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Original Research Communications |
1 From the Departments of Epidemiology and Nutrition, the Harvard School of Public Health; the Channing Laboratory; and the Division of Preventive Medicine, the Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston.
2 Supported by CA40356, the main Nurses' Health Study Grant, and Nutrition Training Grant T32DK07703 from the US National Institutes of Health. 3 Address reprint requests to S Liu, Division of Preventive Medicine, Department of Medicine, Harvard Medical School, 900 Commonwealth Avenue East, Boston, MA 02215. E-mail. simin.liu{at}channing.harvard.edu.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: The objective of this study was to prospectively evaluate the relations of the amount and type of carbohydrates with risk of CHD.
Design: A cohort of 75521 women aged 38–63 y with no previous diagnosis of diabetes mellitus, myocardial infarction, angina, stroke, or other cardiovascular diseases in 1984 was followed for 10 y. Each participant's dietary glycemic load was calculated as a function of glycemic index, carbohydrate content, and frequency of intake of individual foods reported on a validated food-frequency questionnaire at baseline. All dietary variables were updated in 1986 and 1990.
Results: During 10 y of follow-up (729472 person-years), 761 cases of CHD (208 fatal and 553 nonfatal) were documented. Dietary glycemic load was directly associated with risk of CHD after adjustment for age, smoking status, total energy intake, and other coronary disease risk factors. The relative risks from the lowest to highest quintiles of glycemic load were 1.00, 1.01, 1.25, 1.51, and 1.98 (95% CI: 1.41, 2.77 for the highest quintile; P for trend < 0.0001). Carbohydrate classified by glycemic index, as opposed to its traditional classification as either simple or complex, was a better predictor of CHD risk. The association between dietary glycemic load and CHD risk was most evident among women with body weights above average [ie, body mass index (in kg/m2) 
23].
Conclusion: These epidemiologic data suggest that a high dietary glycemic load from refined carbohydrates increases the risk of CHD, independent of known coronary disease risk factors.
Key Words: Diet • carbohydrate • fiber • glycemic load • glycemic index • coronary heart disease • Nurses' Health Study • women
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Carbohydrates with different physical forms, chemical structures, particle sizes, and fiber contents induce distinct plasma glucose and insulin responses (8). This physiologic response to carbohydrate can be quantified by glycemic indexes (8–10), which compare the plasma glucose response to specific foods with the response induced by the same amount of carbohydrate from a standard carbohydrate source, usually white bread or pure glucose. In metabolic studies and dietary trials, substituting foods with low glycemic indexes for those with high indexes reduced serum insulin and glucose responses (11–13), urinary C-peptide excretion (a marker of insulin production), and glycated hemoglobin concentrations in diabetic and nondiabetic subjects (9, 13–16). In addition, greater intake of starches with high glycemic indexes leads to insulin resistance in animals (17, 18) and is associated with insulin resistance (12, 13, 19), a lower concentration of HDL (13, 20), and hypertriglyceridemia (13, 14) in humans. Furthermore, a high dietary glycemic load (ie, the product of the glycemic index of a specific food and its carbohydrate content—a variable representing the quality and quantity of carbohydrate and the interaction between the 2) has been associated with an increased risk of type 2 diabetes (21, 22). Although the available evidence strongly indicates that insulin resistance, hyperglycemia, and associated disorders of lipid metabolism are important determinants of CHD (23–25), the association between dietary glycemic load and risk of CHD has not been examined in humans.
In this 10-y follow-up study of female nurses, we examined prospectively 1) whether dietary glycemic load is related to risk of CHD, 2) whether the glycemic index can predict risk of CHD better than can the traditional classification of carbohydrates into simple and complex forms, and 3) whether the relation of glycemic load to risk of CHD is modified by adiposity.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Measurements of dietary intake were repeated in 1986 and 1990 by using almost identical SFFQs. For each food, a commonly used unit or portion size (eg, one slice of bread) was specified and the subject was asked how often during the previous year, on average, she had consumed that amount. Nine responses were possible, ranging from "never" to "6 times per day." Nutrient scores were computed by multiplying the frequency of consumption of each unit of food from the SFFQ by the nutrient content of the specified portion according to food-composition tables from the US Department of Agriculture (26) and other sources. A full description of the SFFQ and the procedures used for calculating nutrient intake, as well as data on reproducibility and validity in this cohort, were reported previously (27). The correlation coefficient for energy-adjusted carbohydrate intake between the SFFQs and diet records was 0.73. The performance of the SFFQ for assessing the individual foods high in carbohydrate has also been documented (28). For example, correlation coefficients were 0.71 for white bread, 0.77 for dark bread, 0.66 for potatoes, and 0.94 for yogurt (28).
The method used to assess glycemic indexes of individual foods and mixed meals, as well as the measurement of dietary glycemic load in the Nurses' Health Study cohort, were reported elsewhere (10, 15, 21). Briefly, we calculated glycemic load by multiplying the carbohydrate content of each food by its glycemic index, then multiplied this value by the frequency of consumption and summed the values from all foods. Dietary glycemic load thus represents the quality and quantity of carbohydrates and the interaction between the 2, given that the product of glycemic index and carbohydrate intake indicates that a higher glycemic index has a greater effect at higher carbohydrate intakes. Each unit of dietary glycemic load represents the equivalent of 1 g carbohydrate from white bread or pure glucose. We also created a variable we termed overall dietary glycemic index by dividing the average daily glycemic load by the average daily carbohydrate intake. Expression of the glycemic load per unit of carbohydrate allowed the carbohydrate content to be matched gram by gram and thus reflects the overall quality of carbohydrate intake.
The primary endpoint for this analysis was incident CHD, which includes fatal CHD and nonfatal MI occurring during the 10-y period between the return of the 1984 questionnaire and 1 June 1994 (29). A diagnosis of nonfatal MI was confirmed by medical records by using criteria proposed by the World Health Organization—symptoms plus either typical electrocardiographic changes or elevation of cardiac enzymes (29). Fatal CHD was ascertained by using the National Death Index and was confirmed by medical records, autopsy reports, or death certificates. Fatal CHD was confirmed if CHD was listed as the cause of death, if it was the underlying and most plausible cause, or if evidence of previous CHD was available. On the basis of all sources combined, mortality follow-up was >98% complete (30).
Statistical analysis
For each study participant, person-years of follow-up (ie, the number of persons studied times the number of years of follow-up) were counted from the date of return of the 1984 questionnaire to the date of CHD diagnosis; the date of death; or 1 June 1994, whichever came first. Women were grouped in quintiles of glycemic load, overall dietary glycemic index, and carbohydrate intake (simple versus complex). Incidence rates were calculated as the number of CHD events divided by the person-time of follow-up in each quintile. Incidence rate ratios were calculated by dividing the incidence rate of CHD in a particular category of exposure by the corresponding rate in the reference category. Tests for trends were conducted by assigning the median value to each quintile and modeling this value as a continuous variable. The log likelihood ratio test was used to assess the significance of interaction terms.
In a multivariate analysis, the estimated relative risks (RRs) were simultaneously adjusted for potential confounding variables by using a pooled logistic regression that was asymptotically equivalent to the Cox proportional-hazards regression (31). To best represent the participants' long-term dietary patterns during follow-up, we used a cumulative average method based on all available measurements of diet up to the beginning of each 2-y interval (32, 33). Other covariates, including age, body mass index (BMI; in kg/m2), smoking status, alcohol intake, physical activity, postmenopausal hormone use, multivitamin use, use of vitamin E supplements, parental history of MI before age 60 y, history of hypertension, and history of hypercholesterolemia, were assessed and updated every 2 y. To control for total energy intake, all nutrients, glycemic load, and overall dietary glycemic index were adjusted for total energy intake by using the residual method (34). In addition, when examining the effect of substituting carbohydrate for fat, we used multivariate nutrient-density models that simultaneously included energy intake, the percentages of energy derived from protein and carbohydrate, and other confounding variables. All reported P values are two-sided. Furthermore, because adiposity (BMI) is an important determinant of insulin resistance (35), we hypothesized a priori that adiposity could modify the relation between glycemic load and CHD and we evaluated this hypothesis in stratified analyses.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). Women with high dietary glycemic loads consumed more carbohydrates, dietary fiber, cereal fiber, vitamin E, and folate but had lower intakes of fats, cholesterol, proteins, and alcohol and smoked less than did women with low glycemic loads (Table 1). Mean age and BMI, history of parental MI before age 60 y, physical activity, and current use of postmenopausal hormones did not vary appreciably across quintiles of dietary glycemic load. Dietary glycemic load did not appear to be determined by any particular food; the 2 most important contributors to dietary glycemic load in this population were mashed or baked potatoes (8%) and cold breakfast cereals (4%); other carbohydrate-containing foods contributed smaller amounts.
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). This association remained essentially unchanged after further adjustment for other known coronary disease risk factors. In an analysis that included age, BMI, smoking status, alcohol intake, parental family history of MI before age 60 y, self-reported history of hypertension, history of high cholesterol, menopausal status, aspirin use, use of multiple vitamins, use of vitamin E supplements, physical activity, protein intake, dietary fiber, dietary vitamin E and folate, and total energy intake (model 2, Table 2), the RR for the highest compared with the lowest quintile of dietary glycemic load was 1.56 (95% CI: 1.17, 2.07; P for trend < 0.0001).
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). In this model, in which all fats, protein, and total energy intake were held constant, glycemic load represented the effect of substituting high-glycemic-index carbohydrates for low-glycemic-index carbohydrates on CHD risk. Compared with carbohydrates with a low glycemic index, carbohydrates with a high glycemic index were strongly associated with increased risk of CHD. The RR for the highest compared with the lowest quintile of glycemic load was 1.98 (95% CI, 1.41, 2.77; P for trend < 0.0001) in this model. Similar findings for glycemic load were observed when all types of fats were replaced with carbohydrate in the multivariate model, with a multivariate adjusted RR of 1.89 (95% CI: 1.45, 2.46; P for trend < 0.0001) when extreme quintiles of glycemic load were compared.
To better understand the relation of dietary glycemic load to risk of CHD in terms of its constituents, we examined intake of total and specific types of carbohydrates. In model 1 (Table 3), based only on food-composition data (without incorporation of glycemic index data of individual foods), total carbohydrate intake, representing the replacement of fat with carbohydrate, appeared to be positively related to CHD risk, but this association was weak and not significant. When total carbohydrate was entered into the multivariate nutrient-density model as a continuous variable, the RR was 1.02 (95% CI: 0.96, 1.08; P = 0.50) for an increase of 5% in energy from total carbohydrate, as compared with the equivalent energy from total fat.
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). In contrast, the quality of carbohydrate as classified by its glycemic index was significantly associated with the risk of CHD in a multivariate model, in which the same covariates were adjusted for [multivariate adjusted RR: 1.31; 95% CI: 1.02, 1.68; P for trend = 0.008 when extreme quintiles were compared (model 3, Table 3)]. Addition of total carbohydrate intake to this model did not change the positive association between overall dietary glycemic index and CHD risk (RR: 1.28; 95% CI: 1.00, 1.64; P for trend = 0.02).
The increased risk of CHD associated with high glycemic load was most evident among women with BMIs >23 (Figure 1). Little relation between glycemic load and CHD risk was found among women with BMIs < 23 (RR: 1.11; 95% CI: 0.74, 1.66 for high compared with low glycemic load; P < 0.01 for test of interaction between BMI and glycemic load).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The prospective design of this study eliminated many potential sources of bias, especially recall bias. One concern was whether women lost to follow-up had disease and exposure experiences that were different from those of the women who remained in the cohort. However, because loss to follow-up accounted for only 
1.8% of the person-years, any potential bias would have been minimal. Although residual confounding by unknown risk factors is theoretically possible, higher glycemic load was not associated with a generally less favorable risk profile. A clear association between dietary glycemic load and risk was evident after adjustment for age and smoking status. This association became stronger with additional adjustment of a multitude of confounding factors, arguing against the possibility of residual confounding.
Misclassification of dietary exposure is always an important concern. Random within-person variation could attenuate any association of interest, but the SFFQ was designed to minimize this kind of error by assessing average long-term dietary intake. Although the total glycemic effect of diets may not be fully captured by the SFFQ, any measurement errors would be expected to be unrelated to the CHD endpoints because of the prospective design of the Nurses' Health Study. Thus, our estimate of the underlying association between glycemic load and risk of CHD may be somewhat conservative. Furthermore, because updated dietary information during the follow-up was used in our analyses, we accounted for changes in dietary habits over time.
The results of 2 previous cohort studies of men suggested a weak inverse association between intake of carbohydrate and CHD (37, 38). In the Honolulu Heart Program, McGee et al (37) documented 456 cases of CHD among 8000 men of Japanese ancestry who were followed up for 10 y. Measuring diets at baseline with a single 24-h recall, these authors showed that men who later experienced coronary events had lower intakes of energy and carbohydrates than did the rest of the cohort. However, no significant associations were found in multivariate analysis when both total energy and carbohydrate intakes were included in the same model. In The Puerto Rico Heart Health Program, Garcia-Palmieri et al (38) measured baseline diets with a 24-h recall among 8218 men and found a significant inverse association between carbohydrate intake and CHD. However, these findings might have resulted from the lack of adjustment for total energy intake. Such an adjustment is important because variation in total energy intake is largely determined by physical activity, a known protective factor against CHD. Neither study measured dietary glycemic load or conducted stratified analysis to examine the effects of carbohydrate intake on MI risk among subgroups of men defined by body weight. In metabolic trials, low-fat, high-carbohydrate diets neither increased insulin sensitivity nor maintained weight loss in diabetic subjects (39, 40) or nondiabetic subjects (41, 42). These diets did, however, worsen dyslipidemia (41, 42), especially among subjects who were prone to insulin resistance (5).
Insulin resistance, hyperglycemia, and related metabolic disorders have long been recognized as important risk factors for CHD (43, 44). In clinical observations and prospective cohorts, diabetic patients have a greater risk of morbidity and mortality from CHD than do nondiabetic patients (25, 45), especially women (25, 46). In the Framingham cohort, a strong positive association was observed between prevalence of CHD and increased glycated hemoglobin concentration, which suggests the important role of hyperglycemia in the development of CHD (47). In the Whitehall study (48), impaired glucose tolerance was associated with increased risk of MI. More recent studies also suggested that the relation between glycated hemoglobin concentration and CHD risk is progressive and continuous in diabetic (49) and nondiabetic (24) subjects. Although the underlying biological mechanisms for our findings remain to be elucidated, a high dietary glycemic load apparently induces hyperglycemia and hyperinsulinemia (9, 13, 50), which can lead, in turn, to hypertension (51), dyslipidemia (52), and possibly impaired fibrinolysis and thrombosis (53, 54), all of which can increase the risk of CHD.
The adverse metabolic responses to diets with a high glycemic load (9, 13, 16, 50) are exacerbated by underlying insulin resistance because increasing amounts of insulin are needed to compensate for the increased glycemic load (13). Our results are consistent with other published reports indicating that high glycemic load does not appear to affect risk of CHD among women with low BMIs (<23) (Figure 1). This finding may partly explain why some populations, such as those in rural China, have low rates of CHD despite high carbohydrate intakes. Traditionally, these populations consume carbohydrates in less refined forms, have high amounts of physical activity, and have a low prevalence of obesity. These factors can improve insulin sensitivity and may lead to greater tolerance of a relatively high glycemic load (25).
Although almost all existing dietary guidelines advise the substitution of complex carbohydrates for simple sugars or fat (55, 56), data that directly relate types of carbohydrate to CHD risk are limited. Our findings cast doubt on the usefulness of the simple versus complex classification of carbohydrates. A better measure would be the glycemic index of carbohydrates, which, in the present study, was more closely related with CHD risk—carbohydrates with a high glycemic index were strongly associated with increased risk of CHD compared with carbohydrates with a low glycemic index after intakes of total energy, protein, fats, and carbohydrates were held constant (model 4, Table 2). Metabolic data indicate that starchy foods, such as white rice and potatoes, are digested and absorbed quickly. Thus, even though these foods are considered to be desirable complex carbohydrates, they each have a high glycemic index and can induce high blood glucose responses. In contrast, the major source of simple sugars in this cohort was fruit and vegetables. Although they are considered to be less-than-desirable simple carbohydrates, these disaccharides (70% fructose and 33% sucrose) have a low glycemic index and yield only a small to moderate blood glucose response (57).
In summary, our findings suggest that a high intake of rapidly digested and absorbed carbohydrate increases the risk of CHD independent of conventional coronary disease risk factors. These data add to the concern that the current low-fat, high-carbohydrate diet recommended in the United States may not be optimal for the prevention of CHD and could actually increase the risk in individuals with high degrees of insulin resistance and glucose intolerance.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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E. B Levitan, C. W Westgren, S. Liu, and A. Wolk Reproducibility and validity of dietary glycemic index, dietary glycemic load, and total carbohydrate intake in 141 Swedish men Am. J. Clinical Nutrition, February 1, 2007; 85(2): 548 - 553. [Abstract] [Full Text] [PDF]
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D. D Motton, N. L Keim, F. A Tenorio, W. F Horn, and J. C Rutledge Postprandial monocyte activation in response to meals with high and low glycemic loads in overweight women Am. J. Clinical Nutrition, January 1, 2007; 85(1): 60 - 65. [Abstract] [Full Text] [PDF]
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A. T Merchant, S. S Anand, L. E Kelemen, V. Vuksan, R. Jacobs, B. Davis, K. Teo, S. Yusuf, and for the SHARE and SHARE-AP Investigators Carbohydrate intake and HDL in a multiethnic population Am. J. Clinical Nutrition, January 1, 2007; 85(1): 225 - 230. [Abstract] [Full Text] [PDF]
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R. M Krauss, P. J Blanche, R. S Rawlings, H. S Fernstrom, and P. T Williams Reply to EC Westman et al Am. J. Clinical Nutrition, December 1, 2006; 84(6): 1550 - 1550. [Full Text] [PDF]
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D. J. A. Jenkins, C. W. C. Kendall, A. R. Josse, S. Salvatore, F. Brighenti, L. S. A. Augustin, P. R. Ellis, E. Vidgen, and A. V. Rao Almonds Decrease Postprandial Glycemia, Insulinemia, and Oxidative Damage in Healthy Individuals J. Nutr., December 1, 2006; 136(12): 2987 - 2992. [Abstract] [Full Text] [PDF]
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 T. L. Halton, W. C. Willett, S. Liu, J. E. Manson, C. M. Albert, K. Rexrode, and F. B. Hu Low-Carbohydrate-Diet Score and the Risk of Coronary Heart Disease in Women N. Engl. J. Med., November 9, 2006; 355(19): 1991 - 2002. [Abstract] [Full Text] [PDF]
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K. A Hatonen, M. E Simila, J. R Virtamo, J. G Eriksson, M.-L. Hannila, H. K Sinkko, J. E Sundvall, H. M Mykkanen, and L. M Valsta Methodologic considerations in the measurement of glycemic index: glycemic response to rye bread, oatmeal porridge, and mashed potato. Am. J. Clinical Nutrition, November 1, 2006; 84(5): 1055 - 1061. [Abstract] [Full Text] [PDF]
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A. Flood, U. Peters, D. J. Jenkins, N. Chatterjee, A. F Subar, T. R Church, R. Bresalier, J. L Weissfeld, R. B Hayes, A. Schatzkin, et al. Carbohydrate, glycemic index, and glycemic load and colorectal adenomas in the Prostate, Lung, Colorectal, and Ovarian Screening Study. Am. J. Clinical Nutrition, November 1, 2006; 84(5): 1184 - 1192. [Abstract] [Full Text] [PDF]
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M. A Pereira Weighing in on glycemic index and body weight. Am. J. Clinical Nutrition, October 1, 2006; 84(4): 677 - 679. [Full Text] [PDF]
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H. Hare-Bruun, A. Flint, and B. L Heitmann Glycemic index and glycemic load in relation to changes in body weight, body fat distribution, and body composition in adult Danes. Am. J. Clinical Nutrition, October 1, 2006; 84(4): 871 - 879. [Abstract] [Full Text] [PDF]
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 I. G.E. Zarraga and E. R. Schwarz Impact of Dietary Patterns and Interventions on Cardiovascular Health Circulation, August 29, 2006; 114(9): 961 - 973. [Full Text] [PDF]
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E. J Stevenson, C. Williams, L. E Mash, B. Phillips, and M. L Nute Influence of high-carbohydrate mixed meals with different glycemic indexes on substrate utilization during subsequent exercise in women. Am. J. Clinical Nutrition, August 1, 2006; 84(2): 354 - 360. [Abstract] [Full Text] [PDF]
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M. C. Casiraghi, M. Garsetti, G. Testolin, and F. Brighenti Post-Prandial Responses to Cereal Products Enriched with Barley {beta}-Glucan. J. Am. Coll. Nutr., August 1, 2006; 25(4): 313 - 320. [Abstract] [Full Text] [PDF]
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S. Liu Lowering dietary glycemic load for weight control and cardiovascular health: a matter of quality. Arch Intern Med, July 24, 2006; 166(14): 1438 - 1439. [Full Text] [PDF]
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T. Kurth, S. C. Moore, J. M. Gaziano, C. S. Kase, M. J. Stampfer, K. Berger, and J. E. Buring Healthy lifestyle and the risk of stroke in women. Arch Intern Med, July 10, 2006; 166(13): 1403 - 1409. [Abstract] [Full Text] [PDF]
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D. J. Jenkins, A. R Josse, R. Labelle, A. Marchie, L. S. Augustin, and C. W. Kendall Nonalcoholic fatty liver, nonalcoholic steatohepatitis, ectopic fat, and the glycemic index Am. J. Clinical Nutrition, July 1, 2006; 84(1): 3 - 4. [Full Text] [PDF]
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Y. Hu, G. Block, E. P Norkus, J. D Morrow, M. Dietrich, and M. Hudes Relations of glycemic index and glycemic load with plasma oxidative stress markers Am. J. Clinical Nutrition, July 1, 2006; 84(1): 70 - 76. [Abstract] [Full Text] [PDF]
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 L. Qi, J. B. Meigs, S. Liu, J. E. Manson, C. Mantzoros, and F. B. Hu Dietary Fibers and Glycemic Load, Obesity, and Plasma Adiponectin Levels in Women With Type 2 Diabetes Diabetes Care, July 1, 2006; 29(7): 1501 - 1505. [Abstract] [Full Text] [PDF]
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 H. A. Bischoff, H. B. Staehelin, and W. C. Willett The effect of undernutrition in the development of frailty in older persons. J. Gerontol. A Biol. Sci. Med. Sci., June 1, 2006; 61(6): 585 - 589. [Full Text] [PDF]
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M. L. Neuhouser, L. F. Tinker, C. Thomson, B. Caan, L. V. Horn, L. Snetselaar, L. M. Parker, R. E. Patterson, R. Robinson-O'Brien, S. A. A. Beresford, et al. Development of a Glycemic Index Database for Food Frequency Questionnaires Used in Epidemiologic Studies J. Nutr., June 1, 2006; 136(6): 1604 - 1609. [Abstract] [Full Text] [PDF]
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K. Murakami, S. Sasaki, Y. Takahashi, H. Okubo, Y. Hosoi, H. Horiguchi, E. Oguma, and F. Kayama Dietary glycemic index and load in relation to metabolic risk factors in Japanese female farmers with traditional dietary habits Am. J. Clinical Nutrition, May 1, 2006; 83(5): 1161 - 1169. [Abstract] [Full Text] [PDF]
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B. J. Venn, A. J. Wallace, J. A. Monro, T. Perry, R. Brown, C. Frampton, and T. J. Green The Glycemic Load Estimated from the Glycemic Index Does Not Differ Greatly from That Measured Using a Standard Curve in Healthy Volunteers J. Nutr., May 1, 2006; 136(5): 1377 - 1381. [Abstract] [Full Text] [PDF]
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C.-J. Chiu, L. D Hubbard, J. Armstrong, G. Rogers, P. F Jacques, L. T Chylack Jr, S. E Hankinson, W. C Willett, and A. Taylor Dietary glycemic index and carbohydrate in relation to early age-related macular degeneration. Am. J. Clinical Nutrition, April 1, 2006; 83(4): 880 - 886. [Abstract] [Full Text] [PDF]
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Y. Ma, Y. Li, D. E. Chiriboga, B. C. Olendzki, J. R. Hebert, W. Li, K. Leung, A. R. Hafner, and I. S. Ockene Association between Carbohydrate Intake and Serum Lipids. J. Am. Coll. Nutr., April 1, 2006; 25(2): 155 - 163. [Abstract] [Full Text] [PDF]
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L. Qi, R. M. van Dam, S. Liu, M. Franz, C. Mantzoros, and F. B. Hu Whole-Grain, Bran, and Cereal Fiber Intakes and Markers of Systemic Inflammation in Diabetic Women Diabetes Care, February 1, 2006; 29(2): 207 - 211. [Abstract] [Full Text] [PDF]
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M. Garsetti, S. Vinoy, V. Lang, S. Holt, S. Loyer, and J. C Brand-Miller The Glycemic and Insulinemic Index of Plain Sweet Biscuits: Relationships to in Vitro Starch Digestibility J. Am. Coll. Nutr., December 1, 2005; 24(6): 441 - 447. [Abstract] [Full Text] [PDF]
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B. Fletcher, K. Berra, P. Ades, L. T. Braun, L. E. Burke, J. L. Durstine, J. M. Fair, G. F. Fletcher, D. Goff, L. L. Hayman, et al. Managing Abnormal Blood Lipids: A Collaborative Approach Circulation, November 15, 2005; 112(20): 3184 - 3209. [Abstract] [Full Text] [PDF]
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 L. Dauchet, P. Amouyel, and J. Dallongeville Fruit and vegetable consumption and risk of stroke: A meta-analysis of cohort studies Neurology, October 25, 2005; 65(8): 1193 - 1197. [Abstract] [Full Text] [PDF]
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L. A. Bazzano, M. Serdula, and S. Liu Prevention of Type 2 Diabetes by Diet and Lifestyle Modification J. Am. Coll. Nutr., October 1, 2005; 24(5): 310 - 319. [Abstract] [Full Text] [PDF]
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S. K. Raatz, C. J. Torkelson, J. B. Redmon, K. P. Reck, C. A. Kwong, J. E. Swanson, C. Liu, W. Thomas, and J. P. Bantle Reduced Glycemic Index and Glycemic Load Diets Do Not Increase the Effects of Energy Restriction on Weight Loss and Insulin Sensitivity in Obese Men and Women J. Nutr., October 1, 2005; 135(10): 2387 - 2391. [Abstract] [Full Text] [PDF]
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 C-J Tsai, M F Leitzmann, W C Willett, and E L Giovannucci Dietary carbohydrates and glycaemic load and the incidence of symptomatic gall stone disease in men Gut, June 1, 2005; 54(6): 823 - 828. [Abstract] [Full Text] [PDF]
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C. Lau, K. Faerch, C. Glumer, I. Tetens, O. Pedersen, B. Carstensen, T. Jorgensen, and K. Borch-Johnsen Dietary Glycemic Index, Glycemic Load, Fiber, Simple Sugars, and Insulin Resistance: The Inter99 study Diabetes Care, June 1, 2005; 28(6): 1397 - 1403. [Abstract] [Full Text] [PDF]
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P. Parikh, M. C. McDaniel, M. D. Ashen, J. I. Miller, M. Sorrentino, V. Chan, R. S. Blumenthal, and L. S. Sperling Diets and Cardiovascular Disease: An Evidence-Based Assessment J. Am. Coll. Cardiol., May 3, 2005; 45(9): 1379 - 1387. [Abstract] [Full Text] [PDF]
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J. Brand-Miller Optimizing the cardiovascular outcomes of weight loss Am. J. Clinical Nutrition, May 1, 2005; 81(5): 949 - 950. [Full Text] [PDF]
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C. B Ebbeling, M. M Leidig, K. B Sinclair, L. G Seger-Shippee, H. A Feldman, and D. S Ludwig Effects of an ad libitum low-glycemic load diet on cardiovascular disease risk factors in obese young adults Am. J. Clinical Nutrition, May 1, 2005; 81(5): 976 - 982. [Abstract] [Full Text] [PDF]
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L. Qi, E. Rimm, S. Liu, N. Rifai, and F. B. Hu Dietary Glycemic Index, Glycemic Load, Cereal Fiber, and Plasma Adiponectin Concentration in Diabetic Men Diabetes Care, May 1, 2005; 28(5): 1022 - 1028. [Abstract] [Full Text] [PDF]
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T. Pischon, C. J Girman, N. Rifai, G. S Hotamisligil, and E. B Rimm Association between dietary factors and plasma adiponectin concentrations in men Am. J. Clinical Nutrition, April 1, 2005; 81(4): 780 - 786. [Abstract] [Full Text] [PDF]
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M. A. Pereira and D. S. Ludwig Low-Glycemic Load Diet and Resting Energy Expenditure--Reply JAMA, March 9, 2005; 293(10): 1189 - 1190. [Full Text] [PDF]
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Y. Ma, B. Olendzki, D. Chiriboga, J. R. Hebert, Y. Li, W. Li, M. Campbell, K. Gendreau, and I. S. Ockene Association between Dietary Carbohydrates and Body Weight Am. J. Epidemiol., February 15, 2005; 161(4): 359 - 367. [Abstract] [Full Text] [PDF]
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 A. S. Bloch Low Carbohydrate Diets, Pro: Time to Rethink Our Current Strategies Nutr Clin Pract, February 1, 2005; 20(1): 3 - 12. [Abstract] [Full Text] [PDF]
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A. Slyper, J. Jurva, J. Pleuss, R. Hoffmann, and D. Gutterman Influence of glycemic load on HDL cholesterol in youth Am. J. Clinical Nutrition, February 1, 2005; 81(2): 376 - 379. [Abstract] [Full Text] [PDF]
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K. Oh, F. B. Hu, E. Cho, K. M. Rexrode, M. J. Stampfer, J. E. Manson, S. Liu, and W. C. Willett Carbohydrate Intake, Glycemic Index, Glycemic Load, and Dietary Fiber in Relation to Risk of Stroke in Women Am. J. Epidemiol., January 15, 2005; 161(2): 161 - 169. [Abstract] [Full Text] [PDF]
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 C. K. Roberts and R. J. Barnard Effects of exercise and diet on chronic disease J Appl Physiol, January 1, 2005; 98(1): 3 - 30. [Abstract] [Full Text] [PDF]
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D. S. Michaud, C. S. Fuchs, S. Liu, W. C. Willett, G. A. Colditz, and E. Giovannucci Dietary Glycemic Load, Carbohydrate, Sugar, and Colorectal Cancer Risk in Men and Women Cancer Epidemiol. Biomarkers Prev., January 1, 2005; 14(1): 138 - 147. [Abstract] [Full Text] [PDF]
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M. A. Pereira, J. Swain, A. B. Goldfine, N. Rifai, and D. S. Ludwig Effects of a Low-Glycemic Load Diet on Resting Energy Expenditure and Heart Disease Risk Factors During Weight Loss JAMA, November 24, 2004; 292(20): 2482 - 2490. [Abstract] [Full Text] [PDF]
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A. Harbis, S. Perdreau, S. Vincent-Baudry, M. Charbonnier, M.-C. Bernard, D. Raccah, M. Senft, A.-M. Lorec, C. Defoort, H. Portugal, et al. Glycemic and insulinemic meal responses modulate postprandial hepatic and intestinal lipoprotein accumulation in obese, insulin-resistant subjects Am. J. Clinical Nutrition, October 1, 2004; 80(4): 896 - 902. [Abstract] [Full Text] [PDF]
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K. Hoffmann, B.-C. Zyriax, H. Boeing, and E. Windler A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease Am. J. Clinical Nutrition, September 1, 2004; 80(3): 633 - 640. [Abstract] [Full Text] [PDF]
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B. Sloth, I. Krog-Mikkelsen, A. Flint, I. Tetens, I. Bjorck, S. Vinoy, H. Elmstahl, A. Astrup, V. Lang, and A. Raben No difference in body weight decrease between a low-glycemic-index and a high-glycemic-index diet but reduced LDL cholesterol after 10-wk ad libitum intake of the low-glycemic-index diet Am. J. Clinical Nutrition, August 1, 2004; 80(2): 337 - 347. [Abstract] [Full Text] [PDF]
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D. A Schaumberg, S. Liu, J. M Seddon, W. C Willett, and S. E Hankinson Dietary glycemic load and risk of age-related cataract Am. J. Clinical Nutrition, August 1, 2004; 80(2): 489 - 495. [Abstract] [Full Text] [PDF]
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S. W. Rizkalla, L. Taghrid, M. Laromiguiere, D. Huet, J. Boillot, A. Rigoir, F. Elgrably, and G. Slama Improved Plasma Glucose Control, Whole-Body Glucose Utilization, and Lipid Profile on a Low-Glycemic Index Diet in Type 2 Diabetic Men: A randomized controlled trial Diabetes Care, August 1, 2004; 27(8): 1866 - 1872. [Abstract] [Full Text] [PDF]
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K. Oh, W. C. Willett, C. S. Fuchs, and E. L. Giovannucci Glycemic Index, Glycemic Load, and Carbohydrate Intake in Relation to Risk of Distal Colorectal Adenoma in Women Cancer Epidemiol. Biomarkers Prev., July 1, 2004; 13(7): 1192 - 1198. [Abstract] [Full Text] [PDF]
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D. J. Jenkins, C. W. Kendall, A. Marchie, and L. S. Augustin Too much sugar, too much carbohydrate, or just too much? Am. J. Clinical Nutrition, May 1, 2004; 79(5): 711 - 712. [Full Text] [PDF]
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L. S Gross, L. Li, E. S Ford, and S. Liu Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment Am. J. Clinical Nutrition, May 1, 2004; 79(5): 774 - 779. [Abstract] [Full Text] [PDF]
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 R. J. Norman, M. Noakes, R. Wu, M. J. Davies, L. Moran, and J. X. Wang Improving reproductive performance in overweight/obese women with effective weight management Hum. Reprod. Update, May 1, 2004; 10(3): 267 - 280. [Abstract] [Full Text] [PDF]
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M. D. Holmes, S. Liu, S. E. Hankinson, G. A. Colditz, D. J. Hunter, and W. C. Willett Dietary Carbohydrates, Fiber, and Breast Cancer Risk Am. J. Epidemiol., April 15, 2004; 159(8): 732 - 739. [Abstract] [Full Text] [PDF]
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P. Nestel, M. Cehun, and A. Chronopoulos Effects of long-term consumption and single meals of chickpeas on plasma glucose, insulin, and triacylglycerol concentrations Am. J. Clinical Nutrition, March 1, 2004; 79(3): 390 - 395. [Abstract] [Full Text] [PDF]
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T. O. Scholl, X. Chen, C. S. Khoo, and C. Lenders The Dietary Glycemic Index during Pregnancy: Influence on Infant Birth Weight, Fetal Growth, and Biomarkers of Carbohydrate Metabolism Am. J. Epidemiol., March 1, 2004; 159(5): 467 - 474. [Abstract] [Full Text] [PDF]
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M. A. Pereira, E. O'Reilly, K. Augustsson, G. E. Fraser, U. Goldbourt, B. L. Heitmann, G. Hallmans, P. Knekt, S. Liu, P. Pietinen, et al. Dietary Fiber and Risk of Coronary Heart Disease: A Pooled Analysis of Cohort Studies Arch Intern Med, February 23, 2004; 164(4): 370 - 376. [Abstract] [Full Text] [PDF]
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 S. Higginbotham, Z.-F. Zhang, I-M. Lee, N. R. Cook, E. Giovannucci, J. E. Buring, and S. Liu Dietary Glycemic Load and Risk of Colorectal Cancer in the Women's Health Study J Natl Cancer Inst, February 4, 2004; 96(3): 229 - 233. [Abstract] [Full Text] [PDF]
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J. W. Anderson, K. M. Randles, C. W. C. Kendall, and D. J. A. Jenkins Carbohydrate and Fiber Recommendations for Individuals with Diabetes: A Quantitative Assessment and Meta-Analysis of the Evidence J. Am. Coll. Nutr., February 1, 2004; 23(1): 5 - 17. [Abstract] [Full Text] [PDF]
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N. M. McKeown, J. B. Meigs, S. Liu, E. Saltzman, P. W.F. Wilson, and P. F. Jacques Carbohydrate Nutrition, Insulin Resistance, and the Prevalence of the Metabolic Syndrome in the Framingham Offspring Cohort Diabetes Care, February 1, 2004; 27(2): 538 - 546. [Abstract] [Full Text] [PDF]
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S. Higginbotham, Z.-F. Zhang, I-M. Lee, N. R. Cook, J. E. Buring, and S. Liu Dietary Glycemic Load and Breast Cancer Risk in the Women's Health Study Cancer Epidemiol. Biomarkers Prev., January 1, 2004; 13(1): 65 - 70. [Abstract] [Full Text] [PDF]
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