Gut Microbiota and Targeted Biomarkers Analysis in Patients With Cognitive Impairment

Lu, Shourong; Yang, Ying; Xu, Qiao; Wang, Shuqiang; Yu, Jie; Zhang, Bingshan; Wang, Zhuo; Zhang, Yunyun; Liu, Wenwei; Hong, Kan

doi:10.3389/fneur.2022.834403

Cited by 15 publications

(25 citation statements)

References 40 publications

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“…Recent studies have shown that intestinal microbes drive liver inflammatory stimuli through the "gut-liver axis" to accelerate liver carcinogenesis [22,23].In addition, gut microbiome can influence the development and progression of hepatocellular carcinoma by regulating the expression and accumulation of immune cells [24]. Studies have demonstrated that gut microbes can be used as noninvasive diagnostic markers for liver cancer, colorectal cancer, type 2 diabetes, and cognitive impairment [25][26][27][28]. However, the potential of gut microbes as biomarkers in clinical HCC patients has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Liang

Chen

et al. 2022

Oxidative Medicine and Cellular Longevity

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Background. Early stage hepatocellular carcinoma (HCC) has a high recurrence rate after surgery and lacks reliable predictive tools. We explored the potential of combining enhanced CT with gut microbiome to develop a predictive model for recurrence after early HCC surgery. Methods. A total of 112 patients with early HCC who underwent hepatectomy from September 2018 to December 2020 were included in this study, and the machine learning method was divided into a training group ( N = 71 ) and a test group ( N = 41 ) with the observed endpoint of recurrence-free survival (RFS). Features were extracted from the arterial and portal phases of enhanced computed tomography (CT) images and gut microbiome, and features with minimum absolute contraction and selection operator regression were created, and the extracted features were scored to create a preoperative prediction model by using the multivariate Cox regression analysis with risk stratification analysis. Results. In the study cohort, the model constructed by combining radiological and gut flora features provided good predictive performance ( C index, 0.811 (0.650-0.972)). The combined radiology and gut flora-based model constructed risk strata with high, intermediate, or low risk of recurrence and different characteristics of recurrent tumor imaging and gut flora. Recurrence of early stage hepatocellular carcinoma may be associated with oxidative stress in the intestinal flora. Conclusions. This study successfully constructs a risk model integrating enhanced CT and gut microbiome characteristics that can be used for the risk of postoperative recurrence in patients with early HCC. In addition, intestinal flora associated with HCC recurrence may be involved in oxidative stress.

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Section: Introductionmentioning

confidence: 99%

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Liang

Chen

et al. 2022

Oxidative Medicine and Cellular Longevity

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“…70 When compared to a control group, the relative abundance of Lachnoclostridium was higher in the cognitive impairment group. 71 This study showed that lower cognitive performance is associated with higher Blautia relative abundances. A previous study also showed higher abundance of Blautia in less physically active adults with insomnia.…”

Section: Discussionmentioning

confidence: 73%

“… 70 When compared to a control group, the relative abundance of Lachnoclostridium was higher in the cognitive impairment group. 71 …”

Section: Discussionmentioning

confidence: 99%

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Variation in Gut Microbiota Composition is Associated with Sleep Quality and Cognitive Performance in Older Adults with Insomnia

Haimov¹,

Magzal²,

Tamir³

et al. 2022

NSS

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Purpose Insomnia, a chronic condition affecting 50% of older adults, is often accompanied by cognitive decline. The mechanism underlying this comorbidity is not fully understood. A growing literature suggests the importance of gut microbiota for brain function. We tested associations between sleep quality and cognitive performance with gut microbiota in older adults with insomnia. Patients and Methods Seventy-two older adults with insomnia (age 73.2 ± 5.73 years, 56 females) provided stool samples for gut microbial sequencing. Microbiota profile was determined using the DADA2 bioinformatics pipeline. Cognition was assessed with the Cambridge Neuropsychological Test Automated Battery. Objective sleep quality was monitored by a two-week actigraphic recording, and participants completed the Insomnia Severity Index (ISI). We used partial canonical correspondence analysis (pCCA) to examine the relative contribution of insomnia, based on actigraphic sleep efficiency (SE) and ISI, and of cognitive status, based on the Multitasking test of Median Reaction Latency (MTTLMD) and the Spatial Working Memory Between Errors (SWMBE), to variance in microbiota composition. We used Pearson correlations to correlate insomnia and cognitive status parameters with microbiota amplicon sequence variants, genera, and families. Results The pCCA revealed that sleep quality and cognitive performance explained a variation of 7.5–7.9% in gut microbiota composition in older adults with insomnia. Correlation analysis demonstrated that Lachnoclostridium (genus) correlates positively with SE (r=0.42; P =0.05) and negatively with MTTLMD (r=−0.29; P =0.03), whereas Blautia (genus) correlates negatively with MTTLMD (r=−0.31; P =0.01). Conclusion Findings demonstrate the associations of sleep quality and cognitive performance with variance in gut microbiota composition and with specific genus abundance in older adults with insomnia. Further studies should validate the findings, determine causal relationships, and evaluate potential interventions for the comorbidity of insomnia and cognitive impairment in older adults with insomnia.

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“…There has been significant interest in developing risk assessment methods based on gut microbiome data ( Li et al, 2017 ; Xu et al, 2019 ; Chu et al, 2021 ; Loftus et al, 2021 ; Shen et al, 2021 ; Yao et al, 2021 ). Attempts have also being made to identify gut bacteria as biomarkers of disease ( Yu et al, 2017 ; Loftus et al, 2021 ; Yao et al, 2021 ; Lu et al, 2022 ). For example, Yao et al (2021) proposed the use of Fusobacterium as an indicator for colorectal cancer (detection) based on the results of linear discriminant analysis and effect size, while Lu et al (2022) identified 15 genera, including Lactobacillus , Bifidobacterium , and Akkermansia , using a random forest classification model as biomarkers (targeted biomarkers) for cognitive impairment.…”

Section: Introductionmentioning

confidence: 99%

Method for estimating disease risk from microbiome data using structural equation modeling

Tokuno¹,

Itoga²,

Kasuga³

et al. 2023

Front. Microbiol.

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The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) (female: 321 participants, male: 99 participants)], and patients afflicted with atopic dermatitis only [AS (female: 45 participants, male: 13 participants)], with atopic dermatitis and other diseases [AM (female: 75 participants, male: 34 participants)], and with other diseases but without atopic dermatitis [OD (female: 1,669 participants, male: 866 participants)] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components (bacterial genera) were Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis (AS, AM) and those without (NC, OD), and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded.

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Gut Microbiota and Targeted Biomarkers Analysis in Patients With Cognitive Impairment

Cited by 15 publications

References 40 publications

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Variation in Gut Microbiota Composition is Associated with Sleep Quality and Cognitive Performance in Older Adults with Insomnia

Method for estimating disease risk from microbiome data using structural equation modeling

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