A State-of-the-Art of Gut Microbiota and Vascular Disease: Advanced Technology

Abstract

This review aims to break through the theoretical framework of traditional risk factors for vascular diseases, systematically elucidate the core role of gut microbiota in vascular health regulation, and focus on the three key bottlenecks currently facing research: difficulty in establishing causal relationships, unclear functional cell targets, and lack of near-physiological validation systems. To this end, we propose and construct a closed-loop research path of “discovery → prediction → localization → verification” supported by advanced technology: using high-throughput sequencing for microbial ecological map discovery, combining machine learning for risk prediction and biomarker screening, integrating multiple omics to construct gene–metabolite–target causal chains; using single-cell sequencing to accurately anchor microbial signals to key subgroups such as vascular smooth muscle cells and macrophages; using Raman spectroscopy to capture metabolic phenotypes in situ to compensate for the incompleteness of genomic information; and relying on organoid platforms to establish a humanized intestinal-vascular dynamic interaction verification system. The core purpose of this review is to systematically demonstrate how this technology loop can systematically reveal the microecological and etiological mechanisms of vascular diseases, promote research from correlation to causal mechanisms, and provide theoretical basis and methodological framework for precise prevention and treatment strategies based on individual microbial community characteristics, thus opening up new directions for the diagnosis and treatment of vascular diseases.

Graphical abstract

Keywords

Vascular diseases / Gut microbiota / High-throughput sequencing / Machine learning / Raman spectroscopy / Organoid

Introduction

Vascular disease is a general term for heterogeneous diseases involving arteries, veins, and microvascular systems^[1], which widely covers atherosclerosis (AS)^[2], hypertension^[3], abdominal aortic aneurysm^[4], peripheral arterial diseases^[5], venous thromboembolism^[6], diabetic retinopathy^[7], and other microvascular diseases^[8–10], and is the primary cause of disability and death worldwide. This specificity arises because a single causal model cannot easily explain all vascular diseases; the sensitivity of different vascular beds to the same risk factor varies; the weight of the role of gut microbiota and its metabolites in different subtypes of vascular diseases may be significantly different^[11,12]. According to the 2019 Global Burden of Disease data, the number of cardiovascular disease cases has risen from 272 million in 1990 to 523 million, and the number of deaths has increased from 12.1 million to 18.6 million, indicating a continued increase in the disease burden^[13].

Classic risk factors such as dyslipidemia, elevated blood pressure^[9], smoking, diabetes^[14], and genetic susceptibility^[15] have been fully clarified. Statins^[16], antihypertensive drugs^[17], and antiplatelet therapy^[18] have significantly improved the prognosis of patients. However, a clinical reality that cannot be ignored is that even in patient populations where traditional risk factors are strictly controlled, major adverse cardiovascular events remain high. The persistent existence of residual risks strongly suggests that the causal network of vascular diseases is much more complex than existing knowledge, and there must be pathogenic links that have not yet been revealed.

The gut microbiota affects vascular health through three core pathways^[19]: (1) Metabolic pathway—the microbiota metabolizes dietary components to produce bioactive molecules such as trimethylamine-N-oxide (TMAO), short chain fatty acids (SCFAs), imidazolpropionic acid (ImP), and secondary bile acids, which enter the circulatory system and directly act on the vascular wall^[20,21]; (2) Immune regulatory pathway—dysbiosis of the microbiota leads to the entry of pro-inflammatory components such as lipopolysaccharide (LPS) into the bloodstream, activating inflammatory pathways such as TLR4 and inducing local inflammation of blood vessels^[22]; (3) Intestinal barrier pathway—dysbiosis of the microbiota impairs the integrity of the intestinal barrier, making it easier for bacterial metabolites and endotoxins to enter the bloodstream, driving systemic low-grade inflammation^[23].

With the maturity of technology, the approximately 10¹⁴ microorganisms residing in the human gut have gradually moved from being “forgotten organs” to the forefront of disease etiology research. The reason why it is called a forgotten organ is that although the microbiota is “invisible”, it has organ-level functional importance; at the same time, it participates in core physiological processes such as digestion, metabolism, and immune regulation, and is as important as classic organs such as the liver and heart. It is now widely recognized as a key target for disease etiology research. This vast microecology is not passively inhabited, but deeply and systematically affects the inflammatory state of vascular walls, endothelial function, smooth muscle cell phenotype, and tendency towards thrombosis through its metabolic product pool, immune regulatory molecules, and continuous shaping of intestinal barrier function^[24]. The gut vascular axis provides a new dimension to the pathogenesis of vascular diseases through microbiology^[25].

However, the key clinical issues and research bottlenecks currently facing this field cannot be avoided. Firstly, it is difficult to establish a causal relationship. The vast majority of studies remain at the cross-sectional correlation level, with regard to the correlation between certain flora abundance or metabolite levels and vascular disease phenotype^[26,27]. For example, TMAO has been confirmed as an independent atherogenic metabolite, which can accelerate plaque formation by inducing macrophage foam cell formation, activating NLRP3 inflammasome, and enhancing platelet reactivity^[28]. However, whether dysbiosis of the microbiota is a driving factor or an accompanying phenomenon of vascular disease, or simply a false association caused by confounding factors, remains uncertain^[29]. The association between TMAO and cardiovascular disease may be partially explained by renal function^[30], suggesting that the establishment of causal relationships requires multiple pieces of evidence such as longitudinal follow-up, Mendelian randomization genetic analysis, and sterile animal colonization experiments for cross-validation^[31]. Secondly, the cellular targets are unclear. Traditional transcriptome analysis is based on tissue homogenates, masking the specific responses of different subgroups such as vascular smooth muscle cells, endothelial cells, macrophages, and T cells to the same microbial signaling. Through single-cell RNA sequencing, it was confirmed that the metabolite of gut microbiota, ImP, can induce a large infiltration of pro-inflammatory monocytes (Ly6C^hi) and Th1/Th17 helper T cells into the aortic wall, directly activating the inflammatory and immune response gene programs of intrinsic cells in the vascular wall. The effect of microbial signaling has high cell type specificity, and it is difficult for traditional methods to capture the fine regulation at this level. Thirdly, the conversion efficiency of animal experimental results to human applications is extremely low due to differences in microbiota composition, metabolic differences, and vascular physiological differences etc., and there is a lack of validation platforms that approximate human physiological states. At present, intervention strategies for the intestinal vascular axis (such as probiotics/prebiotics regulation, trimethylamine [TMA] lyase inhibitor, fecal bacteria transplantation) have shown the potential to improve atherosclerosis in animal models, but human clinical trial evidence is still very scarce^[32].

These bottlenecks collectively point to a core scientific question: how to systematically construct a complete causal chain from gut microbiota function to specific subpopulations of vascular wall cells, and translate it into new strategies for precise prevention and treatment of vascular diseases^[33].

This review proposes precision medicine for vascular diseases based on individual microecological characteristics, breaking through the limitations of traditional risk factor control and systematically explaining the key role of gut microbiota in vascular health regulation (Figure 1).

Advanced technology

Systematically introducing cutting-edge technologies such as high-throughput sequencing, machine learning, multi-omics, single-cell testing, Raman spectroscopy, and organoids into the study of vascular diseases and gut microbiota is not only a key path to breaking through current bottlenecks in the field, but also has the potential to fundamentally revolutionize the understanding of the etiology of vascular diseases^[34], to promote prevention and treatment strategies from traditional risk factor control to a new era of microecological precision medicine based on individual microbiota characteristics, and has significant basic scientific significance and broad clinical application prospects^[35].

High-throughput sequencing

High-throughput sequencing, also known as next-generation sequencing technology, refers to an advanced technology platform that can simultaneously perform large-scale parallel sequencing of tens of thousands to millions of DNA or RNA molecules^[36].

The genome-wide association study of carotid atherosclerosis (CAS) was conducted by whole-genome shotgun sequencing. In 31 patients and 51 healthy controls, 21 strains and 142 metabolic pathways were identified in the CAS group. Among them, Bacteroides eggerthii, Escherichia coli, and Klebsiella pneumoniae were the most dominant strains, and these strains were significantly positively correlated with clinical indicators^[37]. In coronary artery disease (CAD), metagenomic sequencing combined with metagenome-assembled genome (MAG) reconstruction revealed that Faecalibacterium prausnitzii, which produces short-chain fatty acids, was significantly depleted in CAD patients, while the urea cycle and L-citrulline biosynthesis pathways were significantly enriched^[38]. In abdominal aortic aneurysm (AAA), 16S rRNA sequencing revealed a decrease in Bifidobacterium adolescentis levels. Subsequent metagenomic analysis further revealed an increase in oral bacteria and a decrease in gene expression related to alpha-lipoic acid (ALA) biosynthesis in the feces of AAA patients^[39]. In hypertension research, high-throughput sequencing has confirmed significant differences in microbiota richness, composition, and function between hypertensive patients and healthy controls. In venous thromboembolism (VTE), 16S rRNA sequencing revealed abnormal proliferation of Blautia, Roseburia, Coprococcus, and Ruminococcus in the intestines of VTE patients. In addition, the 96-week longitudinal 16S rRNA sequencing study on the progression of atherosclerosis in HIV-infected people showed that Agathobacter and Ruminococcus 2 were continuously related to the progression of carotid intima‑media thickness (CIMT)^[40].

Machine learning

Machine learning (ML) is a core branch of artificial intelligence, whose basic idea is to enable computers to automatically learn patterns and regularities from data, rather than relying on fixed rules pre-set by humans. In the study of gut microbiota and vascular diseases, machine learning plays a core role in identifying disease characteristics^[41], establishing early warning models, and screening key intervention targets from high-dimensional microbiota and metabolome data^[42,43]. The application of machine learning to gut microbiota and vascular diseases has evolved from simple feature classification to a core technology engine driving disease prediction, biomarker discovery, and personalized intervention. Its application depth and breadth have significantly expanded^[44].

By analyzing the microbiome data of 2573 samples from 13 groups, using 67 million statistical models and machine learning methods, deeper causal inference and feature mining were conducted. The shared microbial genetic characteristics of coronary heart disease, inflammatory bowel disease, and cirrhosis have been identified, indicating that disease risk can be more accurately predicted based on bacterial genetic markers rather than species classification^[45]. The inclusion of microbiota characteristics can enhance the strength of the association between inflammatory bowel diseases (IBD) and unhealthy status, but the association with cardiovascular disease is weakened after incorporating lifestyle variables, suggesting that some previously reported microbiota disease associations may be influenced by confounding factors, and more evidence is needed for the independent association between microbiota and cardiovascular disease^[46].

Multi-omics

Multi-omics integration constructs a complete causal chain of “microbiota metabolite host targets” by combining metagenomics, metabolomics, and host transcriptome/genome^[47]. Multi-omics integration can reveal a multi-level metabolic network that links microbial activity with atherosclerosis. This paradigm has been adopted by international consensus as a cutting-edge methodological framework for microbiota vascular research^[13].

The tripartite causal relationships between five genera (Actinomyces, Bacteroides, Eisenbergiella, Gemella, and Veillonella), two metabolites (ethanol and hydrogen peroxide), and two host genes (FANCD2 and GPX2) were systematically identified from 456 atherosclerosis metagenomic samples and 420 peripheral blood host transcriptome data^[48](Figure 2).

Through multi-omics inference, for the first time, specific microbial functional genes were directly linked to gene expression changes in host vascular pathological pathways. In addition, a study on elderly Chinese patients with coronary heart disease integrated 16S rRNA sequencing and plasma metabolomics, and found that the combination of increased Sphingomonas abundance and 155 differential metabolites had a combined diagnostic area under the curve (AUC) of 0.908, significantly better than the single omics AUC of 0.742^[47].

In a subclinical atherosclerosis population, through metagenomic and phenotypic group network analysis, it was confirmed that intestinal flora could explain 16.5% of the mediating effect of lifestyle on carotid atherosclerosis^[49].

Single-cell RNA sequencing

The breakthrough progress of single-cell RNA sequencing (scRNA-seq) technology is fundamentally changing the research paradigm of gut microbiota and vascular diseases. The core value of scRNA-seq technology lies in its ability to accurately identify specific vascular cell subpopulations and their pathogenic molecules regulated by microbial signaling at the single-cell level, thereby anchoring macroscopic microbial associations to specific cellular targets and achieving cell-level precise localization of mechanism analysis.

It is a breakthrough application in atherosclerosis research. Weighted gene co-expression network analysis (WGCNA) and random forest algorithm were used to screen five flora-related macrophage genes PLEK, IRF8, CD68, CCR1, and BTK from atherosclerosis data set. Subsequent single-cell analysis confirmed the specific expression pattern of these genes in macrophages, and clinical sample validation showed that PLEK was highly expressed in unstable atherosclerotic plaques. This study not only reveals the complex interactions between microbiota and macrophages, but also provides potential biomarkers for the diagnosis of plaque instability^[50].

Through Mendelian randomization analysis, it was confirmed that the abundance of Intestinimonas in the microbiota has a protective causal relationship with the risk of aortic aneurysm. The study subsequently integrated batch and single-cell RNA sequencing data to precisely identify that the expression of CCDC3 gene is specifically reduced in vascular smooth muscle cells (VSMCs) of arterial aneurysm tissue, and CCDC3 deficiency activates the pathogenic CXCL12–CXCR4 chemotaxis in VSMCs, thereby recruiting neutrophils and promoting the formation of neutrophil extracellular traps (NETs). This discovery precisely anchors the interaction between gut microbiota and host genes to VSMCs, a specific cell type, providing precise cellular basis for targeted therapy^[51].

Non-targeted metabonomics found that the intestinal microbial metabolite ImP was significantly associated with the degree of atherosclerosis in mice and two independent human cohorts. The research team further utilized single-cell RNA sequencing and phosphoproteomics analysis to accurately reveal the significant inflammation-related transcriptome changes observed in macrophages, fibroblasts, and endothelial cells in the aorta, which are cellular targets after ImP treatment. In mechanism, ImP induces atherosclerosis by activating the imidazoline-1 receptor (I1R) in myeloid cells, without affecting the level of blood lipids^[52].

The integration of metagenomics, metabolomics, and single-cell analysis revealed the complete mechanism of L. murinus and L. johnsonii inhibiting macrophage pyroptosis and alleviating atherosclerosis through butyric acid GPR109A–GSDMD axis. Single-cell analysis plays a crucial role in anchoring the anti-apoptotic effect of butyric acid to macrophages, a specific cell type^[53](Figure 3).

Embedding and cell proportions labeled by cell type of cells derived from whole aorta, based on transcriptomic profiles. Dendritic cells (DCs); endothelial cells (ECs); fibroblasts (FBs); macrophages (MFs); vascular smooth muscle cells (VSMCs)^[52]. Butyrate inhibits macrophage pyroptosis through activating GPR109A^[53].

Raman spectroscopy

Raman spectroscopy is based on the inelastic light scattering effect to generate molecular “fingerprint” spectra, which can analyze the molecular composition of biological samples in real time and without labeling. Compared with traditional methods, Raman spectroscopy does not require complex sample pretreatment, does not damage the sample, and has lower detection costs. Surface-enhanced Raman spectroscopy (SERS) greatly enhances Raman signals through nano metal substrates, significantly improving detection sensitivity and facilitating in situ rapid detection of SCFAs such as acetic acid, propionic acid, and butyric acid. SCFAs are the core protective metabolites in the gut vascular axis^[54].

The antibiotic-resistant bacteria in human gut microbiota have a significant impact on human health. Raman microscopy and deuterium isotope probing (Raman–DIP) and Raman-activated cell sorting (RACS) can be useful to unravel both phenotypes and genotypes of antibiotic-resistant bacteria in human gut microbiota at the single-cell level^[55,56]. Although there are few studies that directly integrate microbiota analysis with vascular lesion assessment, Raman spectroscopy has made significant progress in the field of liquid biopsy for vascular diseases. SERS technology combined with deep learning models has been used for label-free serum diagnosis of acute myocardial infarction and aortic dissection^[57]. The integration of microbial metabolite detection and vascular disease biomarker analysis on the same platform in the future provides a technological foundation (Figure 4).

Although Raman spectroscopy studies that directly integrate microbial metabolite detection are not yet common, SERS technology has shown diagnostic potential in serum metabolic fingerprinting analysis. The principle is that the state of vascular disease changes the overall spectral fingerprint of lipoproteins, amino acids, and metabolites in the blood. Currently, no research reports have been retrieved on the direct use of SERS for combined analysis of microbiota and metabolites in AS or AAA patients. The application of Raman spectroscopy in the cross-study of gut microbiota and vascular diseases still faces key technical bottlenecks: (1) The accumulation of Raman spectroscopy data directly targeting the interaction between microbiota, metabolites, and blood vessels in patients with vascular diseases is extremely limited, and there is still a lack of systematic evidence; (2) The differentiation between microbial metabolites and host background signals in complex biological samples still requires algorithm optimization; (3) The detection sensitivity and quantitative accuracy still need to be improved in trace metabolite analysis. In the future, the deep integration of Raman spectroscopy with microfluidic chips and machine learning is expected to construct an integrated perfusion model of “gut blood vessels”, which can track the real-time cross-organ transport of microbial metabolites and their dynamic effects on vascular endothelial function.

Organoid

The existing research models have limitations, as they cannot simulate the dynamic changes of microbial metabolites and the interactions between various cells in cellular models. Organoids are miniature organ-like structures formed by the self-organizing differentiation of adult stem cells or pluripotent stem cells in a three-dimensional matrix^[58], whose core feature lies in self-assembly: cells arrange themselves into a three-dimensional structure similar to internal tissues through internal developmental programs^[59], such as the crypt-villus-like structure that can be formed in intestinal organoids^[60]. Organ-on-chip is an engineered cell culture platform based on microfluidic technology, which co-cultivates multiple cell types in micrometer-level channels^[60].

The breakthrough in organoid and organ-on-a-chip technology provides a powerful platform for simulating the human gut-vascular axis in vitro^[61], and is making up for the shortcomings of traditional animal models and simple cell models^[62,63]. Intestinal organoids have been co-cultured with various cell types and microorganisms to more accurately simulate the in vivo microenvironment^[64].

Traditional intestinal organoids mainly simulate intestinal epithelium and lack key components such as blood vessels and nerves^[65]. Therefore, in order to break through this limitation, the construction of functional vascular networks is required. Using intestinal stem cell niche factor Epiregulin (EREG) instead of conventional EGF can induce PSC-derived human intestinal organoid (HIOs) to coordinate the differentiation of epithelial, mesenchymal, glial, endothelial cells, and organized smooth muscle cells. These EREG HIOs not only form a functional vascular network, but can also anastomose with host blood vessels and achieve blood perfusion after transplantation^[66].

The co-culture system of organoids, microorganisms, and blood vessels is becoming increasingly mature. There are multiple approaches ranging from simple organoid co-culture to complex microfluidic systems. By co-culturing intestinal organoids with endothelial cells, immune cells, etc., the effect of microbiota on intestinal vascular barrier and distal metastasis can be simulated^[65]. Building a microfluidic platform that integrates the intestinal and vascular systems, such as intestinal chambers containing cell layers that simulate epithelium, and vascular channels that simulate endothelial cells and introduce blood flow shear forces, which provides a more precise in vitro tool for studying gut-heart interactions and the impact of inflammatory diseases on blood vessels^[67], liver, and kidney^[68](Figure 5).

Summary and outlook

The collaborative development of technologies such as high-throughput sequencing, machine learning, multi-omics, single-cell sequencing, Raman spectroscopy, and organoid chips is driving the research on gut microbiota vascular diseases from describing associations to a new stage of causal verification^[69]. High-throughput sequencing and multi-omics are used to “discover” functional signals, machine learning is used to predict disease risk, single-cell sequencing is used to locate cell targets, Raman spectroscopy and organoids are used for verification. This is expected to construct a complete pathogenic landscape from microbiota functional genes to specific cells on the vascular wall, promoting the prevention and treatment of vascular diseases towards a new era of precision medicine based on individual microecological characteristics.

Establishing a standardized full-chain closed loop of “multi-omics discovery → machine learning prediction → single-cell localization → organoid chip verification” is the core path to promote clinical translation in this field. Specific directions include: (1) Advance large-scale longitudinal cohort and Mendelian randomization research, strengthening the causal evidence chain; (2) Develop annotation-free sequence-level deep learning frameworks to directly extract disease signals from metagenomic raw sequences, breaking through the bottleneck of classification annotation; (3) Promote co-culture of vascularized intestinal organoids and patient-derived microbiota to achieve personalized mechanism validation; (4) Integrate a Raman spectroscopy-microfluidic platform with clinical sample resources to fill the data gap between the technology platform and vascular disease research. The deep integration of multiple technologies is expected to drive vascular diseases from traditional risk factor control to a new stage of precision medicine based on individual microecological characteristics.

The role of gut microbiota in vascular health regulation has shifted from correlation description to causal mechanism exploration, but this leap still faces three core bottlenecks: difficulty in establishing causal relationships, unclear functional cell targets, and lack of near-physiological validation systems^[70]. Current research has confirmed that intestinal flora imbalance and its metabolites are closely related to vascular diseases such as atherosclerosis, but most evidence still remains at the correlation level, and the establishment of causal chain is limited by traditional research methods^[71]. The true value of this technological cycle lies in driving vascular disease research from observational association to intervention causality. However, challenges still exist: the clinical generalization ability of machine learning models is limited, and how organoid platforms systematically incorporate complex factors such as immune cells and microbial metabolites remains a challenge. In the future, it is necessary to further integrate multiple omics data to construct gene–metabolite–phenotype causal chains, and promote the development of organoid models towards high-throughput and standardization, so that microbial-targeted intervention can truly become a part of precision medicine for vascular diseases.

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