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Research Article

Mendelian Randomization Analysis of NETs-Associated Inflammatory Traits and Type 2 Diabetes and its Complications

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DOI:

10.3791/71230

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August 21st, 2026

 ,  ,  ,  ,  ,  ,  ,  , 
1Department of Laboratory Medicine, Zhongshan People's Hospital, 2Department of Advanced Diagnostic and Clinical Medicine, Zhongshan People's Hospital

In This Article

Summary

This two-sample MR study identified potential associations of genetically predicted NETs-associated inflammatory traits with T2DM complications. Genetically predicted IL-6 was inversely associated with diabetic CAD, while NETs were associated with renal and peripheral circulatory complications in T2DM. Sensitivity analyses generally supported the stability of these significant associations.

Abstract

Neutrophil extracellular traps (NETs) -associated inflammatory traits play a significant role in type 2 diabetes mellitus (T2DM) and its complications. Notably, IL-6, a key inflammatory cytokine, is intricately linked to the formation of NETs and the pathogenesis of T2DM and its complications. This study aimed to explore the causal association between NETs-associated inflammatory traits and T2DM, as well as its complications, using a Mendelian Randomization (MR) approach. This study utilized a two-sample MR design with data from Genome-Wide Association Studies (GWAS), comprising a large European population-based meta-analysis for T2DM and its complications. The primary method of analysis was the inverse variance weighted (IVW) approach, complemented by MR-Egger regression, weighted median, and weighted mode methods. Sensitivity analyses included MR-Egger, MR-PRESSO, Cochran’s Q, and leave-one-out methods to assess the robustness of the findings. The study indicated that genetically predicted levels of interleukin-6 (IL-6) were inversely associated with diabetic coronary artery disease (CAD) (OR = 0.8997, 95% CI: 0.8257–0.9803, P = 0.0158). Additionally, NETs showed significant associations with T2DM with renal complications (OR=0.97, 95% CI 0.9428–0.998, P = 0.0358) and T2DM with peripheral circulatory complications(OR = 1.0342, 95% CI 1.002–1.0673, P = 0.037). The significant IVW associations showed no evidence of heterogeneity or horizontal pleiotropy. This study suggests that genetically predicted NETs-associated inflammatory traits are associated with specific T2DM complications. Genetically predicted IL-6 was inversely associated with diabetic CAD, whereas NETs were associated with renal and peripheral circulatory complications in T2DM.

Introduction

Type 2 diabetes mellitus (T2DM), characterized by insulin resistance and often accompanied by progressive beta-cell dysfunction, has reached epidemic proportions worldwide1,2. Including cardiovascular disease, renal failure, neuropathy, and retinopathy, these complications of T2DM impose a substantial burden not only on individuals in terms of their health and quality of life but also on healthcare systems worldwide, straining resources and increasing costs, and on economies3,4. Considering the multifaceted nature of T2DM, it necessitates a nuanced exploration into its etiological factors. Accumulating evidence has implicated neutrophil extracellular traps (NETs) in the pathophysiology of various metabolic disorders, especially associated with T2DM.

Neutrophil extracellular traps (NETs) are extracellular networks of decondensed DNA and granule proteins that possess potent microbicidal properties5,6. Markers such as cell-free DNA and citrullinated histone H3 serve as key indicators of NETosis, reflecting inflammatory activity in various diseases7,8. Accumulating evidence suggests that diabetes promotes the formation of NETs, thereby propagating inflammation and tissue injury9,10. Clinically, elevated NETs markers correlate with hyperglycemia and adverse outcomes across multiple diabetic complications. For instance, increased dsDNA levels are linked to glucose dysregulation in STEMI patients11, while NETs components are associated with the progression of diabetic retinopathy12 and predict amputation risk in diabetic foot ulcers13. Despite these associations, the precise causal relationship between NETs and the development of T2DM and its complications remains to be fully elucidated. Accumulating evidence highlights a bidirectional interplay between IL-6 and NETs in metabolic inflammation. Hyperglycemia primes neutrophils for NETosis, with IL-6 serving as a potent inducer of this energy-dependent process14. Clinically, IL-6 independently predicts elevated NETs markers (e.g., H3Cit and cell-free DNA) in type 2 diabetes, which correlate strongly with prothrombotic states and microvascular complications like diabetic retinopathy15,16. Furthermore, infection-induced NETosis can enhance IL-6 trans-signaling, establishing a reciprocal regulatory loop17. Collectively, these findings underscore the tightly coupled pathophysiology of IL-6 and NETs, particularly within the diabetic milieu.

Mendelian Randomization (MR) is a powerful statistical method that leverages naturally occurring genetic variants as instrumental variables to estimate causal effects of modifiable exposures on health outcomes, offering insights into complex disease pathways18. This approach, rooted in Mendel’s laws of inheritance, allows researchers to infer causality in observational studies by exploiting the random assortment of alleles during meiosis, thereby circumventing some of the limitations of traditional epidemiological methods in establishing cause-and-effect relationships19.

To date, there have been limited reports on MR studies investigating the causal relationship between genetically predicted NETs-associated inflammatory traits and T2DM and its complications. Therefore, the present study aims to explore the potential causal associations between NETs-associated inflammatory traits and T2DM, as well as its complications, using a two-sample MR analysis method.

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Protocol

Study Design

This research utilized a two-sample MR approach to explore the causal relationship between NETs-associated inflammatory traits and T2DM and its complications. The MR framework relies on three key assumptions20: 1) the genetic variant must be robustly associated with the exposure, 2) the genetic variant must only influence the outcome through the exposure (exclusion of pleiotropy), and 3) the genetic variant must not be associated with any confounding factors. For a detailed overview of this study’s process, please refer to Figure 1. The manuscript adheres to the MR-STROBE guidelines for reporting MR studies, ensuring rigorous standards of transparency and reproducibility21.

Data Sources

The Genome-Wide Association Studies (GWAS) data utilized in this MR analysis were sourced from the GWAS public databases. In terms of the summary-level GWASs concerning T2DM, incorporating the largest meta-analysis of a European population, comprising 74,124 cases and 824,006 controls, alongside a prospective nested case-cohort study conducted in Europe, featuring 9,978 cases and 12,348 controls22. For diabetic complications, including T2DM with renal complications and T2DM with peripheral circulatory complications, this study retrieved GWAS summary statistics from studies in which cases were T2DM patients with the specific complication and controls were individuals without T2DM. Detailed information on data sources can be found in Supplementary Table 1.

GWAS data for NETs-associated inflammatory traits were obtained from the GWAS catalog (see Supplementary Table 2). To address the heterogeneity in biological specificity, this study categorized the included exposures into two distinct groups based on their functional roles in neutrophil biology: (1) Core NETosis-related Factors: This category includes markers directly involved in the structural formation of NETs or the enzymatic process of chromatin decondensation. Specifically, this study included NETs themselves, Myeloperoxidase (MPO), Neutrophil Elastase (NE), and the MPO-DNA complex. MPO and NE are essential enzymes for histone degradation and chromatin decondensation, while the MPO-DNA complex is a specific surrogate marker for NETs23. (2) NETs-associated Inflammatory Mediators: This category comprises cytokines and mediators that act as upstream regulators or downstream effectors closely linked to NETosis but are also involved in broader inflammatory pathways. This group includes Interleukin-6 (IL-6)24, Tumor Necrosis Factor-alpha (TNF-α)25, Neutrophil gelatinase-associated lipocalin levels (NGAL)26, and Cellular Communication Network Factor 1 (CCN1)27.

The data used in this study were sourced from open-access databases or GWAS published in previous studies; therefore, ethical approval was not required for this research.

Selection of NETs-Related Genetic Instruments

Given that NETs formation represents a dynamic biological process not directly measured in conventional GWAS, this study employed a gene-based approach to identify genetic instruments for NETs-associated inflammatory traits. A comprehensive set of 257 genes known to be critically involved in NETs formation and regulation, such as MPO, along with genes encoding histones and neutrophil granule proteins, was curated from established mechanistic studies and published literature28.

Instrumental Variable Selection

To identify robust genetic instruments for NETs-associated inflammatory traits, this study implemented a sequential filtering workflow using GWAS summary statistics. The specific operational steps were as follows:

Initial Screening: Genetic variants significantly associated with the exposure traits were extracted29 based on a significance threshold of P < 5 × 10⁻6.

Minor allele frequency (MAF) Filtering: To ensure statistical power, Single Nucleotide Polymorphisms (SNPs) with a MAF ≤ 0.01 were excluded30.

Linkage Disequilibrium (LD) Clumping: To mitigate linkage disequilibrium (LD) confounding, independent SNPs were selected using the clumping function with parameters set to r2 < 0.001 within a 10,000 kb window31.

Instrument Strength Assessment: The strength of each remaining IV was quantified using the F-statistic, calculated as F = R2 × (N-2) / (1-R2). Only SNPs with an F-statistic > 10 were retained to minimize weak instrument bias32.

Proxy SNP Identification and Data Harmonization

To address missing SNPs in the outcome dataset, this study performed a proxy search and data harmonization process:

Proxy Substitution: When a target SNP from the exposure GWAS was unavailable in the outcome GWAS, this study utilized the LDproxy() function based on the 1,000 Genomes Project European reference panel. A candidate proxy SNP was selected only if it exhibited high LD with the original SNP (r2> 0.8). If no qualified proxy was found, the SNP was excluded.

Harmonization: Exposure and outcome datasets were aligned using the harmonise_data() function from the TwoSampleMR package (R version 4.0.5). This study set the parameter action = 2 to automatically align all SNPs to the forward strand and remove palindromic SNPs with ambiguous strand orientation.

Verification: Post-harmonization, this study manually inspected the harmonized dataset to confirm that the effect allele frequencies were consistent between the exposure and outcome data.

MR Analysis and Sensitivity Tests

Causal inference was conducted using the TwoSampleMR package (R version 4.0.5).

Primary and Secondary Analyses: This study applied the Inverse Variance Weighted (IVW) method as the primary approach33. Complementary analyses were performed using MR-Egger34, weighted median, and weighted mode methods to ensure robustness35.

Sensitivity Assessments: Heterogeneity among IVs was evaluated using Cochran’s Q test36 via the mr_heterogeneity() function. Horizontal pleiotropy was assessed using the MR-Egger34 intercept test (mr_pleiotropy_test()).

Outlier Detection: This study employed the MR-PRESSO package to detect potential outliers37. The mr_presso() function was executed with 1,000 simulations. Identified outlier SNPs (P < 0.05) were removed, and the causal estimates were recalculated to verify result stability. Additionally, a leave-one-out analysis was performed to ensure that the causal association was not driven by a single SNP38.

Statistical Correction

To account for multiple testing, P-values obtained from the MR analyses were corrected using the False Discovery Rate (FDR) method. This was implemented using the P.adjust() function in R with the parameter method = "fdr". Associations with a corrected P-value (PFDR) < 0.05 were considered statistically significant.

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Results

Selected Instrumental Variables

This study conducted MR analysis using NETs-associated inflammatory traits as the exposure. A total of 106 IVs were identified for this purpose, with details provided in Supplementary Table 3. The F-statistics of these IVs had an average value of 26.46, ranging from a minimum of 20.6 to a maximum of 250.4.

When adopting T2DM and its various complications (diabetic ketoacidosis, renal complications, ophthalmic complica...

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Discussion

This study employs a two-sample MR approach to investigate the associations between NETs-associated inflammatory traits and T2DM, as well as its complications. The primary findings suggest a statistically significant inverse association between genetically predicted IL-6 levels and diabetic CAD. This finding may reflect the complex genetic architecture of IL-6 signaling pathways rather than a direct protective effect of circulating IL-6 on cardiovascular complications of diabetes. Additionally, this study reveals associa...

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Disclosures

The authors declare that they have no competing interests.

Acknowledgements

The study was supported by the Social Science and Technology Research Major Project of Zhongshan (Grant number: 2021B3013) and the Social Science and Technology Research Project of Zhongshan (Grant number: 2022B1080). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CCN1GWAS CatalogGCST90277805http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90277001-GCST90278000/GCST90277805/harmonised/GCST90277805.h.tsv.gz
Diabetic CADGWAS CatalogGCST006405http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006405/Fall_gwas_results_any_chd_tab.txt.gz
IL-6GWAS CatalogGCST90010146http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90010001-GCST90011000/GCST90010146/harmonised/33303764-GCST90010146-EFO_0007937.h.tsv.gz
MPOGWAS CatalogGCST90087967http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90087001-GCST90088000/GCST90087967/harmonised/35078996-GCST90087967-EFO_0011039.h.tsv.gz
MPO-DNAGWAS CatalogGCST90013658http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90013001-GCST90014000/GCST90013658/harmonised/33717105-GCST90013658-EFO_0005243.h.tsv.gz
MR-PRESSO PackageGitHub (rondolab)Version 1.0R package used to detect and pleiotropy-corrected causal estimates.
NEGWAS CatalogGCST90162366http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90162001-GCST90163000/GCST90162366/harmonised/GCST90162366.h.tsv.gz
NETsGWAS CatalogGCST90137414http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90137001-GCST90138000/GCST90137414/harmonised/GCST90137414.h.tsv.gz
NGALGWAS CatalogGCST90161504http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90161001-GCST90162000/GCST90161504/harmonised/GCST90161504.h.tsv.gz
R SoftwareR Foundation for Statistical ComputingVersion 4.0.5Main statistical computing environment used for all analyses.
T2DFINN R9T2Dhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_T2D.gz
T2D with ketoacidosisFINN R9E4_DM2KETOhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_E4_DM2KETO.gz
T2D with neurological complicationsFINN R9E4_DM2NEUhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_E4_DM2NEU.gz
T2D with ophthalmic complicationFINN R9E4_DM2OPTHhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_E4_DM2OPTH.gz
T2D with peripheral circulatoryFINN R9E4_DM2PERIPHhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_E4_DM2PERIPH.gz
T2D with renal complicationsFINN R9E4_DM2RENhttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_E4_DM2REN.gz
TNF-aGWAS CatalogGCST004426http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004426/harmonised/27989323-GCST004426-EFO_0004684.h.tsv.gz
TwoSampleMR PackageGitHub (MRCIEU)Version 0.6.22R package used for performing the Mendelian Randomization analysis.

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