Microbiome of diseased and healthy implants-a comprehensive microbial data analysis.

Objective: The purpose of this systematic bioinformatics analysis was to describe the compositions and differences in submucosal microbial profiles of peri-implants' diseases and healthy implant. Material and methods: PubMed, Embase, ETH Z, Scopus, CNKI, and Wanfang databases were searched to screen relevant literature on the analysis of peri-implant microflora based on the sequencing analysis technique of 16S ribosomal RNA (16S rRNA) gene. High-throughput sequencing of the 16S rRNA gene of microorganisms from healthy implants, peri-implant mucositis, and peri-implantitis was downloaded from the screened articles. EasyAmplicon and Usearch global algorithm were used to match the reads from each dataset to a full length of 16S rRNA or ITS gene sequence. The microorganisms based on the Human Oral Microbiome Database (HOMD) were re-classified, and the microbial diversity, flora composition, and differential species of the samples were re-analyzed, including taxonomic classification and alpha and beta diversity calculations. The co-occurrence network was also re-analyzed. Results: A total of seven articles with 240 implants were included. Among them, 51 were healthy implants (HI), 43 were in the peri-implant mucositis (PM) group, and 146 were in the peri-implantitis (PI) group. A total of 26,483 OTUs were obtained, and 877 microorganisms were annotated. The alpha diversity including Chao1 (healthy implants, 121.04 ± 92.76; peri-implant mucositis, 128.21 ± 66.77; peri-implantitis, 131.15 ± 84.69) and Shannon (healthy implants, 3.25 ± 0.65; peri-implant mucositis, 3.73 ± 0.61; peri-implantitis, 3.53 ± 0.67) of the samples from the three groups showed a significant difference. The beta diversity of the three samples was statistically different among groups. The genera of Treponema and Fretibacterium were significantly more abundant in the PI group than in the other two groups, and the genus of Streptococcus was more abundant in the HI group. The relative abundance of Porphyromonas in the peri-implantitis group was 6.1%. The results of the co-occurrence network showed differences in the network topology among the three groups of samples. The most connected three genera in the healthy implants were Halomonas, Fusobacterium, and Fretibacterium. The most connected three genera in peri-implant mucositis were Alistipes, Clostridia UCG-014, and Candidatus Saccharimonas. The most connected three genera in the peri-implantitis group were Lachnoanaerobaculum, Fusobacterium, and Atopobium. The betweenness of Porphvromonas gingivalis (red complex) in the PI group (7,900) was higher than in the HI group (23). Conclusions: The community compositions of peri-implant submucosal microorganisms were significantly different in healthy implants, peri-implant mucositis, and peri-implantitis. The submucosal microbial communities in peri-implantitis were characterized by high species richness and diversity compared with the healthy implants; the relative abundance of red complex, some members of the yellow complex, and some novel periodontal pathogens was higher in the peri-implantitis and peri-implant mucositis groups than in the healthy implant group. The core flora of the co-occurrence network of healthy implants, peri-implant mucositis, and peri-implantitis varied considerably. The peri-implantitis site presented a relative disequilibrium microbial community, and Porphyromonas may play an important role in the co-occurrence network.

Obesity-Associated Anxiety Is Prevalent among College Students and Alleviated by Calorie Restriction.

Anxiety is a common disorder among college students, especially those with obesity. Obesity contributes to metabolic disorders and disturbs the neural functions, further leading to anxiety. In this cross-sectional study, we aimed to determine the association between obesity and anxiety among college students and identified the potential factors for obesity-associated anxiety. We evaluated the intervention effects of calorie restriction on anxiety. Self-reported questionnaires were distributed to 1381 college students from January to March in 2021. Anxiety was measured by the State-Trait Anxiety Inventory (STAI). Participants were classified into anxiety and non-anxiety groups according to their STAI scores. Chi-squared test and logistic regression were used to analyze the potential factors. We found that 383 college students exhibited anxiety, accounting for 30.1% among all included college students, which was higher than the global average. The association between anxiety and obesity was observed among college students (p = 0.009), especially in males (p = 0.007). We identified that pre-obesity (p = 0.012), unhealthy calorie intake (p = 0.001), dieting (p = 0.003) and high academic year (p = 0.006) as the risk factors for anxiety and found that the long sleep duration was a protective factor for anxiety (p < 0.001). We found that more obese students showed an improvement of anxiety than the underweight students after calorie restriction (p < 0.001). Collectively, our findings suggest that obesity-associated anxiety is prevalent among the college students and could be alleviated by moderate calorie restriction. It is necessary for students to receive anxiety management in their college life. Additionally, the proper calorie restriction should be promoted to help students protect against obesity and obesity-associated anxiety.