Contribution of preoperative gut microbiota in postoperative neurocognitive dysfunction in elderly patients undergoing orthopedic surgery

Jiangjiang Bi, Yifan Xu, Shiyong Li, Gaofeng Zhan, Dongyu Hua, Juan Tan, Xiaohui Chi, Hongbing Xiang, Fengjing Guo, Ailin Luo, Jiangjiang Bi, Yifan Xu, Shiyong Li, Gaofeng Zhan, Dongyu Hua, Juan Tan, Xiaohui Chi, Hongbing Xiang, Fengjing Guo, Ailin Luo

Abstract

Objective: To investigate the role of gut microbiota and metabolites in POCD in elderly orthopedic patients, and screen the preoperative diagnostic indicators of gut microbiota in elderly POCD.

Method: 40 elderly patients undergoing orthopedic surgery were enrolled and divided into Control group and POCD group following neuropsychological assessments. Gut microbiota was determined by 16S rRNA MiSeq sequencing, and metabolomics of GC-MS and LC-MS was used to screen the differential metabolites. We then analyzed the pathways enriched by metabolites.

Result: There was no difference in alpha or beta diversity between Control group and POCD group. There were significant differences in 39 ASV and 20 genera bacterium in the relative abundance. Significant diagnostic efficiency analyzed by the ROC curves were found in 6 genera bacterium. Differential metabolites in the two groups including acetic acid, arachidic acid, pyrophosphate etc. were screened out and enriched to certain metabolic pathways which impacted the cognition function profoundly.

Conclusion: Gut microbiota disorders exist preoperatively in the elderly POCD patients, by which there could be a chance to predict the susceptible population.

Clinical trial registration: [http://www.chictr.org.cn/edit.aspx?pid=133843&htm=4], identifier [ChiCTR2100051162].

Keywords: elderly patients; gut microbiota; metabolites; orthopedic surgery; postoperative neurocognitive dysfunction.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2023 Bi, Xu, Li, Zhan, Hua, Tan, Chi, Xiang, Guo and Luo.

Figures

Figure1
Figure1
Comparisons of relative abundance of gut microbiota. (A) Histogram results for the top 20 in order of relative abundance at phylum level, which shows Bacteroidota, Firmicutes, Proteobacteria etc. contribute the main components of gut microbiota, (B) Comparisons between the two groups using Wilcoxon rank sum test, significant differences in relative abundance were observed in 20 genus (*p < 0.05), and (C–L) Relative abundance of differential genera bacterium, analyzed by two-tailed Wilcoxon rank-sum test, data represent the means ± SEM, n = 16 in each group, *p < 0.05 compared with Control group.
Figure 2
Figure 2
Alpha diversity and beta diversity in gut microbiota between the two groups. (A–C) There was no significant difference of alpha diversity as measured by the Chao1 diversity index (p = 0.098). The Shannon index (p = 0.065) and ACE index (p = 0.110) indicated species diversity and evenness, did not show significant differences. (D,E) There was no difference between the two groups measured by PCA and PCoA.
Figure 3
Figure 3
ROC curves of the gut genus bacterium relative abundance for the diagnosis of POCD. Vertical coordinate indicated the sensitivity of diagnosis, horizontal coordinate indicated the 1-specificity of diagnosis, AUC > 0.5 indicated a diagnosis efficiency of the gut bacterium.
Figure 4
Figure 4
(A,B) It showed significant differences between two groups at the metabolic profiling level both in PCA and OPLS-DA (R2 = 0.961, Q2 = −0.268), (C,D) Differential metabolites between the two groups performed by T-test, and visualization by their value of ps and Fold change values using volcano plots. The red dots represent metabolites that were significantly upregulated in the POCD group, the blue dots represent metabolites that were significantly downregulated and the grey dots represent metabolites that were not significant.
Figure 5
Figure 5
Heat map of differential metabolites. We performed hierarchical clustering of all significantly differential metabolites expressions, including 27 metabolites in GC–MS (A), and 125 metabolites in LC–MS (B). The horizontal coordinates indicate sample names and the vertical coordinates indicate differential metabolites. The colour ranges from blue to red, a redder colour indicates a higher abundance of expression of the differential metabolite.
Figure 6
Figure 6
Metabolic pathway enrichment bubble chart. The value of p of the metabolic pathway is the significance of the enrichment of the metabolic pathway, and the significant enrichment pathway was selected for bubble plotting. The vertical coordinate is the name of the metabolic pathway; the horizontal coordinate is the enrichment factor (Rich factor = number of significantly differential metabolites/total number of metabolites in the pathway), the larger the Rich factor, the greater the enrichment; the colour from green to red indicates that the value of p decreases; the larger the bubble, the greater the number of metabolites enriched to that pathway. (A) Metabolic pathway enrichment of GC-MS platform. (B) Metabolic pathway enrichment of LC-MS platform.
Figure 7
Figure 7
Heat map of correlation between microbiome and metabolites. Each column is for a different species and each row corresponds to a metabolite. The graph shows positive correlations in orange and negative correlations in blue, with darker colours representing greater correlations and colours closer to white representing correlations closer to zero. A *** in the graph represents a correlation p value less than 0.001, a ** in the graph represents a correlation p value less than 0.01 and a * in the graph represents a correlation p value less than 0.05.

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Source: PubMed

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