This study aims to assess the risks associated with drug-induced macular edema and to examine the epidemiological characteristics of this condition. This study analyzed data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from January 2004 to June 2024 to conduct a disproportionality analysis identifying drugs with positive signals of drug-induced ME. Additionally, the onset time of ME associated with these drugs was examined. In the FAERS database, a total of 490 drugs were reported to pose a risk of drug-induced ME. Disproportional analysis and screening further identified 8 drugs that significantly increased this risk. Among these, one is ophthalmic drugs, including Latanoprost (ROR = 5.51), and ten are non-ophthalmic drugs, including Cefuroxime (ROR = 75.93), Fingolimod (ROR = 30.69), and Siponimod (ROR = 20.51). This study utilizes the FAERS database to investigate potential associations between drug use and the occurrence of ME, rapidly identify drugs that may induce the condition, and propose research strategies. These findings hold significant value for guiding clinical medication practices.

Drug-related conjunctivitis can compromise ocular health and quality of life. To evaluate its epidemiology, we analyzed reports from the FDA Adverse Event Reporting System (FAERS) spanning January 2004 to June 2024. The control group in this study comprised individuals using non-target drugs, while the experimental group consisted of individuals using target drugs. Using disproportionality analysis, we identified drugs with a positive signal for conjunctivitis and stratified their risk levels; we also examined induction periods to assess the speed of onset. Among 38 drugs most frequently reported for conjunctivitis, two ophthalmic agents—brimonidine (ROR = 23.04) and latanoprost (ROR = 10.55)—and eight non-ophthalmic drugs, including tralokinumab (ROR = 83.3), dupilumab (ROR = 18.92), and allopurinol (ROR = 5.04), were associated with positive signals. Tralokinumab, brimonidine, dupilumab, and latanoprost were identified as high-association medications. Notably, ophthalmic agents had a significantly shorter induction period than non-ophthalmic drugs (mean 125.9 vs. 298.4 days). These findings underscore the need for vigilant pharmacovigilance and further investigation into the etiology and prevention of drug-related conjunctivitis.

The objective of this study is to investigate the association between the Systemic Immune-Inflammation Index (SII) and cataracts. This cross-sectional study analyzed data from the 2005–2008 NHANES to examine the relationship between the SII and cataract prevalence. Covariates included age, race/ethnicity, gender, education level, marital status, Body Mass Index (BMI), smoking, alcohol consumption, hypertension, hyperlipidemia, and diabetes. Multivariable logistic regression was used to assess the association, while spline curve fitting explored potential non-linear relationships. Threshold analysis identified critical inflection points. To address age-related bias, Propensity Score Matching (PSM) was performed, aligning cataract patients with comparable non-cataract individuals for further evaluation. Our study included 3,623 participants, of whom 730 (20.15%) were diagnosed with cataracts. After adjusting for all covariates, multivariable logistic regression analysis demonstrated that elevated levels of the SII were significantly associated with increased odds of cataracts (Model1: OR = 1.56; 95%CI [1.33–1.85]; Model2: OR = 1.55; 95%CI [1.32–1.84]; Model3: OR = 1.57; 95%CI [1.33–1.86]). In the spline curve fitting model, the relationship between ln-SII and cataract prevalence was non-linear (P < 0.001), with a critical inflection point identified at an SII of 428.38. SII levels remained significantly associated with cataract prevalence following PSM adjustments (Model 1: OR = 1.48; 95% CI [1.21–1.80]; Model 2: OR = 1.48; 95% CI [1.21–1.80]; Model 3: OR = 1.46; 95% CI [1.20–1.78]). Elevated SII levels are associated with a higher prevalence of cataracts, underscoring the pivotal role of systemic inflammation in cataract development. These findings indicate that SII could serve as a valuable biomarker for assessing cataract risk, further emphasizing the significance of managing systemic inflammation as a potential strategy for cataract prevention.

Objective: Although numerous drugs have been associated with cataracts, the risk for most drugs remains unclear. This study aimed to investigate the risk factors for drug-induced cataracts by analyzing large-scale data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).Methods: We used the reporting odds ratio (ROR) to evaluate reports of drug-induced cataracts in FAERS from the first quarter of 2004 to the third quarter of 2024. A univariate analysis, LASSO (least absolute shrinkage and selection operator) regression, and a multivariate regression analysis were performed to identify drug-related risk factors for cataracts, and Bonferroni correction was applied for multiple comparisons.Results: Multivariate logistic regression ultimately identified 15 drugs as independent risk factors, including immunomodulators (6/15), antineoplastic drugs (3/15), psychotropic drugs (1/15), respiratory drugs (1/15), gastrointestinal drugs (1/15), orthopedic drugs (1/15), metabolic regulators (1/15), and ophthalmic drugs (1/15). The median time to onset of drug-induced cataracts was 449 days (interquartile range [IQR]: 150–901 days), with approximately 75% of adverse events occurring within 747 days.Conclusion: These findings may help clinicians detect drug-related cataracts at an early stage and provide valuable insights for future research on the mechanisms of drug-induced cataracts.

Background: Glaucoma, a leading global cause of blindness, has garnered increasing research attention in recent years concerning its potential association with environmental factors. This study investigates the influence of various environmental pollutants on glaucoma prevalence among middle-aged and older adult populations in China, a country with a high incidence of the disease.Methods: Using data from 17,184 participants in the 2015 China Health and Retirement Longitudinal Study (CHARLS), individuals were grouped based on glaucoma diagnosis. Environmental pollutant exposure levels were derived from satellite-based spatiotemporal models. Standardized linear regression and restricted cubic spline (RCS) analysis were applied to evaluate the impact of pollutants on glaucoma across different covariate-adjusted models, while logistic regression was used to assess significant variables for constructing and evaluating a predictive model.Results: After adjusting for covariates, six pollutants (PM_2.5, PM₁₀, PM₁, NH₄, Cl, and NO₃) demonstrated significant associations with glaucoma incidence. Subsequent logistic regression revealed that the occurrence of glaucoma may be influenced by a combination of environmental pollutants (NH₄ and NO₃), regional categories, gender, age, educational level, and diabetes history.Conclusion: In conclusion, this study offers a novel perspective on glaucoma risk prediction by integrating environmental pollutants, potentially contributing to enhanced preventive strategies for this condition.

Breast cancer is one of the most common malignant tumors in women worldwide. Inflammation plays an important role in the occurrence and development of breast cancer. This study aimed to evaluate the association between inflammatory markers and breast cancer prevalence and explore their potential biomarker value. Using data from the NHANES, approximately 20,000 participants were analyzed to assess the association between six systemic inflammatory markers and breast cancer prevalence. Statistical methods including multivariable logistic regression, subgroup analysis, and interaction testing were employed. ROC curves were used to evaluate and compare their diagnostic capabilities. This study included 19,734 participants. We observed a significant positive correlation between platelet-to-lymphocyte ratio (PLR) and breast cancer prevalence (OR = 1.35; 95% CI:1.09, 1.67), with PLR demonstrating good predictive performance for breast cancer. Additionally, monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index(SIRI) and aggregate index of systemic inflammation(AISI) were also found to be positively associated with breast cancer prevalence. Subgroup analysis and interaction tests revealed that the association between PLR and BC did not differ significantly among population groups. ROC curve analysis indicated that PLR (AUC = 0.59; 95%CI: 0.56, 0.63) outperformed other inflammatory markers in predicting BC. Systemic inflammatory markers, especially PLR, are significantly associated with BC prevalence and demonstrate potential as biomarkers for early detectionn. People with elevated inflammation markers should pay close attention to the latent prevalence of BC.