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A new study links urinary metabolites from organophosphorus pesticides to increased age-related macular degeneration risk.
(Image Credit: AdobeStock/New Africa)
The risk of age-related macular degeneration (AMD) was found to increase as a result of exposure to the urinary dialkyl phosphate (DAP) metabolites in organophosphorus pesticides (OPPs), according to a new Chinese study.1 The authors, led by first author Yu-Xin Jiang, MD, are from the Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; the National Clinical Research Center for Eye Diseases; the Shanghai Key Laboratory of Fundus Diseases; and the Engineering Center for Visual Science and Photomedicine, all in Shanghai, China.
The investigators pointed out that AMD is a multifactorial disease resulting from aging, genetic susceptibility, lifestyle habits, and environmental exposures, which make the pathogenesis of AMD highly intractable to prediction and interpretation. Considering that patient responses to current intravitreal treatments vary and complications are associated with treatment,2 early prevention of AMD from exposure to numerous risk factors is the most effective and feasible measure.
“Among all factors, the adverse effects from environmental chemical exposures on AMD have been heatedly discussed in population-based epidemiologic studies. For example, several researchers have emphasized the impacts of heavy metals,3 air pollutants,4 and radiation5 exposure on the development of AMD. OPPs, a group of organophosphate or phosphate sulfide esters, are prevalent insecticides commonly applied worldwide in agricultural, residential, and commercial settings with the advantages of cost-effectiveness and high efficacy in controlling pests and preventing insect-borne diseases.6 Nevertheless, the persistent non-biodegradable nature and propensity of residue accumulation in soil and water bodies, in conjunction with multiple routes of human exposure to OPPs, for instance, ingestion, inhalation, and skin contact, have raised public attention to concern about their toxic effects on human health and ecosystems,7” they said.
The OPPs can be swiftly absorbed, metabolized, and eliminated as urinary DAP metabolites from the body, commonly used as biomarkers in cohort studies.8
Previous research has identified that OPPs are relevant to diverse diseases, including cancer,9 central nervous system disorders (Parkinson’s disease 10and depression),11 sleep problems,12 diabetes,13 hypertension,14 sex hormone function,15 and atopic diseases.16
The investigators identified patients from the National Health and Nutrition Examination Survey17 between 2005 and 2008. Urinary DAP metabolites were used to construct a machine learning (ML) model for AMD prediction, they explained.
They used interpretability pipelines, ie, permutation feature importance (PFI), partial dependence plot (PDP), and SHapley Additive exPlanations (SHAP), to analyze the effects of exposure features to prediction outcomes.
The authors reported that of the 1,845 patients in the study, 137 had been diagnosed with AMD. Receiver operating characteristic curve analysis evaluated Random Forests as the best ML model with its optimal predictive performance among 11 models. PFI and SHAP analyses indicated that DAP metabolites were of significant contribution weights in AMD risk prediction, higher than most of the sociodemographic covariates. Shapley values and waterfall plots of randomly selected AMD individuals emphasized the predictive capacity of ML with high accuracy and sensitivity in each case. The relationships and interactions visualized by graphical plots and supported by statistical measures showed the indispensable effects of six DAP metabolites to the prediction of AMD risk, the investigators reported.
The authors believe this study yields a novel insight into the link between environmental factors and health outcomes.
The study concluded, “Urinary DAP metabolites of OPPs exposure are associated with AMD risk, and ML algorithms show excellent generalizability and differentiability in the course of AMD risk prediction.”
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