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American Journal of Clinical Nutrition, Vol. 84, No. 1, 70-76, July 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Relations of glycemic index and glycemic load with plasma oxidative stress markers1,2,3

Youqing Hu, Gladys Block, Edward P Norkus, Jason D Morrow, Marion Dietrich and Mark Hudes

1 From the Division of Epidemiology, School of Public Health (YH and GB), and the Department of Nutritional Sciences and Toxicology (MH), University of California, Berkeley, Berkeley, CA; the Department of Medical Research, Our Lady of Mercy Medical Center, Bronx, NY (EPN); the Division of Clinical Pharmacology, School of Medicine, Vanderbilt University, Nashville, TN (JDM); and the Department of Nutritional Epidemiology, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA (MD)

2 Supported by grants no. 6RT-0008 and no. 7RT-0160 from the University of California Tobacco-Related Disease Research Program and grants no. P30 ES01896 and no. DA9561 from the National Institutes of Health.

3 Address reprint requests and correspondence to G Block, 426 Warren Hall, School of Public Health, University of California, Berkeley, CA 94720. E-mail: gblock{at}berkeley.edu.

See corresponding CME exam on page 266.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Recent data suggest that acute hyperglycemia may increase in vivo free radical production. This increased production has been implicated in many disease processes.

Objective: The objective was to investigate whether a diet with a high glycemic index (GI) or glycemic load (GL) is associated with greater oxidative stress as measured by 2 lipid peroxidation markers, malondialdehyde (MDA) and F2-isoprostanes (IsoPs).

Design: Plasma MDA and IsoP concentrations were measured in 292 healthy adults, and dietary GI and GL were assessed by using a validated food-frequency questionnaire. Cross-sectional associations between GI, GL, and the 2 markers were examined by using multiple regression techniques with adjustment for potential confounding variables.

Results: Dietary GI was positively associated with both plasma MDA and IsoPs. The mean multivariate-adjusted MDA concentrations increased from 0.55 to 0.73 µmol/L as GI increased from the lowest to the highest quartile (P for trend = 0.02); the corresponding IsoP concentrations increased from 0.034 to 0.040 ng/mL (P for trend = 0.03). GL was positively associated with both MDA and IsoPs, but the linear relation was significant only for MDA. In addition, a marginally significant interaction between overall GI and body mass index (BMI; in kg/m2) for plasma MDA was observed (P = 0.09). The positive association between overall GI and MDA was stronger in those with a BMI < 26.5 than for those with a BMI ≥ 26.5.

Conclusions: Chronic consumption of high-GI foods may lead to chronically high oxidative stress. A low-GI diet, not a low-carbohydrate diet, appears to be beneficial in reducing oxidative stress.

Key Words: Glycemic index • glycemic load • oxidative stress • malondialdehyde • F2-isoprostanes


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
It has been suggested that oxidative stress—ie, the imbalance between free radical production and in vivo antioxidant defenses—is implicated in the pathogenesis of cardiovascular disease (1). Reactive free radicals can cause oxidative modification of LDL, which is believed to be one of the key factors in the development of atherosclerosis (2). Antioxidant intervention studies in animals and humans have found that antioxidants reduce LDL oxidation and susceptibility to oxidation, increase the lag period of in vitro LDL oxidation, and reduce lesion formation (3, 4). Higher indexes of oxidative stress have also been associated with many other conditions, which suggests that oxidative damage may be one of the earliest events in the onset and progression of many diseases (5).

Evidence is growing that postprandial hyperglycemia is an important risk factor for cardiovascular morbidity and mortality in the general population (6). For example, in the Hoorn Study (7), a 2-SD increase in 2-h postload plasma glucose (a surrogate of postprandial glucose) was associated with a higher cardiovascular mortality (relative risk: 3.0) after an 8-y follow-up in a general population. In the 1980s, the glycemic index (GI) concept was introduced as a way of quantifying the postprandial blood glucose responses to the carbohydrate in different foods (8). In a prospective study (9), both the overall GI and the glycemic load (GL; the product of the GI of a specific food and its carbohydrate content) of the diet were found to be independent risk factors for cardiovascular events.

Experimental data suggest that oxidative stress may be an important mechanism linking acute hyperglycemia to increased cardiovascular risk (10). Acute increases in blood glucose concentrations may increase the production of free radicals by nonenzymatic glycation and by an imbalance in the ratio of NADH to NAD+ induced by glucose in cells (11). Direct evidence from studies in both normal subjects and those with diabetes showed that induced hyperglycemia (12) or meal intake and its attendant increase in glucose (13, 14) can induce oxidative stress and reduce antioxidant defenses, and the increase in oxidative stress was significantly greater after meals that produced a greater degree of hyperglycemia (15). On the basis of these short-term experimental studies, we hypothesized that chronic consumption of high-GI carbohydrates may lead to chronically high oxidative stress. In the current cross-sectional study of 292 healthy subjects, we investigated whether a high-GI or high-GL diet is associated with high oxidative stress as measured by using 2 widely used lipid peroxidation markers, malondialdehyde (MDA) and F2-isoprostanes (IsoPs). MDA is a decomposition product of peroxidized polyunsaturated fatty acids (16). IsoPs are prostaglandin F2 (PGF2)-like compounds formed by cyclooxygenase-independent free radical peroxidation of arachidonic acid (17). High plasma MDA and IsoP concentrations have been associated with a variety of disease conditions (18-21) and with smoking (22, 23).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study population
Subjects for this analysis were selected from a cohort of 146 cigarette smokers, 81 persons passively exposed to cigarette smoke, and 98 nonsmokers who were enrolled in an intervention study that examined the effects of antioxidant supplements on oxidative damage among active smokers and nonsmokers passively exposed to cigarette smoke (24, 25). The study was conducted in Berkeley and Oakland, CA, between June 1998 and June 1999. The inclusion criteria for the study were as follows. Smokers were eligible if they smoked ≥15 cigarettes/d. Subjects passively exposed to cigarette smoke were eligible if they had not smoked cigarettes in the past year but were exposed, indoors, to the smoke of ≥1 cigarette/d on ≥5 d/wk. Nonsmokers were eligible if they had not smoked cigarettes for ≥1 y and were not passively exposed to cigarette smoke. Exclusion criteria included self-reported consumption of ≥4 servings of fruit and vegetables per day; intake of >2 alcoholic drinks/d; history of alcohol abuse within the previous year; current pregnancy; use of blood-thinning drugs; hemochromatosis; history of kidney stones or other kidney disease; cancer, stroke, or heart attack within the previous 5 y; hepatitis; diabetes; HIV infection; and consumption of >800 IU/d of iron or vitamin E supplements. Users of other vitamin supplements underwent a washout period of ≥5 wk before baseline data collection, during which no supplement use was allowed.

This analysis was based on cross-sectional data for the cohort at entry to the study. Participants were excluded from the analysis if their food-frequency questionnaire (FFQ) data were judged invalid (ie, if they reported total energy intake ≤ 500 kcal/d or ≥5000 kcal/d, or skipped ≥15 food items listed on the FFQ, or reported consumption of ≤2 or ≥17 solid food items/d; n = 20) or if covariate information was missing (n = 13). The resulting subsample comprised 292 subjects (126 active smokers, 74 subjects passively exposed to cigarette smoke, and 92 nonsmokers). In addition, MDA and IsoP data were missing for 1 and 3 subjects, respectively, and they were excluded from the corresponding analysis.

Written informed consent was obtained from all participants. The study protocol was approved by the institutional review boards of the University of California, Berkeley, and Kaiser Permanente Division of Research.

Dietary data
All participants completed a Block98 FFQ that assessed usual dietary intake over the previous year (26). The questionnaire contained {approx}100 food items, which were identified as the major contributors of nutrients to the US diet, according to the third National Health and Nutrition Examination Survey (NHANES III; 27). Frequency of consumption was reported at 9 levels, ranging from "never" to "every day." Portion sizes for all foods were estimated by using portion-size pictures. Nutrient intakes were calculated by multiplying the frequency of consumption of each food by its nutrient content and the reported portion size and summing those value for all foods. Servings of food groups were calculated by multiplying the frequency of consumption of each food by the reported portion size, summing the consumption of each food group (in g), and dividing by the standard serving size as defined in the US Department of Agriculture Food Guide Pyramid (28). The validity of Block questionnaires has been assessed in numerous validation studies; they have been found to correlate well with a variety of reference data (29-31).

GI values (with glucose as the reference) for food items on the FFQ were based on data provided by the University of North Carolina Department of Nutrition, Clinical Nutrition Research Center, which in turn were derived in part from published data (32). The GI of a food is defined as the incremental area under the curve for blood glucose response after consumption of 50 g carbohydrate from the test food; it is expressed as a percentage of the area under the curve produced by the same amount of carbohydrate from a reference food, usually white bread or glucose (8). The dietary GL of a subject was calculated by multiplying the GI of each food by its nonfiber carbohydrate content, reported frequency of consumption, and reported portion size and summing those values for all foods. Dietary GL thus represents both the quality and the quantity of carbohydrate intake (33). In addition, a subject’s overall dietary GI was calculated by dividing the dietary GL by the total amount of nonfiber carbohydrate consumed; this figure represents the overall quality of carbohydrate in the diet (33).

Laboratory methods
Blood samples were collected after an overnight fast, drawn into tubes containing EDTA, centrifuged at 1200 x g for 10 min at 5 °C, protected from light, and stored at –70 °C. Plasma MDA concentrations were measured by using lipid peroxidation analysis kits (Oxis International Inc, Portland, OR). The method was based on the reaction of a chromogenic reagent, N-methyl-2-phenylindole, with MDA to form a stable carbocyanine dye with a maximum absorption at 586 nm. Plasma MDA concentrations were derived after calculating the 3rd derivative of each spectrum as a way of improving the specificity and sensitivity of the assay. Derivative spectroscopy mathematically minimizes interference from other lipid peroxidation products in samples, such as 4-hydroxyalkenals. Using this technique, we obtained total plasma MDA estimates that were similar to concentrations measured by HPLC (34-37). The within-run and total CVs ranged from 1.2% to 3.4% and 1.6% to 3.7%, respectively.

Free IsoPs in plasma were quantified, after purification and derivatization, by using gas chromatography/negative ion chemical ionization–mass spectrometry with [2H4]8-iso-PGF2{alpha} as an internal standard (38). Compounds were analyzed as trimethylsilyl ether derivatives by monitoring mass-to-charge ratios of 569 and 573 for endogenous IsoPs and the [2H4]8-iso-PGF2{alpha} internal standard, respectively. Both the within-run and between-run CVs averaged 9%.

Statistical analysis
Statistical analyses were conducted by using SAS software (version 9.0; SAS Institute, Cary, NC). P values < 0.05 were considered significant, and P values < 0.10 were considered marginally significant. Baseline characteristics of the participants were described by using means and proportions. Dietary GL and overall dietary GI were examined across demographic categories and quartiles of dietary factors, and linear trend tests were performed for ordered categories. To examine the relation between carbohydrate nutrition and plasma oxidative stress markers, we calculated mean plasma MDA and IsoP concentrations across quartiles of dietary GL, overall GI, and carbohydrate intake. Analyses were performed on square-root–transformed MDA data and log-transformed IsoP data. Multivariate analyses were conducted to adjust for potential confounding factors, including age, sex, race, BMI, current smoking status, alcohol consumption, total energy intake, percentage of energy from protein, and intakes of fiber, folate, and cholesterol. Linear trend tests were performed to assess trends across quartiles. Potential effect modification by sex, race, smoking status, and BMI was examined by evaluating interaction terms of interest, and a marginally significant overall glycemic index x BMI interaction for plasma MDA concentrations was observed. Subgroup analyses by BMI (above or below the median) were therefore conducted.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The 292 subjects (113 men and 179 women) in this study ranged in age from 18 to 75 y and had a mean BMI of 27.6 (Table 1Go). Approximately 40% of the subjects were current smokers, and 25% were exposed to second-hand smoke. The mean dietary GL for this cohort was 118, and the mean overall dietary GI was 55. After adjustment for total energy intake, dietary GL was lower among whites and was inversely associated with age and intakes of alcohol and meat (Table 2Go). Overall GI was lower with increasing consumption of alcohol, fruit, and dairy foods in this cohort.


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  		TABLE 1. Characteristics of study participants1

 

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  		TABLE 2. Relation of demographic and dietary factors to dietary glycemic load and glycemic index in study participants1

 
In multivariate analyses of MDA, both dietary GL and overall GI were positively associated with plasma MDA concentrations after adjustment for age, sex, race, BMI, current smoking status, and alcohol consumption (multivariate model 1; Table 3Go). The multivariate-adjusted mean MDA concentrations increased from 0.52 to 0.66 µmol/L as dietary GL rose from the lowest to the highest quartile (P for trend = 0.03), and increased from 0.55 to 0.73 µmol/L as overall GI rose from the lowest to the highest quartile (P for trend = 0.02). For GL, the effect is most notable in the contrast between the lowest quartile and all higher quartiles. These associations remained significant after further adjustment for intakes of total energy, protein, fiber, folate, and cholesterol (multivariate model 2). Total carbohydrate intake was not associated with plasma MDA concentrations in either model.


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  		TABLE 3. Plasma malondialdehyde concentrations in study participants by quartile (Q) of dietary glycemic load, overall glycemic index, and carbohydrate intake1

 
There was a marginally significant (P = 0.09) interaction between overall GI and BMI category (above or below the median; Figure 1Go). The positive association between overall GI and plasma MDA was stronger for subjects with a BMI < 26.5 (median) than in those with a BMI ≥ 26.5. For the lowest and highest quartiles of dietary GI, the multivariate-adjusted mean MDA concentrations were 0.44 and 0.78 µmol/L in the low-BMI group (P for trend = 0.002) and 0.67 and 0.70 µmol/L in the high-BMI group (P for trend = 0.67).


Figure 1
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  		FIGURE 1.. Mean (95% CI) multivariate-adjusted plasma malondialdehyde concentrations by quartile (Q) of dietary glycemic index in subjects with BMI (in kg/m2) above (≥26.5; B) or below (<26.5; A) the median. A: Q1, 0.44; Q2, 0.65; Q3, 0.54; and Q4, 0.78 µmol/L; P for trend = 0.002. B: Q1, 0.67; Q2, 0.65; Q3, 0.71; and Q4, 0.70 µmol/L; P for trend = 0.67. Multivariate analysis was performed on square root–transformed data. Least-squares means were obtained from analysis by using quartiles of dietary glycemic index as class variables, and back-transformed results are shown. Linear trend tests were performed to assess trends across quartiles. The multivariate model was adjusted for age, sex, race, BMI, current smoking status, and alcohol consumption. The BMI category x quartiles of dietary glycemic index interaction was marginally significant, P = 0.09. The mean dietary glycemic index of the quartiles (for all BMIs) is as follows: Q1, 50.3; Q2, 53.6; Q3, 55.8; and Q4, 59.9.

 
The relation between carbohydrate nutrition and plasma IsoP concentrations is shown in Table 4Go. As dietary GI rose from the lowest to the highest quartile, the multivariate-adjusted mean IsoP concentrations increased from 0.034 to 0.040 ng/mL after adjustment for potential confounding variables (multivariate model 1; P for trend = 0.03). This association did not change after further adjustment for diet (multivariate model 2). The highest quartile of GL had higher IsoP than did the lowest quartile, but there was no linear trend. Carbohydrate intake was not significantly associated with plasma IsoP concentrations in either model.


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  		TABLE 4. Plasma F2-isoprostane concentrations in study participants by quartile (Q) of dietary glycemic load, overall glycemic index, and carbohydrate intake1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In this study of 292 healthy adults, we found that dietary GI was positively associated with oxidative stress as measured by plasma MDA and IsoP concentrations. Dietary GL was also positively associated with plasma MDA, but no linear relation with IsoPs was observed. Moreover, the association between dietary GI and plasma MDA was more evident among subjects whose BMI was below the median. To our knowledge, this is the first observational study to examine the associations of dietary GI and GL with oxidative stress. Although this study was limited to subjects who consumed <4 servings of fruit and vegetables per day, the finding from the 2003 Behavioral Risk Factor Surveillance System data that 77% of US adults do not eat the recommended ≥5 servings of fruit and vegetables per day (39) supports the generalizability of our findings to a substantial proportion of the US population.

Experimental data suggest that acute hyperglycemia may increase oxidative stress by several mechanisms (11). Several intervention studies further support this hypothesis. Ceriello et al (12) found that total radical-trapping antioxidant parameter, a measure of plasma antioxidant capacity, was significantly reduced during the oral-glucose-tolerance test, in 10 normal subjects as well as in 10 patients with type 2 diabetes. In another crossover study by the same authors (15), 2 standard meals designed to produce different degrees of postprandial hyperglycemia were administered in random order to 10 patients with type 2 diabetes. Plasma MDA was found to increase and total radical-trapping antioxidant parameter to decrease after either meal, but the variations in both measures were significantly greater after the meal that produced a higher degree of hyperglycemia. Our results are consistent with these findings, because high-GI foods produce higher glycemic responses than do low-GI foods, and the dietary GI assessed by the FFQ reflects the overall quality of carbohydrate in the usual diet.

For both MDA and IsoPs, the association with GL was nonlinear, and, in the case of IsoPs, it was not significant. This difference from GI could be explained by the confounding effect of carbohydrate. GL represents both the quality and quantity of carbohydrate. According to Brand-Miller et al (40), the carbohydrate content alone could explain 68% of the variance in GL values, and the GI could account for 49% of the variance. In our dataset, dietary GL had an extremely high correlation with total carbohydrate intake (r = 0.98, P < 0.0001) and had a lower correlation with GI (r = 0.28, P < 0.0001). In our analysis, carbohydrate was inversely related to IsoPs, which weakened any potential effect of GL on IsoPs. In addition, differences in effects on the 2 biomarkers could be due to their different oxidation pathways. The 2 markers also had different relations with smoking and BMI (23), which may reflect differences in sensitivity to specific oxidation pathways.

Of the dietary factors, besides dietary GI, only vitamin C and fruit intake were significantly associated with both MDA and IsoPs in this cohort, whereas neither total fat intake nor fat subtype was associated with the 2 markers (23). Measurement errors in dietary data are always a concern. However, the potential misclassification should be unrelated to measurement errors of MDA and IsoPs. In addition, because the questionnaire we used was designed to assess the average dietary intake during the previous year, the associations of dietary GI and GL with the 2 markers that we observed may be somewhat conservative.

In the current study, the positive association between dietary GI and plasma MDA concentration was stronger in subjects with a BMI < 26.5 than in those with a BMI ≥ 26.5. This stronger effect of GI in leaner persons is different from results from the Nurses’ Health Study and Women’s Health Study, which found that the effect of GL on triacylglycerols and C-reactive protein was stronger in postmenopausal women with a BMI > 25 than in their leaner counterparts (33, 41). Several explanations for this difference are possible. First, the observed interaction with BMI could be due to chance, because the interaction term was only marginally significant (P = 0.09), and no interaction was seen with IsoPs or GL. Second, there may be a different effect of BMI on the relation between GI and oxidative stress than on the relation between GL and triacylglycerols or C-reactive protein. Third, the different findings from the other 2 studies could be attributable to different subject characteristics. Our population included men and premenopausal women and had a wider range of BMIs than do most other studies—ie, one-third of subjects were normal-weight, one-third were overweight, and one-third were obese—a range that is very similar to the current US population distribution. Indeed, 11% of our subjects were in the World Health Organization’s category called Obese II, with a BMI ≥ 35 (42). We have shown previously that MDA was substantially higher in persons in this category (23). It is possible that, in the subgroup with an above-median BMI of 26.5, MDA was already elevated to a point at which GI could have little further effect. Other potential reasons for the observed interaction between GI and BMI require further research.

Extensive data suggest that oxidative damage may play a key role in the pathogenesis of many human diseases. In the current study, we observed a direct relation between dietary GI and 2 widely used markers of oxidative stress, independent of other factors. The observed rise in plasma MDA and IsoP concentrations from the lowest to the highest quartile of GI is comparable to the differences in those concentrations found between persons passively exposed to cigarette smoke and active smokers and between normal-weight and overweight subjects, respectively (23). Although these cross-sectional data cannot prove causality, they suggest that chronic consumption of high-GI foods may lead to chronically elevated oxidative stress. Therefore, increasing dietary intakes of low-GI foods, such as most fruit, vegetables, dairy products, and whole grains, may be beneficial in terms of reduced oxidative stress. However, more research is needed before any firm conclusions can be drawn.


   ACKNOWLEDGMENTS
 
The authors thanks Anna Maria Siega-Riz at the University of North Carolina Department of Nutrition, Clinical Nutrition Research Center, for providing glycemic index values.

YH performed the statistical analysis and drafted the manuscript. GB designed the study, provided oversight, and contributed significantly to data interpretation and manuscript revision. EPN and JDM were responsible for laboratory analysis. MD reviewed the manuscript. MH provided consultation on statistical analysis of the data. None of the authors had any personal or financial conflict of interest.


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 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication August 22, 2005. Accepted for publication February 2, 2006.


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