3.1 Temporal distribution map of the literature
The quantity of publications within a certain time frame can serve as an indication of the research speed and trends within a particular field. As depicted in Fig. 2, since 2005, this field has garnered significant attention, with a steady increase in publication quantity until 2021, apart from a slight decline in 2016. From 2020 onward, the number of publications has remained relatively stable at around 1000, suggesting a field that is both stable and consistently sought after. The average citation count has consistently exceeded 3 since 2007. The decrease in citations in 2023 is likely due to the truncation of the literature search, but it signifies widespread recognition and relevance of research achievements within this field.
3.2 National/Regional and Author Distribution
As depicted in Table 1, the United States, China, and the United Kingdom have the highest number of published literature, accounting for 31.598%, 17.655%, and 9.137% of the total reports, respectively. The fact that these three countries contribute to over 50% of the literature indicates a substantial investment in research within this field. The average citation rate of literature from the United States (55.10), the United Kingdom (47.90), and Canada (41.10) is the highest, highlighting their significant influence and contribution to this research domain. Among the top 10 countries/regions, four have a centrality index equal to or greater than 0.1, namely the United States (0.27), the United Kingdom (0.20), Canada (0.12), and Italy (0.10), signifying their crucial role in research within this field.
Table 1
The Leading 10 Productive Countries/Regions in Biomarkers of CVD
Rank | Countries/Regions | Record Count | % Of 11776 | Total link strength | Average Article Citations | Centrality |
1 | USA | 3721 | 31.598 | 2937 | 55.10 | 0.27 |
2 | CHINA | 2079 | 17.655 | 625 | 15.70 | 0.00 |
3 | UK | 1076 | 9.137 | 2133 | 47.90 | 0.20 |
4 | GERMANY | 872 | 7.405 | 1514 | 35.80 | 0.03 |
5 | JAPAN | 652 | 5.537 | 318 | 24.50 | 0.00 |
6 | ITALY | 622 | 5.282 | 1106 | 36.70 | 0.10 |
7 | NETHERLANDS | 579 | 4.917 | 1116 | 32.70 | 0.07 |
8 | CANADA | 528 | 4.484 | 768 | 41.10 | 0.12 |
9 | AUSTRALIA | 502 | 4.263 | 788 | 38.10 | 0.04 |
10 | SPAIN | 490 | 4.161 | 838 | 28.20 | 0.01 |
CiteSpace parameters were set as follows: time slicing (2004– 2023), node type (country), selection criteria (top N = 50). The other parameters remained unchanged. As a result, a network consisting of 101 nodes and 1481 connections was generated. In Fig. 3A, the color transitions from pink to purple, and the varying sizes of the circles represent the publication volume for the corresponding years.
VOSviewer parameters were set as follows
the method used was either "Linlog" or "modularity," and the minimum number of country documents was set to 5. The analysis yielded results from 132 countries, with 68 countries meeting the threshold and being divided into four clusters. Figure 3B illustrates the collaboration network among countries/regions in the field of cardiovascular biomarker research. The different colors of the nodes represent different clusters, with the yellow cluster indicating the primary research countries/regions. The size of each node corresponds to the number of documents, while the thickness of the lines represents the number of connections between nodes. The figure reveals close collaborations between the United States and countries such as China, Japan, Canada, and South Korea. Additionally, there is extensive collaboration among Italy, Spain, Poland, Switzerland, and Brazil. Furthermore, the United Kingdom collaborates closely with Germany, Sweden, Turkey, and Denmark. Notably, France, Singapore, Iran, and New Zealand also exhibit close collaboration. Figure 3C indicates that countries such as the United States, the United Kingdom, and Germany are pioneers in cardiovascular biomarker research, while countries like China are relatively newcomers in this field.
The rankings of top ten authors by publication volume are presented in Table 2. Among them, ZHANG Y (95 papers, 0.807%) has the highest number of publications, followed by WANG Y (77 papers, 0.654%) and LIU Y (76 papers, 0.645%). This indicates the outstanding contributions made by these three authors in the field. The H-index is a comprehensive evaluation method that measures an author's influence based on the number of publications and citations [11]. Professor VASAN RS from the National Heart, Lung, and Blood Institute's Framingham Heart Study has the highest H-index. Professor VASAN RS has conducted in-depth research on various CVD biomarkers, including MI, AS, atrial fibrillation, stroke, and coronary artery disease [12–13]. The biomarkers studied by Prof. VASAN RS are mainly focused on metabolomics [14] and genomics [15], covering a wide range of fields. The next highest H-index is held by Professor Tanja Zeller from the German Center for Cardiovascular Research. She has contributed to the important "BiomarCaRE" research project, which reveals that serum metabolomic information can predict the incidence of coronary heart disease in the general population [16]. Additionally, Professor Tanja Zeller has also discovered novel biomarkers associated with CVD in research areas such as gene expression [17] and metabolomics [18].
Table 2
Top 10 authors in Biomarkers of CVD
Rank | Author | Record Count | % Of 11776 | Articles Fractionalized | LocalCitations | H-index |
1 | ZHANG Y | 95 | 0.807 | 11.81 | 99 | 23 |
2 | WANG Y | 77 | 0.654 | 10.52 | 57 | 22 |
3 | LIU Y | 76 | 0.645 | 9.68 | 110 | 18 |
4 | LI Y | 74 | 0.628 | 11.01 | 242 | 19 |
5 | WANG J | 61 | 0.518 | 8.43 | 70 | 19 |
6 | LI J | 59 | 0.501 | 8.26 | 44 | 19 |
7 | VASAN RS | 55 | 0.467 | 6.64 | 545 | 33 |
8 | ZHANG L | 50 | 0.425 | 7.17 | 31 | 19 |
9 | LIU J | 45 | 0.382 | 5.94 | 41 | 18 |
10 | ZELLER T | 44 | 0.347 | 4.61 | 297 | 26 |
3.3 Biomarkers for CVDs
CiteSpace parameters were configured as follows: temporal interval (2004–2023), node type (Keyword), selection criteria (top N = 50), while keeping other parameters constant. After manually filtering out irrelevant keywords, a network consisting of 118 nodes and 1240 connections was obtained.
In Fig. 4A, CVDs most closely associated with biomarkers include coronary heart disease(CHD), coronary artery disease(CAD), atherosclerosis(AS), myocardial infarction(MI), heart failure(HF), hypertension(HP), stroke, acute coronary syndrome(ACS), and congenital heart disease. Factors related to CVDs include inflammation, metabolic syndrome, oxidative stress, obesity, and endothelial dysfunction. Biomarkers associated with CVDs include C-reactive protein(CRP), natriuretic peptides(NP), plasma homocysteine(HC), interleukin-6(IL-6), cholesterol(CHOL), nitric oxide(NO), high-density lipoprotein(HDL), low-density lipoprotein(LDL), and others. VOSviewer parameters were set as follows: Method (Linlog/modularity), minimum number of keyword occurrences: 67, and selecting the top 60% most relevant keywords. Out of the total 173,790 keywords, 596 met the thresholds. Finally, after manually removing irrelevant keywords to the research objectives, a co-occurrence keyword network was formed, as shown in Fig. 4B.
The keywords related to CVDs in Fig. 4B are divided into five clusters. The red cluster is associated with CHD and AS, including CVDs such as CHD, HF, and chest pain. Biomarkers in this cluster include N-terminal pro-B-Type natriuretic peptide(NT-porBNP), somatostatin receptor 2 (sST2), cardiac troponin (cTn), microRNA, and the Neutrophil-to-Lymphocyte Ratio (NLR).The green cluster is related to inflammation and lipids, with a focus on AS. Biomarkers in this cluster include hsCRP, inflammatory cytokines, Tumor Necrosis Factor Alpha(TNF-α), reactive oxygen species (ROS), membrane co-factor protein (MCP), malonaldehyde(MDA), phospholipase, asymmetric dimethylarginine (ADMA), lipoprotein E and polycyclic aromatic hydrocarbons(PAH).The blue cluster primarily focuses on lipids and is associated with HP. The main influencing factor is BMI, and biomarkers in this cluster include GHOL, HDL, LDL, adiponectin(ADP), leptin, and lipoprotein B.The yellow cluster is associated with HF, inflammation, and lipids. CVDs in this cluster include heart rate variability, ventricular hypertrophy, and arterial stiffness. Biomarkers in this cluster include fibroblast growth factor (FGF) and epidermal growth factor receptor (EGFR).The purple cluster is primarily focused on lipids, with biomarkers such as Eicosapentaenoic Acid(EPA) and fatty acids(FA) associated with CVDs.
In Fig. 4C, the different colors represent the years in which the keywords appeared. The more purple the color, the earlier the keywords appeared, while the more yellow the color, the later the keywords appeared. From the figure, we can observe that the earlier hot topics of research include AS and ventricular hypertrophy. The earlier hot CVD biomarkers include high sensitivity CRP, CHOL, ADP, leptin, and FA. The more recent hot topics of research include HF, with the corresponding hot CVD biomarkers being NT-porBNP, sST2, cTn, microRNA, FGF, EGFR and NLR.
3.4 Biomarkers associated with CVDs in blood.
The parameters set for CiteSpace were as follows: time slicing (2004–2023), node type (Keyword), selection criteria (top N = 50), while keeping other parameters unchanged. After manual screening to exclude irrelevant keywords, a network consisting of 439 nodes and 4756 connections was obtained.In Fig. 5A, CVDs most closely related to biomarkers for CVDs in blood include AS, HF, MI, HP, endothelial cell dysfunction, and acute myocardial infarction(AMI). Biomarkers for CVDs in blood includeCRP, GHOL, brain natriuretic peptide(BNP), LDL, microRNA, HDL, IL-6, cystatin C(CysC), HC, Scr, NO, docosahexaenoic acid(DHA), vitamin D(VitD), DNA methylation, ADP, arachidonic acid, and cell adhesion molecules among others.
The parameters set for VOSviewer were as follows: Method (Linlog/modularity), a minimum number of occurrences of a keyword: 42, and selection of the 60% most relevant keywords. There were 114,316 keywords, with 611 keywords meeting the thresholds. After manual elimination of irrelevant keywords, a co-occurrence network of keywords was formed, as shown in Fig. 5B.In Fig. 5B, the keywords for biomarkers of CVDs are divided into six clusters. The keywords in the green cluster are related to heart diseases, including HF, congenital heart disease, congestive cardiomyopathy, and cardiovascular fibrosis. The biomarkers in this cluster include NT-proBNP, matrix metalloproteinases(MMP), NP, and serum uric acid. The keywords in the purple cluster are related to heart diseases and lipid disorders, including AS, PAD, and ventricular hypertrophy. The biomarkers in this cluster include lipocalin, serum creatinine(Scr), EGFR, albuminuria, osteoprotegerin(OPG), and VitD. The keywords in the yellow cluster are mainly related to lipid disorders, with the main CVD being HP. The biomarkers in this cluster include CHOL, triglycerides, ADP, glucose, and hs CRP. The keywords in the light blue cluster are related to lipid disorders and CHD, and the biomarkers include FA, DHA, and Polyunsaturated Fatty Acid(PUFA). The keywords in the red cluster are related to inflammation, lipid disorders, and CHD. The CVDs in this cluster mainly include AS and thrombosis, and the biomarkers include inflammatory cytokines, TNF-α, LDL, apob, phospholipase, macrophages, telomere length, and genotypes. The keywords in the dark blue cluster are mainly related to CHD, including CHD, ACS, MI, coronary atherosclerosis, unstable angina, and ST-Segment Elevation Myocardial Infarction(STEMI). The biomarkers in this cluster include cTnI, microRNA, and trimethylamine N-oxide(TMAO).The keywords related to CVD biomarkers have been classified into different colors based on their appearance in different years, as shown in Fig. 5C. Earlier studies on CVDs include CHF, unstable angina, AS, PAD, ventricular hypertrophy, HP, thrombosis, and arteriosclerosis. Biomarkers for these earlier CVDs include phospholipase, genotype, FA, DHA, adiponectin, OPG, lipocalin, Scr, triglycerides, CHOL, glucose, LDL, macrophages, serum uric acid, and NP. Recent research on cardiovascular diseases includes HF, CHD, MI, ACS, CAD, STEMI, myocardial fibrosis, and coronary atherosclerosis, with corresponding biomarkers such as cTnI, NT-proBNP, microRNA, PUFA, VitD, hs-CRP, telomere length, EGFR, TMAO, inflammatory cytokines, apoB, and TNF-α.
The occurrence frequency of key words related to blood CVD biomarkers in Citespace was analyzed. The key words were divided into two categories: biomarkers and diseases. The frequencies of occurrence were recorded in descending order. Figure 5D demonstrates the blood CVD biomarkers with frequencies greater than 2. The highest occurrence frequency was observed for CRP, which appeared 703 times. It was followed by CHOL (138 times), BNP (111 times), LDL (71 times), microRNA (52 times), HDL (50 times), IL-6 (35 times), CysC (31 times), HC (27 times), Scr and NO (both 23 times), DHA (20 times), Vitd (18 times), DNA methylation (15 times), ADP (13 times), EPA (11 times), adhesion molecules(AM) (10 times), and other substances with frequencies less than 10 times. The respective diseases associated with these blood CVD biomarkers had varying occurrence frequencies. The highest occurrence frequency was observed for CHD, with 936 times. It was followed by AS (720 times), HF (358 times), MI (323 times), HP (279 times), endothelial dysfunction (272 times), and AMI (252 times). The frequencies for other diseases were all less than 200 times, As shown in Fig. 5E.
3.5 Biomarkers for CVDs in urine
The parameters set for CiteSpace were as follows
time range (2004–2023), node type (Keyword), selection criteria (top N = 50). Other parameters remained unchanged. After manually excluding irrelevant keywords, a network consisting of 470 nodes and 3041 connections was obtained.
In Fig. 6A, CVDs most closely associated with biomarkers in urine included CHD, AS, HF, MI, peripheral artery disease(PAD), angina pectoris, ischemic heart disease, and congenital heart disease. In urine, biomarkers related to CVDs comprised microalbuminuria, CRP, creatinine, BNP, CysC, 8-iso-prostaglandin F2-alpha, neutrophil gelatinase-associated lipocalin (NGAL), Acid Binding Protein(ABP), PAH, lipid peroxidation(LPO), beta-D-glucosaminidase, VitD, urinary uromodulin, urinary proteomics, urinary heavy metal, and urinary 1-hydroxypyrene concentration.
The parameters set for VOSviewer were as follows: analysis method (Linlog/modularity), minimum occurrence of a keyword: 6, and selection of the 60% most relevant keywords. With a total of 16,898 keywords, 484 keywords met the thresholds. After manually removing keywords irrelevant to the research objectives, a co-occurrence network of keywords was formed, as shown in Fig. 6B. In Fig. 6B, the keywords for urinary CVD biomarkers are divided into eight clusters. The yellow cluster includes CVDs such as AS, ACS, and PAD. The urinary CVD biomarkers in this cluster include lipocalin, CysC, Scr, and urinary NGAL. The green cluster revolves around cardiovascular mortality, with biomarkers such as BNP, urinary NT-proBNP, and MMP being associated with it. Arsenic exposure may increase cardiovascular mortality. The red cluster encompasses urinary CVD biomarkers like isoprostane, prostaglandin F(PGF), FA, polyphenol, flavonoid, flavonoid, alkylresorcinol metabolite, riboflavin, VitD, magnesium, and TMAO. Both the deep blue and light blue clusters are lipid-related. The CVDs in these clusters include AS and hypercholesterolemia. The urinary CVD biomarkers in these clusters include urinary creatinine, Intestinal Fatty Acid-Binding Protein, LDL, catalase, phthalate, and bisphenol. In the purple cluster, the urinary CVD biomarkers include FGF, FGF-23, and uric acid. The orange cluster's biomarkers consist of urinary albumin and galectin, with vascular complications being associated with CVD. The brown cluster is hormone-related, with biomarkers such as renin, aldosterone, and cortiso(Cor). In VOSviewer, keywords were categorized into different colors based on the years of their appearance, as shown in Fig. 6C. The earliest studied CVDs were hypercholesterolemia, cardiovascular mortality, ACS, vascular complications, and AS. The earliest studied biomarkers included PGF, isoprostane, alkylresorcinol metabolite, lipocalin, CysC, SCR, BNP, FGF, renin, urinary NGAL, polyphenol, FA, Cor, and urinary albumin. The most recent studies included PAD and arterial stiffness as diseases, and phthalate, TMAO, catalase, galectin, LDL, urinary nt-proBNP, and MMPs as biomarkers.
The frequency of occurrence of urinary CVD biomarkers was calculated in Citeseer. The keywords were categorized into biomarkers and diseases, and their frequencies were recorded in descending order. Figure 6D shows the times of occurrence of urinary CVD biomarkers that exceeded two times. At the top of the list is trace albuminuria (36 times), followed by CRP (28 times), CysC (18 times), Scr (12 times), NP (12 times), NGAL (9 times), BNP (8 times), ABP (8 times), PAHs (6 times), LPO (4 times), and β-D-glucosaminidase (3 times). The frequencies of other biomarkers were all less than 3 times. Figure 6E provides CVD information associated with urinary biomarkers. CHD ranks first (61 times), followed by AS (36 times), HP (27 times), HF (18 times), MI (12 times), heart disease (6 times), carcinoid syndrome heart disease (4 times), PAD (3 times), vascular disease (2 times), vascular complications (2 times), vascular calcification (2 times), ischemic heart disease (2 times), and congenital heart disease (2 times). Angina pectoris appears only once.
3.6 Biomarkers for CVDs in saliva
The parameters set for CiteSpace were as follows
time range (2004–2023), node type (Keyword), selection criteria ( g-index K = 25). Other parameters remained unchanged. After manually excluding irrelevant keywords, a network consisting of 441 nodes and 1980 connections was obtained.
In Fig. 7A, the CVDs most closely associated with salivary biomarkers include AMI, HF, AS, HP, and stroke. Salivary biomarkers for CVDs encompass CRP, salivary Cor, TNF-α, alpha-amylase, NO, inorganic nitrate, uric acid, AM, MMP, carbon-tetrachloride, microRNAs, 16S rRNA, fatty acid-binding protein, beta glucuronidase, BNP, high-sensitivity troponin, chromogranin-A, glutathione, extracellular vesicles, and DNA methylation, among others. The parameters set for VOSviewer were as follows: Linlog/Modularity as the method, a minimum of four occurrences of a keyword, and the selection of the top 60% most relevant keywords. Among the 11,196 keywords, 463 keywords met the specified thresholds after manual filtering. The resulting co-occurrence keyword network can be seen in Fig. 7B. In Fig. 7B, the key terms for saliva biomarkers of CVD are divided into eight clusters. The yellow cluster primarily relates to CYT, with saliva biomarkers such as growth factor, salivary TNF-a, chemokine, and nitrate. The light blue cluster is associated with blood lipids, including biomarkers such as HDL, salivary Cor, CHOL, and lipoprotein. The purple cluster is linked to oxidative damage, with CVD biomarkers like ischemic stroke(IS), and biomarkers including catalase, uric acid, ROS, and iron. The dark blue cluster consists of biomarkers such as microRNA, nucleic acid, creatine kinase, and circrna. The brown cluster contains biomarkers like phosphate and alpha-amylase. The green cluster is associated with CVD like ACS, with biomarkers including hs CRP and tissue inhibitor. The orange cluster contains CVD such as HP, with biomarkers such as estradiol. The red cluster includes CVDs like HF and AMI, with biomarkers including NT-proBNP and cTnI.The keywords related to salivary biomarkers for CVDs were color-coded based on the different years in which they were mentioned (Fig. 7C). The earliest studies focused on AMI and ACS, with tissue inhibitor, estradiol, hs CRP, CHOL, lipoprotein, nitrate, and growth factor as the earliest investigated biomarkers. The most recent research explored HF, IS and HP as the disease of interest, with NT-proBNP, salivary TNF-a, chemokine, microRNA, circRNA, catalase, uric acid, phosphate, creatine kinase, HDL, cTnI, alpha-amylase, nucleic acid, iron, and salivary Cor as the newly investigated biomarkers.
The keywords for saliva CVD biomarkers can be categorized into two groups: biomarkers and diseases. The frequency of their occurrence in CiteSpace is documented in both Fig. 7D and Fig. 7E. Figure 7D presents substances with a frequency of appearance greater than 2. Amongst these, CRP appears the most frequently (36 times), followed by NO (10 times), Salivary Cor (9 times), TNF-α (6 times), NP (6 times), Alpha-Amylase (5 times), Uric Acid (4 times), MicroRNAs (4 times), Inorganic Nitrate (3 times), Troponin-l (2 times), Nitrate (2 times), mmp 8 (2 times), LPO (2 times), High Sensitivity Troponin (2 times), Glutathione (2 times), Extracellular Vesicles (2 times), DNA Methylation (2 times), Beta Glucuronidase Activity (2 times), and ABP (2 times). Figure 7E records the CVDs associated with these biomarkers. HF appears 23 times, AMI appears 22 times, AS appears 19 times, CHD appears 17 times, MI appears 9 times, HP appears 8 times, ACS appears 8 times, stroke appears 7 times, acute IS appears 2 times, acute heart failure appears 1 time, aortic aneurysm and dissection appears 1 time.
3.7 Biomarkers associated with CVDs in sweat.
The parameters for CiteSpace were set as follows: time range (2004–2023), type of nodes (Keywords), selection criteria (top N = 100), and other parameters remained unchanged. After manual screening to remove irrelevant keywords, a network consisting of 441 nodes and 1742 connections was obtained.
In Fig. 8, the biomarkers for CVDs detected in sweat include lactic acid, alpha-synuclein, urocanic acid, tyrosine hydroxylase, gamma carboxyglutamic acid, CRP, proline-rich proteins, neuropeptide Y, ng2 proteoglycan, vitamin K, iii beta-tubulin, matrix gla protein, leukemia inhibitory factor, metanephrines, and Cor, among others. The CVDs associated with these sweat biomarkers include stroke, HP, AMI, unstable angina, PAD, vascular inflammation, and arterial calcification. In Fig. 8, the color of each node represents the time of related research, with purple indicating earlier studies and red indicating more recent ones. The earliest studied CVDs were unstable angina and AMI, while the earliest studied biomarkers were bone morphogenetic proteins, cholinergic differentiation factor, leukemia inhibitory factor, lectin binding, calcitonin-gene-related peptide, CYT, Colony-Stimulating Factors, substance p, vasoactive intestinal peptide, and neuropeptide y. Mid-term studied CVDs included arterial calcification, vascular inflammation, PAD, AS, and heart rate variability. The mid-term studied biomarkers for CVDs included VitK, gamma carboxyglutamic acid, gamma-glutamyl carboxylase, iii beta-tubulin, Cor, carbonic anhydrase, matrix gla protein, alpha-synuclein, ng2 proteoglycan, RNA, and tyrosine hydroxylase. The most recent research focused on stroke and HP, with the latest studied biomarkers being lactic-acid, Glial fibrillary acidic protein, metanephrines, acetylcholinesterase, and CRP.
A detailed list of the sweat-based biomarkers for CVDs can be found in Table 3, which includes a total of 42 recorded biomarkers. The CVDs associated with these sweat biomarkers are detailed in Table 4, with stroke appearing 3 times and AMI, HF, and heart rate variability. each appearing twice, while the remaining diseases are mentioned only once.
Table 3
Biomarkers for CVDs in sweat
Cytokines | Gamma-Glutamyl Carboxylase | Gamma Carboxyglutamic Acid |
TGF-β | Superoxide | Urocanic Acid |
Calcitonin-gene-related peptide | Hydroquinone Oxidase | Substance p |
Carbonic anhydrase | Ng2 Proteoglycan | Tyrosine Hydroxylase |
Coenzyme q | Lactic-Acid | Leukemia Inhibitory Factor |
Colony-Stimulating Factors | Mannose-Binding Lectin | RNA |
18-Beta-Glycyrrhetinic acid | Matrix Gla Protein | Vasoactive Intestinal Peptide |
Acetylcholinesterase | Metanephrines | VitK |
Ascorbic-Acid | Myospryn | III Beta-Tubulin |
Alpha-Synuclein | Nadh Oxidase | Cor |
Bone Morphogenetic Proteins | Neuropeptide y | Proline Rich Proteins |
C Reactive Protein | Glial Fibrillary Acidic Protein | Protein-Phosphorylation |
Cholinergic Differentiation Factor | Polycyclic Aromatic Hydrocarbons | Free Amino Acids |
Table 4
Sweat biomarkers associated with CVDs
cardiovascular diseases | Count |
stroke | 3 |
acute coronary syndrome | 2 |
heart-failure | 2 |
heart-rate-variability | 2 |
hypertension | 2 |
acute myocardialinfarction | 1 |
aortic dissection | 1 |
arterial calcification | 1 |
atherosclerosis | 1 |
atrial fibrillation | 1 |
atrioventricular block | 1 |
hypotension | 1 |
peripheral artery disease | 1 |
unstable angina | 1 |
vascular inflammation | 1 |