Glutathione S-transferase gene polymorphisms in diabetes mellitus: A bibliometric analysis and future directions

1. Introduction

Diabetes mellitus (DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia, which is caused due to impaired insulin secretion, insulin resistance, or both. According to the International Diabetes Federation, over 590 million people worldwide are affected by diabetes as of 2024, with projections estimating more than 853 million cases by 2050 (International Diabetes Federation, 2025). Oxidative stress is a central feature in the pathogenesis of diabetes and its complications. Hyperglycemia increases mitochondrial superoxide production, promotes NADPH oxidase activity, and impairs antioxidant defense mechanisms. The imbalance between reactive oxygen species (ROS) and antioxidants causes oxidative damage to cellular proteins, lipids, and DNA. This stress contributes to β-cell apoptosis, insulin resistance, and vascular injury (Bhatti et al., 2022).

Glutathione S-transferases (GSTs) are phase II detoxification enzymes. These enzymes catalyze the conjugation of reduced glutathione (GSH) to electrophilic compounds, including ROS, xenobiotics, carcinogens, and products of lipid peroxidation. The GST family includes several classes: α(GSTA), µ(GSTM), π(GSTP), θ(GSTT), ω(GSTO), σ(GSTS), and ζ(GSTZ). Each class plays distinct roles in detoxification pathways and cellular redox regulation (Mazari et al., 2023). GST enzymes are expressed in various tissues such as the liver, pancreas, kidneys, and endothelial cells. GSTs protect pancreatic β-cells from oxidative damage by neutralizing ROS and maintaining redox homeostasis (Aloke et al., 2024). This protective function is especially important because β-cells have low intrinsic antioxidant capacity. Reduced GST activity may exacerbate oxidative stress and contribute to β-cell dysfunction and insulin resistance (Hurrle and Hsu, 2017).

GST genes exhibit substantial polymorphic variation. The most common genetic variants include gene deletions and single-nucleotide polymorphisms (SNPs) that reduce enzyme activity or predispose individuals to clinical manifestations of several etiologies (Abdalhabib et al., 2022; Abdalhabib et al., 2021; Chatterjee and Gupta, 2018; Mazari et al., 2023). Homozygous deletions (null genotypes) of GSTM1 and GSTT1 have been shown association with increased risk of diabetic complications (Gusti et al., 2021; Kim and Hong, 2012; Klusek et al., 2020; Ramprasath et al., 2011; Tala and Sari, 2021; Wang et al., 2006). A common SNP of GSTP1, i.e., Ile105Val (rs1695), alters the catalytic activity of GSTP1, thermal stability, and substrate specificity (Mastana et al., 2013; Sobha et al., 2025; Song et al., 2021). Individuals carrying the GSTP1-Ile105Val variant, either in the homozygous (GG) or heterozygous (AG) form, have been reported to have a fourfold increased risk of developing type 2 diabetes (Mergani et al., 2016). GSTO1 and GSTA1 polymorphisms may also modulate antioxidant defenses and have been linked to metabolic diseases and diabetic complications (Alnasser, 2025; Pavlovic et al., 2023). These polymorphisms modulate individual responses to oxidative stress, xenobiotic exposure, and metabolic disturbances. As such, they have gained attention as genetic risk factors in multifactorial diseases, including diabetes.

A bibliometric analysis provides an objective method to quantify research activity, collaboration patterns, and thematic evolution in a specific field. By mapping global research output, bibliometric analysis can identify influential authors, institutions, journals, and knowledge gaps, thereby guiding future investigations (Jiang et al., 2023). To date, no comprehensive bibliometric analysis has synthesized the global research output on GST polymorphisms in relation to DM. This gap limits the ability to assess the maturity of the field and prioritize future investigations.

Prior efforts to synthesize the literature on GST polymorphisms in diabetes have largely been limited to narrative reviews (Banerjee and Vats, 2014; Tabatabaei-Malazy et al., 2017), with one meta-analysis providing pooled evidence of associations between GST variants and diabetes risk (Nath et al., 2019). While these works offer valuable insights into specific genetic effects, they do not provide a comprehensive overview of global research activity. To date, no bibliometric study has systematically mapped publication output, collaboration networks, or thematic evolution in this area. Addressing this gap is essential to contextualize existing evidence, identify knowledge gaps, and guide future research priorities. This study is significant because it represents the first bibliometric evaluation of global research on GST polymorphisms in DM. It highlights patterns of research productivity, collaboration, and thematic development. The analysis identifies critical gaps, including limited functional genomic evidence, underrepresentation of diverse populations, and insufficient integration with translational models. By delineating these gaps, the study provides direction for future investigations and supports the application of GST polymorphism research within precision-medicine frameworks. The present study aimed to conduct a bibliometric analysis on GST gene polymorphisms and their association with DM and its complications. This analysis assessed the distribution, impact, and collaboration patterns of research publications. The study also examined the evolution of key themes, research hotspots, and emerging areas.

2. Materials and Methods

2.1 Data search and retrieval strategy

The Scopus database was selected as the primary data source due to its broad coverage of peer-reviewed biomedical literature and compatibility with bibliometric software. A systematic search was conducted on June 19, 2025 using the terms (TITLE-ABS-KEY(glutathione AND s-transferases AND genes AND polymorphisms AND diabetes AND mellitus) OR TITLE-ABS-KEY(glutathione AND s-transferases AND genetic AND polymorphisms AND diabetes AND mellitus) OR TITLE-ABS-KEY (gstm1 AND polymorphisms AND diabetes AND mellitus) OR TITLE-ABS-KEY (gstt1 AND polymorphisms AND diabetes AND mellitus) OR TITLE-ABS-KEY (gstp1 AND polymorphisms AND diabetes AND mellitus) OR TITLE-ABS-KEY (gsto1 AND gene AND polymorphism AND diabetes AND mellitus) OR TITLE-ABS-KEY (gst AND polymorphism AND diabetes AND mellitus). Keywords were selected through an initial scoping review of the literature in Scopus and PubMed to identify common terminologies used for GST polymorphisms in diabetes. The final Boolean string included gene family members (GSTM1, GSTT1, GSTP1, GSTO1), their known polymorphic variants, and the disease term ‘diabetes mellitus.’ Both ‘gene polymorphism’ and ‘genetic polymorphism’ were incorporated to account for variations in indexing terminology. This approach ensured comprehensive retrieval of studies while maintaining specificity to GST polymorphism-diabetes research. The process of record identification, screening, exclusion, and final inclusion is summarized in the PRISMA flow diagram (Fig. 1).

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Fig. 1.

Scopus data search strategy and data collection.

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The search was restricted to the title, abstract, and keyword fields and conducted without language restriction to encompass a global perspective. Inclusion criteria were: (i) peer-reviewed original research articles, reviews, editorials, book chapters, or conference papers; (ii) studies addressing GST gene polymorphisms in the context of DM or its complications; and (iii) availability of bibliographic metadata in Scopus. Exclusion criteria were: (i) records unrelated to GST gene variants (e.g., studies on other antioxidant enzymes without a GST focus); (ii) studies not linked to DM; (iii) duplicate records. Articles excluded as ‘irrelevant’ were those that mentioned GSTs or diabetes in passing but did not evaluate GST polymorphisms in relation to diabetes risk, pathophysiology, or outcomes. The data were reviewed by an independent reviewer.

3. Results

3.1 Publication output

A total of 170 publications were retrieved from the Scopus database using the terms mentioned in the methods section. The majority of included publications were original research articles (n = 145), followed by review articles (n = 19). Book chapters and letters (n = 2 each), as well as conference proceedings and editorials (n = 1 each), were minimally represented. Of the 170 documents retrieved, 45 were excluded due to irrelevance. The combined analyses of annual publications and citations demonstrated a progressive rise in both research output and academic impact from 2008 onward. The annual publication trend demonstrated notable variability, with peaks in research output observed in 2015 and again in 2022, the latter representing the highest productivity. Citation frequency showed distinct surges in 2012, 2013, 2019, and 2022, indicating that multiple periods of research activity have contributed substantially to the academic impact of this field (Fig. 2). The peak in publications and citations observed from 2012-2013 can be attributed to multiple high-impact studies. In 2012, influential works such as the environmental health perspectives study on GST polymorphisms, air pollutants, and insulin resistance (102 citations), and chemosphere on GSTO1 polymorphisms and metabolic syndrome (65 citations) received wide attention. The same year, case-control studies from Turkey, Iran, and Europe further broadened the evidence base linking GST variants to diabetes and its complications. In 2013, several highly cited meta-analyses, including those published in PLoS ONE (50 citations) and Gene (33 citations), synthesized associations between GSTM1/GSTT1 null genotypes and type 2 diabetes. Together, these studies account for the surge in academic output and citations during this period. The multidisciplinary nature of research on GST polymorphisms in diabetes is evident from the distribution of subject areas (as illustrated in Fig. 3) with leading contributions from Medicine, Biochemistry, Genetics and Molecular Biology, followed by Pharmacology, Toxicology, and Pharmaceutics.

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Fig. 2.

Annual publication and citation trends of studies on GST polymorphisms and DM.

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Fig. 3.

Distribution of publications by major subject areas.

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3.2 Country-wise distribution, institutional contributions, and collaboration

India led in research output, contributing nearly 31.25% of the total publications, followed by the United States (15%) and China (10%). Several countries from Asia, South America, and the Middle East, including Iran, Russia, Brazil, Egypt, and Saudi Arabia, also demonstrated significant contributions (Table 1). Among other contributors, Japan, Sweden, and Brazil each accounted for approximately 3.3% of the total research output, reflecting modest yet meaningful participation in the global literature on GST polymorphisms and DM.

Table 1. Country-wise distribution of publications on GST polymorphisms and DM.

Country	Documents	% Publications	Citations	Total link strength
India	25	31.25	613	132
United States	12	15	450	26
Iran	11	13.75	157	84
Egypt	9	11.25	161	61
China	8	10	180	92
Russian Federation	7	8.75	219	21
Slovenia	7	8.75	192	20
Saudi Arabia	6	7.5	63	32
Taiwan	5	6.25	173	20
Turkey	5	6.25	138	37

The top cited institutions include Biomedical Research Laboratories, Regulatory Affairs Department, Tokyo, Japan (n=175), Lund University Diabetes Centre, Clinical Research Centre, Malmö, Sweden (n=151), Molecular and Human Genetics Laboratory, University of Lucknow, Lucknow, India (n=108), and Harvard School of Public Health, Boston, MA, United States (n=106).

Among the 37 countries identified in the dataset, co-authorship analysis showed that only 12 engaged in collaborative networks, which were organized into two primary clusters. India emerged as the most prominent contributor in terms of collaboration strength. While earlier research activity was predominantly driven by countries such as the United States and Taiwan, more recent efforts have been led by India, Egypt, Iran, Saudi Arabia, and Brazil. This pattern suggests a notable geographical shift in research focus toward Asia and the Middle East (Fig. 4).

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Fig. 4.

International co-authorship network of countries involved in GST polymorphism research.

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3.6 Keyword co-occurrence network

A total of 1276 keywords were identified. However, the analysis was limited to 131 keywords that appeared at least three times across the document corpus. The network visualization of keyword co-occurrence highlights five major thematic clusters. These clusters were formed based on the frequency and co-occurrence relationships among other keywords.

The network visualization of keyword co-occurrence identified distinct thematic clusters centered around genetic risk, polymorphism, oxidative stress, and metabolic markers (Fig. 5). Core terms such as GST, SNP, genotyping, and genetic risk were highly connected, indicating their centrality in the research domain. Associated keywords such as hemoglobin A1c, insulin, cardiovascular risk, diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy illustrate a strong clinical orientation of GST-related genetic studies.

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Fig. 5.

Keyword co-occurrence network of studies on GST polymorphisms and DM

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The overlay visualization (Fig. 6) reveals a chronological evolution of research themes on GST polymorphisms and DM. Early research activity (dark blue) focused on core molecular concepts such as genotype, oxidative stress, allele (gene), and GST. More recent studies (yellow) emphasized applied and clinical terms, including gestational diabetes, hypertension, cholesterol, and glycosylated hemoglobin, which highlights an increased translational orientation in the literature.

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Fig. 6.

Overlay visualization of keyword co-occurrence network.

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4. Discussion

The results of this bibliometric analysis provide a comprehensive overview of the global research on polymorphisms in GST genes in DM. The increased publication volume, with a peak in 2022, indicates growing scientific focus on the relationship between genetic susceptibility and oxidative stress in diabetes. The citations count also peaked in the same year, showing that the field has achieved both academic maturity and clinical relevance.

India emerged as the most productive country, accounting for nearly 31.25% followed by the United States, Iran, Egypt, and China. This distribution indicates that while high-income countries maintain a strong research presence, middle-income countries, particularly in Asia and the Middle East, have become increasingly prominent. Several factors may explain this shift: high diabetes prevalence in these regions, availability of genetic epidemiology funding, and regional interest in pharmacogenomics (International Diabetes Federation, 2025). Moreover, emerging economies have prioritized non-communicable disease research due to the growing burden of DM within their populations (Hossain et al., 2024; Jakovljevic and Milovanovic, 2015).

Furthermore, Asia, especially countries like China and India, bears the largest share of the global diabetes burden, with over 60% of cases worldwide. This epidemiological reality drives both clinical and research priorities in the region (Rhee, 2015). Asian populations exhibit unique genetic risk profiles for diabetes, including a higher prevalence of certain GST polymorphisms and a greater susceptibility to type 2 diabetes at lower BMI levels compared to Europeans. This has spurred region-specific genetic studies (Rhee, 2015; Seah et al., 2023). Additionally, accelerated urbanization, dietary shifts, and lifestyle changes in Asia have led to increased diabetes incidence, prompting more research into gene-environment interactions (Rhee, 2015). In contrast, while the US and Europe maintain strong research traditions, the relative growth in Asia reflects both the scale of the diabetes epidemic and the region’s increasing scientific capacity.

GST polymorphisms are increasingly recognized as potential biomarkers for assessing diabetes risk and guiding therapeutic decision-making. The progressive growth in research output and citation frequency aligns with accumulating evidence that implicates oxidative stress as a central pathophysiological mechanism in DM (Alnasser, 2025; Mergani et al., 2016; Orhan et al., 2014; Ramprasath et al., 2011; Sobha et al., 2025; Song et al., 2021; Tala and Sari, 2021). The consistent prominence of keywords such as oxidative stress, GSH, GSTM1, and GSTT1, as demonstrated in both the co-occurrence and overlay visualizations, further underscores the mechanistic foundation of GST-related research. The association between GST polymorphisms and reduced detoxification capacity for ROS represents a biologically plausible mechanism that links genetic variability to the onset and progression of diabetes (Amer et al., 2012; Mastana et al., 2013).

GST gene variants, particularly the deletion polymorphisms of GSTM1 and GSTT1, result in reduced or absent enzymatic detoxification of ROS, thereby amplifying oxidative damage in pancreatic β-cells and insulin-responsive tissues. This disruption in redox balance contributes to impaired insulin secretion, glucose intolerance, and systemic inflammation (Hurrle and Hsu, 2017). These associations establish a biologically plausible link between GST polymorphisms and the onset and progression of both type 1 and type 2 diabetes. The strong representation of GST-null genotypes in early research, reflected in the network’s dominant clusters, demonstrates their foundational role in shaping the molecular understanding of diabetes pathogenesis.

Recent shifts in keyword usage, highlighted in the overlay visualization, indicate a broadening research focus. Emerging themes such as gestational diabetes, meta-analysis, case-control study, cholesterol, and glycosylated hemoglobin (hemoglobin A1c) reflect a transition toward translational and population-based studies. These trends point to efforts aimed at validating gene-disease associations, refining genotype-phenotype correlations, and applying molecular findings in clinical settings. Furthermore, the emphasis on systematic reviews and meta-analyses reflects the maturation of the research field, where scholars now aim to reconcile prior inconsistencies across diverse populations and study designs. This evolution of research priorities, substantiated by both quantitative metrics and keyword trajectories, confirms that GST polymorphism research in diabetes has advanced from basic genetic exploration to a translational domain with direct clinical implications. The observed shift from mechanistic research to clinical applications likely reflects both scientific and public health drivers. Early studies (2010-2014) focused on elucidating oxidative stress pathways and establishing the role of GSTM1, GSTT1, and GSTP1 variants in redox regulation. As these mechanisms became well documented, research emphasis progressively moved toward clinical translation, including the evaluation of GST polymorphisms as risk markers for type 2 diabetes, gestational diabetes, and diabetes-related complications. Advances in molecular techniques (e.g., polymerase chain reaction-based genotyping), the availability of larger patient cohorts, and the growing demand for precision-medicine approaches further enabled this transition. Additionally, the global rise in diabetes prevalence, particularly in Asia, created urgency for population-specific risk stratification and predictive models, which directed attention to clinical applications of GST polymorphism research.

Among the thematic clusters identified, gestational diabetes emerged as a distinct and clinically relevant focus. Unlike broader clusters such as oxidative stress or meta-analysis, the recurrence of gestational diabetes as a keyword highlights increasing recognition of GST polymorphisms in pregnancy-related glucose disorders. This reflects global concern regarding the dual impact of gestational diabetes on maternal and neonatal outcomes and the potential contribution of GST variants in modulating oxidative stress during pregnancy. Evidence indicates that GSTM1 and GSTT1 null genotypes increase susceptibility to gestational diabetes by impairing antioxidant defenses and predisposing women to dysregulated glucose metabolism under gestational stress. The bibliometric prominence of gestational diabetes underscores its translational significance, suggesting that GST genotyping could support early risk stratification, predictive modeling, and preventive strategies for women at risk of gestational diabetes and related complications.

The presence of environmental modifiers such as smoking and alcohol consumption highlights the relevance of gene-environment interactions. Cigarette smoke contains numerous electrophilic compounds and ROS that are detoxified by GST enzymes. Individuals with GSTM1 or GSTT1 null genotypes have reduced enzymatic capacity, making them more susceptible to oxidative damage from smoking. Studies have shown that smoking can amplify the risk of diabetes and its complications in individuals with GST gene deletions, highlighting a gene-environment interaction that is especially relevant in populations with high smoking prevalence (Geng and Huang, 2020). Beyond smoking, exposure to environmental toxins such as heavy metals, air pollutants, and dietary carcinogens can interact with GST polymorphisms to modulate diabetes risk (Hollman et al., 2016). These findings contribute to the expanding narrative that diabetes pathogenesis is shaped by both inherited genetic traits and modifiable external exposures. The integration of molecular and environmental risk factors offers a comprehensive framework for understanding the etiopathogenesis of diabetes and guiding the development of precision medicine strategies.

These patterns also suggest underexplored yet promising areas such as gene-nutrient interactions, longitudinal cohort studies in diverse populations, and GST polymorphisms in less commonly studied diabetic phenotypes (e.g., latent autoimmune diabetes or maturity-onset diabetes of the young). The observed co-occurrence networks not only reflect the current state of research but also provide a conceptual roadmap for future investigations aimed at understanding and managing diabetes through a molecular and precision health perspective.

5. Limitations

Like others, this study also has several limitations that warrant consideration. First, the analysis relied exclusively on the Scopus database, which, although comprehensive, may not capture all relevant publications indexed in other sources such as Web of Science. Second, while no language restrictions were applied, Scopus indexing may inherently favor English-language journals, introducing a potential language bias. Third, citation-based metrics such as h-index and citation counts may be influenced by self-citation practices and time since publication, which can overrepresent older articles. Finally, although VOSviewer thesaurus files were used to harmonize keywords, some nuances in terminology may have been overlooked. These limitations should be considered when interpreting the findings, and future work may benefit from incorporating multiple databases and triangulating bibliometric outputs with qualitative assessments.

6. Conclusions

The bibliometric analysis confirms that GST polymorphism research in diabetes has transitioned from a mechanistic foundation to a translational frontier, with significant promise for precision medicine in endocrinology and public health genetics. The emergence of gestational diabetes, gene-environment interactions, and SNP-level analyses highlights the diversification of research themes. Furthermore, increased international collaboration, meta-analytical work, and functional genomic studies are recommended to explain genetic insights into diagnostic and therapeutic innovations in diabetes care.