A Non-Randomized Trial Investigating The Impact Of Brown Rice Consumption On Gut Microbiota, Attention, And Short-Term Working Memory in Thai School-Aged Children Part 3

Previous studies have shown that the absolute abundance of early-life gut microbiota significantly varied during the first two or three years of life [43,44]. In this study, the impact of age on gut microbiota profiles was also observed in school-aged children. 

In recent years, more and more studies have shown that there is a close relationship between gut microbiota and memory. Microbiota diversity and abundance are significantly linked to improved memory performance.

First, the impact of the gut microbiota on the nervous system is significant. The microbiota can send signals to the central nervous system through the gut-brain axis, thereby affecting human behavior and cognition. In addition, certain substances in the microbiota can also affect the function of the blood-brain barrier and thus affect brain function.

Secondly, probiotics in the microbiota can modulate the function of the immune system and reduce the level of inflammation, thereby enhancing the function of the nervous system and improving memory and learning abilities. Imbalance of intestinal microbiota will lead to increased inflammatory response, thereby reducing learning and memory.

Finally, in studies of the human gut microbiota, different species of gut microbes are associated with different cognitive functions. For example, a higher abundance of the Bifidobacterium species in the gut has been linked to improved cognitive function. Lactic acid bacteria may be beneficial to learning and memory, while anaerobic bacteria can promote the formation of antidepressant neurotransmitters.

In summary, there is a close relationship between intestinal microbiota and memory. By improving intestinal microecology and enhancing nervous system and immune system functions, memory and learning abilities can be improved. At the same time, we also need to pay attention to diet and lifestyle to maintain intestinal health and guide the intestinal microbiota to form a pattern of diversity and abundance to better protect health and cognitive abilities. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in a variety of ways.

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Children receiving Sinlek rice exhibited increases in Firmicutes and decreases in Bacteroidetes abundances that negatively and positively correlated with age, respectively, in Phase II. It also appears that age influences the abundances of the above two major phyla, as their proportions in the gut have been found to shift throughout life. 

While Firmicutes and Bacteroidetes are the main phyla contributing to an adult-like gut microbiome structure during the first 4 years of life [45], their abundances were significantly varied when comparing children (9.8 years) to adults (≥40 years) [3], and adults (≥30 years) to the elderly (≥65 years) [46]. Our results suggest that these phyla might interact with age among school-aged children, irrespective of intervention. 

Although the abundance of Gammaproteobacteria was substantially lower for the SLR intervention group at Week 61 (2.2 times lower than the control group), we observed within each group a transient change in this class during Phases I and II. We hypothesize that the depletion of Gammaproteobacteria could be due to dietary shifts in children rather than a direct effect of the intervention since the school reopened after months of closure due to the COVID-19 pandemic. Such a reduction in the abundance of this taxon, especially at Week 61, hinted that the class Gammaproteobacteria might be more sensitive to changes in diet (from home-cooked meals to school lunches) in younger children (aged between 7 and 8 years) as it correlated with age in our sample set. 

While the decline in the population of this class has not been established previously among school-aged children, a rapid response characteristic of the gut microbiota to altered diets might support our findings [47]. Moreover, the abundance of Bacteroides gradually decreased and was lowest at the trial endpoint among both children receiving white rice and Sinlek rice. 

A similar pattern was noted for Ruminococcus. We also observed a higher ratio of Prevotella/Bacteroides (P/B), which was negatively correlated with age (rho = −0.31, q = 0.02) at Week 71 (Figures S8 and S9). These bacteria are the predominant enterotypes in the human gut microbiome [48], and their composition can be altered by dietary components in the short term [49–51]; however, changing an enterotype's status would require long-term dietary intervention [52]. Further, numerous studies have demonstrated that Prevotella and Ruminococcus are associated with dietary fiber, whereas Bacteroides dominate the gut of individuals consuming a Western diet [53,54].

 The abundance of these taxa among school-age children might therefore be modulated by dietary changes, as previously mentioned.

Recent studies focusing on weight loss interventions have shown that changes in microbial profiles are associated with weight loss and an increase in the level of Akkermansia, regardless of intervention types [55–57]. In this study, we observed a decrease in BMI z-score in a few samples following the SLR intervention in Phase I. One sample (BH210) was obese at the baseline and then lost weight, to be later classified as overweight at Weeks 4 and 15. 

The BMI z-score of the other two samples (BH249 and BH273) reduced from overweight at the baseline to normal at Weeks 4 and 15. When looking at the gut microbiota profile of these individuals, a decreasing trend was observed in most bacterial taxa in the BH210 sample, except for Bacteroidetes (Figure S10). The abundance of gut microbiota was also observed to highly fluctuate in the BH249 and BH273 samples (Figure S11). 

Although statistical analysis was not possible due to the small number of samples, it would be interesting to study whether the SLR intervention affects changes in BMI and its interaction with the gut microbiota in larger sample sizes.

While previous studies showed that animal-source food intervention [58] and high protein consumption [59] are associated with a greater degree of cognitive ability in school-aged children, the use of Sinlek rice intervention was not found to affect children's cognitive performance, except in Phase II, during which the SLR group outperformed the control group (controlling for a week), despite having a smaller sample size. Moreover, the cognitive outcomes that displayed an age-dependent pattern were consistent with prior reports of improved function with age on both the CBT [60] and PVT [61].

Repeated administration of PVT-B has not been shown to change PVT outcomes, including response time and lapses [62]. In our study, older children (aged between 9 and 12 years) performed better on all cognitive assessments compared to younger children (aged between 6 and 8 years). 

Our data demonstrated that older children in this study had greater memory span capacity (higher MMG) and better attention (lower RT and lapses) than younger children. Moreover, when age and cognitive outcomes were integrated with the gut microbiota, the first two variables were highly correlated. Such a relationship might be due to brain development in school-aged children, with no relation to changes in the gut microbiota. The CBT is a measure of visuospatial short-term working memory [63], which is often associated with hippocampal function [64,65]. 

The hippocampus is known to develop with age, with associated age-related improvements in memory (Riggins et al., 2018). Attention engages multiple brain regions, including the prefrontal cortex, motor cortex, and basal ganglia [66], regions that are also sensitive to age-related development. 

The basal ganglia are not fully developed in childhood and decrease in volume between the ages of 7 and 24 years [67], similar to other cortical regions that continue to develop throughout adolescence [68,69]. Our results confirm prior findings that cognitive outcomes improved as children developed, with this study adding contextual information about the microbial communities present in our adolescent population.

Consistent with previous findings [20,21], we hypothesized that brown rice may help improve cognitive abilities (attention and short-term working memory) in the understudied population of school-aged children. Our randomized sampling design, with clinical assessment and identification of biomarkers [70], allowed us to monitor more closely how brown rice intervention could provide a beneficial effect on cognitive function, including the potential impacts on mental health that were not included in the present study. Comparison between children and adults in the future would also be worthwhile.

Both cross-sectional and intervention (e.g., probiotics and prebiotics) studies have demonstrated that gut microbiota can influence cognitive health, with most findings indicating an improvement in cognitive outcomes as well as highlighting communication between the gut microbiota and the brain [71]. One study showed that individuals with high adherence to the Mediterranean diet have a high abundance of SCFA-producing bacteria, including Faecalibacterium and Roseburia. Both of these taxa were positively correlated with cognitive assessments such as Babcock memory and constructional praxis [72]. Here, we observed a negative relationship between Roseburia and lapses in the SLR group at Week 4, but this association was not maintained over time. Faecalibacterium, however, showed a positive relationship with RT despite treatments. Although these two genera are dominant butyrate-producing bacteria, their abundances might have a different effect on human cognition, as previously observed in patients with cognitive impairments [73,74]. 

Furthermore, an intervention dose of Sinlek rice, which was provided in a 1:1 ratio with white rice, may therefore not be sufficient to influence either the gut microbiome or cognitive performance of school-aged children. Future research using a full dose of Sinlek rice and studying metabolic profiles may help to unravel the complex relationship between gut microbiota and cognitive function.

Lactobacillus is one of the dominant bacteria found in breast milk, which can be transferred through breastfeeding [75]. In our study, we observed a negative association between Lactobacillus and age at the baseline and Week 4. More than 80% of children in the first phase of intervention were breastfed during infancy. An observed downward trend of this probiotic bacterium in older school-aged children implies that a decrease in the impact of breastfeeding could be a driving force during middle childhood.

Selecting an approach for microbiome profiling can be challenging, particularly when it is related to intervention, health, or diseases. The adoption of several approaches may add variability to the outcomes. Our quantification approach (qPCR), in particular, allowed us to determine the absolute abundances of gut microbiota and how much they changed following the intervention, whereas many studies utilizing 16S rRNA gene sequencing analyze microbial composition based on relative abundances [27,76,77]. 

Although the latter method aids in identifying the entire gut microbiome, interpreting compositional data generated by this method may make it difficult to identify the group of bacteria that are truly influenced by an intervention or health status [78]. Considering absolute abundance estimates of taxa may thus be beneficial in keeping track of the target bacteria and correlating their actual composition to studied conditions.

The main strength of this study is that it describes the interplay of a Sinlek rice intervention, the gut microbiota, and the cognitive performance of school-aged children, with age having a significant influence on both microbial profiles and cognitive outcomes. Nonetheless, several limitations need to be acknowledged. The small sample size and unequal numbers of subjects in the control and intervention groups may reduce the statistical power of our study. Comparisons of gut microbiota and cognitive performance within subjects over time during the intervention could not be established due to missing subjects in Phase II with incomplete uptake of the intervention. A

ge, as a potential confounder, should be considered in future intervention studies. Although we focus on attention and short-term working memory, the cognitive assessments may need to be broadened (e.g., to include social function, planning, verbal tasks, and symbolism) to adequately describe the functional abilities of children, including mental health. 

Other variables that could have an influence on cognitive function, such as nutrition, well-being/socioeconomic status, and iron levels, were not collected due to language and cultural barriers, as the children were from various ethnic backgrounds. As our study involved children, intervention dosage was also a potential limitation. Moreover, it should be noted that the children's diet outside of school hours was not controlled. 

The time between the two phases was also significantly extended due to the COVID-19 pandemic, and it was not possible to record the dietary patterns of children during that period. A recent study suggests that the pandemic caused some temporary changes in food consumption patterns [79]. As a result, there could be diet-induced variation in the microbiome or cognitive outcomes that may obscure any intervention effects that do exist. A metabolomics approach may help to clarify the connection between gut microbiota and cognitive function.

In conclusion, this non-randomized clinical trial revealed that Sinlek rice intervention did not significantly affect the abundance of gut microbiota or the cognitive performance of school-aged children. It did, however, find that age was significantly associated with variations in the abundance of gut microbiota and cognitive outcomes in both phases. 

Older children outperformed younger children on all cognitive assessments. A negative association between Roseburia and lapses was noted in the SLR group. Increasing the SLR dose or metabolic profiling would be needed to further understand whether Sinlek rice could exert a positive effect on the gut microbiota and improve cognitive function in children. Our findings indicate that age is directly related to gut microbiota profiles and cognition in school-aged children in northern Thailand.

Supplementary Materials: The following supporting information can be downloaded at https://www. mdpi.com/article/10.3390/nu14235176/s1, Figure S1: Boxplots represent normalized bacterial abundances based on log10 qPCR 16S rRNA copy number per gram of feces across the weeks of Phase I. Differences in the mean absolute abundances of gut microbiota between the time points of each phase (within subjects) were determined using either pairwise t-tests or Wilcoxon signed rank tests with Benjamini-Hochberg (BH) p-value correction, following significant results from a one-way repeated measures ANOVA or the Friedman test (p < 0.05). **** q < 0.0001, *** q < < 0.01, * q < 0.05. WR, white rice (control); SLR, Sinlek rice intervention. 

Figure S2: Boxplots represent normalized bacterial abundances based on log10 qPCR 16S rRNA copy number per gram of feces across the weeks of Phase II. Differences in the mean absolute abundances of gut microbiota between the time points of each phase (within subjects) were determined using either pairwise t-tests or Wilcoxon signed rank tests with Benjamini-Hochberg (BH) p-value correction, following significant results from a one-way repeated measures ANOVA or the Friedman test (p < 0.05). **** q < 0.0001, *** q < 0.001, ** q < 0.01, * q < 0.05. WR, white rice (control); SLR, Sinlek rice intervention. Figure S3: Barplots displaying the cognitive performance of school-aged children in a non-randomized clinical trial. 

The difference in mean across the weeks of each phase (within subjects) was determined using the Wilcoxon signed rank test with Benjamini-Hochberg (BH) p-value correction, following significant results from the Friedman test (p < 0.05). *** q < 0.001, ** q < 0.01, * q < 0.05. WR, white rice (control); SLR, Sinlek rice intervention; MMG = memory matching game; OVP = overall performance (%); RT = reaction times. Figure S4: Barplots displaying the cognitive performance of school-aged children in a non-randomized clinical trial. The difference in mean across the weeks of each phase (within subjects) was determined using the Wilcoxon signed rank test with Benjamini-Hochberg (BH) p-value correction, following significant results from the Friedman test (p < 0.05). *** q < 0.001, ** q < 0.01, * q < 0.05. WR, white rice (control); SLR, Sinlek rice intervention; MMG = memory matching game; OVP = overall performance (%); RT = reaction times. Figure S5: RDA plots displaying the effect of the intervention on the cognitive performance of school-aged children in Phase I ((a) the control group (WR treatment) and (b) the intervention group (SLR treatment)). 

Treatment, gender, and age were used as constrained explanatory variables, and cognitive performance was used as the response variable. Biplot arrows in the RDA plots represent cognitive performance (blue arrows) and constrained explanatory variables (brown arrows). A triangle denotes the centroid of each explanatory variable. The angle between a pair of vectors reflects their correlation. The significance of constraints was assessed using an ANOVA-like permutation test. Sample IDs were constrained within each treatment group, and between and within variance were quantified by each week (baseline, Week 4, and Week 15). WR, white rice (control); SLR, Sinlek rice intervention; MMG, memory matching game; OVP, the overall performance (%); RT, reaction times (millisecond); lapses (millisecond). 

Figure S6: RDA plots displaying the effect of intervention on the cognitive performance of school-aged children in Phase I (a) and Phase II (b). Treatment, gender, and age were used as constrained explanatory variables and cognitive performance was used as the response variable. Biplot arrows in the RDA plots represent cognitive performance (blue arrows) and constrained explanatory variables (brown arrows). A triangle denotes the centroid of each explanatory variable. The angle between a pair of vectors reflects their correlation. The significance of constraints was assessed using an ANOVA-like permutation test. A weak variable was constrained within each phase, and between and within variance were quantified by treatment. WR, white rice (control); SLR, Sinlek rice intervention; MMG, memory matching game; OVP, the overall performance (%); RT, reaction times (millisecond); lapses (millisecond). Figure S7: The relationship between gut microbiota and cognitive performance of school-aged children in a non-randomized clinical trial (Phase I: (a–c), Phase II: (d–f)). Associations between gut microbiota and cognitive performance were determined by using Spearman's rank correlation coefficient. Benjamini-Hochberg (BH) p-value correction was used for multiple testing adjustments (q-value). 

A q-value less than 0.05 is statistically significant. WR, white rice (control); SLR, Sinlek rice intervention; MMG, memory matching game; OVP, the overall performance (%); RT, reaction times (millisecond); lapses (millisecond). Figure S8: Boxplots displaying Prevotella/Bacteroides ratios based on log10 qPCR 16S rRNA copy number per gram of feces across the weeks of Phase II. The differences in mean absolute abundances of gut microbiota between time points of each phase (within subjects) were determined using the Wilcoxon signed rank test with Benjamini-Hochberg (BH) p-value correction, following significant results from the Friedman test (p < 0.05). **** q < 0.0001, *** q < 0.001 ** q < 0.01, * q < 0.05. WR, white rice (control); SLR, Sinlek rice intervention. Figure S9: Boxplots displaying Prevotella/Bacteroides ratios based on log10 qPCR 16S rRNA copy number per gram of feces for the control and Sinlek rice intervention groups in Phase II. 

The differences in mean absolute abundances of gut microbiota between treatment groups were determined using the Wilcoxon rank sum test with Benjamini-Hochberg (BH) p-value correction. *** q < 0.001, ** q < 0.01, * q < 0.05. An association between the Prevotella/Bacteroides ratio and the age of school-aged children was determined using Spearman's rank correlation coefficient. WR, white rice (control); SLR, Sinlek rice intervention. Figure S10: Boxplots displaying the normalized bacterial abundances based on log10 qPCR 16S rRNA copy number per gram of feces of the BH210 sample across the weeks of the SLR intervention (Phase I). This sample was obese at the baseline and then became overweight at Weeks 4 and 15. WR, white SLR, Sinlek rice intervention. Figure S11: Boxplots displaying the normalized bacterial abundances based on log10 qPCR 16S rRNA copy number per gram of feces of the BH2489 and BH273 samples across the weeks of the SLR intervention (Phase I). 

These samples were overweight at the baseline and then became normal at Weeks 4 and 15. SLR, Sinlek rice intervention. Table S1: Primer pairs targeting bacterial 16S rRNA genes [80–90]. Table S2: Demographics of school-aged children in the control and intervention groups at baseline. Table S3: Demographics of school-aged children in the control and intervention groups at Week 4. Table S4: Demographics of school-aged children in the control and intervention groups at Week 15. Table S5: Demographics of school-aged children in the control and intervention groups at Week 56. Table S6: Demographics of school-aged children in the control and intervention groups at Week 61. 

Table S7: Demographics of school-aged children in the control and intervention groups at Week 71. Supplementary Data: demographic variables, the abundance of selected gut microbiota, and cognitive outcomes of school-aged children in Phases I and II. Supplementary File S1: Multivariate comparisons using PERMANOVA for each week of Sinlek rice intervention. Supplementary File S2: Effect of time points at each treatment level (repeated measurement). Supplementary File S3: The effect of treatment, time point, and demographic variables on the abundance of gut microbiota (PERMANOVA). Supplementary File S4: Multiple factor analysis (MFA) of the association between host variables (age and gender), gut microbiota, and cognitive outcomes.

Author Contributions: Conceptualization, methodology, L.K.M., E.G., K.K., J.D., J.S. and S.P.; formal analysis, L.G.; validation, L.K.M., T.J.S., J.S., and S.P.; visualization, L.G.; writing-original draft preparation, L.G., L.K.M., and S.P.; writing-review and editing, L.G., L.K.M., E.G., K.K., J.D., T.J.S., J.S., and S.P.; supervision, J.D., J.S., and S.P.; funding acquisition, J.D., J.S., and S.P. All authors have read and agreed to the published version of the manuscript.

Funding: This collaborative study was funded by OHSU Global (Portland, OR, USA). The Let's Get Healthy! platform used for data collection was developed with the OHSU Clinical and Translational Research Institute (OCTRI; 1UL1TR002369) through funding from the National Institutes of Health (NIH), including Science Education Partnership Awards (R25OD01496, R25GM129840) and infrastructure developed by NIH grants R25RR020443-05S1, UL1RR024140-04S3, RR026008, 3P30CA-69553-13S9, and UL1TR002369. The Gut Microbiome research group was funded by Mae Fah Luang University.

Institutional Review Board Statement: The study was conducted by the Declaration of Helsinki and approved by the Ethics committee of Mae Fah Luang University (Ethics license: REH-61204).

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Acknowledgments: The authors would like to thank all participants who contributed fecal samples and demographic information. We would like to express our appreciation to Channarong Wanthanjai for his technical assistance. We appreciate Angie Setthavongsack for her assistance with cognitive analysis, whose effort was supported by the National Institutes of Health Common Fund and Office of Scientific Workforce Diversity under three linked awards RL5GM118963, TL4GM118965, and UL1GM118964, administered by the National Institute of General Medical Sciences. We thank Mae Fah Luang University for supporting the Gut Microbiome Research Group.

Conflicts of Interest: K.K. is the founder of Sooksatharana (Social Enterprise) Co., Ltd. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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