with increased breast cancer risk. We found a significant linear trend in increased risk across quintiles of relative fat. Mutual adjustment did not substantially affect risk estimates for total alcohol or relative fat intakes. The specific energy-adjustment approach (i.e., adjusting for TE or NAE) did not influence associations differentially.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

The MDC study was supported by grants from the Swedish Cancer Society (2684-B93-05XAA) and the Swedish Medical Research Council (B93-39X-09534-03C), the European Commission, the Swedish Dairy Association, the Albert Påhlssons Foundation and the City of Malmö. I.M. has also received support from the Ernhold Lundström Foundation. We thank the participants and staff of the MDC study.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES
  • 1
    Zhang Y, Kreger BE, Dorgan JF, Splansky GL, Cupples AL, Ellison CR. Alcohol consumption and risk of breast cancer: the Framingham Study revisited. Am J Epidemiol 1999; 149: 93101.
  • 2
    Ellison CR, Zhang Y, McLennan CE, Rothman KJ. Exploring the relation of alcohol consumption to risk of breast cancer. Am J Epidemiol 2001; 154: 7407.
  • 3
    Longnecker MP. Alcoholic beverage consumption in relation to risk of breast cancer: meta-analysis and review. Cancer Causes Control 1994; 5: 7382.
  • 4
    Longnecker MP, Berlin JA, Orza MJ, Chalmers TC. A meta-analysis of alcohol consumption in relation to risk of breast cancer. JAMA 1988; 260: 6526.
  • 5
    Smith-Warner SA, Spiegelman D, Yaun S-S, van den Brandt PA, Folsom AR, Goldbohm AR, Graham S, Holmberg L, Howe GR, Marshall J, Miller AB, Potter JD, et al. Alcohol and breast cancer in women: a pooled analysis of cohort studies. JAMA 1998; 279: 53540.
  • 6
    Howe GR, Rohan TE, Decarli A, Iscovich JM, Kaldor J, Katsouyanni K, Marubini E, Miller AB, Riboli E, Toniolo P, Trichopoulos D. The association between alcohol and breast cancer risk: evidence from the combined analysis of six dietary case-control studies. Int J Cancer 1991; 47: 70710.
  • 7
    Collaborative Group on Hormonal Factors in Breast Cancer. Alcohol, tobacco and breast cancer—collaborative reanalysis of individual data from 53 epidemiological studies, including 58,515 women with breast cancer and 95,067 women without the disease. Br J Cancer 2002; 87: 123445.
  • 8
    Martin-Moreno JM, Boyle P, Gorgojo L, Willett WC, Gonzales J, Villar F, Maisonneuve P. Alcoholic beverage consumption and risk of breast cancer in Spain. Cancer Causes Control 1993; 4: 34553.
  • 9
    Rohan TE, Jain MG, Howe GR, Miller AB. Alcohol consumption and risk of breast cancer: a cohort study. Cancer Causes Control 2000; 11: 23947.
  • 10
    Kropp S, Becher H, Nieters A, Chang-Claude J. Low to moderate alcohol consumption and breast cancer risk by age 50 years among women in Germany. Am J Epidemiol 2001; 154: 62434.
  • 11
    Hiatt RA, Bawol RD. Alcoholic beverage consumption and breast cancer incidence. Am J Epidemiol 1984; 120: 67683.
  • 12
    Tjonneland A, Gronbaek M, Stripp C, Overvad K. Wine intake and diet in a random sample of 48,763 Danish men and women. Am J Clin Nutr 1999; 69: 4954.
  • 13
    Chatenoud L, Negri E, La Vecchia C, Volpato O, Franceschi S. Wine drinking and diet in Italy. Eur J Clin Nutr 2000; 54: 1779.
  • 14
    Sellers TA, Kushi LH, Cerhan JR, Vierkant RA, Gapstur SM, Vachon CM, Olson JE, Therneau TM, Folsom AR. Dietary folate intake, alcohol, and risk of breast cancer in a prospective study of postmenopausal women. Epidemiology 2001; 12: 4208.
  • 15
    Rohan TE, Jain MG, Howe GR, Miller AB. Dietary folate consumption and breast cancer risk. J Natl Cancer Inst 2000; 92: 2669.
  • 16
    Lee MM, Lin SS. Dietary fat and breast cancer. Annu Rev Nutr 2000; 20: 22148.
  • 17
    Nordiska Ministerrådet. Nutrient recommendations for the Nordic countries [in Swedish]. Copenhagen: Nordiska Ministerrådet (Nordic Council of ministers), 1996. 28.
  • 18
    Manjer J, Carlsson S, Elmståhl S, Gullberg B, Janzon L, Lindström M, Mattisson I, Berglund G. The Malmö Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev 2001; 10: 48999.
  • 19
    Morabia A, Flandre P. Misclassification bias related to definition of menopausal status in case-control studies of breast cancer. Int J Epidemiol 1992; 21: 2228.
  • 20
    Riboli E, Elmståhl S, Saracci R, Gullberg Bo, Lindgärde F. The Malmö Food Study: validity of two dietary assessment methods for measuring nutrient intake. Int J Epidemiol 1997; 26: S16173.
  • 21
    Elmståhl S, Riboli E, Lindgärde F, Gullberg Bo, Saracci R. The Malmö Food Study: the relative validity of a modified diet history method and an extensive food frequency questionnaire for measuring food intake. Eur J Clin Nutr 1996; 50: 14351.
  • 22
    Elmståhl S, Gullberg Bo, Riboli E, Saracci R, Lindgärde F. The Malmö Food Study: the reproducibility of a novel diet history method and an extensive food frequency questionnaire. Eur J Clin Nutr 1996; 50: 13442.
  • 23
    Royal College of Psychiatrists. Alcohol: our favourite drug. London: Tavistock, 1986.
  • 24
    Wallström P, Wirfält E, Janzon L, Mattisson I, Elmståhl S, Johansson U, Berglund G. Fruit and vegetable consumption in relation to risk factors for cancer: a report from the Malmö Diet and Cancer Study. Public Health Nutr 2000; 3: 26371.
  • 25
    Wirfält E, Mattisson I, Johansson U, Gullberg B, Wallström P, Berglund G. A methodological report from the Malmö Diet and Cancer study: development and evaluation of altered routines in dietary data processing. Nutr J 2002; 1: 3. www.nutritionj.com
  • 26
    Merlo J, Berglund G, Wirfält E, Gullberg B, Hedblad B, Manjer J, Hovelius B, Janzon L, Hanson BS, Östergren P-O. Self-administered questionnaire compared with a personal diary for assessment of current use of hormone therapy: an analysis of 16,060 women. Am J Epidemiol 2000; 152: 78892.
  • 27
    Haftenberger M, Schuit AJ, Tormo MJ, Boeing H, Wareham N, Bueno-de-Mesquita BH, Kumle M, Hjartåker A, Chirlaque MD, Ardanaz E, Andrén C, Lindahl B, et al. Physical activity of subjects aged 50–64 years involved in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 2002; 5: 116377.
  • 28
    Mattisson I, Wirfält E, Gullberg B, Berglund G. Fat intake is more strongly associated with life style factors than with socio-economic characteristics, regardless of energy adjustment approach. Eur J Clin Nutr 2001; 55: 45261.
  • 29
    Black AE, Goldberg GR, Jebb SA, Livingstone MBE, Cole TJ, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 2. Evaluating the results of published surveys. Eur J Clin Nutr 1991; 45: 58399.
  • 30
    Goldberg GR, Black AE. Assessment of the validity of reported energy intakes—review and recent development. Scand J Nutr 1998; 42: 69.
  • 31
    Black AE. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes 2000; 24: 111930.
  • 32
    Black AE. The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity. Eur J Clin Nutr 2000; 54: 395404.
  • 33
    WHO. Energy and protein requirements. Report of a joint FAO/WHO/UNU expert consultation. WHO Technical Reports Series. Geneva: WHO, 1985.
  • 34
    Kipnis V, Freedman LS, Brown CC, Hartman AM, Schatzkin A, Wacholder S. Interpretation of energy adjustment models for nutritional epidemiology. Am J Epidemiol 1993; 137: 137680.
  • 35
    Kipnis V, Freedman LS, Brown CC, Hartman AM, Schatzkin A, Wacholder S. Effect of measurement error on energy-adjustment models in nutritional epidemiology. Am J Epidemiol 1997; 146: 84255.
  • 36
    Bellach B, Kohlmeier L. Energy adjustment does not control for differential recall bias in nutritional epidemiology. J Clin Epidemiol 1998; 51: 3938.
  • 37
    Wacholder S, Schatzkin A, Freedman LS, Kipnis V, Hartman AM, Brown CC. Can energy adjustment separate the effects of energy from those of specific macronutrients? Am J Epidemiol 1995; 140: 84855.
  • 38
    Kelsey JL, Gammon MD, John EM. Reproductive factors and breast cancer. Epidemiol Rev 1993; 15: 3647.
  • 39
    Longnecker MP, Newcomb PA, Mittendorf R, Greenberg RE, Clapp RW, Bogdan GF, Baron J, MacMahon B, Willett WC. Risk of breast cancer in relation to lifetime alcohol consumption. J Natl Cancer Inst 1995; 87: 9239.
  • 40
    Ginsburg ES. Estrogen, alcohol and breast cancer risk. J Steroid Biochem Mol Biol 1999; 69: 299306.
  • 41
    Wright RM, McManaman JL, Repine JE. Alcohol-induced breast cancer: a proposed mechanism. Free Radic Biol Med 1999; 26: 34854.
  • 42
    Singletary KW, Gapstur SM. Alcohol and breast cancer. Review of epidemiologic and experimental evidence and potential mechanisms. JAMA 2001; 286: 214351.
  • 43
    Freudenheim JL, Ambrosone CB, Moysich KB, Vena JE, Graham S, Marshall J, Muti P, Laughlin RH, Nemoto T, Harty LC. Alcohol dehydrogenase 3 genotype modification of the association of alcohol consumption with breast cancer risk. Cancer Causes Control 1999; 10: 36977.
  • 44
    Gavaler JS, Love K. Detection of the relationship between moderate alcoholic beverage intake and serum levels of estradiol in normal postmenopausal women: effects of alcoholic consumption quantitation methods and sample size adequacy. J Stud Alcohol 1992; 53: 38994.
  • 45
    Plant ML. Alcohol and breast cancer: a review. Int J Addictions 1992; 27: 10728.
  • 46
    Armstrong B, Doll R. Environmental factors and cancer incidence and mortality in different countries, with special reference to dietary practices. Int J Cancer 1975; 15: 61731.
  • 47
    Prentice RL, Sheppard L. Dietary fat and cancer: consistency of the epidemiological data, and disease prevention that may follow from a practical reduction in fat consumption. Cancer Causes Control 1990; 1: 8197.
  • 48
    FAO. Fats and oils in human nutrition. Report of a joint FAO/WHO/UNU expert consultation. FAO Food and Nutrition Papers 57. Rome: FAO, 1998.
  • 49
    Howe GR, Hirohata T, Hislop GT, Iscovich JM, Yuan J-M, Katsouyanni K, Lubin F, Marubini E, Modan B, Rohan TE, Toniolo P. Dietary factors and risk of breast cancer: combined analysis of 12 case-control studies. J Natl Cancer Inst 1990; 82: 5619.
  • 50
    Lipworth L. Epidemiology of breast cancer. Eur J Cancer Prev 1995; 4: 730.
  • 51
    Hunter DJ, Spiegelman D, Adami H-O, Beeson LW, van den Brandt PA, Folsom AR, Fraser GE, Goldbohm AR, Graham S, Howe GR, Kushi LH, Marshall J, et al. Cohort studies of fat intake and the risk of breast cancer—a pooled analysis. N Engl J Med 1996; 334: 35661.
  • 52
    Kushi LH, Sellers TA, Potter JD, Nelson CL, Munger RG, Kaye SA, Folsom AR. Dietary fat and postmenopausal breast cancer. J Natl Cancer Inst 1992; 84: 10929.
  • 53
    Greenwald P. Role of dietary fat in the causation of breast cancer: point. Cancer Epidemiol Biomarkers Prev 1999; 8: 37.
  • 54
    Hunter DJ. Role of dietary fat in the causation of breast cancer: counterpoint. Cancer Epidemiol Biomarkers Prev 1999; 8: 913.
  • 55
    Wu AH, Pike MC, Stram DO. Meta-analysis: dietary fat intake, serum estrogen levels, and the risk of breast cancer. J Natl Cancer Inst 1999; 91: 52934.
  • 56
    Kaaks R. Nutrition, hormones, and breast cancer: is insulin the missing link? Cancer Causes Control 1996; 7: 60525.
  • 57
    Wynder EL, Cohen LA, Muscat JE, Winters B, Dwyer JT, Blackburn G. Breast cancer: weighing the evidence for a promoting role of dietary fat. J Natl Cancer Inst 1997; 89: 76675.
  • 58
    Wirfält E, Mattisson I, Gullberg Bo, Johansson U, Olsson H, Berglund G. Post-menopausal breast cancer is associated with high intakes of ω-6 fatty acids. Cancer Causes Control 2002; 13: 88393.
  • 59
    Wolk A, Bergström R, Hunter DJ, Willett WC, Ljung H, Holmberg L, Bergkvist L, Bruce Å, Adami H-O. A prospective study of association of monounsaturated fat and other types of fat with risk of breast cancer. Arch Intern Med 1998; 158: 415.
  • 60
    Velie E, Kulldorf M, Schairer C, Block G, Albanes D, Schatzkin A. Dietary fat, fat subtypes, and breast cancer in postmenopausal women: a prospective cohort study. J Natl Cancer Inst 2000; 92: 8339.
  • 61
    Mattisson I, Wirfält E, Andrén C, Gullberg Bo, Berglund G. Dietary fat intake—food sources and dietary correlates in the Malmö Diet and Cancer cohort. Public Health Nutr 2003; 6: 55969.
  • 62
    Linseisen J, Bergström E, Gafá L, Gonzalez CA, Thiebaut A, Trichopoulou A, Tumino R, Navarro C, Martinez C, Mattisson I, Nilsson S, Welch A, et al. Consumption of added fats and oils in the European Prospective Investigation into Cancer and Nutrition (EPIC) centres across 10 European countries as assessed by 24-hour dietary recalls. Public Health Nutr 2002; 5: 122742.
  • 63
    Becker W. Food habits and nutrient intake in Sweden 1989 [in Swedish]. Uppsala: Statens Livsmedelsverk (National Food Administration), 1994.
  • 64
    Freudenheim JL, Krogh V, D′Amicis A, Scaccini C, Sette S, Ferro-Luzzi A, Trevisan M. Food sources of nutrients in the diet of elderly Italians: I. Macronutrients and lipids. Int J Epidemiol 1993; 22: 85568.