Research Article
* These authors contributed equally
This study integrates FAERS pharmacovigilance with Mendelian randomization to assess associations between TNF pathway biomarkers and hip fracture risk. Combining real-world adverse-event surveillance with genetic causal inference, the workflow offers a reproducible approach for evaluating biomarker–outcome relationships when randomized trials are unavailable, impractical, or ethically difficult to conduct in practice.
Hip fracture is a common and serious condition in elderly individuals, often associated with altered TNF-α signaling. However, few studies have explored whether changes in TNF pathway biomarkers contribute to the risk of hip fracture. In this study, adverse events related to hip fracture reported for five TNF inhibitors were analyzed using data from the first quarter of 2014 to the fourth quarter of 2023. After data standardization and cleaning, four disproportionality methods, including the reporting odds ratio, proportional reporting ratio, multi-item gamma Poisson shrinker, and Bayesian confidence propagation neural network, were applied to assess the association between TNF inhibitor exposure and hip fracture outcomes. Complementary mendelian randomization analyses were further conducted using TNF-α, sTNFR1, and sTNFR2 as exposures and hip fracture as the outcome. Pharmacovigilance analyses revealed no significant association between TNF inhibitor exposure and hip fracture-related adverse events across the four algorithms. Mendelian randomization analyses identified no significant direct causal association between genetically predicted TNF-α and hip fracture risk, and additional analyses of sTNFR1 and sTNFR2 yielded consistent results. These findings suggest that TNF pathway biomarkers are unlikely to act as independent direct determinants of hip fracture risk and may provide a useful basis for future mechanistic studies and prevention strategies.
Hip fracture is a common and serious clinical condition in elderly individuals, and its clinical manifestations are usually characterized by hip pain and dysfunction1. Hip fracture not only has a serious impact on patients but also imposes a heavy burden on families and society. For patients, hip fracture is often accompanied by a long period of hospitalization and rehabilitation, which has a great impact on the physical and mental health of elderly patients2,3,4. At the family level, patients usually require long-term care from family members, which can increase the financial and emotional burden on the family, especially for low-income families, which may have a greater impact5. At the societal level, the high incidence of hip fractures not only increases the consumption of healthcare resources but also poses a challenge to the health security system in an aging society6,7. From an epidemiological point of view, the incidence of hip fracture has shown an increasing trend annually globally, which is closely related to population aging8. Elderly people, especially elderly women, are at high risk for hip fracture, and approximately 75% of cases of hip fracture occur in the elderly female population over 65 years of age9,10,11. Therefore, it is necessary to carry out relevant research on hip fractures to explore the mechanism of their occurrence and development, which is conducive to better prevention and treatment of hip fractures. Previous studies have suggested that hip fracture may be accompanied by altered TNF-α expression and inflammatory activation12,13. However, whether genetically predicted alterations in TNF pathway biomarkers are causally associated with hip fracture risk remains unclear14. Therefore, the present study investigated whether TNF pathway biomarkers are causally associated with hip fracture risk. Five TNF inhibitors, including etanercept15, adalimumab16, infliximab17, certolizumab pegol18, and golimumab19, were selected for pharmacovigilance analysis. The FAERS database was used to identify hip fracture adverse event reports associated with these agents, and complementary mendelian randomization (MR) analyses were performed to further evaluate causality.
Several methodological approaches can be used to investigate this question, but each has important limitations. Traditional observational studies, such as cohort and case-control studies, are useful for identifying associations in clinical populations, but they are often affected by residual confounding, indication bias, reverse causation, and incomplete adjustment for disease severity, comorbidities, or concomitant medications20. Randomized controlled trials can provide stronger causal evidence, but they are often expensive, time-consuming, and difficult to conduct for long-term outcomes such as hip fracture, especially when the exposure of interest involves inflammatory biomarkers or post-marketing drug safety events21. Standalone pharmacovigilance analyses can detect potential safety signals in large real-world populations, but spontaneous reporting data cannot independently establish causality because of underreporting, duplicate reports, missing data,reporting bias, and lack of denominator information22. Conversely, standalone mendelian randomization can strengthen causal inference by using genetic variants as instrumental variables, but it does not directly capture real-world post-marketing drug safety patterns and may be limited when valid instruments are unavailable or horizontal pleiotropy is present23.
The FDA adverse event reporting system (FAERS) is a database established by the U.S. Food and Drug Administration (FDA) to collect and monitor drug safety information24. It records adverse drug event reports from multiple sources, including patients, healthcare professionals, and drug manufacturers, and is an important tool for monitoring and evaluating adverse drug reactions25. The information in the FAERS database is widely used in drug safety analysis and research, and many academic studies and clinical practice guidelines base their decisions on data from this platform26. An example is monitoring the risk of adverse events related to immunosuppressants and other related adverse events27,28. Compared with conventional clinical studies, spontaneous reporting systems can capture rare or delayed adverse events in large real-world populations at relatively low cost29. However, they are susceptible to underreporting, duplicate reports, missing data, and reporting bias, and therefore cannot independently establish causality30,31.
Mendelian randomization is a statistical method used to study the causal relationship between exposure and disease by using genetic variation as an instrumental variable32. In the study of adverse drug reactions, mendelian randomization can be used to identify potential causal relationships and provide reliable evidence to support them. The validity of Mendelian randomization relies on three assumptions, as shown in Figure 133,34,35: the selected variants are strongly associated with the exposure; the variants influence the outcome only through the exposure pathway; and the variants are independent of major confounding factors. Compared with traditional observational studies, Mendelian randomization is less vulnerable to residual confounding and reverse causation because genetic variants are randomly allocated at conception and remain largely stable throughout life36,37. However, this approach may be less suitable when valid instrumental variables are unavailable, when horizontal pleiotropy is substantial, or when exposure effects are strongly time-dependent38.
By integrating FAERS pharmacovigilance analysis with Mendelian randomization, the present workflow combines real-world safety surveillance with genetic causal inference. This combined approach is particularly suitable when randomized controlled trials are unavailable, impractical, or unethical, and when complementary evidence is needed to evaluate biomarker-outcome relationships39. In practical terms, this FAERS-Mendelian randomization workflow is most suitable when the research question meets the following conditions: first, the drug-event or biomarker-outcome relationship is clinically important but cannot be adequately tested in randomized trials40; second, post-marketing real-world safety data are available and can be used to screen or characterize adverse event signals; third, appropriate genome-wide association study summary statistics are available for both the exposure and outcome; and fourth, the investigator aims to combine signal detection with genetic causal inference rather than relying on either method alone. Under these conditions, FAERS can provide real-world evidence on whether a potential safety signal exists, whereas Mendelian randomization can further assess whether the related biomarker or pathway is likely to have a direct causal relationship with the outcome. This combined workflow is especially useful for evaluating rare, delayed, or ethically difficult-to-study outcomes and for generating more robust evidence when conventional epidemiological designs are limited41.
Building on this rationale, this study aimed to establish and demonstrate an integrated FAERS-Mendelian randomization workflow for evaluating drug-event safety signals and biomarker-outcome causal relationships when conventional causal inference approaches are limited. FAERS pharmacovigilance analysis was first used to screen and characterize hip fracture-related adverse event reports associated with TNF inhibitors. Mendelian randomization was then applied to assess whether genetically predicted TNF pathway biomarkers, including TNF-α, sTNFR1, and sTNFR2, were causally associated with hip fracture risk. By combining post-marketing safety surveillance with genetic causal inference, this workflow provides a reproducible methodological reference for future studies investigating clinically important drug-event or biomarker-outcome relationships.
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FAERS data sources
The real-world data for this study were obtained from the FAERS database (https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html). This is a publicly accessible and anonymized database, so ethical approval was not required for this study. Information about the five drugs included is shown in Table 1. The search was performed by first limiting the adverse events to hip fracture, and the search time span was from Q1 2014 to Q4 2023. To ensure reliable and stable data, the study standardized the terminology of reported adverse events via the MedDRA Dictionary version 26.142. FAERS quarterly ASCII files from Q1 2014 to Q4 2023 were downloaded and imported for analysis. The extracted FAERS tables included DEMO, DRUG, REAC, THER, RPSR, and OUTC. These tables were merged across all quarters before screening. Reports were linked using CASEID and PRIMARYID to ensure consistency across demographic, drug, reaction, therapy, reporter, and outcome information. The target drugs included etanercept, adalimumab, infliximab, certolizumab pegol and golimumab. Drug names in the DRUG table were standardized by converting text to uppercase, removing extra spaces and checking spelling variants when necessary. Target drugs were identified using standardized generic names in the DRUG table, and drug-role restriction was performed using ROLE_COD = “PS”, indicating the primary suspect drug43. The specific study screening technique roadmap is shown in Figure 2. After data cleaning and screening, a unique and analyzable dataset of eligible hip fracture reports was obtained for subsequent analyses.
Duplicate FAERS reports were removed before signal detection. Duplicates were identified according to CASEID and PRIMARYID. When multiple reports shared the same CASEID, the most recent report was retained according to FDA_DT. If multiple reports had the same CASEID and FDA_DT, the report with the highest PRIMARYID was retained. After deduplication, each CASEID contributed only one record to the final analytic dataset. Reports were included if they met all of the following criteria: reporting date between Q1 2014 and Q4 2023; the adverse event was coded as “Hip fracture”; at least one of the five TNF inhibitors was recorded in the DRUG table; and the drug role was coded as primary suspect. Reports were excluded if they were duplicate records, lacked valid CASEID or PRIMARYID information, had no corresponding DRUG or REAC entry, did not include the target adverse event, or listed the target TNF inhibitor only as a concomitant or secondary suspect drug.
GWAS data sources for Mendelian randomization
The exposure data for TNF-α in the Mendelian randomization for this study were obtained from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/), with GWAS ID prot-c-3722_49_2 from the study of Suhre K et al. The study population was of European ancestry, and the number of SNPs was 501,42844.
The exposure data for sTNFR1 in the mendelian randomization for this study were obtained from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/), with GWAS ID prot-c-2654_19_1 from the study of Suhre K et al. The study population was of European ancestry, and the number of SNPs was 501,42844.
The exposure data for sTNFR2 in the Mendelian randomization for this study were obtained from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/), with GWAS ID prot-c-3152_57_1 from the study of Suhre K et al. The study population was of European ancestry, and the number of SNPs was 501,42844.
The outcome data for hip fracture, GWAS ID GCST90161240, deposited in the GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST90161240), are data from a meta-analysis of a large-scale GWAS that included 11,516 hip fracture cases and 723,838 controls45. The disease classification aligns with the International Classification of Diseases (ICD; ICD-10 codes S72.0–S72.2 and ICD-9 code 820).
Ethical approval and informed consent had been obtained in the original GWAS studies. Because the present study used publicly available, anonymized FAERS data and publicly available GWAS summary statistics, no additional ethical approval was required.
Software environment and workflow implementation
All analyses were performed using R version 4.3.2. FAERS data import, cleaning, merging, and tabulation were performed using R-based data management workflows. Data tables were imported using functions such as data.table::fread() or readr::read_delim(), merged using CASEID and PRIMARYID, and processed using dplyr functions. Descriptive statistics and 2 × 2 contingency tables were generated using custom R scripts.
Mendelian randomization analyses were conducted using TwoSampleMR version 0.5.6. Exposure instruments were extracted using a significance threshold of P < 1 × 10⁻5 or formatted from GWAS summary statistics using TwoSampleMR-compatible input structures. Instrument clumping was performed using clump_data() with clump_r2 = 0.001 and clump_kb = 10,000. Outcome data were extracted or formatted using extract_outcome_data() or read_outcome_data(), depending on the source format. Exposure and outcome datasets were harmonized using harmonise_data(). Causal estimates were generated using mr() with the following mendelian randomization methods: MR-Egger, weighted median, inverse variance weighted, simple mode, and weighted mode. Heterogeneity was assessed using mr_heterogeneity(), and horizontal pleiotropy was assessed using mr_pleiotropy_test(). All datasets were imported, cleaned, harmonized, and analyzed within this software environment to ensure a consistent and reproducible analytical workflow.
Pharmacovigilance analysis
Descriptive analyses were used to summarize hip fracture-related adverse events associated with the five drugs. Signal detection analyses were then performed using four disproportionality algorithms, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS) and Bayesian confidence propagation neural network (BCPNN). The criteria for the four major algorithms are shown in Table 246.
Mendelian randomization analysis
Summary statistics for TNF-α, sTNFR1, and sTNFR2 were extracted as exposure datasets, and hip fracture summary statistics were extracted as the outcome dataset. Analyses were restricted to European ancestry datasets when available to reduce population stratification bias.
To minimize bias caused by linkage disequilibrium and weak instruments, the following criteria were applied: genome-wide significance threshold P < 1 × 10⁻5, linkage disequilibrium threshold r2 < 0.001, clumping window of 10,000 kb, and F-statistic > 20. The F-statistic was calculated for each retained instrumental variable as beta2/se2 to evaluate instrument strength. SNPs with F-statistic ≤ 20 were excluded from downstream analyses.
After SNP selection, exposure and outcome datasets were harmonized to align effect alleles. During harmonization, effect alleles and other alleles were aligned between the exposure and outcome datasets. SNPs with incompatible alleles were removed, and palindromic SNPs with ambiguous allele frequencies were excluded when strand orientation could not be determined. After harmonization, the retained SNPs were checked to confirm that beta coefficients corresponded to the same effect allele in both datasets. The number of SNPs retained after clumping and harmonization was recorded for each exposure as an intermediate reproducibility checkpoint.
Five Mendelian randomization methods were applied, including MR-Egger, weighted median, inverse variance weighted, simple mode, and weighted mode. Potential heterogeneity of instrumental variables was assessed using Cochran’s Q test, and P < 0.05 was considered indicative of significant heterogeneity. Potential horizontal pleiotropy was evaluated using the MR-Egger intercept, and P < 0.05 indicated pleiotropy, suggesting reduced reliability of the causal estimate47. These analyses generated causal effect estimates together with heterogeneity and pleiotropy statistics for each exposure.
Intermediate checkpoints for reproducibility
Intermediate checkpoints were recorded after each major processing step to ensure workflow reproducibility. For the FAERS workflow, checkpoints included the number of DEMO records imported, the number of unique records after deduplication, the number of reports containing hip fracture as the target adverse event, the number of reports involving the five TNF inhibitors, and the final number of eligible reports in which TNF inhibitors were recorded as primary suspect drugs. For the Mendelian randomization workflow, checkpoints included the number of SNPs extracted for each exposure, the number of SNPs retained after linkage disequilibrium clumping, the number of SNPs available in the outcome dataset, the number of SNPs retained after harmonization, and the final number of instrumental variables used in each Mendelian randomization analysis.
Statistical reporting
Continuous results were reported with corresponding effect estimates, 95% confidence intervals (95% CI), and P values. Unless otherwise specified, statistical significance was defined as a two-sided P < 0.05. For pharmacovigilance analysis, descriptive counts and disproportionality estimates were reported for each individual TNF inhibitor and for the pooled TNF inhibitor group. For Mendelian randomization analysis, causal estimates, standard errors, 95% confidence intervals, P values, heterogeneity statistics, pleiotropy test results, and the number of retained SNPs were reported for each exposure.
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Results of descriptive analysis
The detailed data-cleaning and screening workflow is summarized in Figure 2. A total of 15,428,448 records were initially retrieved from the DEMO table. After duplicate removal, 13,447,292 unique DEMO records remained. After data standardization, deduplication, and cross-table linkage, the final numbers of records available in each FAERS table were 27,260,995 in DRUG, 18,851,426 in REAC, 12,573,760 in THER, 6,448,18...
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In this study, five TNF inhibitors identified from real-world pharmacovigilance data were integrated with Mendelian randomization analyses to evaluate whether TNF pathway biomarkers are causally associated with hip fracture risk. The findings consistently indicated that no significant direct causal association between TNF-α and hip fracture risk was identified, and additional analyses of sTNFR1 and sTNFR2 yielded concordant results. These results support the value of combining spontaneous-report safety data with gen...
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The authors declare no competing interests.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| Generic laboratory computer workstation | Equipment | Generic laboratory computer | N/A |
| FDA Adverse Event Reporting System database | Public pharmacovigilance database | U.S. Food and Drug Administration | https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html |
| FAERS quarterly ASCII files | Public pharmacovigilance data files | U.S. Food and Drug Administration | Q1 2014 to Q4 2023; DEMO, DRUG, REAC, THER, RPSR, and OUTC tables |
| MedDRA Dictionary version 26.1 | Medical terminology resource | MedDRA Maintenance and Support Services Organization | https://www.meddra.org/ |
| IEU OpenGWAS database | Public GWAS database | MRC Integrative Epidemiology Unit, University of Bristol | https://gwas.mrcieu.ac.uk/ |
| TNF-α GWAS summary statistics | Exposure GWAS dataset | IEU OpenGWAS database | GWAS ID: prot-c-3722_49_2 |
| sTNFR1 GWAS summary statistics | Exposure GWAS dataset | IEU OpenGWAS database | GWAS ID: prot-c-2654_19_1 |
| sTNFR2 GWAS summary statistics | Exposure GWAS dataset | IEU OpenGWAS database | GWAS ID: prot-c-3152_57_1 |
| GWAS Catalog | Public GWAS database | European Bioinformatics Institute | https://www.ebi.ac.uk/gwas/ |
| Hip fracture GWAS summary statistics | Outcome GWAS dataset | GWAS Catalog | Accession ID: GCST90161240; https://www.ebi.ac.uk/gwas/studies/GCST90161240 |
| R version 4.3.2 | Statistical computing environment | R Foundation for Statistical Computing | https://www.r-project.org/ |
| data.table package | R package | R package repository / R community | https://cran.r-project.org/package=data.table |
| dplyr package | R package | R package repository / R community | https://cran.r-project.org/package=dplyr |
| readr package | R package | R package repository / R community | https://cran.r-project.org/package=readr |
| stringr package | R package | R package repository / R community | https://cran.r-project.org/package=stringr |
| vroom package | R package | R package repository / R community | https://cran.r-project.org/package=vroom |
| TwoSampleMR version 0.5.6 | R package for Mendelian randomization | MRC Integrative Epidemiology Unit | https://mrcieu.github.io/TwoSampleMR/ |
| Custom R scripts for FAERS processing | Analysis script | Prepared by the study authors | Provided as supplementary files |
| Custom R scripts for Mendelian randomization | Analysis script | Prepared by the study authors | Provided as supplementary files |
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