Recent advances have highlighted the gut microbiota as a significant contributor to the development and progression of atherosclerosis, which is an inflammatory cardiovascular disease (CVD) characterized by plaque buildup within arterial walls. The gut microbiota, consisting of a diverse collection of microorganisms, impacts the host’s metabolism, immune responses, and lipid processing, all of which contribute to atherosclerosis. This review explores the complex mechanisms through which gut dysbiosis promotes atherogenesis. We emphasize the potential of integrating microbiota modulation with traditional cardiovascular care, offering a holistic approach to managing atherosclerosis. Important pathways involve the translocation of inflammatory microbial components, modulation of lipid metabolism through metabolites such as trimethylamine-N-oxide (TMAO), and the production of short-chain fatty acids (SCFAs) that influence vascular health. Studies reveal distinct microbial profiles in atherosclerosis patients, with increased pathogenic bacteria (Megamonas, Veillonella, Streptococcus) and reduced anti-inflammatory genera (Bifidobacterium, Roseburia), highlighting the potential of these profiles as biomarkers and therapeutic targets. Probiotics are live microorganisms that have health benefits on the host. Prebiotics are non-digestible dietary fibers that stimulate the growth and activity of beneficial gut bacteria. Interventions targeting microbiota, such as probiotics, prebiotics, dietary modifications, and faecal microbiota transplantation (FMT), present effective approaches for restoring microbial equilibrium and justifying cardiovascular risk. Future research should focus on longitudinal, multi-omics studies to clarify causal links and refine therapeutic applications.

The gut microbiota has acquired significant attention in recent years for its potential as a diagnostic biomarker for colorectal cancer (CRC). In this literature review, we looked at the studies exploring alterations in gut microbiota composition associated with CRC, the potential mechanisms linking gut dysbiosis to CRC development, and the diagnostic approaches utilizing gut microbiota analysis. Our research has led to the conclusion that individuals with CRC often display alterations in their gut microbiota composition compared to healthy individuals. These alterations can include changes in the diversity, abundance, and type of bacteria present in the gut. While the use of gut microbiota as a diagnostic biomarker for CRC holds promise, further research is needed to validate its effectiveness and standardize testing protocols. Additionally, considerations such as variability in the microbiota composition among individuals and potential factors must be addressed before microbiota-based tests can be widely implemented in clinical practice.

Our study highlighted the immune changes by pro-inflammatory biomarkers in the gut–liver-axis-linked ROS-cell death mechanisms in chronic and acute inflammations when gut cells are exposed to endotoxins in patients with hepatic cirrhosis or steatosis. In duodenal tissue samples, gut immune barrier dysfunction was analyzed by pro-inflammatory biomarker expressions, oxidative stress, and cell death by flow cytometry methods. A significant innate and adaptative immune system reaction was observed as result of persistent endotoxin action in gut cells in chronic inflammation tissue samples recovered from hepatic cirrhosis with the A-B child stage. Instead, in patients with C child stage of HC, the endotoxin tolerance was installed in cells, characterized by T lymphocyte silent activation and increased Th1 cytokines expression. Interesting mechanisms of ROS-cell death were observed in chronic and acute inflammation samples when gut cells were exposed to endotoxins and immune changes in the gut–liver axis. Late apoptosis represents the chronic response to injury induction by the gut immune barrier dysfunction, oxidative stress, and liver-dysregulated barrier. Meanwhile, necrosis represents an acute and severe reply to endotoxin action on gut cells when the immune system reacts to pro-inflammatory Th1 and Th2 cytokines releasing, offering protection against PAMPs/DAMPs by monocytes and T lymphocyte activation. Flow cytometric analysis of pro-inflammatory biomarkers linked to oxidative stress-cell death mechanisms shown in our study recommends laboratory techniques in diagnostic fields.

Autophagy and mitophagy are critical cellular processes that maintain homeostasis by removing damaged organelles and promoting cellular survival under stress conditions. In the context of diabetic kidney disease, these mechanisms play essential roles in mitigating cellular damage. This review provides an in-depth analysis of the recent literature on the relationship between autophagy, mitophagy, and diabetic kidney disease, highlighting the current state of knowledge, existing research gaps, and potential areas for future investigations. Diabetic nephropathy (DN) is traditionally defined as a specific form of kidney disease caused by long-standing diabetes, characterized by the classic histological lesions in the kidney, including mesangial expansion, glomerular basement membrane thickening, nodular glomerulosclerosis (Kimmelstiel–Wilson nodules), and podocyte injury. Clinical markers for DN are albuminuria and the gradual decline in glomerular filtration rate (GFR). Diabetic kidney disease (DKD) is a broader and more inclusive term, for all forms of chronic kidney disease (CKD) in individuals with diabetes, regardless of the underlying pathology. This includes patients who may have diabetes-associated kidney damage without the typical histological findings of diabetic nephropathy. It also accounts for patients with other coexisting kidney diseases (e.g., hypertensive nephrosclerosis, ischemic nephropathy, tubulointerstitial nephropathies), even in the absence of albuminuria, such as a reduction in GFR.

Recent research indicates that the microbiome has a significant impact on the progression of inflammatory bowel disease (IBD) and that creating therapies that change its composition could positively impact the outcomes of IBD treatment. This review summarizes the results of extensive studies that examined IBD patients undergoing several therapies, including anti-TNF medication, vedolizumab, ustekinumab, probiotics, and fecal microbiota transplantation (FMT), and the alterations in their gut microbiota’s composition and function. The objective was to investigate the variety and effectiveness of microbial species in order to discover new biomarkers or therapeutic targets that could improve the outcome of treatment for these patients. This research aimed to offer useful insights into personalized medicine techniques for managing IBD. Beneficial bacteria such as Faecalibacterium prausnitzii and Roseburia have been consistently linked to favorable clinical outcomes, whereas pathogenic bacteria such as Escherichia coli and Clostridioides difficile are associated with worsening disease conditions. Although many studies have examined the role of gut microbiota in IBD, there is still a need for more targeted research on the connection between specific microbial communities and treatment outcomes. This study sought to address this gap by exploring the intricate relationship between the gut microbiota composition and the effectiveness of IBD medications.

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine.