Original Communication

European Journal of Clinical Nutrition (2004) 58, 1472–1478. doi:10.1038/sj.ejcn.1601992 Published online 5 May 2004

Correlation between dietary glycemic index and cardiovascular disease risk factors among Japanese women

Y Amano1, K Kawakubo2, J S Lee1, A C Tang1, M Sugiyama3 and K Mori1

  1. 1Department of Health Promotion Sciences, Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, Japan
  2. 2Department of Food Sciences and Nutrition, Kyoritsu Women's University, Japan
  3. 3School of Nutrition & Dietetics, Faculty of Health & Social Work, Kanagawa University of Human Services, Japan

Correspondence: Y Amano, Department of Health Promotion Sciences, Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, 7-3-1 Bunkyo-Ku Hongo, Japan. E-mail: yk-amano@umin.ac.jp

Guarantor: Y Amano.

Contributors: YA carried out the literature research, designed and implemented the study, interpreted the data, and wrote the paper. KK, JSL, ACT, and MS contributed to the design and interpretation of the study data and the writing of the paper. KM conducted the statistical analysis.

Received 1 November 2003; Revised 11 March 2004; Accepted 30 March 2004; Published online 5 May 2004.

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Abstract

Objective: To examine the correlation between dietary glycemic index (GI) and cardiovascular disease (CVD) risk factors among subjects who consume white rice as a staple food.

Design: A cross-sectional study was conducted to explore the associations between dietary GI, dietary glycemic load (GL) and dietary intakes, and CVD risk factors. Dietary GI and GL were calculated from a 3-day (including two consecutive weekdays and one holiday) dietary records.

Setting: A weight-reduction program at a municipal health center in Tokyo, Japan.

Subjects: A total of 32 women aged 52.5plusminus7.2 y participated in the weight-reduction program.

Result: The GI food list made for the current study calculated for 91% of carbohydrate intakes measured. The mean dietary GI was 64plusminus6, and the mean dietary GL was 150plusminus37. Individuals in the highest tertile of GI consumed more carbohydrate, mostly from white rice (P<0.001), and less fat (P<0.01). Individuals in all three groups by tertile of GL showed similar tendencies. In the lowest GI tertile, the highest concentration of HDL-cholesterol and lowest concentration of triacylglycerol and immunoreactive insulin were observed (P<0.01). In the lowest GL tertile, the highest concentration of HDL-cholesterol and the lowest concentration of triacylglycerol were observed (P<0.05).

Conclusion: Calculated dietary GI and GL were positively associated with CVD risk factors among the Japanese women who consumed white rice as a staple food.

Keywords:

glycemic index, glycemic load, white rice, cardiovascular disease, lipid, glucose, women

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Introduction

Nutrition education for diabetic or obese patients has mostly been based on the quantitative modifications of energy from foods and nutrition intakes. Nutrition education assumes that an increase in postprandial glucose is proportional to the quantity of carbohydrate ingested. On the other hand, Jenkins proposed the glycemic index (GI) as a physiologic basis for qualitative classification of carbohydrate-containing foods, where low GI foods produce less glycemia than high GI foods (Jenkins et al, 1981). Since then, many metabolic studies showed that a low GI diet improves overall blood glucose and lipid profiles in normal subjects (Jenkins et al, 1987a), and patients with hyperlipidemia (Jenkins et al, 1985, 1987b) or diabetes (Jenkins et al, 1988; Brand et al, 1991; Wolever et al, 1992a, 1992b; Jarvi et al, 1999; Luscombe et al, 1999). The GI values of 565 foods were compiled to form the international GI table in 1995. And later, nearly 1300 foods were listed in the table (Foster-Powell & Miller, 1995; Foster-Powell et al, 2002).

Glycemic response to mixed meals has been calculated from the GI values of the constituent foods (FAO/WHO, 1998). In recent epidemiologic studies, dietary GI or glycemic load (GL: a measure of carbohydrate quality and quantity; GI times dietary carbohydrate content) were calculated from dietary records or food frequency questionnaires. Higher dietary GI or GL was significantly associated with the incidence of diabetes (Salmeron et al, 1997a, 1997b) and coronary heart disease (CHD) (Liu et al, 2000). Furthermore, an association between higher dietary GI or GL and risk factors of cardiovascular disease (CVD) such as lower HDL-cholesterol (Frost et al, 1999; Ford & Liu, 2001; Liu et al, 2001), higher triacylglycerol (Wolever et al, 1995; Liu et al, 2001) and higher HbA1c (Wolever et al, 1995; Buyken et al, 2001) as observed. These findings suggested the possible benefits that low GI or GL diet might have in the prevention and management of lifestyle-related disease such as diabetes and CVD. In 1998, FAO/WHO recommended the reduction of dietary GI to prevent and manage metabolic diseases (FAO/WHO, 1998).

In most GI studies, GI values were based on Western foods, where white bread, pasta, potatoes, or others, are consumed as staple foods. In many Asian countries, white rice is consumed as a staple food and the overall dietary habits are different from those in western countries. Recently, GI values of foods using white rice as a reference food have been reported (Sugiyama et al, 2003). Few studies have examined the correlations between dietary GI and risk factors for CVD among people who mainly consumed white rice, which is high in GI. Correlations between GI and these parameters would make GI a useful marker in nutrition education among Asian people. The objectives of the current study were: to make a GI food list that matched the Japanese food table to calculate the dietary GI and GL based on the GI food list and to study the correlations between dietary GI or GL and CVD risk factors among a group of Japanese women.

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Subjects and measurement methods

A total of 32 women who participated in a weight-reduction program at a municipal health center in Tokyo participated in the study.

Nutritional intake was determined by a 3-day (two weekdays and one holiday) dietary record (Toeller et al, 1997) before the subjects joined the program. Three dietitians instructed each subject on how to record detailed descriptions of all foods and beverages consumed (ingredients, cooking methods, and information on whether foods were dined in or dined out). The dietitians checked the dietary records of each participant and personally clarified any ambiguous information to ensure completeness. Furthermore, they confirmed the actual portion sizes with the subjects using food models, especially for the starchy foods. For other nonstarchy foods, the standard portion sizes were calculated with a software (OLYMPUS Nutritional Consultation Ver.2.1). Energy, macronutrient, cholesterol, and dietary fibers were calculated using the Standard Tables of Food Composition in JAPAN (fifth revised edition) (Resources Council, Science and Technology Agency, 2000) using Microsoft® Excel 2000 program. Daily intakes of different foods were grouped according to food groups.

Assessment of individual CVD risk factors was performed before the subjects joined the program. Physical check-ups for body weight, body fat and height measurement, blood sampling, and medical interview were carried out. These measurements were performed on the same day the dietary records were adjusted by the dietitians. BMI was calculated from weight (kg) divided by height (m2) of each subject. After an overnight fast, serum total cholesterol (TC), HDL-cholesterol (HDL-c), triacylglycerol (TG), fasting plasma glucose (FPG), and immunoreactive insulin (IRI) were measured. LDL-cholesterol (LDL-c) was calculated using the Friedewald equation (TC - HDL-c - TG/5).

The frequency and duration of exercise, frequency and quantity of alcohol consumed, smoking status, and history of past and current illnesses were obtained through a questionnaire or a medical interview. Exercise time (total minutes/week) was determined by multiplying the frequency (per week) and duration (minutes). Alcohol consumption (g/week) was calculated by multiplying the frequency (per week) and quantity (g). Smoking status was assessed as amount of smoking (number of cigarettes per day).

Informed consent was obtained from each of the subjects before their participation in the program.

GI food list of Japanese foods

A table of glucose-based GI food list containing Japanese foods (Sugiyama et al, 2003) and foods listed in the International GI table (Foster-Powell et al, 2002) was made for the calculation of dietary GI and GL. These foods were selected from the Standard Tables of Food Composition in JAPAN (fifth revised edition). Computation was carried out on an Excel spreadsheet.

The white rice-based GI values of foods were transformed into glucose-based GI by multiplying white rice-based GI by 0.8 (100/122) (Sugiyama et al, 2003). GI values of foods with no available published GI value were estimated from similar foods or calculated from constituent foods (Buyken et al, 2001). We did not include the GI values of most vegetables, since they contained little carbohydrate per serving.

Calculating the dietary GI and GL

The dietary GI was determined by multiplying the amount of carbohydrate content (g) of the food item by the food's GI. The sum of these products was then divided by the total daily carbohydrate intake (Wolever et al, 1991). Fiber content was subtracted from the carbohydrate content. We obtained dietary GI per participant per day as follows (dietary GL was equal to the numerator of the formula).

Dietary GI=sum{(GI of food item) times (grams of carbohydrate of the food item)}/total daily carbohydrate intake.

Statistics

The results are expressed as means plusminuss.d.s. To confirm the correlation between dietary GI or GL and foods and nutrition intakes and CVD risk factors (body weight, BMI, body fat, TC, HDL-c, TG, LDL-c, FPG, and IRI), subjects were divided into three groups by tertiles of dietary GI or GL. The differences between these groups were analyzed by Kruskal–Wallis test and multiple comparisons with Bonferroni-adjusted test. CVD risk factors were adjusted for total energy intake, dietary fiber intake (g/1000 kcal), age, and exercise time (total minutes/week) by using the residual method advocated by Willett & Stampfer (1986) and then examined by tertile groups. To exclude the outliers, we used the Grubbs–Smirnov test. We also excluded subjects who were taking the medication for hypercholesterolemia from the analysis of TC. The differences were considered statistically significant if P<0.05. All statistical analyses were carried out using the SAS (Ver 8.2).

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Results

The 32 women in the study had a mean age of 52.5plusminus7.2 y and a mean BMI of 26.2plusminus2.7 kg/m2. Of these subjects, 18.8% were under medical treatment, out of which 12.5% were hypertensive and 9.4% were hypercholesterolemic. No subject took blood glucose lowering medication. In all, 59% of the women were postmenopausal. The mean exercise time was 70.9plusminus95.2 min/week, and the mean alcohol consumption was 18.4plusminus36.1 g/week. Only three subjects (9.4%) were current smokers. The mean daily energy intake was 1843plusminus308 kcal (7715plusminus1289 kJ), energy% from protein (protein E%) was 15.4plusminus2.0%, fat E% was 27.3plusminus5.0%, carbohydrate E% was 54.1plusminus7.7%, and dietary fiber (g)/1000 kcal was 7.8plusminus1.7 g.

GI food list made in the current study

The 3-day dietary records of all subjects contained 427 different foods. Of these foods, 120 (28%) were meat, fish, or egg, having no effect on dietary GI, 143 (33%) were nonstarchy foods containing less than 1 g carbohydrate per every serving, and 164 (38%) contained more than 1 g carbohydrate per every intake. Among the carbohydrate-containing foods, 127 were identified in the GI food list made in the current study.

The carbohydrate content of these foods contributed 91% of the 3-day total carbohydrate intake.

Dietary GI and GL

The mean dietary GI was 64plusminus6 (50–72), and the median was 64. Tertile means were 57, 64, and 70 with differences of approximately seven between each group. The contributions of each food group to the dietary GI were 76% from cereal foods (rices, breads, noodles, and breakfast cereals), 9% from cakes and desserts, 5% from sugars, and 4% from fruits. The mean dietary GL was 150plusminus37 (68–239), and the median was 150. Tertile means were 109, 148, and 188 with differences of approximately 40 between each group. The contributions of each food group to the dietary GL were almost the same as the dietary GI.

Correlation between dietary GI or GL and demographic data

We compared the demographic differences among the three dietary GI or GL tertile groups (Table 1). There were no significant differences in age, exercise time, and alcohol consumption among dietary GI tertiles. But among the dietary GL tertile groups, alcohol consumption was significantly different, and the lowest GL tertile was seen highest in terms of alcohol consumption (P=0.007), and all smokers (three subjects) belonged to the lowest tertile.


Foods and nutrition intakes

The daily food and nutrition intakes among tertiles of dietary GI and GL are shown in Table 2. The daily food intakes among tertiles of dietary GI were significantly different for cereals, meats, and milk and dairy products (P<0.05). But there was no specific tendency of either increasing or decreasing the amount of these food intakes with regard to an increasing dietary GI. Carbohydrate from white rice as a proportion of total carbohydrate intake was lower in the lowest tertile of GI and higher in the highest tertile of GI (P<0.001). For dietary GL, intake of cereals was significantly different among three tertile groups (P<0.001). The individuals in the lowest tertile of GL consumed less cereals and those in highest tertile of GL consumed more cereals.


For daily nutrition intake in the tertiles of dietary GI, significantly less carbohydrate and more total fat and monounsaturated fat intake were seen among the lowest GI tertile (P<0.05). Similarly, individuals in the lowest tertile of GL consumed less carbohydrate but more intake of total fat, monounsaturated fat, and polyunsaturated fat (P<0.01).

CVD risk factors

The correlation between the dietary GI, dietary GL, and the risk factors, adjusted for total energy intake, dietary fiber intake (g/1000 kcal), age, and exercise time (total minutes/week), are shown in Table 3. For dietary GI, the differences in HDL-c, TG, and IRI concentrations were significantly different among the three tertile groups (P<0.01). Higher HDL-c and lower TG and IRI concentrations were observed in the lowest tertile of GI. BMI, TC, LDL-c, and FPG concentrations were not associated with dietary GI. Similarly, for dietary GL, HDL-c and TG concentrations were significantly different among the three groups (P<0.05). Higher HDL-c and lower TG concentrations were observed especially in the lowest tertile of GL.


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Discussion

We examined the correlations between dietary GI, dietary GL, and CVD risk factors among 32 women who participated in a weight-reduction program. There were significant correlations between dietary GI and HDL-c, TG, and IRI as well as between dietary GL and HDL-c and TG.

In the study by Jarvi et al (1999), dietary GI was calculated for 94% of the consumed dietary carbohydrate. In the current study, in terms of carbohydrate intake, we were able to calculate 91% thereof from the GI food list made based on the Japanese food table. Thus this GI food list would be applicable to future nutrition education in Japan.

The mean dietary GI obtained among the subjects was 64 (50–72). As cereal foods accounted for 76% of the dietary GI, dietary GI was mostly determined by starchy staple foods. In particular, the most important factor related to dietary GI was white rice, where higher white rice intake was related to higher dietary GI. Individuals with low dietary GI consumed more fat and less carbohydrate.

Dietary GI value depended on different foods in various regions. Among type I diabetics in Europe (Buyken et al, 2001), the mean (range) dietary GI was reported to be 57 (41–78), but regional differences existed in the correlation between dietary GI value and foods. Among subjects in southern Europe, the lowest GI quartile consumed more carbohydrate from pasta but less from white bread and potatoes than did subjects in the highest GI quartile. Among subjects in northern, western, and eastern Europe, the lowest GI quartile ingested more carbohydrate from whole-meal or whole-grain bread and less carbohydrate from white bread and potatoes than did subjects in the highest GI quartile.

Among type II diabetics in Canada (Wolever et al, 1994), the mean dietary GI was 60 (46–68) and was inversely correlated with consumption of simple sugars. Among Dutch elderly men (Van Dam et al, 2000), the mean dietary GI was 57 (55–59) and was positively correlated with consumption of wheat bread and sugar products and inversely with fruits and milk consumption.

The mean dietary GL of the subjects in the current study was 150 (68–239), and the intake of cereals was significantly different between the GL tertile groups. The dietary GL difference between the lowest and the highest tertiles was approximately 80, which is equivalent to an additional intake of 250 g white rice or 240 g bread per day. Individuals in the lowest tertile of dietary GL consumed more fat and less carbohydrate. In a study of female nurses in America (Liu et al, 2000), the mean dietary GL in the lowest quintile and the highest quintile were 82 and 144, respectively, where women with high dietary GL consumed more carbohydrate, dietary fiber, and vitamin E but women with low dietary GL had higher intake of fats, cholesterol, proteins, and alcohol.

In the current study, dietary GI difference between the lowest and the highest tertiles was 13. After adjusting for total energy intake, dietary fiber intake, age, and exercise time, there were significant correlations between dietary GI and HDL-c, TG, and IRI as well as between dietary GL and HDL-c, and TG. These results indicated that dietary GI and GL could be useful markers in nutrition education as predictors of CVD risk factor. Although dietary GL partly depended on the amount of carbohydrate consumed, difference of CVD risk factors among the tertiles of total carbohydrate (energy %) was significant only for TG differences (P=0.004, data not shown). So, dietary GL would be a stronger predictor of CVD risk factor than carbohydrate intake. Other researches have examined the correlations between dietary GI or GL and risk factors adjusting further confounding factors such as smoking status and alcohol consumption (Frost et al, 1999; Buyken et al, 2001; Ford & Liu, 2001; Liu et al, 2001). In the current study, after adjusting for smoking status (number of cigarettes per day) and alcohol consumption (g/week) in addition to total energy intake, dietary fiber intake, age, and exercise time, there were still significant correlations between dietary GI and HDL-c (P=0.011), TG (P=0.045), and IRI (P=0.021) (data not shown). However, there was no significant correlation between any risk factors and dietary GL after adjusting the confounding factors including smoking status and alcohol consumption. Among the subjects in this study, all current smokers (three subjects) and all subjects who were drinking alcohol more than 70 g/week (three subjects) belonged to the lowest tertile of dietary GL. So we did not include smoking status and alcohol consumption as covariates.

The correlations between dietary GI and HDL-c, TG and IRI as well as correlations between dietary GL and HDL-c and TG were in agreement with the results of other studies. Albrink suggested that hepatic lipogenesis, especially with respect to triacylglycerol synthesis, could be reduced by minimizing postprandial glucose and optimizing insulin (Albrink et al, 1979). Miller showed that measurable improvements in HbA1c, C-peptide, and TG were associated with diets in which the GI has been reduced by greater than or equal to11 (Miller, 1994). Total fat and saturated fat intake have been reported to be positively correlated to insulin secretion (Storlien et al, 1996). A high-carbohydrate, low-fiber diet (Mancini et al, 1973; Albrink et al, 1979; Jeppesen et al, 1997) was also reported to be positively correlated to insulin secretion. Further studies are needed to address whether different ratios of macronutrient or types of fat intake affect insulin secretion.

Medication might affect correlation between dietary GI or GL and blood lipid or glucose. In our subjects, only three had cholesterol-lowering medication. When these subjects were excluded from analysis, the differences of HDL-c, TG, and IRI among tertiles of dietary GI were still significant (P<0.05, data not shown). Only the difference of HDL-c among tertiles of dietary GL became weaker (P=0.063).

Subjects in the current study with low dietary GI consumed less carbohydrate especially from white rice, and subjects with low dietary GL consumed less cereal. Generally, fat intake is inversely related to carbohydrate intake. There is some evidence that increasing the proportion or absolute amount of carbohydrate with low-fat, low-GI foods may be beneficial in relation to glucose and lipid metabolism (Jenkins et al, 1984). It seemed difficult to change the staple food (especially white rice) to a low GI food with an ideal ratio of macronutrient. In Japan, dietary habit is becoming more westernized, where there is a decrease in the consumption of cereals such as white rice and an increase in the consumption of fats. It is an important issue in nutrition education to apply GI as a qualitative marker of carbohydrate while maintaining an ideal macronutrient balance.

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Conclusion

Among the Japanese women who participated in a weight-reduction program, there were significant correlations between dietary GI and HDL-c, TG, and IRI concentrations. Moreover, dietary GL also correlated with HDL-c and TG. These findings suggested that calculating dietary GI or GL as a qualitative measurement of carbohydrate could support nutrition education practice also in a country where white rice is consumed as a staple food.

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