Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Capsaicin ameliorates diabetic retinopathy by inhibiting poldip2-induced oxidative stress.

BACKGROUND: Oxidative stress and the resultant hyperpermeability play a vital role in the pathogenesis of diabetic retinopathy (DR). Poldip2 has been implicated in H2O2 production, but the effects of capsaicin on poldip2 have not been reported. METHODS: Diabetic Sprague-Dawley (SD) rats induced with STZ were treated with capsaicin or AAV9-poldip2-shRNA, and human retinal microvascular endothelial cells (HRMECs) were treated with capsaicin or poldip2 siRNA. RESULTS: Current data indicated that the expression of PPARγ, poldip2, Nox4, VCAM-1, HIF-1α, and VEGF increased in rat retinas with DR and in HRMECs treated with high glucose. The production of ROS or H2O2 in the tissues, serum, and cells increased, and the paracellular permeability of cultured HRMECs with high glucose significantly increased. In addition, overt hyperpermeability of retinal microvessels and increased retinal neovascularization in diabetic rats were observed. However, capsaicin treatment inhibited these increases and suppressed the expression of PPARγ by enhancing its phosphorylation and ubiquitination in the retinas of DR rats. Poldip2 knockdown in HRMECs or its silencing in the retina of DR rats concomitantly led to reduced levels of Nox4, VCAM-1, HIF-1α, VEGF, ROS, and H2O2, and the paracellular permeability of HRMECs or the hyperpermeability of retinal microvessels in diabetic rat retinas decreased. Similarly, after PPARγ knockdown in HRMECs, poldip2, Nox4, HIF-1α, VEGF, ROS, and H2O2 decreased, and the monolayer paracellular permeability was reduced accordingly. CONCLUSION: Capsaicin may ameliorate diabetic retinopathy by activating TRPV1 and suppressing the PPARγ-poldip2-Nox4 pathway.

Association between meteorological factors and hospital admissions for uveitis in Hefei, China: a time-series study.

Meteorological variables are regarded as risk factors for inflammatory diseases, but their associations with uveitis, one of the leading causes accounting for blindness worldwide with an estimated prevalence of 38-714 cases/100,000 person-years, have not been thoroughly investigated. The present study explored the short-term association between meteorological variables and hospital admissions for uveitis in Hefei City, China. Daily data on uveitis hospital admissions and meteorological variables including mean temperature (MT) (°C), diurnal temperature range (DTR) (°C), and relative humidity (RH) (%), from 2014 to 2020, were collected. A time-series study using generalized linear model combined with distributed lag non-linear model was applied. Totally, 1911 admissions for uveitis including 894 first admissions and 1017 readmissions were reported during the study period. The associations of high percentile of MT (75th, 24.5℃) and low percentile of DTR (25th, 5.4℃) with uveitis admissions were observed to be statistically significant from lag9 (RR = 1.041, 95%CI: 1.002-1.081) to lag11 (RR = 1.053, 95%CI: 1.003-1.104) and lag4 (RR = 1.053, 95%CI: 1.019-1.088) to lag5 (RR = 1.052, 95%CI: 1.020-1.085), respectively. Moreover, a significant association between low percentile of RH (1th, 44%) and uveitis admissions appeared at lag0-8 (RR = 1.869, 95%CI: 1.017-3.434) and lasted until lag0-13 (RR = 2.539, 95%CI: 1.102-5.850) in the cumulative lag structure. Subgroup analyses indicated that males and the young seemed to be more sensitive to high MT exposure, while females appeared to be more vulnerable to low DTR exposure. Interestingly, both the young and the elderly are susceptible to low DTR and low RH exposure. Furthermore, high MT and low DTR exposure were associated with increased risk of first admission for uveitis. In summary, exposure to high-value of MT and low-value of DTR and RH may increase the hospital admissions for uveitis, especially for the status of first admission.