Commentary: Association between metallic implants and stroke in US adults from NHANES 2015-2023 a cross-sectional study

li xiang ming^1,2huang pei xin²huang gui zhong^2*

• ¹Shantou Hospital of Traditional Chinese Medicine, Shantou, China
• ²Shantou Hospital of Traditional Chinese Medicine, Guangzhou University of Traditional Chinese Medicine, shantou, China

2. Impact of Key Information Deficiency on Research ResultsThe NHANES database lacks detailed records of metal-based biomedical implants. Crucial information such as the types of implants, their quantities, the duration of their presence in the body, as well as the corrosion rates and ion release characteristics of different metallic materials, is absent. This deficiency renders it impossible for the study to comprehensively evaluate the potential impact of different metal types on stroke risk. Implanted metal-containing devices vary in terms of their functions, biomechanical loads, and in - vivo degradation processes. These differences can influence the research results, enhance inter - group heterogeneity, and reduce the clinical specificity of the research conclusions. 3. Impact of Unmeasured Confounding Factors on the Reliability of ResultsAlthough the study incorporated some potential confounding factors, such as gender, age, race, poverty status, educational level, body mass index, smoking, diabetes, coronary heart disease, and hypertension, as covariates(Wu et al., 2024), there may still exist unmeasured or unknown confounding factors. These factors can lead to residual confounding effects in the study, interfering with the accurate assessment of the relationship between metallic implants and stroke and undermining the reliability of the research conclusions. For instance, potential confounding factors like cholesterol (Ma et al., n.d.), triglycerides (Fu et al., 2025), cancer (Qu et al., 2023), sleep conditions(Huang et al., 2025)and dietary factors(Liu and Huang, 2025)could further enhance the reliability of the research results if they were further adjusted for.4. Impact of Incomplete Subgroup AnalysisThe authors conducted a highly detailed subgroup analysis, yet failed to perform subgrouping based on race and socioeconomic status. Given that there are differences in stroke incidence or the prevalence of metal-based prosthetic devices among populations of different races and socioeconomic statuses (Cheng et al., 2024; Pantoja-Ruiz et al., 2025), this unbalanced distribution may still conceal specific associations within certain subgroups. As a result, the research conclusions may not accurately reflect the relationship between implanted metallic devices and stroke among populations of different races and socioeconomic statuses. We believe that adding subgroup analyses for different racial and socioeconomic status groups would be highly valuable.5. Interference of Blood Heavy Metal Factors with Research ResultsImplanted metallic devices in the body may release metal ions, thereby causing changes in the heavy metal content in the blood. For example, orthopedic implants such as cobalt - chromium alloys, when interacting with the human body's humoral environment over an extended period, release metal ions like cobalt and chromium into the bloodstream, increasing the concentration of relevant heavy metals in the blood (Houdek et al., 2024). This establishes a direct link between metal-containing prosthetic devices and elevated heavy metals in the blood. A study by Tobias Reiner et al. (Reiner et al., 2019)demonstrated that patients undergoing anatomic or reverse total shoulder arthroplasty (TSA) exhibited significantly higher blood metal ion concentrations compared to 23 healthy controls, with cobalt (0.18 μg/L vs. 0.11 μg/L), chromium (0.48 μg/L vs. 0.14 μg/L), and titanium (1.31 μg/L vs. 0.62 μg/L) levels showing statistically significant elevations postoperatively in a cohort of 40 TSA patients.A meta-analysis demonstrated significant increases in titanium (MD 0.81, 95% CI 0.32–1.30) and chromium levels (OR 23.50, 95% CI 5.56–99.31) following instrumented spinal surgery, with approximately 70% of patients showing elevated chromium levels(Burgos et al., 2024). Furthermore, emerging evidence indicates that fluctuations in blood concentrations of neurotoxic heavy metals (e.g., lead, cadmium) are significantly associated with stroke risk through multiple pathways(He et al., 2024). However, current evidence on systemic metal toxicity from surgical metal devices remains inconclusive. A large-scale review by Zywiel et al. (Zywiel et al., 2016) noted that elevated blood metal levels from orthopedic implants rarely correlate with systemic cardiovascular complications in the general population. Similarly, studies focusing on hypersensitivity reactions to metal implants (Teo and Schalock, 2016) emphasize localized immune responses rather than systemic stroke risk.Since blood-borne heavy metals are both influenced by biomedical metal implants and implicated in stroke risk, failure to adequately account for them in research may confound the accurate assessment of the association between such implants and stroke. This could either exaggerate or obscure the true relationship, potentially leading to biased or misleading conclusions. While localized ion release from implanted metal components is plausible, its systemic clinical relevance to stroke pathogenesis appears limited based on current evidence. Future studies should prioritize direct biomarker measurements (serum metal ion levels) alongside rigorous adjustment for confounders to clarify whether observed associations reflect causation or residual confounding.6. Impact of the Limitations of Stroke Diagnosis MethodsFurthermore, in this study, the diagnosis of stroke relied on self - reported measurements. The subjectivity and incompleteness of self - reporting have affected the research outcomes in multiple ways. On one hand, due to limitations in participants' memory or medical knowledge, misdiagnoses or case omissions are likely to occur. This affects the statistical count of stroke cases, interferes with the assessment of the strength of the association between surgical metal devices and stroke, and leads to biased conclusions. On the other hand, self - reporting tends to miss key information such as stroke types, onset times, and severity levels. This hinders the analysis of the relationship between implanted metal components and different types and stages of stroke, potentially concealing the true association between them. Additionally, inaccurate diagnoses can misclassify stroke cases. During subgroup analysis, this confuses the associations between metal-based medical devices and stroke in different subgroups, rendering the subgroup analysis results unreliable and unable to accurately present the risk differences among different populations.