Relationship Between Plasma Lipopolysaccharides,Gut Microbiota,and Dementia: A Cross-Sectional Study

Abstract

Background:

Previous studies have demonstrated associations between gut microbiota, microbial metabolites, and cognitive decline. However, relationships between these factors and lipopolysaccharides (LPS; molecules of the outer membrane of gram-negative bacteria) remain controversial.

Objective:

To evaluate associations between plasma LPS, gut microbiota, and cognitive function.

Methods:

We performed a cross-sectional sub-analysis of data of 127 participants (women: 58%, mean age: 76 years) from our prospective cohort study regarding the relationship between gut microbiota and cognitive function. We enrolled patients who visited our memory clinic and assessed demographics, dementia-related risk factors, cognitive function, brain imaging, gut microbiomes, and microbial metabolites. We evaluated relationships between cognitive decline and plasma LPS using multivariable logistic regression analyses.

Results:

Plasma LPS concentration increased with increasing degree of cognitive decline and total cerebral small vessel disease (SVD) score (Kruskal-Wallis test; p = 0.016 and 0.007, respectively). Participants with high plasma LPS concentrations tended to have lower concentrations of gut microbial metabolites, such as lactic acid and acetic acid, and were less likely to consume fish and shellfish (44.7% versus 69.6%, p = 0.027) than those with low plasma LPS concentrations. Multivariable analyses revealed that plasma LPS concentration was independently associated with the presence of mild cognitive impairment in participants without dementia (odds ratio: 2.09, 95% confidence interval: 1.14–3.84, p = 0.007).

Conclusion:

In this preliminary study, plasma LPS concentration was associated with both cognitive decline and cerebral SVD and significantly correlated with beneficial gut microbial metabolites. Plasma LPS may be a risk factor for cognitive decline.

Keywords

Biomarkers cerebral small vessel disease cognitive decline dementia gut microbiota

INTRODUCTION

Dementia is a substantial healthcare problem; in 2015, 47 million individuals worldwide were living with dementia [1]. The number of individuals with dementia is expected to increase to 66 million by 2030 and nearly 130 million by 2050 [2]. Furthermore, the global cost of dementia was estimated to be $820 billion (USD) in 2015, and this cost has continued to increase [1, 2]. Japan is also facing a similar healthcare problem because of the increasing proportion of individuals with dementia; therefore, a comprehensive strategy for dementia research has been introduced in Japan to address this issue [3].

Recently, there has been a focus on the associations between gut microbiota and cognitive decline [4 –6], as researchers suspect they may reveal the mechanisms underlying the onset of dementia. Previous studies have suggested that disruption of the neuroinflammatory system [7], vascular inflammation [8], and remote relationships driven by various metabolites [9] are mechanisms underlying cognitive decline that are caused by the gut microbiome. Furthermore, multiple direct (e.g., vagus nerve) and indirect (e.g., short-chain fatty acids, cytokines, and dietary amino acids) gut–brain pathways may be involved in the impairment of cognitive function, and the gut–brain axis is modulated by the gut microbiota via these pathways [6]. The degree of gut dysbiosis worsens with the progression of the disease from mild cognitive impairment (MCI), predementia stage, to dementia [10]. Moreover, the fecal microbial composition of patients with Alzheimer’s disease (AD) is altered, characterized by the depletion of gut microbial metabolite-producing and enriched inflammation-promoting bacteria, which correlates with the severity of AD [11]. Thus, microbiome analysis contributes to the classical predictors of dementia severity [11]. However, knowledge regarding the effects of gut microbiota on cognitive function and the associations regarding blood biomarkers remains limited.

Lipopolysaccharides (LPS), which are molecules of the outer membrane of gram-negative bacteria, induce the release of critical pro-inflammatory cytokines that are necessary to activate a potent immune response, and this increases amyloid-β (Aβ) production and accumulation and the hyperphosphorylation of the tau protein [12]. Bacterial LPS in the brain lysates from the hippocampus and superior temporal lobe neocortex of AD brains [13]. Additionally, LPS progressively accumulate in neuronal parenchyma and appear to preferentially associate with the periphery of neuronal nuclei in patients with sporadic AD [14]. Therefore, LPS are useful as a surrogate marker for cognitive decline, neurodegeneration, and gut microbial dysbiosis.

We conducted an observational study that was originally designed to investigate the relationship between gut microbiota and cognitive function. In our previous study, gut microbial dysregulation was revealed to be associated with cognitive decline [15, 16], vascular risk factors [17], and white matter hyperintensity (WMH) [18]. In addition, gut microbial metabolites have been shown to be associated with both cognitive decline [19] and adherence to a Japanese-style diet [20]. These factors indicate the presence of a diet–microbiome–dementia cascade. However, we previously found that plasma neurofilament light chain (NfL), which is a disease-non-specific marker of neural damage, is not significantly correlated with gut microbial metabolites [21]. NfL is an emerging biomarker of neuronal degradation that is receiving increasing attention [22]. NfL is an integral component of axons and is released into the bloodstream and cerebrospinal fluid during neurodegeneration [22]. Examining another biomarker in addition to NfL, such as LPS, that indicates both dysbiosis and neural damage, and identifying links between the biomarker, gut microbiota, and cognitive function may clarify the unidentified pathway of the gut–brain axis.

Thus, the present study aimed to evaluate the relationships between LPS, gut microbiota, and cognitive function by performing a sub-analysis of the data from our ongoing clinical study. We hypothesized that in our cohort data, plasma LPS would be associated with cognitive decline, and there would be an association between plasma LPS, the gut microbiome, and microbial metabolites.

MATERIALS AND METHODS

Study design

We performed a cross-sectional sub-analysis of data from a hospital-based prospective cohort study, the Gerontological Investigation of Microbiome: a Longitudinal Estimation Study (the Gimlet study), conducted at the National Center for Geriatrics and Gerontology (NCGG) in Japan [15]. Briefly, patients who visited the Memory Clinic at the NCGG and agreed to undergo both a medical assessment of their cognitive function and a fecal examination were enrolled. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the NCGG (no. 1191-3). Written informed consent was obtained from all patients and their families before their participated in the study. The Gimlet study is registered on the UMIN Clinical Trials Registry (UMIN000031851). Detailed information is provided in the Supplementary Materials.

Participants

We enrolled 128 patients (women: 59%, mean age: 74.2±8.7 years) from the Gimlet study who had subjective memory impairment and visited the Memory Clinic at the NCGG between March 2016 and March 2017. Participants in the Gimlet study were eligible for this sub-study if they met the following criteria: 1) able to undergo brain magnetic resonance imaging (MRI); 2) blood samples were successfully preserved; and 3) provided written informed consent. One participant was excluded from the current sub-analysis because of a lack of blood sample for the measurement of LPS, which resulted in a total of 127 participants in the analysis.

Assessments

All participants underwent a comprehensive geriatric assessment based on the following features: 1) demographic characteristics; 2) risk factors, such as hypertension, dyslipidemia, diabetes mellitus, ischemic heart disease, chronic kidney disease, and a history of stroke; 3) activities of daily living; 4) global cognitive function, which was assessed using the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) scales; 5) neuropsychological testing using evaluations, such as the Alzheimer’s Disease Assessment Scale-Cognitive Subscale and Frontal Assessment Battery; 6) behavioral and psychological symptoms assessed using the Dementia Behavior Disturbance Scale (DBDS); 7) burden on caregivers; 8) depression status, which was assessed using the Geriatric Depression Scale; 9) laboratory parameters, such as apolipoprotein E ɛ4, plasma NfL, and C-reactive protein; 10) ankle brachial index and pulse wave velocity as indicators of arteriosclerosis [23] and the ‘impact’ of pulse [24], respectively; 11) results of brain imaging, such as MRI and single-photon emission-computed tomography (SPECT); and 12) dietary assessments based on the Japanese Diet Index [20, 25]. Clinical samples and data were provided by the NCGG Biobank, which collects clinical data for research. In this sub-analysis, we used a single dataset that was obtained at enrollment in the Gimlet study.

Brain imaging

Participants underwent a 1.5-T brain MRI (Philips Ingenia, Eindhoven, Netherlands). The presence of cerebral small vessel disease (SVD) and its components, such as silent lacunar infarcts (SLIs), WMH, cerebral microbleeds (CMBs), and enlarged periventricular space, was assessed. Following previous studies [18, 26], we rated the total MRI burden of SVD on an ordinal scale from 0 to 4 by summing the presence of each of these four features. The voxel-based specific regional analysis system for Alzheimer’s Disease (VSRAD) software (Eisai Co., Ltd., Tokyo, Japan) was used to quantify cortical and hippocampal atrophy. In detail, medial temporal structures involving the entire region of the entorhinal cortex, hippocampus, and amygdala show significant atrophy in the patients with very mild AD and are specifically determined by VSRAD software program [27]. Thus, VSRAD is a useful indicator of early AD [27]. In addition, previous studies show that VSRAD can distinguish between AD and MCI [28] and between AD and dementia with Lewy bodies [29]. Participants also underwent N-isopropyl-p-[¹²³I]-iodoamphetamine SPECT, in which low blood flow in the area of the posterior cingulate gyrus and/or precuneus was regarded as a surrogate marker of AD [30].

Classification of cognitive function

Dementia was defined as an MMSE score of < 20 and/or a CDR score of≥1 [15]. Participants who did not have dementia were further categorized as having either MCI or normal cognition (NC). MCI was defined as an MMSE score of≥20 and a CDR score of 0.5, which indicated possible, very mild dementia and a higher risk of developing dementia [16]. NC was defined as an MMSE score of≥20 and a CDR score of 0.

Lipopolysaccharides

Blood samples were collected, processed onsite to isolate plasma, aliquoted, and frozen at –81°C in the NCGG Biobank. Plasma LPS concentration was measured using a Limulus amebocyte lysate assay (No. K50-643J; Lonza Inc., Basel, Switzerland) according to manufacturer instructions. The plasma was diluted 10-fold in pyrogen-free water and inactivated for 15 min at 90°C. LPS measurements were performed in pyrogen-free glass tubes, Eppendorf tubes, and plates [31]. All samples were measured blinded.

Gut microbiome

Fecal samples were collected, frozen, and stored at –81°C in the NCGG Biobank. The gut microbiome of each participant was analyzed by Techno Suruga Laboratory (Shizuoka, Japan) using terminal restriction fragment length polymorphism (T-RFLP) analysis [32] to classify gut microbes. By referencing the human fecal microbiome T-RFLP profile, each gut microbiome was categorized as representing one of three enterotypes: enterotype I, which comprised Bacteroides at > 30%; enterotype II, which comprised Prevotella at > 15%; and enterotype III, which comprised other combinations of microorganisms. The Firmicutes/Bacteroidetes (F/B) ratio was also calculated because a high ratio is indicative of dysbiosis [33].

Analysis of microbial metabolites in feces

Fecal concentrations of organic acids, short-chain fatty acids, ammonium ions, indoles, phenol, skatole, and p-cresol were measured, as previously described [19]. The concentrations of organic acids and short-chain fatty acids (acetic, propionic, butyric, iso-butyric, succinic, lactic, formic, valeric, and iso-valeric acids) were measured using high-performance liquid chromatography. Ammonium ion concentration was quantified using ion chromatography, and fecal concentrations of indoles, phenol, skatole, and p-cresol were quantified using gas chromatography/mass spectrometry.

Statistical analysis

Continuous, ordinal, and categorical variables are expressed as means±standard deviations, medians and interquartile ranges, or frequencies and proportions (percentage), respectively. Data were compared using Student’s unpaired t-tests, Wilcoxon rank-sum tests, and χ² tests, respectively. Natural logarithm-transformed plasma LPS concentrations were occasionally used to reduce the rightward skew of plasma LPS concentrations for subsequent analysis as well as for plasma NfL. Firstly, we compared clinical characteristics and plasma LPS concentrations between participants with dementia and those without dementia, and between participants with MCI and those with NC. Subsequently, we compared clinical characteristics between participants with high concentrations of plasma LPS and those with low concentrations, who were divided according to the median value of plasma LPS concentration. Furthermore, we compared the dietary composition between these two groups. Thirdly, relationships between the gut microbiome, microbial metabolites, and plasma LPS concentration were evaluated. Finally, multivariable logistic regression models were used to identify independent associations between the presence of dementia (or MCI among those who did not have dementia) and plasma LPS concentration, and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. All comparisons were two-tailed, and p < 0.05 represented statistical significance. Data were analyzed using the JMP 14.3 software package (SAS Institute Inc., Cary, NC, USA).

RESULTS

Participant characteristics

The number of participants who were classified into NC, MCI, and dementia were as follows: NC (n = 23), MCI (n = 71), and dementia (n = 33). Women were more likely than men to have a lower MMSE score (women versus men; 24 versus 26, respectively, p = 0.018), body mass index (22.2 versus 23.2, respectively, p = 0.045), and MNA-SF score (12 versus. 13, respectively, p = 0.046) and were less likely to have a smoking habit (4.1% versus 54.7%, respectively, p < 0.001) and alcohol consumption (21.6% versus 62.3%, respectively, p < 0.001). However, there were no significant differences between women and men in age (median age; 76 versus 75 years, respectively, p = 0.227), plasma NfL concentration (median value: 26.1 versus 23.0 pg/mL, respectively, p = 0.141), or plasma LPS concentration (median value: 4.5 versus 4.4 EU/mL, respectively, p = 0.207, Supplementary Table 1).

Dementia versus non-dementia

Compared with participants without dementia, participants with dementia tended to be women (84.9% versus 48.9%, respectively, p < 0.001), be apolipoprotein E ɛ4 carriers (54.6% versus 21.3%, respectively, p < 0.001), not be enterotype I (15.2% versus 44.7%, respectively, p = 0.003), have cerebral SVD, such as SLIs (24.2% versus 5.3%, respectively, p = 0.005) and CMBs (39.4% versus 16.0%, respectively, p = 0.008), have higher F/B ratio (median value: 2.05 versus 1.38, respectively, p = 0.019), have higher plasma NfL (median value: 33.2 versus 21. pg/mL, respectively, p < 0.001), and have higher scores in the VSRAD (median score: 2.0 versus 0.9, respectively, p < 0.001). Plasma LPS concentrations in participants with dementia were slightly higher than those in participants without dementia; however, the difference was not significant (median score: 4.95 versus 4.40 EU/mL, respectively, p = 0.272, Table 1).

Table 1

Comparison of clinical biomarkers between participants with and without dementia, participants with enterotype I and those with other enterotypes, and participants with mild cognitive impairment (MCI) and normal cognition (NC)

	Dementia (+) (n = 33)	Dementia (–) (n = 94)	p
Plasma LPS, EU/mL	4.95, 3.94–5.89	4.40, 3.83–5.25	0.272
Plasma NfL, pg/mL	33.2, 26.5–49.4	21.3, 15.5–27.2	< 0.001
Enterotype I, n (%)	5 (15.2)	42 (44.7)	0.003
F/B ratio	2.05, 1.24–2.85	1.38, 0.81–2.25	0.019
	Enterotype I (+) (n = 47)	Enterotype I (–) (n = 80)	p
Plasma LPS, EU/mL	4.39, 3.93–5.23	4.47, 3.76–5.47	0.747
Plasma NfL, pg/mL	23.4, 17.9–36.4	24.3, 19.0–34.0	0.621
Dementia, n (%)	5 (10.6)	28 (35.0)	0.003
F/B ratio	0.77, 0.60–1.19	2.12, 1.51–3.12	< 0.001
	MCI (n = 71)	NC (n = 23)	p
Plasma LPS, EU/mL	4.66, 4.01–5.35	4.04, 3.49–4.41	0.007
Plasma NfL, pg/mL	23.4, 17.6–33.3	18.2, 12.8–20.8	< 0.001
Enterotype I, n (%)	37 (52.1)	5 (21.7)	0.015
F/B ratio	1.25, 0.70–2.25	1.65, 1.11–2.26	0.082

Data are represented as medians and interquartile ranges. Wilcoxon rank-sum test and χ² tests were used. Enterotype I: Bacteroides > 30%. F/B ratio, Firmicutes/Bacteroidetes ratio; NC, normal cognition; NfL, neurofilament light chain; LPS, lipopolysaccharides; MCI, mild cognitive impairment.

MCI versus NC

Compared with participants with NC, participants with MCI tended to be older (MCI versus NC: 77 versus 69 years, p < 0.001), be hypertensive (64.8% versus 39.1%, p = 0.050), be enterotype I (52.1% versus 21.7%, p = 0.0028), have WMH (33.8% versus 4.4%, p = 0.006), have higher plasma NfL (median value: 23.4 versus 18.2 pg/mL, p < 0.001), and have higher scores in the VSRAD (median score: 1.0 versus 0.6, p = 0.015). Plasma LPS concentrations in participants with MCI were significantly higher than those in participants with NC (median value: 4.66 versus 4.04 EU/mL, p = 0.007, Table 1).

Clinical data and plasma LPS

Plasma LPS concentration significantly increased with increasing degree of cognitive decline (median values of 4.04 EU/mL for NC participants, 4.66 for MCI participants, and 4.94 for dementia participants; p = 0.016; Fig. 1A). Compared with participants with low concentrations of plasma LPS, those with high concentrations tended to have lower cognitive function (high versus low LPS concentrations: median MMSE score: 24 versus 25, p = 0.035; median CDR-sum of boxes (SB) score: 2.0 versus 1.5, p = 0.070; median Logical Memory subtest I of the Wechsler Memory Scale-Revised (LM-WMSR I) score: 7 versus 10, p = 0.031), higher concentrations of plasma NfL (median value: 26.3 versus 21.9 pg/mL p = 0.096), and higher scores in the VSRAD (median score: 1.20 versus 0.87, p = 0.055, Supplementary Table 2). In participants without dementia, plasma LPS concentrations were significantly higher in participants with cerebral SVD than in those without cerebral SVD (median values [cerebral SVD score]: 4.20 EU/mL [0], 4.75 [1–2], 5.28 [3–4], p = 0.007, Fig. 1B). In participants without dementia, compared with participants with low concentrations of plasma LPS, those with high concentrations tended to have MCI (high versus low LPS concentrations: 88.4% versus 64.7%, p = 0.009), instrumental activities of daily living (IADL) impairment (50.0% versus 26.8%, p = 0.017), higher scores in the DBDS (median score: 11 versus 6, p = 0.016), and slightly higher concentrations of plasma NfL (median value: 24.9 versus 21.1 pg/mL, p = 0.090, Supplementary Table 3). Natural logarithm-transformed plasma LPS was significantly correlated with natural logarithm-transformed plasma NfL (β= 0.419, 95% CI: 0.043–0.795, p = 0.029).

Fig. 1

Comparisons of plasma lipopolysaccharides (LPS) concentration and (A) cognitive function and (B) total cerebral small vessel disease (SVD) scores The x-axes show (A) cognitive function stratified into normal cognition (NC), mild cognitive impairment (MCI), and dementia groups; (B) total cerebral SVD scores stratified as scores of 0, 1–2, or 3–4 in participants without dementia. The y-axes show plasma LPS concentrations. Plasma LPS concentration increased with increasing degree of cognitive decline and total cerebral SVD score (Kruskal-Wallis test). Post hoc analysis was performed using Dunnett’s test. ^*p < 0.05; ^**p < 0.01.

Dietary assessment and LPS

Compared with participants with high concentrations of plasma LPS, those with low concentrations tended to be more likely to consume fish and shellfish (high versus low LPS concentrations: greater intake: 44.7% versus 69.6%, p = 0.027; Supplementary Table 4). Similarly, in participants without dementia, compared with participants with high concentrations of plasma LPS, those with low concentrations tended to be more likely to consume fish and shellfish (high versus low LPS concentrations: greater intake: 46.4% versus 73.2%, p = 0.042; Supplementary Table 5).

Gut microbiota, metabolites, and LPS

There were no significant differences in plasma concentrations of both LPS and NfL between participants with enterotype I and those with other enterotypes (Table 1). Regarding the gut microbial metabolites, concentrations of both lactic acid and acetic acid in participants with high LPS concentrations tended to be lower than those in participants with low LPS concentrations (high versus low: median value [25–75^th percentile]: lactic acid 0.03 [0.03–0.03] versus 0.03 [0.03–8.97] mg/g, p = 0.002 and acetic acid 2.25 [0.03–5.02] versus 3.58 [0.44–9.24] mg/g, p = 0.048). In participants without dementia, concentrations of both lactic acid and acetic acid in participants with high LPS concentrations also tended to be lower than those in participants with low LPS concentrations (high versus low: median value 25–75th percentile]: lactic acid 0.03 [0.03–0.03] versus 0.03 [0.03–8.97] mg/g, p = 0.002 and acetic acid 2.26 [0.03–4.96] versus 3.58 [0.83–9.24] mg/g, p = 0.071; Supplementary Table 6).

Multivariable analyses

Multivariable logistic regression analyses did not reveal significant associations between plasma LPS and dementia (univariate analysis: OR: 1.18, 95% CI: 0.85–1.66, p = 0.139; adjusted for age and sex: OR: 1.12, 95% CI: 0.78–1.63, p = 0.537; Table 2A). In participants without dementia, multivariable analyses revealed that plasma LPS was independently associated with the presence of MCI (univariate analysis: OR: 2.14, 95% CI: 1.22–3.75, p = 0.003; multivariate analysis: OR: 2.09, 95% CI: 1.14–3.84, p = 0.007; Tables 2B and 3). Furthermore, plasma NfL (OR: 1.12), the presence of enterotype I (OR: 3.78), and apolipoprotein E ɛ4 carrier (OR: 4.22) were also associated with the presence of MCI (Table 3). The plasma LPS cutoff value calculated according to the Youden index for the detection of MCI was 4.41 EU/mL, with 56% sensitivity and 78% specificity (area under the curve = 0.69, p = 0.003). Compared with participants with low plasma LPS concentrations, those with high concentrations had a higher OR (OR: 3.96, 95% CI: 1.20–13.1, p = 0.017) calculated in the stepwise multivariable logistic regression analyses after adjusting for other confounding covariables (Table 2B).

Table 2

Multivariable logistic regression analyses for the presence of (A) dementia and (B) mild cognitive impairment (MCI)

(A) Dementia	OR	95% CI	p
Plasma LPS, per 1 pg/mL
Model 1	1.18	0.85–1.66	0.139
Model 2	1.12	0.78–1.63	0.537
High versus low concentration of plasma LPS^*
Model 1	1.82	0.81–4.09	0.141
Model 2	1.66	0.70–3.92	0.248
(A) The dependent variable was the presence of dementia.
(B) MCI	OR	95% CI	p
Plasma LPS, per 1 EU/mL
Model 1	2.14	1.22–3.75	0.003
Model 2	1.90	1.06–3.43	0.019
Model 3	1.91	1.05–3.45	0.019
Model 4	1.93	1.06–3.51	0.032
High versus low concentration of plasma LPS^*
Model 1	4.15	1.39–12.40	0.006
Model 2	3.92	1.21–12.70	0.016
Model 3	3.96	1.20–13.08	0.017
Model 4	2.63	0.77–8.96	0.122

(B)The dependent variable was the presence of MCI. ^*Participants were allocated to two groups according to plasma LPS concentrations: high LPS group if the LPS value was above the median or the low LPS group if the LPS value was below the median. Model 1: univariate analyses. Model 2: adjusted for age and sex. Model 3: backward stepwise multivariable logistic regression analyses adjusted for model 2 factors, years of education, and risk factors, such as hypertension, dyslipidemia, diabetes mellitus, ischemic heart disease, chronic kidney disease, smoking, alcohol consumption, and apolipoprotein E ɛ4 carrier. Model 4: backward stepwise multivariable logistic regression analyses adjusted for model 3 factors, enterotype I (Bacteroides > 30%), Firmicutes/Bacteroidetes ratio, neurofilament light chain, SLIs, WMH, CMBs, enlarged perivascular space (EPVS), score of the voxel-based specific regional analysis system for Alzheimer’s disease, single-photon emission-computed tomography findings (presence or absence of low blood flow in the area of the posterior cingulate gyrus and/or precuneus). CI, confidence interval; LPS: lipopolysaccharides; OR, odds ratio.

Table 3

Multivariable logistic regression analyses for the presence of mild cognitive impairment (MCI)

	OR	95% CI	p
Plasma LPS, per 1 EU/mL	2.09	1.14–3.84	0.007
Plasma NfL, per 1 pg/mL	1.12	1.03–1.22	0.001
Enterotype I	3.78	1.11–12.8	0.025
Apolipoprotein E ɛ4 carrier	4.22	0.88–20.1	0.050

The dependent variable was the presence of MCI. Backward stepwise multivariable logistic regression analyses adjusted for age, sex, years of education, risk factors (e.g., hypertension, dyslipidemia, diabetes mellitus, ischemic heart disease, chronic kidney disease, smoking, alcohol consumption, and apolipoprotein E ɛ4 carrier), enterotype I (Bacteroides > 30%), Firmicutes/Bacteroidetes ratio, and plasma concentration of neurofilament light chain (NfL). CI, confidence interval; LPS: lipopolysaccharides; OR, odds ratio.

DISCUSSION

The main finding of the present study was that plasma LPS concentration significantly increased with increasing degrees of cognitive decline and cerebral SVD. Moreover, plasma LPS concentration was independently associated with the presence of MCI in participants without dementia. Furthermore, a low concentration of plasma LPS was significantly associated with a greater intake of fish and shellfish and a lower concentration of microbial metabolites, such as lactic acid and acetic acid. However, plasma LPS concentration was not independently associated with the presence of dementia in participants who were enrolled in the present sub-analysis.

LPS are the major outer membrane components of gram-negative bacteria and are capable of triggering systemic inflammation and the release of pro-inflammatory cytokines [34]. Recently, Andreadou et al. reported that levels of LPS are increased in the serum and cerebrospinal fluid of patients with AD and the serum of patients with MCI [12], which is consistent with our data. Furthermore, we revealed associations between gut microbial metabolites and the level of plasma LPS. This is the first report that demonstrates associations between plasma LPS concentration, gut microbial metabolites, and cognitive decline. However, Voigt et al. reported that the LPS-binding protein, a marker of intestinal barrier integrity, is not associated with incident AD or MCI [35]. This discrepancy may be attributed to the different factors measured between the studies: LPS and LPS-binding protein. Thus, this issue should be investigated in future studies.

We found that plasma LPS concentration was significantly correlated with gut microbial metabolites, such as lactic acid and acetic acid. These metabolites are products caused by Bifidobacterium, which is a healthy bacterium that has probiotic effects on human health, such as the suppression of neuroinflammation [36]. Therefore, a lower concentration of plasma LPS is expected to be associated with a higher concentration of beneficial microbial metabolites. Recently, we reported that several gut microbial metabolites are associated with dementia [19]; we found that several metabolites are positively associated with dementia, whereas others are inversely associated with dementia. Furthermore, previous studies have shown that some microbial metabolites, such as indole and propionic acid, promote brain health [37], and microbiota-derived short-chain fatty acids are critical mediators along the gut–brain axis and promote Aβ deposition [38]. Alkasir et al. reported the presence of a brain–gut link and proposed a novel strategy for the management of dementia by modulating microbiota [39]. These findings suggest a multiplex linkage among the gut microbiome, microbial metabolites, and cognitive function.

In our previous study, plasma NfL was significantly associated with cognitive decline [21]. In the present sub-analysis, we similarly found that both plasma LPS and NfL concentrations were significantly associated with MCI, independent of enterotype, apolipoprotein E ɛ4, and other dementia-related risk factors, such as age and years of education. Furthermore, plasma LPS concentration was positively correlated with plasma NfL concentration. Thus, plasma LPS concentration may be a novel risk factor for MCI. However, the relationship between the level of LPS and the presence of dementia was not statistically significant in this study. We speculate that this discrepancy is because the level of plasma LPS had already reached a high level at the stage of early cognitive decline, indicated by MCI, which resulted in a non-significant difference in plasma LPS level between participants with MCI and those with dementia. Previous reports also suggested that patients with AD and MCI have reduced microbial diversity, changes in gut microbiota can be detected in the early stages of AD, and that dysbiosis can occur in the MCI stage [40]. In addition, the criteria of dementia and MCI in our study were dependent on the CDR score. A CDR score of 0.5 includes patients at the very early stage of dementia [41]. Thus, the difference in plasma LPS concentration between our participants with dementia (CDR score of≥1) and those with MCI (CDR score of 0.5) was likely small. Even after the early cognitive decline stage, levels of NfL can continue to increase according to the progression of neurodegeneration and/or neural damage.

Plasma LPS concentrations did not significantly differ between the different categories of gut microbiomes, which were categorized according to enterotypes. This negative finding may be a result of the methodological limitations of our T-RFLP analysis. Furthermore, there was no significant difference F/B ratio, an indicator of dysbiosis, between groups with high and low concentrations of plasma LPS. We speculate that F/B ratio is not specifically associated with the presence of plasma LPS because F/B ratio simply indicates the ratio of the presence of Firmicutes to Bacteroidetes. The use of next-generation sequencing can reveal more detailed microbial data than can be revealed by the T-RFLP method, and such assessments will likely clarify the presence of the gut–microbiome–brain axis.

Another notable result of the present study was that greater consumption of fish and shellfish was associated with a lower concentration of plasma LPS and thus may protect against cognitive decline. Previously, we revealed that a healthier diet, as measured by the Japanese-style diet index, was associated with a lower concentration of plasma NfL [21]. Greater consumption of fish and shellfish was associated with a lower concentration of plasma LPS, which indicated that these foods alter the gut microbiota, increase beneficial microbial metabolites, and suppress neurodegeneration and/or neural injury reflected by the increase in plasma NfL, resulting in the preservation of cognitive function. Previously, Otsuka et al. showed that a moderately high level of serum docosahexaenoic acid, accompanied by greater consumption of fish, prevents cognitive decline among community-dwelling older adult individuals [42]. Our data are in line with this previous study, although we did not assess the level of serum docosahexaenoic acid in this sub-analysis. This mechanism will be investigated in more detail in future studies.

The present study has several strengths. Firstly, we revealed novel relationships among plasma LPS concentration, gut microbial metabolites, and cognitive decline. Specifically, plasma LPS concentration was strongly associated with MCI and plasma NfL concentration, the latter of which is a robust biomarker of neurodegeneration. Secondly, we also revealed that LPS level was correlated with several gut microbial metabolites. Although we did not observe a positive association that included participants with dementia, this is the first study to analyze such associations and thus, our findings offer new research directions in this emerging field. Thirdly, we confirmed the utility of plasma LPS concentration for detecting the presence of MCI. Although the sensitivity of plasma LPS was not high, it was statistically significant. Finally, we systematically evaluated cognitive function using a comprehensive geriatric assessment and a range of neuropsychological tests. Our findings encourage further explorations of the relationships between blood biomarkers and cognitive function through examinations of the gut microbiome.

The present study has several limitations. A causal relationship between plasma LPS concentration and cognitive decline could not be established owing to the cross-sectional design. Moreover, we did not assess Aβ or tau in the present study because the Gimlet study did not perform cerebrospinal fluid testing or positron emission tomography to identify these biomarkers, as previously reported [21]. We did not discuss in detail the dementia subtypes because of the small number of participants with dementia. Furthermore, the small number of participants and the large number of potential variables may have statistically underpowered our study. However, because this was a sub-analysis, we did not perform sample size calculation. Selection bias may exist because this was a single hospital-based cohort study. In addition, we did not perform any assessment of bile acids and gastrointestinal infections. Bile acids are known as microbe-derived neuroactive molecules [43], and recurring gastrointestinal infections affect the nervous system and can increase the risk of developing dementia [44]. Physical activity, which we did not assess in this study, can also significantly modulate the gut microbiome [45]. A systematic assessment including other biomarkers, such as the amyloid-β protein precursor and non-neurodegenerative inflammatory biomarkers, may also be useful given their association with the risk of the development of cognitive impairment [46].

Although this sub-study was a preliminary analysis, we provided evidence for the relationships between plasma LPS, gut microbial metabolites, and cognitive function. Detailed assessments of these relationships should be conducted in future studies to determine the underlying mechanisms.

Conclusions

High plasma LPS concentration was independently associated with MCI in participants without dementia. Plasma LPS concentration was also associated with cerebral SVD in participants without dementia and significantly correlated with gut microbial metabolites, such as lactic acid and acetic acid. Furthermore, a low concentration of plasma LPS was significantly correlated with a greater intake of fish and shellfish. Further studies are warranted to examine these associations in relation to the microbiome–gut–brain axis.

Footnotes

ACKNOWLEDGMENTS

This study was supported by a Grant-in-Aid for Scientific Research (C), JSPS KAKENHI (20k07861), grants from the Research Funding of Longevity Sciences from the NCGG (19–24), the NARO Bio-Oriented Technology Research Advancement Institution Project (Advanced Integration Research for Agriculture and Interdisciplinary Fields), the Danone Institute of Japan Foundation, the Honjo International Scholarship Foundation (to Dr. Saji), and the PRIME from the Japan Agency for Medical Research and Development (AMED) under Grant Number 18069370 (to Dr. Yamashita). We would like to thank Yukie Ohsaki, Maki Yamamoto, Hana Saito, and Ayaka Suzuki (NCGG) for their technical and secretarial assistance. We also thank the BioBank and NCGG for quality control of the clinical samples and data. Finally, we thank Sarina Iwabuchi, PhD, from Edanz () for editing a draft of this manuscript.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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