The type and frequency of consumption of carbohydrate-rich foods may play a role in the clinical expression of insulin resistance during pregnancy. (Original Research).

Abstract

Objectives: To assess differences in patterns of carbohydrate consumption between women diagnosed with gestational diabetes mellitus (GDM) and a group of glucose tolerant pregnant women.

Design: A cross-sectional survey of the usual dietary intakes of a sample of women with GDM, newly diagnosed and taken at the first clinic visit, and a sample of women without GDM, matched for age and prepregnancy weight, at the beginning of the third trimester of pregnancy.

Subjects: 16 women newly diagnosed with GDM and 24 healthy pregnant women. Subjects with a diagnosis of GDM in a previous pregnancy and other risk factors for GDM were not excluded.

Setting: The Illawarra area health service, Diabetes service and prenatal clinic at the Wollongong hospital.

Main outcome measures: Reported patterns of carbohydrate consumption in terms of total intakes, amounts of carbohydrate-rich food groups and foods grouped according to glycaemic index (GI) values. These were expressed as energy (kJ) or amount of carbohydrate (g), and frequency of consumption. An average diet GI score was calculated.

Statistical analyses: Differences in dietary intake variables between groups were assessed using independent two tailed t-tests.

Results: There were no significant differences between the two groups for total intakes of carbohydrate in kJ or g. Women with GDM, however, reported significantly less carbohydrate (g) from pasta, fruit juice and milk products (P < 0.05) and from foods with low GI values (P < 0.05). The GDM group reported less frequent intakes of foods with higher GI values (P < 0.05). The average diet GI was significantly higher for the GDM group when adjusted for total energy intake (P < 0.05).

Conclusions: This small qualitative study of the food habits of free-living pregnant women assessed patterns of carbohydrate consumption not readily identifiable using a nutrient approach. Compared to glucose tolerant pregnant controls, women with GDM reported lower intakes of carbohydrate-rich foods with low GI values and a reduced frequency of consumption of foods with higher GI values. These 'non-beneficial' patterns of intake likely impact on blood glucose control and have implications for the dietary management of this group. (Nutr Diet 2002;59:135-143)

Key words: diabetes, gestational, carbohydrate, dietary survey, glycaemic index

Introduction

Gestational diabetes mellitus (GDM), or glucose intolerance during pregnancy, has an incidence of 7.2% in the Illawarra region of New South Wales (1). Associated adverse outcomes of GDM can include assisted delivery, caesarian section and admission of the newborn to special care (2,3). The risk of subsequent development of type 2 diabetes, in both mother and child, is also greatly enhanced (4-6). Those most at risk tend to be older, overweight, multiparous, have specific ethnic origins, a family history of diabetes (7,8) and/or history of GDM (9). However, even women with no identifiable risk factors can display clinical symptoms of insulin resistance at an appreciable rate (3%) (7), suggesting other factors may play a role.

An increased insulin resistance is characteristic of pregnancy and women become glucose intolerant if they are unable to increase endogenous insulin secretion to overcome this physiological resistance (10-14). Controlling blood glucose concentrations may help preserve both optimal beta-cell function and maximise insulin sensitivity (15,16). Hence, a diet that assists glycaemic control might be beneficial in ameliorating the features of insulin resistance for all women during pregnancy. While clinical trials have shown that managing the consumption of the type and frequency of carbohydrate-rich foods can benefit glycaemic control and reduce insulin demand (16-19), few studies have assessed patterns of intake in the diets of free-living pregnant women and those who develop GDM. We have previously reported that women who develop a recurrence of GDM report higher fat intakes than those who do not (20), but were unable to detect a significant difference in total carbohydrate intake. Large prospective studies have also been unable to detect a relationship between total carbohydrate intake and risk of diabetes (21-23). This type of assessment, however, does not account for the glycaemic effect of carbohydrate-rich foods or the ability of foods to increase demands for insulin. Consequently, a qualitative assessment of the consumption of both the type and frequency of carbohydrate-rich foods may uncover patterns of intake during pregnancy likely to play a role in the clinical expression of insulin resistance.

In this study, we compared the patterns of consumption of carbohydrate-rich foods reported by a group of free-living women newly diagnosed with GDM with a control group of healthy glucose tolerant pregnant women.

Methods

Subjects

All pregnant women attending the prenatal clinic at the Wollongong hospital are offered an oral glucose tolerance test (OGTT) at the beginning of the third trimester of pregnancy to determine GDM status. Diagnosis is based on the recommendations of the Australasian Diabetes In Pregnancy Society (ADIPS) (24). It is conventional practice for any women diagnosed with GDM to be referred to the Illawarra area health service Diabetes Centre for medical management. These women are usually seen within two working days. Between August 1999 through January 2000 a convenience sample of 16 pregnant women newly diagnosed with GDM and 24 healthy normal glucose tolerant pregnant controls (as determined by OGTT), and matched for age and prepregnancy weight, were recruited on request on first attendance at the Diabetes Centre (prior to any dietary intervention) and at the prenatal clinic, respectively.

All women were of similar gestational age (28 to 33 weeks). Data on age, prepregnancy weight, parity, and incidence of GDM in a previous pregnancy and family history of diabetes (yes/no), were collected by self-report. Height and current weight were measured at the time of interview. Body mass indices (BMI), kg/[m.sup.2], were calculated for both prepregnancy and current weight, while weight gain during pregnancy was assessed as the difference between the two weights. The human research ethics committee of the University of Wollongong and the Illawarra area health service provided ethical approval for this research.

Dietary assessment and analysis

Four dietitians administered an open-ended diet history questionnaire developed specifically for the study of diet in pregnant women screened for GDM in the Wollongong area of the Illawarra region, Australia (25,26). The women were asked to describe their 'usual' intake ('usual' meaning for the previous two- to three-month period). All women were asked if they had changed their 'usual' diet (yes/no) on first becoming pregnant and/or over the period of the pregnancy and, for those with GDM, since being diagnosed. Dietary data were entered into Foodworks Nutrition Software, (Xyris Software, Brisbane, Queensland, version 2.03, 1999) for nutrient analysis. This software used the NUTTAB 95 Australian food composition database (27).

Food intake data were analysed for energy and macro-nutrient composition, expressed as kilojoules (kJ) and grams (g) respectively, and reported as means [+ or -] standard deviation (SD). Carbohydrate-rich foods were classified according to five broad food groups: cereal and grains, fruits, vegetables, milk and sugar-rich foods, then divided into 15 subgroups in order to identify, more specifically, the food sources of carbohydrate intake (Table 1). For each subject, the total energy (kJ) and carbohydrate (g) provided by each food group and subgroup were calculated, and a mean intake value was determined. In addition, the frequency of consumption of carbohydrate-rich food groups (defined as the number of times the food group was reported in a meal or mid-meal regardless of serving size) was assessed.

A glycaemic index (GI) value was assigned to each food within the broad food groups, according to published values (28,29). If the GI of a food was unknown and a suitably similar food could not be substituted, the food was excluded from the analysis. Tables 2 and 3 outline the food substitutions and exclusions, respectively.

The assignment of GI values enabled foods to be classified into subgroups according to GI (high, intermediate or low), which were similarly assessed for amount and frequency of consumption. An 'average' diet GI score for each subject's overall diet was also calculated, as follows:

Diet GI=Sum of GI value of individual foods X carbohydrate content (g) Total carbohydrate content for the day--excluded carbohydrate (30)

Individual meal GI scores were obtained using the method described for mixed meals (31). A glycaemic load score was obtained for overall diet and individual meals from the sum of [GI value of the individual foods X the carbohydrate content (g)] (32). To control for total energy intake, all nutrients as well as the scores for diet 01 and glycaemic load, were expressed as percent energy (32) using the calculation [mean nutrient intake (or diet score) I energy intake] X 100.

Statistical analysis

Differences between the two study groups were assessed using independent two-tailed t-tests and results expressed as means [+ or -] standard deviation (SD), with the level of significance reported at P < 0.05. Such comparisons were made for: demographic data defining age, gestation, weight and weight gain; dietary data comparing total intakes for energy (kJ), macronutrients and fatty acids according to class of saturation (percent energy), total intakes from carbohydrate and starch, sugar and fibre (g); energy (kJ) and carbohydrate (g) from the carbohydrate-rich food groups and subgroups, as well as the frequency of consumption of these foods (a simple count of the number of times a food was reported); average diet 01 and meal GI scores and GI load scores, respectively.

Results

Characteristics of the GDM and control groups are shown in Table 4. No significant differences were found between groups for matched variables (age and prepregnancy weight), or for other measures of weight and weight gain during pregnancy, or stage of gestation at the time of the survey. The women with GDM, however, were shorter and had a greater parity compared to women in the control group (P <0.05). Four of 16 women with GDM and one of 24 control women had a previous pregnancy in which GDM was diagnosed, while six women with GDM and three from the control group reported a family history of diabetes. As 14 of 16 women with GDM were born in Australia or the UK, ethnicity was not considered to influence the results of this dietary study.

Eight of 16 women with GDM and 16 of 24 control women reported a dietary change on first becoming pregnant, while seven women with GDM and 16 controls reported a dietary change over the period of the pregnancy. For the GDM group, seven of 16 women reported a dietary change since diagnosis (in most cases two to three days prior to the survey). Only three GDM and two control women reported no dietary change at all during the current pregnancy.

The dietary intakes of energy and carbohydrate were not significantly different between the two groups (see Table 5). The GDM group consumed smaller amounts of carbohydrate (g) from pasta, fruit juice, and milk products (P < 0.05) (Figure 1). There was a trend by the GDM group to consume less carbohydrate (g) from milk and yoghurt (P = 0.09). The GDM group reported a lower frequency of consumption from high carbohydrate vegetables or foods and beverages from the sugar-rich group (P < 0.05), despite similar intakes in terms of total kilojoules and carbohydrate (g) from these foods. A trend for less frequent intakes was also demonstrated for those foods listed in the cereal and grains group (P = 0.06).

The GDM group of women consumed significantly less total energy and carbohydrate from low GI foods, expressed in terms of kilojoules (2451 [+ or -] 1256 kJ GDM vs 3592 [+ or -] 1645 kJ control) (P = 0.02) and g of carbohydrate (P = 0.01) (Figure 2). This observation also applied to dinner meals, (530 [+ or -] 502 kJ GDM vs 920 [+ or -] 565 kJ control, P<0.05 and 15.9 [+ or -] 14.8 g GDM vs 28.8 [+ or -] 18.5 g control, P <0.05). The women with GDM also reported consuming less low GI cereal and grains, both in terms of kilojoules (2413 [+ or -] 1323 kJ GDM vs 3625 [+ or -] 1717 kJ control) (P <0.05) and g of carbohydrate (79.1 [+ or -] 39.5 g GDM vs 117.1 [+ or -] 55.2 g control) (P < 0.05).

No differences were found for energy or carbohydrate intakes from high or intermediate GI foods, however, consumption of these foods were reported less frequently by the women with GDM over an average day (high GI foods: 5.9 [+ or -] 2.4 GDM vs 8.3 [+ or -] 4.1 control, P < 0.05); (intermediate GI foods: 15.6 [+ or -] 6.2 GDM vs 19.5 [+ or -] 5.3 control, P < 0.05). In particular, consumption of high GI vegetables were reported less often by the GDM group (2.9 [+ or -] 1.5 GDM vs 4.7 [+ or -] 2.9 control, P < 0.05).

The mean diet GI for each group was not significantly different (58.3 [+ or -] 4.4 GDM vs 56.7 [+ or -] 4.3 control). However, when adjusted for total energy intake the GDM group had a significantly higher diet GI score than the control group (74.8 [+ or -] 31.8 GDM vs 57.6 [+ or -] 16.3 control, P < 0.05). No differences were found for mean glycaemic load scores (14 130.5 [+ or -] 6018.8 GDM vs 16 234.8 [+ or -] 4027.2 nor for glycaemic load as a percentage of energy (159.5 [+ or -] 24.0 GDM vs 157.9 [+ or -] 26.1 control). The mean dinner meals 01 score for the GDM group was significantly higher than for the control group (65.6 [+ or -] 6.6 GDM vs 59.2 [+ or -] 8.0 control) (P = 0.01). Furthermore, the average standard deviation across all meal GI scores was higher for the GDM group (14.3 GDM vs 9.3 control), indicating greater variation in GI scores for meals across the day, although no further differences for meal comparisons of GI were found. Similar comparisons for meal glycaemic load scores were not si gnificantly different between the two groups.

Foods of unknown GI, excluded from the analyses, made up [less than or equal to] 10% of the carbohydrate intake for 15 of 16 women with GDM and 23 of 24 control women, with no significant differences between the two groups for the amount of food excluded as a percentage of either total energy (kJ) or total carbohydrate (g).

Discussion

Much of the evidence for dietary intervention in the management of GDM comes from clinical trials using individual foods and set meals to modify blood glucose concentrations (33-38). Far fewer studies have reported on the food choice patterns of pregnant women under 'free living' conditions. In particular, the diets of women with GDM are very much under-reported (20,39,40). Those who have examined populations at risk of diabetes have focused mainly on macronutrient intakes and 'suspect' foods and have failed to identify dietary differences that warrant specific recommendations (41,42).

In this study we found differences in the patterns of carbohydrate intake between women diagnosed with GDM and a control group of glucose tolerant pregnant women. By controlling for other risk factors related to GDM (age and prepregnancy weight), and recruiting at similar stages of gestation, few differences other than diet were found between the two groups, except the GDM women were shorter and had greater parity, as has been previously reported for women with GDM (43,44). While significant differences in energy and macronutrient intakes were not found in this sample, the trend by the GDM group to consume less energy and carbohydrate is a phenomenon observed for other diabetic populations (20,41). Whether lower intakes reflect actual dietary changes or recall bias in response to diagnosis is uncertain and may represent a limitation of our study. The types of foods restricted by the GDM group, however, are not those normally associated with under-reporting (45).

In our study, the GDM group reported consuming less carbohydrate from low GI cereal and grain foods, in particular pasta, and from fruit juice and milk products. While none of these foods have a high GI value, it could be speculated that these women perceived these foods as worthy of restriction. Although we did not assess this, all but three of the women in the GDM group reported some type of dietary change during the current pregnancy. Similarly, women with GDM consumed high carbohydrate (starchy) vegetables less frequently, suggesting these women were trying to limit their intakes of these foods. Hence, the food choice patterns described diverge from those recommended for pregnant women, a nutritionally at-risk group with additional requirements for the nutrients contained in these food groups, e.g. vitamin C and calcium (46). Additionally, although the total amount consumed from foods with higher GI values were similar for both groups, the GDM group reported selecting them on fewer occasions during the day. Again, these behaviours are contrary to current diabetes management strategies that focus on smaller portions and more frequent intakes over the day (47).

The patterns reported by the women with GDM contributed to a significantly higher average diet GI score, expressed as a percentage of total energy intakes, when compared to the control group. The GDM group also reported significantly lower intakes of low GI foods at dinner and subsequently higher GI meal scores for that meal.

Even modest reductions in GI scores, within the range of values found in a free-living diabetic population, have been shown to improve blood glucose control (48,49). Similarly, foods with comparatively higher GI values, consumed more regularly across the day, have been shown to modify blood glucose concentrations by spreading the GI load (18,19). The lack of any significant difference in our study between the two groups for total GI load scores further suggests partitioning the load may be more important for controlling blood glucose concentrations than the load per se, although this was not apparent from differences in meal glycaemic load scores. It should be noted, however, that unless fluctuations in meal GI and load scores for each person in the group coincide for different meal-times, mean representations may be unable to detect any significant differences between the two groups. Arranging individual meal scores from lowest to highest regardless of time frame may prove more informative. Despite this, the dinner meal, possibly the main meal of the day, was significantly different for meal GI scores between the two study groups, and may have been the only meal with food intakes consistent enough between individuals to detect a significant difference. How these meal scores impact on other meals such as breakfast the next day is uncertain, but clinical evidence suggests an effect for glucose and insulin responses to later meals (50,51).

These results suggest qualitative differences in food choice patterns between the two study groups that have the potential to provide physiological benefits during pregnancy. While both groups reported dietary change during the course of the current pregnancy, different perceptions about the relationship between diet, health and disease, as well as previous exposure to dietary advice may account for differences observed between the two groups. While the women with GDM had not yet received any specific dietary advice for diabetes management of the current pregnancy, four of the 16 women with GDM had a previous diagnosis of GDM and may have been exposed to previous dietary advice. While one might expect this to translate into beneficial patterns of intake for the current pregnancy, traditional 'negative' nutrition messages can translate into food restrictions that impact on food variety and frequency of consumption. Resultant nutritional and metabolic susceptibility to disease (52,53) may, therefore, be an important consequence of dietary change and should be of interest to those involved in the dietary management of this group.

In this study, we have focused on food patterns as a means for understanding dietary relationships in GDM. Analyses of food choice patterns are less dependent on the inadequacies of nutrient databases and allow the diet to be described in a manner that is useful outside the clinical setting (53). A limitation is that it is sensitive to the way food consumption data are categorised (54). Accordingly, several criteria were used to define food groups and identify patterns of intake. Food source and GI identify foods of similar components and metabolic effect and have been used as classifications for other dietary analyses (32,48). Diet GI scores obtained from free-living subjects demonstrate a normal distribution, and are considered valid for use in common statistical procedures (48), however, GI scores expressed as percent energy may be biased by the possibility of under-reporting by the GDM group. Consequently, foods organised into groups related to a more rounded estimate of the body's response (high, interme diate and low GI) may eliminate some of the error related to assigning foods exact GI values, discussed elsewhere (55,56).

Appropriateness of the methodology can also reduce the degree of error for estimating dietary intakes (53,54). The pattern of intake is dependent on the way consumption is reported and, a meal-based diet history that requires a sequential re-telling of regular intakes, is sensitive to the daily spread of individual foods and food groups (59). The modified version used for this study was developed specifically for use with pregnant women screened for GDM and is considered sufficiently valid and reliable for use in studies of pregnant women in the Wollongong area (26). While a diet history taken prior to diagnosis and the exclusion of women with risk factors for diabetes during the current pregnancy may have avoided the possible introduction of dietary and other forms of bias, the omission of data obtained from women with a previous GDM pregnancy did not significantly alter our results. Nonetheless, the results do indicate that a qualitative assessment of the patterns of carbohydrate intakes of a convenience sample of women with and without GDM can uncover dietary differences not readily identifiable using traditional macronutrient comparisons.

Women who develop GDM fail to overcome insulin resistance in pregnancy due to a variety of factors, the most alterable of which may be diet. Clinical trials using low GI carbohydrate-rich foods and increased frequency of intake to modify blood glucose concentrations have shown the greatest potential benefit (17-19,33-38). This small qualitative study of the food habits of free-living pregnant women has identified patterns of intake from carbohydrate-rich foods that support this view. Compared to glucose tolerant pregnant controls, women with GDM reported less carbohydrate-rich foods with low GI values, in particular, pasta, fruit juice and milk products, and a reduced spread of consumption of foods with higher GI values. While both groups reported dietary changes in response to pregnancy, the 'non-beneficial' patterns of intake reported by the GDM group likely impact on glycaeniic control and have implications for the dietary management of these women. A larger prospective dietary intervention study is required in which women with GDM are provided with prescriptive versus 'standard' dietary advice to: compare patterns of intake resulting from different dietary management approaches; and assess the role of these patterns of intake in the clinical expression of insulin resistance during pregnancy.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

Table 1

Carbohydrate-rich food groups and subgroups used or analysis of food
intake patterns

Cereals and grains  Fruits                 Vegetables

Bakery goods        Whole                  Total
                    (fresh/frozen/canned)  (fresh/frozen/canned)
Biscuits            Juice                  High carbohydrate (b)
Breads                                     Low carbohydrate (c)
Breakfast cereals
Pasta
Rice

Cereals and grains  Milk                 Sugar-rich (a)

Bakery goods        Milk/Yoghurt         Foods

Biscuits            Other milk products  Beverages
Breads
Breakfast cereals
Pasta
Rice

(a) Where carbohydrate content is > 50 g/100 g and sugar contributes >
50% of total carbohydrate content

(b) Where carbohydrate content is [greater than or equal to] 10 g/100 g

(c) Where carbohydrate content is < 10 g/100 g
Table 2

Food substitutions used for GI analysis

Food Item                          Substitution

Cereal and grains

Bread

Breadcrumbs, commercial            Bread, white (wheat flour), mean
Bread, Lehanese, white             Pita bread
Bread, mixed grain                 Multi-grain, Tip Top
Raisin toast, commercial           Bread, white + sultanas + sucrose,
                                   mean

Breakfast Cereal

Muesli slice, home prepared        Muesli, breakfast cereal, toasted
Muesli bar, choc-coated            Muesli bar + chocolate, milk, mean
Muesli, swiss-style                Muesli, breakfast cereal, non-toasted

Bakery Goods

Cake, carrot/chocolate/plain       Cake, pound
Cake, fruit                        Cake, pound + sultanas, mean
Cake, rock                         Cake, pound
McMuffin, bacon & egg              English muffin bread
Meat pie/pasty                     Pastry, flaky
Muffin, banana/bran/chocolate      Mean of all muffins
Pie, apple, high, family           Pastry + apple, mean
Pie, cottage                       Pastry, flaky + potato, mashed
Pudding, sponge, steamed           Cake, sponge
Sausage roll                       Pastry, flaky + sausage, mean
Scone, plain, commercial           Bread, white (wheat flour), mean
Vol au vent                        Pastry, flaky

Biscuits

Biscuit, plain sweet, unspecified  Arrowroot + Morning coffee, mean
Cruskits, crispbread               Mean of all crackers
Tiny teddy biscuits                Arrowroot + Morning coffee, mean

Pasta

Lasagna/cannelloni                 Ravioli, meat-filled, cooked

Rice

Rice, creamed, canned              Rice, Calrose, white + milk, whole,
                                   fluid + sucrose, mean
Rice, fried, Chinese restaurant    Mean of all white rice
Rice, white, boiled, unspecified   Rice, Calrose, white, cooked

Fruit

Salad, fruit, fresh                Fruit cocktail, canned in natural
                                   juice
Apple, canned, sweetened           Apple + sucrose, mean
Apple, stewed                      Apple
Fruit drink, apple                 Apple juice + sucrose, mean
Fruit drink, orange                Orange juice + sicrose, mean
Jam, 100% fruit or unspecified     Mean of all jam
Jam, stone fruit                   Jam, Glen Ewin, apricot spread
Mandarin                           Orange
Nectarine                          Peach

Vegetables

Nachos, home prepared              Corn chips, Doritos
Potato, chips, commercial          French fries, fine cut
Potato, mashed, dried              Instant potato, prepared
Potato, new, peeled, boiled        Potato, new, canned, drained
Potato, pale skin, baked           Potato, Pontiac, baked in oven, mean
Potato, pale skin, mashed          Potato, Pontiac, mashed, mean
Potato, pale, skin, peeled,        Potato, Sebago, peeled, boiled, mean
boiled
Taco shell, El Paso                Corn chips, Doritos

Milk Products

Flavoured milk                     Milk, whole, fluid + sucrose, mean
Frozen yoghurt                     Mean of all yoghurt
Smoothie, banana                   Milk, whole, fluid + banana
Sundae, McDonalds                  Ice cream, full fat + sucrose, mean
Thickshake, McDonalds              Milk, whole, fluid + ice cream
                                   + sucrose, mean
Sugar-rich products

Sugar-rich beverages

Mineral water, flavoured           Soft drink, Fanta
Soft drink, all flavours           Soft drink, Fanta

Sugar-rich, foods

Chocolate, candy-coated            Chocolate, milk + sucrose, mean
Chocolate, dark/milk               Chocolate, milk
Chocolate, milk, with nuts         Chocolate, milk + peanuts, mean
Golden syrup                       Sucrose, mean
Gums                               Sucrose, mean
Ice confection                     Sucrose, mean
Licorice                           Sucrose, mean
Picnic bar                         Snickers bar
Sugar confectionery, jell          Jelly beans

Miscellaneous foods

Beer nut, salted                   Peanuts, roasted, salted, mean
Beans, mixed, canned               Mean of all beans
Dumpling                           Bread, white (wheat flour), mean
Falafel/hummus                     Chick peas, boiled, mean
Soup, bean, home prepared          Mean of all beans
Sauce, black bean                  Black beans, boiled
Soup, chicken noodle, dry mix      Noodles, 2-minute

Food Item                          GI

Cereal and grains

Bread

Breadcrumbs, commercial            70
Bread, Lehanese, white             57
Bread, mixed grain                 43
Raisin toast, commercial           46


Breakfast Cereal

Muesli slice, home prepared        43
Muesli bar, choc-coated            55
Muesli, swiss-style                56

Bakery Goods

Cake, carrot/chocolate/plain       54
Cake, fruit                        55
Cake, rock                         54
McMuffin, bacon & egg              77
Meat pie/pasty                     59
Muffin, banana/bran/chocolate      60
Pie, apple, high, family           49
Pie, cottage                       75
Pudding, sponge, steamed           46
Sausage roll                       44
Scone, plain, commercial           70
Vol au vent                        59

Biscuits

Biscuit, plain sweet, unspecified  70
Cruskits, crispbread               71
Tiny teddy biscuits                70

Pasta

Lasagna/cannelloni                 39

Rice

Rice, creamed, canned              60

Rice, fried, Chinese restaurant    83
Rice, white, boiled, unspecified   87

Fruit

Salad, fruit, fresh                55

Apple, canned, sweetened           52
Apple, stewed                      38
Fruit drink, apple                 53
Fruit drink, orange                55
Jam, 100% fruit or unspecified     53
Jam, stone fruit                   55
Mandarin                           44
Nectarine                          42

Vegetables

Nachos, home prepared              42
Potato, chips, commercial          75
Potato, mashed, dried              86
Potato, new, peeled, boiled        65
Potato, pale skin, baked           93
Potato, pale skin, mashed          91
Potato, pale, skin, peeled,        87
boiled
Taco shell, El Paso                42

Milk Products

Flavoured milk                     46
Frozen yoghurt                     25
Smoothie, banana                   41
Sundae, McDonalds                  63
Thickshake, McDonalds              51

Sugar-rich products

Sugar-rich beverages

Mineral water, flavoured           68
Soft drink, all flavours           68

Sugar-rich, foods

Chocolate, candy-coated            57
Chocolate, dark/milk               49
Chocolate, milk, with nuts         32
Golden syrup                       65
Gums                               65
Ice confection                     65
Licorice                           65
Picnic bar                         41
Sugar confectionery, jell          80

Miscellaneous foods

Beer nut, salted                   14
Beans, mixed, canned               37
Dumpling                           70
Falafel/hummus                     33
Soup, bean, home prepared          37
Sauce, black bean                  30
Soup, chicken noodle, dry mix      46
Table 3

Carbohydrate-rich foods of unknown GI excluded from GI analysis

Cereal and grains

Biscuit, choc-coated
Biscuit, chocolate chip
Biscuit, cream-filled
Biscuit, cream-filled, chocolate
Bun, fruit, iced
Cake, iced
Chocolate bavarian
Cornflour
Ice cream cone
Lamington
Mint slice, biscuit
Nut feast, Uncle Toby's
Pancake
Pastry, shortcrust
Rice cracker, plain, Sakata
Rice pudding
Spring roll, deep fried
Spring roll, Thai, restaurant
Tabbouleh
Tim Tam, Arnotts

Fruit

Apricot nectar
Fruit bar
Prune

Vegetables

Coleslaw
Potato gems
Potato salad
Potato scalop, deep freid
Rhubarb, stewed, sugar added

Milk products

Beverage, chocolate, Cadbury
Buttermilk, cultured
Fruche
Malted milk powder
Paddle pop

Sugar-rich products

Cherry ripe
Chocolate topping
Crunch, chocolate
Kit kat
Violet crumble

Miscellaneous foods

Cheesecake
Devon/fritz
Ice cream cone
Moussaka
Pie. fruit
Pie. lemon
Puding, self-saucing
Quiche
Slice, vanilla
Soup, minestrone
Soup, pea & ham
Tart, custard
Twisties, chips
Table 4

Maternal characteristics of the women with gestational diabetes mellitus
(GDM) and normal glucose tolerant control group (a)

                                        GDM
Characteristics                        n = 16

Age (y)                           31.4 [+ or -] 4.8
Gestation (wk)                    29.4 [+ or -] 1.7
parity                             1.9 [+ or -] 1.5
Prepregnancy weight (kg)          63.1 [+ or -] 14.7
Prepregnancy BMI (kg/[m.sup.2])   24.3 [+ or -] 5.6
Weight (kg)                       73.4 [+ or -] 14.8
BMI (kg/[m.sup.2])                28.3 [+ or -] 5.5
Pregnancy weight gain (kg)        11.5 [+ or -] 4.4
Height (cm)                      161.2 [+ or -] 4.3

                                        Control
Characteristics                          n = 24

Age (y)                             9.2 [+ or -] 5.6
Gestation (wk)                     29.4 [+ or -] 1.2
parity                              0.6 [+ or -] 0.7 (b)
Prepregnancy weight (kg)           67.0 [+ or -] 18.5
Prepregnancy BMI (kg/[m.sup.2])    24.2 [+ or -] 5.5
Weight (kg)                        76.6 [+ or -] 9.9
BMI (kg/[m.sup.2])                 27.4 [+ or -] 2.5
Pregnancy weight gain (kg)         10.6 [+ or -] 5.2
Height (cm)                       167.8 [+ or -] 6.0 (b)

(a) Mean [+ or -] standard deviation.

(b) P < 0.05 by two tailed t-tests.
Table 5

Mean total dietary intakes for gestational diabetes mellitus (GDM) and
control groups (a)

                            GDM (n = 16)       Control (n = 24)

Total energy (kJ)         8814 [+ or -] 2889  10384 [+ or -] 2344
Carbohydrate (g)         263.1 [+ or -] 96.2  309.0 [+ or -] 71.3
Carbohydrate (% energy)   48.0 [+ or -] 7.3    48.0 [+ or -] 6.8
protein (% energy)        18.4 [+ or -] 4.7    17.4 [+ or -] 2.2
Fat (% energy)            31.0 [+ or -] 8.9    32.5 [+ or -] 7.2
MUFA (% energy) (c)       10.6 [+ or -] 3.0    10.7 [+ or -] 2.3
PUFA (% energy) (d)        6.6 [+ or -] 5.1     5.1 [+ or -] 2.2
SFA (% energy) (c)        12.6 [+ or -] 4.4    14.4 [+ or -] 4.8
Fibre (g)                 25.1 [+ or -] 7.9    25.2 [+ or -] 8.2
Sugar (g)                128.2 [+ or -] 78.8  162.1 [+ or -] 53.6
Starch (g)               128.6 [+ or -] 37.9  141.5 [+ or -] 34.9

                         P-value (b)

Total energy (kJ)           0.07
Carbohydrate (g)            0.09
Carbohydrate (% energy)     1.00
protein (% energy)          0.39
Fat (% energy)              0.55
MUFA (% energy) (c)         0.86
PUFA (% energy) (d)         0.17
SFA (% energy) (c)          0.24
Fibre (g)                   0.97
Sugar (g)                   0.11
Starch (g)                  0.28

(a) Mean [+ or -] standard diviation.

(b) Determined by t-tests for differences between groups.

(c) MUFA monounsaturated fatty acids

(d) PUFA polyunsaturated fatty acids

(e) SFA saturated fatty acids

References

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Smart Foods Centre and Department of Biomedical Science, University or Wollongong, New South Wales 

L. Gillen, BNutrDiet(Hons), APD, PhD candidate

L. Tapsell, BSc, DipNutrDiet, MHPEd, PhD, APD, Associate Professor

G. Martin, BSc, BNutrDiet (Hons). APD, PhD candidate

Diabetes Service, Illawarra Area Health Service, Wollongong

S. Daniells, MSc (NutrDiet), Dietitian, Illawarra Diabetes Service

S. Knights, MNutrDiet, APD, Manager, Illawarra Diabetes Service

R. Moses, MD, Director, Illawarra Diabetes Service

Correspondence: L. Gillen, Smart Foods Centre, University of Wollongong, Northfields, Avenue, Wollongong, NSW 2522. Email: [e-mail]