Using human genetics to improve safety assessment of therapeutics

New evidence: Metformin unsuitable as routine adjuvant for breast cancer: a drug-target mendelian randomization analysis

PURPOSE

The potential efficacy of metformin in breast cancer (BC) has been hotly discussed but never conclusive. This genetics-based study aimed to evaluate the relationships between metformin targets and BC risk.

METHODS

Metformin targets from DrugBank and genome-wide association study (GWAS) data from IEU OpenGWAS and FinnGen were used to investigate the breast cancer (BC)-metformin causal link with various Mendelian Randomization (MR) methods (e.g., inverse-variance-weighting). The genetic association between type 2 diabetes (T2D) and the drug target of metformin was also analyzed as a positive control. Sensitivity and pleiotropic tests ensured reliability.

RESULTS

The primary targets of metformin are PRKAB1, ETFDH and GPD1L. We found a causal association between PRKAB1 and T2D (odds ratio [OR] 0.959, P = 0.002), but no causal relationship was observed between metformin targets and overall BC risk (PRKAB1: OR 0.990, P = 0.530; ETFDH: OR 0.986, P = 0.592; GPD1L: OR 1.002, P = 0.806). A noteworthy causal relationship was observed between ETFDH and estrogen receptor (ER)-positive BC (OR 0.867, P = 0.018), and between GPD1L and human epidermal growth factor receptor 2 (HER2)-negative BC (OR 0.966, P = 0.040). Other group analyses did not yield positive results.

CONCLUSION

The star target of metformin, PRKAB1, does not exhibit a substantial causal association with the risk of BC. Conversely, metformin, acting as an inhibitor of ETFDH and GPD1L, may potentially elevate the likelihood of developing ER-positive BC and HER2-negative BC. Consequently, it is not advisable to employ metformin as a standard supplementary therapy for BC patients without T2D.

ICH S1 prospective evaluation study and weight of evidence assessments: commentary from industry representatives

Industry representatives on the ICH S1B(R1) Expert Working Group (EWG) worked closely with colleagues from the Drug Regulatory Authorities to develop an addendum to the ICH S1B guideline on carcinogenicity studies that allows for a weight-of-evidence (WoE) carcinogenicity assessment in some cases, rather than conducting a 2-year rat carcinogenicity study. A subgroup of the EWG composed of regulators have published in this issue a detailed analysis of the Prospective Evaluation Study (PES) conducted under the auspices of the ICH S1B(R1) EWG. Based on the experience gained through the Prospective Evaluation Study (PES) process, industry members of the EWG have prepared the following commentary to aid sponsors in assessing the standard WoE factors, considering how novel investigative approaches may be used to support a WoE assessment, and preparing appropriate documentation of the WoE assessment for presentation to regulatory authorities. The commentary also reviews some of the implementation challenges sponsors must consider in developing a carcinogenicity assessment strategy. Finally, case examples drawn from previously marketed products are provided as a supplement to this commentary to provide additional examples of how WoE criteria may be applied. The information and opinions expressed in this commentary are aimed at increasing the quality of WoE assessments to ensure the successful implementation of this approach.

The state of the art in secondary pharmacology and its impact on the safety of new medicines

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Causal association of plasma circulating metabolites with nephritis: a Mendelian randomization study

Background

Nephritis is a pivotal catalyst in chronic kidney disease (CKD) progression. Although epidemiological studies have explored the impact of plasma circulating metabolites and drugs on nephritis, few have harnessed genetic methodologies to establish causal relationships.

Methods

Through Mendelian randomization (MR) in two substantial cohorts, spanning large sample sizes, we evaluated over 100 plasma circulating metabolites and 263 drugs to discern their causal effects on nephritis risk. The primary analytical tool was the inverse variance weighted (IVW) analysis. Our bioinformatic scrutiny of GSE115857 (IgA nephropathy, 86 samples) and GSE72326 (lupus nephritis, 238 samples) unveiled anomalies in lipid metabolism and immunological characteristics in nephritis. Thorough sensitivity analyses (MR-Egger, MR-PRESSO, leave-one-out analysis) were undertaken to verify the instrumental variables' (IVs) assumptions.

Results

Unique lipoprotein-related molecules established causal links with diverse nephritis subtypes. Notably, docosahexaenoic acid (DHA) emerged as a protective factor for acute tubulointerstitial nephritis (ATIN) (OR1 = 0.84, [95% CI 0.78-0.90], p1 = 0.013; OR2 = 0.89, [95% CI 0.82-0.97], p2 = 0.007). Conversely, multivitamin supplementation minus minerals notably increased the risk of ATIN (OR = 31.25, [95% CI 9.23-105.85], p = 0.004). Reduced α-linolenic acid (ALA) levels due to lipid-lowering drugs were linked to both ATIN (OR = 4.88, [95% CI 3.52-6.77], p < 0.001) and tubulointerstitial nephritis (TIN) (OR = 7.52, [95% CI 2.78-20.30], p = 0.042). While the non-renal drug indivina showed promise for TIN treatment, the use of digoxin, hydroxocobalamin, and liothyronine elevated the risk of chronic tubulointerstitial nephritis (CTIN). Transcriptome analysis affirmed that anomalous lipid metabolism and immune infiltration are characteristic of IgA nephropathy and lupus nephritis. The robustness of these causal links was reinforced by sensitivity analyses and leave-one-out tests, indicating no signs of pleiotropy.

Conclusion

Dyslipidemia significantly contributes to nephritis development. Strategies aimed at reducing plasma low-density lipoprotein levels or ALA supplementation may enhance the efficacy of existing lipid-lowering drug regimens for nephritis treatment. Renal functional status should also be judiciously considered with regard to the use of nonrenal medications.

Refining the impact of genetic evidence on clinical success

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG.

METHODS

By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk.

RESULTS

We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk.

CONCLUSIONS

This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

Digital biobanks are underutilized in dermatology and create opportunities to reduce the burden of skin disease

Digital biobanks that integrate genetic data with health data captured by digital sources are used routinely to discover genes, biomarkers, gene–environment interactions and pharmacogenetic relationships across many clinical areas. There remain many opportunities in dermatology to further use biobank data to increase our knowledge about the genetic architecture of skin disease, to resolve disease mechanisms that can be modulated by medical interventions and to discover genetically derived disease relationships that inform on drug repurposing and adverse events. Such knowledge promises to reduce the global burden of skin disease and facilitates the development of tailored medical care.

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania

Background

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects.

Methods

Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets.

Results

After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes.

Conclusion

SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

Genetic insights into associations of multisite chronic pain with common diseases and biomarkers using data from the UK Biobank

BACKGROUND

Chronic pain is a common, complex, and challenging condition, for which specialised healthcare is required. We investigated the relationship between multisite chronic pain (MCP) and different disease traits identify safe biomarker interventions that can prevent MCP.

METHODS

Univariable and multivariable Mendelian randomisation (MR) analysis were conducted to investigate associations between MCP and 36 common diseases in the UK Biobank. Subsequently, we estimated the potential effect of expression of 4774 proteins on MCP utilising existing plasma protein quantitative trait locus data. For the significant biomarkers, we performed phenome-wide MR (Phe-MR) with 1658 outcomes to predict potential safety profiles linked to biomarker intervention.

RESULTS

Multisite chronic pain had a substantial impact on psychiatric and neurodevelopmental traits (major depression and attention deficit hyperactivity disorder), cardiovascular diseases (myocardial infarction, coronary artery disease, and heart failure), respiratory outcomes (asthma, chronic obstructive pulmonary disease, and sleep apnoea), arthropathies, type 2 diabetes mellitus, and cholelithiasis. Higher genetically predicted levels of S100A6, DOCK9, ferritin, and ferritin light chain were associated with a risk of MCP, whereas PTN9 and NEUG were linked to decreased MCP risk. Phe-MR results suggested that genetic inhibition of DOCK9 increased the risk of 21 types of disease, whereas the other biomarker interventions were relatively safe.

CONCLUSIONS

We established that MCP has an effect on health conditions covering various physiological systems and identified six novel biomarkers for intervention. In particular, S100A6, PTN9, NEUG, and ferritin light chain represent promising targets for MCP prevention, as no significant side-effects were predicted in our study.

PharmGWAS: a GWAS-based knowledgebase for drug repurposing

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Looking to the Future: Drug Delivery and Targeting in the Prophylaxis and Therapy of Severe and Chronic Diseases

High molecular weight actives and cell-based therapy have the potential to revolutionize the prophylaxis and therapy of severe diseases. Yet, the size and nature of the agents - proteins, nucleic acids, cells - challenge drug delivery and thus formulation development. Moreover, off-target effects may result in severe adverse drug reactions. This makes delivery and targeting an essential component of high-end drug development. Loading to nanoparticles facilitates delivery and enables targeted mRNA vaccines and tumor therapeutics. Stem cell therapy opens up a new horizon in diabetes type 1 among other domains which may enhance the quality of life and life expectancy. Cell encapsulation protects transplants against the recipient's immune system, may ensure long-term efficacy, avoid severe adverse reactions, and simplify the management of rare and fatal diseases.The knowledge gained so far encourages to widen the spectrum of potential indications. Co-development of the active agent and the vehicle has the potential to accelerate drug research. One recommended starting point is the use of computational approaches. Transferability of preclinical data to humans will benefit from performing studies first on validated human 3D disease models reflecting the target tissue, followed by studies on validated animal models. This makes approaching a new level in drug development a multidisciplinary but ultimately worthwhile and attainable challenge. Intense monitoring of the patients after drug approval and periodic reporting to physicians and scientists remain essential for the safe use of drugs especially in rare diseases and pave future research.

Association of HMGCR inhibition with rheumatoid arthritis: a Mendelian randomization and colocalization study

Objective

The objective of this study was to investigate the association between hydroxymethylglutaryl coenzyme A reductase (HMGCR) inhibition and rheumatoid arthritis (RA) using drug-target Mendelian randomization (MR) and genetic colocalization analyses.

Methods

Two sets of genetic instruments were employed to proxy HMGCR inhibitors: expression quantitative trait loci (eQTLs) of target genes from the eQTLGen Consortium and genetic variants associated with low-density lipoprotein cholesterol (LDL-C) levels with HMGCR locus from open genome-wide association studies (GWAS). Positive control analyses were conducted on type 2 diabetes and coronary heart disease, and multiple sensitivity analyses were performed.

Results

Genetically proxied expression of eQTL was associated with a lower risk of RA (OR=0.996, 95% CI =0.992-0.999, p= 0.032). Similarly, hydroxymethylglutaryl coenzyme A reductase (HMGCR)-mediated low-density lipoprotein cholesterol was negatively associated with risk of RA (OR=0.995, 95% CI =0.991-0.998, p= 0.007) in the inverse variance weighted (IVW) method. Colocalization analysis suggested a 74.6% posterior probability of sharing a causal variant within the SNPs locus (PH4 = 74.6%). A causal relationship also existed between HMGCR-mediated LDL and RA risk factors. The results were also confirmed by multiple sensitivity analyses. The results in positive control were consistent with the previous study.

Conclusion

Our study suggested that HMGCR inhibition was associated with an increased risk of RA while also highlighting an increased risk of current smoking and obesity. These findings contribute to a growing body of evidence regarding the adverse effects of HMGCR inhibition on RA risk, calling for further research on alternative approaches using HMGCR inhibitors in RA management.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

Assessing network-based methods in the context of system toxicology

Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark. Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner. Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug's mechanism of action. Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications.

Identification of PTPN22 as a potential genetic biomarker for abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a severe life-threatening disease that is generally asymptomatic and is diagnosed at a very late stage. The genetic component underpinning AAA is considerable, with an estimated heritability of up to 70%. Therefore, identifying genetic biomarkers for AAA is valuable for predicting high-risk populations. We used integrative bioinformatics and cellular AAA model-based validation to reveal that the gene encoding protein tyrosine phosphatase non-receptor type 22 (PTPN22) may be a potentially useful diagnostic biomarker for AAA. Integrative bioinformatics analyses of clinical specimens showed that PTPN22 expression was consistently upregulated in aortic tissues and peripheral blood mononuclear cells (PBMCs) derived from patients with AAA. Moreover, transcriptomics data revealed that PTPN22 is a potential biomarker for AAA with limited diagnostic value in patients with thoracic aortic aneurysm/dissection. Single-cell RNA sequencing-based findings further highlight PTPN22 expression in aortic immune cells and vascular smooth muscle cells (VSMCs) is consistently upregulated in patients with AAA. A cellular AAA model was eventually employed to verify the increase in PTPN22 expression. Collectively, the results indicate that PTPN22 could be a potentially useful diagnostic biomarker for AAA.