What this is
- This analysis explores trends in research on circadian rhythms in cardiovascular diseases (CVD) from 2002 to 2022.
- It identifies key research areas, prolific authors, and influential journals in this field.
- The findings suggest a growing interest in how circadian rhythms impact cardiovascular health.
Essence
- The study reveals a significant increase in publications on circadian rhythms in CVD, highlighting inflammation, atherosclerosis, and myocardial infarction as key research areas. The United States leads in research output and citations.
Key takeaways
- The number of publications on circadian rhythms in CVD has increased annually, indicating growing research interest. The United States is the most prolific country, contributing significantly to the literature.
- Key research hotspots include inflammation, atherosclerosis, and myocardial infarction, which are critical for understanding the role of circadian rhythms in CVD. These areas may inform future therapeutic strategies.
Caveats
- The analysis is limited to English literature and data from the Web of Science Core Collection, potentially introducing bias and excluding relevant studies from other databases.
- Reliance on machine algorithms for data selection may overlook emerging research areas, affecting the comprehensiveness of the findings.
Definitions
- circadian rhythm: Biological processes that follow a roughly 24-hour cycle, influencing sleep, hormone secretion, and other physiological functions.
- bibliometric analysis: A quantitative method used to analyze academic publications, providing insights into research trends and productivity.
Simplified
Introduction
Circadian rhythms are fundamental to the biology of almost all life on the planet [1]. These rhythms are driven by an internal biological clock, which is known as the circadian clock. The circadian clock is responsible for regulating a wide range of physiological processes that occur on a daily basis, including sleep–wake cycles [2], hormone secretion [3, 4], metabolism [5, 6], Immune system [7], and gene expression [8–10]. In many organisms, including humans, the circadian clock is located in the suprachiasmatic nucleus of the hypothalamus [11]. The suprachiasmatic nucleus receives input from the eyes, which allows it to synchronize the circadian clock to the external light–dark cycle. This synchronization ensures that the circadian rhythms remain aligned with the environmental cycles and are able to provide maximum survival and competitive advantage. When the body’s natural circadian rhythm is disrupted, it can lead to a range of health problems. Research has shown that disturbances to circadian rhythmicity can increase the likelihood of acute myocardial infarction [12–14], stroke [15–17], arrhythmias [18–20], and other unfavorable cardiovascular events. In addition, the circadian rhythm is recognized to affect several cardiovascular events, including endothelial function [21–23], thrombus formation [24, 25], blood pressure [26, 27], and heart rate [28]. Thus, it is not surprising that incidence of adverse cardiovascular events fluctuates depending on the time of day given the diurnal regulation. Myocardial infarctions are more likely to occur in the early morning than at night [29]. This matutinal clustering is also seen in the frequency of strokes, arrhythmias, and sudden cardiac death, as well as the rupture of abdominal aortic aneurysms [30–32].
Bibliometrics is a method for analyzing publications qualitatively and quantitatively. This method allows researchers to gain immediate insight into the thematic evolution, primary study domains, and future research paths in a certain research field [33]. Bibliometrics is now frequently employed as an auxiliary research tool in a wide range of subjects. However, there are few bibliometric studies on circadian rhythm in CVD.
In this study, we utilized bibliometric approaches to evaluate the research state, present research emphasis, and develop research trends in the field of circadian rhythm in CVD during the last two decades, highlighting potential avenues for future research.
Materials and methods
Data source and search strategy

The flowchart illustrates the search formula and selection process in this study. Formula: TS = (“Circadian Rhythm” OR “Diurnal Rhythm” OR “Twenty Four Hour Rhythm” OR “Circadian Clocks” OR “Clock System”) AND TS = (cardiovascular OR heart)
Eligibility criteria and data collection
The document types were only articles and reviews. Duplicate studies were removed. All the information, including the number of papers and citations, publication year, titles, authors, affiliations, keywords, countries, journals, and references, was collected for bibliometric analysis.
Statistical analysis
In this study, VOSviewer (version 1.6.18), HistCite (version 12.03.17), and CiteSpace (version 5.8.R3) were used to perform the bibliometric analysis.
VOSviewer was used to visualize complex co-citation networks [34], such as the collaboration and time trends among countries, institutions, and individuals. The size of the nodes represents the number of publications; the thickness of the line represents the strength of the link, and the colors of the nodes represent different clusters or times.
HistCite was utilized to determine the total number of publication records, total global citation score (TGCS), and total local citation score (TLCS) for each publishing year, active nations, top institutions, core journals, and authors. The TLCS refers to the number of times an article is cited in the current database, which means attracting attention from the same field. Therefore, TLCS is used to rank the authors and journals. More crucially, it was used to find the sample citation routes of relevant references [35].
CiteSpace was used to assess the visual study of the knowledge domain and developing trends [36], such as cluster analysis, dual-map citation overlay, timeline or time zone views, references, and keywords citation bursts [37]. Cluster analysis may be used to categorize references and keywords as well as highlight interesting study topics on circadian rhythmicity in CVD. In cluster analysis, the modularity Q and mean silhouette are two key assessment metrics. Q greater than 0.3 implies that the clustering structure is substantial. The presence of a mean silhouette greater than 0.5 shows that the clustering results are credible. Keywords and references bursts are frequently employed to discover new research trends in the subject [38]. It has two interpretations: (1) in keyword analysis, it represents the frequency of words or phrases used in the citing document. (2) In co-cited references analysis, it represents the frequency of citations received by the cited document.
Results
Overall distribution

Overall distribution of annual output trends and growth rate on circadian rhythm in CVD
Countries and regions

Leading countries related to circadian rhythms in CVD research.Geographical distribution of global output;pie chart of the countries;visual cluster analysis of countries;publication number by country over time A B C D
| Rank | Country | Publications ()n | TLCS | TGCS | Average citation | Centrality |
|---|---|---|---|---|---|---|
| 1 | USA | 619 | 1928 | 28,803 | 46.53 | 0.72 |
| 2 | Japan | 275 | 688 | 7258 | 26.39 | 0.06 |
| 3 | China | 209 | 203 | 2823 | 13.51 | 0.2 |
| 4 | Italy | 165 | 426 | 5599 | 33.93 | 0.09 |
| 5 | UK | 159 | 197 | 4274 | 26.88 | 0.19 |
| 6 | Germany | 136 | 460 | 4460 | 32.79 | 0.08 |
| 7 | Spain | 105 | 206 | 3268 | 31.12 | 0.01 |
| 8 | Netherlands | 92 | 103 | 3269 | 35.53 | 0.15 |
| 9 | France | 86 | 336 | 3041 | 35.36 | 0.18 |
| 10 | Canada | 84 | 90 | 2014 | 23.98 | 0.04 |
Institutions and authors
Through TLCS data, we discovered that the study of Universidade de Vigo and its researchers piqued the interest of a large number of scholars. But Fig. 4A shows that the link lines between Universidade de Vigo and other universities were insufficient and sparse, implying that Universidade de Vigo lacked collaboration with others. This result seems inharmonious, implying that there may be some concealed bias in Vigo University’s work. Their work, for example, may lack independent source data validation from non-host universities [39]. The visualization analysis of the author is shown in Fig. 4B. It is apparent that collaboration between authors from other institutions is lacking, and they prefer to collaborate inside their affiliations.

Visualization of institutes and authors collaboration analysis. (–, respectively) Cluster analysis of institutes and authors’ network A B
| Rank | Institution | Country | Publication counts | TGCS | TLCS | Average citation |
|---|---|---|---|---|---|---|
| 1 | Harvard Univ | USA | 39 | 4244 | 307 | 108.82 |
| 2 | Univ. Tokyo | Japan | 35 | 929 | 172 | 26.54 |
| 3 | Univ. Alabama Birmingham | USA | 33 | 955 | 150 | 28.94 |
| 4 | Univ. Ferrara | Italy | 31 | 1766 | 323 | 56.97 |
| 5 | Univ. Vigo | Spain | 31 | 1652 | 361 | 53.29 |
| 6 | Brigham & Womens Hosp. | USA | 29 | 1209 | 191 | 41.69 |
| 7 | Univ. Minnesota | USA | 29 | 362 | 34 | 12.48 |
| 8 | Harvard Med. Sch | USA | 26 | 229 | 13 | 8.81 |
| 9 | Univ. Penn | USA | 26 | 1341 | 223 | 51.58 |
| 10 | Univ. Copenhagen | Denmark | 25 | 681 | 41 | 27.24 |
| Rank | Name | Country | Affiliation | Counts | TLCS | TGCS | H-index |
|---|---|---|---|---|---|---|---|
| 1 | Hermida RC | Spain | Universidade de Vigo | 34 | 398 | 1797 | 56 |
| 2 | Ayala DE | Spain | Universidade de Vigo | 24 | 287 | 1279 | 51 |
| 3 | Portaluppi F | Italy | University of Ferrara | 19 | 271 | 1376 | 58 |
| 4 | Young ME | USA | University of Alabama Birmingham | 25 | 255 | 1032 | 58 |
| 5 | FitzGerald GA | USA | University of Pennsylvania | 6 | 217 | 661 | 114 |
| 6 | Mojon A | Spain | Universidade de Vigo | 19 | 215 | 933 | 42 |
| 7 | Fernandez JR | Spain | Universidade de Vigo | 19 | 209 | 948 | 55 |
| 8 | Smolensky MH | USA | University of Texas Austin | 21 | 196 | 1062 | 47 |
| 9 | Scheer FAJL | USA | Harvard Medical School | 19 | 175 | 1118 | 58 |
| 10 | Martino TA | Canada | University of Guelph | 11 | 166 | 728 | 28 |
Journals

The dual-map overlay shows the distribution of publications related to circadian rhythm research in CVD. This graphical representation consists of major clusters, which are denoted by terms extracted from the titles of journals in the corresponding clusters. The journal in which a source article is published is called a “citing journal”. The journal in which a reference is published is called a “cited journal”. Notably, each spline curve depicted in the overlay originates from a citing journal located on the left-hand side of the base map and terminates at a cited journal situated on the right-hand side of the base map
| Rank | Source | Publications ()n | TLCS | TGCS | Average citation | 2021 IF |
|---|---|---|---|---|---|---|
| 1 | Chronobiology International | 135 | 467 | 3371 | 24.97 | 3.749 |
| 2 | Circulation | 19 | 327 | 2031 | 106.89 | 39.922 |
| 3 | Circulation Research | 12 | 226 | 1547 | 128.92 | 23.218 |
| 4 | Hypertension | 29 | 164 | 2615 | 90.17 | 9.897 |
| 5 | American Journal of Physiology-heart and Circulatory Physiology | 27 | 138 | 1197 | 44.33 | 5.125 |
| 6 | Proceedings of The National Academy of Sciences of The United States of America | 11 | 108 | 1173 | 106.64 | 12.779 |
| 7 | Nature | 2 | 98 | 1580 | 790 | 69.504 |
| 8 | Sleep Medicine Reviews | 15 | 97 | 1366 | 91.07 | 11.401 |
| 9 | Hypertension Research | 32 | 93 | 739 | 23.09 | 5.525 |
| 10 | Journal of Hypertension | 21 | 87 | 702 | 33.43 | 4.776 |
Analysis of keywords

Visualization of keyword analysis.The word cloud.Timeline distribution of cluster analysis of keywords.The top 25 keywords with the strongest bursts A B C
| Cluster ID | Size | Silhouette | Mean year | Top terms | Log (likelihood ratio) |
|---|---|---|---|---|---|
| #0 | 153 | 0.59 | 2012 | Sleep quality | 2374.76 |
| #1 | 136 | 0.7 | 2009 | Circadian clock | 2687.78 |
| #2 | 124 | 0.69 | 2009 | Ambulatory blood pressure | 4084.14 |
| #3 | 120 | 0.59 | 2009 | Heart rate variability | 1731.1 |
| #4 | 51 | 0.82 | 2013 | Cardiac troponin | 1599.12 |
| #5 | 44 | 0.74 | 2006 | Stress-induced change | 439.02 |
Co-cited references

Visualization of co-cited reference analysis.Cluster analysis of co-cited references.Timeline distribution of the top nine clusters.The top 25 references with the strongest citation bursts A B C
| Rank | First author | Journal | Year | 2021 IF | Cluster | TGCS | TLCS |
|---|---|---|---|---|---|---|---|
| 1 | Storch KF | Nature | 2002 | 69.504 | #3 | 1122 | 91 |
| 2 | Curtis AM | Proceedings of The National Academy of Sciences of The United States of America | 2007 | 12.779 | #3 | 245 | 80 |
| 3 | Viswambharan H | Circulation | 2007 | 39.922 | #1 | 145 | 53 |
| 4 | Durgan DJ | American Journal of Physiology-heart and Circulatory Physiology | 2005 | 5.125 | #3 | 115 | 51 |
| 5 | Durgan DJ | Circulation Research | 2010 | 23.218 | #1 | 178 | 48 |
| 6 | Guo YF | American Heart Journal | 2003 | 5.099 | #3 | 111 | 46 |
| 7 | Anea CB | Circulation | 2009 | 39.922 | #1 | 178 | 45 |
| 8 | Portaluppi F | Sleep Medicine Reviews | 2012 | 11.401 | #1 | 169 | 42 |
| 9 | Otto ME | Circulation | 2004 | 39.922 | #5 | 186 | 40 |
| 10 | Westgate EJ | Circulation | 2008 | 39.922 | #1 | 95 | 39 |
| Cluster ID | Size | Silhouette | Mean year | Top term | Log (likelihood ratio) |
|---|---|---|---|---|---|
| #0 | 205 | 0.879 | 2016 | Inflammation | 19.75 |
| #1 | 118 | 0.898 | 2006 | Endothelium | 16.75 |
| #2 | 87 | 0.928 | 2014 | Myocardial infarction | 10.89 |
| #3 | 83 | 0.961 | 2001 | Peripheral oscillators | 17.92 |
| #4 | 73 | 0.924 | 2011 | Metabolism | 8.01 |
| #5 | 72 | 0.971 | 2003 | Valsartan | 11.99 |
| #6 | 69 | 0.977 | 2010 | Ambulatory blood pressure monitoring | 43.83 |
| #7 | 53 | 0.97 | 1998 | Hormones | 7.75 |
| #8 | 49 | 0.924 | 2007 | Melatonin | 20.66 |
Discussion
This study used multiple softwares to perform a bibliometric analysis of the global scientific outputs published related to the circadian rhythm in CVD from 2002 to 2022. The records from the WoSCC were examined from a variety of perspectives, with the results provided in tables and knowledge network maps. The results revealed that annual publications on circadian rhythm in CVD are on the rise. These studies prompt that circadian rhythm has taken an important role in the research of CVD.
The United States is at the forefront of promoting research on circadian rhythms in CVD, with six of the top 10 most productive institutions based there. Harvard University, which published 39 articles, cited 4244 times, was the main representative, being the most cited institution [41]. Hermida et al. from Universidade de Vigo contributed with most of the publications (published 34 articles, cited 1797 times), and with a long-term research focused on chronotherapy and risk of cardiac and cardiovascular risk [43, 44]. Simultaneously, a clinical trial from this group showed a provocative result, the authors reported that giving anti-hypertensives in the evening rather than the morning reduced the incidence of major cardiovascular events by 45% [45]. But as we found in the visual analysis, the existing data are not enough to support the large-scale promotion of their projects in clinical practice [46]. Concurrently, this seemingly ideal finding has garnered significant attention and, thus, has been subject to scrutiny [47].
Chronobiology International, in particular, had by far the highest number of articles published and citations among the top 10 core journals, indicating that this journal was the most popular journal for scholars who studied the field of circadian rhythm in CVD. In recent years, Chronobiology International has mainly focused on research exploring the relationship between circadian rhythm and diseases. For example, a recent article published in Chronobiology International found that in adults, a blunted rest-activity circadian rhythm is linked to higher white blood cell-based inflammatory indices, implying that lifestyle interventions aimed at restoring circadian rhythm could be a unique way to promote overall health [48]. In terms of several publications, Hypertension Research ranked second among the top 10 journals. This demonstrated that it is also appealing to researchers in this field. Hypertension Research has mainly focused on research discussing the correlation between hypertension and circadian rhythms. A recent study published in Hypertension Research discovered that increased overnight systolic blood pressure adds to the influence of high NT-proBNP levels on the risk of CVD [49]. Molecular biology and medicine are two interrelated fields that share an interest in investigating the fundamental biological mechanisms of life. Molecular biology explores the underlying molecular processes that drive biological activity, while medicine applies scientific knowledge to the prevention, diagnosis, and treatment of disease. According to the result of the dual-map overlay, the significant interest in investigating the link between circadian rhythms and cardiovascular health among researchers in the fields of molecular biology and medicine is understandable. It highlights the fact that researchers are actively exploring the molecular mechanisms that underlie this connection and are striving to develop new interventions and treatments for CVD based on circadian biology [50].
Following the cluster analysis of co-cited references, “hormones” was identified as an early hotspot in the research of circadian rhythms in CVD. After retrieving the publications in this cluster (#7), a study with the highest TGCS on the relationship between leptin and sleep was located, which demonstrated that sleep modulation is a major element of the neuroendocrine control of appetite [51]. Subsequently, Scheer et al. discovered that taking melatonin regularly lowered systolic and diastolic blood pressure during sleep [52]. Similarly, Tutuncu et al. discovered a negative correlation between nocturnal melatonin levels and the degree of nocturnal systolic blood pressure drop [53]. In addition, “secretion” had the strongest burst strength and longest bursts times in the early years, so we retrieved it from the database and found that almost all of the highest TGCS research had a focus on the relationship between the circadian rhythms and hormones—such as leptin [54], melatonin [55], cortisol [56], insulin [57] and oxytocin [58]. Therefore, it is revealed that the studies frequently focused on the influence of circadian rhythm on hormone levels in the early stage of this field.
With the advancement of circadian rhythms in CVD, certain developing study domains are progressively gaining attention from researchers. It is well known that atherosclerosis involves an ongoing inflammatory response [59], and coronary atherosclerosis is the main cause of myocardial infarction [60]. This also explains why the timeline view of references showed that “myocardial infarction” and “inflammation” have attracted the attention of scholars in recent years. According to representative burst references, a clinical study demonstrated that circadian misalignment per se increases blood pressure and inflammatory markers, which will increase hypertension, inflammation, and the risk of CVD [42]. Therefore, circadian rhythmicity appears to have an important role in atherosclerosis by affecting inflammatory processes underlying atherosclerosis, according to growing research [61–65]. All together, these findings suggest that circadian rhythmicity is becoming increasingly important in CVD. Based on these recent discoveries, it is evident that circadian rhythmicity is already a viable target for therapeutic strategies in CVD [66–68]. There have been inconsistencies in clinical evidence of circadian-based therapies for CVD, however, the recent TIME trial suggested that chronotherapy has no negative outcomes [69].
In conclusion, this bibliometric analysis has provided an overview of the research landscape related to circadian rhythm in CVD from 2002 to 2022. The findings reveal the importance of circadian rhythmicity in CVD research and highlight the potential for incorporating it into therapeutic strategies. Furthermore, researchers should focus on investigating how to effectively integrate circadian rhythm as a therapeutic treatment in clinical applications, making it more affordable and accessible for patients. By addressing these limitations and focusing on the potential benefits of circadian rhythmicity in CVD, our study contributes to the ongoing efforts to understand and develop novel therapeutic strategies in the field of CVD.
Limitation
Although bibliometric analysis provides greater insight into research topics and trends than traditional assessments, it has several limitations. To begin with, this article excludes non-English literature, which might introduce bias. Additionally, the data used in this study were obtained solely from the WoSCC database due to the reliability of the publications and citations. We cannot perform relevant analyses, such as co-citation analysis, on PubMed or other databases due to software limitations (lack of information on the references), which further contributes to the study bias. Consequently, the local dataset we downloaded from the WoSCC may have fewer articles and journals compared to other databases like PubMed, resulting in less comprehensive study findings.
Moreover, we examined the characteristics of the data we collected to highlight the most important aspects. As a result, some information may be overlooked. Lastly, most of the results in this study are based on a machine algorithm, which significantly lacks human selection. (For example, the selection of terms in cluster analysis will cause readers a certain degree of confusion.) Due to the sensitivity of machine algorithms, several emerging research areas related to circadian rhythmicity in CVD may not have been included.