Altered Gut Microbiota Composition Is Associated with Difficulty in Explicit Emotion Regulation in Young Children

Abstract

Executive function (EF) consists of explicit emotion regulation (EER) and cognitive control (CC). Childhood EER in particular predicts mental and physical health in adulthood. Identifying factors affecting EER development has implications for lifelong physical and mental health. Gut microbiota (GM) has attracted attention as a potential biomarker for risk of physical and mental problems in adulthood. Furthermore, GM is related to brain function/structure, which plays a crucial role in emotional processing. However, little is known about how GM compositions are associated with the development of emotion regulation in early childhood. Therefore, in this study, we examined 257 children aged 3–4 to investigate links between GM and risk to EF. EF was measured using the Mother-Reported Behavior Rating Inventory of Executive Function–Preschool version. GM composition (alpha/beta diversity and genus abundance) was evaluated using 16S rRNA gene sequencing and compared between EF-risk and non-risk groups. Our results show that children with EER-risk (an index of inhibitory self-control) had a higher abundance of the genera Actinomyces and Sutterella. Although we have not established a direct link between GM and CC risk, our findings indicate that GM of preschoolers is closely associated with emotional processing and that EERrisk children have more inflammation-related bacteria.

Temperament in early childhood is associated with gut microbiota composition and diversity

“…Longitudinal studies are required to investigate whether these characteristics at age 3–4 affect developmental outcomes at the age of 5–6, which is a critical period in the development of executive functions involved in emotion regulation and the risk of mental problems. In this regard, another of our studies [42] has already found that children at risk for emotional control have higher levels of inflammation-associated bacteria. Second, comparing the current study to previous research involving infants reveals the importance of exploring the relationship between gut microbiota and temperament while considering alterations in gut microbiota composition during infancy and childhood.…”

The Potential Harmful Effects of Genetically Engineered Microorganisms (GEMs) on the Intestinal Microbiome and Public Health

“…The intestinal microbiota represents a potential biomarker for the risk of mental and behavioral morbidities. Basically, gut eubiotic diversity and synergistic composition affect brain function, thus playing a pivotal role in emotional processing [ 28 , 73 , 89 , 112 , 113 , 114 , 115 , 116 ]. Recently, the following neuropsychiatric conditions were reported to be dysbiotic-dependent: Alzheimer’s disease, attention deficit hyperactivity disorder, amyotrophic lateral sclerosis, anorexia nervosa, bipolar disorder, generalized anxiety disorder, major depressive disorder, multiple sclerosis and schizophrenia [ 117 ].…”

“…The intestinal microbiota represents a potential biomarker for the risk of mental and behavioral morbidities. Basically, gut eubiotic diversity and synergistic composition affect brain function, thus playing a pivotal role in emotional processing [28,73,89,[112][113][114][115][116].…”

“…Table 2 summarizes the potential involvement of GEMs in chronic human diseases driven by perturbated gut homeostasis. Neurodevelopment and behavior Bipolar, depression, anxiety, ADHD, migraines and headaches [28,73,89,[112][113][114][115][116][117] infertility Female and male infertility [120,121] Abbreviations: MM-multiple sclerosis, MG-myasthenia gravis, GBS-Guillain-Barré syndrome, ALS-amyotrophic lateral sclerosis, ADHD-attention deficit hyperactivity disorder.…”

Relationship of Metabolic Dysfunction-associated Steatohepatitis-related Hepatocellular Carcinoma with Oral and Intestinal Microbiota: A Cross-Sectional Pilot Study

“…Detailed sequencing methods are described in previous report [18]. Data processing and assignment were performed using the QIIME2 pipeline (version 2020.8), and based on the work of Fujihara et al [19].…”