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Journal of the American College of Nutrition, Vol. 18, No. 3, 242-247 (1999)
Published by the American College of Nutrition

Original Paper

Day-to-Day Consistency in Amount and Source of Carbohydrate Intake Associated with Improved Blood Glucose Control in Type 1 Diabetes

Thomas M.S. Wolever, MD, PhD, FACN,, Safa Hamad, MSc, Jean-Louis Chiasson, MD, Robert G. Josse, MD,, Lawrence A. Leiter, MD,, N. Wilson Rodger, MD, Stuart A. Ross, MD and Edmond A. Ryan, MD

Department of Nutritional Sciences, University of Toronto, Division of Endocrinology and Metabolism, St. Michael’s Hospital (T.M.S.W., R.G.J., L.A.L.), Toronto, Onario, Research Centre, CHUM
Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital (T.M.S.W., S.H., R.G.J., L.A.L.), Toronto, Onario, Research Centre, CHUM
Hôtel-Dieu de Montreal Hospital, Montreal, Quebec (J.-L.C.), CANADA
St. Joseph’s Health Centre, University of Western Ontario, London, Ontario (N.W.R.), CANADA
Diabetes Education and Research Centre, Calgary (S.A.R.), CANADA
Heritage Medical Research Centre, University of Alberta, Edmonton, Alberta (E.A.R.), CANADA

Address reprint requests to: Dr. Thomas MS Wolever, Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada M5S 3E2


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Objective: To determine if a relationship exists between blood glucose control and variability in nutrient intake from day-to-day in subjects with type 1 diabetes.

Methods: Two three-day diet records and one measurement of glycated hemoglobin (HbA1c) were obtained from 272 subjects with type 1 diabetes treated with a mixture of regular and NPH insulins before breakfast and supper and using a standardized algorithm to adjust insulin dose according to the results of self-monitoring of blood glucose two to four times daily. Day-to-day variation in nutrient intake was expressed as the coefficient of variation (CV=SDx100/mean).

Results: Nutrient intakes in the study population (mean±SD) were energy 8.35±2.43 MJ, fat 81±30 g, protein 94±28 g, carbohydrate 227±68 g, starch 126±38 g and dietary fiber 20±6 g with diet glycemic index being 84.2±7.4. Neither energy, nutrient intakes nor insulin dose was significantly related to HbA1c. Day-to-day variation of carbohydrate (p=0.0097) and starch (p=0.0016) intakes and diet glycemic index (p=0.033) was positively related to HbA1c, and the associations remained significant when adjusted for age, sex, duration of diabetes and BMI. Day-to-day variation in energy, protein or fat intakes was not related to HbA1c.

Conclusions: Consistency in the amount and source of carbohydrate intake from day-to-day is associated with improved blood glucose control in people with type 1 diabetes, a result which supports continued educational efforts to achieve adherence to a diabetes diet plan. This conclusion may not apply to people on intensified insulin therapy who adjust their insulin dose based on their actual carbohydrate intake at each meal.

Key words: diet, humans, type 1 diabetes, carbohydrate, glycemic index, blood glucose control


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The main goal of nutrition therapy for individuals with diabetes is to have an individualized meal plan which provides a nutritionally adequate diet and helps minimize short- and long-term complications [1]. In type 1 diabetes, special attention is normally paid to balancing the insulin dose with episodes of activity and the quantity and timing of food intake to prevent acute episodes of hypo- and hyperglycemia. In the past, the philosophy of treatment was that the diet and lifestyle of the person with diabetes needed to be adjusted to meet the requirements of an inflexible insulin dose. The advent of self-monitoring of blood glucose (SMBG) and flexible, multiple insulin doses has allowed people with type 1 diabetes to adjust their insulin dose to suit their chosen activities and diet and, thus, to gain more control over their lifestyles. In addition, this approach to treatment may result in improved blood glucose control [2].

In both normal [3] and diabetic [4,5] subjects, food intake varies considerably from day-to-day. In people with type 1 diabetes, variation in diet from day-to-day may make it more difficult to adjust the insulin dose for optimal blood glucose control. Some studies [6,7,8,9], but not all [10,11,12], suggest that consistency in the timing and amount of food intake from day-to-day is associated with improved blood glucose control. However, most of these studies used qualitative measures of compliance to a diabetic meal plan. Therefore, we wished to determine if there was a relationship between blood glucose control and a quantitative measure of variability in nutrient intake from day-to-day in subjects with type 1 diabetes. Our hypothesis was that low variability in the source and amount of dietary carbohydrate from day-to-day would be associated with improved blood glucose control.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
The data in this report were obtained from 272 subjects with type 1 diabetes from seven centers in Canada (Vancouver, Edmonton, Calgary, London, Toronto, Montreal and Halifax) during the pre-treatment, baseline run-in period of a clinical trial of the effects of an {alpha}-glucosidase inhibitor on blood glucose control in subjects with type 1 diabetes. The protocol of the study was approved by the ethics review committee at each participating institution, and all subjects consented to participate by signing the approved consent form. Details of the subjects included are shown in Table 1. To be eligible for the study, subjects had to have a well-established diagnosis of type 1 diabetes, to be taking a mixture of NPH and regular insulins before breakfast and before supper, to have demonstrated adequate knowledge (scoring at least 90% on a written questionnaire) about a standardized algorithm for adjusting insulin dose based on the results of SMBG, and to be performing SMBG 2 to 4 times per day and using the algorithm to adjust their insulin doses. Based on the results of SMBG, NPH insulin was adjusted in increments of two units, and regular insulin was adjusted in increments of 0.2 units per 10 g carbohydrate in the dietary prescription. The dietary prescription was provided by the dietitian, based on the amount of carbohydrate typically consumed at each meal.


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  		Table 1. Details of Subjects Studied

 
Subjects were seen on three occasions over an approximately eight-week period before being randomized to one of three treatment groups (placebo or two different doses of active therapy). On the first visit, subjects were seen by a dietitian who provided advice about following the dietary guidelines recommended by the Canadian Diabetes Association [13] and instructed subjects about how to record the time, type and quantity of all foods and drinks consumed for three days (two weekdays and one weekend day) in standardized booklets. About two weeks later, at visit two, a three-day diet record was handed in and the subjects were started on placebo therapy for six weeks to assess their ability to comply with the study protocol. Visit three occurred at the end of the six-week placebo period, at which time subjects handed in a second three-day diet record and a fasting blood sample was obtained for measurement of glycosylated hemoglobin (HbA1c); compliant subjects were then randomized to start the study therapy. The diet records were reviewed with the subject for accuracy by the dietitian who coded the diets using a standard computerized program developed for the study and provided to each center.

The dietary database was derived from the Canadian condensed Nutrient File [14] which has nutrient data for approximately 650 foods. The database was modified to include information about simple sugars (mono- and disaccharides), oligosaccharides, dietary fiber and glycemic index. Values for sugars and oligosaccharides were obtained from manufacturer’s information or other food tables [15]. Values for dietary fiber were obtained from food tables [15] or direct analysis using a gravimetric method [16]. Values for carbohydrate in the database were adjusted to reflect glycemic carbohydrate [17], which was defined as total carbohydrate by difference minus dietary fiber. Starch was taken to be the difference between glycemic carbohydrate and the sum of simple sugars and oligosaccharides. Glycemic index values were based on data in the literature [18], with values for unknown foods estimated as previously described [19]. Diet glycemic index was calculated for each day as the weighted mean of the glycemic index values of each food in the diet as previously described [19].

Coded diets were saved on diskettes and sent to a central location where they were translated and compiled. The mean was calculated for each nutrient and the day-to-day variation of intakes was expressed as the coefficient of variation (CV=SDx100/mean). Complete three-day diet records for both visits two and three were obtained from 258 (95%) of the 272 subjects. The mean and variability of intakes of energy, protein, fat, carbohydrate, sugars, starch and fiber did not differ significantly between visits two and three. Therefore, the six days of diet records were pooled for further statistical analysis.

Blood samples were sent by courier on wet ice packs to a central laboratory for measurement of HbA1c using an HPLC method (Diamat, Bio Rad Laboratories, Mississauga, ON; normal range, 3.5 to 6.5%).

Results are given as means±SD. The relationships between means and CVs of nutrient intakes, total insulin dose and HbA1c were evaluated by simple univariate regression analysis and Pearson correlation coefficients. Least squares linear regression analysis was performed to determine if associations existed between HbA1c and the potentially confounding variables of age, sex, duration of diabetes, height, weight, body mass index (BMI) and insulin dose. Multiple regression analysis was used to adjust relationships between diet and HbA1c for confounding variables. Data management and statistical analysis was performed using a computer spreadsheet (Lotus 1-2-3, Release 5.0, Lotus Development Corp, Cambridge, MA). Relationships were considered significant if p<0.05 (two-tailed).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Mean nutrient intakes for the population studied are shown in Table 2. Fat contributed 36.2±5.3% of energy intake, protein 19.1±2.9%, carbohydrate 43.6±5.5% and alcohol 1.2±2.3%. Intakes of energy, protein, fat, carbohydrate, sugars, starch, fiber or glycemic index were not significantly associated with HbA1c, whether expressed in grams (Table 2) or as % of energy (not shown). Intakes of energy, protein, total fat, saturated, monounsaturated and polyunsaturated fat, carbohydrate, starch and sugars were highly correlated with insulin dose (Table 2) when intakes were expressed in grams; however, when intakes were expressed as % of energy, none of the correlation coefficients were significant (not shown). There was no relationship between insulin dose and intakes of alcohol and fiber and diet glycemic index.


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  		Table 2. Dietary Intake of 272 Subjects with Type 1 Diabetes and Correlation between Nutrients and HbA1c and Insulin Dose

 
Table 3 shows the day-to-day variation in intake of selected nutrients expressed as the coefficient of variation. There was no relationship between HbA1c and the variability of energy, protein or fat intakes. However, there were significant relationships between day-to-day variation in carbohydrate and starch intakes and HbA1c and between the variation in diet glycemic index and HbA1c (Table 3). There were no significant relationships between HbA1c and the potentially confounding variables considered, including gender (p=0.16), age (r=0.067, p=0.27), duration of diabetes (r=0.047, p=0.44), insulin dose (r=-0.096, p=0.11), height (r=0–0.22, p=0.72), weight (r=-0.097, p=0.11) and body mass index (BMI; r=-0.106, p=0.083). Thus, after adjusting for these seven confounding variables, the partial correlations between HbA1c and the CV of carbohydrate (p=0.0067) and starch (p=0.0007) intakes and the CV of diet glycemic index (p=0.029) remained significant (Fig. 1).


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  		Table 3. Day-to-Day Variation in Intake of Selected Nutrients and Univariate Correlations between Variation of Intakes and HbA1c

 


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  		Fig. 1. Partial correlations between blood HbA1c and the CV of starch intake (left) and between HbA1c and CV of diet glycemic index (GI; right) in 272 subjects with type 1 diabetes after adjusting for seven confounding variables (age, gender, duration of diabetes, insulin dose, height, weight and body mass index). Residuals are the differences between the observed values for HbA1c, CV starch and CV of diet GI and the values predicted from the confounding variables by multiple correlation analysis.

 
To illustrate the magnitude of differences in dietary variation, Fig. 2 shows the day-to-day variation in carbohydrate and starch intakes and diet glycemic index for subjects with CVs approximately equal to the mean±1 SD for each variable. The dietary variability of 25 to 29% of subjects was outside the range encompassed by the mean±1 SD. After adjusting for age, gender, height, weight, BMI, insulin dose and duration of diabetes, the magnitude of differences in the day-to-day variation illustrated in Fig. 2 for carbohydrate and starch intakes and diet glycemic index, respectively, were associated with 0.46%, 0.56% and 0.37% differences in HbA1c.



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  		Fig. 2. Examples of daily intakes of carbohydrate (top) and starch (middle) and diet glycemic index (bottom) for six days in subjects with CVs equivalent to the mean plus 1 SD (left) and the mean minus 1 SD (right).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
It has traditionally been considered that constancy of carbohydrate intake from day-to-day is an essential feature of the diabetes diet [20]. Carbohydrate counting continues to be an important part of nutrition education for people with diabetes [1,21]. Previous studies in diabetic subjects have demonstrated mean CVs of carbohydrate intake from 12 to 16% [4,5,7] and CVs of energy intake from 11 to 14% [5,7]. This degree of variation was thought to be so large that it was considered a fallacy to believe that diabetic patients could control their carbohydrate intake [4] and that the variability in diet made manipulation of oral hypglycemic agents and insulin difficult and arbitrary [7]. The existence of dietary variability has been used to support the argument for a qualitative approach to dietary prescriptions for people with diabetes [5]. However, it has been pointed out that people with diabetes actually have less variation in carbohydrate intake from day-to-day than people without diabetes [22], indicating that diabetic patients are at least partly successful in controlling their carbohydrate intake. The present results suggest that consistency in both the amount and the source of carbohydrate in the diet is associated with improved blood glucose control and, thus, support continued educational efforts to this end.

The mean CVs of carbohydrate and energy intakes estimated in this study, 18% and 20%, respectively, are larger than the values cited above from previous studies, which are in the range of 11 to 16%. One reason for this may be that the subjects in this study self-adjusted their insulin dose based on frequent home blood-glucose monitoring and may, therefore, have felt that controlling carbohydrate intake was less important for them than did the diabetic subjects in less recent studies, who were on fixed doses of oral agents or insulin. Also, it is likely that methodologic differences account, at least in part, for the higher mean CVs we observed. In previous studies, the CV of energy and carbohydrate intakes were calculated from diet records collected on three to seven consecutive days. In contrast, in this study, the CVs were calculated based on two three-day diet records separated by a period of six weeks. Use of adjacent-day diet records underestimates true within-subject variation compared to random sampling of days throughout a one-year period [23] because of the existence in some individuals of long-term trends in energy intake. Variation in food intake on different days of the week is well known to exist, and this is the reason why two weekdays and one weekend day are sampled when collecting three-day food records. However, 23 of 29 subjects who recorded their food intake for 365 consecutive days had significant patterns in food intake with cycles having a duration greater than seven days [3]. If diet records collected over seven sequential days are used to estimate within-subject variation intake, the variation due to long-term patterns of food intake is not included, resulting in an underestimate of the true variability in food intake. In the current study, we used two three-day diet records collected six weeks apart to estimate day-to-day variation and therefore had a greater chance of including in our CV estimate variation due to long-term dietary patterns than had we used a single seven-day diet record.

It is of interest that blood glucose control in the subjects with type 1 diabetes was not related to the composition of the diet, whereas, in subjects with type 2 diabetes, blood glucose control tended to be better with lower diet glycemic index and higher carbohydrate and dietary fiber intakes [24]. This suggests that, as long as insulin dose is adjusted to reflect food intake, the composition of the diet may not be as important for optimizing blood glucose control for people with type 1 diabetes as it is for those with type 2 diabetes. On the other hand, the total dose of insulin used per day was directly related to the amount of energy, fat, protein and carbohydrate consumed—a fact which is not surprising since insulin dose was based, at least in part, on carbohydrate intake.

While the current results suggest that the composition of the diet is not related to blood glucose control in people with type 1 diabetes, they do suggest that consistency in the diet is important. It could be argued that the relationships we observed are clinically insignificant because the correlation coefficients, though statistically significant, are very small, and that a large improvement in diet consistency was associated with only a small difference in HbA1c. However, it has been recognized that it is impossible to estimate the dietary intake of individuals without error and that the high random error associated with estimates of nutrient intake biasses correlation coefficients and slopes of regression lines toward zero [25]. The problem of imprecise estimates of usual nutrient intakes probably also extends to estimates of the day-to-day variability of food intake because of long-term patterns of food intake in individuals [3]. Thus, the effect on HbA1c of changes in the consistency of food intake is likely to be larger than that estimated here.

Diet variation was found to have a greater effect on HbA1c than body mass index and insulin dose. It is widely accepted that a high body mass index is associated with an increased risk of developing type 2 diabetes [26] and with blood glucose control in type 2 diabetes [27]. However, in type 1 diabetes, the picture is not so clear; starting intensive insulin therapy results in long-term improvement in blood glucose control despite weight gain [2, 28], and a number of studies have shown no effect on glycemic control of increased body weight in adolescents with type 1 diabetes [29, 30].

The current results appear to support the official position of the American Diabetes Association that, for diabetes diet planning, the amount of carbohydrate is more important than the source [1], in that the variation in starch intake is more closely related to HbA1c than variation in diet glycemic index. However, this is due to the fact that variation in diet glycemic index was much smaller than variation in the amount of carbohydrate consumed from day-to-day. Indeed, our data could be interpreted to mean that variation in glycemic index has more effect on HbA1c than variation in the amount of carbohydrate consumed, since the slope of the regression lines of CV on HbA1c (Fig. 1) show that change in HbA1c associated with a 1% change in CV of diet glycemic index (0.079) is about three times that for a 1% change in CV of dietary starch intake (0.026). The fact that variation in both the amount and glycemic index of dietary carbohydrate is associated with HbA1c in type 1 diabetes is consistent with studies showing that both of these variables influence the acute blood glucose response to meals in normal [31], type 2 [32] and type 1 [33, 34] diabetic subjects.

One possible implication of the current findings is that they support educational efforts aimed at having people with diabetes control their diets and be consistent from day-to-day in the sources and amounts of carbohydrate foods consumed. However, people with diabetes regard following a strict diet a burden, and some studies suggest that, with an appropriate educational program, liberalization of the diet may relieve this burden without adverse effects on glycemic control [35]. Subjects in this study adjusted their insulin based on their prescribed carbohydrate intake and did not adjust for day-to-day variation in carbohydrate intake. The educational program associated with successful liberalization of the diet includes adjustment of insulin dose to match the carbohydrate about to be consumed at the next meal [35]. The current results suggest that, to achieve optimal glycemic control with a liberalized diet, education about how to adjust insulin for both the amount and the glycemic index of dietary carbohydrate in the next meal may be needed. Further research is needed to determine how to adjust insulin accurately for variation in dietary intake and to see if this approach improves quality of life and glycemic control.

We conclude that consistency in the amount and source of carbohydrate intake from day-to-day is associated with improved blood glucose control in people with type 1 diabetes, a result which supports continued educational efforts to achieve adherence to a diabetes diet plan. This conclusion may not apply to people on intensified insulin therapy who adjust their insulin dose based on their actual carbohydrate intake at each meal.


   ACKNOWLEDGMENTS
 
Supported by Bayer Inc.

Received September 1, 1998. Accepted February 1, 1999.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. American Diabetes Association Position Statement: Nutrition recommendations and principles for people with diabetes mellitus. Diabetes Care 19: S16–S19, 1996.
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  4. Henry CL, Heaton KW, Manhire A, Hartog M: Diet and the diabetic: the fallacy of a controlled carbohydrate intake. J Hum Nutr 35: 245–246, 1981.[Medline]
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  25. Beaton GH: Approaches to analysis of dietary data: relationship between planned analyses and choice of methodology. Am J Clin Nutr 59(S): 253S–261S, 1994.[Abstract/Free Full Text]
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