Dietary carbohydrates, fiber, and breast cancer risk in Chinese women 1 3

AJCN. First published ahead of print December 3, 2008 as doi: 10.3945/ajcn.2008.26356. Dietary carbohydrates, fiber, and breast cancer risk in Chinese women 1 3 Wanqing Wen, Xiao Ou Shu, Honglan Li, Gong Yang, Bu-Tian Ji, Hui Cai, Yu-Tang Gao, and Wei Zheng ABSTRACT Background: Few studies have investigated the association of dietary carbohydrate and fiber intake with breast cancer risk in women in China, where carbohydrate intake is traditionally high. Objective: The objective was to prospectively evaluate the association of dietary carbohydrates, glycemic index, glycemic load, and dietary fiber with breast cancer risk and to determine whether the effect of these dietary intakes is modified by age and selected insulin- or estrogen-related risk factors. Design: A total of 74,942 women aged 40 70 y were recruited into the Shanghai Women s Health Study, a population-based cohort study. Dietary intake was assessed by in-person interviews. A Cox proportional hazards regression model was used to evaluate associations. Results: During an average of 7.35 y of follow-up, 616 incident breast cancer cases were documented. A higher carbohydrate intake was associated with a higher risk of premenopausal breast cancer (P for trend ¼ 0.002). Compared with the lowest quintile, the hazard ratios (and 95% CIs) were 1.47 (1.00, 2.32) and 2.01 (1.26, 3.19) for the fourth and fifth quintiles, respectively. A similar pattern was found for glycemic load. The association between carbohydrate intake and breast cancer was significantly modified by age; the increased breast cancer risk associated with carbohydrate intake was restricted to women who were younger than 50 y. No significant association of breast cancer risk with glycemic index or dietary fiber intake was found. Conclusion: Our data suggest that a high carbohydrate intake and a diet with a high glycemic load may be associated with breast cancer risk in premenopausal women or women,50 y. Am J Clin Nutr 2009;89:1 7. INTRODUCTION Recently, the association of dietary carbohydrate intake with breast cancer risk has received a significant amount of attention based on the hypothesis that a high dietary carbohydrate intake increases insulin resistance and increases plasma concentrations of insulin conditions that may be associated with breast cancer risk (1 4). The specific composition of dietary carbohydrates consumed may be particularly important in breast cancer risk. Consumption of high-glycemic-index foods was associated with higher postprandial or fasting plasma insulin concentrations (5, 6). We recently reported that a high intake of foods with a high glycemic index and a high glycemic load increases the risk of type 2 diabetes mellitus in Chinese women (7). Some case-control studies (8 10) and a prospective study (11) have reported that high-glycemic-index foods may be associated with an increased risk of breast cancer. Some prospective studies have shown that the effect of carbohydrates on breast cancer risk may be modified by nondietary risk factors that are associated with estrogen metabolism and insulin resistance, such as menopausal status, body weight, and physical activity (11 13) Most prospective studies, however, have shown no overall association between dietary carbohydrate intake and breast cancer risk (12 17). Dietary fiber, on the other hand, may decrease breast cancer risk through mechanisms that decrease circulating estrogens (18, 19). A considerable part of the Chinese diet consists of resistant starch, which contributes to dietary fiber and has a low glycemic index. Epidemiologic studies have reported inconsistent findings on these hypotheses. Some case-control studies have provided evidence to support these hypotheses (20, 21). Some recent prospective studies have reported inverse associations between dietary fiber intake and breast cancer risk (22, 23), whereas others have reported no association (12, 14, 24). Traditional ecological analysis suggests a positive correlation between national per capita fat consumption and breast cancer incidence and mortality (25). Chinese women historically have consumed a high amount of carbohydrates, yet have a low risk of breast cancer. As rapid economic development continues in Shanghai, China, there have been substantial dietary changes among Chinese women characterized by increasing fat intake and decreasing carbohydrate intake (26, 27). Coincidently, Chinese women in Shanghai have experienced a dramatic increase in breast cancer incidence in recent years (27). However, these ecological correlations were generally not confirmed by epidemiologic studies, most of which were conducted in Western populations, where fat intake is high but carbohydrate intake is low. This study investigated the association of dietary carbohydrate with breast cancer risk in a Chinese population in whom 1 From the Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN (WW, XOS, GY, HC, and WZ); the Department of Epidemiology, Shanghai Cancer Institute, People s Republic of China (HL and Y-TG); and the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, MD (B-TJ). 2 Supported by US Public Health Service grant number R01 CA070867. 3 Address reprint requests and correspondence to W Wen,Vanderbilt Epidemiology Center, Institute of Medicine & Public Health, Vanderbilt University Medical Center, Sixth Floor, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738. E-mail: wanqing.wen@vanderbilt.edu. Received May 1, 2008. Accepted for publication October 19, 2008. doi: 10.3945/ajcn.2008.26356. Am J Clin Nutr 2009;89:1 7. Printed in USA. Ó 2009 American Society for Nutrition 1 Copyright (C) 2008 by the American Society for Nutrition

2 WEN ET AL carbohydrate intake is traditionally high but which is decreasing in younger women (26) and may provide an opportunity to disprove the traditional ecological analysis of negative correlation between carbohydrate intake and breast cancer. In the present study, we prospectively evaluated the association of dietary carbohydrate intake, glycemic index, glycemic load, and dietaryfiberintakewithbreastcancer risk inthe ShanghaiWomen s Cohort Study (SWHS). In addition, we investigated whether the effect of these dietary intakes are modified by age and selected insulin- or estrogen-related risk factors in women. SUBJECTS AND METHODS Study participants The SWHS, initiated in March 1997, is an ongoing prospective cohort study being conducted in urban Shanghai, China. The study was approved by the relevant institutional review boards for human research, and the details of the study design were described elsewhere (28, 29). Briefly, 81,170 women aged 40 70 y who resided in 7 geographically defined communities in urban Shanghai were approached, and 75,221 participated in the study; the response rate was 92.7%. Of those who completed the survey, 279 women were later found to be younger than 40 y or older than 70 y at the time of the baseline interview and thus were excluded from the cohort. The remaining 74,942 women constituted the cohort. All subjects were interviewed in person by trained interviewers using a questionnaire. The questionnaire included, among other items, questions on sociodemographic factors, dietary and lifestyle habits, menstrual and reproductive history, hormone use, and medical history. Anthropometric measurements, including current weight, height, and circumferences of the waist and hips, were made by trained interviewers who are retired health professional according to standard procedures (30). All measurements were taken twice with a preset tolerance of 1 kg for weight and 1 cm for height, waist, and hip circumferences. A third measurement was taken if the difference between the 2 measurements was larger than the tolerance limit. The averages of the 2 closest measurements were used in the current analysis. Dietary assessment A quantitative food-frequency questionnaire (FFQ) was used to assess usual dietary intake at the baseline survey and again at the first follow-up survey conducted 2 3 y after the baseline measurement. The FFQ was validated against the average of multiple 24-h dietary recalls. The correlation coefficients for macronutrients between the FFQ and the 24-h recall ranged from 0.59 to 0.66 (28). The FFQ covered.90% of foods commonly consumed in urban Shanghai (28, 29). During the in-person interviews, each participant was first asked how often, on average, during the past 12 mo she had consumed a specific food or food group (the possible responses ware daily, weekly, monthly, yearly, or never) and then how much they consumed in grams per unit of time. Macronutrient intakes from each food were calculated by multiplying the amount of food consumed by the nutrient content per gram of the food, as obtained from the Chinese food-composition tables (31). The total dietary intake of macronutrients was calculated by summing across all food items. The glycemic index ranks foods on the basis of the relative postprandial blood glucose response per gram of carbohydrate (32). Glycemic index values for individual food items from published data were added to the nutrient database (7). A food s glycemic load was calculated by multiplying the carbohydrate content of each food by its glycemic index values and its frequency of consumption (33). Dietary glycemic load for a participant was calculated by summing the values of glycemic load for all food items. Overall dietary glycemic index for a participant was calculated by dividing glycemic load by the amount of carbohydrates consumed. Identification of breast cancer cases The cohort has been followed by a combination of in-person surveys and periodic linkage with records kept by the Shanghai Tumor Registry. Every 2 y, the cohort members are interviewed to record details of their interim health history, including their history of cancer, cardiovascular disease, stroke, and other chronic diseases. Every year, we conduct a record linkage of cohort member information with the cancer registry and death certificate registry to ensure a timely and complete ascertainment of new cancer cases and deaths in the study cohort. All possible matches are checked manually and are verified through home visits. Copies of medical charts are obtained to verify cancer diagnoses and to collect detailed information on the pathological characteristics of cancers. Statistical analysis The end date of the observation in this study was set as the date of cancer diagnosis or the date of death for deceased cohort members or the date of the last follow-up. Forthoseparticipants whose last inperson contact was before 31 December 2005, the end date of the observation was set as 31 December 2005, 1 y before the most recent record linkage, to allow for delays in records processing. Hazard ratios and 95% CIs were estimated by using the Cox proportional hazards regression model (34). Survival was modeled as a function of age and stratified by birth cohort intervals to control for age and cohort effects (35). Left truncation was set to the date of the baseline survey. Dietary intake estimated from only the baseline interview and the average dietary intake calculated from both the baseline interview and the first follow-up interview were analyzed. To prevent bias due to dietary changes related to disease development, the baseline dietary intake was used to replace the average dietary intake for study participants who were diagnosed with cardiovascular diseases, cancers, and diabetes within 1 y before the first follow-up interview. Carbohydrate and fiber intakes, glycemic index, and glycemic load were adjusted for total energy intake with the regressionresidual method (36). The energy-adjusted dietary intakes were categorized into quintiles and also analyzed as continuous variables to evaluate linear trends. The interaction of continuous dietary intake variables with age under observation and other common risk factors for breast cancer risk was evaluated with a likelihood ratio test by comparing the Cox models with and without the interaction term. Information on menopausal status was updated at each follow-up interview. Menopausal status and age of observation were treated as time-varying variables when evaluating their interaction with dietary intakes (34). In addition to total energy intake, age at start of follow-up, education level, body mass index (BMI), age at first birth, breast cancer history in

TABLE 1 Baseline characteristics of women in the Shanghai Women s Cohort Study and the association of breast cancer risk with common risk factors Variables CARBOHYDRATES, FIBER, AND BREAST CANCER RISK 3 Value (n ¼ 73,328) Percentage of study population (n ¼ 73,328) HR (95% CI) 1 P for trend Age (y) 52.5 6 9.1 2 Age at menopause (y) 3 49.1 6 4.0 Energy intake (kcal/d) 1675.4 6 400.5 Carbohydrate intake 68.5 6 6.9 (% of energy) Energy-adjusted glycemic index 70.7 6 5.2 Energy-adjusted glycemic load 202.0 6 31.2 Fiber intake (g/100 kcal) 11.0 6 3.3 Waist-to-hip ratio 0.81 6 0.05.0.81 47.9 1.20 (1.02, 1.41) BMI (kg/m 2 ) 24.0 6 3.4.25 35.1 1.31 (1.11, 1.55) Age at menarche 14.9 6 1.7 0.347.15 y 35.9 1.00 (reference) 14 15 y 42.1 1.07 (0.89, 1.30) 13 y 22.1 1.08 (0.87, 1.35) Age at first birth 25.6 6 4.1 0.002 4,25 y 39.2 1.00 (reference) 25 29 y 46.0 1.22 (0.99, 1.50) 30 y 11.5 1.56 (1.19,2.03) Nulliparous 3.3 1.46 (0.96, 2.23) Education level,middle school 21.4 1.00 (reference),0.001 Middle school 37.2 1.52 (1.13, 2.07) High school 27.9 2.21 (1.63, 3.00).High school 13.5 1.97 (1.41, 2.75) Breast cancer history in first-degree relatives 1.9 2.14 (1.45, 3.15) Personal history of benign breast disease 16.9 1.60 (1.32, 1.94) Physical activity 35.1 0.85 (0.71, 1.00) Hormone replacement therapy use 2.1 1.10 (0.68, 1.77) Ever smoked 2.8 1.25 (0.68, 2.28) Ever drank alcohol 2.3 1.15 (0.63, 2.10) 1 The hazard ratios (HRs) and 95% CIs were derived from the Cox regression model with age as the time scale. All nondietary risk factors listed were included in the same model for mutual adjustment. 2 Mean 6 SD (all such values). 3 Among postmenopausal women. 4 For parous women only. first-degree relatives, personal history of benign breast diseases, and physical activity were also adjusted for in the analyses. RESULTS For the current analysis, we excluded 1576 women with a history of cancer at baseline, 28 women with outlier values for dietary intake (.3 SDs from the mean; ie, women with a carbohydrate intake,50 or.705 g/d or a total energy intake,388 or.4577 kcal/d), and 10 women who were lost to follow-up shortly after study enrollment. We documented 616 incident breast cancer cases in the remaining 73,328 women during an average of 7.35 y of follow-up (538,899 person-years). Of 616 incident breast cancer cases, 593 cases were invasive carcinoma, 23 cases were in situ carcinoma, 190 cases occurred before menopause, and 426 cases occurred after menopause. The characteristics of women in the SWHS cohort and the association of known nondietary risk factors with breast cancer risk are shown in Table 1. Consistent with previous study findings, women who were obese, were older at first birth, had higher levels of education, had a history of breast cancer in a first-degree relative, had a personal history of benign breast disease, or were physically inactive had an increased risk of breast cancer. These risk factors were adjusted for with a Cox regression model in the analyses of dietary intakes. The association of breast cancer risk with carbohydrate intake, glycemic index, glycemic load, and fiber intake measured at the baseline interview by quintiles for all women in the cohort and separated by menopausal status are shown in Table 2. Carbohydrate intake was significantly associated with elevated breast cancer risk in premenopausal women (P for linear trend ¼ 0.001). Compared with the lowest quintile, the hazard ratios (and 95% CIs) were 1.47 (1.00, 2.32) for the fourth quintile and 2.01 (1.26, 3.19) for the fifth quintile. However, this association was not seen in postmenopausal women. A similar association pattern was found for glycemic load, which was highly correlated with

4 WEN ET AL TABLE 2 Hazard ratios (and 95% CIs) for the association of dietary carbohydrate and fiber intake with breast cancer risk by quintile (Q) 1 Median value All subjects Premenopausal women Postmenopausal women (n ¼ 616) 2 (n ¼ 190) 2 (n ¼ 426) 2 Carbohydrate intake Q1 257.5 1.00 (reference) 1.00 (reference) 1.00 (reference) Q2 263.2 1.06 (0.82, 1.37) 1.17 (0.74, 1.85) 1.02 (0.75, 1.38) Q3 273.8 1.06 0.82, 1.36) 1.11 (0.69, 1.77) 1.03 (0.76, 1.39) Q4 289.3 1.08 (0.83, 1.39) 1.47 (1.00, 2.32) 0.93 (0.68, 1.27) Q5 343.5 1.22 (0.94, 1.58) 2.01 (1.26, 3.19) 0.98 (0.72, 1.34) P for trend 0.204 0.001 0.549 P for interaction 0.001 Glycemic index Q1 63.9 1.00 (reference) 1.00 (reference) 1.00 (reference) Q2 68.5 1.09 (0.85, 1.38) 0.97 (0.62, 1.51) 1.14 (0.85, 1.52) Q3 71.2 1.01 (0.79, 1.29) 1.08 (0.70, 1.68) 0.98 (0.72, 1.32) Q4 73.6 0.93 (0.72, 1.20) 1.39 (0.90, 2.13) 0.76 (0.55, 1.05) Q5 76.8 1.03 (0.79, 1.34) 1.19 (0.73, 1.94) 0.96 (0.70, 1.31) P for trend 0.472 0.256 0.093 P for interaction 0.068 Glycemic load Q1 163.8 1.00 (reference) 1.00 (reference) 1.00 (reference) Q2 187.5 1.02 (0.79, 1.30) 0.86 (0.54, 1.37) 1.08 (0.80, 1.45) Q3 202.5 0.99 (0.77, 1.27) 0.93 (0.59, 1.48) 1.00 (0.74, 1.35) Q4 216.7 1.07 (0.83, 1.38) 1.63 (1.07, 2.48) 0.86 (0.63, 1.18) Q5 239.4 1.07 (0.82, 1.39) 1.53 (0.96, 2.45) 0.91 (0.67, 1.25) P for trend 0.552 0.008 0.291 P for interaction 0.007 Fiber intake Q1 7.7 1.00 (reference) 1.00 (reference) 1.00 (reference) Q2 8.8 1.05 (0.81, 1.35) 0.95 (0.61, 1.49) 1.08 (0.79, 1.49) Q3 10.2 1.03 (0.80, 1.33) 0.85 (0.54, 1.34) 1.13 (0.82, 1.54) Q4 12.0 1.01 (0.78, 1.31) 0.71 (0.44, 1.15) 1.17 (0.86, 1.59) Q5 16.3 1.09 (0.84, 1.40) 1.01 (0.64, 1.57) 1.12 (0.83, 1.53) P for trend 0.482 0.635 0.267 P for interaction 0.225 1 The hazard ratios (HRs) and 95% CIs were derived from the Cox regression model with age as the time scale and were adjusted for age at the start of follow-up, total energy intake, education level, BMI, age at first birth, breast cancer history in first-degree relative, personal history of benign breast diseases, and physical activity. 2 Number of breast cancer cases. carbohydrate intake in this study (correlation coefficient ¼ 0.95). No significant association of breast cancer risk with glycemic index or fiber intake was found in either premenopausal women or postmenopausal women. We examined whether the association between carbohydrate intakeandbreastcancerrisk was modifiedby age or other riskfactors for breast cancer, including education levels, personal history of benign breast disease, and selected insulin- or estrogen-related risk factors, including BMI, physical activity, age atmenarche, and age at first birth. As presented in Table 3, the association of dietary carbohydrate intake with breast cancer was significantly modified by age (P ¼ 0.002 in all women, P ¼ 0.012 in premenopausal women). The increased breast cancer risk associated with carbohydrate intake was restricted to women who were,50 y. No age-carbohydrate interaction was found in postmenopausal women (P ¼ 0.940; data not shown). We found no significant interaction between carbohydrate intake and other factors when the analyses included all women, premenopausal women only (as presented in Table 3), or postmenopausal women only (data not shown). We conducted similar analyses for the average dietary intake calculated from both the baseline interview and the first follow-up interview. We also conducted analyses restricted to invasive breast cancer cases only and excluding breast cancer cases that occurred within 1 y of the baseline interview to minimize the possible effect of dietary changes related to subclinical breast cancer. All of these analyses produced results similar to those reported here. DISCUSSION In this prospective study we found that a high dietary carbohydrate intake and glycemic load were associated with elevated breast cancer risk in premenopausal, but not in postmenopausal, women in a dose-response manner. We found no clear association between glycemic index or dietary fiber intake and breast cancer risk. Using an energy-partition model (36) to examine the effect of dietary fat, protein, and carbohydrate intakes, we found that the effect of carbohydrate intake was opposite that of dietary fat and protein intakes. Using a residual model to adjust for total energy intake, we interpreted the coefficients for carbohydrate intake as the effect of substituting a certain amount of carbohydrate (eg, 50 g) for the same amount of energy from noncarbohydrate sources (ie,

CARBOHYDRATES, FIBER, AND BREAST CANCER RISK 5 TABLE 3 Association of dietary carbohydrate intake with breast cancer risk by common risk factors All women Premenopausal women No. of breast No. of breast cancer cases HR (95% CI) 1 P 2 cancer cases HR (95% CI) 1 P 2 Overall 616 1.07 (0.93, 1.24) 190 1.59 (1.20, 2.10) Age,50 y 179 1.72 (1.28, 2.31) 137 1.84 (1.32, 2.59) 50 59 y 207 0.83 (0.66, 1.05) 53 1.11 (0.67, 1.84) 60 y 230 1.01 (0.80, 1.27) 0.002 0 0.012 Education level High school 282 1.23 (0.99, 1.51) 86 1.90 (1.24, 2.91).High school 334 0.97 (0.80, 1.19) 0.295 104 1.41 (0.97, 2.04) 0.638 BMI 25 kg/m 2 377 1.09 (0.90, 1.31) 143 1.54 (1.10, 2.16).25 kg/m 2 239 1.06 (0.85, 1.31) 0.590 47 1.71 (1.05, 2.80) 0.334 Physical activity No 415 1.11 (0.93, 1.33) 153 1.57 (1.15, 2.14) Yes 201 0.99 (0.78, 1.27) 0.788 37 1.70 (0.89, 3.26) 0.222 Personal history of benign breast diseases No 461 1.14 (0.97, 1.35) 129 1.76 (1.26, 2.47) Yes 155 0.89 (0.66, 1.18) 0.506 61 1.25 (0.75, 2.07) 0.369 Age at menarche.15 y 191 1.09 (0.85, 1.39) 51 1.91 (1.13, 3.25) 14 15 y 268 1.15 (0.92, 1.43) 93 1.44 (0.97, 2.15) 13 y 157 0.96 (0.72, 1.28) 0.151 46 1.51 (0.83, 2.74) 0.237 Age at first birth,25 y 199 1.22 (0.95, 1.57) 23 2.30 (0.98, 5.39) 25 29 y 294 1.10 (0.89, 1.36) 120 1.80 (1.26, 2.60) 30 y or nulliparous 123 0.87 (0.64, 1.17) 0.750 47 1.12 (0.68, 1.85) 0.714 1 The hazard ratios (HRs) and 95% CIs were derived from the Cox regression model with age as the time scale and were adjusted for age at start of follow-up, total energy intake, education level, BMI, age at first birth, breast cancer history in firstdegree relative, personal history of benign breast diseases, and physical activity. The increment is 50 g of increase in carbohydrate intake for the HRs and 95% CIs. 2 P values for the tests of interaction. fat and protein) while holding constant the intakes of total energy. Thus, we were able to compare the effect of dietary carbohydrate with that of dietary fat and protein and found that dietary carbohydrate intake was responsible for the increase in breast cancer risk in premenopausal women. The proposed mechanisms for a possible etiologic role of dietary carbohydrate intake in breast cancer risk are related to the development or exacerbation of insulin resistance or chronic hyperinsulinemia (1, 3, 37). Insulin increases cell proliferation, and insulin receptors are expressed in normal and malignant breast tissue; thus, insulin may play an important role in breast cancer etiology (38, 39). In addition, insulin inhibits the synthesis of insulin-like growth factor binding protein I, increases the bioavailability of insulin-like growth factor I (IGF-I) (2, 40), and thus increases the risk of breast cancer, particularly in premenopausal women (2, 41 43). Previous prospective studies have generally shown no association between dietary carbohydrate intake and breast cancer risk (12 17). A recent prospective study (44), however, reported that starch-rich foods were associated with an increased risk of breast cancer and ovarian cancer (odds ratio: 1.85; 95% CI: 1.37, 2.48) for the highest consumption quartile compared with the lowest quartile. Another prospective study (12) reported that body weight modified the association of carbohydrate intake with breast cancer risk and that carbohydrate intake was positively related to breast cancer risk only among overweight premenopausal women. The authors of the study postulated that obesity, as an important determinant of insulin resistance, might exaggerate adverse metabolic responses related to carbohydrate intake. In our study, we did not find that body weight or other risk factors for breast cancer modified the association between carbohydrate intake and breast cancer risk. However, we found that the effect of carbohydrates on breast cancer risk was modified by menopausal status and age, with the increased risk associated with high dietary carbohydrate intakes being observed only among premenopausal women or women younger than 50 y. Previous studies have shown that plasma IGF-I is particularly relevant to breast cancer risk in premenopausal women (41 43), given that estradiol enhances the action of IGF-I in the breast (45). Our findings support the hypothesis that the effect of carbohydrate intake on breast cancer risk may be mediated by insulin and IGF-I. Traditional ecological analysis (25) and the fact that while carbohydrate intake has been decreasing in Shanghai, breast cancer incidence has been increasing (26, 27) indicate a negative association between carbohydrate intake and breast cancer. In contrast, however, we found a positive association between carbohydrate intake and breast cancer in premenopausal women and women younger than 50 y in this study. The heterogeneity of the association

6 WEN ET AL between carbohydrate intake and breast cancer across age groups (effect modification by age group) may be the reason for such a large ecological bias (46). In this study, glycemic load, a product of carbohydrate quantity and quality, was highly correlated with total carbohydrate intake (correlation coefficient ¼ 0.95). Therefore, glycemic load has an effect on breast cancer risk similar to that of total carbohydrate intake. Glycemic index, on the other hand, was not found to be associated with breast cancer risk. A recent study (47) reported similar findings in which endometrial cancer risk was associated with total carbohydrate intake and glycemic load, but not with glycemic index. A possible explanation for these findings was ascribed to the narrow range and centered distribution of the observed glycemic index values present in the data (47, 48). In our data, the glycemic index range was also narrow (interquartile range ¼ 6.6; CV ¼ 7.4%), and 2 food groups, rice and noodles (73.9%) and steamed bread (73%), contributed to.80% of the dietary glycemic load in the study population (7). In addition, the well-known carbohydrates (eg, sugar, fructose, or fat-containing foods) have a low or medium glycemic index. It is difficult to determine the effect of glycemic index with this type of distribution. The strengths of this study include its large sample size, its prospective cohort design, its extraordinarily high participation rate (92.7%) at baseline recruitment, and its collection of detailed information on many potential confounders. As with any epidemiologic study using an FFQ, a potential limitation is that the assessment of dietary intake is prone to measurement errors. However, dietary intake was measured twice, first during the baseline interview and then at the first follow-up survey. We analyzed the average dietary intakes from the 2 measurements and found similar results when only dietary intakes from the baseline interview were analyzed. For example, the hazard ratios (and 95% CIs) for every 50-g increase in carbohydrate intake in premenopausal women were 1.59 (1.20, 2.10) when analyzing the baseline data and 1.51 (1.23, 1.86) when analyzing the averaged data (data not shown in the tables). This comparison and the other strengths of this study may alleviate some concerns about dietary measurement errors. Another limitation of this study was that we did not analyze the effect of specific types of carbohydrates (eg, fructose and sucrose), because we were not able to derive these variables from the FFQ data. Some studies have suggested that a high intake of fructose and sucrose may increase the risk of cancer (49, 50). In conclusion, our data suggest no association of dietary fiber with breast cancer risk and a positive linear association of dietary carbohydrate intake and glycemic load with breast cancer risk in premenopausal women or women younger than 50 y. The authors responsibilities were as follows WW, XOS, GY, B-TJ, Y-TG, and WZ: study design; HL and Y-TG: data acquisition; WW, XOS, B-TJ, HC, and WZ: data analysis and interpretation; WW: draft of manuscript; and XOS, HC, B-TJ, and WZ: critical revision of manuscript. None of the authors had any personal or financial conflicts of interest. REFERENCES 1. Kaaks R, Lukanova A. Energy balance and cancer: the role of insulin and insulin-like growth factor-i. Proc Nutr Soc 2001;60:91 106. 2. Kaaks R. Nutrition, insulin, IGF-1 metabolism and cancer risk: a summary of epidemiological evidence. Novartis Found Symp 2004;262:247 60. 3. Gupta K, Krishnaswamy G, Karnad A, Peiris AN. Insulin: a novel factor in carcinogenesis. Am J Med Sci 2002;323:140 5. 4. Del Giudice ME, Fantus IG, Ezzat S, Keown-Eyssen G, Page D, Goodwin PJ. Insulin and related factors in premenopausal breast cancer risk. Breast Cancer Res Treat 1998;47:111 20. 5. Holt SH, Miller JC, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr 1997;66:1264 76. 6. Pereira MA, Jacobs DR Jr, Pins JJ, et al. Effect of whole grains on insulin sensitivity in overweight hyperinsulinemic adults. Am J Clin Nutr 2002;75:848 55. 7. Villegas R, Liu S, Gao YT, et al. Prospective study of dietary carbohydrates, glycemic index, glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women. Arch Intern Med 2007; 167:2310 6. 8. Augustin LS, Dal ML, La VC, et al. Dietary glycemic index and glycemic load, and breast cancer risk: a case-control study. Ann Oncol 2001;12:1533 8. 9. Potischman N, Coates RJ, Swanson CA, et al. Increased risk of early-stage breast cancer related to consumption of sweet foods among women less than age 45 in the United States. Cancer Causes Control 2002;13:937 46. 10. Tavani A, Giordano L, Gallus S, et al. Consumption of sweet foods and breast cancer risk in Italy. Ann Oncol 2006;17:341 5. 11. Silvera SA, Jain M, Howe GR, Miller AB, Rohan TE. Dietary carbohydrates and breast cancer risk: a prospective study of the roles of overall glycemic index and glycemic load. Int J Cancer 2005;114:653 8. 12. Cho E, Spiegelman D, Hunter DJ, Chen WY, Colditz GA, Willett WC. Premenopausal dietary carbohydrate, glycemic index, glycemic load, and fiber in relation to risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2003;12:1153 8. 13. Higginbotham S, Zhang ZF, Lee IM, Cook NR, Buring JE, Liu S. Dietary glycemic load and breast cancer risk in the Women s Health Study. Cancer Epidemiol Biomarkers Prev 2004;13:65 70. 14. Holmes MD, Liu S, Hankinson SE, Colditz GA, Hunter DJ, Willett WC. Dietary carbohydrates, fiber, and breast cancer risk. Am J Epidemiol 2004;159:732 9. 15. Horn-Ross PL, Hoggatt KJ, West DW, et al. Recent diet and breast cancer risk: the California Teachers Study (USA). Cancer Causes Control 2002;13:407 15. 16. Jonas CR, McCullough ML, Teras LR, Walker-Thurmond KA, Thun MJ, Calle EE. Dietary glycemic index, glycemic load, and risk of incident breast cancer in postmenopausal women. Cancer Epidemiol Biomarkers Prev 2003;12:573 7. 17. Nielsen TG, Olsen A, Christensen J, Overvad K, Tjonneland A. Dietary carbohydrate intake is not associated with the breast cancer incidence rate ratio in postmenopausal Danish women. J Nutr 2005;135:124 8. 18. Cohen LA. Dietary fiber and breast cancer. Anticancer Res 1999; 19:3685 8. 19. Gerber M. Fibre and breast cancer. Eur J Cancer Prev 1998;7(suppl 2):S63 7. 20. Challier B, Perarnau JM, Viel JF. Garlic, onion and cereal fibre as protective factors for breast cancer: a French case-control study. Eur J Epidemiol 1998;14:737 47. 21. Ronco A, De SE, Boffetta P, Deneo-Pellegrini H, Mendilaharsu M, Leborgne F. Vegetables, fruits, and related nutrients and risk of breast cancer: a case-control study in Uruguay. Nutr Cancer 1999;35:111 9. 22. Cade JE, Burley VJ, Greenwood DC. Dietary fibre and risk of breast cancer in the UK Women s Cohort Study. Int J Epidemiol 2007; 36:431 8. 23. Suzuki R, Rylander-Rudqvist T, Ye W, Saji S, Adlercreutz H, Wolk A. Dietary fiber intake and risk of postmenopausal breast cancer defined by estrogen and progesterone receptor status: a prospective cohort study among Swedish women. Int J Cancer 2008;122:403 12. 24. Terry P, Jain M, Miller AB, Howe GR, Rohan TE. No association among total dietary fiber, fiber fractions, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2002;11:1507 8. 25. Willett WC. Diet and breast cancer. J Intern Med 2001;249:395 411. 26. Chen Z, Shu XO, Yang G, et al. Nutrient intake among Chinese women living in Shanghai, China. Br J Nutr 2006;96:393 9. 27. Jin F, Devesa SS, Chow WH, et al. Cancer incidence trends in urban shanghai, 1972-1994: an update. Int J Cancer 1999;83:435 40. 28. Shu XO, Yang G, Jin F, et al. Validity and reproducibility of the food frequency questionnaire used in the Shanghai Women s Health Study. Eur J Clin Nutr 2004;58:17 23. 29. Zheng W, Chow WH, Yang G, et al. The Shanghai Women s Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005;162:1123 31. 30. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics Books.

CARBOHYDRATES, FIBER, AND BREAST CANCER RISK 7 31. Wang GY, Shen ZP. Chinese food composition tables. Beijing, People s Republic of China: People s Health Publishing House, 1991. 32. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr 2002; 76:5 56. 33. Wolever TM, Jenkins DJ, Jenkins AL, Josse RG. The glycemic index: methodology and clinical implications. Am J Clin Nutr 1991;54:846 54. 34. Hosmer DW, Lemeshow S. Applied survival analysis: regression modeling of time to event data. New York, NY: John Wiley & Sons, 1999. 35. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol 1997;145:72 80. 36. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 1997;65:1220S 8S. 37. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA 2002;287:2414 23. 38. Milazzo G, Giorgino F, Damante G, et al. Insulin receptor expression and function in human breast cancer cell lines. Cancer Res 1992;52: 3924 30. 39. Papa V, Milazzo G, Goldfine ID, Waldman FM, Vigneri R. Sporadic amplification of the insulin receptor gene in human breast cancer. J Endocrinol Invest 1997;20:531 6. 40. Conover CA, Lee PD, Kanaley JA, Clarkson JT, Jensen MD. Insulin regulation of insulin-like growth factor binding protein-1 in obese and nonobese humans. J Clin Endocrinol Metab 1992;74:1355 60. 41. Hankinson SE, Willett WC, Colditz GA, et al. Circulating concentrations of insulin-like growth factor-i and risk of breast cancer. Lancet 1998;351:1393 6. 42. Toniolo P, Bruning PF, Akhmedkhanov A, et al. Serum insulin-like growth factor-i and breast cancer. Int J Cancer 2000;88:828 32. 43. Yu H, Jin F, Shu XO, et al. Insulin-like growth factors and breast cancer risk in Chinese women. Cancer Epidemiol Biomarkers Prev 2002;11: 705 12. 44. Edefonti V, Decarli A, La VC, et al. Nutrient dietary patterns and the risk of breast and ovarian cancers. Int J Cancer 2008;122:609 13. 45. Ruan W, Catanese V, Wieczorek R, Feldman M, Kleinberg DL. Estradiol enhances the stimulatory effect of insulin-like growth factor-i (IGF-I) on mammary development and growth hormone-induced IGF-I messenger ribonucleic acid. Endocrinology 1995;136:1296 302. 46. Rothman KJ, Greenland S. Modern epidemiology. Philadelphia, PA: Lippincott-Raven Publishers, 1998. 47. Cust AE, Slimani N, Kaaks R, et al. Dietary carbohydrates, glycemic index, glycemic load, and endometrial cancer risk within the European Prospective Investigation into Cancer and Nutrition Cohort. Am J Epidemiol 2007;166:912 23. 48. Flood A, Peters U, Jenkins DJ, et al. Carbohydrate, glycemic index, and glycemic load and colorectal adenomas in the Prostate, Lung, Colorectal, and Ovarian Screening Study. Am J Clin Nutr 2006;84:1184 92. 49. Higginbotham S, Zhang ZF, Lee IM, et al. Dietary glycemic load and risk of colorectal cancer in the Women s Health Study. J Natl Cancer Inst 2004;96:229 33. 50. Nothlings U, Murphy SP, Wilkens LR, Henderson BE, Kolonel LN. Dietary glycemic load, added sugars, and carbohydrates as risk factors for pancreatic cancer: the Multiethnic Cohort Study. Am J Clin Nutr 2007;86:1495 501.