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Dietary Carbohydrates and Glycated Proteins in the Blood in Non Diabetic Subjects

Laboratory of Epidemiology (G.M., A.M.C., V.G.), IRCCS "S. De Bellis," Research Hospital for Digestive Diseases, Castellana, Bari, ITALY
Laboratory of Clinical Pathology (G.D.M.), IRCCS "S. De Bellis," Research Hospital for Digestive Diseases, Castellana, Bari, ITALY
Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston (G.L.)
Department of Social and Preventive Medicine, School of Public Health and Allied Health Professions, University at Buffalo, Buffalo (J.L.F.)

Address reprint requests to: Giovanni Misciagna, MD, PhD, Laboratorio di Epidemiologia, IRCCS "S. De Bellis," Ospedale Gastroenterologico, Castellana, (Bari), 70013, ITALY. E-mail: gmisciag{at}libero.it

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSIONS REFERENCES

 
Objectives: To evaluate in non diabetic subjects the association of dietary carbohydrates with fructosamine, a measure of total non enzymatic glycated proteins in the blood associated with mortality, particularly from cardiovascular diseases.

Methods: A population sample of 252 subjects (137 men and 115 women, mean age 57) without diabetes and with fasting serum glucose <126 mg/100 mL, participated in the study. Diet and dietary glycemic load were measured with a validated food frequency questionnaire. Fructosamine was measured with a standard colorimetric method. Multiple linear regression was used to analyze the data.

Results: Serum fructosamine was positively associated with dietary glycemic load. Moreover, it was positively associated with intake of polyunsaturated fats and alcohol; and negatively with intake of monounsaturated fats, and with physical activity.

Conclusion: The quality of carbohydrate and fat, as well as physical activity, may explain the variation of non enzymatic glycated serum proteins in non diabetic subjects.

Key words: fructosamine, dietary carbohydrates, dietary fats, glycemic load, non diabetic subjects

	INTRODUCTION

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Hyperglycemia in asymptomatic subjects has been found to be associated with cardiovascular disease [1]. Glucose reacts non-enzymatically in the blood with amino groups of many plasma and tissue proteins to form glycated proteins, and then advanced glycated end products (AGE), sometimes also altering their function [2,3]. Glycated hemoglobin (HbA _1c) and fructosamine, a measure of total non enzymatically glycated proteins in the blood, are biomarkers of long term glucose homeostasis in diabetic subjects [4,5]. In non-diabetics, elevated HbA _1c has been shown to be associated with increased risk of diabetes [6], and of prevalent [7], and incident cardiovascular disease [8], and also fructosamine has been associated with all cause mortality, particularly for cardiovascular diseases [9].

Blood glucose does not fully explain HbA _1c variations in non diabetic subjects, and diet and other lifestyle variables have been studied as determinants of HbA _1c in many studies [10–15]. However, in none of them was there a clear correlation of HbA _1c with dietary carbohydrates.

Little is known about how diet and other lifestyle factors influence fructosamine in non-diabetic subjects. The correlation between HbA _1c and fructosamine is high (r = 0.88) in diabetic subjects and low in non diabetic subjects (r = 0.01) [16]. HbA _1c and glycated proteins do not react in the same way to variation in blood glucose. In diabetic rats, glycated albumin is more sensitive than HbA _1c to changes in serum glucose [17]. Furthermore, the change in concentration of glycated albumin and of glycated hemoglobin induced by dietary manipulations is different: glycated albumin is more sensitive to change in sugar levels [18,19].

The objective of this study was to evaluate in non diabetic subjects the association with fructosamine of some dietary and non dietary factors that had already been evaluated for their association with HbA _1c, and more specifically dietary carbohydrates.

	METHODS

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Subjects
Participants in this study were individuals included as controls in a population case-control study on diet, physical activity and gallstones [20]. In that study 312 control subjects (age range 36–77 years) were selected from the gallstone-free population. All these subjects had completed a questionnaire on socio demographic status, medical history, dietary habits, and physical activity pertaining to the previous year, and a blood sample was also taken, always in the morning with the subjects fasting for at least 12 hours. Of these 312 control subjects, 290 (93%) subjects had completed the food frequency questionnaire, and 252 subjects entered this study. Thirty subjects were not eligible because they had a physician’s diagnosis of diabetes or because their blood glucose was above 126 mg/100 mL. For eight subjects there was insufficient serum to analyze for fructosamine.

Dietary and Physical Activity Measurement
Diet was assessed with a semi-quantitative food-frequency questionnaire (available from the corresponding author on demand) already used in a study on diet and gallstones [20]. In the questionnaire, the frequency of consumption of 96 food items in the year before the examination was to be attributed to one of eight categories (no consumption, less than once/month, 1–3 times/month, 1 time/week, 2–3 times/week, 4–6 times/week, 1 time/day, 2+ times/day). The usual portion according to three different categories (small, medium, large portion size, illustrated on the top of each page), was also requested for each item. The list of foods and beverages was grouped into 12 separate sections, according to the principal food groups [21]. Finally, some questions queried modifications in diet related to changes in life style for medical purposes, and alteration in body weight in the year before the interview. Information on demographic characteristics and smoking habits was also requested. The reproducibility and accuracy of the questionnaire was documented in a validation study in which it was compared with two seven-day dietary records completed over 6 months [22]. Total energy and macro- and micronutrients intakes were calculated using Italian food composition tables [23,24]. Energy-adjusted nutrient intakes were computed as the residual from the regression model, with total energy intake as the independent variable and absolute nutrient intake as the dependent variable [25].

Physical activity was ascertained with the use of 10 questions designed to measure both leisure time and work activities. The items were selected on the basis of a previous report investigating the activity patterns of elderly populations in rural areas [26] and from a local survey of individuals consulting the outpatient department of the local hospital. Questions probed the time (hours and minutes) spent in bed (sleeping and resting), performing household activities (cooking and cleaning), and performing recreational activities (e.g., gardening, walking, bicycling, and exercising). Residual time (time not accounted for by the listed activities) was assumed to be spent on light-to-moderate activities. Daily energy expenditures in kilojoules were calculated from these items according to the procedures described by James and Schofield [27] and described in [20].

Glycemic Load
Glycemic load is a measure that is indicative of the blood glucose load resulting from consumption of single foods in a fasting state. We calculated the glycemic load of each food [28] by multiplying the available carbohydrate content of a specific portion of that food by its glycemic index [29]. We then multiplied this glycemic load value by the frequency of consumption per year obtained from the food frequency questionnaire, summed these products over all food items, and divided by 365 to produce the dietary glycemic load per day. Each unit of glycemic load represents the equivalent of 1 g of carbohydrate from white bread.

Serum Fructosamine
Serum was stored at –80C before being analyzed. Serum fructosamine was measured using the "Dri FRUTTOSOAMINE kit" on the Beckman Synchron CX5 Clinical System, a colorimetric assay based on the ability of ketoamines to reduce nitroblue tetrazolium to formazan. Venous serum was added to carbonate buffer at pH 10.8 containing nitroblue tetrazolium (NBT) 0.48 mmol/L, the absorbance at 550 nm was measured 10 and 15 minutes after mixing and compared with a fructosamine calibrator (BAH 140, 315 mmol/L). The whole assay was carried out at 37C. The coefficient of variation in the same day, used to measure the precision of the measurement, was 1.1%.

Statistical Analysis
All respondents were included in the analysis of diet because all questionnaires were at least 90% complete. Mean and standard deviation were used for the descriptive statistics of male and female study subjects. Multiple linear regression was used to evaluate the relationship of fructosamine with each of the dietary variables and physical activity, controlling for age, BMI, total serum protein and calories, and stratifying by gender. The normalcy of the distribution of the variables was tested before introducing them in the regression models. We also tested the interaction of the single dietary variables, glycemic load and physical activity with gender, controlling for the above confounders. The final most parsimonious multiple linear regression model, forward method, was used to select dietary factors corrected for calories and non-dietary factors significantly associated with fructosamine (t test of the coefficients of the multiple regression statistically significant at p-value <0.10, two tails), holding age, gender, BMI, and serum protein in the model as main covariates [30,31]. Linearity was explored by introducing quadratic terms in the model and the independent variables in quartiles, and only first order interactions were explored. All the statistical computations were made using STATA 6.0 Statistical Software (Stata Corporation, College Station, Texas, USA).

	RESULTS

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The descriptive statistics of the dietary and non dietary variables in males and females are shown in Table 1. It is clear that both males and females have a mean BMI in the overweight range, while fat intake is primarily from monounsaturated fats, and male alcohol intake (in this area above all wine with meals) is three times that of females. Linear regression modeling of fructosamine separately on each dietary variable and on physical activity, stratifying by gender and controlling for age, BMI, total serum protein and calories, was estimated. For the most part, the correlations between reported nutrient intake and serum fructosamine levels are relatively weak and not statistically significant. Physical activity was inversely associated with fructosamine both in men and women, but the association was statistically significant only in women (p = 0.04). The influence of dietary nutrients, glycemic load, and physical activity on serum fructosamine was similar in males and females (p-value >0.20 of no interaction of these variables with gender, controlling for age, BMI, serum proteins, and calories), so we decided to study males and females together in the same model. Table 2 shows some regression models of the relationship between serum fructosamine and the glycemic load in quartiles, controlling for different dietary variables as follows: only glycemic load (model 1); the different type of fats (model 2); alcohol (model 3); and both fat and alcohol (model 4). Model 4 shows that when both fat and alcohol are in the regression model of fructosamine on glycemic load, glycemic load is positively related to fructosamine (p = 0.05). Alcohol is also a strong confounder of the relationship between fructosamine and saturated fat, changing the direction of the relationship from negative to positive when introduced in model 4. Table 3 depicts the final model of the linear regression of fructosamine on all the dietary and non dietary variables, without (model 1) and with interaction (model 2) of glycemic load with saturated fat. Also level of education and smoking, that are not in the final model 1, were evaluated. Smoking was positively associated with fructosamine but only in women who smoked more than 20 cigarettes per day. Because there were only five subjects who smoked that much, the variable ‘smoking’ was excluded from the final model. For all the dietary variables not linearly associated with fructosamine we tested a quadratic term for non linearity, and they were also introduced in the model in quartiles, as a dummy variable, or as median of the quartile to test for trend. The interaction of all the variables with gender, and then the interaction of the glycemic load with dietary protein, different type of fats, and alcohol, was tested. In model 1, age, glycemic load (t test for trend, p = 0.06), dietary polyunsaturated fats, and alcohol were positively associated with fructosamine; female gender, dietary monounsaturated fats, and physical activity (t test for trend, p = 0.02) were inversely associated. Dietary protein was not retained in the model, because it was not associated with serum fructosamine. Saturated fat was retained in model 1, even though its association with fructosamine was not statistically significant, because there was a negative interaction between glycemic load and saturated fats, as shown in model 2.

	DISCUSSION

Measurement of fructosamine was made with a standard laboratory method under strict quality control, and there is evidence that fructosamine is stable after storage [32]. Measurement of diet and physical activity was made with a standardized and validated semi-quantitative food frequency questionnaire [20]. Statistical analysis was able to detect only the relationships between nutrients and fructosamine that had an important linear component. To investigate non linearity the relationship of fructosamine with nutrients in quartiles was explored, and quadratic terms of the nutrients were tested in the model. Serum glucose was not introduced in the model as a confounder, because of the possibility that it could be an intermediate in the relationship between nutrients and fructosamine. However, we introduced serum glucose in the final model and the regression coefficients of the other variables did not change. Further, it was not associated with fructosamine, probably because only subjects with serum glucose <126 mg/100 mL were studied. Other potential confounders not considered in the model were vitamin supplements, as these are not widely used in this population, or a family history of diabetes. Because most subjects had at least one first degree relative who had migrated to northern Italy or abroad, it was difficult, or impossible, to collect family history information.

Diet and Fructosamine
Carbohydrate.
In this study fructosamine, a marker of serum glycated proteins, was associated with the glycemic load of the diet. The association of HbA _1c with glycemic load was not explored in any of the three most recent observational studies about HbA _1c and diet [10–12]. In a cross-over experimental study on six healthy male subjects, two weeks of intake of a low glycemic load diet significantly decreased serum fructosamine in comparison with intake of a high glycemic load diet over the same period [33]. In this short term study HbA _1c was not used as a marker of serum glycation of protein. Many studies have investigated the effect of carbohydrates on glycated proteins and/or HbA _1c. In healthy volunteers, after 75 g or 50 g of an oral glucose load [34,35] stable HbA _1c (ketoamine) increases after 30 days, and returns to normal values after 60 days. The labile aldimine fraction of HbA _1c increases two hours after 75 g of glucose load [36]. A carbohydrate-rich diet (fat 10%, carbohydrates 75%) increased glycated HbA _1c in 9 normal subjects after one week [37]. Calorie restriction decreases HbA _1c in healthy subjects [38]. Eleven non diabetic subjects were randomized to a diet with high soluble fiber/low glucose or low soluble fiber/high glucose for 6 weeks, and HbA _1c and fructosamine were analyzed [18]. Only fructosamine increased with the high, and decreased with the low glucose diet. In an observational study of 248 subjects with normal glucose tolerance in Israel [39] there was no relationship of HbA _1c with total energy intake, or with intake of any specific food components. Yudkin compared the diet of 12 high and 13 low hemoglobin glycators and found no significant differences in intake of total carbohydrates, sugars, glucose equivalents, total and soluble fiber or vitamin B6 or Vitamin C, even when nutrient intake was expressed as a proportion of total energy intake [40]. In a cross-sectional study of 745 men and 1028 women over forty, HbA _1c was not associated with energy-adjusted disaccharides, polysaccharides, or fiber, controlling for age, gender, obesity, physical activity level, educational attainment, smoking status, and use of supplements [10]. In 9772 non diabetic subjects, 4684 men and 5088 women, age range 16–94 years, HbA _1c was higher in subjects who took sugar in tea or in coffee, controlling for age, gender, obesity, physical activity level, educational attainment, smoking status, and consumption of alcohol [11]. In another recent cross-sectional study on 2759 men and 3464 women, age range 45–74 years, HbA _1c was inversely associated with fruit and green leafy vegetables, in the fully adjusted analysis and controlling also for dietary fiber, saturated fat, plasma and dietary vitamin C [13]. Although these recent observational studies on diet and HbA _1c did not explore the relationship of glycemic load with HbA _1c, the results are not in opposition with our findings on glycemic load and fructosamine. The negative findings in Boeing’s study [10] could be justified by the fact that HbA _1c is less sensitive to carbohydrates than fructosamine. Added sugar in the diet, found by Gulliford [11] to be directly associated with HbA _1c, has a high glycemic index and fruit and green leafy vegetables, found to be inversely associated with HbA _1c in the Epic-Norfolk study [13], have a low glycemic index.

Fat.
In this study we found (Table 3, model 1 and 2) monounsaturated fat, mainly vegetable fat from olive oil, to be inversely associated with fructosamine, and saturated and polyunsaturated fats, mainly animal fats from cheese and meat directly associated with glycated proteins, even if the association with saturated fats was not statistically significant.

There are some problems with interpretation of these associations and their comparison with the association between fats and HbA _1c found in other studies. Firstly, monounsaturated fat is highly correlated with saturated fat in a typical Western diet because of the shared food source (e.g. beef and dairy products). In our subjects’ diet, still largely typically Mediterranean, intake of monounsaturated fat can be investigated with minimal confounding by intake of saturated fat because of the high consumption of olive oil; moreover, mean intake of saturated fats in our subjects is half than that in Northern Europe [10,11], while the polyunsaturated fats are not vegetable fats as in Germany [10], or England [12] but animal fat.

In the Epic-Potsdam study, Boeing [10] found that HbA _1c was associated with energy-adjusted saturated fat, controlling for the most important known confounders of the relationship. He did not find a significant association of HbA _1c with monounsaturated fat (mainly from meat and cheese) and polyunsaturated fat (from vegetable oil), even if they seem to be directly and inversely associated with HbA _1c, respectively. Gulliford, in his Health Survey for England 1994 [11], in non diabetic subjects, found that HbA _1c was higher in subjects who used solid fat for cooking or whole milk or butter or hard margarine, all foods with a high content of animal fat, saturated or mono or polyunsaturated. In another cross sectional study in England, the Epic-Norfolk study [12], in the fully adjusted analysis HbA _1c was inversely associated with the ratio of polyunsaturated to saturated fat, and directly associated with total fat intake. In the same study, in the regression model on different types of fats, HbA _1c was associated only with saturated fat, and not with mono or polyunsaturated fat, results that overlap with the results of the Epic-Norfolk study, that has the same "Epic" methodology and the same type of Western population. There are no other studies in non diabetic subjects, apart from this study in Southern Italy, exploring the relationship between monounsaturated fats from olive oil, and glycated hemoglobin or fructosamine. Other studies have been made in diabetics, or in normal subjects on fasting plasma glucose or insulin sensitivity. No association of monounsaturated fat with HbA _1c and fructosamine was found in an experimental study in subjects with non insulin dependent diabetes [41]. An Italian study found that monounsaturated fat, from olive oil, was associated with lower fasting plasma concentration of glucose [42]. In an experimental study on 162 healthy subjects, the KWANU study, insulin sensitivity was significantly impaired on the saturated fatty acid diet, but did not change on the monounsaturated fatty acid diet [43].

It is interesting to consider the negative interaction between saturated fats and high glycemic load in their effect on fructosamine, present above all at high glycemic load (Table 3, model 2). A possible interpretation of this negative interaction is that it is caused by the effect of dietary carbohydrates on blood lipids. A high glycemic load determines not only a high glucose level in the blood, but also an increase in fasting plasma triacylglycerols and their apoproteins [44,45]. The consequence of this increase of blood lipids and their protein carriers could be that at high glycemic load more glucose in the blood also sticks to the amino groups of apoproteins, building glycated lipoproteins [46,47] not measured by the fructosamine test, but found in human atherosclerotic plaques [48].

Vitamin C and E.
Vitamin C has been found in several studies to be inversely associated with HbA _1c. In the Beaver Dam Eye Study [49], in 1982 healthy non diabetic subjects, HbA _1c was inversely associated with the dietary intake of Vitamin C, controlling for age, gender, plasma glucose, and smoking status. Furthermore, controlling for major confounders the energy-adjusted intake of vitamin C was inversely associated with HbA _1c also in two recent cross sectional studies [10,15]. However, in an experimental study in 12 healthy volunteers [50], three months of Vitamin C administration caused an 18% decrease of HbA _1c, a 33% decrease of glycated albumin but only a 5% decrease of fructosamine. Vitamin C seems to protect hemoglobin and albumin from glycation more than other proteins, in agreement with the weak association we found in our study on fructosamine, and the strong one found in the other studies on HbA _1c [10,15,49]. We did not find an association between vitamin E and fructosamine. In non diabetic subjects vitamin E was associated with HbA _1c in one study [10], and not associated in another [49].

Alcohol.
Alcohol consumption in this population is primarily wine with meals. We found a direct association between calorie-adjusted alcohol intake and fructosamine, controlling not only for age, gender, BMI, and serum proteins, but also for physical activity and other nutrients. In some recent observational studies, energy-adjusted alcohol intake, after controlling for gender, age, obesity index, physical activity, educational attainment, smoking status and supplement use, but not for other nutrient intake, was inversely associated with HbA _1c [10,11]. In Gulliford’s study the association was statistically significant only in men, maybe because they drink more than women. However, in 38 consecutive non diabetic, non cirrhotic male drinkers [51], after one week alcohol withdrawal determined a significant decrease of fructosamine, and no decrease of HbA _1c (however the time window of a week is too short to see an effect on HbA _1c). Furthermore, HbA _1c has been reported to be normal or subnormal in alcoholics, whereas the other glycated hemoglobin fractions HbA _1a–b were found to be elevated [52,53]. The studies on alcohol intake and the risk of diabetes, insulin sensitivity, and carbohydrate metabolism have produced conflicting results, depending also on the quantity of alcohol intake, with a possible U- or J-shaped association. In this study of ours alcohol was not only associated positively with fructosamine, but was also a strong confounder of the association of other nutrients, above all fats and glycemic load with fructosamine.

Physical Activity and Fructosamine
In non diabetic subjects, fructosamine was inversely associated with physical activity, significantly so only in women. Physical activity in non diabetic subjects has been shown to be inversely associated with HbA _1c, but to a significant degree only in men [11]. In two other observational studies there was no association between HbA _1c and physical activity [10,39]. Physical activity was implicated in many studies as having a protective effect against type two diabetes [54–56]. A key mechanism underlying this relationship between physical activity and diabetes has to do with the influence of physical activity on improving insulin sensitivity [57].

	CONCLUSIONS

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The most important findings of this study are the positive association of glycemic load, saturated and polyunsaturated fat (animal fats) and alcohol with fructosamine, and its inverse relationship with monounsaturated fat (vegetable fat), and physical activity. These results have been obtained in a typical Mediterranean country, where the population is still highly physically active. The variables in our final regression model explain only 20% of the variation of fructosamine. While a small decrease of fructosamine may be important at the population level in decreasing mortality from cardiovascular disease, the majority of the variation is still unexplained. One unknown factor could be stress, which has been found to be associated with glycated hemoglobin both in non diabetic subjects [58] and in subjects with non insulin dependent diabetes [59], and also with glycated albumin in diabetic subjects [60]. Another missed factor could be inheritance; genetic factors explain more than 60% of the variation of HbA _1c [61].

To conclude, the results of this study support the hypothesis that not only the quantity but also the quality of carbohydrate and fat are important for control of fructosamine, a measure of glycated serum proteins, and a marker associated with both cardiovascular disease and overall mortality.

Received November 26, 2003. Accepted June 27, 2004.

	REFERENCES

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