Trends and hotspots in familial hypercholesterolemia: A bibliometric systematic review from 2002 to 2022

Background:

We visually assessed the research hotspots of familial hypercholesterolemia (FH) using bibliometrics and knowledge mapping in light of the research state and development trend of FH.

Methods:

We employed bibliometric tools, such as CiteSpace and the alluvial generator, to illustrate the scientific accomplishments on FH by extracting pertinent literature on FH from the Web of Science Core Collection database from January 1, 2002, to December 31, 2022.

Results:

A total of 4402 papers in total were selected for study; 29.2% of all articles globally were from the USA, followed by the Netherlands and England. The University of Amsterdam, University of Oslo, and University of Western Australia are the 3 institutions with the most publications in this area. Gerald F. Watts, Raul D. Santos, and John J. P. Kastelein wrote the majority of the pieces that were published. The New England Journal of Medicine, Circulation, and Atherosclerosis were the journals with the greatest number of papers in this field. Prevalence and genetic analysis of FH, proprotein convertase subtilisin/kexin 9 inhibitors, and inclisiran are current research hotspots for the condition. Future research in this area will be focused on gene therapy.

Conclusions:

FH research has shown shows a trend of ascending followed by leveling off. The prevalence and diagnosis of FH, proprotein convertase subtilisin/kexin 9 inhibitors, inclisiran, and gene therapy are current research hotspots. This report may serve as a reference for current research trends.

1. Introduction

The most clinically significant and treatable monogenic defect is familial hypercholesterolemia (FH), a co-dominant and highly penetrable condition that affects the liver’s ability to clear low-density lipoprotein (LDL) via the LDL receptor, resulting in a classic phenotype that includes early atherosclerotic cardiovascular disease.^[1–3] The availability of novel biological therapeutics and advancements in diagnostic gene technologies have changed perceptions of FH and made it an example of how precision medicine may be used to prevent early cardiovascular disease in families in our communities. If untreated, this rather frequent co-dominant condition causes early coronary events because it represents the cumulative impact of increased LDL cholesterol (LDL-C) levels from birth on the formation of atherosclerosis.^[1,4,5] FH is still not well identified or treated,^[6,7] but clinical researchers and scientists throughout the world are now actively addressing this care gap.^[4–6,8,9]

Researchers’ exceptional performance in FH has substantially improved over the last ten years. However, these studies have not been measured systematically. With the aid of bibliometrics analysis software, a review of this literature was conducted. We hope that there were significant findings in our study that could be helpful for the advancement of FH research and that we could offer some advice and inspiration to the scientists working on FH studies and treatments.

2. Materials and methods

We used CiteSpace 6.1.R6 (64-bit) Advanced ((c) 2003-2023 Chaomei Chen. All rights reserved), Alluvial Generator (reduced by Daniel Edler, Anton Holmgren Coding & ideas, et al researchers and developers at Umeå University with a background in physics), and Microsoft Excel (2021MSO) (Microsoft Corporation) to analyze research trends, references, and keywords analysis of FH (Fig. 1). No ethical approval was required for this systematic review.

Figure 1.

Flow diagram of the publication screening process and methodologies for bibliometric analysis. SCI = Science Citation Index, SSCI = Social Science Citation Index.

2.1. Data source and search strategy

On January 7, 2023, literature was downloaded within a day from the Web of Science Core Collection (WoSCC) database. The search terms were as follows: TS = (“*amilial *ypercholesterol*mia “), and the dates of the search were January 1, 2002, to December 31, 2022, resulting in 9365 records. We chose a time period from 2002 to 2022 since the literature on FH has grown explosively in the last 20 years compared to the preceding 20 years, and to better highlight the advancement of FH research. Publications from the Social Science Citation Index and the Science Citation Index Expanded were chosen, as indicated in Figure 1. In this analysis, only original English-language articles were taken into account.

A total of 4402 original articles were included, and there were no duplicates after being examined by CiteSpace. CiteSpace and Alluvial Generator were used to further analyze these papers. The data were chosen and independently recorded by 2 writers, L.C. and H.P. All differences were discussed until an agreement was reached.

2.2. Bibliometric analysis and software assistance

In this study, using the CiteSpace v.5.8 R3 (64-bit) application, a knowledge map of FH’s research status and hotspots was constructed. CiteSpace is a Java-based information visualization program created by Professor Chao-Mei Chen. In bibliographic databases, visual exploration and knowledge discovery are supported by CiteSpace. It provides a visual mapping tool for a wide range of users to examine areas of expertise and the establishment of research themes inside knowledge domains, as well as to find new patterns and trends in the body of scientific literature.^[10,11]

CiteSpace generates visual graphs with nodes and lines. The frequency is represented by the node’s size. The strength of collaboration is also represented by the thickness of the link between the nodes; the thicker the connection, the greater the cooperation. Centrality is a network parameter that measures the importance of nodes. The greater the centrality larger than 0.1, the more important the node. The higher a node’s centrality, the more frequently it communicates with other nodes and the more significant it is in the overall network. In the CiteSpace network, nodes with centrality >0.1 are referred to as key nodes. Additionally, Purple circles are commonly used to signify nodes with betweenness centrality >0.1. The more centrality a node has, the more helpful it is to other nodes. The log-likelihood ratio technique was selected to group related terms and references. There are numerous closely related terms in each cluster. A cluster has more references or keywords if there are fewer cluster labels. The silhouette (S) value refers to the cluster’s average contour value. In general, it is believed that the cluster is acceptable with a S > 0.5 and compelling with a S > 0.7.^[12]

CiteSpace’s particular settings were set for this investigation as follows: Time Slicing: from January 1, 2002, to December 31, 2022; Years Per Slice: 3; Term Source: Title, Abstract, Author, Keywords, and Keyword Plus; Node Types: Author, Institution, Country, Keyword, Reference, Cited Author, and Cited Journal; selection criteria: Top N = 50.

An alluvial flow map depicting the evolution of co-cited papers over the previous 5 years was created using the alluvial generator (2018–2022). The web-based program MapEquation’s alluvial flow diagram illustrates how the network changes over time, which obviates the propensity of fields. The intellectual structure of a certain topic can be built numerically and graphically for assessing literature performance, identifying fundamental issues, and resolving disciplinary quandaries by combining bibliometrics with data visualization. The publications’ modules that have been mentioned throughout the course of these 5 years in a row are colored, signifying that they have attracted a lot of interest during that period. Microsoft Excel was used to create tables, rose charts, and show annual national trends in publications.

3. Results

3.1. Annual publications and trends

There were 4402 publications on FH in the WoSCC database’s Science Citation Index Expanded and Social Science Citation Index from 2002 to 2022. According to Figure 2, the number of publications on FH increased gradually between 2002 and 2018 until it reached 341 pieces. In the next 4 years, there was a little decline in the number of publications, although they still reached a high level.

Figure 2.

The number of annual publications on FH research between 2002 and 2022. The year of publishing is indicated by the horizontal coordinates. The number of publications is shown by the vertical coordinates. FH = familial hypercholesterolemia.

3.2. Analysis of countries, institutions, and authors

The analysis of the worldwide collaboration network is displayed in Figure 3A to help comprehend the contribution made by each nation to the FH research area. The USA (1284, 29.2%) had the most publications, followed by Netherlands (480, 10.9%), England (440, 10.0%), Canada (376, 8.5%), and China (365, 8.3%) (Fig. 3B). Regarding the centrality of countries, Belgium (0.60) ranked first, followed by Argentina (0.58), Hungary (0.53), Singapore (0.43), and Israel (0.41) (Table 1). The USA was a significant FH research nation, and it collaborated closely with France in this area. Scholars can find a base for looking for partnering institutions while doing research by understanding the global distribution of research institutions studying FH through the analysis of research institutions (Fig. 3C) by the number of publications originating from 7 different nations, namely the Netherlands (University of Amsterdam), Norway (University of Oslo), Australia (the University of Western Australia, and Royal Perth Hospital), Brazil (The University of São Paulo), Canada (University of Montreal), and the USA (Harvard University, University of Pennsylvania, and Baylor College of Medicine). The institution with the most publications was University of Amsterdam (241), followed by University of Oslo (159), the University of Western Australia (157), Harvard University (133), and University of Montreal (119). In terms of centrality, the top 5 institutions were Harvard University (1.05), Hospital Israelita Albert Einstein (0.86), University of Cape Town (0.85), The University of Iowa (0.76) and Baylor College of Medicine (0.72) (Fig. 3D; Table 1). Figure 3E shows the collaboration network of authors, which provides a basis for finding research partners and identifying industry giants. The author with the most publications was John J.P. Kastelein (125), followed by Gerald F. Watts (122), Raul D. Santos (77), G. Kees Hovingh (74), and Carol Jing Pang (57) (Fig. 3F). The top 5 authors according to centrality were Mafalda Bourbon (0.32), Olivier S. Descamps (0.31), John J.P. Kastelein (0.30), and Albert Wiegman (0.29) (Table 1).

Figure 3.

Co-authorship between countries, institutions, and authors in the field of FH. (A) The collaboration network of countries. (B) Rose chart of the top 10 productive countries. (C) The collaboration network of institutions. (D) Rose chart of the top 10 productive institutions. (E) The collaboration network of authors. (F) Rose chart of the top 10 productive authors. FH = familial hypercholesterolemia.

Table 1.

Top 10 publication counts and centralities of countries, institutions, and authors.

Items	Rank	Count	Name	Rank	Centrality	Name
Country	1	1284	USA	1	0.60	Belgium
	2	480	The Netherlands	2	0.58	Argentina
	3	440	England	3	0.53	Hungary
	4	376	Canada	4	0.43	Singapore
	5	365	Peoples R. China	5	0.41	Israel
	6	339	Italy	6	0.38	Bulgaria
	7	334	Japan	7	0.34	Serbia
	8	291	Spain	8	0.29	Tunisia
	9	277	France	9	0.28	Oman
	10	268	Germany	10	0.27	Thailand
Institution	1	241	Univ Amsterdam (the Netherlands)	1	1.05	Harvard Univ (the USA)
	2	159	Univ Oslo (Norway)	2	0.86	Hosp Israelita Albert Einstein (Brazil)
	3	157	Univ Western Australia (Australia)	3	0.85	Univ Cape Town (South Africa)
	4	133	Harvard Univ (the USA)	4	0.76	Univ Iowa (the USA)
	5	119	Univ Montreal (Canada)	5	0.72	Baylor Coll Med (the USA)
	6	104	Univ Penn (the USA)	6	0.61	Univ Witwatersrand (South Africa)
	7	103	UCL (England)	7	0.55	Fdn Hipercolesterolemia Familiar (Spain)
	8	97	Univ Sao Paulo (Brazil)	8	0.53	Ctr Adv Metab Med & Nutr (Chile)
	9	93	Royal Perth Hosp (Australia)	9	0.46	Metab & Atherosclerosis Res Ctr (the USA)
	10	80	Baylor Coll Med (the USA)	10	0.44	Sanofi (France)
Author	1	125	John J.P. Kastelein (the Netherlands)	1	0.32	Mafalda Bourbon (Portugal)
	2	122	Gerald F. Watts (Australia)	2	0.31	Olivier S. Descamps (Belgium)
	3	77	Raul D. Santos (Brazil)	3	0.30	John J.P. Kastelein (the Netherlands)
	4	74	G. Kees Hovingh (the Netherlands)	4	0.29	Wiegman Albert (the Netherlands)
	5	57	Jing Pang (Australia)	5	0.28	Marianne Abifadel (France)
	6	54	Pedro Mata (Spain)	6	0.13	Frederick J. Raal(South Africa)
	7	51	Eric, Bruckert (France)	7	0.12	Catherine Boileau (France)
	8	50	Steve E. Humphries (England)	8	0.11	Handrean Soran (England)
	9	49	Mariko Harada-shiba (Japan)	9	0.10	Martin P. Bogsrud (Norway)
	10	49	Daniel Gaudet (Canada)	10	0.10	Roeland Huijgen (the Netherlands)
	11	49	Hayato Tada (Japan)

3.3. Analysis of co-cited authors and co-cited journals

Figure 4A and Table 2 display the co-cited authors in this field of study. The top 5 co-cited authors by citation frequency were Børge G. Nordestgaard (1009), Joseph L. Goldstein (820), Frederick J. Raal (587), Marianne Abifadel (486), and Michael S. Brown (483). The top 5 co-cited authors for centrality were Eli M. Roth (1.11), followed by Jennifer G. Robinson (1.10), Marc S. Sabatine (1.01), Mary Jane Koren (0.96), and Dave Sullivan (0.96) (Table 2). In the analysis of co-cited journals (Fig. 4B), identifying the core journals in the field is beneficial. The highest frequency among co-cited journals was Atherosclerosis (3201), followed by Circulation (2736), New England Journal of Medicine (2246), the Lancet (2220), and Arteriosclerosis, Thrombosis, and Vascular Biology (2136). The top 5 co-cited journals for centrality were Atherosclerosis (1.03), Arteriosclerosis, Thrombosis, and Vascular Biology (0.88), Circulation (0.75), Journal of Biological Chemistry (0.72), and Journal of Clinical Investigation (0.66).

Figure 4.

Analysis of co-cited authors and journals in the field of FH. (A) The collaboration network of co-cited authors. (B) The collaboration network of co-cited journals. FH = familial hypercholesterolemia.

Table 2.

Top 10 publication counts and centralities of co-cited authors and co-cited journals.

Items	Rank	Count	Name	Rank	Centrality	Name
Co-cited author	1	1009	Børge G. Nordestgaard	1	1.11	Eli M. Roth
	2	820	Joseph L. Goldstein	2	1.10	Jennifer G. Robinson
	3	587	Frederick J. Raal	3	1.01	Marc S. Sabatine
	4	486	Marianne Abifadel	4	0.96	Mary Jane Koren
	5	483	Michael S. Brown	5	0.96	Dave Sullivan
	6	446	Scott M. Grundy	6	0.95	Jonathan C. Cohen
	7	425	Evan A. Stein	7	0.95	James M McKenney
	8	407	Marianne Benn	8	0.94	Robert Patrick Giugliano
	9	402	Marina Cuchel	9	0.93	Thomas A. Lagace
	10	402	Albert Wiegman	10	0.91	Da-Wei Zhang
Co-cited Journal	1	3201	Atherosclerosis	1	1.03	Atherosclerosis
	2	2736	Circulation	2	0.88	Arterioscl Throm Vas
	3	2246	New Engl J Med	3	0.75	Circulation
	4	2220	Lancet	4	0.72	J Biol Chem
	5	2136	Arterioscl Throm Vas	5	0.66	J Clin Invest
	6	2084	Eur Heart J	6	0.52	Eur Heart J
	7	1964	J Am Coll Cardiol	7	0.48	Nature
	8	1667	J Lipid Res	8	0.44	Nat Genet
	9	1604	Am J Cardiol	9	0.35	Hum Mutat
	10	1483	JAMA-J Am Med Assoc	10	0.20	Lancet

3.4. Analysis of references

As shown in Figure 5A and Table 3, “Nordestgaard BG (2013), Sabatine MS (2017), Khera AV (2016), Cuchel M (2014), and Robinson JG (2015)” were frequently cited references. In the last 21 years, there have been 15 major study subjects that have been concentrated in the area of FH, as illustrated in Figure 5B, where 15 clusters of varying colors and sizes were developed. In Table 4, we provided details for each cluster. The 15 clusters’ silhouettes, which were all above 0.9, showed that their homogeneity was considerably greater. Clusters #2 (“carotid arteries “), #3 (“LDLR gene mutations”), #5 (“risk factors “) and #8 (“lathosterol”) had the earliest average publication year among their members (1999, 2000, 2002, and 2001, respectively), indicating that they were early research topics in this area. The largest cluster for co-cited reference, cluster #0, was labeled as “Familial hypercholesterolemia.” The timeline view of co-cited references is also shown in Figure 5C, which reveals that the most recent areas are clusters #0 (“Familial hypercholesterolemia”), #1 (“inclisiran “), and #15 (“genetic analysis “), and second most recent clusters were #7 (“evolocumab “), and #9 (“alirocumab “). The top 5 referred and referring references in these 5 clusters are displayed in Supplementary Table S1, Supplemental Digital Content, http://links.lww.com/MD/J230, 2, http://links.lww.com/MD/J231. What’s more, the top 25 references with the strongest citation bursts are shown in Figure 5C. The highest burst strength was from Joseph L. Goldstein (2001). Moreover, François Mach (2020), Amy C Sturm (2018), Ilse K Luirink (2019), François Mach (2019), and Gregory G. Schwartz (2018) received more attention in recent years. The alluvial flow map shown in Figure 6 represents the most cited references from 2018 to 2022, with Sabatine MS (2017), Robinson JG (2015), Kastelein JJP (2015), Cannon CP (2015), and Kereiakes DJ (2015) cited with the largest alluvial flow.

Figure 5.

Analysis of co-citation references in the field of FH. (A) The network of co-cited references. (B) The network map of co-citation clusters. Fifteen clusters with different research topics were formed, reflecting different colors on the map. (C) The timeline view network of co-cited references. (D) The top 25 references with the strongest citation. FH = familial hypercholesterolemia.

Table 3.

Top 10 co-cited references.

Rank	Title	Article type	Author	Year	Journal	Citation
1	Familial hypercholesterolemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease	Guideline	Børge G. Nordestgaard	2013	European Heart Journal	438
2	Evolocumab and clinical outcomes in patients with cardiovascular disease	Original article	Marc S. Sabatine	2017	N Engl J Med	228
3	Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia	Original article	Amit V Khera, Hong-Hee Won, Gina M Peloso	2016	J Am Coll Cardiol	191
4	Homozygous familial hypercholesterolemia: new insights and guidance for clinicians to improve detection and clinical management. A position paper from the Consensus Panel on Familial Hypercholesterolaemia of the European Atherosclerosis Society	Guideline	Marina Cuchel	2014	Eur Heart J	190
5	Efficacy and safety of alirocumab in reducing lipids and cardiovascular events	Original article	Jennifer G. Robinson	2015	N Engl J Med	170
6	Ezetimibe added to statin therapy after acute coronary syndromes	Original article	Christopher P. Cannon	2015	N Engl J Med	129
7	2013 ACC/AHA Guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults	Guideline	Neil J. Stone	2014	Circulation	126
8	Inhibition of PCSK9 with evolocumab in homozygous familial hypercholesterolemia (TESLA Part B): a randomized, double-blind, placebo-controlled trial	Original article	Frederick J Raal	2014	Lancet	126
9	Mutations causative of familial hypercholesterolemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217	Original article	Marianne Benn	2016	Eur Heart J	126
10	Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel	Review	Brian A. Ference	2017	Eur Heart J	126
11	The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association	Review	Samuel S. Gidding	2015	Circulation	126

AHA = American Heart Association, PCSK9 = proprotein convertase subtilisin/kexin 9.

Table 4.

Top 10 largest clusters of co-cited references.

Cluster ID	Size	Silhouette	Mean Year	Top terms (LLR)
#0	23	0.986	2016	Familial hypercholesterolemia; atherosclerosis
#1	21	1	2018	Inclisiran
#2	17	0.977	1999	Carotid arteries
#3	17	0.959	2000	LDL receptor gene mutations
#4	17	0.941	2010	Proprotein convertase subtilisin/kexin type 9
#5	16	1	2002	Risk factors
#6	16	0.986	2007	Low-density lipoprotein receptor
#7	16	1	2013	Evolocumab
#8	15	1	2001	Lathosterol
#9	14	0.962	2015	Alirocumab
#10	14	0.947	2003	Narc-1
#11	14	1	2011	Survey
#12	14	0.904	2011	Lomitapide
#13	14	0.91	2008	Mutations
#14	14	0.979	2005	LDL receptor
#15	10	1	2018	Genetic analysis

LDL = low-density lipoprotein, LLR = log-likelihood ratio, Narc-1 = neural apoptosis-regulated convertase.

Figure 6.

Alluvial flow map of co-cited references in the last 5 years. Each line represented a study, and colored and continuous lines referred to articles that had been cited continuously or with the largest alluvial flow.

The goal of alluvial flow diagrams is to uncover time patterns in evolutionary networks. We created an alluvial map to easily observe the citation changes of the top 5 cited references from 2018 to 2022. The data were first obtained from CiteSpace, then networks of co-cited references were created. The longest-presence nodes in the import network are highlighted by coloring the flows they form. The flow increases as the flow lines become thicker, reflecting the significance of the co-cited reference. The top largest flow of co-cited references in the alluvial flow map is shown in Table 5.

Table 5.

Top 10 largest flow of co-cited references. The flow increases as the flow lines become thicker, reflecting the significance of the co-cited reference.

Rank	Article title	Author	Year	Flow
1	Evolocumab and clinical outcomes in patients with cardiovascular disease	Marc S. Sabatine	2017	0.0657
2	Efficacy and safety of alirocumab in reducing lipids and cardiovascular events	Jennifer G. Robinson	2015	0.0585
3	ODYSSEY FH I and FH II: 78 week results with alirocumab treatment in 735 patients with heterozygous familial hypercholesterolemia	John J.P. Kastelein	2015	0.0582
4	Efficacy and safety of alirocumab in high cardiovascular risk patients with inadequately controlled hypercholesterolemia on maximally tolerated doses of statins: the ODYSSEY COMBO II randomized controlled trial	Christopher P Cannon	2015	0.0519
5	Efficacy and safety of the proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab among high cardiovascular risk patients on maximally tolerated statin therapy: The ODYSSEY COMBO I study	Dean J. Kereiakes	2015	0.0510
6	Alirocumab as add-on to atorvastatin versus other lipid treatment strategies: ODYSSEY OPTIONS I randomized trial	Harold Bays	2015	0.0484
7	Efficacy and safety of alirocumab in patients with heterozygous familial hypercholesterolemia and LDL-C of 160 mg/dL or higher	Henry N Ginsberg	2016	0.0482
8	Efficacy and safety of alirocumab vs ezetimibe in statin-intolerant patients, with a statin rechallenge arm: The ODYSSEY ALTERNATIVE randomized trial	Patrick M. Moriarty	2015	0.0441
9	Effect of alirocumab, a monoclonal antibody to PCSK9, on long-term cardiovascular outcomes following acute coronary syndromes: rationale and design of the ODYSSEY outcomes trial	Gregory G. Schwartz	2014	0.0432
10	Efficacy and safety of evolocumab in reducing lipids and cardiovascular events	Marc S. Sabatine	2015	0.0366

LDL-C = low-density lipoprotein cholesterol, PCSK9 = proprotein convertase subtilisin/kexin 9.

3.5. Analysis of keywords

Research topics with high-frequency keyword performance were considered research hotspots in the field. “Coronary heart disease,” “risk factor,” “cardiovascular disease,” “cholesterol,” and “LDL” were high-frequency keywords (Fig. 7A). “Randomized controlled trial,” “density lipoprotein cholesterol,” “PCSK9 inhibitor,” “degradation,” “monoclonal antibody,” “PCSK9,” “coronary heart disease,” “mutation,” “autosomal dominant hypercholesterolemia,” and “identification” were the top ten keywords for centrality (Table 6). Burst keywords may detect changes in research patterns and intuitively depict research hotspots over time. Figure 7B depicts the top 25 terms with the greatest citation bursts. “plasma” was the phrase with the most burst strength. Furthermore, the most recent terms that exploded in the previous 6 years were “guideline/recommendation,” “management,” “general population,” “care,” “clinician,” “Evolocumab,” and “variant.”

Figure 7.

Keywords co-occurrence map of publications on the myocardial bridge. (A) The co-occurrence network of keywords; (B) The top 25 keywords with the strongest citation bursts.

Table 6.

Top 10 keywords by frequency and centrality

Rank	Count	Keyword	Rank	Centrality	Keyword
1	1022	Coronary heart disease	1	1.17	Randomized controlled trial
2	781	Risk factor	2	1.06	Density lipoprotein cholesterol
3	711	Cardiovascular disease	3	1.03	PCSK9 inhibitor
4	609	Cholesterol	4	1.02	Degradation
5	543	LDL	5	0.95	Monoclonal antibody
6	500	Atherosclerosis	6	0.92	PCSK9
7	487	Disease	7	0.89	Coronary heart disease
8	477	Mutation	8	0.78	Mutation
9	436	LDL-C	9	0.78	Autosomal dominant hypercholesterolemia
10	398	Guideline/recommendation	10	0.56	Identification

LDL = low-density lipoprotein, LDL-C = low-density lipoprotein cholesterol, PCSK9 = proprotein convertase subtilisin/kexin 9.

4. Discussion

4.1. General information

The increasing annual number of articles on FH before 2018 and remaining high-level recent years indicates that this research field remains heated. This was the first bibliometric examination of worldwide FH articles.

We can observe through a graphic study of the distribution of the nations and institutions that FH research is primarily conducted in the US. The research on FH piqued the interest of nations from all over the world, including countries from Europe, Asia, Australia, and Africa. In the previous 21 years, the USA published approximately 2.7 times as many papers as the Netherlands. The most frequently cited document in the USA (citations = 1962) was published by Kasey C. Vickers in 2011. It primarily identified a new intercellular communication channel that involved HDL-mediated microRNA transport and cellular distribution.^[13] Belgium has the highest centrality, signifying the tight interaction it has with other nations.

University of Amsterdam in the Netherlands, with 241 papers addressing the risk factors, prevalence, genetics, therapy, and other facets of FH, was the organization with the most publications.^[14–20] The most popular of them was a randomized experiment conducted by Jennifer G. Robinson to assess 2341 patients from 27 nations in North and South America, Europe, and Africa. Alirocumab or placebo was administered as a 1-ml subcutaneous injection every 2 weeks for a total of 78 weeks. The findings of this research demonstrated that the addition of alirocumab to statin treatment at the highest tolerated dose significantly decreased LDL-C levels and the frequency of cardiovascular events.^[20]

In terms of publications, 3 of the top ten universities were from the US: Harvard University was ranked fourth, University of Pennsylvania was ranked sixth, and Baylor College of Medicine was ranked tenth. Marc S. Sabatine produced the article with the most citations (1122) at Harvard University in 2015. It was a randomized control trial to assess the long-term effects of Evolocumab, a monoclonal antibody that inhibits proprotein convertase subtilisin/kexin 9 (PCSK9). In a predetermined but exploratory study, the use of evolocumab in addition to conventional medication considerably decreased LDL-C levels and decreased the incidence of cardiovascular events over the course of about a year of treatment.^[21]

According to the findings, John J.P. Kastelein (University of Amsterdam, Amsterdam, Netherlands) ranked first in terms of both publications and centrality. His main focus was on the treatment of FH through randomized control trials, his main focus was on the treatment of FH through randomized control trials, demonstrating the ineffectiveness of ezetimibe in reducing carotid artery intima-media thickness (citation = 531),^[22] the efficacy of alirocumab in lowering LDL-C levels in patients with heterozygotes FH (HeFH),^[23] and the failure of torcetrapib to slow the progression of atherosclerosis in HeFH.^[24] The journal that received the most co-citations was Atherosclerosis. Only a tiny percentage of patients with HeFH achieve the LDL-C treatment target of 2.5 mmol/l, according to one of the most widely quoted articles in atherosclerosis, which was also written by the team of Kastelein, JJP.^[25] One of the journal’s most recent studies examined the SAFEHEART risk prediction model’s external validity in patients with FH in an English routine care cohort.^[26]

4.2. Hotspots and frontiers

4.2.1. Prevalence and genetic analysis of FH.

The term “Familial Hypercholesterolemia” was assigned to the greatest cluster for co-cited references. Amit V. Khera’s paper from cluster #0, which was the most often mentioned, discussed the likelihood of an FH mutation in those with severe hypercholesterolemia and the risk of developing coronary artery disease. They discovered that 1.7% of patients with LDL-C > 190 mg/dL had an FH mutation detected by genome sequencing. FH mutation carriers, however, exhibited a markedly elevated risk for coronary artery disease for any detected LDL-C.^[27] The purpose of number 2 of the cluster, which was published by Marianne Benn, was to determine the prevalence and predictors of FH-causing mutations in general populations. It was discovered that these mutations are estimated to occur in the general population in 1:217 (0.46%) cases.^[28] Additionally, the third edition, released by Samuel S. Gidding et al on behalf of the American Heart Association, was a scientific statement that gave an agenda for further advancement, building on the foundation set by current recommendations and assessments of advancements in diagnosis and treatment.^[4] Samantha Karr was the cluster’s most important citer. He has covered the epidemiology and treatment of FH and other forms of hyperlipidemia, and he offers guidelines for best practices in clinical care to help medical practitioners effectively manage patients with these disorders.^[29]

The word “prevalence” appeared 52, 133, and 138 times from 2014 to 2016, 2017 to 2019, and 2020 to 2022, respectively, in CiteSpace when we clicked the node “prevalence” in Figure 7A to view the history of appearance. This indicates that the prevalence of FH has truly attracted widespread attention, as can be seen in Figure 7B. In 2022, there were about 15 articles focusing on the frequency of FH in various areas.^[30–34] The predicted prevalence of probable/definite FH in the US as a whole was 0.40%, or 1:250.^[35] In the meta-analyses conducted in 2020, the prevalence of HeFH in the general population was estimated as 1:313 and 1:311.^[36,37] According to 2 of the CEFH criteria, the prevalence of FH in the Chinese population aged 35 to 75 years was 0.13% (or around 1 in 769), and the patients were severely undertreated and undercontrolled, according to recent research by Haobo Teng et al.^[34]

However, we must determine the diagnostic criteria that were applied and if genetic analysis was performed each time we cite prevalence now. This is one of the main causes for the current boom in interest in genetic analysis as a field of study. Prior research approximated the frequency of FH without taking genetic testing into account,^[34–36,38] but in the Netherlands, Norway, UK, Spain, Denmark, Belgium, Canada, Australia, New Zealand, and South Africa, among others, FH genetic testing has been carried out more widely and/or at the population level.^[5,39,40] According to research by Mark Trinder et al, both monogenic and polygenic hypercholesterolemia were substantially linked to an elevated risk of CVD events among people with similar levels of LDL-C, as opposed to people with hypercholesterolemia without a known genetic basis.^[41] The prevalence of FH was determined to be 1:270 using the DLCN clinical criteria alone, 1:263 with genetic testing alone, and 1:152 when clinical criteria and genetic testing were combined, according to research by Brandon K. Bellows. Additionally, in young individuals aged 20 to 39 years, using clinical criteria alone was predicted to be 1:769; however, when clinical criteria and genetic testing were combined, this number increased to 1:238.^[42]

“Clinical Genetic Testing for Familial Hypercholesterolemia,” a 2018 JACC Scientific Expert Panel publication, had the highest citations of the cluster #15 (genetic analysis) articles and is the No. 22 reference with the greatest citation bursts in Figure 5D. The standard of treatment for individuals with definite or probable FH and their at-risk relatives should include FH genetic testing, according to their recommendations. LDL receptor, apolipoprotein B, and PCSK9 genes should be tested; depending on the patient’s condition, more genes may need to be examined. Greater diagnoses, more efficient cascade testing, faster therapy initiation, and more precise risk classification are anticipated consequences.^[3] And the articles citing most references in the cluster were guidelines for FH in Australia^[43] and Brazil,^[44] respectively. They all concur that genetic testing and phenotypic criteria should both be used to make the diagnosis of FH, but that phenotypic criteria should be used in the absence of genetic evidence.

4.2.2. PCSK9 inhibitor.

PCSK9 inhibitor is one of the research hotspots, as shown by the burst keyword analysis. The articles with the highest alluvial flow in Figure 6 and the No. 25 article in Figure 5D both discussed the clinical efficacy of PCSK9 inhibitors, evolocumab, or alirocumab. The proprotein convertases, which are enzymes that change proproteins into their functional protein forms, include PSCK9. By attaching to the LDL-C cellular receptors on hepatic cells, its active form signals their degradation. As a result, there is a general decline in LDL-C receptor activation, which raises LDL-C. Due to the fact that LDL-C requires receptor binding in order to enter cells, LDL-C levels in the blood start to rise if it doesn’t. More LDL-C receptors are expressed as a result of PCSK9 medicines’ blocking of PCSK9 proteins, which decreases the LDC-C level.^[45,46] IgG1 and IgG2 monoclonal antibodies called alirocumab and evolocumab, respectively, block the PCSK9 enzyme and stop the loss of LDL receptors.^[47,48] Lower levels of LDL-C are caused by an increase in LDL receptors on the surface of hepatocytes.^[48]

Phase III clinical studies of alirocumab revealed a substantial reduction in LDL-C.^[49] Additionally, its mid-term and long-term efficacy has previously been demonstrated.^[20,50,51] Evolocumab treatment significantly decreased LDL-C in both adult and pediatric patients with HeFH.^[52,53] In individuals with homozygous FH (HoFH), alirocumab and evolocumab were both secure and well-tolerated.^[54,55] Recently, several researchers were interested in how evolocumab affected cognitive function. They discovered that evolocumab had no detrimental effects on cognition in either adults or children.^[56–58]

4.2.3. Inclisiran – new approaches to reduce LDL-C.

The cluster analysis of co-cited references and keywords revealed that inclisiran, a different strategy for targeting PCSK9 that uses RNA interference, was one of the research hotspots. In contemporary biomedical engineering and clinical illness treatment, gene therapy has emerged as a significant study area. New technologies, including clustered regulatory interspaced short tandem repeats, antisense oligonucleotides, small interfering ribonucleic acid (siRNA), antisense oligonucleotides, and new transport techniques, like nanomaterials and lipid carriers, have recently emerged, greatly promoting the use of gene therapy in clinical settings.^[59] A long-acting synthetic double-stranded siRNA called Inclisiran selectively binds to the triantennary n-acetylgalactosamine carbohydrate ligand and the glucose-lowering glycoprotein receptor to target PCSK9 mRNA.^[60] The PCSK9 synthesis-inhibiting small interfering RNA (siRNA) drug inclisiran lowered LDL-C by up to 50% in a phase I and phase II study. The reduction was dose-dependent. For around 6 months, PCSK9 and LDL-C levels were kept lower.^[61,62] No particular severe adverse effects were noted. 15,000 people with a history of a myocardial infarction or stroke are now participating in the HPS4/TIMI65/ORION4 study, which has a mean length of 5 years and compares inclisiran versus placebo. Kausik K. Ray et al merged individual patient’s data from 3 Phase III lipid-lowering trials to assess the impact of inclisiran treatment or placebo on the risk of cardiovascular events and provide preliminary insights into the potential of this therapeutic approach. Inclisiran significantly decreased composite major adverse cardiovascular events, but not fatal and nonfatal myocardial infarctions or fatal and nonfatal stroke, according to their findings.^[63]

The appropriate treatment and management of this patient group have proven to be a formidable issue for the worldwide medical community due to the very significant risk of cardiovascular events in FH. Patients with HoFH now have hope for a cure because of the advancement of gene therapy technologies.^[64] The development from classical gene substitution to gene editing has opened up countless treatment options for FH. Gene therapy may eventually enable FH patients to receive lasting advantages from a single therapy in a safe and ethical manner as technology advances, significantly lowering the financial burden that FH has on society at large. Future study may focus on the efficacy and safety of gene therapy.

With regard to the current status of research and the trajectory of specific research areas, there are many studies that use bibliometrics and knowledge mapping.^[65–68] CiteSpace and the alluvial generator, which were used in this study, are the 2 of the most bibliometric tools. Our analysis strategy is similar to those employed in previous research. However, there are several restrictions on this study. To begin with, we only included scientific papers from WoSCC and omitted material from other databases like Google Scholar and PubMed. This may have led to bias. Second, some crucial information or viewpoints could have been left out by software because the material we first downloaded wasn’t the entire text. Our research, however, is based entirely on information that was acquired without any bias from supervisors. Third, because synonyms were used in the analysis, it is possible that subjectivity might have influenced the results. Co-reference analysis may nevertheless result in unavoidable accuracy loss. Finally, the authors may still be ignoring certain literature and therefore missing out on some more important study areas.

5. Conclusions

This study systematically assessed the research publications on FH by bibliometric analysis. It is sufficient to demonstrate that this study area has consistently piqued the attention of many academics by the number of publications, which has been rising yearly until 2018 and has continued to be at a high level in the years after. Evaluations of publications from various nations, organizations, authors, and journals demonstrated their contributions to the FH research and may also be used to direct future study. We have identified the FH research hotspots and trends for the future by the examination of references and keywords. The incidence and genetic diagnosis of FH have garnered a lot of attention, and the patients were significantly undertreated and poorly managed. The effects of PCSK9 inhibitors on adults or kids have caught the interest of researchers. The most promising treatment for patients with HoFH may be gene therapy, which includes the PCSK9 siRNA medication. The efficacy and safety of gene therapy could be the subject of future research. Researchers that are interested in this topic may find the study’s reference on research trends and savings in time while looking for research frontiers and hotspots.

Acknowledgments

We would like to express our gratitude to all those who helped us during the writing of this manuscript. Thanks to all the peer reviewers for their opinions and suggestions.

Author contributions

Conceptualization: Liang Chen, Hao Peng, Yang Yu.

Data curation: Liang Chen, Hao Peng, Bo-Lin Wang, Wen-Yuan Yu.

Formal analysis: Liang Chen, Hao Peng.

Funding acquisition: Yang Yu.

Methodology: Liang Chen, Xiao-Hang Ding.

Software: Liang Chen, Hao Peng.

Supervision: Yang Yu.

Validation: Bo-Lin Wang, Wen-Yuan Yu, Xiao-Hang Ding.

Visualization: Liang Chen, Hao Peng.

Writing – original draft: Liang Chen.

Writing – review & editing: Bo-Lin Wang, Wen-Yuan Yu, Xiao-Hang Ding, Ming-Xin Gao, Yang Yu.