Bibliometric and Visual Analysis on Coronary Artery Calcification from 2003 to 2024

Bibliometric and Visual Analysis on Coronary Artery Calcification from 2003 to 2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bibliometric and Visual Analysis on Coronary Artery Calcification from 2003 to 2024 Lin Zhao, Liying Zheng, Xiao Gong, Senfu Han, Leshun Liu, Mei Xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5921952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study utilized the Web of Science core collection database to retrieve all CAC-related publications from January 1, 2003, to August 31, 2024. Microsoft Excel was used to analyze publication volume, trends, and journal publication volume. VOSviewer software was applied to visualize the collaboration network among authors, while Citespace software was used for analyzing institutions, keywords co-occurrence, keywords clustering, and keywords burst detection. The study collected 3,069 publications on CAC from the Web of Science database between 2003 and 2024. Budoff, Matthew J. was identified as the most prolific author, with 161 articles. Nine of the top ten institutions in terms of publication volume located in the U.S., indicating that the U.S. is leading in this research area. Current research on CAC focuses on quantitative assessment and its association with the progression of cardiovascular diseases, particularly studies related to coronary artery disease and atherosclerosis, which have emerged as research hotspots in recent years. Research on coronary artery calcification has centered on early diagnosis and risk assessment, especially through quantification of calcification scores using CT imaging to predict cardiovascular event risk. Furthermore, there is a focus on the biological mechanisms of calcification and its relationship with atherosclerosis. Health sciences/Cardiology/Cardiovascular biology/Calcification Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases coronary artery calcification bibliometrics CiteSpace VOSviewer visual analytics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coronary artery calcification (CAC) is the process of calcium salt deposition in the walls of the coronary arteries and is considered a key feature of atherosclerotic lesions. CAC is a distinct manifestation of coronary artery disease (CAD) and typically arises during the development of atherosclerotic plaques [ 1 ] . The prevalence of CAC is widespread, with epidemiological data showing that approximately 50% of people aged 40 to 49 have evidence of coronary artery calcification, a figure that rises to 80% in individuals aged 60 to 69. The incidence of CAC significantly increases with advancing age [ 2 ] . The development of CAC is closely related to pathological changes in the intimal and medial layers of the coronary arteries. During atherosclerosis (AS) progression, low-density lipoprotein cholesterol (LDL-C) and other lipids accumulate in the inner layer of the arterial wall, provoking localized inflammation that leads to arterial wall thickening and plaque formation. As atherosclerosis progresses, these plaques undergo calcification, leading to CAC [ 3 ] . CAC is an objective indicator of atherosclerotic burden, commonly assessed by computed tomography (CT). The coronary artery calcium score (CACS), frequently calculated using the Agatston scoring method, quantifies the extent of calcification. Numerous studies have found a strong association between CACS and the risk of CAD as well as other cardiovascular events. Furthermore, CAC is an independent predictor of cardiovascular events, particularly in asymptomatic individuals, aiding in the identification of high-risk patients who may not be identified by traditional risk assessments [ 4 , 5 ] . Bibliometrics is a research method that uses quantitative analysis of publications (such as academic papers, books, and conference proceedings) and their citations to explore the development patterns and characteristics of scientific and technological literature [ 6 ] . By statistically analyzing the number of publications, authors, sources, keywords, and citation relationships, bibliometrics helps to identify research hotspots and trends in academic fields [ 7 ] . CAC is an important marker of AS and CAD, and its detection and evaluation are crucial for cardiovascular risk stratification and the development of personalized treatment strategies. Consequently, a significant number of high-quality studies and reviews on CAC have been published in recent years. However, there is currently a lack of bibliometric analysis regarding CAC research. Therefore, this study aims to provide a visual analysis of the research status, hotspots, and trends in CAC from 2003 to 2024, based on the literature published in this field. Materials and Methods 1.1 Data Retrieval This study retrieved all publications related to CAC from the Core Collection of the Web of Science database, covering the period from January 1, 2003, to August 31, 2024. The search strategy was as follows: ((((((((((TS=(Score)) OR TS=(Detection)) OR TS=(Measurement)) OR TS=(Risk Factor)) OR TS=(Clinical Significance)) OR TS=(Prognosis)) OR TS=(Imaging)) OR TS=(Cure)) OR TS=(Diagnosis)) OR TS=(Mechanism)) OR TS=(Evaluation) AND ((TS=(Coronary Artery Calcification)) OR TS=(Calcified Coronary Artery)) OR TS=(Coronary Artery Calcium)). The document types were limited to "Article" and "Review Article," and the language was restricted to English. The search was completed within one day (August 31, 2024) to avoid bias from daily data updates. 1.2 Inclusion Criteria Publications related to the topic of CAC. 1.3 Exclusion Criteria Duplicate publications; conference papers, technical reports, and theses, publications lacking complete information on authors, institutions, or keywords. 1.4 Data Collection and Conversion Titles, keywords, and abstracts were reviewed, and publications that met the inclusion and exclusion criteria were selected from the Core Collection of the Web of Science database. Eligible articles were exported in plain text format. 1.5 Software Selection and Parameter Settings Microsoft Excel was used for analyzing publication volume, publication trends, and journal contributions. VOSviewer software (version 1.6.20) was employed to visualize co-authorship networks, with adjustments made to the frequency thresholds for authors and keywords based on visualization clarity. The "Linlog/modularity" method was used, with default settings for clustering. Layout, scale, and labels were adjusted for aesthetic purposes, and the network visualization view was used to display co-authorship and keywords co-occurrence. Citespace software (version 6.2.R4) was used for analyzing institutions, keywords co-occurrence, keywords clustering (using the Likelihood Ratio, LLR algorithm), and keywords bursts. The time span was set from January 2003 to August 2024, with a time slice of one year. Other parameters (Text Processing, Links, and Selection Criteria) were kept at default settings. The research flow was shown in Fig. 1 . Results 2.1 Literature Search Outcomes A total of 13,851 articles were identified. After excluding 10,619 articles that were unrelated to the topic of CAC and removing 163 duplicates, 3,069 articles were included in the final analysis. These comprised 2,840 original articles and 229 review articles. 2.2 Analysis of Annual Publication Volume and Trends The annual volume of publications reflects the development trends and pace of research in this field. An analysis of the yearly publication volume was conducted using Excel, as shown in Figure 2 . Over the past 20 years, the number of studies on CAC has generally shown a steady increase, with a peak in publications observed from 2021 to 2023, averaging over 200 papers per year. This indicates a growing interest in CAC in recent years. The trendline suggests that the volume of publications in this field is likely to continue increasing. 2.3 Visual Analysis of the Literature 2.3.1 Distribution of Authors A total of 15,437 authors have contributed to CAC-related research. The top 10 authors by publication volume are presented in Table 1 , with Budoff, Matthew J. being the most prolific author, having published 161 articles. Using VOSviewer, a co-authorship network analysis of core authors was conducted. The core authors were identified based on Price's law, where the minimum number of publications, n = 0. 749×Öηmax (with ηmax being the number of publications by the most prolific author), was used to set the threshold. The minimum publication threshold for authors was set at 10, resulting in a co-authorship network map ( Figure 3 ). The map shows that the top six authors in terms of publication volume have closely collaborated, forming research teams primarily led by Budoff, Matthew J. and Nasir, Khurram. 2.3.2 Institutional Distribution A total of 3,276 institutions have been involved in CAC-related research. The top 10 institutions by publication volume are presented in Table 2 , with Johns Hopkins University being the most prolific, having published 138 articles. A co-authorship network analysis of core institutions was performed using VOSviewer, setting a minimum publication threshold of 9. Based on this, a co-authorship network map of core institutions was constructed ( Figure 4 ). Notably, nine of the top ten institutions by publication volume are located in the United States, highlighting the leading position of the U.S. in current CAC-related research. 2.3.3 Keywords Analysis (1) Keywords Co-occurrence Keywords provide a concise summary of the main content of an article, helping readers quickly grasp the author’s viewpoint and the core content of the paper. Analyzing keywords co-occurrence networks can reveal the research hotspots and development directions in a field, with frequently occurring keywords reflecting the research focus during a specific period [8] . Citespace software was used to perform a keywords co-occurrence analysis on the included literature, and a keyword co-occurrence map was generated ( Figure 5 ). The top 15 keywords by occurrence frequency are presented in Table 3. The analysis of the results indicates that current research in this field mainly focuses on the quantitative assessment of CAC and the association between CAC progression and cardiovascular diseases. The color of the loops in the keywords network shows that research related to "coronary artery disease" and "atherosclerosis" has been a recent hotspot in this field. The connecting lines between the keywords suggest a strong co-occurrence relationship among them. (2) Keywords Clustering To further understand the research hotspots in this field over the past 20 years, we used Citespace software with the Log-Likelihood Ratio (LLR) algorithm to perform a keywords clustering analysis. The clustering analysis identified seven major clusters, as shown in Table 4 and Figure 6 . The results indicated that the Q value was 0.34 (greater than 0.3), and S was 0.68 (greater than 0.5), suggesting that the clustering results were reasonable. The top seven clusters were: #0 Vascular Calcification, #1 Risk Factors, #2 Metabolic Syndrome, #3 Computed Tomography, #4 Coronary Artery Disease, #5 Percutaneous Coronary Intervention, and #6 Impairment. The keywords clusters indicate that arterial calcification and coronary artery calcification scoring are key research hotspots in the field of CAC. (3) Keywords Burst Detection To further analyze the research trends and frontiers in this field, a burst detection analysis of keywords was conducted, which helps in predicting future research directions. Burst detection provided by CiteSpace can identify burst keywords from the subject terms in the literature, clearly indicating the research frontiers and development trends of a discipline. Figure 7 presents the top 25 burst keywords in chronological order, along with their burst intensity and start and end periods. The red lines indicate the duration of each keywords burst. As shown in the figure, early CAC research focused on exploring methods for calcification detection, which is why early research keywords are mostly related to CT. As detection techniques have matured, the recent rise in the popularity of keywords such as "management," "outcome," and "impact" indicates a shift in the focus of CAC research towards risk assessment, patient management, and surgical interventions. Discussion 3.1 Current Research Status In this study, we conducted a comprehensive bibliometric analysis of journal articles related to CAC published over the past 20 years (2003-2024) in the WoS database. Using visualization software such as CiteSpace and VOSviewer, we created knowledge maps to illustrate the development status of the field, identify research hotspots, obtain frontier information, and predict potential trends. Based on the annual publication volume of Chinese and English literature in this field, it is evident that research on CAC has shown an upward trend, increasing from 60 articles in 2003 to 213 in 2023. The presence and extent of CAC are generally closely related to the incidence and severity of CAD [1] . Additionally, the occurrence of CAC is strongly associated with traditional cardiovascular risk factors such as hypertension, hyperlipidemia, diabetes, smoking, and family history [9] . Therefore, CAC has become an important indicator for predicting the risk of cardiovascular events, such as myocardial infarction, angina, and cardiovascular death [10] , making it a current research hotspot. Analysis of author and institution distribution showed that 15,437 authors participated in research in this field, with Budoff, Matthew J. being the most prolific, with 161 articles. He is also the most cited scholar, with 12,419 citations, far exceeding other researchers. In 2023, he reviewed global CAC-related guidelines, discussing differences in CAC scoring ranges, risk cut-offs, treatment thresholds, and stratification of specific patient subgroups, contributing significantly to the establishment of global guidelines for CAC scoring and the prevention and management of CVD [11] . 3.2 Research Hotspots and Trends Based on the analysis of the top 25 keywords by frequency and the keywords clustering results, the research hotspots on CAC mainly focus on the following areas: 3.2.1 Advances in Imaging Techniques for CAC The development of imaging techniques for coronary artery calcification has evolved through several stages, from early conventional X-rays to modern Multi-Detector Computed Tomography (MDCT). The CAC score was initially assessed using Electron Beam Computed Tomography (EBCT) [12] . With continuous advancements in imaging technology, CAC detection has become more precise and widespread. MDCT, Dual-Source CT (DSCT), and High-Resolution CT have significantly improved in terms of spatial resolution, image quality, and scanning time, becoming mainstream technologies. These imaging methods not only assess the degree of coronary artery stenosis but also accurately reflect the degree of calcification, aiding clinicians in more refined patient risk stratification and the development of personalized treatment plans [13] . High-resolution imaging technologies also help visualize extensive vascular calcification in small vessel lumens and distinguish between calcified and non-calcified plaque components, allowing for the identification of vulnerable plaques, which is crucial for the diagnosis and risk assessment of patients with high coronary artery calcification loads [14] . In addition to non-invasive techniques such as CT, intravascular ultrasound (IVUS) and optical coherence tomography (OCT) performed during percutaneous coronary intervention (PCI) can more accurately assess the location, extent, and length of CAC. These intravascular imaging techniques have reduced the incidence of surgical adverse events and postoperative restenosis [15] . 3.2.2 Application of CAC in Cardiovascular Risk Assessment CACS is an important indicator for assessing coronary heart disease risk. Recent studies have mainly focused on the application of CACS in cardiovascular disease risk prediction, its comparison with other biomarkers, and its applicability in different populations. CACS is widely used for cardiovascular risk stratification in asymptomatic individuals. Multiple studies have shown that CACS can independently predict the risk of future cardiovascular events, especially in intermediate-risk populations, where CACS helps further refine risk assessment. Research indicates that a CACS of 0 is associated with a protective effect against the occurrence of cardiovascular events in CAD [16] . Patients with an initial CACS of 0 have a markedly low risk of coronary artery and cardiovascular events within a 5-year period [17.18] . Currently, several studies have established risk models for patients with a calcium score of 0, with the goal of accurately predicting the cardiovascular event risk in such CAD patients [19.20] . A recent large cohort study showed a positive correlation between CACS and the occurrence of cardiovascular events, and CACS can improve the predictive ability of traditional risk scoring models [21] . Researchers have also focused on the differences in the applicability of CACS among different races, genders, and age groups. Some recent studies have suggested that the predictive power of CACS may vary among women, younger individuals, and certain races (e.g., Asians), requiring further research to optimize risk assessment strategies for these populations [22.23] . With the development of AI and machine learning, automated analysis and diagnostic techniques for CAC imaging have been widely applied. In recent years, AI algorithms, especially deep learning techniques, have been used to automatically detect and quantify CAC, effectively reducing human error and improving diagnostic accuracy and efficiency, potentially representing a future direction for CAC use in cardiovascular risk assessment [24] . 3.2.3 Pathophysiological Mechanisms of CAC The occurrence of CAC is closely related to coronary atherosclerosis. In the late stages of atherosclerosis, the accumulation of foam cells and necrotic cell debris induces the migration and proliferation of smooth muscle cells while releasing pro-calcification factors such as Bone Morphogenetic Proteins (BMPs) and Matrix Vesicles [25] . These factors promote the transdifferentiation of vascular smooth muscle cells (VSMCs) into osteoblast-like cells by inducing osteogenic cell phenotype transformation. Transdifferentiated VSMCs begin to secrete bone matrix proteins (such as osteocalcin and osteopontin), thereby promoting calcium deposition [26] . Chronic inflammation plays a key role in the occurrence and progression of CAC. Macrophages and other immune cells release pro-inflammatory cytokines (such as IL-1, IL-6, and TNF-α), which not only exacerbate endothelial cell damage but also promote the osteogenic transdifferentiation process of VSMCs, further accelerating calcification [27] . In addition, inflammatory responses can enhance the expression of calcification factors by activating the NF-κB signaling pathway and oxidative stress pathways [28] . Elevated Lp(a) and Osteoprotegerin levels have also been found to be associated with CAC occurrence [29.30] . The occurrence and progression of CAC are also influenced by gene and protein expression. Recent studies have found that upregulation of PCSK9 expression is associated with CAC development, and the use of PCSK9 inhibitors can effectively reduce the annual incidence of CAC [31.32] . Moreover, genetic studies suggest that certain specific gene variants (e.g., 9p21.3 locus) are associated with CAC occurrence [33] . 3.2.4 Interventions for CAC In the management of coronary artery calcification, factors such as hypertension, hyperlipidemia, and smoking are associated with CAC occurrence, and improving related risk factors [34] can reduce the progression of CAC. In recent years, multiple studies have evaluated the role of statins in CAC progression. Results have shown that statin use does not slow down CAC development and may even accelerate it. However, statin use is associated with a lower incidence of cardiovascular events, which may be related to its effects in reducing inflammation and stabilizing plaques [35-37] . CAC often has a negative impact on post-procedural revascularization following PCI. Therefore, direct intervention of calcified areas during the procedure is of significant importance for improving patient outcomes. Commonly used interventional methods for calcified sites during PCI include intravascular lithotripsy (IVL) and rotational atherectomy [38] . Some studies have indicated that for patients with severe coronary artery calcification, IVL is more effective in reducing the incidence of cardiovascular adverse events [39.40] . 3.2.5 Future Research Directions for CAC In-depth research on CAC is significant for the prevention of cardiovascular diseases. Since the Agatston calcium score was developed in 1900, the diagnosis of CAC based on imaging techniques has become increasingly accurate. Future advancements may facilitate more precise measurement and quantification of CAC severity utilizing AI algorithms and deep learning technologies. Moreover, there has been continuous refinement of CAC scoring models adapted for patients with varying genders, regions, and ages. These advancements play a crucial guiding role in risk assessment and the formulation of prevention strategies for patients with coronary heart disease. Research into the mechanisms underlying CAC occurrence has progressed to protein and gene levels, and the identification of novel protein and gene targets is poised to provide new avenues for early screening and personalized treatment of CAC. The correlation between the morphology and location of calcified coronary artery plaques and the occurrence of coronary artery events, the appropriate preventive measures for asymptomatic coronary heart disease patients with different CAC scores, as well as research into the pathogenesis and heritability of CAC, may represent promising areas of future investigation. In conclusion, coronary artery calcification, serving as a vital tool for assessing cardiovascular disease risk, is increasingly recognized for its expanding applications and value in both clinical practice and research. Future research will continue to explore its pathophysiological mechanisms and intervention strategies to further improve patient outcomes. 4. Strengths and Limitations This study provides an objective and comprehensive analysis of the research status and development trends in this field over the past 20 years using bibliometric tools. It helps researchers understand recent developments in the field, grasp research hotspots, and clarify research directions. However, this study also has certain limitations. It only retrieved articles from the WoS database; although WoS is the most widely used retrieval database, it inevitably omits some articles. Additionally, bibliometric software only analyzes specific information and does not provide a comprehensive evaluation of the full text, which may result in missing some detailed information during visualization. Conclusion This study analyzed 3,069 publications on CAC from the WoS database, published between 2003 and 2024. The research hotspots in CAC primarily focus on early diagnosis and risk assessment, including the quantification of calcification scores using CT imaging techniques to predict cardiovascular event risks. Additionally, studies have explored the biological mechanisms of calcification and its relationship with atherosclerosis. Future trends may involve leveraging AI and machine learning to optimize imaging analysis, developing personalized treatment strategies, and identifying protein and gene targets related to calcification to enhance prevention and treatment outcomes. Declarations Competing interests The authors declare no competing interests. Author Contribution MX, ZL, and LYZ designed the research and extracted relevant data. LSL and XG organized the data. SFH, and LSL rechecked the data. LZ and LYZ analyzed the data and wrote the original manuscript, X reviewed and revised the original manuscript. LYZ and LZ had equal contributions to the study. All authors contributed to the article and approved the submitted version. Acknowledgement This study is supported by Major research project of scientific and technological innovation proiect of Chinese Academy Chinese Medicine sciences (No.C12021A00913), National Natural science Foundation of China (No. 81973686), and National Key Programme for Research and Development from the Ministry of Science and Technology, China (No. 2019YFC0840608) and Hospital capability enhancement project of Xiyuan Hospital, CACMS (NO.XYZX0201-018). Data Availability The data that support the findings of this study are available from the corresponding author, Mei Xue, upon reasonable request. References Greenland, P., Blaha, M. J., Budoff, M. J., Erbel, R. & Watson, K. E. Coronary Calcium Score and Cardiovascular Risk. J. Am. Coll. Cardiol. 72 , 434–447. 10.1016/j.jacc.2018.05.027 (2018). Wexler, L. et al. Coronary artery calcification: pathophysiology, epidemiology, imaging methods, and clinical implications. A statement for health professionals from the American Heart Association. Writ. Group. Circulation . 94 , 1175–1192. 10.1161/01.cir.94.5.1175 (1996). Onnis, C. et al. Coronary Artery Calcification: Current Concepts and Clinical Implications. Circulation 149 , 251–266. 10.1161/circulationaha.123.065657 (2024). Nasir, K. & Cainzos-Achirica, M. Role of coronary artery calcium score in the primary prevention of cardiovascular disease. BMJ (Clinical Res. ed) . 373 , n776. 10.1136/bmj.n776 (2021). Khan, S. S. et al. Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events. Jama 329 , 1768–1777. 10.1001/jama.2023.7575 (2023). Masic, I. et al. The First Mediterranean Seminar on Science Writing, Editing and Publishing, Sarajevo, December 2–3, Acta informatica medica: AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH 24, 424–435, (2016). 10.5455/aim.2016.24.424-435 (2016). Ninkov, A., Frank, J. R., Maggio, L. A. & Bibliometrics Methods for studying academic publishing. Perspect. Med. Educ. 11 , 173–176. 10.1007/s40037-021-00695-4 (2022). Radhakrishnan, S., Erbis, S., Isaacs, J. A. & Kamarthi, S. Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PloS one . 12 , e0172778. 10.1371/journal.pone.0172778 (2017). Silverman, M. G. et al. Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: the Multi-Ethnic Study of Atherosclerosis. Eur. Heart J. 35 , 2232–2241. 10.1093/eurheartj/eht508 (2014). Lehker, A. & Mukherjee, D. Coronary Calcium Risk Score and Cardiovascular Risk. Curr. Vasc. Pharmacol. 19 , 280–284. 10.2174/1570161118666200403143518 (2021). Golub, I. S. et al. Major Global Coronary Artery Calcium Guidelines. JACC Cardiovasc. Imaging . 16 , 98–117. 10.1016/j.jcmg.2022.06.018 (2023). Agatston, A. S. et al. Quantification of coronary artery calcium using ultrafast computed tomography. J. Am. Coll. Cardiol. 15 , 827–832. 10.1016/0735-1097(90)90282-t (1990). Osborne-Grinter, M., Ali, A. & Williams, M. C. Prevalence and clinical implications of coronary artery calcium scoring on non-gated thoracic computed tomography: a systematic review and meta-analysis. Eur. Radiol. 34 , 4459–4474. 10.1007/s00330-023-10439-z (2024). Mergen, V. et al. Ultra-High-Resolution Coronary CT Angiography With Photon-Counting Detector CT: Feasibility and Image Characterization. Invest. Radiol. 57 , 780–788. 10.1097/rli.0000000000000897 (2022). Truesdell, A. G. et al. Intravascular Imaging During Percutaneous Coronary Intervention: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 81 , 590–605. 10.1016/j.jacc.2022.11.045 (2023). Agha, A. M. et al. The Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain: A Meta-Analysis. JACC Cardiovasc. Imaging . 15 , 1745–1757. 10.1016/j.jcmg.2022.03.031 (2022). Chen, C. L., Wu, Y. J., Yang, S. C. & Wu, F. Z. New look at the power of zero coronary artery calcium (CAC) in Asian population: a systemic review and meta-analysis. Cardiovasc. diagnosis therapy . 14 , 377–387. 10.21037/cdt-23-474 (2024). Lehmann, N. et al. Value of Progression of Coronary Artery Calcification for Risk Prediction of Coronary and Cardiovascular Events: Result of the HNR Study (Heinz Nixdorf Recall). Circulation 137 , 665–679. 10.1161/circulationaha.116.027034 (2018). Wu, Y. J., Mar, G. Y., Wu, M. T. & Wu, F. Z. A LASSO-Derived Risk Model for Subclinical CAC Progression in Asian Population With an Initial Score of Zero. Front. Cardiovasc. Med. 7 , 619798. 10.3389/fcvm.2020.619798 (2020). Shen, Y. W. et al. Natural course of coronary artery calcium progression in Asian population with an initial score of zero. BMC Cardiovasc. Disord. 20 , 212. 10.1186/s12872-020-01498-x (2020). Blaha, M. J. et al. Comparing Risk Scores in the Prediction of Coronary and Cardiovascular Deaths: Coronary Artery Calcium Consortium. JACC Cardiovasc. Imaging . 14 , 411–421. 10.1016/j.jcmg.2019.12.010 (2021). Shaikh, K., Nakanishi, R., Kim, N. & Budoff, M. J. Coronary artery calcification and ethnicity. J. Cardiovasc. Comput. Tomogr. 13 , 353–359. 10.1016/j.jcct.2018.10.002 (2019). McClelland, R. L., Chung, H., Detrano, R., Post, W. & Kronmal, R. A. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 113 , 30–37. 10.1161/circulationaha.105.580696 (2006). Eng, D. et al. Automated coronary calcium scoring using deep learning with multicenter external validation. NPJ Digit. Med. 4 , 88. 10.1038/s41746-021-00460-1 (2021). Kim, J. S. & Hwang, H. S. Vascular Calcification in Chronic Kidney Disease: Distinct Features of Pathogenesis and Clinical Implication. Korean circulation J. 51 , 961–982. 10.4070/kcj.2021.0995 (2021). Maniatis, K. et al. Osteoprotegerin and Osteopontin Serum Levels are Associated with Vascular Function and Inflammation in Coronary Artery Disease Patients. Curr. Vasc. Pharmacol. 18 , 523–530. 10.2174/1570161117666191022095246 (2020). Lee, H. Y., Lim, S. & Park, S. Role of Inflammation in Arterial Calcification. Korean circulation J. 51 , 114–125. 10.4070/kcj.2020.0517 (2021). Demer, L. L. & Tintut, Y. Inflammatory, metabolic, and genetic mechanisms of vascular calcification. Arteriosclerosis, thrombosis, and vascular biology 34 , 715–723, (2014). 10.1161/atvbaha.113.302070 Ong, K. L. et al. Lipoprotein (a) and coronary artery calcification: prospective study assessing interactions with other risk factors. Metab. Clin. Exp. 116 , 154706. 10.1016/j.metabol.2021.154706 (2021). Samadi, S. et al. Prognostic role of osteoprotegerin and risk of coronary artery calcification: a systematic review and meta-analysis. Biomark. Med. 17 , 171–180. 10.2217/bmm-2022-0621 (2023). Alonso, R. et al. PCSK9 and lipoprotein (a) levels are two predictors of coronary artery calcification in asymptomatic patients with familial hypercholesterolemia. Atherosclerosis 254 , 249–253. 10.1016/j.atherosclerosis.2016.08.038 (2016). Gao, F. et al. Effect of Alirocumab on Coronary Calcification in Patients With Coronary Artery Disease. Front. Cardiovasc. Med. 9 , 907662. 10.3389/fcvm.2022.907662 (2022). Choi, S. Y. et al. Genome-wide association study of coronary artery calcification in asymptomatic Korean populations. PloS one . 14 , e0214370. 10.1371/journal.pone.0214370 (2019). Xia, C. et al. Cardiovascular Risk Factors and Coronary Calcification in a Middle-aged Dutch Population: The ImaLife Study. J. Thorac. Imaging. 36 , 174–180. 10.1097/rti.0000000000000566 (2021). Xinyu, Z. et al. Statins Accelerate Coronary Calcification and Reduce the Risk of Cardiovascular Events. Cardiol. Rev. 31 , 293–298. 10.1097/crd.0000000000000438 (2023). Mitchell, J. D. et al. Impact of Statins on Cardiovascular Outcomes Following Coronary Artery Calcium Scoring. J. Am. Coll. Cardiol. 72 , 3233–3242. 10.1016/j.jacc.2018.09.051 (2018). Shahraki, M. N., Jouabadi, S. M., Bos, D., Stricker, B. H. & Ahmadizar, F. Statin Use and Coronary Artery Calcification: a Systematic Review and Meta-analysis of Observational Studies and Randomized Controlled Trials. Curr. Atheroscler. Rep. 25 , 769–784. 10.1007/s11883-023-01151-w (2023). Lee, M. S., Yang, T., Lasala, J. & Cox, D. Impact of coronary artery calcification in percutaneous coronary intervention with paclitaxel-eluting stents: Two-year clinical outcomes of paclitaxel-eluting stents in patients from the ARRIVE program. Catheterization Cardiovasc. interventions: official J. Soc. Cardiac Angiography Interventions . 88 , 891–897. 10.1002/ccd.26395 (2016). Zhao, Y., Wang, P., Zheng, Z., Shi, Y. & Liu, J. Comparison of intravascular lithotripsy versus rotational atherectomy for the treatment of severe coronary artery calcification. BMC Cardiovasc. Disord. 24 , 311. 10.1186/s12872-024-03965-1 (2024). Visinoni, Z. M. et al. Coronary intravascular lithotripsy for severe coronary artery calcification: The Disrupt CAD I-IV trials. Cardiovasc. revascularization medicine: including Mol. interventions . 65 , 81–87. 10.1016/j.carrev.2024.03.001 (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5921952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":408980683,"identity":"0dd53e02-f9c3-4eea-9294-1fb88a7166c4","order_by":0,"name":"Lin Zhao","email":"","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhao","suffix":""},{"id":408980684,"identity":"fa681dfd-c3a5-48d5-8418-d39343d12535","order_by":1,"name":"Liying Zheng","email":"","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Zheng","suffix":""},{"id":408980685,"identity":"740dfb30-3cf5-4900-8511-09dcd416e7ea","order_by":2,"name":"Xiao Gong","email":"","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Gong","suffix":""},{"id":408980686,"identity":"36cde276-c771-4f8a-8947-7ecefda3cdae","order_by":3,"name":"Senfu Han","email":"","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Senfu","middleName":"","lastName":"Han","suffix":""},{"id":408980687,"identity":"6e1511e6-712a-4d80-a108-d7912461f029","order_by":4,"name":"Leshun Liu","email":"","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leshun","middleName":"","lastName":"Liu","suffix":""},{"id":408980688,"identity":"ef59697b-8710-470b-95e2-dff13f747b12","order_by":5,"name":"Mei Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFADZuYDQNKCgYGHeC1sCQwMCRKkaGHgMSBOi8GNHDNp3jabaH52no+PeX9IyPHzHGD88DEHtxbJGTlmkjPb0nJnNvNuNuZJkDCW7G1glpy5DbcWfokcM4mPbYdzNxzm3SYN1JK44TwDGzMvHi1sIC2Jbf9z9x/meUacFqgtB3I3MPOwQbScbcCvRbLnWbHljHPJuTMOsxkbzkkD+qXnYDNevxgcT954m6fMLre///DDB29sbIAhlnzww0c8WhgEMkwk0IQYG/CoB3nm+OMP+FWMglEwCkbBiAcAj+lKh087fXsAAAAASUVORK5CYII=","orcid":"","institution":"Xiyuan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mei","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2025-01-29 05:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5921952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5921952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75408636,"identity":"96779e1d-a653-47dd-b722-513807bd6fc9","added_by":"auto","created_at":"2025-02-04 09:00:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":268658,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/1cee4e4d464c5de4afd4b583.png"},{"id":75412113,"identity":"bdbee0fc-f3c1-43ee-a085-f0eb7625786b","added_by":"auto","created_at":"2025-02-04 09:16:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136743,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/eea196a96ad21a349b1c64f7.png"},{"id":75410561,"identity":"34170a35-bcd6-4ef1-9ae4-9c736f6a342e","added_by":"auto","created_at":"2025-02-04 09:08:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":377186,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/bf60d7c0276ec5e22ef8db06.png"},{"id":75408647,"identity":"5d83b4de-376e-4a7d-a3d7-de37bf7712e1","added_by":"auto","created_at":"2025-02-04 09:00:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":438905,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/682f32ff7649e42f5b24974a.png"},{"id":75408651,"identity":"66992baf-3039-49b4-8923-11bee5a92e7d","added_by":"auto","created_at":"2025-02-04 09:00:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":384325,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/4aa9fb6f235fe26db36c146c.png"},{"id":75410559,"identity":"f84b3cd9-90e8-470c-9f36-c77b78976395","added_by":"auto","created_at":"2025-02-04 09:08:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216340,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/c67cefdf610805d3f15ea32b.png"},{"id":75408649,"identity":"d176ad3f-2a71-4e6d-91e7-c8fc9e1ad403","added_by":"auto","created_at":"2025-02-04 09:00:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":430760,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/5b20c5dd98b8c2d18221ce18.png"},{"id":76078943,"identity":"0c7fdc0d-7c85-49ca-97fc-bc26ab6b9210","added_by":"auto","created_at":"2025-02-12 06:16:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3007189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5921952/v1/8f0f848c-d947-4aa0-85a8-3a1954326061.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bibliometric and Visual Analysis on Coronary Artery Calcification from 2003 to 2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery calcification (CAC) is the process of calcium salt deposition in the walls of the coronary arteries and is considered a key feature of atherosclerotic lesions. CAC is a distinct manifestation of coronary artery disease (CAD) and typically arises during the development of atherosclerotic plaques \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The prevalence of CAC is widespread, with epidemiological data showing that approximately 50% of people aged 40 to 49 have evidence of coronary artery calcification, a figure that rises to 80% in individuals aged 60 to 69. The incidence of CAC significantly increases with advancing age \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The development of CAC is closely related to pathological changes in the intimal and medial layers of the coronary arteries. During atherosclerosis (AS) progression, low-density lipoprotein cholesterol (LDL-C) and other lipids accumulate in the inner layer of the arterial wall, provoking localized inflammation that leads to arterial wall thickening and plaque formation. As atherosclerosis progresses, these plaques undergo calcification, leading to CAC \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. CAC is an objective indicator of atherosclerotic burden, commonly assessed by computed tomography (CT). The coronary artery calcium score (CACS), frequently calculated using the Agatston scoring method, quantifies the extent of calcification. Numerous studies have found a strong association between CACS and the risk of CAD as well as other cardiovascular events. Furthermore, CAC is an independent predictor of cardiovascular events, particularly in asymptomatic individuals, aiding in the identification of high-risk patients who may not be identified by traditional risk assessments \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBibliometrics is a research method that uses quantitative analysis of publications (such as academic papers, books, and conference proceedings) and their citations to explore the development patterns and characteristics of scientific and technological literature \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. By statistically analyzing the number of publications, authors, sources, keywords, and citation relationships, bibliometrics helps to identify research hotspots and trends in academic fields \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. CAC is an important marker of AS and CAD, and its detection and evaluation are crucial for cardiovascular risk stratification and the development of personalized treatment strategies. Consequently, a significant number of high-quality studies and reviews on CAC have been published in recent years. However, there is currently a lack of bibliometric analysis regarding CAC research. Therefore, this study aims to provide a visual analysis of the research status, hotspots, and trends in CAC from 2003 to 2024, based on the literature published in this field.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Data Retrieval\u003c/h2\u003e \u003cp\u003eThis study retrieved all publications related to CAC from the Core Collection of the Web of Science database, covering the period from January 1, 2003, to August 31, 2024. The search strategy was as follows: ((((((((((TS=(Score)) OR TS=(Detection)) OR TS=(Measurement)) OR TS=(Risk Factor)) OR TS=(Clinical Significance)) OR TS=(Prognosis)) OR TS=(Imaging)) OR TS=(Cure)) OR TS=(Diagnosis)) OR TS=(Mechanism)) OR TS=(Evaluation) AND ((TS=(Coronary Artery Calcification)) OR TS=(Calcified Coronary Artery)) OR TS=(Coronary Artery Calcium)). The document types were limited to \"Article\" and \"Review Article,\" and the language was restricted to English. The search was completed within one day (August 31, 2024) to avoid bias from daily data updates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Inclusion Criteria\u003c/h3\u003e\n\u003cp\u003ePublications related to the topic of CAC.\u003c/p\u003e\n\u003ch3\u003e1.3 Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eDuplicate publications; conference papers, technical reports, and theses, publications lacking complete information on authors, institutions, or keywords.\u003c/p\u003e\n\u003ch3\u003e1.4 Data Collection and Conversion\u003c/h3\u003e\n\u003cp\u003eTitles, keywords, and abstracts were reviewed, and publications that met the inclusion and exclusion criteria were selected from the Core Collection of the Web of Science database. Eligible articles were exported in plain text format.\u003c/p\u003e\n\u003ch3\u003e1.5 Software Selection and Parameter Settings\u003c/h3\u003e\n\u003cp\u003eMicrosoft Excel was used for analyzing publication volume, publication trends, and journal contributions. VOSviewer software (version 1.6.20) was employed to visualize co-authorship networks, with adjustments made to the frequency thresholds for authors and keywords based on visualization clarity. The \"Linlog/modularity\" method was used, with default settings for clustering. Layout, scale, and labels were adjusted for aesthetic purposes, and the network visualization view was used to display co-authorship and keywords co-occurrence. Citespace software (version 6.2.R4) was used for analyzing institutions, keywords co-occurrence, keywords clustering (using the Likelihood Ratio, LLR algorithm), and keywords bursts. The time span was set from January 2003 to August 2024, with a time slice of one year. Other parameters (Text Processing, Links, and Selection Criteria) were kept at default settings. The research flow was shown in \u003cem\u003eFig.\u0026nbsp;1\u003c/em\u003e.\u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Literature Search Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 13,851 articles were identified. After excluding 10,619 articles that were unrelated to the topic of CAC and removing 163 duplicates, 3,069 articles were included in the final analysis. These comprised 2,840 original articles and 229 review articles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Analysis of Annual Publication Volume and Trends\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe annual volume of publications reflects the development trends and pace of research in this field. An analysis of the yearly publication volume was conducted using Excel, as shown in \u003cem\u003eFigure 2\u003c/em\u003e. Over the past 20 years, the number of studies on CAC has generally shown a steady increase, with a peak in publications observed from 2021 to 2023, averaging over 200 papers per year. This indicates a growing interest in CAC in recent years. The trendline suggests that the volume of publications in this field is likely to continue increasing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Visual Analysis of the Literature\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e2.3.1 Distribution of Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 15,437 authors have contributed to CAC-related research. The top 10 authors by publication volume are presented in \u003cem\u003eTable 1\u003c/em\u003e, with Budoff, Matthew J. being the most prolific author, having published 161 articles. Using VOSviewer, a co-authorship network analysis of core authors was conducted. The core authors were identified based on Price's law, where the minimum number of publications, n = 0. 749×Öηmax\u0026nbsp;(with ηmax being the number of publications by the most prolific author), was used to set the threshold. The minimum publication threshold for authors was set at 10, resulting in a co-authorship network map (\u003cem\u003eFigure 3\u003c/em\u003e). The map shows that the top six authors in terms of publication volume have closely collaborated, forming research teams primarily led by Budoff, Matthew J. and Nasir, Khurram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 Institutional Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,276 institutions have been involved in CAC-related research. The top 10 institutions by publication volume are presented in \u003cem\u003eTable 2\u003c/em\u003e, with Johns Hopkins University being the most prolific, having published 138 articles. A co-authorship network analysis of core institutions was performed using VOSviewer, setting a minimum publication threshold of 9. Based on this, a co-authorship network map of core institutions was constructed (\u003cem\u003eFigure 4\u003c/em\u003e). Notably, nine of the top ten institutions by publication volume are located in the United States, highlighting the leading position of the U.S. in current CAC-related research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 Keywords Analysis\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(1) Keywords Co-occurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeywords provide a concise summary of the main content of an article, helping readers quickly grasp the author’s viewpoint and the core content of the paper. Analyzing keywords co-occurrence networks can reveal the research hotspots and development directions in a field, with frequently occurring keywords reflecting the research focus during a specific period \u003csup\u003e[8]\u003c/sup\u003e. Citespace software was used to perform a keywords co-occurrence analysis on the included literature, and a keyword co-occurrence map was generated (\u003cem\u003eFigure 5\u003c/em\u003e). The top 15 keywords by occurrence frequency are presented in Table 3. The analysis of the results indicates that current research in this field mainly focuses on the quantitative assessment of CAC and the association between CAC progression and cardiovascular diseases. The color of the loops in the keywords network shows that research related to \"coronary artery disease\" and \"atherosclerosis\" has been a recent hotspot in this field. The connecting lines between the keywords suggest a strong co-occurrence relationship among them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Keywords Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further understand the research hotspots in this field over the past 20 years, we used Citespace software with the Log-Likelihood Ratio (LLR) algorithm to perform a keywords clustering analysis. The clustering analysis identified seven major clusters, as shown in \u003cem\u003eTable 4\u003c/em\u003e and \u003cem\u003eFigure 6\u003c/em\u003e. The results indicated that the Q value was 0.34 (greater than 0.3), and S was 0.68 (greater than 0.5), suggesting that the clustering results were reasonable. The top seven clusters were: #0 Vascular Calcification, #1 Risk Factors, #2 Metabolic Syndrome, #3 Computed Tomography, #4 Coronary Artery Disease, #5 Percutaneous Coronary Intervention, and #6 Impairment. The keywords clusters indicate that arterial calcification and coronary artery calcification scoring are key research hotspots in the field of CAC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Keywords Burst Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further analyze the research trends and frontiers in this field, a burst detection analysis of keywords was conducted, which helps in predicting future research directions. Burst detection provided by CiteSpace can identify burst keywords from the subject terms in the literature, clearly indicating the research frontiers and development trends of a discipline. \u003cem\u003eFigure 7\u003c/em\u003e presents the top 25 burst keywords in chronological order, along with their burst intensity and start and end periods. The red lines indicate the duration of each keywords burst. As shown in the figure, early CAC research focused on exploring methods for calcification detection, which is why early research keywords are mostly related to CT. As detection techniques have matured, the recent rise in the popularity of keywords such as \"management,\" \"outcome,\" and \"impact\" indicates a shift in the focus of CAC research towards risk assessment, patient management, and surgical interventions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Current Research Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted a comprehensive bibliometric analysis of journal articles related to CAC published over the past 20 years (2003-2024) in the WoS database. Using visualization software such as CiteSpace and VOSviewer, we created knowledge maps to illustrate the development status of the field, identify research hotspots, obtain frontier information, and predict potential trends.\u003c/p\u003e\n\u003cp\u003eBased on the annual publication volume of Chinese and English literature in this field, it is evident that research on CAC has shown an upward trend, increasing from 60 articles in 2003 to 213 in 2023. The presence and extent of CAC are generally closely related to the incidence and severity of CAD\u003csup\u003e\u0026nbsp;[1]\u003c/sup\u003e. Additionally, the occurrence of CAC is strongly associated with traditional cardiovascular risk factors such as hypertension, hyperlipidemia, diabetes, smoking, and family history\u003csup\u003e\u0026nbsp;[9]\u003c/sup\u003e. Therefore, CAC has become an important indicator for predicting the risk of cardiovascular events, such as myocardial infarction, angina, and cardiovascular death \u003csup\u003e[10]\u003c/sup\u003e, making it a current research hotspot.\u003c/p\u003e\n\u003cp\u003eAnalysis of author and institution distribution showed that 15,437 authors participated in research in this field, with Budoff, Matthew J. being the most prolific, with 161 articles. He is also the most cited scholar, with 12,419 citations, far exceeding other researchers. In 2023, he reviewed global CAC-related guidelines, discussing differences in CAC scoring ranges, risk cut-offs, treatment thresholds, and stratification of specific patient subgroups, contributing significantly to the establishment of global guidelines for CAC scoring and the prevention and management of CVD \u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Research Hotspots and Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the analysis of the top 25 keywords by frequency and the keywords clustering results, the research hotspots on CAC mainly focus on the following areas:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Advances in Imaging Techniques for CAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe development of imaging techniques for coronary artery calcification has evolved through several stages, from early conventional X-rays to modern Multi-Detector Computed Tomography (MDCT). The CAC score was initially assessed using Electron Beam Computed Tomography (EBCT)\u003csup\u003e\u0026nbsp;[12]\u003c/sup\u003e. With continuous advancements in imaging technology, CAC detection has become more precise and widespread. MDCT, Dual-Source CT (DSCT), and High-Resolution CT have significantly improved in terms of spatial resolution, image quality, and scanning time, becoming mainstream technologies. These imaging methods not only assess the degree of coronary artery stenosis but also accurately reflect the degree of calcification, aiding clinicians in more refined patient risk stratification and the development of personalized treatment plans \u003csup\u003e[13]\u003c/sup\u003e. High-resolution imaging technologies also help visualize extensive vascular calcification in small vessel lumens and distinguish between calcified and non-calcified plaque components, allowing for the identification of vulnerable plaques, which is crucial for the diagnosis and risk assessment of patients with high coronary artery calcification loads \u003csup\u003e[14]\u003c/sup\u003e.\u0026nbsp;In addition to non-invasive techniques such as CT, intravascular ultrasound (IVUS) and optical coherence tomography (OCT) performed during percutaneous coronary intervention (PCI) can more accurately assess the location, extent, and length of CAC. These intravascular imaging techniques have reduced the incidence of surgical adverse events and postoperative restenosis\u003csup\u003e\u0026nbsp;[15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Application of CAC in Cardiovascular Risk Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCACS is an important indicator for assessing coronary heart disease risk. Recent studies have mainly focused on the application of CACS in cardiovascular disease risk prediction, its comparison with other biomarkers, and its applicability in different populations. CACS is widely used for cardiovascular risk stratification in asymptomatic individuals. Multiple studies have shown that CACS can independently predict the risk of future cardiovascular events, especially in intermediate-risk populations, where CACS helps further refine risk assessment. Research indicates that a CACS of 0 is associated with a protective effect against the occurrence of cardiovascular events in CAD\u003csup\u003e\u0026nbsp;[16]\u003c/sup\u003e. Patients with an initial CACS of 0 have a markedly low risk of coronary artery and cardiovascular events within a 5-year period\u003csup\u003e\u0026nbsp;[17.18]\u003c/sup\u003e. Currently, several studies have established risk models for patients with a calcium score of 0, with the goal of accurately predicting the cardiovascular event risk in such CAD patients\u003csup\u003e\u0026nbsp;[19.20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA recent large cohort study showed a positive correlation between CACS and the occurrence of cardiovascular events, and CACS can improve the predictive ability of traditional risk scoring models \u003csup\u003e[21]\u003c/sup\u003e. Researchers have also focused on the differences in the applicability of CACS among different races, genders, and age groups. Some recent studies have suggested that the predictive power of CACS may vary among women, younger individuals, and certain races (e.g., Asians), requiring further research to optimize risk assessment strategies for these populations \u003csup\u003e[22.23]\u003c/sup\u003e. With the development of AI and machine learning, automated analysis and diagnostic techniques for CAC imaging have been widely applied. In recent years, AI algorithms, especially deep learning techniques, have been used to automatically detect and quantify CAC, effectively reducing human error and improving diagnostic accuracy and efficiency, potentially representing a future direction for CAC use in cardiovascular risk assessment\u003csup\u003e\u0026nbsp;[24]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Pathophysiological Mechanisms of CAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe occurrence of CAC is closely related to coronary atherosclerosis. In the late stages of atherosclerosis, the accumulation of foam cells and necrotic cell debris induces the migration and proliferation of smooth muscle cells while releasing pro-calcification factors such as Bone Morphogenetic Proteins (BMPs) and Matrix Vesicles \u003csup\u003e[25]\u003c/sup\u003e. These factors promote the transdifferentiation of vascular smooth muscle cells (VSMCs) into osteoblast-like cells by inducing osteogenic cell phenotype transformation. Transdifferentiated VSMCs begin to secrete bone matrix proteins (such as osteocalcin and osteopontin), thereby promoting calcium deposition \u003csup\u003e[26]\u003c/sup\u003e. Chronic inflammation plays a key role in the occurrence and progression of CAC. Macrophages and other immune cells release pro-inflammatory cytokines (such as IL-1, IL-6, and TNF-α), which not only exacerbate endothelial cell damage but also promote the osteogenic transdifferentiation process of VSMCs, further accelerating calcification \u003csup\u003e[27]\u003c/sup\u003e. In addition, inflammatory responses can enhance the expression of calcification factors by activating the NF-κB signaling pathway and oxidative stress pathways\u003csup\u003e\u0026nbsp;[28]\u003c/sup\u003e. Elevated Lp(a) and Osteoprotegerin levels have also been found to be associated with CAC occurrence \u003csup\u003e[29.30]\u003c/sup\u003e. The occurrence and progression of CAC are also influenced by gene and protein expression. Recent studies have found that upregulation of PCSK9 expression is associated with CAC development, and the use of PCSK9 inhibitors can effectively reduce the annual incidence of CAC \u003csup\u003e[31.32]\u003c/sup\u003e. Moreover, genetic studies suggest that certain specific gene variants (e.g., 9p21.3 locus) are associated with CAC occurrence\u003csup\u003e\u0026nbsp;[33]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.4 Interventions for CAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the management of coronary artery calcification, factors such as hypertension, hyperlipidemia, and smoking are associated with CAC occurrence, and improving related risk factors\u003csup\u003e\u0026nbsp;[34]\u003c/sup\u003e can reduce the progression of CAC. In recent years, multiple studies have evaluated the role of statins in CAC progression. Results have shown that statin use does not slow down CAC development and may even accelerate it. However, statin use is associated with a lower incidence of cardiovascular events, which may be related to its effects in reducing inflammation and stabilizing plaques \u003csup\u003e[35-37]\u003c/sup\u003e.\u0026nbsp;CAC often has a negative impact on post-procedural revascularization following PCI. Therefore, direct intervention of calcified areas during the procedure is of significant importance for improving patient outcomes. Commonly used interventional methods for calcified sites during PCI include intravascular lithotripsy (IVL) and rotational atherectomy\u003csup\u003e\u0026nbsp;[38]\u003c/sup\u003e. Some studies have indicated that for patients with severe coronary artery calcification, IVL is more effective in reducing the incidence of cardiovascular adverse events\u003csup\u003e\u0026nbsp;[39.40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.5 Future Research Directions for CAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn-depth research on CAC is significant for the prevention of cardiovascular diseases. Since the Agatston calcium score was developed in 1900, the diagnosis of CAC based on imaging techniques has become increasingly accurate. Future advancements may facilitate more precise measurement and quantification of CAC severity utilizing AI algorithms and deep learning technologies. Moreover, there has been continuous refinement of CAC scoring models adapted for patients with varying genders, regions, and ages. These advancements play a crucial guiding role in risk assessment and the formulation of prevention strategies for patients with coronary heart disease. Research into the mechanisms underlying CAC occurrence has progressed to protein and gene levels, and the identification of novel protein and gene targets is poised to provide new avenues for early screening and personalized treatment of CAC. The correlation between the morphology and location of calcified coronary artery plaques and the occurrence of coronary artery events, the appropriate preventive measures for asymptomatic coronary heart disease patients with different CAC scores, as well as research into the pathogenesis and heritability of CAC, may represent promising areas of future investigation.\u003c/p\u003e\n\u003cp\u003eIn conclusion, coronary artery calcification, serving as a vital tool for assessing cardiovascular disease risk, is increasingly recognized for its expanding applications and value in both clinical practice and research. Future research will continue to explore its pathophysiological mechanisms and intervention strategies to further improve patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Strengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides an objective and comprehensive analysis of the research status and development trends in this field over the past 20 years using bibliometric tools. It helps researchers understand recent developments in the field, grasp research hotspots, and clarify research directions. However, this study also has certain limitations. It only retrieved articles from the WoS database; although WoS is the most widely used retrieval database, it inevitably omits some articles. Additionally, bibliometric software only analyzes specific information and does not provide a comprehensive evaluation of the full text, which may result in missing some detailed information during visualization.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analyzed 3,069 publications on CAC from the WoS database, published between 2003 and 2024. The research hotspots in CAC primarily focus on early diagnosis and risk assessment, including the quantification of calcification scores using CT imaging techniques to predict cardiovascular event risks. Additionally, studies have explored the biological mechanisms of calcification and its relationship with atherosclerosis. Future trends may involve leveraging AI and machine learning to optimize imaging analysis, developing personalized treatment strategies, and identifying protein and gene targets related to calcification to enhance prevention and treatment outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e Competing interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMX, ZL, and LYZ designed the research and extracted relevant data. LSL and XG organized the data. SFH, and LSL rechecked the data. LZ and LYZ analyzed the data and wrote the original manuscript, X reviewed and revised the original manuscript. LYZ and LZ had equal contributions to the study. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study is supported by Major research project of scientific and technological innovation proiect of Chinese Academy Chinese Medicine sciences (No.C12021A00913), National Natural science Foundation of China (No. 81973686), and National Key Programme for Research and Development from the Ministry of Science and Technology, China (No. 2019YFC0840608) and Hospital capability enhancement project of Xiyuan Hospital, CACMS (NO.XYZX0201-018).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, Mei Xue, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGreenland, P., Blaha, M. J., Budoff, M. J., Erbel, R. \u0026amp; Watson, K. E. Coronary Calcium Score and Cardiovascular Risk. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 434\u0026ndash;447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2018.05.027\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2018.05.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWexler, L. et al. Coronary artery calcification: pathophysiology, epidemiology, imaging methods, and clinical implications. A statement for health professionals from the American Heart Association. \u003cem\u003eWrit. Group. Circulation\u003c/em\u003e. \u003cb\u003e94\u003c/b\u003e, 1175\u0026ndash;1192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.cir.94.5.1175\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.94.5.1175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnnis, C. et al. Coronary Artery Calcification: Current Concepts and Clinical Implications. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e, 251\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.123.065657\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.123.065657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasir, K. \u0026amp; Cainzos-Achirica, M. Role of coronary artery calcium score in the primary prevention of cardiovascular disease. \u003cem\u003eBMJ (Clinical Res. ed)\u003c/em\u003e. \u003cb\u003e373\u003c/b\u003e, n776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.n776\u003c/span\u003e\u003cspan address=\"10.1136/bmj.n776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, S. S. et al. Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e, 1768\u0026ndash;1777. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2023.7575\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.7575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasic, I. et al. The First Mediterranean Seminar on Science Writing, Editing and Publishing, Sarajevo, December 2\u0026ndash;3, Acta informatica medica: AIM : journal of the Society for Medical Informatics of Bosnia \u0026amp; Herzegovina : casopis Drustva za medicinsku informatiku BiH 24, 424\u0026ndash;435, (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5455/aim.2016.24.424-435\u003c/span\u003e\u003cspan address=\"10.5455/aim.2016.24.424-435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNinkov, A., Frank, J. R., Maggio, L. A. \u0026amp; Bibliometrics Methods for studying academic publishing. \u003cem\u003ePerspect. Med. Educ.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 173\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40037-021-00695-4\u003c/span\u003e\u003cspan address=\"10.1007/s40037-021-00695-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadhakrishnan, S., Erbis, S., Isaacs, J. A. \u0026amp; Kamarthi, S. Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, e0172778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0172778\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0172778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilverman, M. G. et al. Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: the Multi-Ethnic Study of Atherosclerosis. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 2232\u0026ndash;2241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/eht508\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/eht508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehker, A. \u0026amp; Mukherjee, D. Coronary Calcium Risk Score and Cardiovascular Risk. \u003cem\u003eCurr. Vasc. Pharmacol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 280\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1570161118666200403143518\u003c/span\u003e\u003cspan address=\"10.2174/1570161118666200403143518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolub, I. S. et al. Major Global Coronary Artery Calcium Guidelines. \u003cem\u003eJACC Cardiovasc. Imaging\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 98\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2022.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2022.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgatston, A. S. et al. Quantification of coronary artery calcium using ultrafast computed tomography. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 827\u0026ndash;832. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0735-1097(90)90282-t\u003c/span\u003e\u003cspan address=\"10.1016/0735-1097(90)90282-t\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne-Grinter, M., Ali, A. \u0026amp; Williams, M. C. Prevalence and clinical implications of coronary artery calcium scoring on non-gated thoracic computed tomography: a systematic review and meta-analysis. \u003cem\u003eEur. Radiol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 4459\u0026ndash;4474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-023-10439-z\u003c/span\u003e\u003cspan address=\"10.1007/s00330-023-10439-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMergen, V. et al. Ultra-High-Resolution Coronary CT Angiography With Photon-Counting Detector CT: Feasibility and Image Characterization. \u003cem\u003eInvest. Radiol.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 780\u0026ndash;788. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/rli.0000000000000897\u003c/span\u003e\u003cspan address=\"10.1097/rli.0000000000000897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTruesdell, A. G. et al. Intravascular Imaging During Percutaneous Coronary Intervention: JACC State-of-the-Art Review. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 590\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2022.11.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2022.11.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgha, A. M. et al. The Prognostic Value of CAC Zero Among Individuals Presenting With Chest Pain: A Meta-Analysis. \u003cem\u003eJACC Cardiovasc. Imaging\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1745\u0026ndash;1757. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2022.03.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2022.03.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C. L., Wu, Y. J., Yang, S. C. \u0026amp; Wu, F. Z. New look at the power of zero coronary artery calcium (CAC) in Asian population: a systemic review and meta-analysis. \u003cem\u003eCardiovasc. diagnosis therapy\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 377\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/cdt-23-474\u003c/span\u003e\u003cspan address=\"10.21037/cdt-23-474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehmann, N. et al. Value of Progression of Coronary Artery Calcification for Risk Prediction of Coronary and Cardiovascular Events: Result of the HNR Study (Heinz Nixdorf Recall). \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e, 665\u0026ndash;679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.116.027034\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.116.027034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Y. J., Mar, G. Y., Wu, M. T. \u0026amp; Wu, F. Z. A LASSO-Derived Risk Model for Subclinical CAC Progression in Asian Population With an Initial Score of Zero. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 619798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2020.619798\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2020.619798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, Y. W. et al. Natural course of coronary artery calcium progression in Asian population with an initial score of zero. \u003cem\u003eBMC Cardiovasc. Disord.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12872-020-01498-x\u003c/span\u003e\u003cspan address=\"10.1186/s12872-020-01498-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaha, M. J. et al. Comparing Risk Scores in the Prediction of Coronary and Cardiovascular Deaths: Coronary Artery Calcium Consortium. \u003cem\u003eJACC Cardiovasc. Imaging\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 411\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2019.12.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2019.12.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaikh, K., Nakanishi, R., Kim, N. \u0026amp; Budoff, M. J. Coronary artery calcification and ethnicity. \u003cem\u003eJ. Cardiovasc. Comput. Tomogr.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 353\u0026ndash;359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcct.2018.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jcct.2018.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcClelland, R. L., Chung, H., Detrano, R., Post, W. \u0026amp; Kronmal, R. A. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e113\u003c/b\u003e, 30\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.105.580696\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.105.580696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEng, D. et al. Automated coronary calcium scoring using deep learning with multicenter external validation. \u003cem\u003eNPJ Digit. Med.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41746-021-00460-1\u003c/span\u003e\u003cspan address=\"10.1038/s41746-021-00460-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J. S. \u0026amp; Hwang, H. S. Vascular Calcification in Chronic Kidney Disease: Distinct Features of Pathogenesis and Clinical Implication. \u003cem\u003eKorean circulation J.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 961\u0026ndash;982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4070/kcj.2021.0995\u003c/span\u003e\u003cspan address=\"10.4070/kcj.2021.0995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManiatis, K. et al. Osteoprotegerin and Osteopontin Serum Levels are Associated with Vascular Function and Inflammation in Coronary Artery Disease Patients. \u003cem\u003eCurr. Vasc. Pharmacol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 523\u0026ndash;530. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1570161117666191022095246\u003c/span\u003e\u003cspan address=\"10.2174/1570161117666191022095246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. Y., Lim, S. \u0026amp; Park, S. Role of Inflammation in Arterial Calcification. \u003cem\u003eKorean circulation J.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 114\u0026ndash;125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4070/kcj.2020.0517\u003c/span\u003e\u003cspan address=\"10.4070/kcj.2020.0517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemer, L. L. \u0026amp; Tintut, Y. Inflammatory, metabolic, and genetic mechanisms of vascular calcification. Arteriosclerosis, thrombosis, and vascular biology \u003cb\u003e34\u003c/b\u003e, 715\u0026ndash;723, (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/atvbaha.113.302070\u003c/span\u003e\u003cspan address=\"10.1161/atvbaha.113.302070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng, K. L. et al. Lipoprotein (a) and coronary artery calcification: prospective study assessing interactions with other risk factors. \u003cem\u003eMetab. Clin. Exp.\u003c/em\u003e \u003cb\u003e116\u003c/b\u003e, 154706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2021.154706\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2021.154706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamadi, S. et al. Prognostic role of osteoprotegerin and risk of coronary artery calcification: a systematic review and meta-analysis. \u003cem\u003eBiomark. Med.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 171\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/bmm-2022-0621\u003c/span\u003e\u003cspan address=\"10.2217/bmm-2022-0621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso, R. et al. PCSK9 and lipoprotein (a) levels are two predictors of coronary artery calcification in asymptomatic patients with familial hypercholesterolemia. \u003cem\u003eAtherosclerosis\u003c/em\u003e \u003cb\u003e254\u003c/b\u003e, 249\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2016.08.038\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2016.08.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, F. et al. Effect of Alirocumab on Coronary Calcification in Patients With Coronary Artery Disease. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 907662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2022.907662\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.907662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi, S. Y. et al. Genome-wide association study of coronary artery calcification in asymptomatic Korean populations. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, e0214370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0214370\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0214370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, C. et al. Cardiovascular Risk Factors and Coronary Calcification in a Middle-aged Dutch Population: The ImaLife Study. \u003cem\u003eJ. Thorac. Imaging.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 174\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/rti.0000000000000566\u003c/span\u003e\u003cspan address=\"10.1097/rti.0000000000000566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXinyu, Z. et al. Statins Accelerate Coronary Calcification and Reduce the Risk of Cardiovascular Events. \u003cem\u003eCardiol. Rev.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 293\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/crd.0000000000000438\u003c/span\u003e\u003cspan address=\"10.1097/crd.0000000000000438\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitchell, J. D. et al. Impact of Statins on Cardiovascular Outcomes Following Coronary Artery Calcium Scoring. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 3233\u0026ndash;3242. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2018.09.051\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2018.09.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahraki, M. N., Jouabadi, S. M., Bos, D., Stricker, B. H. \u0026amp; Ahmadizar, F. Statin Use and Coronary Artery Calcification: a Systematic Review and Meta-analysis of Observational Studies and Randomized Controlled Trials. \u003cem\u003eCurr. Atheroscler. Rep.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 769\u0026ndash;784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11883-023-01151-w\u003c/span\u003e\u003cspan address=\"10.1007/s11883-023-01151-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, M. S., Yang, T., Lasala, J. \u0026amp; Cox, D. Impact of coronary artery calcification in percutaneous coronary intervention with paclitaxel-eluting stents: Two-year clinical outcomes of paclitaxel-eluting stents in patients from the ARRIVE program. \u003cem\u003eCatheterization Cardiovasc. interventions: official J. Soc. Cardiac Angiography Interventions\u003c/em\u003e. \u003cb\u003e88\u003c/b\u003e, 891\u0026ndash;897. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ccd.26395\u003c/span\u003e\u003cspan address=\"10.1002/ccd.26395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., Wang, P., Zheng, Z., Shi, Y. \u0026amp; Liu, J. Comparison of intravascular lithotripsy versus rotational atherectomy for the treatment of severe coronary artery calcification. \u003cem\u003eBMC Cardiovasc. Disord.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12872-024-03965-1\u003c/span\u003e\u003cspan address=\"10.1186/s12872-024-03965-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisinoni, Z. M. et al. Coronary intravascular lithotripsy for severe coronary artery calcification: The Disrupt CAD I-IV trials. \u003cem\u003eCardiovasc. revascularization medicine: including Mol. interventions\u003c/em\u003e. \u003cb\u003e65\u003c/b\u003e, 81\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.carrev.2024.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.carrev.2024.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coronary artery calcification, bibliometrics, CiteSpace, VOSviewer, visual analytics","lastPublishedDoi":"10.21203/rs.3.rs-5921952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5921952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study utilized the Web of Science core collection database to retrieve all CAC-related publications from January 1, 2003, to August 31, 2024. Microsoft Excel was used to analyze publication volume, trends, and journal publication volume. VOSviewer software was applied to visualize the collaboration network among authors, while Citespace software was used for analyzing institutions, keywords co-occurrence, keywords clustering, and keywords burst detection. The study collected 3,069 publications on CAC from the Web of Science database between 2003 and 2024. Budoff, Matthew J. was identified as the most prolific author, with 161 articles. Nine of the top ten institutions in terms of publication volume located in the U.S., indicating that the U.S. is leading in this research area. Current research on CAC focuses on quantitative assessment and its association with the progression of cardiovascular diseases, particularly studies related to coronary artery disease and atherosclerosis, which have emerged as research hotspots in recent years. Research on coronary artery calcification has centered on early diagnosis and risk assessment, especially through quantification of calcification scores using CT imaging to predict cardiovascular event risk. Furthermore, there is a focus on the biological mechanisms of calcification and its relationship with atherosclerosis.\u003c/p\u003e","manuscriptTitle":"Bibliometric and Visual Analysis on Coronary Artery Calcification from 2003 to 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 09:00:27","doi":"10.21203/rs.3.rs-5921952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ec520e7-15e7-4e08-b2a6-6ce5795838b2","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43609989,"name":"Health sciences/Cardiology/Cardiovascular biology/Calcification"},{"id":43609990,"name":"Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases"}],"tags":[],"updatedAt":"2025-02-12T06:08:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-04 09:00:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5921952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5921952","identity":"rs-5921952","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}