Glycemic load and brain function -
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View by Specialty. Endocrinology Choose a specialty. Cardiometabolic Disorders Choose a specialty. Children were instructed to press a defined button each time a stimulus matched a stimulus 2 trials before. The test consisted of 21 correct trials.
Each stimulus was presented for a maximum ms with an interval of ms regardless of whether the participant responded within the limited time of ms or not. There was no positive or negative feedback. RT was calculated only for correctly responded trials. Measures of accuracy were the ratio of false alarms response to wrong trial and the ratio of missings no reaction to correct trial.
A simple response task was used with two white targets cross and circle individually presented on black background in the middle of the screen Fig. When a white circle appeared, the participants had to press a button as quickly as possible maximal RT ms , the cross required no reaction.
The outcome variables were the mean RT ms and the deviation of RT ms for speed and the number of omission errors no reaction after ms and the number of commission errors reaction during the presence of the cross for accuracy. All analyses were performed using the statistical software package IBM ® SPSS ® Statistics for Windows, version Interval-scaled parameters of the cognitive tasks were used as outcome variables switch task: switch costs, visual search letters, visual search numbers; 2-back task: RT, ratio of missings, ratio of false alarms; alertness: mean RT, Deviation of RT, count of omission errors, count of commission errors.
The sums of the two individual values of the outcome variables cognitive parameters of period 1 and 2 were compared between both sequence groups using an unpaired t test for normally distributed data and the Wilcoxon rank-sum test for non-normally distributed data to examine potential carryover effects of the treatment [ 21 , 22 ].
Since no carryover effects were observed, results from both days were considered for the treatment effect. Treatment effects were analyzed using a linear mixed model. First, non-normally distributed outcomes were transformed logarithm, square root, or reciprocal transformation.
Period, sequence and GI were treated as fixed effects, subjects as random. With this model period effects i. Because lunch portion size varied between participants, associations of GI with cognitive parameters were additionally adjusted for GL fixed effects: GL, period, sequence, GI; random effects: subjects.
Per protocol analysis was performed by excluding participants who did not follow the study protocol. Out of eligible children, confirmed their participation and met the inclusion criteria Fig. Written informed consent was available for children.
Finally, a modified intention-to-treat analysis was performed with data from children who participated on both experimental days. For per-protocol-analysis, 21 children were excluded who did not follow the instructions for refraining from eating anything besides the study food between lunch and cognitive testing.
Sex distribution and estimated GL, stratified by sequence group h-mGI and m-hGI are presented in Table 1. Overall, the average serving size was g of the total weight of the meal, that of rice g. The GL of the meal consumed with hGI rice Jasmin Rice was significantly higher Table 1.
Participants ate less of the medium GI rice on the second test day. Performing per-protocol analysis, considering only participants who had fully adhered to the study protocol, showed similar results of average serving size Table S1. Not all participants completed all tests: results from two participants were missing for 2-back task and one for alertness.
Of the included participants, 33 had to be excluded for task switching analysis due to implausible negative switch costs at least one of the trials was not finished in time.
Intention-to-treat analysis revealed no significant differences between lunches based on mGI or hGI rice for the examined cognitive parameters, switch costs, visual search letters and visual search numbers Table 2. For two parameters period effects were detected.
Switch costs improved in period 2 compared to period 1 in both groups as did the RT for visual search letters. Performing per-protocol analysis showed similar results of cognitive performance Table S2. None of the tested parameters showed statistically significant differences.
After consumption of lunch with hGI rice the ratio of missings was slightly higher Table 2. However, after applying Bonferroni correction these differences vanished.
The RT and ratio of false alarms did not differ between mGI and hGI. However, the ratio of false alarms improved in period 2 but at the same time ratio of missings increased.
Per-protocol analysis revealed no statistically significant differences in tested parameters between mGI and hGI Table S2. No parameter of the attention task, i. However, period effects were detected. The mean RT and the deviation of RT of the alertness task increased in the second period.
In addition, even after excluding participants who did not follow the protocol, no differences were detected between mGI and hGI. Estimated lunch GL differed significantly between both periods Table 1.
The GL of lunch with hGI rice was higher in both periods compared to that with the GL of lunch with mGI rice. However, models including GL as a covariate revealed no significant GI effects on any cognitive outcome parameter Table 3. Performing per protocol analysis by excluding participants who had not fully adhered to the study protocol showed no differences of the cognitive performance between both intervention groups as well Table S3.
The current study aimed to investigate whether the dietary GI of lunch had short-term effects on cognitive performance of schoolchildren 90 min after lunch. In our previous CogniDO GI study, we did not see differences in selected cognitive parameters after eating lunch differing in estimated dietary GI at an interval of 45 min within the same crossover approach.
Extending the postprandial time to 90 min still had no influence. Overall, studies on the interrelations of lunch and cognition are scarce, in adults as well as in children. So far, only studies investigating effects of lunch on vigilance per se are available. It was shown that eating lunch can cause a post-lunch dip with impaired cognitive performance in adults [ 23 , 24 ].
Herein, negative effects of lunch seem to increase with age [ 25 ], which we supported in our previous studies showing that children do not suffer from post-lunch dip but even might profit from lunch [ 13 , 18 , 19 ].
However, studies on the estimated dietary GI of lunch and cognition have not been evaluated, yet. The majority of studies investigating GI effects on cognitive performance in children focused on breakfast.
For instance, two studies showed that a breakfast with lower GI foods enhanced cognitive functions with respect to reaction speed, accuracy, and in part spatial memory [ 9 , 27 ]. On the contrary, Smith et al. revealed that breakfast with high GI foods improved verbal episodic memory in adolescents age 14—17 years after 50 and 90 min [ 11 ].
The reasons why our study intervention revealed any cognitive changes are probably manifold. The main difference between studies on breakfast and lunch is that influences of foods with different GI consumed at breakfast were examined after overnight fasting.
The glycemic response after a fasting period for 8—12 h might be more pronounced. For instance, Ogata et al. showed that skipping breakfast leads to much more pronounced glycemic response after eating lunch in healthy young individuals [ 28 ], thus fasting time before having a meal seems to be of relevance.
This is in line with a recent study of 10—year-old children in a school-based testing environment, which showed that an ad libitum breakfast improved reaction speed, visual-sustained attention, and visual—spatial memory [ 29 ]. Typically, blood glucose concentrations return to baseline or below 3—4 h after ingestion.
Thus, the time frame between breakfast and lunch might have been too short in our intervention design for blood glucose levels to return to baseline. Overall, it is difficult to draw conclusions about the acute effects of lunch composition on cognition.
The conflicting results of breakfast studies with children on attention and working memory tasks suggest that metabolic criteria such as body weight and the glucose-mediated insulin response, as well as intervention adherence and lunch type are relevant [ 15 , 32 ].
Another important factor is the GI difference between both rice types. Based on data from others, there can be significant differences in the GI between basmati rice and jasmine rice low GI vs. high GI [ 33 ]. These rice types have already been chosen for our pervious study after testing for sensory properties and their acceptance by children.
To ensure comparability with the current study the same rice types were used. GI-values changed from formerly 62 vs. Jasmine rice to 64 vs. Possibly, the differences between both rice types with regard to the GI were not sufficient to detect effects on cognition. A controlled laboratory environment might have delivered more unequivocal results, but would have lacked transferability into everyday life.
Nevertheless, we chose to analyze the selected rice types in a certified lab according to ISO standards instead of solely relying on theoretical reference values.
However, the study has also some limitations. Although breakfast was standardized in terms of food components, the amount of breakfast consumed was not controlled thereby potentially influencing the outcome. Equally important, blood glucose regulation varies individually [ 15 ]. Baseline and post-intervention glucose measurements as well as information on body composition e.
However, it is a common difficulty in pediatrics that parents are not willing to consent to extensive examination, particularly in the case of healthy children. Furthermore, our study design lacks baseline and late postprandial cognitive assessments.
In addition, the cognitive test run lasted 45 min so that cognition was recorded in a time frame of 90— min. Together with our previous study in which we examined the same cognitive parameters 45 min after lunch [ 12 ], a very broad time window was covered. Short-term postprandial effects of the GI of foods on cognition might be of more relevance in the morning.
Whether other nutrients have short-term effects on cognition in children under real-life conditions, needs to be established.
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Patindol JA, Siebenmorgen TJ, Wang YJ Impact of environmental factors on rice starch structure: a review. Download references. The authors thank all participants and teachers, the school management and kitchen staff for supporting this study. We acknowledge Jennie Brand-Miller and Fiona Atkinson from SUGiRS for GI measurements and Ludger Blanke ALA Institute for the excellent technical support of the cognition tests.
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