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The glycaemic potency of breakfast and cognitive function in school children

Abstract

Objectives:

The aim of this study was to assess how the glycaemic potency (blood glucose (BG)-raising potential) of breakfast is associated with cognitive function (CF) in school children, taking into account important confounders, including iron status, underlying physiological adaptations and socio-economic status.

Methods:

Sixty children aged 11–14 years were selected on the basis of having breakfast habitually. Their breakfast and any snacks eaten on the morning of the study were recorded. They were categorized into four groups according to the glycaemic index (GI) and glycaemic load (GL) of the breakfast: low-GI, high-GL; high-GI, high-GL; low-GI, low-GL and high-GI, low-GL above or below the median for GI=61 and GL=27. BG levels were measured in finger-prick blood samples immediately before and immediately after the CF tests.

Results:

A low-GI, high-GL breakfast was associated with better performance on a speed of information processing (P<0.01) and a serial sevens (P<0.001) task 90 min later; a high-GI breakfast with better performance on an immediate word recall task (P<0.01); and a high-GL breakfast with better performance on a Matrices task (P<0.01).

Conclusions:

GI, GL or both were associated with performance on the majority of the CF tests (4 of 7) used. This study describes the macronutrient composition of breakfast that could have a positive influence on the cognition of school children, proposes the use of both GI and GL to estimate exposure, and discusses future directions in this area of research.

Introduction

The plethora of studies investigating the effects of breakfast on cognitive function (CF) support the hypothesis that skipping breakfast adversely affects cognition, and that the brain is thus vulnerable to brief fasting (Pollitt et al., 1981, 1982; Conners and Blouin, 1982; Simeon and Grantham-McGregor, 1989; Chandler et al., 1995; Vaisman et al., 1996; Benton and Parker, 1998; Smith et al., 1999; Wesnes et al., 2003; Mahoney et al., 2005). Despite the wealth of studies that have been conducted in this area, the macronutrient composition of breakfast that could selectively facilitate CF after an overnight fast is not well established. The relationships between breakfast consumption and CF in school children are not clear, with considerably conflicting results in prior studies, in part due to differences in study design and the type of breakfast consumed, as well as lack of control for potentially important confounders, including iron status (Halterman et al., 2001; Gordon, 2003), variation from habitual breakfast eating habits (Lloyd et al., 1996), timing and selection of CF tests (Dye et al., 2000), the underlying physiological adaptations (Dolan, 2002), and socio-economic status (Bradley and Corwyn, 2002).

Research on glucose consumption and CF (Kennedy and Scholey, 2000; Scholey and Kennedy, 2004) suggests that the brain may be sensitive to short-term fluctuations of glucose supply, and thus supports the hypothesis that glucose content may induce the memory-enhancing effects of breakfast, by producing metabolic alterations, mainly changes in circulating glucose levels (glycaemic response) (Dye et al., 2000; Gibson and Green, 2002; Messier, 2004; Riby, 2004; Hoyland et al., 2008). As pure glucose will be rarely consumed as part of a normal diet, current research has focused on varying the carbohydrate (CHO) content of breakfast meals, and hence their subsequent glycaemic responses (that is, the potential mediator), to investigate the effects of breakfast consumption on CF (Benton et al., 2003, 2007; Mahoney et al., 2005; Ingwersen et al., 2007). The effects of CHO-containing meals on blood glucose (BG) can be modelled by using the glycaemic index (GI) (Jenkins et al., 1981) and the glycaemic load (GL) (Salmeron et al., 1997). When foods with equal amounts of CHO content are compared, foods with high-GI induce a greater rise and fall in BG and insulin, leading to lower concentrations of the body's two main fuels (BG and fatty acids) in the immediate post-absorptive period. Thus, GI reflects the qualities of CHO in relation to digestion and absorption, but not the amount. Conversely, GL considers both the amount and type of CHO (that is, GL=GI × CHO); in fact, GL predicts BG in an approximately linear manner (that is, higher the GL, higher the BG response) (Brand-Miller et al., 2003). Previous studies have considered either GI or GL, but not both when investigating the effects of different CHO-containing breakfast meals on CF. It has been hypothesized that a low-GI meal versus a high-GI meal can facilitate performance by minimizing glycaemia fluctuations, and a high-GL meal versus a low-GL meal can facilitate performance by preserving the substrate (that is, higher circulating glucose levels) for a longer period of time (1–2 h after a meal) (Benton et al., 2003, 2007; Mahoney et al., 2005; Ingwersen et al., 2007). Nonetheless, although for example a low-GI, high-CHO meal and a high-GI, low-CHO meal can produce the same GL, the metabolic effects produced by the two meals will be different (Barclay et al., 2005). Thus, GI and GL should be used in conjunction to best describe the glycaemic potency (BG-raising potential) of a food or meal (ADA, 2004).

To address this important question and elucidate the conflicting results of previous studies, we investigated whether, after an overnight fast, breakfasts differing in both their GI and GL are associated with differences in CF. We focused on adolescents aged 11–14, as previous work has focused on younger children (Wesnes et al., 2003; Mahoney et al., 2005; Ingwersen et al., 2007). We took into account the confounders listed above and used CF tests reported to be sensitive to variations in the glucose supply. It could be argued that a low-GI meal would minimize glycaemia fluctuations and facilitate performance for longer after breakfast consumption compared with a high-GI meal, and that a high-GL meal would potentiate the glycaemic potency of the meal. We therefore hypothesized that 90–120 min after breakfast (a) a low-GI, high-GL breakfast would be associated with the best CF, a high-GI, low-GL would be associated with the lowest CF, and the other two breakfast types would be associated with intermediate levels of CF; and (b) the high-GL meals would be associated with higher BG levels compared with the low-GL meals (Ludwig, 2002).

Methods

Study design

Sixty pupils (24 boys, 36 girls) aged 11–14 years from two schools in South London participated; all of them were in good health and free from learning disabilities. Children who said that they never had breakfast were excluded. The study was approved by the King's College's Research Ethics Committee.

Questionnaires

A screening questionnaire was filled in by the parents/carers to determine socio-economic group (Government Statistical Service, 1990), and to exclude children for medical reasons (that is, anaemia or other blood disorders, food allergies, diabetes or glucose intolerance, other acute or chronic illnesses/diseases, colour blindness, severe learning disabilities and mood disorders). The type of breakfast typically consumed and the usual breakfast eating habits were also recorded.

A self-rating mood questionnaire was developed from the Profile of Mood States bipolar form (POMS-BI) (Lorr and McNair, 1984) and the short form of the Activation-Deactivation Adjective Checklist. It was modified from previous research (Rogers et al., 1995); 22 terms were used to assess mood, energy levels, hunger and thirst. Responses were made on integer scales from zero (not at all) to four (extremely) (see Appendix A for the list of 22 mood states assessed).

In a self-reported task demand questionnaire, participants rated how difficult, effortful and tiring they found the tests to be, using the same rating scale.

Interview

Participants were interviewed about their eating habits, physical activity, current health status, medication/supplements, sleeping patterns, and menstrual status, including food and drink consumed at home on the morning of the CF testing (‘breakfast’); anything else eaten or drunk on their way to school or since arriving at school (‘snack’); and the last meal they had the night before their appointment (‘dinner’). A photographic atlas of food portion sizes (Nelson et al., 1997) was used to quantify the amounts of food and drink consumed. The pupils were weighed in their school uniform, after removing their blazer and shoes (Salter scale) and measured for height (Chasmores Ltd portable stadiometer).

Blood measurements

Duplicate finger-prick blood samples were obtained before (mean±s.e.: 105.1±3.1 min after breakfast) and after the CF tests (149.2±3.2 min after breakfast). Haemoglobin (Hb) was measured using a ‘HemoCue’ 201+ analyzer (coefficient of variation (CV)=1.3%) (HemoCue Ltd, Lake Forest, CA, USA). Blood glucose was measured using a plasma-calibrated Glucose Freestyle Mini meter (CV<5%) (Abbott Laboratories, Maidenhead, UK) (ADA, 1996; DH, 2005).

CF tests

CF tests were those used in previous studies to detect differences in CF induced by glucose administration (Donohoe and Benton, 1999; Kennedy and Scholey, 2000; Sunram-Lea et al., 2001). The tests were administered in the same order: word generation task (1), immediate word recall (2), Stroop task (3), matrices (4), number search task (5), serial sevens (6) and delayed word recall (7) (see Appendix A for a detailed description of the CF tests).

Procedure

Each subject was seen once between 0945 and 1015 hours, 90–120 min after the start of usual breakfast, as stated on the parents' screening questionnaire. If a participant had eaten a ‘snack’ as well, they were seen 90 min after the start of the snack if it contained >10 g CHO.

The order of procedures was as follows: anthropometric measurements; first finger-prick blood sample; mood scales (before); CF tests; mood scales (after); second finger-prick blood sample; task demand questions; interview.

Computation of GI and GL, and classification of pupils

Microdiet (Downlee Systems Ltd, High Peak, UK) was used for nutrient analysis. The GI value of the individual foods that comprised ‘breakfast’, ‘snacks’ and ‘dinner’ was derived from published sources (Foster-Powell et al., 2002). The GI of the composite meals was calculated as the sum of the weighted GI values of the foods comprising the meal (Wolever and Jenkins, 1986). The GL of a food was calculated as follows (Salmeron et al., 1997):

The GL of a meal was calculated as the sum of the GL values of the individual foods. The GI and GL values included snacks contributing 10 g or more of CHO. Participants were classified using a 2 × 2 GI × GL grid for breakfast: above or below the median for GI=61 and for GL=27.

Statistical analysis

To establish whether the four GI × GL groups as classified by the median for GI and GL were well matched, we performed one-way analysis of variance (ANOVA) to assess the mean differences in the descriptive characteristics (age, height, weight, body mass index), the BG and Hb levels, and the GI, GL and macronutrient composition of the breakfast between the four GI × GL groups; Bonferroni post hoc tests were further performed to assess the statistically significant differences in the macronutrient composition of the breakfasts corresponding to the GI × GL classification. Two-way ANOVA was used to assess differences in the CF test scores between the four GI × GL groups, using GI and GL as main factors, and gender and socio-economic group as potential confounders. Further potential confounders were explored by performing correlation and multiple regression analyses, including the order of administration of the tests; ‘having a snack’; descriptive characteristics (age, height, weight, body mass index); finger-prick blood measurements (Hb, BG); total energy, protein, fat and CHO content of the breakfast eaten; the GI and GL of the dinner the night before (52±1.6 and 46±3.9, respectively); hours of sleep (8.3±0.2); time between breakfast and the first CF test (113±3 min); time between waking up and the first CF test (149±4 min); mood scores before the CF tests; and exercise on the day and the day before. The significant potential confounders (in addition to gender and socio-economic group) identified, including age, height, weight, body mass index, Hb levels, BG levels, ‘happy’ mood score before the CF tests, and time between breakfast and the first CF test, were all initially included as covariates in the two-way ANOVA. The ANOVA model for each CF test was then further refined, removing the non-significant interactions first (starting with the non-significant interaction with the highest P-value), then removing the non-significant factors and covariates, until all of the non-significant interactions, factors and covariates had been removed from the model. Correlation analyses were further performed between the mood scales ‘after’ and the task demand scores and CF; either measure could not be considered as a predictor of performance on the CF tests as it was recorded on completion of the testing. Nonetheless, mood scales ‘after’ and task demand scores were separately considered to uncover potential associations between these measures and cognitive performance. All P-values were two-tailed (alpha=0.05). All analyses were performed using SPSS 15.0.

Results

Table 1 presents the descriptive characteristics of the sample. The four GI × GL groups were well matched, with no significant differences in age, height, weight, Hb, body mass index or BG. BG was higher in the high-GL group before the tests compared with the low-GL group (105.6±2.1 versus 99.4±1.7 mg/dl, P=0.025), and the decrease in BG (−2.6±2.6 mg/dl) in the high-GL group was statistically significantly different from the rise in BG (4.7±2.3 mg/dl) in the low-GL group (P=0.04), suggesting that BG levels were returning to baseline for the high-GL meals and recovering from baseline for the low-GL meals. These two findings support the original expectation that between 90 and 120 min after breakfast, high-GL meals would be associated with higher BG levels.

Table 1 Descriptive characteristics, GI–GL values and finger-prick blood measures in 60 children participating in the study, in all children and in the four GI and GL groups

Table 2 presents the macronutrient composition of breakfast corresponding to the GI × GL classification. The four breakfast meals differed in their energy content, and % energy from fat and CHO; % energy from protein was not statistically different between the breakfast meals. Although the two low-GL meals (high-GI and low-GI) had similar energy content, the low-GI, high-GL meal had 32% higher energy content than the high-GI, high-GL breakfast. Post-hoc tests showed that this difference between the two high-GL meals was not statistically significant, and that it was evident between the low-GI, low-GL and the low-GI, high-GL breakfast (P=0.003); % energy from fat and CHO differed between the low-GI, low-GL and high-GI, high-GL meal (P<0.05 for both). Table 3 shows examples of the breakfasts corresponding to the GI and GL classifications.

Table 2 Macronutrient composition of breakfast in 60 children participating in the study, in all children and in the four GI and GL groups
Table 3 Examples of breakfast meals corresponding to the GI and GL classification

The reported mood before the CF tests was considered as a potential predictor of performance on the CF tests (see Supplementary Table for average mood scores before the CF tests among the four GI and GL groups). In general, before the tests ‘positive’ feelings such as ‘friendly’, ‘happy’, ‘relaxed’ and ‘calm’ were significantly correlated with lower scores on a number of CF tests, whereas other mood states, including hunger and thirst, were not associated with CF; the most consistent finding was that the more ‘happy’ a child the worse their performance (data not shown). After the tests ‘negative’ feelings such as ‘drowsy’, ‘tired’ and ‘sluggish’ were generally associated with higher CF scores. Correlation analyses between self-reported task demand and CF showed that higher perceived difficulty was significantly associated with lower scores on all CF tests performed; perceived effort and tiredness were not significantly associated with CF scores (data not shown). On the basis of students' scores, serial sevens (3.0±0.1) and speed of information processing (2.7±0.1) were the two most difficult tasks (Friedman test, P<0.001).

Table 4 shows mean performance scores across the four GI × GL groups. Among several potential confounders explored, the order of administration of the tasks, ‘having a snack’, the macronutrient composition of the breakfast, the GI and GL of the dinner the night before, hours of sleep, time between waking up and the first CF test, mood states before the testing (with the exception of feeling ‘happy’), and exercise on the morning of the testing and the night before were unrelated to the CF tasks. Only the significantly associated confounders were included as covariates in the two-way ANOVA between the four GI × GL groups and CF test scores. The results of the two-way ANOVA taking GI, GL and all relevant covariates into account are summarized in Table 5. Performance on the word generation, Stroop and delayed word recall task was not significantly associated with either GI or GL. The remaining four tasks, immediate word recall, matrices, speed of information processing and serial sevens, were significantly associated with either GI, GL or both. High-GI meals were associated with better performance on the immediate word recall task; low-GI meals with better performance on the speed of information processing and serial sevens task; and high-GL meals with better performance on the matrices, speed of information processing and serial sevens task. Overall, findings were similar for boys and girls (that is, gender was not a statistically significant associated covariate), with the exception of the serial sevens task, where boys performed better than girls.

Table 4 Average cognitive function test scores among the four GI and GL groups
Table 5 Cognitive function performance and statistically significantly associated factors

Discussion

The main finding of our study is that four of seven CF tests administered were associated with either GI or GL, or both. The method of classifying participants by both GI and GL has proved useful for investigating the effects of the glycaemic potency of breakfast on CF. Specifically, high-GI was associated with better immediate recall (short-term memory), high-GL with better matrices performance (inductive reasoning), and low-GI and high-GL with better speed of information processing (vigilance, sustained attention) and serial sevens performance (vigilance, working memory). We hypothesized that a low-GI, high-GL breakfast would be associated with the best CF. Our findings are therefore consistent with the hypothesis for a low-GI, high-GL effect for the speed of information and serial sevens task, and for a high-GL but not a GI effect for the matrices. It is not consistent with the hypothesis for a low-GI effect for the immediate word recall.

The low-GI, high-GL breakfast was associated with better scores on the two CF tasks (serial sevens and speed of information processing) that were reported by the participants to be the most difficult (that is, mentally demanding). This finding is consistent with previous studies (Owens et al., 1997; Donohoe and Benton, 1999; Scholey et al., 2001; Sunram-Lea et al., 2001) showing that in order for an effect of glucose on cognition to be observed, the tasks need to be sufficiently mentally demanding. Although it is difficult to quantify cognitive demand, the duration of the task, its complexity and time pressure probably all contributed to the ratings of cognitive demand obtained in this study. Thus, it could be argued that a low-GI, high-GL breakfast could selectively facilitate mentally demanding CF tasks. Nonetheless, GI or GL were also shown to be associated with performance on the immediate word recall (high-GI) and matrices (high-GL) tasks, which were not ranked by the participants among the most difficult tasks. Therefore, it appears that GI might be differentially associated with different cognitive domains; low-GI was associated with improved performance on two vigilance tasks (how quickly information is being processed), but worse performance on a memory task. The reason for this difference remains unclear, although potential unmeasured interactions between gluco-regulatory processes, arousal and subsequent cortisol secretion could partially account for that. Lastly, the order of administration of the tests was not significantly associated with CF performance, suggesting that testing fatigue (that is, the later tests only being affected) was not driving the observed associations.

Only recently has GI or GL been used as a tool to assess the effects of CHO-containing foods or meals on CF. In 71 young female adults, a low-GI breakfast cereal predicted improved performance only on the difficult abstract words of a word recall task 150 and 210 min after breakfast, but not earlier (Benton et al., 2003). Mahoney et al. (2005) administered oatmeal (low-GI), ready-to-eat cereal (high-GI) or no breakfast to school children aged 9–11 (15 males, 15 females) and 6–8 (15 males, 15 females). Spatial memory and short-term working memory (in girls only) in both age groups, and auditory attention in 6–8 year olds, were improved 1 h after the low-GI breakfast only. Visual recall memory, visual attention and verbal memory (recall) were not affected. Benton et al. (2003) and Mahoney et al. (2005) considered GI (but not GL) and maintained the same energy and macronutrient content for the two breakfasts. Ingwersen et al. (2007) found that in 64 children (26 boys, 38 girls) aged 6–11, there was less decline in accuracy of attention and recall memory (visual and verbal) 2 h after a low-GI breakfast as compared with a high-GI breakfast. Working memory, speed of memory and vigilance were not affected. The latter study did not control for GL; furthermore, the macronutrient composition of the meals was different (high-GI cereal: 133 kcal, low-GI cereal: 98 kcal; 36% difference in energy alone). To test if GI alone is having an effect, the macronutrient composition should be similar and only the CHO-source varied. In the study by Benton et al. (2007), which included 9 boys and 10 girls aged 6–7, no associations were observed between GL and any of the CF domains assessed, that is memory (visual recall, spatial memory) and the ability to sustain attention. This study did not control for GI, and the macronutrient composition of the meals was different. Among 189 adult females, high-GL predicted improved reaction times and vigilance, only in people with better gluco-regulation; verbal memory was unaffected (Nabb and Benton, 2006). Nonetheless, this study was not designed to investigate the effects of GI or GL, and it was unbalanced.

Across all of these studies (including this study), there are inconsistencies regarding all cognitive domains, including declarative verbal memory and memory recall. One would expect that these measures would be consistently affected by GI and/or GL, as glucose administration has been shown to improve both domains (Owens et al., 1997; Donohoe and Benton, 1999; Scholey et al., 2001; Sunram-Lea et al., 2001). Nonetheless, a CHO-rich breakfast may not produce the same effects on CF and mood as glucose, as it will be different in terms of absorption rates, gastric emptying, metabolic effects and secondary hormonal responses. Furthermore, the tasks selected between studies are not identical; therefore, lack of sensitivity of the task, rather than lack of an effect, could account for the inconsistencies. Most importantly though, none of these studies investigated the effects of both GI and GL, and with the exception of two (Benton et al., 2003; Mahoney et al., 2005), none of the remaining controlled for GL when investigating GI (Ingwersen et al., 2007), and vice versa (Nabb and Benton, 2006; Benton et al., 2007).

Specifically, there should be a distinction between a low glycaemic response as determined by both GI and GL (the recommended approach) and a low glycaemic response as determined solely by GI or GL. The literature to-date generally predicts that a low glycaemic response is beneficial, but it does not distinguish between a high, intermediate and a truly low glycaemic response (that is, the lowest among the meals compared when both GI and GL are taken into account). This lack of distinction can be attributed to none of the studies in this field assessing the impact of both GI and GL. Therefore, when a low glycaemic response is suggested as beneficial, this should be interpreted with caution, as results for the same test can differ according to whether GI (Ingwersen et al., 2007) or GL (Nabb and Benton, 2006) is measured. Future studies in this field should investigate whether low-GI and high-GI meals selectively facilitate different cognitive domains (for example, low-GI facilitating vigilance, high-GI facilitating memory or vice versa, as there are no firm findings supporting either one) by controlling for GL, and including a selection of tests that assess both mnemonic processes and vigilance. High-GL seems to be more consistently associated with improved performance (present findings; Nabb and Benton, 2006). Also, other studies have not reported the possible impact of mood as a confounder. The finding that pupils who were feeling ‘happy’ did less well might be explained because they were not motivated/aroused enough by the testing or the mental load, and hence did not perform as well.

There are important limitations that need to be considered. First, the cross-sectional design means the observed associations may not be causal, leaving open the possibility that the relationships observed might be explained by unmeasured confounders. Second, the GI and GL values are based on what the participants reported having eaten for breakfast; this age group is prone to under-reporting (NDNS, 2000). Any resulting misclassification may somewhat underestimate the observed associations.

Third, the low-GI, high-GL breakfast had the highest energy content in comparison with the other three meals. It would have been desirable that within the same GL groups, not only the energy content but also the macronutrient composition to be similar for the GI meals. (It would, of course, differ between the GL groups). Although this difference in self-selected meals was not statistically significant, it could have promoted higher BG levels and, thus (assuming the hypothesis is correct), better performance 90–120 min later. While this was not evident from the present findings, the effect of the energy, fat and protein content per se cannot be strictly differentiated from the effect of the glycaemic potency. A further extension of this problem relates to some differences in the macronutrient composition of the meals. Nonetheless, the differences in composition do not explain differences in CF as consistently as the observed combined effects of GL and GI in the two CF measures affected, the high-GL and the low-GI groups performing better (in accordance with the hypothesis).

Another limitation is that any snacks that contained <10 g of available CHO were not included in the total breakfast meal. These snacks were excluded on the basis that they would not change BG levels, and to provide consistent classification of breakfast GI and GL in relation to the timing of the CF tests. Nonetheless, it should be noted that introduction of other macronutrients into the stomach might, to a small degree, alter the absorption and digestion profile of the breakfast meals, thus potentially altering glycaemic, insulinaemic and other hormonal responses that in turn might have an effect on CF test scores. However, ‘having a snack’ did not appear as a significant factor predicting CF test outcomes.

This study, like other studies in the field (Benton et al., 2003; Mahoney et al., 2005; Nabb and Benton, 2006; Gibson, 2007; Ingwersen et al., 2007), did not have a baseline measure of performance, as the interest was in short-term differences of high-GI or low-GI and high-GL or low-GL meals on CF, and not on whether there is an improvement or decline in overall CF as a result of a meal. In all these studies, the GI calculations were based on published values (Foster-Powell et al., 2002), which may have introduced error in the estimation of the exposure. Any resulting misclassification would be likely to attenuate the significant associations observed here rather than reveal associations, which do not exist.

Despite these limitations this study suggests that the GI and GL of breakfast may affect performance in specific cognitive domains and under real-life conditions, and that performance in the classroom might be better after a low-GI, high-GL breakfast. This study is the first of its kind to consider both GI and GL when assessing the effects of breakfast on CF in teenage school children, and to show that both are associated with specific cognitive outcomes. The fact that performance in only four of the seven tests administered was associated with differences in GI and GL does not undermine the overall findings. There was never the expectation that all of the CF tests would be affected. Indeed, theory predicts that differences in gluco-regulatory processes and cortisol secretion under stress (that is, arousal) may differentially affect performance after administration of meals differing in their GI and/or GL (Gibson, 2007); to what extent remains to be elucidated.

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Acknowledgements

All authors revised the manuscript for important intellectual content, and approved final manuscript for submission. We are grateful to Ms Julia Forbes and Ms Kathryn Lowes, who helped in carrying out fieldwork and data coding, and to all the enthusiastic volunteers who participated in this trial. Financial support was obtained from the Harokopeio University PhD scholarship.

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Correspondence to R Micha.

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The authors declare no conflict of interest.

Additional information

Contributors: RM performed statistical analysis, data interpretation, selected cognitive function tests and prepared the manuscript. PR selected and developed the cognitive function tests, mood scales and task demand questionnaire. MN supervised the project, statistical analysis, data interpretation and manuscript preparation. All authors revised the manuscript for important intellectual content, and approved final manuscript for submission.

Supplementary Information accompanies the paper on European Journal of Clinical Nutrition website

Supplementary information

Appendix A

Appendix A

List of 22 mood states assessed

  1. 1

    Friendly

  2. 2

    Nervous

  3. 3

    Drowsy

  4. 4

    Happy

  5. 5

    Calm

  6. 6

    Uncertain

  7. 7

    Sad

  8. 8

    Energetic

  9. 9

    Muddled

  10. 10

    Relaxed

  11. 11

    Dissatisfied

  12. 12

    Alert

  13. 13

    Confident

  14. 14

    Tired

  15. 15

    Angry

  16. 16

    Contented

  17. 17

    Lively

  18. 18

    Tense

  19. 19

    Sluggish

  20. 20

    Clearheaded

  21. 21

    Hungry

  22. 22

    Thirsty

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Micha, R., Rogers, P. & Nelson, M. The glycaemic potency of breakfast and cognitive function in school children. Eur J Clin Nutr 64, 948–957 (2010). https://doi.org/10.1038/ejcn.2010.96

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Keywords

  • breakfast
  • glycaemic index
  • glycaemic load
  • cognitive function
  • school children

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