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Keywords: PDB database, B-factor, Graphlete Degree Vector, protein flexibility
Published in RUP: 14.09.2025; Views: 462; Downloads: 5
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Abstract: Background: Early time-restricted eating combined with energy restriction (eTRE + ER) has been shown to reduce fat mass, diastolic blood pressure (DBP) and fasting glucose more effectively than late TRE with energy restriction (lTRE + ER) or energy restriction (ER) alone. Given the gut microbiome’s sensitivity to circadian rhythms, we examined whether adding TRE, particularly eTRE, to ER alters gut microbiota composition beyond ER alone, and whether such effects persist during follow-up. Methods: We analysed anthropometric, biochemical and gut microbiome data from 76 participants at baseline and after a 3-month intervention (eTRE + ER: n = 33; lTRE + ER: n = 23; ER: n = 20). Follow-up microbiome data 6-months after the end of intervention were available for 43 participants. Gut microbiota composition was assessed via 16S rRNA gene sequencing of stool samples. Results: No significant between-group differences in beta diversity were observed over time. However, changes in alpha diversity differed significantly across groups at the end of the intervention (Shannon: F = 5.72, p < 0.001; Simpson: F = 6.72, p < 0.001; Richness: F = 3.99, p = 0.01) and at follow-up (Richness: F = 3.77, p = 0.02). lTRE + ER led to the greatest reductions in diversity post intervention, while ER was least favourable during follow-up. Although no significant between-group differences were observed at the phylum level either at the end of the intervention or during follow-up, only the eTRE + ER group exhibited a significant decrease in Bacillota and an increase in Bacteroidota during follow-up. At the genus level, differential abundance analysis revealed significant shifts in taxa such as Faecalibacterium, Subdoligranulum, and other genera within the Ruminococcaceae and Oscillospiraceae families. In the eTRE + ER, Faecalibacterium and Subdoligranulum increased, while in other groups decreased. Notably, the changes in Faecalibacterium were negatively correlated with fasting glucose, while the increase in Subdoligranulum was inversely associated with DBP; however, both associations were weak in strength. Conclusions: eTRE + ER may promote beneficial, lasting shifts in the gut microbiome associated with improved metabolic outcomes. These results support further research into personalized TRE strategies for treatment of obesity.
Keywords: eating window, energy restriction, microbiota, alpha and beta diversity, metabolic health, obesity
Published in RUP: 17.07.2025; Views: 723; Downloads: 7
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Keywords: protein, binding site, virus, in silico, composition, amino acid
Published in RUP: 12.07.2024; Views: 2710; Downloads: 19
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Keywords: molecular docking, CmDock, ligand, decoy, receptor, roc curve, enrichment
Published in RUP: 27.10.2022; Views: 3354; Downloads: 30
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Abstract: : The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host%s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25%50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.
Keywords: gut microbiota, nutrition, obesity, lifestyle parameters, clustering
Published in RUP: 10.09.2020; Views: 2846; Downloads: 81
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