Carbohydrates and Cognitive Function -
Negative switch costs were regarded as implausible and excluded. To assess capability in holding and manipulating information for a short time, the n -back task was used in a two-back condition. Subjects were asked to monitor a sequence of consecutive pictures of fruits and vegetables presented in the middle of the screen Fig.
No feedback was given. Twenty-one pictures were targets same picture as two trials before. The outcome variables were: ratio of missings no reaction while reaction was required , ratio of false alarms reaction while no reaction was required , and mean RT while reaction was required.
A simple reaction task was used to measure tonic alertness. A white fixation cross was presented on a black screen Fig. The test included 50 items. All analyses were performed using the statistical software package IBM® SPSS® Statistics for Windows, version The parameters of the cognitive tasks were used as outcome variables, all interval-scaled.
Statistical analysis was performed in accordance with the Consolidated Standards of Reporting Trials statement and Wellek and Blettner [ 18 , 19 ]. The sums of the two individual values of the outcome variables of period 1 and 2 were compared between both arms 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.
If no carryover effects were observed, results from both days were considered for the treatment effect. Briefly, individual differences of the particular outcomes of both test days test day 1—test day 2 were compared between both sequences hGI—mGI vs.
In case of carryover effects, only results from day 1 were considered. Associations of GI with cognitive parameters were adjusted for GL using a linear mixed model. In addition, period effects were determined with this model.
GL, period, and GI were treated as fixed effects, subjects as random. Of these, four were excluded due to diagnosed learning disorder, one student did not eat lunch on one of the test days Fig. Thus, intention-to-treat analysis was performed using cognitive performance data of subjects. Characteristics of the included participants are shown in Table 1.
Statistical analyses revealed no significant differences between lunch based on medium or high GI rice for most parameters of the selected cognitive outcomes Table 2.
For one outcome of the two-back task RT and one of the alertness task count of commission error carryover effects were detected. Thus, statistical analysis was only performed for period 1. Eating lunch with hGI rice resulted in shorter RT compared with lunch with mGI rice.
Similarly, number of errors of the alertness task were lower after lunch with hGI rice. There were period effects for some parameters: switch costs improved in period 2 compared with period 1 in both groups while the RT for visual search numbers slowed down.
Within the two-back task and the alertness task, RT and the ratio of false alarms decreased in both groups in period 2 while the ratio of missings increased. Estimated meal GL differed significantly between both periods, while the amount of lunch consumed did not differ Table 1.
Including the estimated meal GL as covariate in the analysis revealed no significant association with cognitive parameters for either GI or GL Table 3.
The outcomes were not different from the results described above data not shown. In our previous CogniDo studies, lunch caused no decline in cognitive performance, even indicating small positive effects [ 15 , 16 ]. In the present study, we investigated for the first time short-term cognitive effects of carbohydrate-rich lunch differing in GI.
Our results indicate small but significant effects favoring hGI rice with regard to two of the tested cognition parameters: working memory updating two-back task: RT and alertness alertness task: count of commission errors , respectively, although only results from period 1 could be considered for this analysis due to carryover effects.
A carryover effect is defined as the effect of the treatment from the first period on the response at the second period.
Accordingly, rice GI would have a lasting impact on cognition for over a week. Thus, our results suggesting that hGI rice would improve these two parameters of cognitive performance in period 1 should be interpreted cautiously. It may be possible that the cognitive parameters of the two groups differed from the start, independent of the consumed rice.
However, cognitive improvements by a dish based on a high GI food are in line with findings from Micha et al. showing an improved short-term memory and vigilance in 11—year-old children after eating a hGI breakfast [ 20 , 21 ]. On the contrary, Cooper et al. Similarly, hGI breakfast has been associated with a significant decline in accuracy of attention compared with low-GI breakfast [ 9 ].
Apart from the differences, we found for the two cognitive parameters in period 1, none of the other parameters were affected by lunch composition within this crossover study.
Some studies investigating the influence of GI in breakfast found positive cognitive effects for either low or hGI. This inconsistency compared with our study could be due to the prolonged fasting before breakfast. In our study, all participants were offered a standardized breakfast to ensure the same conditions for the cognition tasks.
In addition, even though breakfast was standardized with regard to the food ingredients, the amount of breakfast consumed was not controlled. This might have influenced the outcome after lunch.
Moreover, it has to be considered that rice was served with ground beef sauce, which makes the prediction of the glycemic response difficult. Principally, the simultaneous intake of rice with meat and fat leads to a prolonged digestion and a reduced glycemic response [ 1 , 22 ].
Thus, blood glucose concentrations may not have differed sufficiently between our intervention conditions. Measuring glucose responses with continuous glucose monitoring would have been helpful to differentiate between both test meals but was impossible in healthy children.
The timeframe between eating and cognitive testing might be of importance as well. Possibly, differences would have been visible later in the afternoon.
In addition to the GI, we considered meal sizes and estimated the GL of the consumed rice portion. Although GL differed significantly between groups, adjusting the effects of dietary GI for GL showed no association with the cognitive performance.
This is in line with findings from Brindal et al. Finally, it must be mentioned that we discovered period effects within our crossover design. Independent of the rice GI, children showed either improved or impaired results for some cognitive parameters on the second test day, may be due to learning effects.
Learning effects appear independently of carryover effects, which in turn would be a metabolically based cognitive consequence of the dietary intervention lasting until the second cognitive test. On the other hand, it is quite challenging to motivate children throughout the testing, especially when a task proceeds rather monotonously.
A strength of the present study is that it was not performed under clinical conditions but tested schoolchildren in their everyday school environment. Thus, our results indicate effects of carbohydrates differing in GI consumed at lunch under real-life conditions.
In addition, the GI of the rice used for our dietary intervention was assessed in a certified lab according to ISO standards. However, our approach under real-life conditions is vulnerable to confounding factors.
Schoolchildren and their peers tend to distract or influence each other when a whole class is tested simultaneously. Although, the children were supervised during the cognitive testing to assure they stay focused, this could be one explanation for the deterioration in some cognitive parameters.
Another limitation is that the GI difference between the rice types may not have been sufficient. Since our aim was to examine our question within an everyday school life, we chose a lunch from which we know that children like it.
Measuring glucose responses with continuous glucose monitoring was not possible. Potential beneficial effects of lunch based on hGI rice on working memory updating and alertness warrant attention.
It is also of interest whether extending time between lunch and testing might influence the cognitive performance. Sünram-lea SI, Owen L. The impact of diet-based glycaemic response and glucose regulation on cognition: evidence across the lifespan.
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At that time, their cognitive function was evaluated by an expert panel of physicians, nurses and neuropsychologists. Of those participants, only the roughly who showed no signs of cognitive impairment were asked to return for follow-up evaluations of their cognitive function.
About four years into the study, of those were beginning to show mild cognitive impairment, problems with memory, language, thinking and judgment that are greater than normal age-related changes.
Those who reported the highest carbohydrate intake at the beginning of the study were 1. Participants with the highest sugar intake were 1. But those whose diets were highest in fat -- compared to the lowest -- were 42 percent less likely to face cognitive impairment, and those who had the highest intake of protein had a reduced risk of 21 percent.
When total fat and protein intake were taken into account, people with the highest carbohydrate intake were 3. However, high levels of sugar may actually prevent the brain from using the sugar -- similar to what we see with type 2 diabetes.
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Abstract Available from publisher site using DOI. A subscription may be required. Hawkins MAW 1 ,. Keirns NG ,. Helms Z. Affiliations 1. Department of Psychology, Oklahoma State University, Stillwater, Oklahoma, USA.
Authors Hawkins MAW 1. Share this article Share with email Share with twitter Share with linkedin Share with facebook. Abstract Purpose of review Recent evidence documents the negative impact of obesity, diabetes mellitus, and other metabolic dysregulation on neurocognitive function.
This review highlights a key dietary factor in these relationships: refined carbohydrates. Recent findings Chronic consumption of refined carbohydrates has been linked to relative neurocognitive deficits across the lifespan. Hippocampal function is especially impacted, but prefrontal and mesolimbic reward pathways may also be altered.
Early life exposure to refined carbohydrates, i. The impact of acute carbohydrate administration is mixed, with some findings showing benefits while others are neutral or negative. Potential mechanisms of the carbohydrate-cognition relationship include dysregulation in metabolic, inflammatory, and vascular factors, whereas moderators include age, genetic factors, physiological e.
Critically, the negative neurocognitive impacts of diets high in refined carbohydrates have been shown to be independent of total body weight. Summary Neurocognitive deficits induced by a diet high in refined carbohydrates may manifest before overt obesity or metabolic disease onset, suggesting that researchers and providers may need to target subclinical metabolic, inflammatory, and vascular dysregulation factors in efforts to preserve cognitive function across the lifespan.
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Dietary Carbohydratea consumption was related to cognitive function. Whereas, there was no study investigate Funchion association of dietary carbohydrate consumption Hand injury prevention cognitive function modification CCarbohydrates daily fasting duration. This study aims to examine the association between dietary carbohydrate consumption and cognitive function among participants with different daily fasting duration. In this cross-sectional study, adults aged over 60 years from the nationally representative data of the National Health and Nutrition Examination Survey NHANES, — were enrolled. Percentage energy from carbohydrates was present in both quartiles and continuous forms. The scope of the application was proposed Catbohydrates fall under a Carbohydrates and Cognitive Function Energy-saving appliances based on newly Funxtion scientific evidence. The Panel considers that the food constituent, glycaemic Body fat percentage and body composition, Congitive is the subject of the health claim, is sufficiently characterised in relation to the claimed effect. Contribution to normal cognitive function is a beneficial physiological effect. Glycaemic carbohydrates contribute to the maintenance of normal brain functions, including cognition. The Panel concludes that a cause and effect relationship has been established between the consumption of glycaemic carbohydrates and contribution to normal cognitive function.
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