Fructosamine, glycated hemoglobin, and dietary carbohydrates

Clinica Chimica Acta 340 (2004) 139 – 147 www.elsevier.com/locate/clinchim

Fructosamine, glycated hemoglobin, and dietary carbohydrates Giovanni Misciagna a,*, Giancarlo Logroscino b, Giampietro De Michele c, Anna M. Cisternino a, Vito Guerra a, Jo L. Freudenheim d a

Laboratory of Epidemiology, IRCCS ‘‘S. De Bellis,’’ Hospital for Digestive Diseases, Castellana, Bari 70013, Italy b Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA c Laboratory of Clinical Pathology, IRCCS ‘‘S. De Bellis,’’ Hospital for Digestive Diseases, Castellana, Bari, Italy d Department of Social and Preventive Medicine, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, USA Received 9 April 2003; received in revised form 6 October 2003; accepted 11 October 2003

Abstract Background: Glycated hemoglobin (HbA1c), a marker of glycemia in the previous 3 months, was found to be associated with dietary saturated, fat but not with carbohydrates, in recent population surveys. Another nonenzymatically glycated substance in the blood, fructosamine, a marker of glycemia in the previous 3 weeks, is poorly correlated with HbA1c in nondiabetic subjects. The aim of this study is to compare the correlation of glycated hemoglobin and fructosamine with dietary carbohydrate intake in the same subjects. Subjects and methods: Seventy-one individuals from a cohort study on diet and cancer entered this study. Serum fructosamine was measured by a standard colorimetric method, and glycated hemoglobin by high-performance liquid chromatography (HPLC). Diet was measured by a validated semiquantitative food frequency questionnaire. The correlation of fructosamine and glycated hemoglobin with dietary variables, corrected for calories, was evaluated by multiple correlation. Results: Fructosamine was more strongly correlated with dietary sugar (r = 0.26, p = 0.05) than HbA1c was (r = 0.001, p = 0.99). Fructosamine was also inversely correlated with energy, and glycated hemoglobin with vitamin C. Conclusions: Fructosamine appears to be more related to dietary sugar intake than glycated hemoglobin and may be a marker of exposure to dietary carbohydrates, particularly simple sugars, in epidemiological studies. D 2003 Elsevier B.V. All rights reserved. Keywords: Dietary carbohydrates; Fructosamine; Glycated hemoglobin

1. Introduction Glycated hemoglobin (HbA1c), a marker of glycemia in the previous 3 months [1], is associated with myocardial infarction in nondiabetic subjects [2,3], and with ultrasound-documented thickness of the arterial intima and media, which has been shown to be a predictor of myocardial infarction [4]. * Corresponding author. Tel./fax: +39-80-4960252. E-mail address: [email protected] (G. Misciagna). 0009-8981/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.cccn.2003.10.024

In recent population surveys in England [5,6] and in Germany [7], HbA1c was found to be associated with dietary intake of fats, particularly saturated fats, but not carbohydrates or sugars. The finding that HbA1c is not associated with carbohydrate intake has two possible implications. It could mean that HbA1c is not sensitive to the effect of dietary carbohydrates, or that the measure of dietary carbohydrate intake was not sufficiently sensitive [8]. Fructosamine, another nonenzymatic glycated substance in the blood, is a marker of glycemia in the previous 3 weeks [1]. Fructosamine,

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like HbA1c, is associated with cardiovascular disease, particularly in women [9]. However, fructosamine is poorly correlated with HbA1c in nondiabetic subjects [10]. In this study, we evaluated the correlation of both HbA1c and fructosamine with the reported intake of carbohydrates and simple sugars in the same subjects.

2. Materials and methods 2.1. Subjects The subjects of this study were drawn from a cohort recruited to evaluate the association of diet with cancer. All subjects over 50 years old, registered in three public health administrative areas of the Apulia region in southern Italy, examined by their family physicians for outpatient problems or for a physical check up in 1992 –1993, and referred to the Clinical Chemistry Laboratories of the area for blood analysis were invited to participate. After giving informed consent, they received a self-administered questionnaire with queries regarding their past medical history and health behavior, including the weekly frequency of intake of 96 food items in the previous year. A portion of their blood sample, drawn for diagnostic purposes on the day they received the questionnaire, was used to study the relationship between diet and disease. Questionnaires with at least 90% of queries completed were provided by 3382 subjects, who formed the cohort. During the period of enrollment, 71 participants (28 men and 43 women; 21 with diabetes, 21 with high cholesterol and/or triglycerides, 4 with past myocardial infarction, 1 with hyperuricemia, and 24 who gave blood samples only for a normal check up) consented to enter the study twice, so that we could evaluate the biological stability of some of the blood variables of the research. Only these 71 subjects of the cohort were studied because they had given two blood samples at different points in time that could show that fructosamine and HbA1c were stable. In fact, blood variables need to be stable to correlate with nutrients from a food frequency questionnaire, which has a temporal window of 1 year. At time 1 of the study, we had serum and questionnaires from all 71 subjects, but red blood cells for HbA1c measurement were available from only 59 of them. At time 2, again the

serum of all 71 subjects was available, but only 48 of them had returned the questionnaire, after having given their consent and accepting to fill it in. Furthermore, in only 39 of these 48 subjects were there enough red blood cells for HbA1c measurement. Thus, there were 59 subjects at time 1 and only 39 subjects at time 2 who had HbA1c, fructosamine, and the completed food frequency questionnaire available, so we used the 59 subjects at time 1 (24 men and 35 women; 17 with diabetes, 18 with high cholesterol and/or triglycerides, 4 with past myocardial infarction, 1 with hyperuricemia, and 19 with blood samples drawn only for a normal check up) for the comparison of the correlation of dietary sugar with fructosamine vs. that with HbA1c. 2.2. Dietary assessment Diet was assessed with a semiquantitative food frequency questionnaire that was filled in by the subjects and returned within a week from blood drawing [13]. The frequency of consumption of 96 food items in the year before the examination was assessed, assigned to one of eight categories (no consumption, less than once a month, one to three times a month, once a week, two to three times a week, four to six times a week, once a day, twice or more a day). Usual portion size was also determined, as participants indicated which of three pictures corresponded to their usual portion. In addition, there were questions regarding modifications in diet related to changes in lifestyle for medical purposes, and/or modification in body weight in the year before the interview. Demographic characteristics and smoking habits were also queried. The reproducibility and relative validity of the questionnaire were documented in a study in which it was compared with two 7-day dietary records completed over 6 months [14]. Total energy and macronutrient and micronutrient intakes were calculated using Italian food composition tables [15,16]. Energy-adjusted nutrient intakes were computed as the residual of the regression model, with total energy intake as the independent variable and absolute nutrient intake as the dependent variable [17]. 2.2.1. 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

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glycemic load of foods [18] by multiplying the portion size of each food by its glycemic index [19]. We then multiplied this glycemic load value by the reported frequency of consumption per year, 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. 2.3. Laboratory methods Blood was drawn in the morning before 9 am, with the subjects fasting for at least 12 h. A blood sample was used for serological analysis; another sample of EDTA- anticoagulated blood was taken for HbA1c measurement. The blood was immediately cooled to 6 jC in a refrigerator . The serum was separated after 120 – 240 min, portioned into microtubes, and held stored at 80 jC for about 7 years until laboratory analyses. One-milliliter samples of whole blood were washed twice with cold physiological solution in a total volume of 10 ml and centrifuged at 300  g at 4 jC. The supernatant was removed and the cells were washed once more with the same volume of physiological solution, and centrifuged at 800  g to pack the cells. The washed erythrocytes were separated and portioned into microtubes, and held stored at 80 jC for about 7 years until laboratory analyses. 2.3.1. Serum glucose and protein The Glucose Chemistry Module determines glucose concentration by an oxygen rate method employing a Beckman Oxygen Electrode. The Beckman Synchron CK9 Clinical System ALX electronics determines the rate of oxygen consumption, which is directly proportional to the concentration of glucose in the sample. The glucose within-day coefficient of variation was 1.4%. The total protein level in serum samples was determined using the Biuret method on an automated analyzer Beckman Synchron CX9 Clinical System. The total protein within-day coefficient of variation was 1.7%. 2.3.2. Serum fructosamine 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

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(NBT) to formazan. Venous serum was added to carbonate buffer at pH 10.8 containing 0.48 mmol/ l NBT, then the absorbance at 550 nm was measured 10 and 15 min after mixing and compared with a fructosamine calibrator (BAH 140, 315 mmol/l). The whole assay was carried out at 37 jC. The serum fructosamine within-day coefficient of variation was 1.1%. 2.3.3. Glycated hemoglobin HbA1c was measured with the VARIANTk Hemoglobin Testing Systems HbA1c/HbA2 Dual Kit (Bio-Rad Laboratories, Milan, Italy), which utilizes the principle of ion exchange high-performance liquid chromatography (HPLC) for the separation of normal and glycated hemoglobins into whole blood samples, without interference from the Schiff base and lipemia. Prior to analysis, a simple preparation of the patient sample is required to hemolyse the blood and remove the Schiff bases by incubation at 15 –30 jC for 10 min. Five microliters of whole blood from each patient sample is put into 1.5-ml sample vials; 1.0 ml of hemolysis reagent is added, mixed by inversion, and put into the VARIANTk. The within-day HbA1c coefficient of variation was 0.56%. 2.4. Statistical analysis Means and standard deviations were calculated for relevant study variables. Pearson correlation coefficients (after normalizing variables not distributed normally) were calculated for glucose, relative fructosamine and HbA1c at baseline, and between glucose, relative fructosamine, and HbA1c measured at two different time points. Relative fructosamine, controlling for the variation in fructosamine due to serum protein [20], was used instead of fructosamine because all correlations were found to be higher using this variable. The formula used to calculate the relative fructosamine value was =[(measured fructosamine value/measured total protein value)  70 g/l] [21]. Then, in all the 71 subjects, we calculated the Pearson multiple partial linear correlations for relative fructosamine at time 1 and the mean of relative fructosamine at two points in time with all the dietary variables, controlling for age, gender, body mass index (BMI), glycemia, and calories. Successively, we calculated the Pearson multiple partial linear correlations for relative fructosamine and HbA1c with all the dietary variables

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in the same 59 subjects at time 1. We compared the correlation coefficients of sugar with fructosamine vs. with HbA1c using a test for dependent samples [22]. Finally, the most parsimonious multiple linear correlation was calculated to evaluate the dietary factors most strongly associated with fructosamine and HbA1c (forward method, t test of the null hypothesis of the multiple partial correlation coefficient, statistically significant at p < 0.10), forcing age, gender, BMI, and blood glucose in the model. The diagnosis of diabetes at the medical history was also introduced in all the models as a potential confounder, but was not considered in the final models because it did not change the correlations of the dietary variables with relative fructosamine or with HbA1c. All the statistical computations were made using STATA 6.0 Statistical Software (Stata, College Station, TX, USA).

3. Results Table 1 shows the descriptive statistics for the dietary and nondietary variables measured in the Table 1 Descriptive statistics in the study group at baseline (n = 71; 28 men, 43 women) Mean

S.D.

Age (year) Weight (kg) Height (cm) BMI (kg/m2)

66.1 69.8 161.9 26.6

8.5 12.1 8.3 4.1

Serum variables Glucose (mmol/l) Relative fructosamine (Amol/l) HbA1c (%)a

6.1 295.6 5.7

1.7 35.1 0.9

Dietary variables Protein (g/day) Saturated fat (g/day) Monounsaturated fat (g/day) Polyunsaturated fat (g/day) Starch (g/day) Sugars (g/day) Glycemic load (U/day) Fiber (g/day) Alcohol (g/day) Vitamin C (mg/day) Energy (MJ/day)

61.0 17.9 35.8 7.3 123.8 85.4 121.6 22.7 15.1 142.0 7.6

22.8 6.9 11.9 2.4 64.3 41.7 56.8 10.0 17.4 84.8 2.5

a

n = 59.

Table 2 Pearson correlation coefficients for serum glucose, relative fructosamine, and HbA1c at baseline (A) and between serum glucose, relative fructosamine, and HbA1c at two time points (B) A Glucose1 – relative fructosamine1 Glucose1 – HbA1c at t1 Relative fructosamine1 – HbA1c at t1 B Glucose1 – glucose2 Relative fructosamine1 – relative fructosamine2 HbA1c at t1; HbA1c at t2

n

r

95% CI

71

0.48

0.26 – 0.66

59 59

0.65 0.41

0.47 – 0.78 0.17 – 0.60

71 71

0.78 0.80

0.61 – 0.88 0.64 – 0.89

39

0.96

0.92 – 0.98

study subjects. Mean BMI was in the overweight range. Mean serum glucose was high but still within the normal limit (V 7 mmol/l). According to the medical history, 21 of 71 subjects (30%) had diabetes, 11 (15%) took drugs for diabetes (none of them insulin), and 13 had glycemia >7 mmol/ l (18%). Fat intake was primarily from monounsaturated fat. Table 2A shows the correlations between fructosamine, HbA1c, and fasting glycemia at baseline, and Table 2B shows the correlations obtained on two successive occasions, at a mean interval of 138 days (from 46 to 335 days). As seen in Table 2A, fasting blood glucose at baseline was more strongly correlated with HbA1c than with fructosamine (r = 0.65 vs. r = 0.48). The correlation between fructosamine and HbA1c was 0.41. In Table 2B, it is clear that both fructosamine and HbA1c are each highly correlated over time (r = 0.80 and r = 0.96, respectively), despite the fact that many subjects are diabetics and under medical treatment. In Table 3, we show the Pearson partial correlation coefficients of dietary variables with relative fructosamine at baseline and with the mean of relative fructosamine at two different time points in all the 71 subjects studied, adjusted for calories, sex, age, BMI, and serum glucose. Because only four subjects smoked, four had a higher level of education than mandatory schooling, and the total number of subjects was small, these variables were not included in the models. Most of the nutrients are weakly correlated with serum fructosamine, apart

G. Misciagna et al. / Clinica Chimica Acta 340 (2004) 139–147 Table 3 Pearson partial correlation coefficients for relative fructosamine at baseline (A), and the mean of relative fructosamine at two different time points (B) with dietary variables, adjusted for calories, sex, age, BMI, and serum glucose (n = 71) Dietary variables Blood variables A

B

Relative p valuea Relative p valuea fructosamine fructosamine (Amol/l) (Amol/l) Total protein (g/day) Saturated (g/day) Monounsaturated (g/day) Polyunsaturated (g/day) Starch (g/day) Sugars (g/day) Glycemic load (U/day) Fibre (g/day) Alcohol (g/day) Vitamin C (g/day) Energy (MJ/day)

0.06

0.61

0.03

0.83

0.01

0.92

0.01

0.91

0.11

0.38

0.14

0.25

0.04

0.76

0.04

0.75

0.05

0.66

0.09

0.47

0.28

0.02

0.33

0.006

0.08

0.51

0.08

0.53

0.21

0.10

0.24

0.05

0.11

0.38

0.07

0.57

0.08

0.50

0.14

0.26

0.24

0.05

0.26

0.03

a p value, t test of the null hypothesis of the partial linear correlation coefficient.

from dietary sugar and fiber. However, the correlation of fructosamine with fiber decreases and is no longer statistically significant when fiber and sugar are both included in the regression models. The association of fructosamine with dietary sugar was stronger when fructosamine is introduced in the model as the mean at two different times than as the baseline value (r = 0.33, p = 0.006 vs. r = 0.28, p = 0.02). Fructosamine was also inversely correlated with energy. Table 4 shows the partial correlation coefficients for relative fructosamine and HbA 1c at time 1 (n = 59), with each dietary variable corrected for calories, and controlling for age, gender, BMI, and fasting glycemia. Fructosamine was correlated with dietary sugar (r = 0.26, p = 0.05); this correlation was

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much higher than the one of HbA1c (r = 0.001, p = 0.99). Among subjects with glycemia < 7 mmol/ l, fructosamine was also more strongly correlated with dietary sugar intake than HbA1c (r = 0.20 vs. r = 0.04). HbA1c was only inversely correlated with vitamin C (r = 0.23, p = 0.09). We compared the correlation coefficients of sugar with fructosamine vs. that of HbA1c [22], and found a probability value of 0.07 for the null hypothesis H0 of equal correlation coefficients of dietary sugar with fructosamine and of dietary sugar with HbA1c. In Table 5, we compared the most parsimonious multiple linear correlation model of dietary variables with fructosamine and HbA1c in the same subjects (n = 59). All dietary variables were corrected for calories, and age, gender, BMI, and fasting blood glucose were forced into the model (forward method, null partial correlation coefficient hypothesis at statistical significance level p < 0.10). Of the dietary factors, only sugar and energy remain in the final model for fructosamine, and vitamin C remains in that for HbA1c. Age and glycemia were correlated both with fructosamine and HbA1c. The correlation of fructosamine with fiber decreases and is no longer statistically significant when fiber and sugar

Table 4 Pearson partial correlation coefficients in the same subjects (n = 59; 24 men, 35 women) for relative fructosamine and HbA1c with dietary variables, adjusted for calories, sex, age, BMI, and serum glucose Dietary variables

Protein (g/day) Saturated fat (g/day) Monounsaturated fat (g/day) Polyunsaturated fat (g/day) Starch (g/day) Sugars (g/day) Glycemic load (U/day) Fiber (g/day) Alcohol (g/day) C vitamin (g/day) Energy (MJ/day)

Blood variables Relative fructosamine (Amol/l) 0.07 (0.63)a 0.0002 (0.99) 0.13 (0.34) 0.10 (0.48) 0.03 (0.80) 0.26 (0.05) 0.15 (0.27) 0.24 (0.07) 0.14 (0.29) 0.08 (0.56) 0.26 (0.06)

HbA1c (%)

0.009 0.09 0.03 0.07 0.03 0.001 0.007 0.09 0.03 0.23 0.06

(0.95) (0.50) (0.83) (0.63) (0.82) (0.99) (0.96) (0.52) (0.82) (0.09) (0.68)

a p value, t test of the null hypothesis of the partial linear correlation coefficient.

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Table 5 Pearson partial correlation coefficient for the most parsimonious multiple linear correlation models in the same subjects (n = 59; 24 men, 35 women) for relative fructosamine (A) and HbA1c (B) with the dietary calorie-adjusted variables, adjusting for model sex, age, BMI, and serum glucose (forward method, level of statistical significance p < 0.10) A

Sex (M/F) Age (years) BMI (kg/m2) Glycemia (mmol/l) Sugars (g/day) Energy (MJ/day)

Relative t test B fructosamine p value (Amol/l) 0.08 0.28 0.11 0.52 0.23 0.23 R2 = 0.41

0.57

Sex (M/F) 0.04 Age (years) 0.41 BMI (kg/m2) < 0.001 Glycemia (mmol/l) 0.09 Vitamin C (mg/day) 0.10

HbA1c (%)

t test p value

0.15

0.28

0.27

0.05

0.09

0.50

0.65

< 0.001

0.23

0.09

R2 = 0.51

are both included in the regression model, as shown in Table 3.

4. Discussion The findings from this study indicate that serum fructosamine is sensitive to dietary sugar intake and is a more sensitive biomarker than HbA1c. Furthermore, fructosamine is inversely correlated with energy, and HbA1c with vitamin C; both fructosamine and HbA1c are correlated with age and fasting blood sugar. We were not able to examine other correlations between these nonenzymatic glycated substances and saturated fats because of the low power of the study (71 subjects) and because the main source of fat in this population is monounsaturated fat. Saturated fat consumption is lower than in the previous studies, reporting a correlation between HbA1c and saturated fat. The correlation of fructosamine with fiber decreases and is no longer statistically significant when fiber and sugar are both included in the regression models. The source of dietary sugar is mainly fruit, which is also the most important source of fiber, explaining the correlation of fiber with fructosamine.

The subjects in this study live in a primarily agricultural region in southern Italy. In this population, the principal meal is at noon, and the diet varies from the classic Mediterranean diet (high in cereals, legumes, fruit, vegetables, olive oil, and wine) to a westernized diet (high in meat and refined sugars) [23]. This wide variation may allow for easier detection of associations than in a population with less variation. Mean fasting blood sugar and the percentage of subjects with diabetes (a medical history of diabetes and/or fasting serum glucose higher than 7 mmol/l) are higher in our study group than the values we found in another population sample from the same area [24]. However, the subjects in this study had been referred by their family physicians to the laboratories in the area, and therefore people with chronic disease might be overrepresented. Notwithstanding the high frequency of diabetes, the nutrient composition in the diets of our subjects is not different from that of a population sample from the same region [13]. Measurements of fructosamine and HbA1c were made with standard laboratory methods under strict quality control. The serum had been stored for about 7 years, and there are few data regarding the stability of fructosamine or glycated hemoglobin over such a period of time. However, there is evidence that fructosamine is stable after 2 years of storage [25], and we found glycated hemoglobin to be associated with vitamin C in our study, as found by others on blood samples stored at 80 jC for a shorter period [7]. Measurement of diet was made with a standardized, validated semiquantitative food frequency questionnaire [15]. Statistical analysis was able to detect only the relationships between nutrients and fructosamine and HbA1c that had an important linear component, and normalization of some skewed variables did not change the results. Morning fasting blood glucose was introduced into the model as a confounder, although it was possible it might be an intermediate in the relationship between nutrients and fructosamine or glycated hemoglobin [26]. Only one other study has compared the effect of diet on both fructosamine and HbA1c [12]. In a cross-over trial, 11 nondiabetic subjects were randomized to a high soluble fiber/low glucose or low soluble fiber/high glucose diet for 6 weeks. Both HbA1c and fructosamine were analyzed, but only fructosamine increased with the high-glucose diet and decreased with the low-

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glucose diet. Most studies have evaluated the effects of dietary carbohydrate only on HbA1c. In most of these, there was no cross-sectional association between carbohydrate intake and HbA1c [7,27,28]. In a study in England, HbA1c was higher in subjects who took sugar in tea or coffee, controlling for age, sex, obesity, physical activity level, educational attainment, smoking status, and consumption of alcohol [5]. However, calories were not controlled for, and the study sample was large so the statistical power was high enough to detect even very small associations. Apart from the Yudkin trial [12], there is only one other study of the effects of dietary carbohydrates on fructosamine in normal subjects. In a cross-over experimental study of six healthy male subjects, 2 weeks of intake of a lowglucose diet significantly decreased serum fructosamine in comparison with a high-glucose load diet over the same period [29]. A high-glucose load diet is probably, but not necessarily, high in dietary sugar, so fructosamine may be more sensitive to dietary intake of simple sugar. Studies of this biomarker are very few, and conducted only on a small number of subjects. The results of the studies on diet and HbA1c or fructosamine in diabetic subjects are less consistent. In two observational studies on diabetic subjects with insulin-dependent diabetes [30] and noninsulin-dependent diabetes [31], carbohydrate intake did not affect HbA1c; energy intake from carbohydrates was very low in these subjects. In the more recent EURODIAB complication study, in 2810 subjects with insulin-dependent diabetes, a glycemic index obtained with a 3-day dietary record was associated with HbA1c, independent of fiber intake [32]. In a withinsubjects design, 21 subjects with type II diabetes were randomized to a high glycemic index diet, to a low glycemic index diet, and to a high glycemic index and high monounsaturated fat diet: there was no variation of fructosamine levels in the three diet groups [33]. However, among diabetic subjects and above all in those under dietary treatment, there may be less variation in blood glucose after meals. The apparent differential effect of dietary carbohydrates on the nonenzymatic glycation of serum proteins and hemoglobin is difficult to explain. The measurement of fructosamine includes many serum glycated proteins, particularly albumin (r = 0.90) [11]. In vitro studies in human blood [34] and in vivo studies [1] show that the rate of albumin

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glycation is much higher than the glycated hemoglobin rate [34]. Given the higher rate of glycation, fructosamine may be more sensitive than HbA1c to peaks in blood glucose caused by meals with a high glycemic load. There is only indirect evidence available to support this hypothesis, obtained in subjects with diabetes. Among 13 pregnant women with established type I diabetes, fructosamine was more strongly correlated with postprandial, than with preprandial, blood glucose whereas the reverse was true for HbA1c [20]. In a randomized double-blind trial, 21 nonobese type II diabetes subjects, controlled only with diet therapy, were randomized to placebo and to an a-glucosidase inhibitor, which delays the absorption of dietary disaccharides and decreases postprandial glycemia. After 8 weeks, there was a statistically significant decrease in fructosamine in the treated group, but no change in HbA1c and fasting blood glucose. It would appear that fructosamine is more strongly associated with postprandial glycemic variation, whereas HbA1c is more strongly associated with overall blood glucose. Both observational and experimental studies have evaluated the relation of vitamin C with nonenzymatic glycated substances in the blood in nondiabetic subjects. In an experimental study in 12 healthy volunteers, the administration of 1 g/day vitamin C for 3 months decreased plasma glycated albumin by 33%, fructosamine by 6%, and glycated hemoglobin by 18%. The level of all three glycated substances returned to baseline after 1 month (fructosamine and HbA1c) or 2 months (HbA1c) from suspension of the vitamin supplementation [35]. Several other studies, like ours, found an inverse cross-sectional association of vitamin C with HbA1c in normal subjects [7,36,37]. Among those investigating fructosamine, its association with vitamin C was weaker than with HbA1c. The mechanism of action for a vitamin C effect on glycation is that of competitive inhibition [38]. The observation of a much less marked decrease in fructosamine concentration than in HbA1c is probably partly due to the fact that fructosamine measures the glycosylation of many proteins that may be affected to varying degrees by vitamin C [11,39]. We found an inverse association of energy intake with fructosamine. This relationship may have been due to differences in physical activity correlated with energy intakes. There are several studies of physical

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activity in normal subjects in relation to HbA1c, but the results are not consistent [5,7,27,37]. Physical activity may be more closely related to fructosamine than to HbA1c because physical activity causes an oscillation in blood glucose, similar to the effect of intake of dietary simple sugars. In this study of diabetics and nondiabetics, we found that fructosamine appears to be related to dietary intake of simple sugars and that it may be a more sensitive indicator than HbA1c of usual intake of these sugars. This finding must be confirmed by other larger studies and in populations with a different dietary composition. The increasing evidence linking the dietary glycemic index with several disease outcomes is provocative, but a clearer understanding of the underlying mechanism is needed before definitive conclusions can be drawn. Identification of good biomarkers for intake of these sugars could significantly aid research in this field. Our findings provide indications that fructosamine might serve as such a biomarker.

[9]

[10]

[11] [12]

[13]

[14]

[15] [16]

References [1] Tahara Y, Shima K. Kinetics of HbA1c glycated albumin, and fructosamine and analysis of their weight functions against preceding plasma glucose level. Diabetes Care 1995;18:440 – 7. [2] Park S, Barrett-Connor E, Wingard DL, Shan J, Edelstein S. HbA1c is a better predictor of cardiovascular disease than fasting or post challenge plasma glucose in women without diabetes. The Rancho Bernardo Study. Diabetes Care 1996;19: 450 – 6. [3] Khaw KT, Wareham N, Luben R, et al. Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of European Prospective Investigation of Cancer and Nutrition (EPIC – Norfolk). BMJ 2001;322:15 – 8. [4] Temelkova-Kurktschjev T, Koehler C, Schaper F, et al. Relationship between fasting plasma glucose, atherosclerosis risk factors and carotid intima media thickness in non-diabetic individuals. Diabetologia 1998;41:706 – 12. [5] Gulliford MC, Ukoumunne OC. Determinants of glycated haemoglobin in the general population: association with diet, alcohol and cigarette smoking. Eur J Clin Nutr 2001;55: 615 – 23. [6] Harding AH, Sargeant LA, Welch A, et al. Fat consumption and HbA1c levels. Diabetes Care 2001;24:1911 – 6. [7] Boeing H, Weisgerber UM, Jeckel A, Rose HJ, Kroke A. Association between glycated hemoglobin and diet and other lifestyle factors in a non diabetic population: cross-sectional evaluation of data from the Potsdam cohort of the EPIC Study. Am J Clin Nutr 2000;71:1115 – 22. [8] Willett W. Reproducibility and validity of food-frequency

[17]

[18]

[19] [20]

[21]

[22] [23]

[24]

[25] [26]

[27]

questionnaires. In: Willett W, editor. Nutritional epidemiology. 2nd ed. New York: Oxford Univ. Press, 1998. p. 101 – 48. Browner WS, Pressman AR, Lui LY, Cummings SR. Association between serum fructosamine and mortality in elderly women. The Study of Osteoporotic Fractures. Am J Epidemiol 1999;149:471 – 5. Narbonne H, Renacco E, Pradel V, Portugal H, Vialettes B. Can fructosamine be a surrogate for HbA 1c in evaluating the achievement of therapeutic goals in diabetes? Diabetes Metab 2001;24:598 – 603. Mosca A, Carenini A, Zoppi F, et al. Plasma protein glycation as measured by fructosamine assay. Clin Chem 1987;33:1141 – 6. Ryle AJ, Davie S, Gould BJ, Yudkin JS. A study of the effect of diet on glycosylated haemoglobin and albumin levels and glucose tolerance in normal subjects. Diabetes Med 1990;7: 865 – 70. Misciagna G, Centonze S, Leoci C, et al. Diet, physical activity and gallstones—a population-based, case – control study in southern Italy. Am J Clin Nutr 1999;69:120 – 6. Leoci C, Centonze S, Guerra V, Cisternino AM, Misciagna G. Validazione e riproducibilita` di un questionario di frequenza alimentare semi-quantitativo autosomministrato. Gior Ital Nutr Clin Prev 1993;2:58 – 9. Carnovale E, Miuccio F. Tabelle di Composizione degli Alimenti. Rome: National Institute of Nutrition, 1987. Fidanza F, Liguori G. Nutrizione umana. Naples: Idelson, 1988. Willett W. Implications of total energy intake for epidemiologic analyses. In: Willett W, editor. Nutritional epidemiology. 2nd ed. New York: Oxford Univ. Press, 1998. p. 273 – 302. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA 1997;277: 472 – 7. Foster-Powell K, Miller JB. International tables of glycemic index. Am J Clin Nutr 1995;62:871s – 93s. [Supplement]. Van Dieijen-Visser MP, Seynaeve C, Brombacher PJ. Influence of variation in albumin or total-protein concentration on serum fructosamine concentration. Clin Chem 1986;32: 1610. Kennedy DM, Johnson AB, Hill PG. A comparison of automated fructosamine and HbA1c methods for monitoring diabetes in pregnancy. Ann Clin Biochem 1998;35:283 – 9. Chen PY, Popovich PM. Correlation. Thousand Oaks, USA: Sage Publications, 2002. p. 9 – 25. Misciagna G, Leoci C, Guerra V, et al. Epidemiology of cholelithiasis in southern Italy: Part II. Risk factors. Eur J Gastroenterol Hepatol 1996;8:585 – 93. Misciagna G, Guerra V, Di Leo A, Correale M, Trevisan M. Insulin and gallstones: a population case – control study in southern Italy. Gut 2000;47:144 – 7. Koskinen P, Irjala K. Stability of serum fructosamine during storage. Clin Chem 1988;34:2545 – 6. Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE. Defining the relationship between plasma glucose and HbA1c. Diabetes Care 2002;25:275 – 8. Modan M, Meytes D, Rozeman P, et al. Significance of high

G. Misciagna et al. / Clinica Chimica Acta 340 (2004) 139–147

[28]

[29]

[30]

[31]

[32]

[33]

HbA1 levels in normal glucose tolerance. Diabetes Care 1988;11:422 – 8. Yudkin J, Forrest RD, Jackson CA, Ryle AJ, Davie S, Gould BJ. Unexplained variability of glycated hemoglobin in nondiabetic subjects not related to glycemia. Diabetologia 1990; 33:208 – 15. Jenkins DJA, Wolever TMS, Collier GR, et al. Metabolic effects of a low-glycemic-index diet. Am J Clin Nutr 1987; 46:968 – 75. Shimakawa T, Warram JH, Herrera-Acena MG, Krolewski AS. Usual dietary intake and hemoglobin A1 level in patients with IDDM. J Am Diet Assoc 1993;93:1409 – 12. Bell RA, Summerson JA, Konen JC. Dietary intakes by level of glycemic control for black and white adults with NIDDM. J Am Coll Nutr 1995;14:144 – 51. Buyken AE, Toeller M, Heitkamp G, Karamanos B, Rottiers R, Muggeo M. Glycemic index in the diet of European outpatients with type 1 diabetes: relations to glycated hemoglobin and serum lipids. Am J Clin Nutr 2001;73:574 – 81. Luscombe ND, Noakes M, Clifton PM. Diets high and low glycemic index versus high monounsaturated fat diets: effects

[34]

[35] [36]

[37]

[38]

[39]

147

on glucose and lipid metabolism in NIDDM. Eur J Clin Nutr 1999;53:473 – 8. Olufemi S, Talwar D, Robb DA. The relative extent of glycation of hemoglobin and albumin. Clin Chim Acta 1987;163: 125 – 36. Davie S, Gould B, Yudkin JS. Effect of vitamin C on glycosylation of proteins. Diabetes 1992;41:167 – 73. Shoff SM, Mares-Perlman JA, Cruickshanks KJ, Klein B, Ritter LI. Glycosylated hemoglobin concentrations and vitamin E, vitamin C, and beta-carotene intake in diabetic and nondiabetic old adults. Am J Clin Nutr 1993;58:412 – 6. Sargeant LA, Wareham NJ, Bingham S, et al. Vitamin C and hyperglycemia in the European Prospective Investigation into Cancer—Norfolk (EPIC—Norfolk) study. Diabetes Care 2000;23:726 – 32. Khatami M, Suldan Z, David I, Weiye I, Rockey J. Inhibitory effects of pyridoxal phosphate, ascorbate, and aminoguanidine on non-enzymatic glycation. Life Sci 1988;43: 1725 – 31. Seng LY, Staley MJ. Plasma fructosamine is a measure of all glycated proteins. Clin Chem 1987;32:560.