383

Asia Pac J Clin Nutr 2010;19 (3):383-392

Original Article

The association of carbohydrate intake, glycemic load,
glycemic index, and selected rice foods with breast
cancer risk: a case-control study in South Korea
Sung Ha Yun MS1, Kirang Kim PhD1,4, Seok Jin Nam PhD2, Gu Kong PhD3,
Mi Kyung Kim PhD1
1

Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, Korea
Department of Surgery, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul,
Korea
3
Department of Pathology, College of Medicine, Hanyang University, Seoul, Korea
4
Department of Food and Nutrition, Hanyang University, Seoul, Korea
2

Despite carbohydrate hypothesis related to breast cancer development, the inter-relationships of carbohydratemeasures with risk of breast cancer are unclear. We evaluated the association between the risk of breast cancer
and total carbohydrate intake, glycemic load, and glycemic index, and types of rice in a hospital-based casecontrol study. Cases were 362 women aged 30-65 years old who were histologically confirmed to have breast
cancer. Controls visiting the same hospital were matched to cases according to their age (±2 years) and menopausal status. Food intake was estimated by a quantitative food frequency questionnaire (FFQ) with 121 items.
Conditional and unconditional logistic regression analysis was used to obtain the odds ratios (ORs) and corresponding 95% confidence intervals. There were no associations between risk of breast cancer and carbohydrate
intake and glycemic load. A positive association with white rice (OR=1.19 per 100 g/d increment, 95% confidence interval (CI)=1.01-1.40), no association with mixed white rice (OR=0.95 per 100 g/d increment, 95%
CI=0.80-1.13), and an inverse association with mixed brown rice (OR=0.76 per 100 g/d increment of mixed
brown rice, 95% CI=0.61-0.95) was found. Additional analysis showed a positive association for white rice andan inverse association for mixed brown rice with breast cancer risk among overweight, postmenopausal women.
These results do not support an association between breast cancer and diets high in carbohydrate, glycemic index,
or glycemic load. However, a higher consumption of mixed brown rice may be associated with a decreased riskof breast cancer, especially in overweight, postmenopausal women.

Key Words: carbohydrate, glycemic load, glycemic index, rice, breast cancer

INTRODUCTION
Some researchers have hypothesized that carbohydrates
may promote cancer growth by increasing insulin levels
or insulin-like growth factor I (IGF-I) levels.1-2 The
physiological mechanisms which may underlie the increase in circulating total IGF-I and insulin have been
investigated. Among them, chronic energy restriction may
cause a dramatic drop in circulating IGF-I levels, but obesity and physical activity was not related with IGF-I andreduced insulin levels appeared with physical activity,
weight loss, and a high fiber diet.1-2 It is unclear how elevated IGF-I or insulin levels may be related to diet. Nevertheless, rapidly-digestible and absorbable carbohydrates
may contribute to elevated IGF-I or insulin levels.1,3 A
measure of the relative impact of carbohydrate-containing
foods on blood glucose is the glycemic index (GI), developed by Jenkins and colleagues in 1981.4 Rapidlydigestible and absorbable carbohydrates have a high GI.
The product of the GI value of a food and its carbohydrate content was also defined by the concept of glycemic
load (GL). These measures allow simultaneous assessment of both the quality and quantity of the carbohydrate
consumed.5 A review on diet and breast cancer showedthat the most recent studies on carbohydrates and breast

cancer risk have included the GL and the GI as carbohydrate nutrition factors.6 However, most previous studies
using GI and GL have not revealed an association withbreast cancer risk.6
Carbohydrate sources range from simple sugars suchas mono- and disaccharides to starches. GL and GI havebeen used as indicators of the quality and quantity of carbohydrates with respect to blood glucose and insulin response after carbohydrate consumption. However, given
the chemical and physical complexity of food, as well as
the variety of defense mechanisms against carcinogenesis,
it is difficult to assign an anticarcinogenic or a carcinogenic effect to an isolated component of consumed food.
Corresponding Author: Dr Mi Kyung Kim, Department ofPreventive Medicine, Medical School Building A-Room 512,
Department of Preventive Medicine, College of Medicine, Hanyang University, 17 Haengdang Dong, Sungdong Gu, Seoul,
133-791, Republic of Korea.
Tel: +82-2-2220-0667; Fax: +82-2-2293-0660
Email: kmkkim@hanyang.ac.kr
Manuscript received 30 December 2009. Initial review completed 21 March 2010. Revision accepted 12 May 2010.

384

SH Yun, K Kim, SJ Nam, G Kong and MK Kim

Cooked rice is the most frequently consumed staple
food item for Koreans and provides more than 55% ofdaily carbohydrate intake. On average, 97% of men and
95% of women eat more than twice daily.7 Although all
Koreans eat rice, there are several types of cooked rice,including refined white rice, white rice with cereals
and/or beans (mixed white rice), and brown rice with cereals and/or beans (mixed brown rice). Components inwhole grains associated with improved health status include dietary fiber, vitamins, minerals, lignans, phytoestrogens, phenolic compounds, and phytic acid, and thesecomponents coexist with starch highly concentrated inendosperm.8 Therefore, it may be difficult to dissociate
the effect of carbohydrates on breast cancer from othercomponents in the same carbohydrate source. However,
the refining process removes most of bran and some of
the germ, thus the relative concentration of starch ishigher in refined grains.8
In the present study, the association of breast cancer
risk with the dietary intake of carbohydrates, GL, and GI
among Korean women was examined. Additionally, it
was evaluated whether the source foods of carbohydrate
such as white rice and brown rice differently affects
breast cancer risk.
MATERIALS AND METHODS
Recruitment of cases and controls
Cases and controls were recruited from October 2004
through June 2006 at Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea. All participants aged 30-65 years were examined by mammography to detect any possibility of breast cancer. Cases had
histologically confirmed breast cancer and controls were
patients visiting one of the dentistry, orthopedic surgery,
general surgery, ophthalmology, dermatology, rehabilitation, or family medicine clinics within the same hospital. We excluded participants who had any history ofcancer (5 cases) or had estimated total energy intakes<500 kcal/d or >4000 kcal/d (16 cases and 13 controls).
Cases and controls were matched with respect to age (±2years) and menopausal status (362 pairs). This study was
conducted according to the guidelines laid down in the
Declaration of Helsinki and all procedures involving
human subjects were approved by the Institutional Review Board of the Samsung Hospital at Sungkyunkwan
University. Written informed consent was obtained fromeach participant.
Data Collection
Cases and controls were interviewed by trained interviewers with a questionnaire that consisted of generalcharacteristics, menstrual and reproductive history, familial history of breast cancer, smoking, drinking, intake
of multivitamins, and the average time spent exercising.
Dietary data was collected by the quantitative food frequency questionnaire (FFQ), modified from a validated
FFQ,9 with visual aids such as food photographs andmodels for item specific units. The validity and reliability
for the total carbohydrates was acceptable (correlation
coefficient with multiple 24 hour recalls=0.47; correlation
coefficient between two FFQs=0.41), although it was notformally validated for GI and GL. The FFQ was com-posed of 121 food items which included some rice foods
such as white rice, mixed white rice, and mixed brownrice. Open-ended questions with standard units such ascup, bowl, and piece etc were used. Subjects were asked
by trained interviewers to recall their usual intake of the
121 food items over a period of 12 months beginningfrom 3 years prior to the time of the interview.10 The average frequency of foods consumed was asked in nine
categories ranging from never or less than once per monthto three times or more per day. All frequencies were standardized into “times per day” by using the conversion
factors 30.4 days or 4.3 weeks per month. Daily food andnutrient intake was calculated with a standardized frequency per day and amount of food consumed. Detailed
information on database collection has been presented inprevious publications elsewhere.11-12
Three carbohydrate measures were used: carbohydrate
intake, GL and GI. To determine GL consumption, thecarbohydrate content of each food item was multiplied by
its GI value, and then all of the GL values were summed.
The overall dietary GI was calculated by dividing the
average daily GL by the average daily carbohydrate intake. The GI value for each food item was obtained fromthe international table of GI values,13 the GI online database maintained by the University of Sydney.14 The reference GI value was glucose (GI for glucose=100). Whenseveral GI values were available for a food item, themean GI value was used. For foods for which a GI value
had not been determined, a value was assigned based on
the most similar food item. In the case of mixed dishes,
the GI value for each ingredient was directly assignedwith the GI value of GI databases from the recipe, such as
boiled or raw. To get the GL value for each ingredient,the matched GI value was multiplied by the carbohydratecontent of that ingredient. The GL value for mixed dishes
was obtained by summing the GL values of all of the ingredients. The overall GI value of each mixed dish was
also calculated by dividing the GL value by the carbohydrate content of that mixed dish. Therefore, the mean value of twelve studies on GI for white rice and that of threestudies for brown rice was assigned (64 and 55, respectively) and overall GI values calculated from each ingredient were assigned for mixed white rice and mixed
brown rice (62 and 54, respectively) (Table 1). Fooditems with a zero or negligible carbohydrate content, suchas meat, fish, poultry, eggs, fats, and oils, were assumed
to produce little glycemic response and were not assigned
GI values. Of the 121 food items listed in the FFQ, GI
values were calculated for 79 items with GI values ranging from 14 (peanuts) to 105 (starch syrup). In the present
study, the carbohydrate content of these 79 food items
contributed to 96.5% (SD=2.2) of the total available carbohydrate intake.
To examine whether the effect of the sources of carbohydrates on breast cancer risk was different, the association between different rice foods, which are major contributors to the daily intake of carbohydrates (16% for
mixed brown rice; 19% for mixed white rice; 33% forwhite rice) or GL (16% for mixed brown rice; 20% for
mixed white rice; 21% for white rice) and breast cancer
was analyzed (Table 1).

Carbohydrate measures, rice, and breast cancer

385

Table 1. The contribution of some rice foods to carbohydrate and glycemic load

White rice, g/dMixed white rice
Mixed brown rice

Carbohydrate for controls
Glycemic index% carbohyPercentage of(glucose=100) Mean±SDdrate fromcontribution toeach foodvariation (%)16463 ± 9120
33
62
62 ± 91191054
58 ± 10216
12

Glycemic load for controls% glycemic
Percentage of
Mean±SD load from each contribution tofoodvariation (%)140 ± 5821
10
38 ± 57
2034
31 ± 55
16
5

Only food items contributing to more than 5% of total energy-unadjusted carbohydrate and glycemic load were shown. Carbohydrate, GI,
and GL were adjusted for total energy intake (kcal/d) by residual method.
Partial R2 from multiple stepwise regression with carbohydrate intake or glycemic load as the dependent variable and carbohydrate intake
or glycemic load from each food as the independent variable.

†

Statistical analysis
All nutrient intakes were total energy-adjusted values by
the residual method.15 Cases and their matched controls
were compared by a paired t-test for continuous variables
and by the McNemar test for categorical variables. The
quintiles of dietary carbohydrate, GI, GL, and other nutrient intakes and the quantiles of daily rice food intake
were used in the analyses. The general linear model andthe Cochran-Mantel-Haenszel analysis were used to assess potential confounders among controls. Variables
which were significantly different between cases and controls and showed significant linear trends across quintilesof carbohydrate, GI, and GL were adjusted in the multivariable models. Conditional logistic regression analysis
was applied to obtain the odds ratios (ORs) and corresponding 95% confidence intervals (CI). The trend tests
were conducted by considering the median values ofeach quantile of dietary carbohydrate, GI, GL, and riceintakes as continuous values in each of the model. Daily
carbohydrate, GI, GL, and rice food intakes were introduced as continuous values, and the units were expressed in increments of 100 g/d, 50 units/d, 10 units/d,and 100 g/d, respectively. After stratification by weightstatus (BMI <23 or ≥23 kg/m2),16 an unconditional logistic regression was used for the multivariable analysis.
All statistical analyses were conducted using SAS software (version 9.2, SAS Institute Inc., Cary, NC).
RESULTSThe comparisons of some general characteristics and theaverage of dietary nutrient and foods intakes between
cases and controls are presented in Table 2. The averageage of the cases and controls was 46 years, and the proportion of menopausal women was 35%. Women in thecontrol group had more children and higher proportionsof multivitamin use and breastfeeding than those in thecase group. Although the proportion of alcohol drinkers
was higher in the controls than in the cases, the daily consumption of alcohol (g/d) was not significantly different
between the cases and controls. The number of drinkers
was very low, and the proportion of drinkers who had one
or more drinks was approximately 3% in both case andcontrol patients in the present study. The average carbohydrate and white rice intake and glycemic index was
higher in cases than in controls. However, mixed white
rice and brown rice was higher in controls, although notstatistically significant.

Table 3 presents selected characteristics of study participants according to quintiles of carbohydrate intake,GL, and overall GI among controls. Higher carbohydrate
intake was related to higher postmenopausal status, older
age, white rice intake, mixed white rice consumption,
mixed brown rice intake, a lower proportion of multivitamin use, and daily intake of folate, soy protein, and vitamin E. A higher dietary GL was related to older age, ahigher daily intake of carbohydrates, white rice, mixed
white rice and a lower daily intake of soy protein, fiber,
folate, vitamin E, and mixed brown rice. In the case of GI,
higher intake was related with a higher BMI, a higher
intake of carbohydrates, and mixed white rice, and a lower daily intake of soy protein, fiber, folate, vitamin E, andmixed brown rice. The GI of the diet was uncorrelatedwith carbohydrate. Total fat intake was not included in
the analyses because of the strong correlation with total
carbohydrate intake (r= –0.70), and there was no difference between cases and controls.
The association between the intake of carbohydrate,
GL, GI, and rice food type and breast cancer risk isshown in Table 4. A higher carbohydrate intake was positively associated with breast cancer risk in the crude
model (5th vs. 1st quintile; OR=1.92, 95% CI=1.19-3.11, p
for trend=0.01), and a 61% increased risk per 100 g/dincrement of carbohydrate intake was also found. However, the adjustment for potential confounders, including
dietary factors such as soy protein, folate, vitamin E, andfiber, attenuated the strength of the association (multivariable model). No association between GL and breast
cancer risk was found in the present study. An inverse
association between GI and breast cancer risk was foundin the crude model for the continuous value (34% decreased risk per 10 units/d increment) and in the multivariable model (5th vs. 1st quintile; OR=0.44, 95% CI=
0.23-0.85, p for trend=0.01, 42% decreased risk per 10
units/d increment in multivariable model).
Interestingly, rice, which is major contributor of daily
intake in this study population, was differently associatedwith breast cancer risk. A higher intake of white rice wasassociated with an increased breast cancer risk (the highest vs. 1st quantile; OR=1.48, 95% CI=1.02-2.16, p fortrend=0.01 in the crude model) and a 100-g/d increment
increased the risk by 20%. Although the adjustment forpotential confounders attenuated the statistical significance of the association between white rice and breast
cancer, the increased risk per 100 g/d increment of white
rice remained (OR=1.19 per 100 g/d, 95% CI=1.01-1.40).

SH Yun, K Kim, SJ Nam, G Kong and MK Kim

386

Table 2. General characteristics and estimated nutrient and food intakes related to carbohydrate exposures among Korean women based on a matched case-control study (362 pairs)

Age, yMenopause,%
Education, yCases(n=362)46.1± 8.5
35
12.7 ± 3.6

Controls(n=362)46.0 ± 8.6
3512.4 ± 3.5

Body mass index, Kg/m2

23.6 ± 3.2

23.4 ± 3.0

0.41

Family history of breast cancer, %Current smokers, %Current alcohol drinkers, %Alcohol consumption, g/dRegular exercise (MET-hours/wk ≥ 22.5), %

8
1.9
29
1.85 ± 6.14
17
8.8

12
3.9
38
2.40 ± 8.07
22
14

0.10
0.13
<0.01
0.29
0.11
0.03

11
1.9 ± 1.0
64
15
14

8
2.1 ± 1.0
77
14
14

0.13
0.01
<0.0001
0.83
0.82

Age at menarche, y14.5 ± 1.7

14.5 ± 1.7

0.41

Age at the first birth, yAge at menopause, y

26 ± 3.5
48 ± 5.4

26 ± 3.4
48 ± 5.1

0.84
0.11

Total energy, kcal/d1945 ± 572

2026 ± 658

0.07

Carbohydrate, g/d

320 ± 41

312 ± 410.008

Fat, g/d40.9 ± 13.0

42.6 ± 13.3

0.07

Glycemic load

181 ± 33

182 ± 32

0.53

Glycemic index
Soy protein, g/d
Folate, μg/d
Vitamin E, mg/d
Fiber, g/d

58 ± 7.5
7.4 ± 4.5284 ± 100
10.6 ± 4.1
10.1 ± 4.5

57 ± 5.5
8.5 ± 4.6297 ± 100
11.2 ± 4.3
10.1 ± 4.1

0.02
0.001
0.07
0.09
0.96

Total vegetables, g/d

84 ± 165

83 ± 89

0.99

Total fruits, g/d

312 ± 385

278 ± 246

0.16

The main staple foods in Korea
White rice, g/dMixed white rice
Mixed brown rice

110±13765±11770±12482±11982±12279±139

0.003
0.07
0.36

Characteristics

Multivitamin users, %Reproductive factors
Nulliparous, %Number of children, N
Breast feeding, %Oral contraceptive use, %Used hormone compound, %p-value
N/A
N/A
0.31

Nutrient and food intakes

N/A, not applicable
Values are expressed as Mean ± SD or Percent
All nutrient intakes were adjusted for total energy intake (kcal/d) by the residual methodp-value: paired t test for continuous variables or McNemar test for categorical variable.

In contrast, mixed brown rice were inversely associatedwith breast cancer risk in multivariable models (for mixed
brown rice with cereals and beans: the highest vs. 1st quintile; OR=0.42, 95% CI=0.20-0.87, p for trend=0.01, 24%
decreased risk per 100 g/d).
Many large epidemiological studies have found that
overweight or obese postmenopausal women are at increased risk of developing breast cancer.17 Insulin levels
are higher in the presence of insulin resistance and obesity.18,19 Therefore, we evaluated whether the associations by weight status were dependent on menopausal
status with continuous dietary data after stratifying sub-jects into four groups (Table 5): 1) premenopausal
women with normal weight status; 2) premenopausal
women with overweight status; 3) postmenopausal
women with normal weight status; and 4) postmenopausal women with overweight status. The GI was inversely associated with breast cancer among overweight
women both with premenopausal status (OR=0.38, 95%
CI=0.17-0.86) and postmenopausal status (OR=0.48,
95% CI=0.23-0.99). An increased risk for breast cancer
per 100g/d increment of white rice in postmenopausal
women with overweight status (OR=1.47, 95% CI=1.042.07) was found. Decreased risk per 100 g/d increment

Carbohydrate measures, rice, and breast cancer

387

Table 3. Selected characteristics according to the quintiles of estimated dietary carbohydrate intake, glycemic load, and glycemic index among controls based on a matched casecontrol study (362 pairs)
Quintiles of carbohydrate intake
MedianAge (yr)
Menopause (yes)
Education (year)
Body mass index (kg/m2)
Family history of breast cancer (%)
Current smoker (%)
Current alcohol drinker (%)
Physical activity (≥22.5MET-hours/wk, %)Multivitamin user (%)
Parity (%)
Number of children
Oral contraceptive use (ever, %)Breastfeeding (ever, %)Dietary intake of
Total Energy (kcal/d)
Soy protein (g/d)
Fiber (g/d)
Folate (µg/d)
Vitamin E (mg/d)
Carbohydrate (g/d)
Glycemic load
Glycemic index
White rice (g/d)
Mixed white rice with cereals and beans (g/d)
Mixed brown rice with cereals and beans (g/d)

Quintiles of glycemic load

Quintiles of glycemic index

Q1

Q3

Q5

p for trend

Q1

Q3

Q5

p for trend

Q1

Q3

Q5

p for trend

261
44
29
12.2
23.3
8
3.2
43
28
22
89
2.1
17
76

316
46
33
12.7
23.5
9
7.2
37
18
14
92
2.1
17
78

369
51
41
11.5
23.4
24
0
35
17
9
96
2.1
11
83

<.0001
0.05
0.27
0.75
0.11
0.66
0.40
0.13
0.02
0.22
0.89
0.36
0.42

143
44
32
12.2
23.2
10.0
3.8
48
26
17
88
2.1
22
71

180
46
35
12.0
23.6
14.8
6.4
31
20
13
91
2.1
15
76

222
48
40
11.7
23.7
14.8
1.8
35
17
13
92
2.1
16
80

0.008
0.07
0.07
0.22
0.39
0.59
0.31
0.34
0.26
0.31
0.54
0.64
0.24

51
48
51
12.8
23.1
10.4
4.6
37
27
15
86
2.0
13
73

57
44
57
12.2
23.0
5.5
7.8
34
28
17
95
2.3
14
78

63
46
63
12.5
24.0
12.4
2.7
35
21
17
90
2.0
16
74

0.24
0.37
0.22
0.03
0.38
0.59
0.60
0.28
0.91
0.49
0.96
0.46
0.49

1963
9.2
9.3
301
12.5
256
150
58
67
45
45

2071
8.8
10.7
309
12.0
317
179
57
79
72
95

1936
6.8
10.0
247
8.1
368
220
60
105
136
105

0.81
0.002
0.10
0.009
<.0001<.0001<.00010.71
0.02<.0001
0.006

2.17
9.5
10.7
317
13.3
261
143
53
46
43
77

2047
8.2
10.6
304
11.6
317
180
57
66
88
98

1937
6.7
7.7
244
8.1
351
222
66
138
133
19

0.75
<.0001<.0001<.0001<.0001<.0001<.0001<.0001<.0001<.0001
0.008

2052
10.9
14.7
361
14.4
296
148
50
12
27
191

2004
8.6
9.1
291
10.4
306
174
57
75
120
40

1991
7.1
7.9
263
10.0
314
210
67
128
74
20

0.33
<.0001<.0001<.0001<.00010.006<.0001<.00010.15
0.01<.0001The prevalence of menopausal status and age were adjusted for respectively and other variables were adjusted for both age and menopausal status. Values were expressed as a mean or percent. P-values for thetrends were determined by the general linear model for continuous variables and by the Cochran-Mantel-Haenszel test for categorical variables.

SH Yun, K Kim, SJ Nam, G Kong and MK Kim

388

Table 4. Odds ratio (OR) and 95% confidence intervals (CIs) for breast cancer risk according to carbohydrate intake, glycemic load, glycemic index, and rice foods among Korean
women based on a matched case-control study (362 pairs)
Quintiles of carbohydrate intake, glycemic load, and glycemic index/ Quantiles of rice foods
Q1
Q2
Q3
Q4
Q5
Carbohydrate intake
MedianCases/Controls
Crude OR
Multivariable OR†
Glycemic load
MedianCases/Controls
Crude OR
Multivariable OR
Glycemic index
MedianCases/Controls
Crude OR
Multivariable OR
White riceMedianCases/Controls
Crude OR
Multivariable OR
Mixed white rice with cereals and beans
MedianCases/Controls
Crude OR
Multivariable OR
Mixed brown rice with cereals and beans
MedianCases/Controls
Crude OR
Multivariable OR

261
61 / 83
1.0
1.0

294
73 / 72
1.38 (0.86, 2.21)
1.38 (0.82, 2.33)

316
68 / 77
1.17 (0.74, 1.86)
1.04 (0.61, 1.79)

342
76 / 69
1.48 (0.93, 2.35)
1.14 (0.66, 1.99)

369
84 / 61
1.92 (1.19, 3.11)
1.59 (0.88, 2.87)

143
73 / 71
1.0
1.0

164
67 /780.80 (0.50, 1.29)
0.89 (0.52, 1.53)

180
64/ 810.76 (0.47, 1.21)
0.62 (0.36, 1.07)

197
79 / 66
1.12 (0.71, 1.75)
0.99 (0.57, 1.69)

222
79 / 66
1.15 (0.73, 1.81)
0.85 (0.48, 1.50)

51
79 / 651.0
1.0

54
69 / 76
0.75 (0.47, 1.20)
0.75 (0.44, 1.27)

57
74 / 71
0.88 (0.55, 1.41)
0.79 (0.42, 1.47)

59
74 / 71
0.87 (0.55, 1.37)
0.62 (0.32, 1.21)

63
66 / 79
0.70 (0.44, 1.10)
0.44 (0.23, 0.85)

0178/189
1.0
1.0

39
23/44
0.57 (0.33, 1.99)
0.77 (0.41, 1.45)

180
74/70
1.10 (0.75, 1.63)
1.10 (0.67, 1.81)

315
87/59
1.48 (1.02, 2.16)
1.51 (0.90, 2.54)0241/197
1.0
1.0

79
54/88
0.51 (0.34, 0.75)
0.67 (0.42, 1.08)

350
67/77
0.74 (0.50, 1.08)
0.97 (0.57, 1.66)

0252/244
1.0
1.0

100
44/43
0.98 (0.62, 1.57)
0.90 (0.47, 1.71)

350
66/75
0.85 (0.59, 1.24)
0.42 (0.20, 0.87)

p for trend

Continuous
per 100g/d

0.01
0.28

1.61 (1.13, 2.30)
1.38 (0.88, 2.16)
per 50 units/d

0.24
0.80

1.07 (0.86, 1.34)
0.91 (0.69, 1.20)
per 10 units/d

0.22
0.01

0.76 (0.60, 0.97)
0.58 (0.41, 0.82)
per 100 g/d

0.01
0.10

1.18 (1.06, 1.33)
1.19 (1.01, 1.40)
per 100 g/d

0.09
0.90

0.90 (0.79, 1.01)
0.95 (0.80, 1.13)
per 100 g/d

0.41
0.01

0.95 (0.85, 1.06)
0.76 (0.61, 0.95)

OR, Odds Ratio; CI, 95% confidence interval†Multivariable included BMI (kg/m2, continuous), alcohol drinking (none, past, current), multivitamin use (yes, no), number of children, breast feeding (yes, no), and dietary factors including soy protein (quintiles), folate (quintiles), vitamin E (quintiles), and fiber (quintiles).

Carbohydrate measures, rice, and breast cancer

389

Table 5. Odds ratio (OR) and 95% confidence intervals (CIs) for breast cancer risk according to carbohydrate intake,glycemic load, glycemic index, and rice foods by menopausal and weight status among Korean women based on amatched case-control study (362 pairs)

No of cases/controls

Premenopausal status
Normal Weight
Overweight status(BMI <23)(BMI ≥23)
123/144112/91

Postmenopausal status
Normal Weight
Overweight status(BMI <23)(BMI ≥23)
36/41
91/86

Carbohydrate intake, per 100g/d

1.38 (0.59, 2.88)

0.93 (0.38, 2.26)

0.01 (0.001, 11.0)

2.54 (0.87, 7.41)

Glycemic load, per 50 units/d
Glycemic index, per 10 units/dWhite rice, per 100 g/d
Mixed white rice with cereals andbeans, per 100 g/d
Mixed brown rice with cereals andbeans, per 100 g/d

0.89(0.57, 1.39)
0.66 (0.39, 1.10)
1.06 (0.80, 1.41)

0.64 (0.34, 1.20)
0.38 (0.17, 0.86)
1.14 (0.83, 1.57)

0.22 (0.01,6.22)
1.25 (0.19, 8.31)
0.35 (0.06, 2.10)

1.02 (0.54, 1.95)
0.48 (0.23, 1.00)
1.47 (1.04, 2.07)

0.90 (0.67, 1.21)

0.90 (0.62, 1.91)

0.82 (0.21, 3.12)

1.05 (0.74, 1.50)

0.88 (0.58, 1.34)

0.80 (0.52, 1.21)

0.25 (0.03, 1.82)

0.58 (0.37, 0.90)

Odds ratios were obtained by unconditional logistic regression analysis after adjusting for age, alcohol drinking (none, past, current), multivitamin use (yes, no), number of children, breast feeding (yes, no), and dietary factors such as soy protein (quintiles), folate (quintiles), vitamin E (quintiles), and fiber (quintiles) as covariatesof mixed brown rice was found only in postmenopausal
women with overweight status (OR=0.58, 95% CI=0.370.90).
DISCUSSION
There was no increased risk of breast cancer with highdietary intake of carbohydrate and GL, and high-GI. A
higher intake of white rice was related to an increased
risk for breast cancer among overweight, postmenopausal
women, whereas a higher intake of mixed brown rice wasrelated to a decreased risk for breast cancer among overweight, postmenopausal women.
In vitro or animal studies have shown that insulin or
IGF-I may directly affect tumor development by stimulating cell proliferation, by inhibiting apoptosis, and bymodifying endogenous sex steroid metabolism.1 Theoretically, rapidly-digestible and absorbable carbohydrates,
foods with a high GI, may elevate IGF-I or insulin levels
and may be associated with breast cancer risk. However,in the present study, any increased risk was not foundwith high carbohydrate, GL, or GI. Unlike the GI andbreast cancer hypothesis, an inverse association between
GI and breast cancer risk was found in the present study.
Previously, there were a few case-control studies andsome prospective studies investigating the links between
dietary carbohydrates, GL, or GI and breast cancer, but
most of those studies have not supported the hypothesis
of GI and breast cancer risk.20-36 Among prospective cohort studies on carbohydrate intake, eleven out of twelve
studies showed null associations with breast cancer.23-30,3233,35-36
One small follow-up study showed an elevated riskof breast cancer with increased carbohydrate.30 A nested
case-control study showed a decreased incidence of breast
cancer with increased carbohydrate intake (RR=0.42
across tertiles, 95% CI=0.18-0.95) among postmenopausal
women.37 Nine cohort studies have looked at GL andGI,25-28,31,32,34-36 but only the Nurses’ Health Study and theCanadian National Breast Screening Study showed anincreased risk of breast cancer with GL and GI among
postmenopausal women, respectively.27,28 A French cohort study showed an increased risk with GL and GI

among overweight postmenopausal women.36 Amongpremenopausal women, only the Hormones and Diet in
the Etiology of Breast Tumors Study (ORDET Study),out of five cohort studies, showed an increased risk ofbreast cancer with GI and GL.26-28,34,35 It is unclear whyhigh GI was rather inversely associated with breast cancer
risk in the present study unlike previous studies. A possible explanation is that GI may be a simple marker of another health-related behavior in Korea. However, in thepresent study, a high GI was related to unhealthy characteristics, relatively high BMI and a high intake of carbohydrate and low intake of soy protein, fiber, folate, vitamin E, and mixed brown rice. Another concern is the usefulness of GI. Although the glycemic index is a significant predictor of the insulin response, only 23% of thevariability in insulinemia is accounted for by the glycemic index.38 The glycemic response may not be an adequate marker of insulin or IGF-I response and an insulin
index of food may be a better measure of the potential
hyperinsulinemic effect of food.
There have not been many prior studies that have examined the association between rice type and breast cancer risk, except for an ecological study that reported that
populations with lower rates of breast cancer are more
likely to have higher consumption of rice;39 however, no
study has shown a link between refined grain intake andbreast cancer risk.40 The present study findings show significant but different results based on the type of rice. A
significantly positive association between white rice andbreast cancer among overweight women was found inpostmenopausal women (OR=1.47, 95% CI=1.04-2.07 in
Table 4). However, there was no association between
mixed white rice and breast cancer risk. Mixed brownrice was inversely associated with breast cancer only
among overweight postmenopausal women (OR=0.58 per
100 g/d increment of mixed brown rice, 95% CI=0.370.90). It is interesting that there were different associations between different rice foods and breast cancer risk.
The positive association with white rice may indicate thateating only the starchy endosperm of rice, which is composed primarily of starch and therefore has a high GI,

390

SH Yun, K Kim, SJ Nam, G Kong and MK Kim

may increase breast cancer risk. It might indirectly support the hypothesis of breast cancer risk linked to carbohydrate in obese women by Holmes et al.27 The lack of an
association between mixed white rice and breast cancer
risk may be due to the anticarcinogenic components of
mixed cereals and beans.8 The inverse association between brown rice intake and breast cancer risk is biologically reasonable because of the high nutritional quality ofbrown rice; the great bulk of nutrients and biologically
active constituents in grain are found in the bran and thegerm.8
Taken together, these findings suggest that a weak association or an unexpected association of carbohydratemeasures with breast cancer may be attributed to the characteristics of the carbohydrate source ingested.
Several limitations should be considered when interpreting the findings of this study. The primary concern isthat the present study was a hospital-based case-control
study, thus, there is a restriction in generalizing the results
to the general population.41 Another potential bias in acase-control study is that cases could change their diet asa result of cancer diagnosis and treatment. However, inthe present study, all cases were interviewed before diagnosis or within one week after diagnosis so that dietarychanges of cases or differential recall biases are not substantial. In addition, the selection of an appropriate control group may be problematic.41 The diets of those who
participated the study may still differ from those who did
not participate. Another limitation in interpreting results
is that the FFQ was not specifically designed to investigate the association between GI or GL or rice with breast
cancer risk and it was not formally validated for them.
Furthermore, the values were generated from international tables and did not reflect the mixed foods eaten at
the same time which does influence GL. However, weobserved strong relationships between GI and GL andlow HDL cholesterolemia among women (OR=1.77 perincrement 10 units of GI, 95% CI=1.06-2.93; OR=2.21
per 50 units/d increment of GL, 95% CI=1.47-3.33 in a
general population in a rural area) with the same GI database in another study,42 although it was not direct evidence for the validity of GI and GL, and the GI values
were comparable to published data for GI of Korean
foods.43
Regardless of these limitations, rice is a major staple
among two-thirds of the world’s population,44 thus it isworthwhile to note different association between brownrice (whole grain) and white rice (refined grain) with breast
cancer risk, as well as results of carbohydrate measures.
In conclusion, results do not support an association between breast cancer and diets high in carbohydrate, glycemic index, or glycemic load and indicate that brownrice with cereals and beans instead of white rice may berelated to a decreased risk of breast cancer among overweight, postmenopausal women.
ACKNOWLEDGEMENTS
Kim MK designed and supervised the execution of the study,
and Yun SA performed the analyses and wrote the manuscriptwith Kim MK. Kim K, Nam SJ and Kong G contributed to datapreparation. All authors participated in interpretation of theresults and editing of the manuscript and have no conflicts ofinterest. This study was supported by the Korean Ministry of
Science and Technology (Grant No. M10418020002-08N180200210).
AUTHOR DISCLOSURESWe declare that we have no conflict of interest.
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392

SH Yun, K Kim, SJ Nam, G Kong and MK Kim

Original Article

The association of carbohydrate intake, glycemic load,
glycemic index, and selected rice foods with breast
cancer risk: a case-control study in South Korea
Sung Ha Yun MS1, Kirang Kim PhD1,4, Seok Jin Nam PhD2, Gu Kong PhD3,
Mi Kyung Kim PhD1
1

Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, Korea
Department of Surgery, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul,
Korea
3
Department of Pathology, College of Medicine, Hanyang University, Seoul, Korea
4
Department of Food and Nutrition, Hanyang University, Seoul, Korea
2醣類攝取、升糖負荷、升糖指數及米食與乳癌危險性
之相關：一個南韓的病例對照研究
雖然醣類被假設與乳癌的發生有關，但醣類攝取的評量與乳癌危險性的相關仍
不清楚。我們在一家醫院進行病例對照研究，評估乳癌危險性與總醣類攝取、
升糖負荷、升糖指數、及米的種類的相關性。病例組為 362 名婦女，年齡 3065 歲，都經組織檢驗證實罹患乳癌。對照組婦女在同一家醫院就診，與病例組
依年齡(±2 歲)和停經狀況配對。飲食攝取以列有 121 項目的定量式飲食頻率問
卷(FFQ)評估。使用條件式及非條件式邏輯迴歸分析，求得勝算比(OR)及 95%
信賴區間。結果發現，罹患乳癌的危險性與醣類攝取及升糖負荷沒有相關性。
但與白米攝取有正相關 (OR=1.19 每增加 100 g/天，95% CI=1.01-1.40)；與混合
白米沒有相關性(OR=0.95 每增加 100 g/天，95% CI=0.80-1.13)，與混合糙米為
負相關(OR=0.76 每增加 100 g/天，95% CI=0.61-0.95)。進一步分析顯示，在體
重過重的停經後婦女，乳癌危險性與白米攝取有正相關，與混合糙米攝取為負相關。這些結果並不支持乳癌與高醣飲食、升糖指數或升糖負荷的相關性。然而，攝取較多的混合糙米可能與降低乳癌危險性有相關，尤其是在體重過重的
停經後婦女。
關鍵字：醣類、升糖負荷、升糖指數、米、乳癌