Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study
Abstract Background Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-025-06466-7 |
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| Summary: | Abstract Background Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. Methods Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC–MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. Results Anxious individuals exhibited significantly higher blood Cd levels than controls (β = 0.50, 95% CI: 0.07–0.93, p < 0.01), with anxiety positively correlating with depression (r = 0.62, p = 0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC = 0.80, validation cohort). Conclusions This first integrative environmental-proteomic study highlights cadmium’s synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants. Graphical Abstract Integrating clinical and demographic data with plasma concentrations of heavy metals, this study developed a predictive model for the comorbidity of mental disorders in middle-aged and elderly individuals. Correlation analysis revealed significant links between plasma cadmium (Cd), lead (Pb), mercury (Hg), and anxiety. By utilizing the XGBoost and LASSO machine learning algorithms, combined with validation through animal experiments, CCDC126 was identified as a diagnostic biomarker derived from the plasma proteome. The clinical prediction model, which incorporates blood cadmium levels, hypertension status, protein expression, and blood lead concentration was formulated as: $$\left[ln\frac{P}{1-P}=-39.414+0.449\times Cd+1.896\times HYPERTENSION+3.586\times CCDC126-0.289\times Pb\right]$$ l n P 1 - P = - 39.414 + 0.449 × C d + 1.896 × H Y P E R T E N S I O N + 3.586 × C C D C 126 - 0.289 × P b . The model achieved an Area Under the Curve (AUC) of 0.8, the model provides an early diagnostic tool for anxiety attributed to environmental pollutants and offers insights into geriatric psychiatric comorbidities. |
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| ISSN: | 1479-5876 |