Atherosclerosis (AS) is a chronic inflammatory vascular disease in which lipid or fibrous plaques deposit on the inner lining of arteries, causing narrowing of the affected vessels, blood flow obstruction, and ischemia of related tissues (Libby et al., 2019). Narrowing of blood vessels coupled with the rupture of unstable plaques can lead to platelet aggregation, thrombosis formation, and acute cardiovascular and cerebrovascular diseases. Cardiovascular disease is the leading cause of death worldwide (Benjamin et al., 2017), and coronary heart disease is a major component of cardiovascular disease. An epidemiological survey conducted in the United States showed that more than 17 million people died from cardiovascular disease in 2015, accounting for 31% of all deaths worldwide, and an estimated 7.4 million people died from coronary heart disease (Organization, 2011). Moreover, over 75% of deaths from cardiovascular disease occur in low- and middle-income countries worldwide (Organization, 2011). The high disability rates associated with cardiovascular disease also impose a heavy economic burden on many developing countries.

The endoplasmic reticulum (ER) is an important organelle in eukaryotic cells that controls protein quality and is responsible for protein synthesis, folding, and transport, as well as participating in calcium storage. Research has shown that ERS is a key factor in the development of AS (Tabas, 2010). The ER membrane contains three types of transmembrane proteins: activating transcription factor 6 (ATF6), double-stranded RNA-dependent protein kinase R-like ER kinase (PERK), and inositol-requiring enzyme 1 (IRE1) (Ron and Walter, 2007; Adolph et al., 2012; Hetz, 2012). When ERS is enhanced, these sensors can recognize and initiate a series of complex adaptive responses to restore ER homeostasis and function, known as the unfolded protein response (UPR). UPR can provide cellular protection when moderately activated, but if the stress exceeds the compensatory capacity of UPR, such as under oxidative stress, hypoxia, or inflammatory reactions, it can disrupt ER homeostasis, enhance ERS, lead to misfolding and accumulation of unfolded proteins in the lumen, along with disturbances in calcium balance. This can activate inflammatory and apoptotic pathways, resulting in cellular and endothelial dysfunction, accelerating plaque formation, and the progression of AS (Lawrence de Koning et al., 2003; Yang et al., 2020). The specific mechanism (shown in Figure 1) involves the separation of the ER membrane-located stress sensors IRE1, PERK, and ATF6 from their protein partner Bip. IRE1 splices XBP1 mRNA to generate spliced XBP1 and recruits apoptosis signal-regulating kinase 1 (ASK1) and TNF receptor-associated factor 2 (TRAF2) (Zhu and Lee, 2015; Lebeaupin et al., 2018), leading to the activation of JNK and NF-kB, inducing inflammation and cell apoptosis. Activated PERK phosphorylates the eukaryotic initiation factor 2α (eIF2α), indirectly inhibiting protein synthesis and RNA transcription (Yang et al., 2020), while enhanced phosphorylated eIF2α promotes ATF4 mRNA translation, upregulating the transcription factor C/EBP homologous protein (CHOP), inducing gene expression involved in autophagy, oxidative stress, and apoptosis (Ren et al., 2021). Unfolded proteins in the ER lumen activate the JNK and CHOP pathways after the dissociation of ATF6 and heavy chain-binding protein (Bip)/GRP78(11). Under sustained ER stress conditions, the activated CHOP/GADD153, Caspase-12, and JNK pathways participate in inducing cell apoptosis. Additionally, the activation of the TRAF2-mediated NF-κB pathway induces the production of NLRP3 inflammasome (NLRP3) and inflammatory factors such as interleukins, triggering an inflammatory response (Bravo et al., 2011; Oakes and Papa, 2015). All of these findings indicate the significant role of ERS in the formation and progression of atherosclerosis.

Figure 1. Mechanisms of ERS for AS. This figure illustrates the process by which the three stress sensors, IRE1, PERK, and ATF6, located on the ER membrane, induce inflammation and cell apoptosis through distinct mechanisms.

Bibliometrics emerged in the early 20th century and developed into an independent discipline in 1969. It has been widely applied in literature analysis. Bibliometrics provides quantitative and qualitative methods for investigating published literature in various fields. By analyzing specific information such as authors, countries, institutions, journals, and references of published literature, bibliometrics enables us to obtain information about the development status, distribution patterns, and research hotspots in relevant fields. It has become one of the popular techniques for evaluating the credibility, quality, and impact of academic work (Ellegaard and Wallin, 2015; Rondanelli et al., 2016). Quantitative analysis of literature-related data combined with visual presentations can make research results more intuitive. A search of literature related to ERS and AS reveals that although numerous studies are exploring this field from different perspectives, the use of bibliometric methods to analyze the research status in this field is lacking.

Web of Science is a globally trusted and authoritative scientific citation database that covers multiple fields including natural sciences, engineering technology, biomedical sciences, social sciences, arts, and humanities. To ensure the coverage and authority of the data, the Web of Science was selected as the data source, the indexes were selected as SSCI and SCIE, the search strategy selected was TS= (“endoplasmic reticulum stress” OR “Endoplasmic Reticulum Stresses” OR “Reticulum Stress, Endoplasmic” OR “Reticulum Stresses, Endoplasmic” OR “Stresses, Endoplasmic Reticulum” OR “Stress, Endoplasmic Reticulum”) AND TS= (“Atherosclerosis” OR “Atheroscleroses” OR “Atherogenesis”). To avoid deviations caused by daily data updates, the time span was set from 1 January 2000 to 1 February 2023. A total of 1,048 documents were obtained, and 1,035 documents were finally used in the study. The search results were exported with “Plain Text file” and the record content chose “Full Record and Cited Reference”, and stored in download_*.txt format. The flowchart was shown in Figure 2.

Figure 2. Flowchart illustrating the search strategy and selection process in ERS and AS.

In this study, CiteSpace, VOSviewer and the R package of " bibliometric” were applied to collect information on ERS and AS and draw knowledge maps. These software programs have complementary advantages. CiteSpace uses a set-theoretic data standardization method to measure the similarity of knowledge units. Similarity algorithms are used to obtain Timezone and Timeline views within time slices, which clearly outline the evolution of knowledge in this field over time, enabling understanding of the development dynamics and exploration of new research hotspots (Chen, 2006). VOSviewer uses a probability-based data standardization method and provides multiple visualizations in co-authorship, co-citation, and keyword sections, including Network Visualization, Overlay Visualization, and Density Visualization, with the advantages of simple operation and beautiful images (van Eck and Waltman, 2010). R language is a programming language and operating environment used for statistical analysis, graphical reports, and reporting. “bibliometric” is an R language software package developed based on bibliometrics. It uses its powerful computing ability to extract multiple analysis indicators of literature and can achieve visual analysis of bibliometric analysis (Aria et al., 2017). Firstly, we used visualized maps of annual publications, countries, authors, institutions, and journal publications to intuitively display the basic output of this field. Secondly, based on highly co-cited journals and literature, we presented the knowledge base of this field. Finally, based on co-occurrence graphs of keywords (including keyword clustering, timeline, and highlight maps), we showed changes in research hotspots in this field and identified and discovered new hotspots.

From January 2000 to December 2023, a total of 1,035 publications were published in this field. Figure 3 shows the trend of literature production in this field over time. The first relevant literature on ERS and AS appeared in 2000, and since then, research on ERS and AS has shown a steady and significant increase. It reached its peak in 2017 with 91 publications. After that, the research intensity on ERS and AS slightly declined but still exhibited a fluctuating upward trend. As the statistics for 2023 are not yet complete, there is a downward trend starting from 2021, and the number of publications in 2023 reached its lowest point at 48.

Figure 3. Trends in the growth of publications and the number of cumarticles.

As shown in Figure 4 and Table 1, China has the highest number of publications, with a total of 348 papers. The following countries are the United States, Canada, Japan, and South Korea, with 318, 70, 62, and 50 publications respectively, accounting for 33.62%, 30.72%, 6.76%, 5.99%, and 4.83% of the total publication count, surpassing 80% of the total. This suggests that these countries have made significant contributions to the field, while also highlighting the uneven development of research in this domain across different countries/regions. Among the top ten countries in terms of publication count displayed in the table, six countries have a centrality score exceeding 0.1: PEOPLES R CHINA (0.26), United States (0.51), Canada (0.1), ITALY (0.14), ENGLAND (0.27), and FRANCE (0.17). These scores indicate that these six countries hold an important position in research on ERS and AS. It is noteworthy that the United States has the highest citation count, reaching 36,719, followed by China and Canada with 9,528 and 5,052 citations respectively. This highlights the high research value of literature from these countries, which has garnered attention from numerous scholars. VOSviewer parameters were set as follows: Methods (Linlog/modularity) and a minimum number of country documents: 5. The obtained results were retrieved from 62 countries, with 30 meeting the thresholds.

Figure 4. Cooperation map of countries/regions in ERS and AS. (A) A visual map for VOSviewer network. (B) Countries/regions involved in ERS and AS research. The links between countries/regions indicate their collaborations and connections.

Table 1. Top 10 most productive countries/regions in ERS and AS.

According to the analysis of collaborative authors, VOSviewer divided countries into different collaboration clusters, distinguished by different colored nodes. The size of the nodes represents the number of publications, and the thickness of the lines indicates the number of connections between nodes (Figure 3A). Figure 3A shows that countries that collaborate more with the United States include China, Canada, the United Kingdom, Japan, France, Germany, Italy, South Korea, and others. Countries that collaborate more with China include the United States, Canada, the United Kingdom, Turkey, and others. Countries that collaborate more with Canada include China, the United States, the United Kingdom, France, and others. Figure 3B displays a collaboration map of authors in ERS and AS based on their countries/regions. The darker the color, the higher the publication count. The lines represent collaborative relationships between countries, and the line thickness represents the strength of collaboration. Interestingly, the more frequent the contributions between countries, the greater the output, as exemplified by China and the United States, where the figure showed that the lines crossing them are numerous and thick, suggesting that China and the United States play an important role in the development of cooperation in this field. Thus China and the United States are both the countries that produce the most output and work closely with Canada, France, Germany, and Japan.

As shown in Table 2, 5 authors have published more than 10 articles. The author with the highest number of publications is Ira Tabas from Columbia University, with a total of 35 articles and 11,329 citations. The second highest is Geoff H Werstuck from McMaster University, with 22 articles and 1,356 citations. Richard C Austin from Henderson Research Center ranks third with 17 articles and 1,582 citations. Yuanyuan Shi from McMaster University is the fourth with 13 articles and 386 citations. Lastly, Ebru Erbay from Ihsan Dogramaci Bilkent University has published 11 articles with 1,454 citations.

Table 2. Top 10 authors in T cell and AS.

VOSviewer parameters were set as follows: Methods (Linlog/modularity) and a minimum number of documents of an author: 3. The obtained results were retrieved for 5,361 authors, and 240 met the thresholds. Based on the analysis of collaborative authors, VOSviewer categorizes authors into 7 clusters. To enhance the visual display, the author co-occurrence network is optimized and presented using Pjake software (Figure 5A). Each node in Figure 5A represents an individual author, where the size of the circle corresponds to the number of publications by each author. The lines between nodes depict the collaborative relationships between authors, with thicker lines indicating stronger collaboration. From the figure, it can be observed that Tabas, Ira has close collaborations with authors such as Tall, Alan R, Seimon, Tracie A, and Fisher, Edward A. Similarly, Erbay, Ebru, Weber, Christian, Mallat, Ziad, and other authors have tight collaborations. Dong, yunzhou, Chen, hong, Cowan, Douglas Bdeng, as well as Zou, Minhui, Xie, Zhonglin, Zhang, Miao, and others also exhibit strong collaborative relationships. Figure 5A clearly demonstrates the clustering of the seven authors, with dense connections within each cluster and relatively sparse and thinner connections between different clusters. This suggests that authors have formed stable collaborative networks, with fewer authors breaking existing collaborations to seek new partners. CiteSpace parameters were set as follows: time slice (2000–2023), year per slice (Benjamin et al., 2017), term source(entire selection), node type(author), and selection criteria (top N = 50). Other parameters were left at the default settings. Figure 4B shows the visual map of authors for CiteSpace network. The size of nodes represents the number of outputs of authors, and different colors represent different years. From 2000 to 2023, the color changes from purple to red.

Figure 5. A collaborative network display between authors and institutions from ERS and AS. (A) A visual map for the VOSviewer network among authors. (B) A visual map for the CiteSpace network among authors. (C) A visual map for the VOSviewer network among institutions. (D) A visual map for the CiteSpace network among institutions.

Based on the data in Table 2 and the author’s co-occurrence maps presented by VOSviewer and CiteSpace, it is evident that Tabas, Ira is an absolute core research force in the field of ERS and AS. Authors such as Werstuck, Geoff H and Austin, Richard C have also made significant contributions to research in this field.

As shown in Figure 5 and Table 3, Columbia University is the institution with the highest number of publications (Battson et al., 2017), followed by McMaster University (Milutinović et al., 2020), University of California System (Esse et al., 2019), Harvard University (Ji et al., 2021), and Capital Medical University (Tabas and Ron, 2011). The top five institutions in terms of citation count are Columbia University (13,154), New York University (3,751), University of California System (3,208), University of Washington (3,205), and Harvard University (2,951).

Table 3. Top 5 institutions based on publications (Rank a) and citations (Rank b).

The VOSviewer was set as follows: the minimum number of institutions was 5. Of the 1,192 organizations, 96 met the thresholds. By utilizing Pjake software, the clustering of institutions was visualized and presented in Figure 5C. The institutions were divided into 11 clusters. The first cluster includes Boston University, Chongqing Medical University, Fudan University, INSERM, Guangzhou Medical University, Nanchang University, and others. The second cluster includes China Pharmaceutical University, Chinese People’s Liberation Army General Hospital, Huazhong University of Science and Technology, Shandong University, Wuhan University, and so on. The third cluster includes Capital Medical University, Chinese Academy of Sciences, China Academy of Chinese Medical Sciences, Nanjing Medical University, and so on. The forth cluster includes the Chinese University of Hong Kong, Dalian Medical University, Harbin Medical University, Monash University and so on. The fifth cluster includes Cleveland Clinic Foundation, McMaster University, University of Iowa, and so on. The sixth cluster includes Baylor College of Medicine, Johns Hopkins University, National Taiwan University, University of Michigan and so on. The seventh cluster includes University of Tokyo, Xian Jiaotong University, King’s College London, Queen Mary University London and so on. The eighth cluster includes Columbia University, New York University, Yale University, and so on. The ninth clusters includes Harvard Medical School, Harvard University, University of Toledo and so on. The 10th cluster includes University of California System, University of Pennsylvania, University of Toronto and so on. The 11th cluster includes University of Washington, Emory University, Temple University, and so on. The CiteSpace parameters were set as follows: time slicing (2000–2023), year per slice(1), term source is all, and other parameters were set to default values. Figure 4D displays the visual map of the results. Nodes representing Columbia University, McMaster University, and the University of California System are relatively larger, indicating significant contributions of these institutions in the ERS and AS fields. In Figure 5C,D, all nodes are interconnected closely, suggesting extensive collaborations among different institutions.

A total of 1,035 articles were published in 373 journals. Figure 6 and Table 4 present the top ten journals with the highest number of publications, along with their JCR zones and impact factors. Arteriosclerosis Thrombosis and Vascular Biology published the highest number of articles in the ERS and AS fields (31.3%), followed by the Journal of Biological Chemistry (29, 2.8%), International Journal of Molecular Sciences (27, 2.61%), Circulation Research (23, 2.22%), and Atherosclerosis (21, 2.03%). Circulation had the highest IF of 37.8. Among the top 10 journals, 7 (Arteriosclerosis Thrombosis and Vascular Biology, International Journal of Molecular sciences, Circulation Research, Antioxidants & Redox Signaling, Frontiers in Pharmacology, Cardiovascular research and Circulation) journals are all located in the Q1 JCR division, and their IF exceed 5. Figure 6A illustrates the publication volume of the top 10 journals, while Figure 6B displays the temporal trend of publication volume for these journals. It can be observed from the figures that the publication volume of each journal has been increasing over the years, particularly since 2010, when there has been a significant surge in the number of publications.

Figure 6. Showcase of issuing journals. (A)Top 10 journals in terms of number of publications. (B) Trends in the number of articles published in the top ten journals over time.

Table 4. The top 10 journals in ERS and AS.

Table 5 presents the top 10 most cited journals, with seven of them ranked in Q1 of JCR. The highest impact factor journal is Nature (64.8), while the top three most cited journals are the Journal of Biological Chemistry (4,011 citations), Arteriosclerosis Thrombosis and Vascular Biology (3,196 citations), and Circulation (2,881 citations), all outstanding journals in JCR zones one and 2. The VOSviewer was set to display a minimum of 200 institutions, with 70 sources meeting the thresholds. Figure 7 shows that the co-citation network of journals is composed of four clusters, corresponding to the four colors in the figure. In the first and second clusters, the research is related to medicine and biochemistry. The first cluster focuses on the microbial level of research, such as cells, while the second cluster is more related to cardiovascular diseases. The third cluster is associated with inflammation and immunity, and the fourth cluster consists of only one review medical journal. The main purpose of citing these clustered journals is to analyze and review existing research, providing theoretical and empirical support for future studies. Among them, the results of the second and third clusters indicate that in the fields of ERS (Environmental and Resources Science) and AS (Aerosol Science), more scholars focus on cardiovascular diseases within the circulatory system. The research hotspot lies in the investigation of inflammatory and immune mechanisms.

Table 5. Top 10 co-cited journals in T cell and AS.

Figure 7. Visualization of co-cited journals. According to the journal content, four clusters—red, green, blue, and yellow—are presented, with the red cluster comprising the most journals and being the largest cluster.

Co-citation refers to the frequency at which two documents are both cited by another document. The table displays the top ten most cited articles. The most frequently cited article is “The Role of Endoplasmic Reticulum Stress in the Progression of Atherosclerosis” by Ira Tabas, published in Circulation Research (66 citations) (Tabas, 2010). The second one is “Reduced Apoptosis and Plaque Necrosis in Advanced Atherosclerotic Lesions of Apoe/and Ldlr/Mice Lacking CHOP” by Edward Thorp, published in Cell Metabolism (58 citations) (Thorp et al., 2009). The third one includes two articles: “Reducing endoplasmic reticulum stress through a macrophage lipid chaperone alleviates atherosclerosis” by Ebru Erbay, published in Nature Medicine (57 citations) (Erbay et al., 2009), and “Increased Endoplasmic Reticulum Stress in Atherosclerotic Plaques Associated With Acute Coronary Syndrome” by Masafumi Myoishi, MD, published in Coronary Heart Disease (57 citations) (Myoishi et al., 2007). Among the top ten cited articles, five are review articles and five are experimental articles. Based on the co-citation map of literature (Figure 8A), co-citation literature emergence detection was performed, and Figure 8B shows the top 20 most prominent reference literature. The blue line represents the timeline, and the red part on the blue timeline represents the start year, end year, and duration of emergence. The strongest emerging reference literature is “The Role of Endoplasmic Reticulum Stress in the Progression of Atherosclerosis” published in 2010, with an intensity of 22.76. These articles are outstanding contributions in the ERS and AS fields. Among them, “The Role of Endoplasmic Reticulum Stress in the Progression of Atherosclerosis” discusses the role and treatment strategies of endoplasmic reticulum stress in mediating cell apoptosis and inflammatory responses in endothelial cells, smooth muscle cells, and macrophages in atherosclerosis (Tabas, 2010). The high citation and high intensity of emergence both indicate the important influence of this review article.

Figure 8. (A) References co-citation network in ERS and AS. (B) Top 20 references with the strongest citation bursts in ERS and AS.

As displayed in Table 6, in addition to endoplasmic reticulum stress(887) and atherosclerosis(586), keywords with higher frequency in this study include apoptosis(254), oxidative stress(248), unfolded protein response(223), inflammation(200), vascular endothelial cell(180), cardiovascular disease(174), activation(160), smooth muscle cells(150). Among these keywords, expression, nf-kappa b, macrophage, insulin resistance, and ldl appeared more than 100 times, indicating that they are the focus of the research.

Table 6. Top 20 keywords in ERS and AS.

CiteSpace parameters were set as follows: time slice (2000–2023), year per slice (Benjamin et al., 2017), term source(entire selection), node type(author), and selection criteria (top N = 50). Other parameters were left at the default settings. The larger the nodes in the graph, the higher the frequency of occurrence of the corresponding keywords. A larger node centrality also indicates the importance of the keyword. The redder the node color, the newer the keyword. Thicker lines between nodes indicate a closer relationship between them. Based on the co-occurrence graph of keywords, we generated keyword clustering graphs, keyword timeline graphs, and the top 25 keywords with the highest prominence intensity (Figure 9).

Figure 9. (A) Keywords network in ERS and AS. (B) Timeline viewer related to ERS and AS. (C) Top 25 keywords with the strongest citation bursts. (D) The cluster of keywords in the studies of ERS and AS.

The timeline graph displays the dynamic evolution path of research hotspots represented by keywords. It explores the evolution process of research hotspots in different periods and helps scholars track current and potential hotspots. Figure 9B visually shows the phased research hotspots and development trends in the ERS and AS fields from a temporal perspective. Multiple clusters are formed, labeled as #0, #1, #2, etc. Figure 9B presents 10 clusters, namely, oxidative stress, cholesterol efflux, insulin resistance, express, endothelial cell, endothelial dysfunction, vitamin D, cell death, unfolded protein response, and cystathionine beta-synthase.

The keyword burst graph intuitively displays the phased changes in research hotspots over time. As shown in Figure 9C, the research hotspots from 2000 to 2010 include vascular endothelial cells, risk factors, nitric oxide, plasma homocysteine, and cystathionine beta-synthase, among others. The hotspots from 2017 to 2023 include cardiovascular disease, lipid metabolism, inhibition, nlrp3 inflammasome, and ischemia reperfusion injury, among others. These keywords are also in the period of research outbreak and their popularity shows no sign of decline over time.

Keyword clustering is a classification based on the degree of association between keywords. In CiteSpace software, the likelihood ratio (LLR) is used to create keyword clustering graphs, where a higher number of clustered nodes indicates a higher research hotspot and a smaller cluster index. The modularity value (Q) and average silhouette value (S) are two metrics used to evaluate the clustering effectiveness of CiteSpace. A Q value greater than 0.3 indicates a significant community structure, while an S value above 0.5 suggests reasonable clustering. When the S value reaches 0.7, it is considered to have high clustering efficiency and is convincing (Yue et al., 2015).

This study formed 10 clusters (Q = 0.5427, S = 0.7818) with convincing clustering results (shown in Table 7). The cluster labels are endothelial dysfunction, inflammation, insulin resistance, unfolded protein response, fatty liver disease, smooth muscle cells, metabolic syndrome, induced apoptosis, scavenger receptor, and cholesterol-loaded macrophages. Clusters #0, #6, and #9 mainly involve the pro-AS effects of ERS on different cells. Clusters #1, #2, #3, #7, and #8 mainly involve the specific mechanism by which ERS promotes AS. Clusters #4 and #6 are related to diseases associated with ERS and AS.

Table 7. Ten keyword clusters.

Co-citation refers to two articles being cited by another article at the same time. Highly co-cited research is usually considered the foundation of a field (Lijun et al., 2019). Table 8 shows the top ten highly cited articles, including five reviews and five experimental articles. These ten articles explore the relationship between ERS and AS from different perspectives.

Table 8. Top 10 cited references of publications in ERS and AS.

A review published by Ira Tabas in the journal Circulation in 2010 systematically discusses the role of endoplasmic reticulum stress (ERS) in the development of atherosclerosis. This article summarizes that under stimuli such as oxidative stress, oxidized sterols, elevated intracellular cholesterol levels, and saturated fatty acids, the unfolded protein response (UPR) can be prolonged and activated. It provides ample evidence to demonstrate the chronic activation of UPR in atherosclerotic lesion cells, such as endothelial cells, smooth muscle cells, and macrophages. The long-term activation of ERS is believed to influence the formation and vulnerability of atherosclerotic plaques by recruiting inflammatory factors, inducing inflammatory responses, and activating multiple pro-apoptotic signaling pathways (Tabas, 2010).

The team led by Edward Thorp discovered through experiments that CHOP expression increases in advanced atherosclerotic lesions. By comparing the size and morphology of aortic root lesions in fat-fed Chop+/+; Apoe−/− and Chop−/−; Apoe−/− mice, they found that plaques and apoptotic cells were reduced in CHOP-deficient mice. They suggest that the CHOP pathway may serve as a potential therapeutic target for treating atherosclerosis (Thorp et al., 2009).

Ebru Erbay and colleagues discovered through genetic and chemical models that adipocyte fatty acid-binding protein 4 (aP2) is a major regulatory factor for lipid-induced endoplasmic reticulum stress in macrophages. They found that upregulating the chemical chaperone 4-phenyl butyric acid (PBA) or inhibiting the lipid chaperone aP2 significantly suppressed endoplasmic reticulum stress, cell death, and atherosclerosis in macrophages (Erbay et al., 2009).

Masafumi Myoishi, MD and colleagues detected the expression of endoplasmic reticulum (ER) chaperones and apoptotic cells in smooth muscle cells and macrophages within thin-cap fibroatheromas and ruptured plaques in coronary artery segments obtained during autopsies of 71 patients. They found a significant increase in ER chaperone expression and apoptotic cells. Oxysterols, such as 7-ketocholesterol (7-KC), contribute to the cytotoxicity of oxidized low-density lipoprotein. The upregulation of 7-KC expression induces ER chaperone upregulation through the generation of reactive oxygen species, leading to increased endoplasmic reticulum stress and activation of the downstream CHOP signaling pathway, which induces apoptosis in smooth muscle cells and macrophages. Further experiments revealed the activation of the Chop-dependent pathway in unstable plaques. Therefore, endoplasmic reticulum stress is considered one of the factors that increase vulnerability of coronary artery plaques, leading to acute coronary syndrome and fatal outcomes in patients with coronary artery disease (Myoishi et al., 2007).

Gökhans S. Hotamisligil reviewed the cross-talk between the endoplasmic reticulum (ER) stress response and inflammatory pathways in the context of metabolic homeostasis and disease, highlighting the importance of ER stress as a crucial mechanism underlying metabolic diseases. The author proposed that ER stress-induced inflammation and autophagy are also significant causes of metabolic dysfunction. By summarizing these viewpoints, the author suggested that there is enormous potential for targeting ER stress in the treatment of metabolic diseases (Hotamisligil, 2010a).

Ira Tabas provided a comprehensive review on the molecular mechanisms of endoplasmic reticulum (ER) stress and cell apoptosis, focusing on the pathophysiological relationship between ER stress and cell apoptosis, as well as the integration and complementarity of various cell apoptosis pathways induced by ER stress. The aim was to provide a basis for future drug intervention strategies (Tabas and Ron, 2011). David Ron and colleagues systematically discussed the three branches of the unfolded protein response (UPR) under ER stress and explained the role of different gene expressions in the secretion pathway. The goal was to preserve the beneficial effects of ER stress in the human body while alleviating its detrimental effects (Ron and Walter, 2007).

Gökhan S. Hotamisligil highlighted the role of endoplasmic reticulum (ER) stress in macrophages exposed to lipotoxic environments, which can activate inflammatory and apoptotic pathways, promoting the formation of atheromatous plaques or accelerating the rupture of vascular plaques and exacerbating metabolic dysfunction. The author proposed that inhibiting or genetically deleting lipid chaperone aP2 could be an important means of enhancing ER folding capacity to alleviate ER stress (Hotamisligil, 2010b). Mete Civelek an