Effects of meals with high soluble fibre, high amylose barley variant on glucose, insulin, satiety and thermic effect of food in healthy lean women

Abstract

Objective:

To examine the effect of barley flour (barley cultivar, Hordeum Vulgare var Himalaya 292) incorporated into breakfast and lunch compared with otherwise identical meals containing white wheat flour on the thermic effect of food (TEF), subsequent food intake and metabolic parameters.

Design:

Randomized single blinded crossover study.

Setting:

Outpatient setting.

Subjects and methods:

Fourteen healthy women consumed a test breakfast at 0700 h. Energy expenditure, respiratory quotient (RQ), appetite ratings using a visual analogue scale (VAS), insulin and glucose levels were measured before and after a test lunch at 1330 h. Food intake was recorded for the remainder of the day.

Results:

The TEF was 5% for both test lunches and meal type did not affect any variable measured by the VAS. There was an increase in post-prandial RQ above baseline (0.80) independent of treatment (0.88 and 0.90 for barley and wheat-containg meals, respectively, P<0.001). Mean area under the glycaemic response curve (AUC) for wheat-containing meals was 4.68±1.67 mmol/l/h, 22% higher than for the barley-containing meals (3.67±1.91 mmol/l/h), P=0.05. AUC of insulin in response to wheat-containing meals (78.1±35.3 mIU/l/h) was 32% greater than barley-containing meals (52.8±24.7 mU/l/h), P<0.02. Ad libitum food intake over the next 10 h was reduced by 23% (9.6 vs 11.0 MJ, P<0.05) after the wheat-containing meals compared to the barley-containing glycaemic index meals.

Conclusion:

Inclusion of an ingredient containing increased soluble fibre and amylose did not reduce spontaneous food intake but rather was associated with higher subsequent energy intakes despite its reduced glycaemic and insulinemic effects.

Sponsorship:

CSIRO, Human Nutrition, Adelaide, Australia.

Introduction

The classification of foods according to their glycaemic response has been used in the management of diabetes mellitus, dyslipidaemia and excess body weight (Jenkins et al., 1981, 1985; Brand-Miller et al., 2002). Consumption of foods with a lower glycaemic response have been promoted to have a number of putative health benefits including promotion of long-term weight loss by influencing satiety and food intake and reduced glucose and insulin responses (Lavin and Read, 1995; Ludwig, 2000). The mechanism by which foods with a reduced glycaemic response or low-glycaemic index (GI) may improve satiety is controversial. In a systematic review of 31 short-term studies (<1day), low GI meals were associated with greater satiety or reduced hunger in 15 studies, 14 studies showed no difference in satiety between the high and low GI meals and the remaining two studies found that the higher GI meals improved satiety compared with the low GI meals (Raben, 2002).

Dietary composition influences the thermic effect of food (TEF) and it has been observed that a high-protein meal had a greater effect on TEF and was more satiating that either a high-carbohydrate or a high-fat meal (Crovetti et al., 1997). TEF and the sensation of fullness were closely linked in this study. Similarly Raben et al. (2003) have observed that protein had a greater effect on TEF than carbohydrate and fat but less that alcohol with no difference in hunger or satiety sensations in ad libitum energy intake. It is unclear if the GI of food influences its TEF as to our knowledge this has not been examined.

The consumption of a low GI meal may also affect the subsequent meal glucose and insulin response (the so-called ‘second-meal effect’). The impact of a low GI meal on the subsequent meal has been shown previously by Liljeberg and Bjorck (2000), who observed lower glucose and insulin responses after lunch when a low-GI meal of pasta was eaten for breakfast. Ostman et al. (2002) altered bread at breakfast by adding lactic acid, which resulted in a reduced post-prandial glucose and insulin response. However, the reasons for these effects are unclear.

The amount of amylose and β-glucans in a food may also have an effect of glucose and insulin responses. We have previously shown that a high-amylose starch meal resulted in a reduction in post-prandial plasma insulin concentration by 17% (Noakes et al., 1996). Other studies have shown that β-glucans have a beneficial effect on glucose and insulin responses (Hallfrisch et al., 1995; Biorklund et al., 2005). Foods with a lower glycaemic response are being developed and one way to achieve this is to include an ingredient likely to blunt the glycaemic response in usual or staple foods, for example, bread, breakfast, cereal or cake.

The aim of this study therefore was to examine in healthy female subjects the effects on the thermic effect of feeding, subjective measures of appetite, energy intake, blood glucose and insulin measured after lunch in a single-blinded crossover study of two identical meals with foods containing either a wheat or barley ingredient at breakfast and lunch, respectively.

We hypothesized that the barley-containing breakfast would blunt glucose and insulin responses to lunch and a barley-containing lunch would blunt this even more leading to stable post-prandial glucose levels and lower subsequent food intake over the next 10 h.

Subjects and methods

Subjects

Healthy women were recruited from within the Commonwealth Scientific and Industrial Research Organisation (CSIRO) volunteer database by internal advertisement. The selection criteria were: aged 20–50 years, with a body mass index (BMI) 18–28 kg/m2, no history of heavy alcohol consumption (>5 standard drinks/day), or widely fluctuating exercise patterns. Volunteers were not pregnant or breast feeding, had no history of hypersensitivity to the test food, no history of diabetes, cancer, liver, respiratory, renal disease and did not have unstable cardiac disease. They were not attempting to lose weight at the time and were not taking medications likely to affect study outcomes.

Fourteen women aged 28.6±8.3 years, BMI 22.8±2.3 kg/m2 participated in the study. We recruited women only as the meal sizes and amount of carbohydrate would need to be different for men and this would complicate the analysis.

The study was approved by the CSIRO Human Nutrition Human Ethics Committee and written informed consent was obtained from all the participants before the start of the study.

Study design

A randomized, single-blind crossover design was used to compare the effects of white wheat flour and barley flour incorporated into similar breakfast and lunch meals. Each test day was planned to be separated by a washout period of 7 days, during which the subjects consumed their usual diet. Randomization was carried out using Clinstat computer program (MS-DOS program in the public domain).

Two test meals were chosen to assess the effect on ad libitum intake following lunch. Subjects were provided with a breakfast meal of cereal and milk, matched for type and quantity of food (Table 1) to consume at home before 0700 h before attending the clinic. All the subjects were asked to avoid heavy activity and alcohol 24 h before the day of the study. Following the breakfast meal, they were asked to maintain light activity and not to eat or drink anything else (water allowed) until they reported to the clinic at 1240 h. Weight and height were measured in light clothing without shoes. A venous blood sample was collected at 1300 h, 6 h after the standard breakfast and subjects were asked to complete a short questionnaire rating their appetite (visual analogue scale, VAS) (Flint et al., 2000). Energy expenditure (EE) was measured 6 h after the standard breakfast for 30 min. The subjects were provided with their lunch meal (Table 1) at 1330 h and allowed 20 min to consume it. The TEF was measured for 3 h after consuming lunch (Luscombe et al., 2003). During this time, further venous blood samples and VAS were taken and recorded at 30, 60, 120 and 180 min after commencement of lunch. At the end of the test period, subjects left the clinical research unit and were asked to maintain a weighed food record of all food consumed for the remainder of the day.

Table 1 Nutritional composition of the study meals

Study meals

Barley flour from a novel, non-GMO barley cultivator with a specific gene defect in starch synthesis leading to a shrunken grain with a lower content of total starch (27%) and a higher proportion of amylose (71% of the starch content) (BarleyMax barley cultivar, Hordeum vulgare var. Himalaya 292) developed by CSIRO was used as the low-GI ingredient (Morell et al., 2003; Topping et al., 2003). The mutation results in a higher content of soluble non-starch polysaccharides (NSP), especially β-glucan. The macronutrient and dietary fibre composition of Hordeum vulgare var. Himalaya 292 has been determined previously (Bird et al., 2004). This flour was used in the bread and muffin for the barley lunch and refined white wheat flour was used in the comparator lunch. The GI of the barley in the form of a cereal made from 100% of the same barley cultivar used in this study had previously been determined by CSIRO Human Nutrition in 30 subjects using the standard methodology of Wolever et al. (1991) and was calculated to be 49 compared to glucose (unpublished) and the GI of refined white bread was taken to be 70 (Foster-Powell et al., 2002). Refined white wheat flour was replaced by 20 and 21% barley flour in bread and muffins, respectively. For breakfast, the volunteers were provided with pre-packaged cereals of either barley flakes (Biorklund et al., 2005) or commercially available wheat bran flakes (GI 74-literature value) (Foster-Powell et al., 2002) and reduced fat (1%) milk (both days) in a tetra pack which they were asked to consume in full. Lunch consisted of a sandwich with test bread filled with tuna, lettuce, reduced fat margarine, reduced fat mayonnaise and a test muffin (both bread and muffin were made from the appropriate flour) and orange juice which was also consumed in full. The meals were identical in appearance. Both the barley and the refined wheat foods had been used in an earlier study at CSIRO (unpublished). The acceptability of all the foods used had previously been determined in a Food Product Assessment Questionnaire in a previous study (unpublished data). The acceptability of the muffins and bread was high, being close to or, in the case of the bread, exceeding the acceptability of the refined product. The total energy content of the barley and wheat diets (breakfast and lunch) was 3590 and 3685 kJ, respectively, providing approximately 47–49% of estimated total energy requirement of an average adult woman (7700–9000 kJ/day). The total energy content from lunch was similar for the two test meals, 3037 and 3071 kJ for the barley and wheat meals, respectively, and this figure was used to determine the TEF. The energy and macronutrient composition of the test meals are shown in Table 1. The dietary fibre analysis of the test foods is shown in Table 2.

Table 2 Dietary fibre analysis of test foods

Methods

Metabolic rate, thermic effects of food and respiratory quotient

Metabolic rate (MR) and respiratory quotient (RQ) were measured over 30 min by using an indirect calorimetry Deltatrac metabolic monitor (Datex Division Instrumentarium Corp., Helsinki, Finland) and a ventilated canopy. The instrument was calibrated every morning before the study started. After measuring weight and height, subjects were asked to lay supine on a bed in a thermoneutral environment with a clear plastic hood over their head and shoulders. Baseline resting metabolic rate (RMR) and RQ were measured for 30 min. The first 10 min of data were discarded to ensure all subjects had reached equilibrium. The remaining 20 min of data were averaged. Subjects were allowed to read and listen to music but were not allowed to sleep during the period of measurement.

After measuring the baseline RMR, subjects consumed the test lunch within 20 min and returned to the ventilated canopy for further measurement of EE. Mean changes in post-prandial TEF and RQ were calculated using the average of every 20 min over 180 min. TEF was calculated as the mean increase in RMR over 180 min and expressed as a percentage of the energy intake of the lunch consumed (Luscombe et al., 2003).

Visual analogue scores

A validated short questionnaire with a linear scale of 100 mm for rating hunger, fullness, satiety, nausea, desire to eat and the amount of food that could be eaten was completed by each of the subjects at baseline and at 30, 60, 120 and 180 min after commencing the test lunch (Flint et al., 2000). The changes in ratings from baseline were quantified following the method described by Porrini et al. (1995). A more positive value for satiety, nausea and fullness indicated greater satiety, nausea and fullness; more negative value for hunger, desire to eat and amount of food that could be eaten indicated lower hunger, a reduced desire to eat and lower amount of food that could be eaten.

Weighed food record

Subjects were asked to record all the food and drink they consumed for the remainder of the day. Subjects were educated on maintaining a food record by a research dietitian and provided with digital kitchen weighing scales to assist in quantifying food intake.

Food intake for the remainder of the day was analyzed using Diet 1 software package (Xyris Software, Highgate Hill, Australia) to determine energy intakes and macronutrient composition (protein, carbohydrate, fat, alcohol and dietary fibre). This database is based on Australian food composition tables and food manufacturers' data (Cashell et al., 1989).

Laboratory analysis

Blood samples for plasma insulin and glucose were collected in sodium fluoride/ethylenediaminetetracetic acid (EDTA) (1g/l) and stored on ice until processed. The plasma was isolated by centrifuging for 10 min at 1500 g at 4°C (Beckman GS-6R Centrifuge CA, USA) and stored at −80°C. All samples for each individual were measured in one assay at the end of the study. Plasma glucose was measured on a Hitachi 902 Automatic Analyzer (Roche, Nutley, NJ, USA) and insulin concentration was measured using the Mercodia Insulin ELISA kit (ALPCO, American Laboratory Products, Salem, NH, USA).

Data analysis

All the data are presented as means±s.d. The effects of the meals on MR and energy intake were compared using paired t-test. VAS and TEF were analyzed by using repeated-measures general linear model for meal alone or meal and time as within-subject factors. Areas under the glycaemic and insulin response curves were calculated using the trapezoidal rule for values above the baseline and using paired t-test to assess the differences (Wolever and Jenkins, 1986). Relationship between satiety and energy intake, AUC for insulin and glucose and AUC for TEF were assessed using Pearson's correlation. All calculations were performed by using SPSS for Windows, version 11.5 (SPSS Inc., Chicago, IL, USA). Significance was set at P<0.05.

Results

Post-prandial insulin and glucose concentration

Serum insulin or plasma glucose concentrations were not different before lunch, 6 h after the barley or wheat breakfast (Table 3). Plasma glucose and insulin responses to the two diets are depicted in Figure 1a and b. The mean area under the glycaemic response curve (AUC) above baseline for the meals containing wheat was 4.68±1.67 mmol/l/h, 22% higher than the barley-containing meals (3.67±1.91 mmol/l/h), P=0.05. AUC of insulin above baseline in response to the white wheat flour meals (78.1±35.3 mIU/l/h) was 32% greater than barley-containing meals (52.8±24.7 mU/l/h), P<0.02.

Table 3 Subject characteristics before luncha
Figure 1
figure1

(a) Change in plasma glucose concentration (mmol/l) over time in minutes after consuming a barley (♦) or wheat (▪) test lunch. (b) Change in serum insulin concentration (mU/l) over time in minutes after consuming a barley () or wheat (▪) test lunch.

Effects of diet on EE, TEF and RQ

There were no differences between treatments in EE measured before lunch or RQ (Table 3). After adjusting for energy intake, total TEF for the wheat meals (4.9±0.4%) was not significantly different to the barley meals (4.4±0.4%) (Figure 2).

Figure 2
figure2

Effect of barley and wheat as an ingredient on EE and TEF over time (3 h). Change in energy expenditure (a) and TEF (b) over time (3 h) after consuming barely (▪) or wheat (−) test lunch.

The RQ, reflecting carbohydrate oxidation, for both diets increased from baseline 0.80 to 0.88 for the barley meals (P<0.001) and to 0.90 (P<0.001) for the wheat meals. There was no difference in glucose or fatty acid oxidation between the two diets, indicating that the wheat meals did not induce significantly higher carbohydrate oxidation compared to the barley meals (Figure 3).

Figure 3
figure3

Effect of barley and wheat as an ingredient on the mean changes in RQ over time (3 h). Change in RQ for 3 h after consuming barley (•) and wheat () lunch (P<0.001). Values presented as mean±s.d.

Effect of diet on subjective appetite rating

There were no significant differences between any of the appetite ratings for nausea, hunger, fullness, satiety and desire to eat, and amount of food that could be eaten between the two meals. There was no correlation found between VAS and serum insulin or plasma glucose concentration after adjusting for baseline and no correlation was found between VAS and energy intake or TEF.

No correlation was found between hunger and satiety measures averaged over 3 h and the TEF over this time.

Effect of diet on subsequent energy intake

The subsequent ad libitum energy intake for the remainder of the day was higher after consuming the barley meals (4685±360 kJ) compared with the wheat meals (3486±345 kJ) (P<0.05). Only three subjects drank alcohol and removal of alcohol from the energy intake data did not affect this outcome.

Protein intake was 39% higher after the wheat meals (P<0.05) but the intake of all other macronutrients was not statistically different. There was no difference in fibre intake (13.4±1.8 g and 10.1±1.3 g barley and wheat meals, respectively).

Discussion

The main finding of this study was that reducing the glycaemic load of a breakfast and lunch meal using a high soluble fibre, high amylose barley variant did not reduce subsequent ad libitum energy intake, but was associated with higher subsequent energy intakes. In contrast, Ludwig et al. (2000) found an increase in the subsequent intake of food 4 h after a high GI, high glycaemic load meal (Ludwig, 2003). The energy intake for the remainder of the day in this study was determined by self-reported dietary recording. It is well established that self-reporting of dietary intake leads to under-reporting of intake (Hill and Davies, 2001), and as the duration increases so does the inaccuracy. However in this study the effects of under-reporting would have been limited because of the crossover design and the short period of recording that was required. High-GI meals are purported to promote higher voluntary intake due to a marked counter regulatory response to a sudden fall in glucose and increased appetite 4 h after the meal.

The inclusion of a high amylose/high soluble fibre ingredient at breakfast and lunch had no effect on TEF. Other studies examining the effect of GI on EE have been performed with simple sugars (fructose compared to glucose) (Ritz et al., 1991; Raben et al., 1994, 1997; Tagliabue et al., 1995), where increased TEF was observed with fructose, which has a lower GI compared to glucose. Significant differences in TEF and satiety with carbohydrate foods were observed by Raben et al. (1994) who noted significantly lower TEF and increased fullness with high-fibre (4.7 g/MJ) compared to low-fibre (1.7 g/MJ) meals. However, a very large amount of fibre was needed to produce TEF differences, which may not be sustainable on a daily basis. In this study, there was a difference in fibre levels between the two diets, 6.1 and 2.5 g/MJ barley and wheat-containing meals, respectively; however, despite this difference in the fibre there was no difference in TEF.

Substituting a single ingredient with a higher fibre and higher amylose at both breakfast and lunch resulted in a lower post-prandial blood glucose and insulin response after lunch. The reduction in glucose reflects both the smaller amount of carbohydrate in the meals, which was 19.0 and 25.0 g carbohydrate, barley and wheat breakfasts, respectively, and 94 and 102 g, respectively, in the lunch meal and the increased amount of low-GI amylose and soluble fibre. However, the insulin level was much lower than this (32%), suggesting the barley ingredient provided a much lower stimulus to the release of insulin and was actually delivering glucose to plasma at a lower rate. Although formal GI tests use equal available amounts of carbohydrate, in this study we have used more physiological equal serve sizes, which means the amount of carbohydrate is slightly different. The consumption of a barley breakfast may also have affected the post-lunch glycaemic response (the so-called ‘second-meal effect’). The impact of a low-GI meal on the following meal has been shown previously by Liljeberg and Bjorck's (2000), who observed lower glucose and insulin responses after lunch when a low-GI meal of pasta was eaten for breakfast. However, in contrast to Liljeberg and Bjorck's (2000) results, we did not observe differences in insulin and glucose immediately before lunch, suggesting that any effects on insulin and glucose were related to the lunch meal and not to the preceding meal. However, the amount of carbohydrate was less, 19 g at the breakfast meal in this study compared with 50 g in the study by Liljeberg and Bjorck's (2000), which may account for this difference. A study by Ostman et al. (2002) of the addition of lactic acid to bread at breakfast resulted in a reduction in the glucose AUC after the following meal with a reduction in the insulin level at 45 min. However, the reason for this effect is unclear. Liljeberg et al. (1999) reported that some low-GI breakfasts could improve glucose tolerance at a subsequent lunch. The higher insulin levels observed after the white wheat flour in this study is consistent with most of the studies using simple sugars or carbohydrate-rich foods as the test meals (Holt and Brand-Miller, 1995; Lavin and Read, 1995; Ludwig, 2002; Schafer et al., 2003). Ludwig et al. (1999) have suggested that meals with different GI may have markedly different effects on the metabolic response. This opinion has been supported by a later similar study (Ball et al., 2003).

The relationship between changes in insulin and glucose concentration and satiety and hunger are unclear. Although some studies report no relationship between blood glucose and insulin levels with satiety (Steward et al., 1997; Anderson and Woodend, 2003), others have found significant inverse relationships between satiety and glucose and insulin responses with carbohydrate-rich breakfast cereals (Holt et al., 1992; Holt and Brand-Miller, 1995) and with different GI beverages (Anderson et al., 2002). In this study, we observed differences in glucose and insulin, however, there were no differences in subjective measures of satiety (as assessed by VAS) between the two meals and no correlation between GI and satiety. Furthermore, there was no significant difference in other VAS variables examined after consuming the two different GI meals. Most studies have found that a low-GI meal is more satiating and produces more fullness, less desire to eat and decreased intake (Lavin and Read, 1995; Ludwig et al., 1999; Ludwig, 2000; Ball et al., 2003) but other studies suggest that GI has no relation with satiety or subsequent energy intake (Holt and Brand-Miller, 1995; Anderson and Woodend, 2003; Doucet et al., 2003). However, inconsistency of the results may be due to widely varying energy and macronutrient compositions of the test meal in these studies.

A major factor in this study may have been the differences in fibre between the two diets (22 vs 9.4 g in the barley and wheat groups, respectively); however, as high-fibre diets are associated with improved satiety and reduced dietary intake (Koh-Bannerjee and Rimm, 2003) this does not contribute to the unexpected result that was observed.

In conclusion, in this study inclusion of a barley ingredient, which produced lower glucose and insulin levels, did not reduce spontaneous food intake over the remainder of the day. These results remain to be confirmed in future studies.

References

  1. Anderson GH, Catherine NLA, Woodend DM, Wolever TMS (2002). Inverse association between the effect of carbohydrates on blood glucose and subsequent short-term food intake in young men. Am J Clin Nutr 76, 1023–1030.

    CAS  Article  Google Scholar 

  2. Anderson GH, Woodend D (2003). Effect of glycemic carbohydrates on short-term satiety and food intake. Nutr Rev 61, S17–S26.

    Article  Google Scholar 

  3. Ball SD, Keller KR, Moyer-Mileur LJ, Ding YW, Donaldson D, Jackson WD (2003). Prolongation of satiety after low versus moderately high glycemic index meals in obese adolescents. Pediatrics 111, 488–494.

    Article  Google Scholar 

  4. Biorklund M, van Rees A, Mensink RP, Onning G (2005). Changes in serum lipids and postprandial glucose and insulin concentrations after consumption of beverages with beta-glucans from oats or barley: a randomised dose-controlled trial. Eur J Clin Nutr 59, 1272–1281.

    CAS  Article  Google Scholar 

  5. Bird AR, Flory C, Davies DA, Usher S, Topping DL (2004). A novel barley cultivar (Himalaya 292) with a specific gene mutation in starch synthase IIa raises large bowel starch and short-chain fatty acids in rats. J Nutr 134, 831–835.

    CAS  Article  Google Scholar 

  6. Brand-Miller JC, Holt SH, Pawlak DB, McMillan J (2002). Glycemic index and obesity. Am J Clin Nutr 76, 281S–285S.

    CAS  Article  Google Scholar 

  7. Cashell K, English R, Lewis J (1989). Composition of foods. AGPS: Canberra, Australia.

    Google Scholar 

  8. Crovetti R, Porrini M, Santangelo A, Testolin G (1997). The influence of thermic effect of food on satiety. Eur J Clin Nutr 52, 482–488.

    Article  Google Scholar 

  9. Doucet E, St-Pierre S, Almeras N, Tremblay A (2003). Relation between appetite ratings before and after a standard meal and estimates of daily energy intake in obese and reduced obese individuals. Appetite 40, 137–143.

    Article  Google Scholar 

  10. Flint A, Raben A, Blundell JE, Astrup A (2000). Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies. Int J Obes Relat Metab Disord 24, 38–48.

    CAS  Article  Google Scholar 

  11. Foster-Powell K, Holt SHA, Brand-Miller JC (2002). International table of glycemic index and glycemic load values. Am J Clin Nutr 76, 5–56.

    CAS  Article  Google Scholar 

  12. Hallfrisch J, Scholfield DJ, Behall KM (1995). Diets containing soluble oat extracts improve glucose and insulin responses of moderately hypercholesterolemic men and women. Am J Clin Nutr 61, 379–384.

    CAS  Article  Google Scholar 

  13. Hill RJ, Davies PSW (2001). The validity of self-reported energy intake as determined using the doubly labeled water technique. Br J Nutr 85, 415–430.

    CAS  Article  Google Scholar 

  14. Holt S, Brand J, Soveny C, Hansky J (1992). Relationship of satiety to postprandial glycaemic, insulin and cholecystokinin responses. Appetite 18, 129–141.

    CAS  Article  Google Scholar 

  15. Holt SHA, Brand-Miller J (1995). Increase insulin responses to ingested foods are associated with lessened satiety. Appetite 24, 43–54.

    CAS  Article  Google Scholar 

  16. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM et al. (1981). Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr 34, 362–366.

    CAS  Article  Google Scholar 

  17. Jenkins DJ, Wolever TM, Kalmusky J, Giudici S, Giordano C, Wong GS et al. (1985). Low glycaemic index carbohydrate foods in the management of hyperlipidemia. Am J Clin Nutr 42, 604–617.

    CAS  Article  Google Scholar 

  18. Koh-Bannerjee P, Rimm EB (2003). Whole grain consumption and weight gain: a review of the epidemiological evidence, potential mechanisms and opportunities for future research. Proc Nut Soc 62, 25–29.

    Article  Google Scholar 

  19. Lavin JH, Read NW (1995). The effect on hunger and satiety of slowing the absorption of glucose: relationship with gastric emptying and postprandial blood glucose and insulin responses. Appetite 25, 89–96.

    CAS  Article  Google Scholar 

  20. Liljeberg HG, Akerberg AK, Bjorck IM (1999). Effect of the glycemic index and content of indigestible carbohydrates of cereal-based breakfast meals on glucose tolerance at lunch in healthy subjects. Am J Clin Nutr 69, 647–655.

    CAS  Article  Google Scholar 

  21. Liljeberg H, Bjorck I (2000). Effects of a low-glycaemic index spaghetti meal on glucose tolerance and lipaemia at a subsequent meal in healthy subjects. Eur J Clin Nutr 54, 24–28.

    CAS  Article  Google Scholar 

  22. Ludwig DS (2000). Symposium: dietary composition and obesity: do we need to look beyond dietary fat? Glycemic index and obesity. J Nutr 130, 280S–283S.

    CAS  Article  Google Scholar 

  23. Ludwig DS (2002). The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA 287, 2414–2423.

    CAS  Article  Google Scholar 

  24. Ludwig DS (2003). Dietary glycemic index and the regulation of body weight. Lipids 38, 117–121.

    CAS  Article  Google Scholar 

  25. Ludwig DS, Majzoub JA, Al-Zahrani A, Dallal GE, Blanco I, Roberts SB (1999). High glycemic index foods, overeating and obesity. Pediatrics 103, E26.

    CAS  Article  Google Scholar 

  26. Luscombe ND, Clifton PM, Noakes M, Fransworth E, Wittert G (2003). Effects of high-protein, energy-restricted diet on weight loss and energy expenditure after weight stabilization in hyperinsulinemic subjects. Int J Obes Relat Metab Disord 27, 582–590.

    CAS  Article  Google Scholar 

  27. Morell MK, Kosar-Hashemi B, Cmiel M, Samuel MS, Chandler P, Rahman S et al. (2003). Barley sex6 mutants lack starch synthase IIa activity and contain a starch with novel properties. Plant J 34, 173–185.

    CAS  Article  Google Scholar 

  28. Noakes M, Clifton PM, Nestel PJ, Le Leu R, McIntosh G (1996). Effect of high-amylose starch and oat bran on metabolic variables and bowel function in subjects with hypertriglyceridemia. Am J Clin Nutr 64, 944–951.

    CAS  Article  Google Scholar 

  29. Ostman EM, Liljeberg Elmstahl HG, Bjorck IM (2002). Barley bread containing lactic acid improves glucose tolerance at a subsequent meal in healthy men and women. J Nutr 132, 1173–1175.

    CAS  Article  Google Scholar 

  30. Porrini M, Crovetti R, Testolin G, Silva S (1995). Evaluation of satiety sensations and food intake after different preloads. Appetite 25, 17–30.

    CAS  Article  Google Scholar 

  31. Raben A (2002). Should obese patients be counselled to follow a low-glycaemic index diet? No Obes Rev 3, 245–256.

    CAS  Article  Google Scholar 

  32. Raben A, Agerholm-Larsen L, Flint A, Holst JJ, Astrup A (2003). Meals with similar energy densities but rich in protein, fat, carbohydrate, or alcohol have different effects on energy expenditure and substrate metabolism but not on appetite and energy intake. Am J Clin Nutr 77, 91–100.

    CAS  Article  Google Scholar 

  33. Raben A, Kiens B, Richter EA (1994). Differences in glycaemia, hormonal response and energy expenditure after a meal rich in mono- and disaccharides compared to a meal rich in polysaccharides in physically fit and sedentary subjects. Clin Physiol 14, 267–280.

    CAS  Article  Google Scholar 

  34. Raben A, Macdonald I, Astrup A (1997). Replacement of dietary fat by sucrose or starch: effects on 14 d ad libitum energy intake, energy expenditure and body weight in formerly obese and never-obese subjects. Int J Obes Relat Metab Disord 21, 846–859.

    CAS  Article  Google Scholar 

  35. Ritz P, Krempf M, Cloarec D, Champ M, Charbonnel B (1991). Comparative continuous-indirect-calorimetry study of two carbohydrates with different glycemic indices. Am J Clin Nutr 54, 855–859.

    CAS  Article  Google Scholar 

  36. Schafer G, Schenk U, Ritzel U, Ramadori G, Leonhardt U (2003). Comparison of the effects of dried peas with those of potatoes in mixed meals on postprandial glucose and insulin concentrations in patients with type 2 diabetes. Am J Clin Nutr 78, 99–103.

    Article  Google Scholar 

  37. Steward SL, Black RM, Wolever TMS, Anderson GH (1997). The relationship between the glycaemic response to breakfast cereals and subjective appetite and food intake. Nutr Res 17, 1249–1260.

    Article  Google Scholar 

  38. Tagliabue A, Raben A, Heijnen ML, Deurenberg P, Pasquali E, Astrup A (1995). The effect of raw potato starch on energy expenditure and substrate oxidation. Am J Clin Nutr 61, 1070–1075.

    CAS  Article  Google Scholar 

  39. Topping DL, Morell MK, King RA, Li Z, Bird AR, Noakes M (2003). Resistant starch and health – Himalaya 292, a novel barley cultivar to deliver benefits to consumers. Starch/Stärke 55, 539–545.

    CAS  Article  Google Scholar 

  40. Wolever TM, Jenkins DJ (1986). The use of the glycemic index in predicting the blood glucose response to mixed meals. Am J Clin Nutr 43, 167–172.

    CAS  Article  Google Scholar 

  41. Wolever TM, Jenkins DJ, Jenkins AL, Josse RG (1991). The glycemic index: methodology and clinical implications. Am J Clin Nutr 54, 846–854.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank David Topping, Russell Heywood, Kathryn Lawrence, Anne McGuffin, Julia Weaver, Rosemary McArthur, Ruth Pinches, Cherie Keatch and Candita Sullivan for their advice and assistance in performing these studies.

Author information

Affiliations

Authors

Corresponding author

Correspondence to P M Clifton.

Additional information

Guarantor: PM Clifton.

Contributors: JBK contributed to the manuscript and also to data analysis; CWHL carried out the study and contributed to data analysis and to the manuscript; MN contributed to the study design, data analysis, the manuscript and oversight of project; JB contributed to the design and conduct of study and the manuscript; PMC contributed to study design, data analysis, contribution to manuscript.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Keogh, J., Lau, C., Noakes, M. et al. Effects of meals with high soluble fibre, high amylose barley variant on glucose, insulin, satiety and thermic effect of food in healthy lean women. Eur J Clin Nutr 61, 597–604 (2007). https://doi.org/10.1038/sj.ejcn.1602564

Download citation

Keywords

  • barley
  • energy expenditure
  • satiety
  • energy intake

Further reading