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American Journal of Clinical Nutrition, Vol. 83, No. 5, 1161-1169, May 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Dietary glycemic index and load in relation to metabolic risk factors in Japanese female farmers with traditional dietary habits1,2,3

Kentaro Murakami, Satoshi Sasaki, Yoshiko Takahashi, Hitomi Okubo, Yoko Hosoi, Hyogo Horiguchi, Etsuko Oguma and Fujio Kayama

1 From the National Institute of Health and Nutrition, Tokyo, Japan (KM, SS, YT, and YH); the Department of Nutrition Sciences, Kagawa Nutrition University, Saitama, Japan (HO); the Division of Environmental Medicine, Center for Community Medicine, Jichi Medical University, Tochigi, Japan (HH, EO, and FK); and Core Research for Evolutional Science and Technology, Japan Science and Technology Cooperation, Kawaguchi City, Japan (EO and FK)

2 Supported mainly by grants from the Japanese Ministry of Health, Labor and Welfare, the Ministry of Agriculture and Forestry, and Core Research for Evolutional Science and Technology, Japan Science and Technology Cooperation.

3 Address reprint requests to F Kayama, Division of Environmental Medicine, Center for Community Medicine, Jichi Medical University, 3311-1 Yakushiji, Minami-Kawachi, Kawachi-gun, Tochigi 329-0498, Japan. E-mail: kayamaf{at}jichi.ac.jp.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Little is known about the relation of dietary glycemic index (GI) and glycemic load (GL) to metabolic risk factors, particularly in non-Western populations.

Objective: We examined the cross-sectional associations between dietary GI and GL and several metabolic risk factors in healthy Japanese women with traditional dietary habits.

Design: The subjects were 1354 Japanese female farmers aged 20–78 y from 5 regions of Japan. Dietary GI and GL were assessed with a self-administered diet-history questionnaire. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m). Fasting blood samples were collected for biochemical measurements.

Results: The mean dietary GI was 67, and the mean dietary GL (/1000 kcal) was 88 (GI for glucose = 100). White rice (GI = 77) was the major contributor to dietary GI and GL (58.5%). After adjustment for potential dietary and nondietary confounding factors, dietary GI was positively correlated with BMI (n = 1354; P for trend = 0.017), fasting triacylglycerol (n = 1349; P for trend = 0.001), fasting glucose (n = 764; P for trend = 0.022), and glycated hemoglobin (n = 845; P for trend = 0.038). Dietary GL was independently negatively correlated with HDL cholesterol (n = 1354; P for trend = 0.004) and positively correlated with fasting triacylglycerol (P for trend = 0.047) and fasting glucose (P for trend = 0.012).

Conclusions: Both dietary GI and GL are independently correlated with several metabolic risk factors in subjects whose dietary GI and GL were primarily determined on the basis of the GI of white rice.

Key Words: Glycemic index • glycemic load • white rice • body mass index • triacylglycerol • glucose • glycated hemoglobin • HDL cholesterol • Japanese women • epidemiology • Japanese Multi-centered Environmental Toxicants Study • JMETS


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Dietary carbohydrates are typically categorized into simple sugars and complex carbohydrates on the basis of their degree of polymerization. Their effects on health, however, may be better categorized according to their physiologic effects, specifically their ability to raise blood glucose (1), because the blood glucose response varies substantially among different carbohydrate-containing foods and cannot be predicted by their chemical composition (2). This varied glycemic response is quantified according to the glycemic index (GI), which is a measure of how much each carbohydrate-containing food raises blood glucose compared with a standard food of either glucose or white bread (per 50 g available carbohydrate) (3). In consideration of the amounts of carbohydrate-containing foods and total dietary carbohydrate, the concept of glycemic load (GL: GI x available carbohydrate content) has also been proposed (4, 5).

Recent results from a limited number of observational studies have suggested that diets with a low GI, a low GL, or both have a beneficial effect on several metabolic risk factors for cardiovascular disease and type 2 diabetes, such as body mass index (BMI; in kg/m2) (6), HDL cholesterol (7–11), triacylglycerol (9, 10, 12), and glycated hemoglobin (Hb A1c) (12, 13). However, almost all studies of dietary GI or dietary GL and metabolic risk factors have been conducted in Western countries, whereas, to our knowledge, only one small study (10) was carried out in Asian countries, including Japan.

For Japanese people, rice is the food that contributes most to total carbohydrate and energy intake (43% and 29%, respectively), which is a characteristic seldom observed in Western people (14). Therefore, a different correlation of dietary GI or dietary GL and metabolic risk factors may exist between Western and Japanese populations. Additionally, whereas cardiovascular disease is the second leading cause of all death in Japan (15), the number of Japanese people with type 2 diabetes is estimated to be no fewer than 6.8 million (16); thus, as is the case in Western people, these are serious health problems in Japan. Consequently, we examined the cross-sectional associations between dietary GI and GL and several metabolic risk factors for cardiovascular disease and type 2 diabetes, including BMI, fasting serum triacylglycerol, fasting plasma glucose, Hb A1c, and serum total, HDL, and LDL cholesterol in a group of apparently healthy Japanese women.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The subjects in the present study were participants in the Japanese Multi-centered Environmental Toxicants Study (JMETS), the main purpose of which was to identify the threshold concentration in the dose-response relation of cadmium renal dysfunction (17, 18). For this purpose, the JMETS was conducted in female farmers in 4 moderately cadmium-polluted areas and 1 non-cadmium-polluted area in Japan; however, no difference in the effects of environmental exposure to cadmium was observed between the 4 polluted areas and 1 nonpolluted area, at least regarding renal function and bone density (17, 18). Thus, the study did not identify evidence that environmental exposure to cadmium, at the level found in the 4 polluted areas, has an adverse affect on health. The 5 areas surveyed consist of rural agricultural communities with inhabitants who remain in the community even after marriage. Thus, most of the farmers in these areas are assumed to have maintained traditional Japanese dietary patterns, consuming their own crops, including rice, for decades. During the winters of 2000 and 2001, female farmers in each area were recruited through the local Agricultural Cooperative to participate in a medical examination organized for the JMETS. One week before the examination, group orientations were held for the study participants, at which the study purpose and protocol were explained and written informed consent was obtained from each participant. In addition, participants were instructed on how to complete questionnaires regarding diet and other lifestyle factors and were asked to bring them to the examination. The protocol of the JMETS was approved by the ethical committee of Jichi Medical University. Additional details about the JMETS were reported elsewhere (17, 18).

A total of 1407 women aged 20–78 y completed both a medical examination and the lifestyle-related questionnaires. Subjects excluded from the present study were those with previously diagnosed diabetes (n = 15) or cardiovascular disease (n = 18), those with extremely low or high energy intakes (<600 or >4000 kcal/d; n = 10), and those with missing covariate information (n = 4). Furthermore, subjects with missing information regarding dependent variables, were excluded from the analysis of LDL cholesterol (n = 6), glucose (n = 609), and Hb A1c (n = 527), and subjects who ate breakfast before blood was drawn were excluded from the analysis of fasting triacylglycerol and glucose (n = 5). Thus, the final sample was 1354 for BMI and serum total and HDL cholesterol, 1348 for serum LDL cholesterol, 1349 for fasting serum triacylglycerol, 764 for fasting plasma glucose, and 845 for Hb A1c; however, some subjects were included in more than one exclusion category. Further exclusion of subjects with a diagnosis of hyperglycemia, dyslipidemia, hypercholesterolemia, or a combination thereof (n = 24 for BMI, cholesterol, and triacylglycerol and n = 17 for glucose and Hb A1c) did not alter the findings of the present study; therefore, these subjects were included in the analyses.

Metabolic risk factors
At the medical examination site, each subject's weight (measured while wearing light clothes and no shoes) was measured with a set of balance scales calibrated to 0.01 kg. Body height was also measured at the site. The BMI of each subject was calculated as weight (kg) divided by the square of height (m). Peripheral blood samples were obtained from subjects after an overnight fast. Blood was collected in evacuated tubes containing no additives, allowed to clot, and centrifuged at 3000 x g for 10 min at room temperature to separate the serum. Blood samples for blood sugar measurement were collected in hydrogen fluoride–containing tubes. All of the following biochemical variables of the samples were assayed at Mitsubishi Kagaku Bio-Clinical Laboratories Inc (Itabashi, Tokyo, Japan) within 3 d of collection to avoid significant degradation. Total cholesterol, HDL cholesterol, and triacylglycerol were measured by enzymatic assay methods. Serum LDL-cholesterol concentrations were calculated by using the Friedewald equation (19) for subjects with fasting serum triacylglycerol concentrations <400 mg/dL. Hb A1c was measured by latex agglutination–turbidimetric immunoassay. In-house quality-control procedures for all of the abovementioned assays were fulfilled at Mitsubishi Kagaku Bio-Clinical Laboratories Inc.

Dietary assessment
Dietary habits during the past month were assessed with a self-administered diet-history questionnaire (DHQ) (20–22), which was completed by each subject at home and was checked by ≥2 dietitians during the medical examination. The DHQ is a 16-page structured questionnaire that consists of the following 7 sections: general dietary behaviors, major cooking methods, consumption frequency and portion size of 6 alcoholic beverages, semiquantitative frequency of intake of 121 selected food and nonalcoholic beverage items, dietary supplements, consumption frequency and amount of 19 staple foods (rice, bread, noodles, and other wheat foods) and miso (fermented soybean paste) soup, and open-ended items for foods consumed regularly (≥1 time/wk) but not appearing in the DHQ. The food and beverage items and portion sizes in the DHQ were derived primarily from data in the National Nutrition Survey of Japan and several recipe books for Japanese dishes (20). Measures of dietary intake for 147 food and beverage items, energy, fat, total carbohydrate, alcohol, and dietary fiber were calculated by using an ad hoc computer algorithm developed for the DHQ, which was based on the Standard Tables of Food Composition in Japan (23). Information on dietary supplements and data from the open-ended questionnaire items were not used in the calculation of dietary intake. Detailed descriptions of the methods used for calculating dietary intake and the validity of the DHQ were published elsewhere (20–22). Pearson's correlation coefficients between the DHQ and 3-d dietary records were 0.48 for energy, 0.55 for fat, and 0.48 for total carbohydrate in 47 women (20). In addition, Pearson's correlation coefficients between the DHQ and 16-d dietary records were 0.79 for alcohol and 0.69 for dietary fiber in 92 women (S Sasaki, unpublished observations, 2004).

Calculation of dietary GI and GL
The GI of a food is defined as the 2-h incremental area under the blood glucose response curve after consumption of a food portion containing a specific amount (usually 50 g) of available carbohydrate, divided by the corresponding area after consumption of a portion of a reference food (usually glucose or white bread) containing the same amount of available carbohydrate, and multiplied by 100 to be expressed as a percentage (24). We calculated dietary GI by multiplying the percentage contribution of each individual food to daily available carbohydrate intake by the food's GI value and summed these products. Available carbohydrate was calculated as total carbohydrate minus dietary fiber (24). We also calculated dietary GL by multiplying the dietary GI by the total amount of daily available carbohydrate intake (divided by 100).

To determine the GI value of each food for these calculations, each food item on the DHQ was directly matched to foods in the international table of GI (24), in several publications about the GI of Japanese foods (25–27), and in a recent article about the GI of potatoes (28). Glucose was used as the reference (GI for glucose = 100). The white bread–based GI values were transformed into glucose-based GI values by multiplying the white bread–based GI by 0.7, as in Western studies (24, 28), or by 0.73 [= 100/137 (white bread–based GI value of white bread/white bread–based GI value of glucose)] as in Japanese studies (27). The white rice–based GI values were transformed into glucose-based GI values by multiplying white rice–based GI by 0.82 [= 100/122 (white rice-based GI of white rice/white rice-based GI of glucose)] (25, 26). When more than one GI value was available, the mean GI values was used. Ten foods for which a GI value had not been determined were assigned a value according to the nearest comparable food, as follows: Chinese noodles were assigned the GI of instant noodles, Japanese-style pancakes were assigned the GI of pizza, jellies were assigned the GI of pudding, lotus roots were assigned the GI of carrots, vegetable juice was assigned the GI of tomato juice, curry and roux in stew were assigned the GI of white rice with curry, nutritional-supplement drinks were assigned the GI of sports drinks, nutritional supplement bars were assigned the GI of a sports bar, and ground fish-meat products and boiled-fish, shellfish, and seaweed in soy sauce were assigned the GI of fish fingers. Although alcoholic beverages contain little carbohydrate, large quantities of several alcoholic beverages, such as beer and sake, may raise glucose concentrations slightly; however, by definition, the GI is based on 50 g available carbohydrate. Thus, we ignored alcoholic beverages during the calculation of dietary GI and GL. Furthermore, foods with a very low available carbohydrate content were excluded because their GI values cannot be tested. The cutoff for exclusion of foods was set at 3.5 g available carbohydrate per serving (6). Of the total 147 food and beverage items included in the DHQ, 6 (4.1%) are alcoholic beverages, 8 (5.4%) contain no available carbohydrate, and 63 (42.9%) contain <3.5 g available carbohydrate per serving. The calculation of dietary GI and GL was thus based on the remaining 70 items with GI values ranging from 16 to 91. The GI value of each item is presented in Table 1Go. In the present study, the available carbohydrate content of these 70 items contributed to 94.0 ± 2.5% (x ± SD) of total available carbohydrate intake, which is comparable with previous studies (6, 10).


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  		TABLE 1. Glycemic index (GI) value of each food and beverage item used in the present study1

 
Other variables
Smoking status, menopausal status, dietary supplement use during the previous month, and rate of eating were self-reported in questionnaires. Body weight at age 20 y was also self-reported, and BMI at age 20 y was computed by dividing self-reported weight (kg) at age 20 y by the square of current measured height (m). In addition, the subjects reported the average times per week spent on 13 activities such as sleeping, household-related activities, leisure-time sporting activities, and leisure-time sedentary activities. The reported number of hours spent on each activity (per week) was divided by 7 to obtain the mean number of hours per day. For subjects whose recorded total hours per day were < or >24 h, the total number of hours spent daily were proportionately increased or decreased to equal 24. Each activity was assigned a metabolic equivalent (MET) value from a previously published table (29, 30). The mean number of hours spent per day on each activity was multiplied by the MET value of that activity, and all MET-hour products were summed to give a total MET-hour score for the day. Total energy expenditure was calculated by multiplying the total MET-hour score by body weight. Physical activity level was calculated by dividing total energy expenditure by basal metabolic rate, which was estimated as standard values of basal metabolic rate for Japanese women multiplied by body weight (31).

Statistical analysis
Dietary GI and GL were examined in relation to the 7 metabolic risk factors: BMI; serum total, HDL, and LDL cholesterol; fasting serum triacylglycerol; fasting plasma glucose; and Hb A1c. We used crude values for dietary GI and energy-adjusted values for dietary GL (/1000 kcal) because, by definition, dietary GI is a measure of carbohydrate quality, not quantity, whereas dietary GL is a measure of the combination of carbohydrate quality and quantity. The mean (±SE) values for these metabolic factors were calculated according to quintiles of dietary GI and GL after multivariate adjustment for potential confounding variables. Confounding variables included residential area (5 categories), age (≤39, 40–49, 50–59, 60–69, and ≥ 70 y), menopausal status (premenopausal or postmenopausal), current smoking (no or yes), dietary supplement use (no or yes), rate of eating (fast, medium, or slow), physical activity level (quintiles), energy intake (quintiles), percentage of energy as fat (quintiles), alcohol intake (nondrinkers, >0 to <1% of energy, or ≥1% of energy), and energy-adjusted intake (g/1000 kcal) of dietary fiber (quintiles). In the analyses, except for the analysis of BMI, current BMI (quintiles) and BMI at age 20 y (quintiles) were also included as confounding variables. Linear trends with increasing levels of dietary GI and GL were tested by assigning each participant the median value for the category and modeling this value as a continuous variable. All statistical analyses were carried out by using SAS statistical software (version 8.2; SAS Institute Inc, Cary, NC). All reported P values are 2-tailed, and a P value <0.05 was considered statistically significant.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Basic characteristics of the 1354 subjects are shown in Table 2Go. The mean intakes of protein, fat, and carbohydrate were 14.0%, 25.3%, and 59.0% of energy, respectively. The mean dietary GI was 66.7 and the mean dietary GL was 88.0 (/1000 kcal; crude mean = 167.7). White rice was the major contributor to dietary GI and GL (58.5%), followed by confectioneries (10.6%), fruit (6.7%), sugars (5.5%), bread (4.3%), noodles (3.4%), other rice (3.2%), and potatoes (2.6%). Potential confounding variables of the 1354 subjects are shown in Table 3Go according to quintiles of dietary GI and GL. Fewer women in the higher quintiles of dietary GI used dietary supplements and more were nondrinkers of alcohol. Women in the higher quintiles of dietary GI had lower mean energy, fat, and dietary fiber intakes. In addition, women in the higher quintiles of dietary GL had higher mean values for age and physical activity level and lower mean energy, fat, and dietary fiber intakes. Fewer women in the higher quintiles of dietary GL were premenopausal, current smokers, and dietary supplement users and more were nondrinkers of alcohol. Similar patterns were observed for potential confounding variables according to quintiles of dietary GI and GL among the subjects included in the analyses of serum LDL cholesterol (n = 1348), fasting serum triacylglycerol (n = 1349), fasting plasma glucose (n = 764), and Hb A1c (n = 845) (data not shown).


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  		TABLE 2. Basic characteristics of the 1354 Japanese women

 

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  		TABLE 3. Selected characteristics of the 1354 Japanese women according to quintiles of dietary glycemic index and load

 
Multivariate-adjusted mean values for metabolic risk factors across quintile categories of dietary GI and GL are shown in Table 4Go. After adjustment for potential confounding variables, dietary GI was significantly positively correlated with BMI (mean difference between the lowest and highest quintiles = 0.7; P for trend = 0.017), fasting serum triacylglycerol (mean difference = 16.0 mg/dL; P for trend = 0.001), fasting plasma glucose (mean difference = 6.4 mg/dL; P for trend = 0.022), and Hb A1c (mean difference = 0.2%; P for trend = 0.038). No correlation was observed between dietary GI and serum concentrations of total, HDL, and LDL cholesterol.


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  		TABLE 4. Metabolic risk factors according to quintiles of dietary glycemic index and load in Japanese women

 
In contrast, after control for potential confounding variables, dietary GL was significantly negatively correlated with serum HDL cholesterol (mean difference = –6.4 mg/dL; P for trend = 0.004) and positively correlated with fasting serum triacylglycerol (mean difference = 14.4 mg/dL; P for trend = 0.047) and fasting plasma glucose (mean difference = 12.5 mg/dL; P for trend = 0.012). Other metabolic risk factors examined, including BMI, serum concentrations of total and LDL cholesterol, and Hb A1c were not significantly correlated with dietary GL. Adjustment for the percentage of energy from carbohydrate instead of the percentage of energy from fat did not change the results materially, which suggests that the observed correlations between dietary GI and GL and metabolic risk factors are independent of carbohydrate intake (data not shown).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Because only limited evidence is available regarding associations between dietary GI and GL and metabolic risk factors, particularly in Asian populations, we investigated these associations in the present cross-sectional study of healthy Japanese female farmers with traditional dietary habits. We found that dietary GI was positively associated with BMI, fasting serum triacylglycerol, fasting plasma glucose, and Hb A1c after control for potentially confounding lifestyle and dietary factors. We also found that dietary GL was independently negatively associated with serum HDL cholesterol and positively associated with serum triacylglycerol and fasting plasma glucose.

Concerns have been expressed regarding the utility of the GI for mixed meals (32, 33). However, many researchers have shown that the GI of a mixed meal can be predicted consistently as the mean of the GI values of each of the component foods, weighted according to their relative contribution to carbohydrate intake (34–36). In reality, studies using standardized techniques have observed high correlation coefficients between observed and calculated GI values, ranging from 0.84 to 0.99 (34–36). Dietary GI and GL values in the present study were similar when compared with those in a previous Japanese study (67 compared with 64 for GI and 168 compared with 150 for GL) (10). However, the dietary GI and GL values observed in the present and previous (10) Japanese studies were considerably higher than the corresponding values in Western countries (48–60 for GI and 84–120 for GL) (4–6, 7–9, 37–40). This may have resulted from the differences in the major food contributors. Dietary GIs and GLs in Western populations are determined by a variety of food items, including potatoes (7–8%), breakfast cereals (4–7%), bread (5%), and rice (5%) (41–43). However, white rice (GI = 77) was the major contributor in the present and previous (10) Japanese studies, accounting for 59% of dietary GI and GL in the present study.

All self-reported dietary assessment methods are subject to measurement error and selective underestimation or overestimation of dietary intake (44). In the present study, however, we used a previously validated DHQ (20–22) to minimize data inaccuracy. Additionally, dietary GI and GL values calculated in the present study are believed to be relatively accurate because the major determinant of dietary GI and GL in the present study, rice (62%), is more accurately reported than are other foods on the DHQ because it is consumed regularly in relatively fixed amounts. Moreover, the same tendency was observed in a repeated analysis of subjects with a physiologically plausible energy intake, ie, subjects with a ratio of energy intake to basal metabolic rate of 1.2–2.5 (45)—{approx}78% of the subjects included in the main analysis (data not shown). Thus, we considered that the correlations observed in the present study reflect true associations, not spurious associations resulting from inaccurate dietary data.

In the present study, dietary GI was positively correlated with BMI. A 5-wk crossover, randomized, controlled trial conducted in overweight nondiabetic men with ad libitum dietary intakes also showed a significantly lower fat mass and a tendency for a higher fat-free mass, but not a lower body weight, after a low-GI diet than after a high-GI diet (46). In contrast, other ad libitum trials conducted in subjects with type 2 diabetes showed no significant differences in body weight change between high-GI and low-GI diets (47–49). However, in a 10-wk ad libitum, randomized, controlled trial conducted in healthy overweight women, decreases in body weight and fat mass were larger in a low-GI diet group than in a high-GI diet group, although these differences were not statistically significant (50). Moreover, as was shown in this study, a recent observational study also showed a positive association between dietary GI and BMI and no association between dietary GL and BMI (6).

Dietary GL has consistently been shown to be inversely correlated with HDL cholesterol in cross-sectional studies (8–11). In contrast, the correlation between dietary GI and HDL cholesterol is not consistent. An inverse correlation has been reported in 3 (7, 8, 10), but not in another 2 (9, 37), cross-sectional studies. Furthermore, recent randomized controlled trials have not supported the beneficial effect of a low-GI diet on HDL cholesterol in contrast with a high-GI diet (46–50). In the present study, we also found an inverse correlation between dietary GL and HDL cholesterol, but no correlation between dietary GI and HDL cholesterol.

Both dietary GI and GL were positively correlated with fasting triacylglycerol in 2 cross-sectional studies (9, 10); however, no association between dietary GI and fasting triacylglycerol was observed in a study of elderly men (37). In the present study, both dietary GI and GL were positively associated with fasting triacylglycerol. Several randomized controlled trials have also shown the beneficial effect of a low-GI diet on triacylglycerol (51), although the lack of an effect of GI has been observed in subjects with low triacylglycerol concentrations (52).

We identified a positive correlation between dietary GI and GL and fasting glucose, whereas no correlation was observed in a cross-sectional study of elderly men (37). Several prospective cohort studies (4, 5, 38), but not others (39, 40, 53), in the United States have shown a positive association between dietary GI, GL, or both and the incidence of type 2 diabetes, which is not in conflict with our finding. Recently, several (48, 49), but not all (46, 47, 50), randomized controlled trials have also shown lower fasting glucose concentrations after consumption of a low-GI diet than after a high-GI diet.

We found a positive correlation between dietary GI and Hb A1c. A positive association was also reported in cross-sectional studies conducted in patients with type 2 diabetes treated by dietary restriction alone (12) and in patients with type 1 diabetes (13). Additionally, a low-GI diet reduced Hb A1c more than did a high-GI diet in several randomized controlled trials (48, 49). Furthermore, a recent meta-analysis of 14 randomized controlled trials has shown the amelioration of Hb A1c through a low-GI diet (54).

Both total and LDL cholesterol were not correlated with dietary GI or GL in the present study, although randomized controlled trials have generally shown that low-GI diets result in lower total and LDL cholesterol concentrations (54). However, similar to our findings, no correlation between dietary GI or GL and total or LDL cholesterol was observed in several cross-sectional studies (7, 10, 37).

Our results may not be extrapolated into general Japanese populations because the subjects in the present study were selected female farmers. Additionally, our DHQ, although similar to most previous epidemiologic studies, was not designed specifically to measure dietary GI and GL; however, the satisfactory validity of this DHQ for total carbohydrate (20) provides some reassurance. Moreover, although we attempted to adjust for a wide range of potential confounding variables, we could not rule out residual confounding because of these or other unknown variables. Furthermore, because the study population consisted of generally healthy persons, the clinical relevance of our findings remains to be elucidated. However, our results should provide valuable insight from a prevention perspective.

In summary, after adjustment for a variety of confounding factors, we observed positive correlations between dietary GI and BMI, fasting serum triacylglycerol, fasting plasma glucose, and Hb A1c and between dietary GL and fasting serum triacylglycerol and fasting plasma glucose and negative correlations between dietary GL and serum HDL cholesterol in healthy Japanese female farmers whose dietary GI and GL were primarily determined by white rice. Because the cross-sectional nature of the present study precludes any causal inferences, more observational and experimental studies are needed before any firm conclusions can be drawn with regard to the effect of dietary GI and GL on metabolic risk factors.


   ACKNOWLEDGMENTS
 
We thank Michiko Sugiyama for technical advice regarding the glycemic index values for Japanese foods.

KM created a table of glycemic index, conducted the statistical analyses, and wrote the manuscript. SS was involved in the design of the dietary study and assisted in the creation of the table and the manuscript. YT assisted in the creation of the table. HO was involved in the management of the dietary dataset and data collection during the dietary study. YH was involved in the data collection for the dietary study. HH and EO were responsible for the research design, data collection, and data management. FK was responsible for the research design, data collection, and overall management. All authors provided suggestions during the preparation of the manuscript and approved the final version submitted for publication. None of the authors had any conflict of interest to declare.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication November 28, 2005. Accepted for publication January 17, 2006.




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