Home Medicine Multiple sclerosis and type 1 diabetes: a Mendelian randomization study of European ancestry
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Multiple sclerosis and type 1 diabetes: a Mendelian randomization study of European ancestry

  • Jing Wang-Chang ORCID logo , Jia Ma ORCID logo , Han Su ORCID logo , Xiaoqiang Wu ORCID logo , Shunfeng Zhao ORCID logo , Yuan Cai ORCID logo , Ning Feng ORCID logo , Lina Sun ORCID logo , Xin Zhao ORCID logo , Kai Feng ORCID logo , Haoxiao Chang ORCID logo EMAIL logo and Haowen Li ORCID logo EMAIL logo
Published/Copyright: December 19, 2025
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Open Medicine
From the journal Open Medicine Volume 20 Issue 1

Abstract

Objectives

Observational studies have indicated that type 1 diabetes (T1D) is prevalent in multiple sclerosis (MS), yet the causality remains unclear. The purpose of this study was to assess the causal association between MS and T1D.

Methods

We employed a Mendelian randomization method using two samples to research the causal association between MS and T1D. The study primarily utilized the inverse variance weighted (IVW) method, and we use three methods (MR Egger, Weighted median, Weighted mode) for auxiliary analysis. To avoid reverse causality, we employed the Steiger Test method to further examine the screened SNPs. Furthermore, sensitivity analysis was conducted to ensure the robustness of the obtained results.

Results

When MS was considered as the exposure variable and T1D as the outcome variable, the results indicated a significant positive correlation (IVW, OR=1.078, 95 % CI: 1.041−1.117; p<0.001). Conversely, when T1D is the exposure in question, the causal relationship with MS remains undetermined. These results were further validated through sensitivity analysis.

Conclusions

The MR analysis results indicate that there is a causal relationship between MS and T1D. We provide compelling genetic evidence to support the causal connection between MS and an increased risk of T1D.

Keywords: multiple sclerosis; type 1 diabetes; Mendelian randomization analysis; genetics

Introduction

Multiple sclerosis (MS) is a disease of the immune system that involves inflammation and damage to the protective layer of nerve fibers in the brain and spinal cord, known as demyelination. This autoimmune condition leads to the progressive deterioration of the central nervous system (CNS) [1]. This disease is believed to result from complex interactions between genetic and environmental factors, primarily affecting middle-aged and young adults [2]. It is a recurrent condition without a cure [3]. Comorbidities are common in MS, and chronic comorbidities can lead to delayed diagnosis, increased mortality, and challenges in clinical management and treatment.

Epidemiological studies have found that MS is more prevalent among individuals with autoimmune diseases, including T1D [4]. Although implicating different organs, autoimmune diseases have the shared pathogenesis or are caused by the shared predisposing factors [5], 6], so comorbidities of autoimmune diseases are common. Previous studies have shown that MS and T1D share immunological and epidemiological characteristics [7]. T1D, a chronic autoimmune disease, is characterized by the destruction of β cells. This ultimately leads to a complete deficiency of insulin [8]. There is evidence to suggest that T1D may directly exacerbate autoimmune dysfunction in MS, suggesting a possible common mechanism in the development of these two diseases [9]. However, the causal relationship between MS and T1D has not been elucidated. In order to examine the genetic causality, we implemented the two sample MR method to scrutinize the plausible causal linkage between MS and T1D.

Mendelian randomization is a statistical approach that harnesses genetic instrumental variables to establish causal links between exposure and outcome [10]. It minimizes bias caused by unmeasured confounding factors and reverse causality [11], providing stronger evidence for causal inference than observational studies [12]. In this study, we implemented the two sample MR method to scrutinize the plausible causal linkage between MS and T1D.

Methods

Study design

This study assessed the Two Sample MR analysis method and Genome-Wide Association Study (GWAS) summary data to estimate the causal effect of exposure on outcomes [13], 14], the objective of this research was to assess the causal association between MS and T1D, its complications. The study is based on three Assumption of Mendelian inheritance [15]: (1) The exposure demonstrates a significant correlation with the selected instrumental variable. (2) No confounding factors are found to be linked to the instrumental variables. (3) The instrumental variables exclusively influence outcomes via exposure and exclude any other mechanisms (Figure 1).

Figure 1: Diagram of the Mendelian randomization study for the association between multiple sclerosis and type 1 diabetes. SNP, single nucleotide polymorphism; IVs, instrumental variables.
Figure 1:

Diagram of the Mendelian randomization study for the association between multiple sclerosis and type 1 diabetes. SNP, single nucleotide polymorphism; IVs, instrumental variables.

By applying these assumptions and the two sample MR method, this study seeks to provide stronger evidence for the causal relationship between MS and T1D minimizing the bias caused by unmeasured confounding factors and reverse causality.

Data resource

For MS, we utilized a large scale GWAS dataset from the International Multiple Sclerosis Genetics Consortium (IMSGC), comprising of 47,429 cases and 68,374 controls [16]. For T1D, we utilized a GWAS data from the UK-Biobank, comprising of 18,942 cases and 501,638 controls [17]. We used the largest and most comprehensive database that has been analyzed to date (Table 1). To decrease the potential deviation in the analysis of MR caused by population stratification, all the data utilized in this study were obtained from individuals of European descent. Given that the research relied on publicly available GWAS aggregated statistical data and did not involve individual-level data analysis, ethical approval was not sought.

Table 1:

Exposure and outcome gwas data information.

Traits Ncase Ncontrol Ncases Year GWAS id
Multiple sclerosis 47,429 68,374 115,803 2019 Ieu-b-18
Type 1 diabetes 18,942 501,638 520,580 2021 Ebi-a-GCST90014023

Ethical approval

All data used in this study were obtained from public databases. Formal approval from the Medical Ethics Review Committee was not required as the Medical Research Involving Human Subjects Act does not apply for this study.

Statistical analysis

Selection of instrumental variables (IVs)

Pursuant to the MR analysis principle, for the MS dataset, the sample size is small, to expand the SNP screening range, we set the screening threshold at P<5 × 10−6, for the T1D dataset, we set the screening threshold at P<5 × 10−8. We set the genetic distance to 10,000 kb and excluded SNPs with R2<0.001. From the exposure dataset, we screened out instrumental variables without a linkage effect, and further filtered those significantly associated with outcome (P<5 × 10−5) among the screened instrumental variables. In addition, we employed the Steiger Test method to further examine the screened SNPs, indicating that the selected instrumental variables did not demonstrate a reverse causal relationship and were relatively robust [18]. To ensure the instrumental variables possessed significant statistical significance, we calculated the F statistics for each SNP using the formula F=R2 × (N−2)/(1–R2) , R2=2 × EAF × (1−EAF) × β2 (R2: genetic variance explained by each SNP; N: sample size of the exposed dataset; EAF: the effect allele frequency; β: the estimated effect of SNP), only SNPs with F statistics greater than 10 were included as instrumental variables for analysis [19].

Mendelian randomization analysis

In our research, we primarily use the Inverse Variance Weighted (IVW) method as our main analysis tool, this is the most commonly used MR analysis method, due to its simplicity and ease of implementation [20]. It estimates the causal effect by combining the results of multiple genetic variants using the inverse of the variance as a weight. The IVW method does not require an intercept term in the regression model and uses the reciprocal of the outcome variance (standard error’s quadratic power) as the weight for fitting [21]. Given the significant heterogeneity among the instrumental variables, we employed the random-effects IVW model as our primary analytical method, as it better accommodates this heterogeneity and provides more robust causal estimates. To enhance the stability of our results, we also employ “MR egger” [22], “Weighted median” [23], and “Weighted mode” as supplementary methods. By combining these approaches, we aim to provide more accurate and reliable estimates of the causal effects associated with genetic variants and their impact on various traits or diseases.

Sensitivity analysis

In sensitivity analysis, to eliminate confounding factors that could introduce bias into the results, we utilized the NHGRI-EBI Catalog (https://www.ebi.ac.uk/gwas/) to meticulously examine each SNP. We excluded those SNPs that were directly associated with the outcome or were suspected of having a potential causal relationship. And then, we employ the MR Egger [22] to assess pleiotropy bias. We utilize MR PRESSO [24] to detect level pleiotropy outliers. Heterogeneity testing is employed to identify disparities between individual instrumental variables. Cochran’s Q statistic utilized for calculating heterogeneity [25]. If genetic variance estimates for causal effects exhibit heterogeneity (p≤0.05), we employ the I2 metric to measure the portion of SNP variation explained by this divergence [26]. We adopt leave one out test to investigate the influence of individual SNPs on causal relationships and to verify the stability of the results [27]. The statistical analyses were using R (version 4.3.0), and the Two Sample MR software package was utilized to analyze all the collected data. All code used in the analysis has been uploaded to GitHub. We set p value<0.05 as statistically significant.

Results

MS was considered as the exposure variable, while T1D was investigated as the outcome variable, we selected 103 SNPs as instrumental variables, and F statistics are all greater than 10 (Table S1). The MR results revealed that the beta values of the four analysis methods were consistent, the odds ratios (OR) assess was 1.078 (IVW, 95 % CI: 1.041−1.117; p<0.001) (Table 2). Sensitivity analysis was also conducted, and heterogeneity testing demonstrated moderate heterogeneity (Q=196.324, p<0.05). Then, we calculated the I2 (I2=48.045) (Table S3), which is considered moderate, indicating that heterogeneity is acceptable [26]. MR Egger showed no pleiotropy (intercept=0.003, p=0.585). When T1D was used as the exposure variable, MS as the outcome variable, we selected 52 SNPs as instrumental variables, and all F statistics were greater than 10 (Table S2). MR results indicated no correlation (IVW, OR=0.994, 95 % CI: 0.947−1.044; p=0.817).

Table 2:

The causal association of multiple sclerosis with type 1 diabetes.

Exposure Outcome Method nSNP Beta Se p-Value OR (95 % CI)
MS T1D Inverse variance weighted 103 0.075 0.018 0.000 1.078 (1.041–1.117)
MR egger 103 0.055 0.041 0.186 1.056 (0.974–1.145)
Weighted median 103 0.041 0.023 0.070 1.042 (0.997–1.090)
Weighted mode 103 0.034 0.032 0.291 1.034 (0.972–1.101)
T1D MS Inverse variance weighted 52 −0.006 0.025 0.817 0.994 (0.947–1.044)
MR egger 52 0.000 0.055 0.993 1.000 (0.898–1.113)
Weighted median 52 −0.004 0.031 0.890 0.996 (0.937–1.058)
Weighted mode 52 −0.008 0.037 0.825 0.992 (0.922–1.067)

  1. MS, multiple sclerosis; T1D, type 1 diabetes.

Scatter plot illustrates the correlation between MS and T1D (Figure S1). Leave One Out [27] analyzing each SNP’s impact by excluding it revealed that the results remained unchanged (Figure S2, Table S4 and S5). Based on the findings, a significant association between MS and T1D is indicated, this implies that MS elevates the likelihood of developing T1D (Figure 2).

Figure 2: The causal effect estimates from various Mendelian randomization methods. This figure shows the effect of multiple sclerosis on type 1 diabetes. MS, multiple sclerosis; T1D, type 1 diabetes; IVW, inverse variance weighted; OR, odds ratio; CI, confidence interval.
Figure 2:

The causal effect estimates from various Mendelian randomization methods. This figure shows the effect of multiple sclerosis on type 1 diabetes. MS, multiple sclerosis; T1D, type 1 diabetes; IVW, inverse variance weighted; OR, odds ratio; CI, confidence interval.

Discussion

This is the first study to Mendelian randomization investigation in Multiple sclerosis and Type 1 diabetes. The current findings from Mendelian randomization research suggest that MS, when considered as an exposure, exhibits a positive causal relationship with T1D. Conversely, when T1D is the exposure in question, the causal relationship with MS remains undetermined.

Previous observational studies have indicated that individuals with MS have a significantly higher risk of developing T1D [9], 28]. In a population-based investigation carried out in Sardinia, it came to light that the frequency of T1D amid MS patients surpasses that of the overall population by fivefold [28].A recent study has identified common genetic characteristics and pathways between T1D and MS through a systems biology approach. Nahid and colleagues analyzed the gene expression profiles of peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells from T1D patients, as well as PBMCs and cerebrospinal fluid (CSF) from MS patients. By integrating the differentially expressed genes with protein-protein interaction data, they constructed an Inquiry-Inquiry Protein-Protein Interaction (QQPPI) network. Further analysis of the QQPPI network revealed that the key genes shared in both T1D and MS diseases include those involved in immune response (IKBKE, NF-κB2, and RAC1), the proteasome (PSMA1), the spliceosome (SRPK1, YBX1, and MYC), and apoptosis (HSP90AB1) [29]. A recent study has further confirmed the shared role of the human leukocyte antigen (HLA) genes, particularly the DRB1 and DQB1 alleles, in the pathogenesis of MS and T1D through meta-analysis and the integration of data from multiple studies [30]. The research findings indicate that certain HLA genetic variants significantly increase the risk of developing both conditions. The perspective of HLA genes acting synergistically in MS and T1D enhances the explanatory power of genetic mechanisms [30]. This phenomenon can be attributed to the presence of DRB1*0405-DQA1*0501-DQB1*0301, DRB1*0301-DQA1*0501-DQB1*0201 haplotypes in this distinctive population, these two haplotypes have been identified as risk factors for both diseases [31]. Genetic variations can only partially explain the concurrent occurrence of MS and T1D, suggesting that other factors are also involved. A recent study has explored in detail the mechanism of enhanced autoimmune response in patients with MS, including abnormal activation of T and B cells, overexpression of inflammatory factors, and disruption of the blood-brain barrier. These immune disorders may increase the risk of T1D in patients with MS [32].

Multiple population-based studies and high-quality clinical investigations involving T1D patients consistently demonstrate an elevated risk of MS development [33], 34]. These results present a fascinating yet perplexing epidemiological phenomenon, which the original researchers described as “together at last” when characterizing the MS-T1D risk association. Notably, despite immunological assertions that HLA patterns of T1D and MS are mutually exclusive [34], rigorous clinical evidence has conclusively established bidirectional disease risk between these conditions. While our MR analysis indicates a causal relationship between MS and T1D, we emphasize that the genetic evidence specifically supports MS as a causal factor for increased T1D risk. Conversely, when T1D is examined as the exposure, the causal relationship with MS remains statistically undetermined. Given that autoimmune diseases typically arise from complex interactions between genetic predisposition and environmental factors [5], we propose that environmental triggers or other modulators may underlie the observed increased MS risk in T1D patients. Alternatively, future studies may benefit from incorporating more comprehensive and ethnically diverse genomic datasets for MR analyses to further elucidate the underlying genetic relationships.

Uncovering this causal relationship between T1D and MS is crucial for disease prevention and management, ultimately reducing the significant burden of disease. In comparison to observational research, this study possesses significant advantages. GWAS data employed in this research are the largest and most publicly available to date [16], 17]. Rather than solely relying on the IVW method as the main analysis technique, this study also employs various MR analysis methods to enhance the accuracy of our findings. In addition, we prevent potential horizontal pleiotropy in genetic instrumental variables, ensuring the validity of our results. Concurrently, we employ the Steiger Test to detect and screen SNPs to avoid reverse causal relationships [18]. We calculate the F statistics of the SNPs and exclude those with F statistics greater than 10, thus guaranteeing the robustness of the final SNPs included in the analysis. Lastly, we perform multiple sensitivity analyses on our results to guarantee their reliability.

However, this study does possess limitations. Firstly, the GWAS data utilized were sourced from European samples, which raises the question of whether the observed results are applicable to other populations. As such, future studies exploring the causal relationship between MS and T1D using the Mendelian method should consider incorporating samples from diverse ethnic groups, thereby enhancing the breadth and universality of the findings. Secondly, the publicly aggregated GWAS data used in this study prevent subgroup analysis, which may lead to some bias in the results. Finally, we acknowledge that this study lacks experimental validation using an independent cohort.

This study is the first to decipher the causal relationship between MS and T1D, supplementing previous observational studies and providing a reasonable biological explanation. Our findings contribute to enhancing the understanding of the comorbidity mechanism between MS and T1D. Thus, this study significantly contributes to the field of MS and T1D research, offering valuable insights into their potential genetic connections. Concurrently, our research offers a novel foundation for early prevention among T1D patients.

Corresponding authors: Haoxiao Chang and Haowen Li, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing 100070, China, E-mail: (H. Chang), (H. Li)
Jing Wang and Jia Ma contributed equally to this work.

Acknowledgements

We thank the International Multiple Sclerosis Genetics Consortium and the UK Biobank consortium for providing GWAS summary statistics data for our analysis.

  1. Funding information: This study was supported in part by the National Science Foundation of China (82301453) and a grant from the Special Fund for Health Development and Scientific Research of Shunyi District, Beijing, China (Wsjkfzkyzx-2023-y-07).

  2. Author contributions: Conception and study design: HC, HL and JW-C. Drafting of the manuscript: HC, JW-C, and JM. Acquisition of data: JW-C, JM, SZ, KF and NF. Analysis or interpretation of data: JW-C, SZ, YC, LS, XW, HS, and XZ. Critical revision of the manuscript for important intellectual content: HC, HS, and HL. All authors contributed to the article and approved the submitted version.

  3. Conflict of Interests: All authors claimed no competing interests.

  4. Data Availability Statement: The GWAS summary datasets for MS (GWAS ID: ieu-b-18) and T1D (GWAS ID: GCST90014023), are available through the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/datasets).

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/med-2025-1363).

Received: 2025-02-05
Accepted: 2025-12-01
Published Online: 2025-12-19

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  32. Core muscle strength and stability-oriented breathing training reduces inter-recti distance in postpartum women
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  36. Analysis of serum NLR combined with intraoperative margin condition to predict the prognosis of cervical HSIL patients undergoing LEEP surgery
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  38. The correlation between epithelial–mesenchymal transition classification and MMP2 expression of circulating tumor cells and prognosis of advanced or metastatic nasopharyngeal carcinoma
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  43. Perform tumor-specific survival analysis for Merkel cell carcinoma patients undergoing surgical resection based on the SEER database by constructing a nomogram chart
  44. Unveiling the role of CXCL10 in pancreatic cancer progression: A novel prognostic indicator
  45. High-dose preoperative intraperitoneal erythropoietin and intravenous methylprednisolone in acute traumatic spinal cord injuries following decompression surgeries
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  48. Clinical efficacy of azacitidine in the treatment of middle- and high-risk myelodysplastic syndrome in middle-aged and elderly patients: A retrospective study
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  50. Expression and clinical significance of ITGA3 in breast cancer
  51. Single-nucleus RNA sequencing reveals ARHGAP28 expression of podocytes as a biomarker in human diabetic nephropathy
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  225. Corrigendum
  226. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  227. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  228. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  229. Retraction
  230. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  231. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  232. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
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  239. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
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  241. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  242. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
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  245. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
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  248. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  249. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  250. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  251. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
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  253. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  254. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
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https://doi.org/10.1515/med-2025-1363
Keywords for this article
multiple sclerosis; type 1 diabetes; Mendelian randomization analysis; genetics
Creative Commons
BY 4.0

Articles in the same Issue

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  32. Core muscle strength and stability-oriented breathing training reduces inter-recti distance in postpartum women
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  36. Analysis of serum NLR combined with intraoperative margin condition to predict the prognosis of cervical HSIL patients undergoing LEEP surgery
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  38. The correlation between epithelial–mesenchymal transition classification and MMP2 expression of circulating tumor cells and prognosis of advanced or metastatic nasopharyngeal carcinoma
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  41. A comparative analysis of the impact of repeated administration of flavan 3-ol on brown, subcutaneous, and visceral adipose tissue
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  48. Clinical efficacy of azacitidine in the treatment of middle- and high-risk myelodysplastic syndrome in middle-aged and elderly patients: A retrospective study
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  92. Exploring the role of succinyl carnitine in the association between CD39⁺ CD4⁺ T cell and ulcerative colitis: A Mendelian randomization study
  93. Dexmedetomidine suppresses microglial activation in postoperative cognitive dysfunction via the mmu-miRNA-125/TRAF6 signaling axis
  94. Analysis of serum metabolomics in patients with different types of chronic heart failure
  95. Diagnostic value of hematological parameters in the early diagnosis of acute cholecystitis
  96. Pachymaran alleviates fat accumulation, hepatocyte degeneration, and injury in mice with nonalcoholic fatty liver disease
  97. Decrease in CD4 and CD8 lymphocytes are predictors of severe clinical picture and unfavorable outcome of the disease in patients with COVID-19
  98. METTL3 blocked the progression of diabetic retinopathy through m6A-modified SOX2
  99. The predictive significance of anti-RO-52 antibody in patients with interstitial pneumonia after treatment of malignant tumors
  100. Exploring cerebrospinal fluid metabolites, cognitive function, and brain atrophy: Insights from Mendelian randomization
  101. Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis
  102. Widespread venous thrombosis: Unveiling a complex case of Behçet’s disease with a literature perspective
  103. Uterine fibroid embolization: An analysis of clinical outcomes and impact on patients’ quality of life
  104. Discovery of lipid metabolism-related diagnostic biomarkers and construction of diagnostic model in steroid-induced osteonecrosis of femoral head
  105. Serum-derived exomiR-188-3p is a promising novel biomarker for early-stage ovarian cancer
  106. Enhancing chronic back pain management: A comparative study of ultrasound–MRI fusion guidance for paravertebral nerve block
  107. Peptide CCAT1-70aa promotes hepatocellular carcinoma proliferation and invasion via the MAPK/ERK pathway
  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
  110. Cell-free adipose extract inhibits hypertrophic scar formation through collagen remodeling and antiangiogenesis
  111. HALP score in Demodex blepharitis: A case–control study
  112. Assessment of SOX2 performance as a marker for circulating cancer stem-like cells (CCSCs) identification in advanced breast cancer patients using CytoTrack system
  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
  114. Comparison of the two intestinal anastomosis methods in pediatric patients
  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. Glutathione attenuates sepsis-associated encephalopathy via dual modulation of NF-κB and PKA/CREB pathways
  125. FAHD1 prevents neuronal ferroptosis by modulating R-loop and the cGAS–STING pathway
  126. Association of placenta weight and morphology with term low birth weight: A case–control study
  127. Investigation of the pathogenic variants induced Sjogren’s syndrome in Turkish population
  128. Nucleotide metabolic abnormalities in post-COVID-19 condition and type 2 diabetes mellitus patients and their association with endocrine dysfunction
  129. TGF-β–Smad2/3 signaling in high-altitude pulmonary hypertension in rats: Role and mechanisms via macrophage M2 polarization
  130. Ultrasound-guided unilateral versus bilateral erector spinae plane block for postoperative analgesia of patients undergoing laparoscopic cholecystectomy
  131. Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment
  132. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker?
  133. Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells
  134. H2valdien3 arrests the cell cycle and induces apoptosis of gastric cancer
  135. Prognostic relevance of PRSS2 and its immune correlates in papillary thyroid carcinoma
  136. Association of SGLT2 inhibition with psychiatric disorders: A Mendelian randomization study
  137. Motivational interviewing for alcohol use reduction in Thai patients
  138. Luteolin alleviates oxygen-glucose deprivation/reoxygenation-induced neuron injury by regulating NLRP3/IL-1β signaling
  139. Polyphyllin II inhibits thyroid cancer cell growth by simultaneously inhibiting glycolysis and oxidative phosphorylation
  140. Relationship between the expression of copper death promoting factor SLC31A1 in papillary thyroid carcinoma and clinicopathological indicators and prognosis
  141. CSF2 polarized neutrophils and invaded renal cancer cells in vitro influence
  142. Proton pump inhibitors-induced thrombocytopenia: A systematic literature analysis of case reports
  143. The current status and influence factors of research ability among community nurses: A sequential qualitative–quantitative study
  144. OKAIN: A comprehensive oncology knowledge base for the interpretation of clinically actionable alterations
  145. The relationship between serum CA50, CA242, and SAA levels and clinical pathological characteristics and prognosis in patients with pancreatic cancer
  146. Identification and external validation of a prognostic signature based on hypoxia–glycolysis-related genes for kidney renal clear cell carcinoma
  147. Engineered RBC-derived nanovesicles functionalized with tumor-targeting ligands: A comparative study on breast cancer targeting efficiency and biocompatibility
  148. Relationship of resting echocardiography combined with serum micronutrients to the severity of low-gradient severe aortic stenosis
  149. Effect of vibration on pain during subcutaneous heparin injection: A randomized, single-blind, placebo-controlled trial
  150. The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
  151. Comparing biofeedback device vs diaphragmatic breathing for bloating relief: A randomized controlled trial
  152. Serum uric acid to albumin ratio and C-reactive protein as predictive biomarkers for chronic total occlusion and coronary collateral circulation quality
  153. Multiple organ scoring systems for predicting in-hospital mortality of sepsis patients in the intensive care unit
  154. Single-cell RNA sequencing data analysis of the inner ear in gentamicin-treated mice via intraperitoneal injection
  155. Suppression of cathepsin B attenuates myocardial injury via limiting cardiomyocyte apoptosis
  156. Influence of sevoflurane combined with propofol anesthesia on the anesthesia effect and adverse reactions in children with acute appendicitis
  157. Identification of hub genes related to acute kidney injury caused by sevoflurane anesthesia and endoplasmic reticulum stress
  158. 10.1515/med-2025-1313
  159. 10.1515/med-2025-1316
  160. Health education pathway for individuals with temporary enterostomies using patient journey mapping
  161. 10.1515/med-2025-1321
  162. 10.1515/med-2025-1324
  163. 10.1515/med-2025-1325
  164. 10.1515/med-2025-1327
  165. 10.1515/med-2025-1331
  166. Effect of timing of cholecystectomy on weight loss after sleeve gastrectomy in morbidly obese individuals with cholelithiasis: a retrospective cohort study
  167. 10.1515/med-2025-1337
  168. 10.1515/med-2025-1347
  169. 10.1515/med-2025-1360
  170. Multiple sclerosis and type 1 diabetes: a Mendelian randomization study of European ancestry
  171. Rapid pathogen identification in peritoneal dialysis effluent by MALDI-TOF MS following blood culture enrichment
  172. Comparison of open and percutaneous A1 pulley release in pediatric trigger thumb: a retrospective cohort study
  173. Review Articles
  174. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  175. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  176. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  177. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  178. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  179. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  180. The application of augmented reality in robotic general surgery: A mini-review
  181. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  182. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  183. Plant polyphenols, terpenes, and terpenoids in oral health
  184. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  185. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  186. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  187. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  188. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  189. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  190. Giant borderline ovarian tumours – review of the literature
  191. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  192. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  193. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  194. Supplement strategies for infertility in overweight women: Evidence and legal insights
  195. Scurvy, a not obsolete disorder: Clinical report in eight young children and literature review
  196. A meta-analysis of the effects of DBS on cognitive function in patients with advanced PD
  197. Protective role of selenium in sepsis: Mechanisms and potential therapeutic strategies
  198. Strategies for hyperkalemia management in dialysis patients: A systematic review
  199. C-reactive protein-to-albumin ratio in peripheral artery disease
  200. 10.1515/med-2025-1251
  201. 10.1515/med-2025-1330
  202. 10.1515/med-2025-1332
  203. Antibiotic prescribing patterns in general dental practice- a scoping review
  204. Clinical and medico-legal reflections on non-invasive prenatal testing
  205. Case Reports
  206. Delayed graft function after renal transplantation
  207. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  208. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  209. Giant right atrial hemangioma presenting with ascites: A case report
  210. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  211. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  212. Molecular hydrogen-rhodiola as an adjuvant therapy for ischemic stroke in internal carotid artery occlusion: A case report
  213. Coronary artery anomalies: A case of the “malignant” left coronary artery and its surgical management
  214. Combined VAT and retroperitoneoscopy for pleural empyema due to nephro-pleuric fistula in xanthogranulomatous pyelonephritis
  215. 10.1515/med-2025-1362
  216. Rapid Communication
  217. Biological properties of valve materials using RGD and EC
  218. A single oral administration of flavanols enhances short-term memory in mice along with increased brain-derived neurotrophic factor
  219. Letter to the Editor
  220. Role of enhanced external counterpulsation in long COVID
  221. Expression of Concern
  222. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  223. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  224. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  225. Corrigendum
  226. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  227. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  228. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  229. Retraction
  230. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  231. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  232. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  233. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  234. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  235. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  236. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  237. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  238. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  239. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  240. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  241. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  242. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  243. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  244. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  245. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  246. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  247. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  248. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  249. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  250. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  251. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  252. A review of brain research on T2DM-related cognitive dysfunction
  253. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  254. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  255. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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