A large proportion of serotonin is produced in the gut Your gut microbes also produce a neurotransmitter called gamma-aminobutyric acid GABA , which helps control feelings of fear and anxiety Studies in laboratory mice have shown that certain probiotics can increase the production of GABA and reduce anxiety and depression-like behavior The trillions of microbes that live in your gut also make other chemicals that affect how your brain works Your gut microbes produce lots of short-chain fatty acids SCFA such as butyrate, propionate and acetate They make SCFA by digesting fiber.

SCFA affect brain function in a number of ways, such as reducing appetite. One study found that consuming propionate can reduce food intake and reduce the activity in the brain related to reward from high-energy food Another SCFA, butyrate, and the microbes that produce it are also important for forming the barrier between the brain and the blood, which is called the blood-brain barrier Gut microbes also metabolize bile acids and amino acids to produce other chemicals that affect the brain Bile acids are chemicals made by the liver that are normally involved in absorbing dietary fats.

However, they may also affect the brain. Two studies in mice found that stress and social disorders reduce the production of bile acids by gut bacteria and alter the genes involved in their production 19 , Gut and gut microbes play an important role in your immune system and inflammation by controlling what is passed into the body and what is excreted Lipopolysaccharide LPS is an inflammatory toxin made by certain bacteria.

It can cause inflammation if too much of it passes from the gut into the blood. This can happen when the gut barrier becomes leaky , which allows bacteria and LPS to cross over into the blood. Inflammation and high LPS in the blood have been associated with a number of brain disorders including severe depression, dementia and schizophrenia Your gut and brain are connected physically through millions of nerves, most importantly the vagus nerve.

The gut and its microbes also control inflammation and make many different compounds that can affect brain health. Gut bacteria affect brain health, so changing your gut bacteria may improve your brain health. Probiotics are live bacteria that impart health benefits if eaten.

However, not all probiotics are the same. Some probiotics have been shown to improve symptoms of stress, anxiety and depression 25 , One small study of people with irritable bowel syndrome and mild-to-moderate anxiety or depression found that taking a probiotic called Bifidobacterium longum NCC for six weeks significantly improved symptoms Prebiotics , which are typically fibers that are fermented by your gut bacteria, may also affect brain health.

One study found that taking a prebiotic called galactooligosaccharides for three weeks significantly reduced the amount of stress hormone in the body, called cortisol Probiotics that affect the brain are also called psychobiotics.

Both probiotics and prebiotics have been shown to reduce levels of anxiety, stress and depression. A number of foods such as oily fish, fermented foods and high-fiber foods may help increase the beneficial bacteria in your gut and improve brain health.

Research shows that the gut and brain are connected, a partnership called the gut-brain axis. The two are linked through biochemical signaling between the nervous system in the digestive tract, called the enteric nervous system, and the central nervous system, which includes the brain.

The primary information connection between the brain and gut is the vagus nerve, the longest nerve in the body. The gut has been called a "second brain" because it produces many of the same neurotransmitters as the brain does, like serotonin, dopamine, and gamma-aminobutyric acid, all of which play a key role in regulating mood.

What affects the gut often affects the brain and vice versa? When your brain senses trouble—the fight-or-flight response—it sends warning signals to the gut, which is why stressful events can cause digestive problems like a nervous or upset stomach.

On the flip side, flares of gastrointestinal issues like irritable bowel syndrome IBS , Crohn's disease, or chronic constipation may trigger anxiety or depression. Memory performance was associated with bacterial abundances from Ruminococcaceae and Lachnospiraceae families. In a rat model of metabolic syndrome, preoperative treadmill exercise increased microbiome diversity increased abundance of Firmicutes and decreased abundance of Bacteroidetes and alleviated postoperative cognitive impairments 84 , a common issue in older adult patients with metabolic syndrome.

Male high- and low-capacity running rats were randomly assigned to receive preoperative exercise 6 weeks with surgery tibia fracture with internal fixation under anesthesia or sham surgery anesthesia only , or no exercise with surgery or sham surgery. Preoperative exercise attenuated memory impairments and lowered neuroinflammation in low-capacity running rats postsurgery.

Exercise increased the abundance of SCFA-producing bacteria and elevated levels of Lactobacillus reuteri, a vitamin B 12 producer.

No differences in spatial memory were observed, but the exercise group showed decreases in the size and number of beta-amyloid plaques in the hippocampus.

The findings mostly from rodent trials provide evidence for the mediating effect of gut microbiota on diet and exercise effects on cognition Figure 1. Western-type diets were associated with decreased microbiota richness and diversity 45 , 51 and poorer spatial and object recognition memory 42 , 43 , 46 , 47 , 49—51 , as well as increased intestinal and neural inflammation 45—47 , 49 , 51 , and decreased BDNF expression Interventions associated with MED eating patterns often resulted in greater microbiota diversity 46 , 60 , 62 and spatial and object recognition memory 54— Following a MED diet intervention in older adults, correlations between diet-associated microbiota changes and global cognition were observed Increased SCFAs and tight junction proteins 45 , 54 , 55 , 62 , hippocampal BDNF expression 58 and reduced endotoxin translocation 45 , 46 , 60 , brain insulin resistance 54 , and neuroinflammation 45 , 54 , 60 , 62 were identified as potential mediating mechanisms in these studies.

Not all diet interventions observed cognitive effects however, and these inconsistencies are likely due to heterogeneity of diets, rodent models, and cognitive assessments.

The most promising evidence comes from high-fiber interventions where increased SCFAs and reduced inflammation along with cognitive changes were commonly observed 45 , 46 , 54— Summary of intervention results.

Dashed arrows denote mixed evidence for cognitive effects. Evidence from a limited number of preclinical exercise trials have shown compositional microbiota changes that were associated with cognition in healthy mice 44 and reduced postoperative cognitive impairment and neuroinflammation in a rat model of metabolic syndrome In AD mice, high-intensity treadmill exercise led to increases in SCFA-producing bacteria and reductions in AD pathology 80 , but not cognitive differences.

The lack of cognitive effects in AD mice may be related to their advanced disease progression and aligns with the notion that interventions should be applied early in the course of cognitive decline Supporting the mediating the role of the microbiome are microbiota changes that precede cognitive changes 45 , antibiotic elimination of cognitive effects related to diet 45 , 46 , microbiota transplant effects 49 , and potential mechanistic links such as SCFA, BDNF, and inflammatory changes.

Associations between altered microbiota and cognition were also frequently observed 42—44 , 51 , 54 , 59 , In particular, bacteria from the Clostridia class and Bacteroidales order, such as Lachnospiraceae, Ruminococcaceae, Coprobacter, and Rikenella, as well as Lactobacillus and Bifidobacterium genera.

The field is predominantly dominated by rodent studies, but their findings set the stage for future human trials in this area. Several ongoing human studies are described in the following section.

Participants will be randomly assigned to one of the following groups: MED diet to maintain body weight; MED diet to achieve weight loss; typical American diet to achieve weight loss. The 3-group design could give insight on whether the MED diet affects microbiome and cognition independent of adiposity and metabolic changes.

The COMBAT study 87 will investigate the impact of 12 weeks of cranberry intake, rich in polyphenols, on gut microbiota and cognition. Blood, urine, and fecal samples will be collected to assess diet and microbiome, and all participants will undergo cognitive testing and magnetic resonance imaging MRI.

Secondary analyses of inflammatory and metabolic markers, BDNF levels, and cerebrovascular hemodynamics will be conducted. Strengths of this study include the recruitment of older adults and a comprehensive brain health assessment.

The use of a single nutrient intervention, however, only informs us about one attribute of a healthy diet. The intervention will consist of a low-fat vegan diet, aerobic and resistance exercise, stress management, and group support.

A waitlist control group will be used, and after 20 weeks, those in the control phase will receive the lifestyle intervention. Cognition and microbiome changes will be assessed at 20 and 40 weeks. The intervention design limits the ability to assess individual lifestyle components and may only be able to inform on the overall combined effects of the intervention.

A study from Sun Yat-sen University in China 89 aims to examine the effects of a combined diet and exercise intervention versus either intervention alone on executive function and intestinal microbiota in undergraduate students. Exercise training will consist of rope skipping 3 cycles of 20 minutes skipping: minute breaks 3 times per week.

The diet intervention consists of 10 hours of restricted eating of a high-fiber diet. Secondary outcomes will include BDNF, CRP, and a variety of inflammatory cytokines.

Limitations of this study include the young study sample, lack of a true control group, and the assessment of only one cognitive domain. Additionally, restricted eating does not necessarily reflect a healthy diet, and rope skipping for long durations does not seem appropriate for adults without a moderate fitness level at baseline.

The aforementioned studies speak to the growing interest in the effects of lifestyle modification on the gut—brain axis, but are limited by various sample populations, intervention protocols, and outcome measures. Strengths of these studies include the study of at-risk populations, high-intensity exercise, and multicomponent intervention effects.

They also provide a sense of appropriate designs and outcome measures. There are still, however, many gaps in the field that need to be addressed. The role of the microbiome in the effects of diet and exercise on cognition is emerging from preclinical trials, but inferences to human physiology, especially in the context of dementia prevention, are uncertain Figure 2.

A major limitation of rodent research is the narrow selection of cognitive tests. Spatial and object recognition are most always reported due to the frequent use of maze testing and fear conditioning paradigms.

Thus, little is known about other cognitive domains, which can be differently impaired in humans experiencing dementia. Human trials provide the opportunity to comprehensively study cognition through neuropsychological assessment and the use of advanced measurement techniques such as MRI. There are numerous studies investigating the effects of diet and exercise on cognition in older adults, but lacking investigation into the microbiome.

We encourage researchers working on these studies to collaborate with microbiome scientists, and attempt to include simple, cost-effective measures of microbiota composition, diversity, and function. The majority of studies investigated singular components of Western and MED eating patterns, and the effects of whole diet interventions on microbiota and cognition are underexplored.

High-fat and high-sucrose diets are common in North America; thus, there is a lot of research interest in their effects. Furthermore, the frequent use of single nutrient interventions may reflect the desire to discover simple forms of treatment.

The general consensus, however, is that the combined attributes of a diet are more important for microbiome composition and cognition than individual components Nutrients that target SCFA-producing bacteria and inflammation appear to have the greatest impact on cognition. Thus, whole diets such as the MED diet, which comprised fruits, vegetables, and healthy fats, are recommended for future studies.

The number of exercise interventions in this field is quite limited compared with dietary interventions. Additional preclinical trials are needed to corroborate the current evidence from a handful of rodent studies and inform the designs of human trials.

Little is known about how exercise intensity influences the gut—brain axis, and thus is an important endeavor for future research, as differing intensities have shown to have varying effects on gut health and other physiological outcomes 77— Researchers should also consider investigating other types of exercise, such as resistance training.

Microbiota transplant and antibiotic treatment designs should also be considered in rodent exercise studies. None of the reviewed exercise interventions measured BDNF, but given that BDNF is elevated by exercise and associated with gut health and cognition, it is recommended that future exercise trials include this as an outcome.

The diet interventions included in this study could warrant their own review entirely; however, it was our intent to present findings from both diet and exercise interventions as there is accruing evidence supporting the synergistic effects of multicomponent lifestyle interventions, and it is of interest whether these are replicated when assessing microbiome and cognitive outcomes 90 , Diet and exercise-associated microbiome and cognitive changes are accompanied by many of the same physiological changes; thus, it is reasonable to predict that synergistic effects may occur.

Factorial designs comparing diet, exercise, and diet combined with exercise are highly encouraged to tease apart these relationships. The majority of studies reviewed included heterogeneous, mostly male rodent models.

Findings from adolescent, young adult, and stress-induced mice may not generalize to at-risk populations, while, conversely, AD rodent models may be too far along in their disease progression.

Most studies used healthy adult rodents, a group of interest considering many risk factors for dementia begin early in adult life, and lifestyle behaviors during adulthood are associated with cognition in late life Findings from studies including middle-aged, senescence-accelerated, and cardiometabolic risk rodent models are perhaps the most relevant to dementia prevention and are recommended for future studies.

Researchers also tend to use male mice exclusively as they are concerned that estrous cycles in female mice will increase variability; however, these claims have been refuted The underrepresentation of female rodents limits our understanding of female biology and may lead to inadequate treatment for females.

Given there are sex and gender differences in cognitive trajectories 94 and lifestyle preferences 95 , 96 , it is important to study the effects of diet and exercise on the gut—brain axis as a function of both sex and gender.

Despite these limitations, we believe that for a research area still in its infancy, it is appropriate to consider findings from all studies available that investigate the interplay between diet, exercise, and the gut—brain axis.

Lastly, a major focus of this review was to infer the mediating role of the microbiome; however, no studies conducted proper mediation analyses.

Instead, our conclusions are drawn from evidence of potential mediating mechanisms, correlations between altered microbiota and cognition, and novel designs such as microbiota transplant and antibiotic treatment. Most studies did not assess microbiota and cognition at baseline, a requirement for running proper mediation analyses.

Figure 3 provides an example of a trial design for future exercise and diet investigating the mediating role of the gut microbiome on cognitive changes in older adults. In addition to baseline and postintervention assessments, midpoint assessments are useful for identifying whether microbiota changes precede cognitive changes.

Measuring potential mediating mechanisms such as SCFAs and tight junction proteins, BDNF, and measures of local, systemic, and neural inflammation are highly recommended. Suggested trial design.

The intervention literature supports the notion that the gut microbiome, at least in part, mediates diet and exercise effects on cognition. In contrast to Western-style diets, interventions encompassing features of the MED diet, and uptake of exercise were associated with improved microbiota diversity, increased SCFA production, and reduced local and systemic inflammation.

The evidence is mainly derived from rodent studies; however, one large MED diet intervention found diet-associated microbiota changes to be correlated with cognitive performance in older adults. Several diet and exercise interventions assessing both microbiome and cognitive outcomes in humans are underway, but are limited by heterogeneous populations and interventions.

We encourage the inclusion of baseline and follow-up measures of microbiome composition, diversity, and function in lifestyle interventions aimed at reducing dementia risk in older adults.

This effort would help to elucidate the mechanisms by which lifestyle modification affects cognition and may help to develop more targeted dementia prevention strategies.

This work was supported by a grant from the Canadian Consortium on Neurodegeneration in Aging CCNA , which is supported by the Canadian Institutes of Health Research CIHR with funding from several partners. The salary of N. was supported by the grant. The sponsors are not involved in the preparation of the paper.

Everyone who has significantly contributed to this work has been listed as coauthor. participated in the conceptualization of the project.

aggregated the data and was the lead writer of the original draft. supported the writing of the original draft. All coauthors were equally involved in reviewing, editing, and approving the final version of this manuscript.

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Cognitive impairment has been identified in numerous disease states, both gastrointestinal and extraintestinal in nature, many of which have also been characterized as having a role for dysbiosis in disease pathogenesis.

This includes, but is not limited to, inflammatory bowel diseases, irritable bowel syndrome, type 1 diabetes, obesity, major depressive disorder, and autism spectrum disorder.

The role of cognition and the microbiome will be discussed in this chapter for all these diseases, as well as evidence for a role in maintaining overall human health and well being.

Finally, evidence for a role for probiotics in beneficially modulating the microbiota and leading to improved cognition will be discussed. Keywords: Cognition; Development; Gastrointestinal physiology; Hippocampus; Probiotics.

Translational Neurodegeneration volume 11 , Article number: 49 Cite this article. Metrics details. Microbiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes, but evidence from large human cohorts is lacking.

This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations.

We included participants from the Guangzhou Nutrition and Health Study GNHS with both gut microbiome and cognitive assessment data available as a discovery cohort, of whom individuals provided fecal samples twice before cognitive assessment.

We selected individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit. Fecal 16S rRNA and shotgun metagenomic sequencing, targeted serum metabolomics, and cytokine measurements were performed in the GNHS. We found protective associations of specific gut microbial genera Odoribacter , Butyricimonas , and Bacteroides with cognitive impairment in both the discovery cohort and the replication study 1.

Result of Bacteroides was further validated in the replication study 2. Odoribacter was positively associated with hippocampal volume β, 0. Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment.

We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition. Our findings reveal that specific gut microbial features are closely associated with cognitive impairment and decreased hippocampal volume, which may play an important role in dementia development.

The number of elderly people living with dementia is rising especially in low- and middle-income countries [ 1 ].

AD patients typically undergo several stages of cognitive impairment preclinical, mild cognitive impairment [MCI], and dementia before diagnosis, and the time delay between initial biochemical and cellular changes in the brain and clinical diagnosis can be more than 10 years [ 2 ].

Thus, early detection and prevention is quite important for improving the prognosis and alleviating the progression of AD, particularly for the preclinical stage or MCI [ 3 ]. Gut microbiome is essential for human health and accumulating evidence supports that gut microbial dysbiosis contributes to the pathogenesis of various neurodegenerative disorders e.

Several case—control studies have reported altered microbiota in both MCI and AD patients compared to normal controls [ 6 , 7 , 8 ]. These findings conjointly emphasize possible effects of gut microbiome on cognitive function and brain structure, which might be further linked to the occurrence and development of dementia [ 10 ].

Nevertheless, direct evidence from large human cohorts is still lacking, leaving a research gap in this field. Here we performed a multi-omics analysis to explore the associations of gut microbiome with age-related cognitive impairment in three independent populations.

We also examined the associations of the identified gut microbial features with brain structure and volumes, as well as associations with circulating metabolites and inflammatory markers.

The main analyses were based on the Guangzhou Nutrition and Health Study GNHS , a community-based prospective cohort study in southern China. Briefly, a total of individuals aged 45—72 years were enrolled during — and followed up every 3 years [ 11 ].

Prior to the cognitive examination, we had collected stool samples from of the included participants. During the further follow-up visit, brain images were collected via 3. To validate the results discovered with cognitive scores, we performed the same analysis in an AD case—control study replication study 1; 30 AD patients, 30 MCI patients, and 30 healthy controls which was published recently [ 8 ].

Detailed information about the three populations was provided in the Additional file 1. The MMSE, established by Folstein in [ 14 ], is one of the most widely used instruments for cognitive screening in clinical settings and epidemiologic surveys.

The MMSE contains five domains, each with an assigned point value totaling orientation 10 points , registration 3 points , attention and calculation 5 points , delayed recall 3 points , and language 9 points.

A higher score indicates better cognitive performance [ 14 ]. In the CHNS, we applied the cognitive screening items from part of the Telephone Interview for Cognitive Status—modified [ 16 ], which is a telephone adaptation of the MMSE. Cognitive performance was quantified as global cognitive score ranging from 0 to 27 points , which was calculated as the sum of all cognitive testing items.

In the GNHS, fecal DNA of participants was extracted according to the protocol [ 17 ]. We applied MiSeq Reagent Kits v2 Illumina Inc. Shotgun metagenomic sequencing was carried out among fecal samples from individuals. In the AD case—control study, fecal DNA was extracted and used for the amplification of V3—V4 regions of the 16S rRNA gene as described [ 8 ].

In the CHNS, fecal DNA extraction and 16S rRNA gene sequencing have been described in detail previously [ 18 ].

Bioinformatic analysis of gut microbiota is shown in the Additional file 1. We performed targeted metabolomics to quantify the concentrations of serum metabolites among participants using an ultra-performance liquid chromatography coupled to tandem mass spectrometry system ACQUITY UPLC-Xevo TQ-S, Waters Corp.

Besides, we conducted electrochemiluminescence-based immunoassays to quantify the levels of six serum cytokines interferon-gamma [IFN-γ], interleukin [IL]-2, IL-4, IL-6, IL-8 and IL among participants, using the MSD V-Plex Proinflammatory Panel 1 human kit Meso Scale Diagnostics, Rockville, MD; Cat.

In the GNHS participants, 3D T1-weighted structural images were acquired with the magnetization prepared rapid acquisition gradient echo sequence on a 3.

We processed and analyzed 3D T1 images using MATLAB version Rb The MathWorks Inc, Natick, MA and Statistical Parametric Mapping software SPM12; The Welcome Department of Imaging Neuroscience, London.

We focused on cognition-related regions of interest ROIs including hippocampus, superior and middle frontal lobe, and insular opercula opercularis, orbitalis, and triangularis [ 19 , 20 ], which were identified by the Automated Anatomical Labeling atlas [ 21 ].

More information is provided in the Additional file 1. Statistical analysis was performed using Stata 15 StataCorp, College Station, TX or R software version 4.

To estimate the associations between α-diversity indices and cognitive impairment, we conducted multinomial logistic regression in the GNHS and linear mixed-effect model which contained a random intercept and random coefficient on the provinces or megacities to adjust the geographic regions in the CHNS.

The covariates included age, gender, body mass index BMI , education, and income in both the GNHS and CHNS. We performed sensitivity analysis by additionally adjusting for Bristol scale, time lag between stool sampling and cognitive assessment, and history of stroke in the GNHS.

For β-diversity, we conducted principal coordinates analysis PCoA and permutational multivariate analysis of variance PERMANOVA vegan function 'adonis'; permutations based on Bray—Curtis dissimilarities at the genus level in both discovery and validation cohorts.

To validate our findings, we further performed LASSO analysis between AD patients and normal controls in the case—control dataset replication study 1. The microbes with non-zero β-coefficients achieved in both the GNHS and the AD case—control study were regarded as key genera for further validation analysis in the CNHS replication study 2.

Ratios between absolute β-coefficient of each genus and the sum of all absolute β-coefficients were calculated to quantify the contribution of each selected genus to the LASSO model.

Relative abundance of genera was standardized as z-scores for the LASSO models for comparability. Considering the influence of geographic regions on gut microbiome [ 18 ], we further validated the relationships between the identified key genera and global cognitive scores using linear mixed-effect models in the CHNS, containing a random intercept and random coefficient on the provinces or megacities.

We also extracted volumetric information of white matter WM , grey matter GM , cerebrospinal fluid CSF and each individual ROI to explore associations between key genera and the volumes using multiple linear regression adjusted for age, gender, BMI, education, income, and TIV. As short-chain fatty acids SCFAs might exert neuroprotective effects as prior studies reported [ 22 ], we tended to reveal latent connections between SCFAs log-transformed and microbiome-gut-brain axis by performing multiple linear regression analysis to investigate associations of: 1 the identified key genera with serum SCFAs adjusted for age, gender, BMI, education, and income; and 2 the SCFAs with the brain area volumes adjusted for age, gender, BMI, education, income, and TIV.

Based on the repeated measurements of gut metagenomics in the GNHS, we performed PCoA and PERMANOVA to illustrate microbial structure alterations quantified by Bray—Curtis dissimilarities at the species level over time across different cognitive groups. Multinomial logistic regression was used to examine the associations of intra-individual alterations in gut microbial composition with cognitive impairment.

We fitted two statistical models: model 1 included age, gender, BMI, education, and income; and model 2, which was as model 1 plus Bristol scale and history of stroke. Kruskal—Wallis test was used to compare concentrations of serum metabolite between the mild and the normal groups or between the questionable and the normal groups.

To identify metagenomic and metabolomic biomarkers that could distinguish participants with cognitive impairment from healthy controls, we constructed two LASSO models: 1 the separated models which were based on metagenomic species only, metagenomic pathways only or serum metabolites only; and 2 the combined model which was based on these three kinds of features selected in the separated models to obtain more consolidated results.

We applied the semi-partial correlation [ 23 ] adjusting for age, gender, BMI, education, and income to estimate correlations between the LASSO-identified features and MMSE domains i. FDR using Benjamini—Hochberg method was calculated to correct the multiple testing.

To examine associations across multi-omics datasets, we first performed logistic regression Mild vs. Then we performed Spearman correlation analysis to estimate interactions among the identified multi-omics features stratified by disease state. The results were visualized in an interaction network using Gephi 0.

The overviews of the study design and multi-omics datasets are shown in Fig. A total of participants from the GNHS aged Generally, the characteristics were comparable among the subgroups stratified by cognitive function, except that the participants with lower levels of education and income were more likely to have worse cognitive performance.

The mean ages were In the GNHS, we finally obtained 36, Overview of study design and analyses. We included participants from the Guangzhou Nutrition and Health Study GNHS as a discovery cohort, all of whom had cognitive assessment and at least one stool sample collection individuals collected stool samples twice.

A subset of individuals underwent magnetic resonance imaging MRI with availability of both gut microbiome and the cognitive assessment data. Image created with BioRender.

We identified no significant associations between α-diversity and cognitive impairment based on 16S rRNA gene sequencing data of participants in the GNHS Additional file 1 : Table S3 , which were consistent with previously reported results [ 8 ].

Distinct variances of β-diversity were observed between groups with different cognitive status in the GNHS and the AD case—control study Fig. Alterations in the gut microbial structure in participants with cognitive impairment.

a — c Scatterplots from principal coordinates analysis PCoA and permutational multivariate analysis of variance PERMANOVA , based on Bray—Curtis distances at genus level from the GNHS, the AD case—control study and the CHNS, respectively.

The phylum-level compositions of gut microbiota among the mild, questionable, and normal groups were illustrated in the circular layout Fig.

The top three abundant phyla were Firmicutes, Bacteroidetes, and Proteobacteria. Firmicutes was more abundant in the mild group and Bacteroidetes was relatively more abundant in the normal group Fig. These findings were also accordant with the previous AD case—control study [ 8 ]. Besides, the results revealed that Odoribacter , Butyricimonas , and Bacteroides were inversely associated with cognitive impairment in the GNHS and the AD case—control study Fig.

Altered phylum- and genus-level taxonomies in participants with cognitive impairment. a The circular layout illustrates the mean relative abundance of phyla 16S rRNA gene sequencing among the mild, questionable, and normal groups in the GNHS.

Colors in upper and lower halves of the outermost circle represent different groups of participants and microbial phyla, respectively. Width of each track highlights mean relative abundance of each phylum contained in different groups of participants. b and c The relative abundance of Firmicutes and Bacteroidetes b , and the ratio of Bacteroidetes to Firmicutes c among the GNHS participants with different degrees of cognitive impairment are presented by the violin plot with included boxplot.

The boxplots show median and interquartile ranges IQR. d Key gut microbial genera that contribute to distinguishing participants with different levels of cognitive impairment Mild vs. Normal in the GNHS; AD vs.

NC in the AD case—control study using LASSO models. The bars are colored according to the direction of association between the genera and cognitive impairment orange for positive correlation [harmful]; dark green for negative correlation [beneficial].

Key genera selected in both populations. e Validation of the relationships between key genera and cognitive performance using linear mixed-effect models in the CHNS. The forest plot shows the result of the association between Bacteroides and global cognitive scores.

The completed result is provided in the Additional file 1 : Table S6. In the CHNS, the participants were classified into T1, T2 and T3 groups according to the tertiles of their global cognitive scores.

These findings indicated a potential role of acetate in mediating the gut microbiota—hippocampus association. Associations between cognition-related genera and brain structure in the GNHS. a Linear regression was used to estimate associations between cognition-related genera and brain structure.

The β-coefficients indicate the corresponding changes in standardized volumes of different brain areas for per 1-standardize unit in SD unit increase of the bacterial relative abundance.

False discovery rate FDR was calculated using the Benjamini—Hochberg method. A broad overview of the metagenomic taxonomy and pathways from the mild and normal groups is shown in Additional file 1 : Fig. S2a Additional file 1 : Table S9. Additional file 1 : Figure S2b shows significant differences in serum metabolites across the three groups mild, questionable, and normal.

Based on the repeated measurements of gut microbiome, we found that the microbial composition at the species level had a substantial alteration among individuals with cognitive impairment, but not among those with normal cognition Fig. After controlling the potential covariates, we found a significantly positive association between microbial alteration quantified by Bray—Curtis dissimilarities across the two time points and cognitive impairment Mild vs.

These findings demonstrated that participants with cognitive impairment may have more alterations in gut microbial composition than their normal counterparts. Associations between gut metagenomic alterations and cognitive function in the GNHS. a — c Principal coordinate analysis PCoA and permutational multivariate analysis of variance PERMANOVA plots of Bray—Curtis BC dissimilarities at the species level display the compositional alterations of gut microbiome over 3 years in groups with different cognitive status.

d Comparison of microbial alterations quantified by Bray—Curtis dissimilarities of species among participants with different cognitive performance. P values were generated from multinomial logistic regression models. Boxplots show median and interquartile ranges IQR.

GNHS Guangzhou Nutrition and Health Study. We identified 5 microbial species, 3 functional pathways, and 4 serum metabolites in LASSO models, which had potential links with age-related cognitive impairment Fig.

In the combined LASSO model, species Dorea longicatena , methylglutaric acid, hyodeoxycholic acid, as well as the pathways of glycogen biosynthesis I from ADP- d -Glucose , formaldehyde oxidation I, and petroselinate biosynthesis were enriched in individuals with impaired cognition, while glyceric acid and L -phenylalanine were elevated in the normal controls Fig.

We further found that these cognition-related bacterial and metabolic features were significantly correlated with MMSE domain scores Additional file 1 : Fig. S3a and Additional file 1 : Table S The species Dorea longicatena , the pathways of formaldehyde oxidation, and methylglutaric acid were correlated with poor scores on the MMSE domain of language.

Meanwhile, hyodeoxycholic acid was negatively correlated with the scores on delayed recall domain, and glyceric acid and L -phenylalanine were positively associated with scores on the language domain. We found significant correlations between the identified serum metabolites e. S3b and Additional file 1 : Table S Moreover, these identified bacterial features also had significant correlations with serum inflammatory cytokines Fig.

Multi-omics interactions and cognitive impairment in the GNHS. a and b Metagenomic and metabolomic markers for distinguishing participants of the mild group from the normal group using the LASSO models based on metagenomic species, pathways or serum metabolites a , or the combination of these three kinds of features selected from the separated models mentioned above b.

The x axis denotes the coefficients of the features in each model. c Semi-partial correlation of key metagenomic features selected from the combined LASSO model with serum inflammatory cytokines. The intensity of color represents correlation coefficients. False discovery rate FDR was calculated using Benjamini—Hochberg method.

d Significant associations among 4 aspects of multi-omics: genera of 16S rRNA gene sequencing, metagenomic pathways, serum metabolites, and brain structure. Spearman correlation was used to calculate pairwise correlations of all the measurements. Size of nodes represents the number of connections with others.

After adjusting for potential confounders, we found that the pathways of formaldehyde assimilation II RuMP Cycle and formaldehyde oxidation I, as well as the methylglutaric acid, were enriched in samples from subjects with impaired cognition Additional file 1 : Fig.

Additionally, several pathways e. Individuals with normal cognition had higher levels of L -valine, isovelarylcarnitine, and L -phenylalanine Additional file 1 : Fig. As shown in Fig. Both distinct and common features were found between the mild and normal groups.

For example, the significant edges of the network were fewer in the mild group than in the normal group vs. A total of 51 and 53 nodes were included in the mild and normal groups, respectively.

CSF volume was significantly associated with several metabolites including L -valine and isovelarylcarnitine among individuals with cognitive decline, while other brain structures e. The degrees of amino acids had a substantial change between the mild and normal groups 16 and 60, respectively.

Of note, the mild group contained six amino acids and the normal group contained eight, with six overlapping, including L -threonine, L -serine, L -glutamine, L -leucine, L -methionine, and L -valine.

Additionally, the pathways of glycogen biosynthesis I from ADP-D-Glucose and formaldehyde oxidation I were found to play central roles in the interaction network among the normal participants, but none of the dominant pathways were found in the interaction network among the mild group.

Detailed connections among cognition-related pathways are presented in Additional file 1 : Fig. There was a total of 15 nodes and 88 edges in the network. C4 photosynthetic carbon assimilation cycle PEPCK type , C4 photosynthetic carbon assimilation cycle NADP-ME type , and glycogen biosynthesis I from ADP-D-Glucose contributed to most of the interactions with other pathways.

In the present study, we found significant differences in the gut microbial composition among people with different cognitive status and revealed that increased intra-individual alterations in gut microbial composition was associated with cognitive decline. We further identified three genera including Odoribacter , Butyricimonas , and Bacteroides which were depleted in participants with cognitive impairment compared with normal controls.

Moreover, higher abundance of Odoribacter was associated with several important features of brain structure, including larger volumes of WM and the right hippocampus as well as smaller CSF volume. We then revealed that chronic inflammation might underlie the associations of gut microbial features with cognitive impairment.

The associations between gut microbial α-diversity and dementia or cognitive function have been controversial. cohort, gut microbial α-diversity was lower in dementia patients compared with healthy controls [ 6 ], while contradictory evidence was reported in a Japanese population [ 25 ].

In the present GNHS study involving more than participants, we did not find significant correlations between α-diversity and cognitive function, which was consistent with a previous study conducted in European participants [ 26 ].

Microbial instability has been associated with a variety of disease outcomes such as metabolic diseases [ 27 ] and allergenic and autoimmune disorders [ 28 ]. To the best of our knowledge, our study is the first to report that the instability of gut microbial composition is correlated with cognitive impairment.

We observed significant differences in β-diversity among participants with distinct cognitive performance in two independent populations. We speculated that intra-individual alterations of microbial structure rather than α-diversity might play an important role in the cognitive maintenance.

Prior studies have demonstrated that the microbiome-gut-brain axis might be involved in the development and progression of cognitive impairment and dementia by altering permeability of the blood—brain barrier and inducing neuroinflammation [ 29 ].

SCFAs including acetate, propionate, and butyrate have been reported to decrease the permeability of the blood—brain barrier and exert anti-neuroinflammatory effects [ 22 ].

Of note, the three cognition-related genera identified in our study, Odoribacter , Butyricimonas , and Bacteroides, are all putative SCFA-producing bacteria which have potent anti-inflammatory and immunomodulatory effects [ 30 ].

Previous studies demonstrated that Odoribacter and Bacteroides are decreased in AD patients [ 31 , 32 ]. Odoribacter has also been shown to be beneficial for hypertension prevention [ 33 ] and blood sugar regulation [ 34 ].

Butyricimonas , known as a protective bacterium, has the ability to produce butyric acid and isoacid salts [ 35 ]. Prior studies have reported that Butyricimonas is depleted in mice with spinal cord injury [ 36 ] and in patients with cystic fibrosis [ 37 ]. Butyricimonas is also associated with decreased adiposity and hepatic steatosis in mice [ 38 ].

Volumetric reduction of cognition-related brain areas is considered a pathological hallmark of neurodegeneration [ 39 ]. Hippocampal atrophy has been robustly linked to cognitive performance and risk of dementia [ 40 ]. In comparison to normal controls, AD patients are found with smaller white matter volume [ 41 ], while a larger volume of CSF tends to be associated with higher dementia risk [ 42 ].

Here, the associations of Odoribacter with the volumes of WM, right hippocampus, and CSF revealed potential neuro-protective effect of putative SCFA-producing bacteria.

The present study indicated that increased Dorea longicatena was associated with worse cognitive performance. Dorea longicatena has been reported to be positively associated with BMI and waist circumference [ 43 ]. Meanwhile, higher abundance of Dorea longicatena exists in individuals with circadian rhythm disturbance [ 44 ].

Recent evidence suggests that Dorea might contribute to elevated intestinal permeability [ 45 ]. In general, Dorea longicatena or Dorea may have a negative effect on the maintenance of a healthy gut.

Furthermore, the present study revealed that Dorea longicatena and the pathway of formaldehyde oxidation I were positively correlated with IFN-γ. IFN-γ is an AD-related pro-inflammatory cytokine [ 46 ] and elevated IFN-γ has been reported in AD and other neurologic disorders, such as stroke and multiple sclerosis [ 47 , 48 , 49 ].

These results suggest that inflammation activation plays a key role in the crosstalk between gut microbiome and the central nervous system CNS. Although IFN-γ and microglial activation have been generally linked to inflammatory stimuli in the CNS, the presence of IFN-γ in the blood is not necessarily associated with chronic inflammation [ 50 ].

Thus, our findings need to be verified by more mechanistic studies. Besides systemic inflammation, the pathways involved in glucose metabolism and mitochondrial dysfunction have also been related with AD pathology [ 51 , 52 ]. Glycogen is critical in energy and glucose metabolism [ 53 ].

Enrichment of microbial pathways of glycogen biosynthesis and degradation among participants with poor cognition in the present study may reflect the essential role of these unbalanced metabolic processes in cognitive disorders.

Another pathway enriched in participants with cognitive impairment is the formaldehyde oxidation. Prior investigations have revealed that endogenous formaldehyde accumulation is mainly stimulated by aging, stroke, diabetes, and oxidative stress [ 54 , 55 ]. Recent studies also support that formaldehyde exposure exhibits adverse effects on cognitive function [ 54 ].

Therefore, gut microbiota may be involved in the regulation of formaldehyde oxidation that eventually affects cognition. In this study, we identified several key metabolites associated with cognitive impairment, of which methylglutaric acid is considered to be neurotoxic through early activation of an oxidative stress response [ 56 ] and increasing the potential for neurodegeneration in rats [ 57 ].

Another key metabolite L -phenylalanine identified is a precursor of catecholamines including dopamine and is essential for biosynthesis of these neurotransmitters [ 58 ]. Moreover, the concentration of L -phenylalanine has been found to be significantly lower in the plasma of AD patients compared to that of healthy controls [ 59 ].

There are several limitations in this study. First, the observational nature of this study makes the results subjected to the influence of potential residual confounders, and the statistically significant differences found in our analysis can only suggest associations of these multi-omics features with the outcomes.

Further experimental studies or clinical trials are required to verify these findings and prove the causality. Second, this study includes only Chinese participants and thus the results may not be generalizable to other ethnic populations.

Finally, although we have adjusted for the lag time 1. A major strength of the present study is that our findings are replicated across three independent populations from different regions in China. Furthermore, the findings based on questionnaire information i. Overall, the present study provides important evidence supporting the close association of gut microbiome with cognitive impairment and alterations of brain structure.

The identified cognition-related gut microbial taxonomies, pathways or serum metabolites may potentially contribute to the development of interventions or drug targets for dementia and cognitive decline in the future.

Other data described in the article will be made available upon request by bona fide researchers for specified scientific purposes via contacting the corresponding authors.

Patterson C. World Alzheimer report Google Scholar. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. N Engl J Med. Article CAS PubMed PubMed Central Google Scholar.

Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: report of the Lancet Commission.

Article PubMed PubMed Central Google Scholar. Ghaisas S, Maher J, Kanthasamy A. Gut microbiome in health and disease: Linking the microbiome—gut—brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases.

Pharmacol Ther. Article CAS PubMed Google Scholar. Harach T, Marungruang N, Duthilleul N, Cheatham V, Mc Coy KD, Frisoni G, et al.

Reduction of Abeta amyloid pathology in APPPS1 transgenic mice in the absence of gut microbiota. Sci Rep. Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, et al.

Article CAS Google Scholar. Liu P, Wu L, Peng G, Han Y, Tang R, Ge J, et al. Brain Behav Immun. Article PubMed Google Scholar.

Li B, He Y, Ma J, Huang P, Du J, Cao L, et al. Alzheimers Dement. Arnoriaga-Rodriguez M, Mayneris-Perxachs J, Burokas A, Contreras-Rodriguez O, Blasco G, Coll C, et al.

Obesity impairs short-term and working memory through gut microbial metabolism of aromatic amino acids. Cell Metab. The gut microbiome in neurological disorders.

Lancet Neurol. Zhang ZQ, He LP, Liu YH, Liu J, Su YX, Chen YM. Association between dietary intake of flavonoid and bone mineral density in middle aged and elderly Chinese women and men.

Osteoporos Int. Katzman R, Zhang MY, Ouang Ya Q, Wang ZY, Liu WT, Yu E, et al. A Chinese version of the mini-mental state examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol.

Popkin BM, Du S, Zhai F, Zhang B. Cohort profile: the China Health and Nutrition Survey—monitoring and understanding socio-economic and health change in China, — Int J Epidemiol. Folstein MF, Folstein SE, McHugh PR.

A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. Perneczky R, Wagenpfeil S, Komossa K, Grimmer T, Diehl J, Kurz A. Mapping scores onto stages: mini-mental state examination and clinical dementia rating.

Am J Geriatr Psychiatry. Brandt J, Welsh KA, Breitner JC, Folstein MF, Helms M, Christian JC. Hereditary influences on cognitive functioning in older men. A study of twin pairs. Arch Neurol. Miao Z, Lin JS, Mao Y, Chen GD, Zeng FF, Dong HL, et al. Erythrocyte n-6 polyunsaturated fatty acids, gut microbiota and incident type 2 diabetes: a prospective cohort study.

Diabetes Care. Sun S, Wang H, Tsilimigras MC, Howard AG, Sha W, Zhang J, et al. Does geographical variation confound the relationship between host factors and the human gut microbiota: a population-based study in China.

BMJ Open.

The intensity of color represents correlation coefficients. False discovery rate FDR was calculated using Benjamini—Hochberg method. d Significant associations among 4 aspects of multi-omics: genera of 16S rRNA gene sequencing, metagenomic pathways, serum metabolites, and brain structure.

Spearman correlation was used to calculate pairwise correlations of all the measurements. Size of nodes represents the number of connections with others. After adjusting for potential confounders, we found that the pathways of formaldehyde assimilation II RuMP Cycle and formaldehyde oxidation I, as well as the methylglutaric acid, were enriched in samples from subjects with impaired cognition Additional file 1 : Fig.

Additionally, several pathways e. Individuals with normal cognition had higher levels of L -valine, isovelarylcarnitine, and L -phenylalanine Additional file 1 : Fig.

As shown in Fig. Both distinct and common features were found between the mild and normal groups. For example, the significant edges of the network were fewer in the mild group than in the normal group vs. A total of 51 and 53 nodes were included in the mild and normal groups, respectively.

CSF volume was significantly associated with several metabolites including L -valine and isovelarylcarnitine among individuals with cognitive decline, while other brain structures e. The degrees of amino acids had a substantial change between the mild and normal groups 16 and 60, respectively.

Of note, the mild group contained six amino acids and the normal group contained eight, with six overlapping, including L -threonine, L -serine, L -glutamine, L -leucine, L -methionine, and L -valine.

Additionally, the pathways of glycogen biosynthesis I from ADP-D-Glucose and formaldehyde oxidation I were found to play central roles in the interaction network among the normal participants, but none of the dominant pathways were found in the interaction network among the mild group.

Detailed connections among cognition-related pathways are presented in Additional file 1 : Fig. There was a total of 15 nodes and 88 edges in the network.

C4 photosynthetic carbon assimilation cycle PEPCK type , C4 photosynthetic carbon assimilation cycle NADP-ME type , and glycogen biosynthesis I from ADP-D-Glucose contributed to most of the interactions with other pathways.

In the present study, we found significant differences in the gut microbial composition among people with different cognitive status and revealed that increased intra-individual alterations in gut microbial composition was associated with cognitive decline.

We further identified three genera including Odoribacter , Butyricimonas , and Bacteroides which were depleted in participants with cognitive impairment compared with normal controls. Moreover, higher abundance of Odoribacter was associated with several important features of brain structure, including larger volumes of WM and the right hippocampus as well as smaller CSF volume.

We then revealed that chronic inflammation might underlie the associations of gut microbial features with cognitive impairment. The associations between gut microbial α-diversity and dementia or cognitive function have been controversial. cohort, gut microbial α-diversity was lower in dementia patients compared with healthy controls [ 6 ], while contradictory evidence was reported in a Japanese population [ 25 ].

In the present GNHS study involving more than participants, we did not find significant correlations between α-diversity and cognitive function, which was consistent with a previous study conducted in European participants [ 26 ]. Microbial instability has been associated with a variety of disease outcomes such as metabolic diseases [ 27 ] and allergenic and autoimmune disorders [ 28 ].

To the best of our knowledge, our study is the first to report that the instability of gut microbial composition is correlated with cognitive impairment. We observed significant differences in β-diversity among participants with distinct cognitive performance in two independent populations. We speculated that intra-individual alterations of microbial structure rather than α-diversity might play an important role in the cognitive maintenance.

Prior studies have demonstrated that the microbiome-gut-brain axis might be involved in the development and progression of cognitive impairment and dementia by altering permeability of the blood—brain barrier and inducing neuroinflammation [ 29 ].

SCFAs including acetate, propionate, and butyrate have been reported to decrease the permeability of the blood—brain barrier and exert anti-neuroinflammatory effects [ 22 ].

Of note, the three cognition-related genera identified in our study, Odoribacter , Butyricimonas , and Bacteroides, are all putative SCFA-producing bacteria which have potent anti-inflammatory and immunomodulatory effects [ 30 ].

Previous studies demonstrated that Odoribacter and Bacteroides are decreased in AD patients [ 31 , 32 ]. Odoribacter has also been shown to be beneficial for hypertension prevention [ 33 ] and blood sugar regulation [ 34 ]. Butyricimonas , known as a protective bacterium, has the ability to produce butyric acid and isoacid salts [ 35 ].

Prior studies have reported that Butyricimonas is depleted in mice with spinal cord injury [ 36 ] and in patients with cystic fibrosis [ 37 ]. Butyricimonas is also associated with decreased adiposity and hepatic steatosis in mice [ 38 ]. Volumetric reduction of cognition-related brain areas is considered a pathological hallmark of neurodegeneration [ 39 ].

Hippocampal atrophy has been robustly linked to cognitive performance and risk of dementia [ 40 ]. In comparison to normal controls, AD patients are found with smaller white matter volume [ 41 ], while a larger volume of CSF tends to be associated with higher dementia risk [ 42 ].

Here, the associations of Odoribacter with the volumes of WM, right hippocampus, and CSF revealed potential neuro-protective effect of putative SCFA-producing bacteria.

The present study indicated that increased Dorea longicatena was associated with worse cognitive performance. Dorea longicatena has been reported to be positively associated with BMI and waist circumference [ 43 ].

Meanwhile, higher abundance of Dorea longicatena exists in individuals with circadian rhythm disturbance [ 44 ]. Recent evidence suggests that Dorea might contribute to elevated intestinal permeability [ 45 ].

In general, Dorea longicatena or Dorea may have a negative effect on the maintenance of a healthy gut. Furthermore, the present study revealed that Dorea longicatena and the pathway of formaldehyde oxidation I were positively correlated with IFN-γ.

IFN-γ is an AD-related pro-inflammatory cytokine [ 46 ] and elevated IFN-γ has been reported in AD and other neurologic disorders, such as stroke and multiple sclerosis [ 47 , 48 , 49 ]. These results suggest that inflammation activation plays a key role in the crosstalk between gut microbiome and the central nervous system CNS.

Although IFN-γ and microglial activation have been generally linked to inflammatory stimuli in the CNS, the presence of IFN-γ in the blood is not necessarily associated with chronic inflammation [ 50 ].

Thus, our findings need to be verified by more mechanistic studies. Besides systemic inflammation, the pathways involved in glucose metabolism and mitochondrial dysfunction have also been related with AD pathology [ 51 , 52 ].

Glycogen is critical in energy and glucose metabolism [ 53 ]. Enrichment of microbial pathways of glycogen biosynthesis and degradation among participants with poor cognition in the present study may reflect the essential role of these unbalanced metabolic processes in cognitive disorders.

Another pathway enriched in participants with cognitive impairment is the formaldehyde oxidation. Prior investigations have revealed that endogenous formaldehyde accumulation is mainly stimulated by aging, stroke, diabetes, and oxidative stress [ 54 , 55 ].

Recent studies also support that formaldehyde exposure exhibits adverse effects on cognitive function [ 54 ]. Therefore, gut microbiota may be involved in the regulation of formaldehyde oxidation that eventually affects cognition. In this study, we identified several key metabolites associated with cognitive impairment, of which methylglutaric acid is considered to be neurotoxic through early activation of an oxidative stress response [ 56 ] and increasing the potential for neurodegeneration in rats [ 57 ].

Another key metabolite L -phenylalanine identified is a precursor of catecholamines including dopamine and is essential for biosynthesis of these neurotransmitters [ 58 ].

Moreover, the concentration of L -phenylalanine has been found to be significantly lower in the plasma of AD patients compared to that of healthy controls [ 59 ]. There are several limitations in this study. First, the observational nature of this study makes the results subjected to the influence of potential residual confounders, and the statistically significant differences found in our analysis can only suggest associations of these multi-omics features with the outcomes.

Further experimental studies or clinical trials are required to verify these findings and prove the causality. Second, this study includes only Chinese participants and thus the results may not be generalizable to other ethnic populations.

Finally, although we have adjusted for the lag time 1. A major strength of the present study is that our findings are replicated across three independent populations from different regions in China. Furthermore, the findings based on questionnaire information i.

Overall, the present study provides important evidence supporting the close association of gut microbiome with cognitive impairment and alterations of brain structure.

The identified cognition-related gut microbial taxonomies, pathways or serum metabolites may potentially contribute to the development of interventions or drug targets for dementia and cognitive decline in the future. Other data described in the article will be made available upon request by bona fide researchers for specified scientific purposes via contacting the corresponding authors.

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Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: report of the Lancet Commission.

Article PubMed PubMed Central Google Scholar. Ghaisas S, Maher J, Kanthasamy A. Gut microbiome in health and disease: Linking the microbiome—gut—brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. Pharmacol Ther. Article CAS PubMed Google Scholar.

Harach T, Marungruang N, Duthilleul N, Cheatham V, Mc Coy KD, Frisoni G, et al. Reduction of Abeta amyloid pathology in APPPS1 transgenic mice in the absence of gut microbiota.

Sci Rep. Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, et al. Article CAS Google Scholar. Liu P, Wu L, Peng G, Han Y, Tang R, Ge J, et al. Brain Behav Immun. Article PubMed Google Scholar. Li B, He Y, Ma J, Huang P, Du J, Cao L, et al.

Alzheimers Dement. Arnoriaga-Rodriguez M, Mayneris-Perxachs J, Burokas A, Contreras-Rodriguez O, Blasco G, Coll C, et al. Obesity impairs short-term and working memory through gut microbial metabolism of aromatic amino acids.

Cell Metab. The gut microbiome in neurological disorders. Lancet Neurol. Zhang ZQ, He LP, Liu YH, Liu J, Su YX, Chen YM. Association between dietary intake of flavonoid and bone mineral density in middle aged and elderly Chinese women and men.

Osteoporos Int. Katzman R, Zhang MY, Ouang Ya Q, Wang ZY, Liu WT, Yu E, et al. A Chinese version of the mini-mental state examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. Popkin BM, Du S, Zhai F, Zhang B. Cohort profile: the China Health and Nutrition Survey—monitoring and understanding socio-economic and health change in China, — Int J Epidemiol.

Folstein MF, Folstein SE, McHugh PR. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. Perneczky R, Wagenpfeil S, Komossa K, Grimmer T, Diehl J, Kurz A.

Mapping scores onto stages: mini-mental state examination and clinical dementia rating. Am J Geriatr Psychiatry. Brandt J, Welsh KA, Breitner JC, Folstein MF, Helms M, Christian JC.

Hereditary influences on cognitive functioning in older men. A study of twin pairs. Arch Neurol. Miao Z, Lin JS, Mao Y, Chen GD, Zeng FF, Dong HL, et al. Erythrocyte n-6 polyunsaturated fatty acids, gut microbiota and incident type 2 diabetes: a prospective cohort study.

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PLoS ONE. Naugle RIKK. Limitations of the Mini-Mental State Examination. Cleve Clin J Med. Download references. We thank all the participants involved in the three populations and Westlake University Supercomputer Center for the assistance in data storage and computation.

We thank the support from Westlake Education Foundation and Westlake Intelligent Biomarker Discovery Lab at the Westlake Laboratory of Life Sciences and Biomedicine. This study was funded by the National Natural Science Foundation of China , , , and , Zhejiang Ten-thousand Talents Program R , and Zhejiang Provincial Natural Science Foundation of China LQ21H CHNS received funding from the National Institutes of Health NIH R01HD, R01AG, P30DK, and R01HD from to and was supported by the National Institutes of Health and National Institute of Diabetes and Digestive and Kidney Diseases R01DK and the Carolina Population Center P2CHD, P30AG The funders had no role in collecting data, study design, interpretation of data or the decision to submit the manuscript for publication.

College of Life Sciences, Zhejiang University, Hangzhou, , China. School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, , China.

Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, , China. Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, , China.

School of Public Health, Hainan Medical University, Haikou, , China. Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, , China.

Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, , China. Key Laboratory of Trace Element Nutrition, National Health Commission, Beijing, , China.

Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, , China. You can also search for this author in PubMed Google Scholar. JSZ, YMC, BZ and SDC conceived the study concept and design. WTC, ZHW, XFJ, CYL, HRL, HLZ, and SDC collected the data.

ZLM, WLG, and MLS processed the biological samples. ZLJ, KZ, YJH, and XXL supported microbial and imaging data for analysis. XXL and YQF did the statistical analysis.

XXL, YQF, and JSZ wrote the manuscript. All authors critically reviewed the article and approved the final manuscript.

Correspondence to Shengdi Chen , Bing Zhang , Yu-ming Chen or Ju-Sheng Zheng. The GNHS study protocol was approved by the Ethics Committee of the School of Public Health at Sun Yat-sen University and the Ethics Committee of Westlake University.

The study protocol of CHNS was approved by the Institutional Review Boards of the Chinese Center for Disease Control and Prevention, University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health.

Informed consents were obtained from all participants. Table S1. Participant characteristics in the GNHS. Table S2. Participant characteristics of the CHNS.

Table S3. Table S4. The primary information connection between the brain and gut is the vagus nerve, the longest nerve in the body. The gut has been called a "second brain" because it produces many of the same neurotransmitters as the brain does, like serotonin, dopamine, and gamma-aminobutyric acid, all of which play a key role in regulating mood.

What affects the gut often affects the brain and vice versa? When your brain senses trouble—the fight-or-flight response—it sends warning signals to the gut, which is why stressful events can cause digestive problems like a nervous or upset stomach.

On the flip side, flares of gastrointestinal issues like irritable bowel syndrome IBS , Crohn's disease, or chronic constipation may trigger anxiety or depression. The brain-gut axis works in other ways, too.

For example, your gut helps regulate appetite by telling the brain when it's time to stop eating. About 20 minutes after you eat, gut microbes produce proteins that can suppress appetite, which coincides with the time it often takes people to begin feeling full.

How might probiotics fit in the gut-brain axis? Some research has found that probiotics may help boost mood and cognitive function and lower stress and anxiety.