Research Trends and Hotspots in Mendelian Randomization Studies of Liver Diseases: A Bibliometric and Visualization Analysis

Abstract

This study conducted a bibliometric analysis of Mendelian randomization (MR) research in liver diseases from 2014 to 2025 using the Web of Science Core Collection (WoSCC) and PubMed databases. A total of 304 WoSCC records and 10 clinical studies were included. Publications showed a steady upward trend, with China contributing the most (n=251), followed by the United States and United Kingdom. Zhejiang University led institutional output (n=18), and Chen Lanlan was the most prolific author (n=8). Frontiers in Endocrinology was the most productive journal (n=21). Current hotspots include gut microbiota, metabolism, and insulin resistance. Clinical studies focused on metabolic dysfunction-associated steatotic liver disease (MASLD), covering risk factors, comorbidities, and clinical outcomes. This is the first comprehensive bibliometric study in this field, providing a reliable reference for future research directions. However, the generalizability of our findings is constrained by the coverage scope of the databases and the language of the included literature, which may introduce selection bias. In addition, the causal inferences drawn from the included MR studies are subject to the validity of instrumental variables and the generalizability across populations, which may limit the applicability of our conclusions.

Introduction

Chronic liver disease is a group of conditions characterized by progressive impairment of liver function. The main causes include viral infections, long-term alcohol consumption, metabolic dysfunction, and autoimmune disorders. These factors lead to persistent inflammation, destruction, and regeneration of liver parenchyma, ultimately resulting in excessive deposition of extracellular matrix and the formation of fibrosis.1 If the disease continues to progress, it can develop into cirrhosis. Cirrhosis is the end stage of chronic liver disease and is also an important risk factor for liver cancer. Currently, liver diseases has become the eleventh leading cause of death globally, resulting in over 2 million deaths annually. This accounts for 4% of the total global mortality, meaning that 1 in every 25 deaths is caused by liver diseases.2 Liver diseases often progresses insidiously, with symptoms typically manifesting only in the late stages, which significantly limits the choice of treatment options. Therefore, early detection, prevention, and treatment are crucial for improving the prognosis of liver diseases.

In recent years, researchers have been committed to delving into the pathogenesis of liver diseases and the causal relationships of related factors, with the aim of controlling the condition of liver diseases and effectively slowing their progression. Randomized controlled trials (RCTs) are considered the gold standard for studying causal relationships in diseases.3,4 However, their implementation often faces significant challenges due to high costs, lengthy timeframes, poor participant compliance, and ethical issues.5,6 Additionally, the presence of confounding factors and reverse causation can potentially bias the accuracy of research findings. Mendelian randomization (MR) has emerged as an alternative approach and has rapidly gained popularity. This method utilizes single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) as instrumental variables (IVs). Under the premise that the core instrumental-variable assumptions are satisfied, MR can reduce biases arising from conventional confounding and reverse causation, thereby providing relatively reliable evidence for causal inference.7,8 However, this method is not intrinsically unbiased; its findings remain vulnerable to horizontal pleiotropy, weak instruments, population stratification, assortative mating, selection bias, sample overlap, and other violations of instrumental-variable assumptions. Nevertheless, owing to its unique advantages in causal inference, MR has been widely applied across various research fields and offers novel perspectives as well as a powerful analytical tool for studies on liver diseases.

Bibliometrics, a statistical method introduced by Alan Pritchard in 1969, enables the systematic analysis of publications within a specific discipline to reveal its research status, hotspots, and developmental trends.9 In recent years, MR methods have been increasingly applied in the field of liver diseases, with a marked rise in the number of related publications. However, a systematic overview of the global research landscape, developmental trajectory, and emerging frontiers in this field is currently lacking. To address this gap, the present study conducted a bibliometric analysis based on the Web of Science Core Collection (WoSCC), examining multiple dimensions—including countries, institutions, journals, authors, references, and keywords—to systematically delineate the research hotspots and emerging trends in MR research related to liver diseases. Furthermore, given the comprehensive coverage of PubMed in the biomedical domain, we supplemented our analysis with clinical studies from PubMed to compensate for potential indexing delays or incomplete coverage of WoSCC in medically oriented applications.10 This approach specifically captures research advances relevant to translational medicine, thereby enhancing the practical value of bibliometric findings for clinical practice.

Materials and Methods

Data Collection and Search Strategy

The data for this study were sourced from two major databases, namely WoSCC and PubMed. As a globally renowned high-quality academic database, the Web of Science (WoS) is widely used due to its comprehensive coverage of high-impact journals across various disciplines and its extensive citation data. It is considered the most suitable database for bibliometric analysis, enabling researchers to gain in-depth insights into the research landscape and developmental trends within academic fields.11,12 We systematically searched and exported literature related to MR studies in liver diseases from the Science Citation Index Expanded (SCI-Expanded) within the WoSCC. The search strategy was as follows: TS = (“Liver Disease” OR “Disease, Liver” OR “Liver Dysfunction” OR “Dysfunction, Liver” OR “Fatty Liver” OR “Hepatitis” OR “Liver Abscess” OR “Liver Cirrhosis” OR “Liver Neoplasms” OR “Hepatomegaly”) AND TS=(“Mendelian randomization”). The search was restricted to English-language publications. Since the earliest relevant literature was published in 2014, we set the search period from January 1, 2014 to April 25, 2025, and downloaded all relevant records on April 26, 2025, to ensure accuracy, consistency, and to avoid potential bias caused by database updates. The PubMed search strategy was as follows: (“Liver Disease”[Title/Abstract] OR “Disease, Liver”[Title/Abstract] OR “Liver Dysfunction”[Title/Abstract] OR “Dysfunction, Liver”[Title/Abstract] OR “Fatty Liver”[Title/Abstract] OR “Hepatitis”[Title/Abstract] OR “Liver Abscess”[Title/Abstract] OR “Liver Cirrhosis”[Title/Abstract] OR “Liver Neoplasms”[Title/Abstract] OR “Hepatomegaly”[Title/Abstract]) AND (“Mendelian randomization”[Title/Abstract]). The remaining search criteria were consistent with those used for WoSCC. In addition, to avoid data heterogeneity and record duplication that may arise from integrating multiple databases, and to ensure the reliability and comparability of the analytical results, the Scopus and Embase databases were not included in this study.

Inclusion and Exclusion Criteria

The WoSCC included only English publications classified as “articles” or “review articles.” PubMed included only English-language clinical studies based on real-world data sources, such as population-based cohorts or clinical registry databases, provided that they contained direct analyses of clinical population data. Pure MR studies that relied solely on publicly available summary statistics from genome-wide association studies (GWAS), as well as pure bioinformatics analyses, were excluded. Both databases further excluded studies with low topical relevance, duplicate publications, retracted articles, and non-original document types such as letters, editorials, news reports, and case reports. As the WoSCC and PubMed datasets served different analytical purposes, their screening workflows were conducted in parallel, as illustrated in Figure 1.

Figure 1.

The detailed process of literature screening.

Data Preprocessing and Analysis

Two authors independently conducted the literature search and screened the records according to the predefined inclusion and exclusion criteria. In cases of disagreement, a third author was consulted to reach a consensus through discussion. To ensure data consistency, we standardized the extracted bibliographic data, including country names, author names, institutional affiliations, journal abbreviations, and variant expressions of the same concept. This standardization process laid a solid foundation for subsequent analysis. For the bibliometric analysis, we utilized specialized software tools including VOSviewer (version 1.6.20), CiteSpace (version 6.4.1 Advanced), and the Bibliometrix R package (version 4.6.0), leveraging their respective strengths to generate corresponding visualizations and conduct in-depth analysis.

VOSviewer, developed by Van Eck and Waltman at Leiden University in the Netherlands in 2009, is primarily employed to analyze collaboration networks among countries, authors, and institutions, as well as keyword co-occurrence networks.13 It effectively visualizes the knowledge structure of research fields through its mapping techniques. CiteSpace, a science mapping tool developed by Professor Chen Chaomei, enables intuitive visualization of journals’ double map overlays, timeline plots, and keyword clustering networks.14,15 The Bibliometrix R package is used to check annual publication and citation volumes. This analytical tool proves particularly valuable for identifying research hotspots and detecting emerging trends.

Results

Analysis of the Publications

According to the established search strategy and screening criteria, this study ultimately included 304 documents. We processed the data using the Bibliometrix R-package and found that the MR began to be applied in the field of liver diseases research since 2014. Between 2014 and 2021, the number of relevant publications was relatively low, with none exceeding 10 articles. However, starting from 2022, the number of publications has increased year by year, with a total of 277 articles published by April 25, 2025, accounting for 91.1% of the total number of publications. Among them, the year with the highest number of publications was 2024, reaching 142 articles. Among all the years, the average number of citations per article was the highest in 2018, at 13.30 (Figure 2). It should be noted that the data for 2025 cover only the first four months of that year (up to April 25), representing partial annual data, and thus are not directly comparable with complete annual data from previous years for trend analysis.

Figure 2.

The annual publications and average citations between 2014 and 2025. *Data for 2025 are limited to the period between January 1 and April 25 and therefore represent partial-year data.

Countries/Regions Analysis

As of now, a total of 36 countries/regions have participated in MR-related research in the field of liver diseases. Among them, China has the highest number of publications, with a total of 251 papers, accounting for 82.6% of the global total, ranking first. It is followed by the United States (n=38), England (n=22), Sweden (n=13), and Italy (n=12). In terms of citation frequency, China also leads with a total of 1760 citations, followed by the United States (n=1242) and the United Kingdom (n=613). Although China has an advantage in both the number of publications and total citations, the average citation per paper is lower than that of other countries. This indicates that China needs to further improve the quality and impact of its papers in the future (Table 1).

Table 1.

Top 10 Most Productive Countries

Ranking	Country/Region	Documents	Citations	Average Citations
1	China	251	1760	7.01
2	USA	38	1242	32.68
3	England	22	613	27.86
4	Sweden	13	592	45.54
5	Italy	12	425	35.42
6	Netherlands	12	334	27.83
7	Germany	11	176	16
8	Denmark	10	394	39.4
9	Canada	9	358	39.78
10	Australia	7	393	56.14

We utilized the Bibliometrix R-package and VOSviewer software to create collaboration network maps of countries, as shown in Figure 3A and B. The analysis indicates that international scientific cooperation is primarily concentrated in regions such as Asia, North America, and Europe. China has established cooperative relationships with 13 countries, with the closest collaboration being with the United States, having a link strength of 16. Cooperation with the United Kingdom follows closely behind, with a link strength of 12. Both the United States and the United Kingdom have a broader range of international partners. This suggests that researchers from China, the United States, and the United Kingdom have made significant contributions to the field and have engaged in extensive collaboration. Single Country Publications (SCP) refer to articles where all authors are from the same country. In contrast, Multiple Country Publications (MCP) imply that the article’s authors are from different countries. As depicted in Figure 3C, China, the United States, and the United Kingdom have numerous foreign collaborators in international cooperation, yet domestic collaboration in China still accounts for a considerable proportion. Looking ahead, China should further enhance its collaboration efforts with international scholars to foster broader academic exchanges and knowledge sharing.

Figure 3.

Contributions of various countries to the research of MR in liver diseases. (A) World map of countries’ cooperation. The lines in the diagram represent the collaborative relationships between countries, with the number of lines reflecting the frequency of collaboration. The shade of color for each country or region is related to the number of publications, with darker colors indicating a higher volume of publications. (B) Map of National Cooperation Contributions. The circles correspond to the number of publications from each country, and the lines represent their collaborations. (C) The 10 countries with the most corresponding authors. (D)Top 10 Countries with the Strongest Citation Bursts.

Figure 3D delineates the citation bursts among the top-ten countries, with red curves providing an intuitive visualization of burst intensity. These surges not only chart the evolutionary trajectory of the field but also pinpoint the temporal characteristics of citation explosions. From 2014 to 2025, the United Kingdom had the highest citation burst intensity (strength = 3.69), followed by Canada (2.19) and the Netherlands (2.08), indicating that research outputs from these countries received relatively high scholarly attention during specific periods.

Institutions and Authors Analysis

We conducted an analysis of the collected literature and found that a total of 469 institutions have participated in the publication of papers related to MR research in the field of liver diseases. Among them, Zhejiang University not only had the highest number of publications but also topped the list in terms of citation frequency. Following closely are Shanghai Jiao Tong University, Sichuan University, Fudan University, Central South University, Chinese Academy of Medical Sciences, Wuhan University, Southern Medical University, Chongqing Medical University, and Wenzhou Medical University. It is noteworthy that all of the top ten institutions in terms of publication volume are located in China (Table 2). To further analyze the data, we selected 45 institutions that have published more than four papers for an in-depth study. The collaboration co-occurrence map illustrates the cooperative relationships among these institutions, with a total of 43 nodes and 114 links, and a total link strength of 160. The data indicates that the collaboration among various institutions is relatively close, with Shanghai Jiao Tong University, Zhejiang University, and Central South University all having extensive cooperative networks (Figure 4).

Table 2.

Top 10 Institutions with the Most Publication

Ranking	Institutions	Documents	Ciationa	Average Citations	Country
1	Zhejiang University	18	367	20.39	China
2	Shanghai Jiao Tong University	15	179	11.93	China
3	Sichuan University	15	95	6.33	China
4	Fudan University	10	81	8.10	China
5	Central South University	10	252	25.20	China
6	Chinese Academy of Medical Sciences	10	145	14.50	China
7	Wuhan University	10	88	8.80	China
8	Southern Medical University	10	83	8.30	China
9	Chongqing Medical University	10	30	3.00	China
10	Wenzhou Medical University	10	30	3.00	China

Figure 4.

Map of institutional Cooperation Contributions. The circles correspond to the number of publications from each institution, and the lines represent the collaborations between institutions.

A total of 1,879 authors worldwide have contributed to research in this field. Among them, Chen Lanlan has published the most papers, with a total of 8. This is followed by Brouwers Martijn C.G.J. and Chen Jie, who have each published 6 papers (Figure 5A). It is worth noting that no author has published more than 10 papers, indicating that research in this area is still relatively scarce and holds significant potential for growth. Figure 5B presents the top 10 most-cited authors, with Liu Wanqing (n=36), Liu Zhipeng (n=33), and Zhang Yang (n=33) taking the top three spots, reflecting that their published work has received relatively higher levels of scholarly attention. Figure 5C illustrates the academic output of authors over time, showing that Brouwers Martijn C.G.J. and Stehouwer Coen D.A. were the pioneers in applying MR to liver diseases research, both publishing their first related papers in 2019 and remaining active through to 2024. We further selected 226 authors who have published more than 2 papers for an in-depth study. Using VOSviewer, we generated a co-authorship network map (Figure 5D), which includes only 18 nodes, all representing Chinese authors. This indicates a close collaboration among Chinese authors, while cooperation among authors globally is relatively less tight.

Figure 5.

Analysis of authors and co-cited authors in MR in liver diseases. (A) Top 10 productive authors. (B) The top 10 most-cited authors. (C) Annual publication trends of the top 10 authors in publication terms. (D) The visualization map of author cooperation network. Circles represent the number of publications by authors, and lines represent interactions between authors.

Journals Analysis

The 304 articles we included are from 145 different journals. Table 3 presents the top 10 journals with the highest number of publications in this field. In terms of publication volume, Frontiers in Endocrinology (n=21, IF = 3.9) leads as the journal with the most publications, followed by Scientific Reports (n=15, IF = 3.8), Liver International (n=13, IF = 6.0), and Frontiers in Genetics (n=13, IF = 2.8). All these journals have published over 10 MR research papers related to liver diseases, meriting increased attention from scholars. According to the Journal Citation Reports (JCR), half of these journals are ranked in the Q1 Quartile. In terms of geographical distribution, four journals are from the United States, and two are from Switzerland. Table 4 lists the top 10 journals with the highest citation counts, all of which are located in the Q1 Quartile. The Journal of Internal Medicine, Hepatology, and Nature Reviews Gastroenterology and Hepatology are the three most-cited journals, each with over 200 citations. The Journal of Internal Medicine has the highest average citation rate at 271, followed by Nature Reviews Gastroenterology and Hepatology with an average of 218. This indicates that the above three journals have received relatively high levels of scholarly attention within the present dataset. Looking at the geographical distribution, four journals are from the United States, three are from the Netherlands, two are from the United Kingdom, and one is from Denmark.

Table 3.

Top 10 Journals with the Most Publication

Ranking	Journal	Documents	Citations	Average Citations	IF	H-Index	JCR	Country
1	Frontiers in Endocrinology	21	72	3.43	3.9	51	2	United States
2	Scientific Reports	15	56	3.73	3.8	149	1	United Kingdom
3	Liver International	13	157	12.08	6.0	98	1	Denmark
4	Frontiers in Genetics	13	64	4.92	2.8	61	2	United States
5	Frontiers in Microbiology	9	58	6.44	4.0	88	2	Switzerland
6	Frontiers in Nutrition	9	48	5.33	4.0	-	2	Switzerland
7	Medicine	8	11	1.38	1.4	135	2	United States
8	EbioMedicine	5	144	28.8	9.7	37	1	Netherlands
9	Hepatology	4	235	58.75	13.0	326	1	United States
10	Digestive and Liver Disease	4	18	4.5	4.0	84	1	Italy

Table 4.

Top 10 Most Cited Journals

Ranking	Journal	Documents	Citations	Average Citations	IF	H-Index	JCR	Country
1	Journal of Internal Medicine	1	271	271	9.0	143	1	United Kingdom
2	Hepatology	4	235	58.75	13.0	326	1	United States
3	Nature Reviews Gastroenterology and Hepatology	1	218	218	46.4	105	1	United States
4	Nature Genetics	2	170	85	31.8	527	1	United States
5	Journal of Hepatology	2	164	82	26.8	216	1	Netherlands
6	European heart Journal	1	162	162	38.1	265	1	United Kingdom
7	Liver International	13	157	12.08	6.0	98	1	Denmark
8	EbioMedicine	5	144	28.8	9.7	37	1	Netherlands
9	European Journal of Epidemiology	2	135	67.5	7.7	96	1	Netherlands
10	Diabetes	2	128	64	6.2	304	1	United States

We set the minimum number of publications per journal to two articles and selected 57 highly productive journals for analysis using VOSviewer, generating a journal cooperation co-occurrence map as shown in the Figure 6A. The map includes 52 nodes and 161 links, with a total link strength of 200, indicating close collaborative relationships among the journals. Among them, Frontiers in Endocrinology is not only the journal with the highest number of publications but also the most active one, having established cooperation with 18 different journals. According to the journal’s annual publication growth chart (Figure 6B), it can be seen that Scientific Reports was the first journal to start publishing literature on this topic. In terms of growth rate, Frontiers in Endocrinology stands out the most, with an annual increase of 10 articles, followed closely by Scientific Reports, with an annual increase of 9 articles.

Figure 6.

Analysis of journal collaborations and publication trends on MR in liver diseases. (A) Map of journal collaborations. Circles represent journals, and lines represent interactions between journals. (B) Annual Publication Trends of Journals.

Co-Cited References Analysis

Among the 304 papers we included, there were a total of 10,477 references cited. Table 5 lists the top 10 most-cited references. Six of these papers originated from the United Kingdom, while three were from the United States. The most frequently cited paper is titled “Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases,” published by Marie Verbanck et al in Nature Genetics in 2018, with 117 citations. Following closely are two papers by Jack Bowden et al, titled “Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator17 and “Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression,18” with 113 and 111 citations respectively. These three papers, each with over 100 citations, have laid a solid foundation for the application of MR in the field of liver diseases. The paper with the highest Impact Factor (IF) (63.5) is titled “Mendelian Randomization”, published by Connor A. Emdin et al in JAMA in 2017. The second highest is a paper by Zobair M. Younossi et al, titled “Global epidemiology of nonalcoholic fatty liver diseases: Meta-analytic assessment of prevalence, incidence, and outcomes,19” published in Hepatology in 2016, with an IF of 58.75. Both papers were published in the United States, and both journals have an impact factor exceeding 50.

Table 5.

The Top 10 Most Co-Cited References

Rank	Title	Published Year	First Author	Journal	IF	Co-Citations	Publication Types	Countries
1	Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases	2018	Marie Verbanck	Nature Genetics	31.8	117	Research	United States
2	Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator	2016	Jack Bowden	Genet Epidemiol	1.7	113	Research	United Kingdom
3	Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression	2015	Jack Bowden	International Journal of Epidemiology	6	111	Research	United Kingdom
4	The MR-Base platform supports systematic causal inference across the human phenome	2018	Gibran Hemani	eLife	6.4	91	Research	United Kingdom
5	Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data	2013	Stephen Burgess	Genet Epidemiol	1.7	65	Research	United Kingdom
6	Interpreting findings from Mendelian randomization using the MR-Egger method	2017	Stephen Burgess	European Journal of Epidemiology	7.7	64	Research	United Kingdom
7	Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease	2021	Nooshin Ghodsian	Cell Reports Medicine	11.7	57	Review	Canada
8	Mendelian randomization: genetic anchors for causal inference in epidemiological studies	2014	George Davey Smith	Human Molecular Genetics	3.1	55	Review	United Kingdom
9	Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes	2016	Zobair M Younossi	Hepatology	58.75	55	Review	United States
10	Mendelian Randomization	2017	Connor A Emdin	JAMA-J AM MED ASSOC	63.5	53	Review	United States

A citation burst refers to a situation where a particular article is frequently cited within a specific period, which helps to reveal the latest research hotspots in a related field. Figure 7 displays the top 15 most-cited articles,16–18,20–31 with the blue line representing the citation timeline from 2014 to 2025, and the red line indicating the period during which the citation peak occurred. Among these, two articles have a citation burst strength exceeding 10 (accounting for 13.3%). Notably, the article with the highest citation burst is “Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases”, published by Marie Verbanck et al in Nature Genetics in 2018 (Strength = 17.96), making it the most frequently cited reference. It is also worth mentioning that the article “Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention,26” published by Zobair Younossi et al in Nature Reviews Gastroenterology and Hepatology, has shown the longest citation burst period, lasting for four years. This suggests that the article has maintained a relatively active citation status over an extended period.

Figure 7.

Analysis of Co-Cited References on MR in liver diseases. The top 15 references with the strongest citation bursts.16–18,20–31

Keyword Analysis

Analysis of Keyword Co-Occurrence

In this study, a total of 1150 keywords emerged. Table 6 presents the top 20 most frequently occurring keywords. “Mendelian randomization” was the most frequent keyword, appearing 238 times. It was followed by “non-alcoholic fatty liver disease” (n=149), “risk” (n=79), “association” (n=59), “epidemiology” (n=50), “instruments” (n=50), “causality” (n=40), “meta-analysis” (n=34), “obesity” (n=34), and “insulin-resistance” (n=33). We conducted a co-occurrence analysis on the 84 keywords that appeared more than five times each (Figure 8A). In the graph, the size of each node represents the frequency of the keyword’s occurrence, the lines between nodes indicate the strength of their association, and the four different colors represent four distinct clusters. The red cluster includes 23 keywords, primarily focusing on the bidirectional relationship between “non-alcoholic fatty liver disease” and “metabolic syndrome”, with a particular emphasis on the impacts of “insulin”, “obesity”, and “inflammation.” The green cluster comprises 22 keywords, centered around “genome-wide association”, “genetics”, “gene”, “loci”, and “epidemiology.” This cluster is concerned with genetics and epidemiology, especially the application of “genome-wide association” in the field of liver diseases. The blue cluster includes 20 keywords, involving research methods such as “mendelian randomization”, “causality”, and “bias” in the study of liver diseases like “cirrhosis/fibrosis” and “chronic hepatitis B”, as well as the relationship between gut microbiota and liver diseases. The yellow cluster contains 19 keywords, including “population”, “inference”, “cancer”, “Primary sclerosing cholangitis”, and “risk loci”, which involve population studies and the identification of risk loci, potentially focusing on research related to cancer and other diseases.

Table 6.

The Top 20 Most Frequent Keywords

Ranking	Keyword	Occurrences	Total Link Strength	Ranking	Keyword	Occurrences	Total Link Strength
1	Mendelian randomization	238	1081	11	Cirrhosis/fibrosis	32	170
2	Non-alcoholic fatty liver disease	149	709	12	Genome-wide association	31	190
3	Risk	79	426	13	Bias	31	174
4	Association	59	326	14	Prevalence	30	185
5	Epidemiology	50	296	15	Gut microbiota	25	105
6	Instruments	50	268	16	Fatty liver-disease	24	137
7	Causality	40	200	17	Hepatocellular carcinoma	21	123
8	Meta-analysis	34	204	18	Primary sclerosing cholangitis	21	113
9	Obesity	34	174	19	Diabetes	20	116
10	Insulin-resistance	33	173	20	Metabolic dysfunction-associated fatty liver disease	20	91

Figure 8.

The keyword mapping of MR in liver diseases. (A) A network map of keywords. Circles represent the frequency of keyword occurrences. (B) The top 15 keywords with the strongest citation bursts. (C) The timeline view of author’s keywords. (D) Trend topics based on keywords plus over time.

A keyword timeline diagram can clearly display the temporal evolution of keywords and their cutting-edge trends within a specific field. As shown in the Figure 8C, keyword clustering has generated 10 labels, namely cluster #0 “nonalcoholic fatty liver disease”, cluster #1 “inflammatory cytokines”, cluster #2 “vitamin D”, cluster #3 “primary sclerosing cholangitis”, cluster #4 “PNPLA3”, cluster #5 “hepatocellular carcinoma”, cluster #6 “type 2 diabetes mellitus”, cluster #7 “fatty liver”, cluster #8 “gut microbiota”, and cluster #9 “liver diseases”. The earliest and largest cluster is cluster #0 “nonalcoholic fatty liver disease”, while the most recent clusters are cluster #8 “gut microbiota” and cluster #9 “liver diseases”. This indicates that researchers initially applied MR to the field of nonalcoholic fatty liver disease, and over time, the scope of research has gradually expanded to a broader range of liver diseases. The current research focus is primarily on the causal relationship between gut microbiota and liver diseases.

Analysis of Keywords Citation Burst

We utilized the CiteSpace tool to identify the top 15 keywords with the most significant citation bursts in the field over the past decade, as shown in Figure 8B. Among them, the term “confers susceptibility” had the highest burst strength, with an index of 4.46, followed by “cardiovascular disease” and “insulin resistance”. “Ischemic heart disease” emerged as the earliest and longest-lasting keyword, experiencing rapid growth between 2014 and 2019. Recently trending keywords include “insulin resistance”, and “bias”. This indicates that “insulin resistance” is a hot topic in the field. We generated a keyword trend chart using R-bibliometric (Figure 8D) and found that research interests have recently shifted towards directions such as “metabolism”, “risk”, and “insulin resistance”.

Clinical Advances

PubMed is widely recognized as a high-quality medical database that indexes a substantial number of high-level clinical trials.32 In this study, we performed a systematic search and screening of the PubMed database, and ultimately included 10 clinical studies that involved liver diseases and employed MR methods, all of which were observational in design (Table 7). Based on their research foci, the included articles were categorized into three major thematic directions-risk factors, comorbid associations, and clinical outcomes-for systematic analysis.

Table 7.

Clinical Studies on Mendelian Randomization in Liver Diseases

Rank	First Author	Published Year	Journal	Title	Study Design	References
1	Shanshan Ran	2025	Journal of hepatology	Association of metabolic signatures of air pollution with MASLD: Observational and Mendelian randomization study	Observational Study	[33]
2	Chun Zhou	2024	Nutrition, metabolism, and cardiovascular diseases:NMCD	Inverse association between lung function and nonalcoholic fatty liver disease: An observational and mendelian randomization study	Observational Study	[34]
3	Fangyuan Jiang	2024	Med (New York, N. Y).	Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease	Observational Study	[35]
4	Qing-Yun Hao	2024	Frontiers in endocrinology	Observational and genetic association of non-alcoholic fatty liver disease and calcific aortic valve disease	Observational Study	[36]
5	Chenghao Weng	2024	Liver international	Association of sex hormones with non-alcoholic fatty liver disease: An observational and Mendelian randomization study	Observational Study	[37]
6	Lin Zeng	2024	Osteoporosis international	Association between fatty liver index and controlled attenuation parameters as markers of metabolic dysfunction-associated fatty liver disease and bone mineral density: observational and two-sample Mendelian randomization studies	Observational Study	[38]
7	Xinting Cai	2023	Frontiers in endocrinology	Association of sex hormones and sex hormone-binding globulin with liver fat in men and women: an observational and Mendelian randomization study	Observational Study	[39]
8	Tianyi Xia	2023	Radiology	Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk	Observational Study	[40]
9	Mengqin Yuan	2024	Chinese medical journal	Hypertension and NAFLD risk: Insights from the NHANES 2017–2018 and Mendelian randomization analyses	Observational Study	[41]
10	Yuetian Yu	2023	Frontiers in endocrinology	Nonalcoholic fatty liver disease and type 2 diabetes: an observational and Mendelian randomization study	Observational Study	[42]

With respect to risk factors, depression has been confirmed as one of the independent risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed non‑alcoholic fatty liver disease (NAFLD). Depression was associated with an increased risk of severe MASLD (HR = 1.21), and MR analysis further supported a causal relationship between genetically predicted depression and MASLD (OR = 1.55).43 Regarding environmental factors, ambient air pollutants, including PM2.5, PM10, NO₂, and NOₓ, were all associated with an increased risk of MASLD, with metabolic traits mediating these associations.33

In terms of pulmonary function, each 1-standard-deviation increase in forced expiratory volume in 1 second (FEV₁) and forced vital capacity (FVC) was associated with a 9% (OR = 0.91) and 10% (OR = 0.90) reduction in MASLD risk, respectively, and MR supported a causal relationship between lung function and MASLD.34 Hormonal regulation exhibited marked sex heterogeneity: sex hormone-binding globulin (SHBG) was linearly associated with a reduced risk of MASLDin women (HR = 0.71); bioavailable testosterone (BAT) showed a protective effect in men (HR = 0.81) but was associated with an increased risk in women (HR = 1.25). Genetic evidence supported a protective role of SHBG against hepatic steatosis in women.37,39 Furthermore, hypertension, as a traditional cardiovascular risk factor, was consistently demonstrated by both observational and MR studies to have a significant positive causal association with increased MASLD risk (OR = 7.203).41

MASLD has been redefined as a systemic disease involving multisystem involvement. A phenome-wide association study based on over 360,000 individuals from the UK Biobank revealed that MASLD was associated with 96 intrahepatic and extrahepatic diseases, including circulatory system disorders, metabolic diseases, and conditions affecting the urogenital, neurological, gastrointestinal, and hematological systems. MR analysis further supported causal relationships between MASLD and various intrahepatic diseases, metabolic disorders, cardiovascular and cerebrovascular diseases, and the risk of ascites; however, no significant association was identified between MASLD and neurological diseases.35 With respect to cardiovascular outcomes, MASLD was independently associated with an increased risk of calcific aortic valve disease (AVC) (HR = 1.58). Even after excluding genes related to impaired very low-density lipoprotein (VLDL) secretion, MR analysis still demonstrated a significant association between MASLD and calcific aortic valve stenosis (CAVS), suggesting that MASLD may promote cardiovascular calcification through pathways independent of conventional dyslipidemia.36 In terms of metabolic outcomes, bidirectional MR analysis confirmed a significant causal relationship between genetically predicted MASLD and an increased risk of type 2 diabetes mellitus (T2DM) (OR = 1.003), whereas no causal association was observed from T2DM to MASLD, indicating that MASLD may act as an upstream driver of T2DM rather than a mere comorbid condition.42 Furthermore, although observational analyses suggested an association between MASLD and reduced heel bone mineral density, this association disappeared after adjustment for shared metabolic factors including obesity and T2DM; MR analysis likewise did not support a direct causal effect between the two, suggesting that their relationship is primarily mediated by shared metabolic factors.38

With respect to liver-related clinical outcomes, genetically predicted liver MRI proton density fat fraction (PDFF) was strongly associated with multiple hepatic disease outcomes, including malignant liver tumors (OR = 4.5), alcoholic liver disease (OR = 1.9), liver fibrosis (OR = 3.0), cirrhosis (OR = 3.8), and MASLD (OR = 4.4). Mediation analysis further revealed that high-density lipoprotein cholesterol, genetic risk of type 2 diabetes mellitus, and waist-to-hip ratio exerted significant mediating effects in the association between PDFF and liver fibrosis/cirrhosis.40 These findings indicate that MRI-PDFF not only serves as a non-invasive quantitative imaging biomarker for assessing hepatic steatosis, but also holds potential clinical utility for liver disease risk stratification and adverse outcome prediction, thereby providing an objective quantitative tool for the precise diagnosis and prognostic evaluation of MASLD.

Discussion

Current Status of MR in Liver Diseases Research

With the widespread adoption of MR research methods, there has been an increasing number of studies in the field of liver diseases. This study included 304 documents and conducted an in-depth analysis of relevant literature from the past decade. These documents were published in 145 journals, authored by 1,879 individuals from 469 institutions across 36 countries and regions, collectively citing 10,477 references. From 2014 to 2021 marks the initial phase of this field, with a substantial increase in related research starting from 2022 to the present, constituting 91.1% of the total publication volume. This indicates a growing emphasis on the application of MR in liver diseases research. Currently, China, the United States, and the United Kingdom are leading in this area, with close collaborative ties. Among them, China is the country with the highest number of published papers, and also has the highest total number of citations (Table 1). The top ten institutions with the highest publication volumes are all located in China (Table 2), with Zhejiang University being the most contributing. It is noteworthy that although Australia has only published 7 papers, it has the highest average citation rate, reaching 56.14. The authors with the most publications and citations both come from China (Figure 5A and B), but Chinese authors tend to collaborate more closely within the country and have less interaction with international authors. Looking at the JCR, half of the top ten journals with the most publications are in Q1, and the other half in Q2 (Table 3). The ten journals with the most citations are all in Q1 (Table 4). This suggests that these journals have a high level of influence in the field, and future cutting-edge topics related to this area are likely to be published in these journals.

Hotspots of MR in Liver Diseases Research

Causal Relationship Between Gut Microbiota and Liver Diseases

The gut microbiota, now regarded as a newly recognized “organ”, is intimately linked to human health.44 The interaction between the intestine and the liver is commonly referred to as the “gut-liver axis”, a bidirectional route formed by the portal vein and the biliary circulation. Intestine-derived molecules, including nutrients and microbial metabolites, are conveyed to the liver via the portal vein, while bile acids, antibodies, and other bioactive compounds secreted by the liver travel through the biliary tract into the intestine, dynamically shaping microbial composition and intestinal barrier function.45,46 Perturbations of this axis can trigger dysbiosis, increase intestinal permeability, and facilitate endotoxin translocation to the liver, thereby contributing to the initiation and progression of various hepatic disorders.47 Accumulating evidence has demonstrated that gut dysbiosis correlates with worsening liver diseases,48 and MR studies continue to establish causal relationships between the gut microbiota and multiple liver diseases. Associations have been reported between the gut microbiota and a range of liver diseases, including chronic hepatitis B (CHB),49 alcoholic liver disease (ALD),50 MASLD,51 autoimmune liver diseases (AILDs),52 cholestatic liver diseases,53,54 drug-induced liver injury,55 liver cirrhosis,56 and primary liver cancer (PLC).57 However, these findings should not be directly equated with evidence of clinical intervention efficacy, and their translational value warrants rigorous validation through further experimental studies and RCTs. In the study of AILDs, research has shown that the gut microbiota can influence disease progression through immune cell mediation. For example, the L-tryptophan biosynthesis pathway, acting on CD14+CD16+ monocytes, is involved in the pathogenesis of autoimmune hepatitis (AIH), while Ruminococcus, acting on CD62L-CD86+ myeloid dendritic cells, is involved in the pathogenesis of primary sclerosing cholangitis (PSC).58 In addition, Zhai et al found that Enterobacterales, Enterobacteriaceae, Lachnospiraceae UCG-004, and Prevotella 9 are associated with an increased risk of MASLD. Reverse MR analysis revealed that MASLD may lead to an increase in the abundance of the genera Holdemania and Ruminococcus 2, further confirming a bidirectional causal relationship between the gut microbiota and MASLD.59 This discovery reveals the dynamic interaction network between the gut microbiota and liver diseases through metabolic and immune pathways, providing a new perspective for understanding disease progression. However, the above conclusions are based on GWAS data derived from a single European-ancestry population, and their generalizability requires cross-ancestry, cross-cohort replication. Moreover, the specific functional differences among distinct strains within the same bacterial family remain to be further elucidated, to avoid masking strain-level functional heterogeneity due to analyses conducted at the family level.

The impact of microbial communities on diseases varies across different taxonomic levels. At the phylum level, most microbial groups (such as Actinobacteria and Proteobacteria) are risk factors for liver diseases. In contrast, at the genus level, microbial communities often exert protective effects on CHB, fatty liver, and PLC (such as Dorea), but tend to have detrimental effects on primary biliary cholangitis (such as Bifidobacteriaceae). The abundance increase of certain bacteria may have a protective effect on one type of liver diseases, while simultaneously increasing the risk of another liver diseases. For instance, Anaerotruncus and Veillonella exert protective effects against PLC, but the former increases the risk of CHB, and the latter is a risk factor for fatty liver.60,61 Lachnospiraceae is capable of producing short-chain fatty acids that have anti-inflammatory effects and protect the intestinal barrier, which is beneficial for non-alcoholic fatty liver disease. However, in liver cirrhosis, it can lead to increased energy absorption and obesity, thereby indirectly exacerbating the liver burden and acting as a risk factor.62 Nevertheless, Zhou et al found that Lachnospiraceae is negatively correlated with the risk of hepatitis B-related cirrhosis.63 This discrepancy may be due to the fact that neither study further subtyped Lachnospiraceae to more specific strains or species. Meanwhile, liver cirrhosis of different etiologies has varying impacts on the gut microbiota, and the abundance of Lachnospiraceae may change with different causes, resulting in opposite directions of its role in diseases. Future research needs to combine more refined microbial subtyping for stratified analysis and verify its specific mechanisms of action through in vivo and in vitro experiments. In addition, combining single-cell sequencing technology to explore the expression differences of microbiota-related genes at different stages of liver diseases will help to transform these findings from basic research into clinically applicable diagnostic biomarkers for early diagnosis and disease monitoring. Meanwhile, exploring the therapeutic potential of microbial metabolites will also provide new strategies for the precise prevention and treatment of liver diseases.

Causal Relationship Between Metabolism and Liver Diseases

Cholesterol (TC) metabolism is essential for hepatic homeostasis. Disruption of cholesterol balance-characterized by increased synthesis and absorption coupled with impaired clearance-leads to intrahepatic cholesterol accumulation. Multiple MR studies now establish a causal link between aberrant cholesterol metabolism and liver diseases. Chen et al identified four core cholesterol-metabolic genes causally implicated in MASLD: MVK and TM7SF2 confer protection, whereas HMGCS1 and FDPS increase risk, and nominated retinoic acid and alendronate as candidate therapeutics, providing novel targets for MASLD research and treatment.64 Zhang et al profiled 23 differentially expressed sphingolipid-metabolism genes and, through MR, validated CD37 and CXCL9 as diagnostic biomarkers for MASLD; their dysregulation appears to drive disease progression by perturbing amino-acid metabolism and promoting M1-macrophage-mediated immune infiltration.65 Multi-species, multi-omics analyses have revealed that alcohol-use-associated genes (most notably WDPCP) drive ALD by reprogramming lipid metabolism, and that increased WDPCP expression is causally linked to hepatic fibrosis and cirrhosis in humans, positioning it as a potential therapeutic target for ALD.66 Multiple MR studies have shown that genetically predicted lower levels of low‑density lipoprotein cholesterol (LDL‑C) and TC are causally associated with an increased risk of HCC.67,68 Tian et al69 also identified a significant causal relationship between apolipoprotein E (APOE) and HCC risk. Furthermore, as HMGCR is the rate‑limiting enzyme in cholesterol synthesis, MR evidence suggests that genetically predicted inhibition of this target may be associated with an increased risk of HCC.70 However, genetically predicted lipid levels or drug‑target associations are not equivalent to the effects of lipid‑lowering agents (including statins) in actual clinical practice; their net impact on HCC development remains unclear and warrants further investigation through in vivo experiments and clinical studies. In addition, methodological heterogeneity across studies—including differences in instrumental variable selection, pleiotropy control, and sample overlap handling—may contribute to inconsistent findings, and some null results may be underreported due to publication bias. Therefore, the causal relationship between cholesterol metabolism and HCC requires further replication across diverse populations and independent data sources.

As the central hub of vitamin metabolism, the liver plays a pivotal role in the storage, activation, and metabolism of vitamins. Recent studies indicate that disturbances in vitamin metabolism are intricately linked to the onset and progression of various liver diseases, yet causal relationships remain to be firmly established. Using MR, Chen et al reported that reduced vitamin A metabolic activity is causally associated with a higher risk of HCC. They further revealed that RDH16, a key gene in the retinol metabolic pathway, is markedly down-regulated in HCC tissues. Suppression of RDH16 promotes cancer-cell proliferation, migration, and invasion while inhibiting apoptosis, thereby exerting a pro-tumorigenic effect. Importantly, RDH16 expression is regulated by miR-665, providing new insights into the molecular diagnosis and targeted therapy of HCC.71 Although cross-sectional studies have reported inverse associations between serum vitamin C or 25-hydroxyvitamin D levels and MASLD risk, MR analyses have not supported a causal relationship.72,73 These findings suggest that previously observed associations may be confounded, and current evidence is insufficient to endorse vitamin C or D supplementation for MASLD prevention or treatment. Similarly, investigations into vitamin D and ALD revealed no statistically significant causal relationship between the two factors,74 underscoring the importance of distinguishing correlation from causation in assessing the role of vitamins in liver diseases.

The liver serves as the central organ for amino acid metabolism, and its metabolic reprogramming plays a pivotal regulatory role in the development and progression of HCC. The research team led by Wei employed a multi-omics survival analysis (SVR) approach to identify for the first time the functional genetic variant rs2074038. This locus significantly impacts the clinical prognosis of hepatitis B virus-related HCC (HBV-HCC) patients by regulating the expression levels of ACCS, a key gene in amino acid metabolism. The study revealed that high expression of the ACCS gene was significantly positively correlated with shorter overall survival (OS) in patients.75 This discovery not only elucidates the crucial role of amino acid metabolic reprogramming in HBV-HCC progression at the molecular level but also provides a translational molecular marker for clinical prognosis assessment.

Causal Relationship Between Insulin Resistance and Liver Diseases

MASLD has become the most prevalent chronic liver diseases worldwide, closely linked to Western diets, obesity, and escalating insulin resistance (IR). IR promotes hepatic lipid accumulation through a dual mechanism: on one hand, it directly enhances hepatic de novo lipogenesis, while on the other hand, it impairs insulin-mediated suppression of adipose tissue lipolysis. This leads to increased peripheral lipolysis, resulting in a massive influx of free fatty acids into the liver, which further exacerbates lipid deposition and metabolic dysregulation.76,77 MR evidence in adults shows that MASLD only heightens IR in severely obese individuals when accompanied by hepatic inflammation; among the general overweight/obese population, MASLD does not add extra IR risk.27 Subsequent MR studies in obese adolescents also confirmed no direct causal relationship between MASLD and IR, with their association likely stemming from shared pathogenic pathways.78 Kong et al discovered that elevated IR increases branched-chain amino acid (BCAA) levels, while heightened alanine exacerbates insulin resistance. This suggests that low birth weight may promote MASLD development through an “alanine-insulin resistance-elevated BCAAs” cascade, providing a theoretical foundation for interventions targeting both insulin resistance and amino acid metabolism.79 Huang et al demonstrated that all obesity phenotypes were significantly associated with increased MASLD risk. Their findings further confirmed both IR and C-reactive protein (CRP) as causal mediators in this association, with IR exhibiting stronger mediating effects. This underscores the pivotal role of IR monitoring in MASLD prevention and management.80 Moreover, IR has been identified as a contributing factor in HCC development.81 IR not only elevates serum levels of insulin-like growth factor 1 (IGF-1) but also enhances its bioactivity. The increased IGF-1 binds to insulin-like growth factor 1 receptor (IGF-1R), activating downstream signaling pathways that drive hepatocyte proliferation while inhibiting apoptosis, ultimately promoting HCC pathogenesis.82 Although recent studies suggest obesity may influence HCC through IR, the causal relationship between IR and HCC remains unestablished.83 Future research should employ large-scale, multi-ethnic MR studies integrated with multi-omics approaches to elucidate the causal relationship between IR and HCC and delineate the underlying molecular mechanisms.

Limitation

Several limitations of this study should be acknowledged. First, the bibliometric analysis was based solely on English-language publications indexed in the WoSCC. It did not include relevant literature from other databases such as Scopus. This may have introduced language and database selection biases, which could, to some extent, affect the comprehensiveness of the literature coverage. Second, variations in visualization tools and software parameter settings may have led to minor information omissions or discrepancies during data presentation, potentially influencing the completeness of the research findings. Finally, due to the dynamic nature of journal database updates, newly published articles and emerging research hotspots beyond the retrieval timeframe could not be captured in real time. Consequently, our findings may not fully reflect the most recent advances in this field, posing a constraint on the timeliness of the conclusions.

Although MR analysis offers unique advantages in causal inference, the potential mechanisms underlying its findings still require further experimental validation. Second, differences in GWAS data sources, statistical models, and parameter settings across studies may contribute to heterogeneity in the observed results, and inconsistencies between positive and null findings may also arise from these methodological variations. Third, current GWAS data are predominantly derived from European-ancestry populations (approximately 78%), reflecting a notable population bias that limits the generalizability of findings to other ethnic groups. Future studies should incorporate data from East Asian, African, and other underrepresented populations to enhance sample diversity and thereby improve the generalizability and robustness of research conclusions. Fourth, although MR can effectively reduce confounding bias inherent in observational studies, its reliance on genetic instrumental variable assumptions may introduce additional bias. Issues such as instrument validity, sample overlap, horizontal pleiotropy, and the lack of independent replication may all affect the accuracy of causal estimates. In addition, all clinical studies included in the present review were observational in design, and the overall level of evidence remains limited. Therefore, MR-based conclusions require cross-validation through RCTs and additional epidemiological evidence to strengthen the robustness of causal inferences. Future research should prioritize the establishment of a cross-ancestry, cross-regional GWAS data-sharing platform and promote the systematic integration of evidence from case series, RCTs, and observational studies within a unified framework to develop a multidimensional and robust approach to causal inference.

Conclusion

This study used bibliometric and visualization analyses to systematically review the research progress and developmental trends in the application of MR in the field of liver diseases. In recent years, this field has developed rapidly, with a steady increase in annual publications. Bibliometric analysis indicated that the current research hotspots are mainly concentrated in three key areas: gut microbiota, metabolic processes, and insulin resistance. Among the PubMed-indexed literature included in this study, clinical research predominantly focused on MASLD, with particular attention to their risk factors, comorbidities, and prognostic outcomes. It should be emphasized that the “research hotspots” identified through bibliometric analysis merely reflect the distribution of scholarly attention and should not be equated with clinically validated causal relationships; the causal hypotheses derived from these hotspots require further confirmation through RCTs and basic mechanistic studies. Future research should expand the spectrum of liver diseases under investigation and further explore the underlying molecular mechanisms. In parallel, a cross-ancestry, cross-regional data-sharing platform for genome-wide association studies (GWAS) should be established to integrate evidence from MR studies, RCTs, and observational studies, thereby constructing a multidimensional causal inference framework. Despite certain limitations, this study contributes by mapping the structure and evolution of the literature, thereby providing valuable directional references for future research.

Funding Statement

This research was funded by the Hunan University of Chinese Medicine Double First-Class Discipline Construction Project (0005001006).

Ethical Approval

This was deemed not applicable.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.