首页|Air Force Medical Center Reports Findings in Chronic Kidney Disease (Comparative mathematical modeling of causal association between metal exposure and developm ent of chronic kidney disease)
Air Force Medical Center Reports Findings in Chronic Kidney Disease (Comparative mathematical modeling of causal association between metal exposure and developm ent of chronic kidney disease)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Kidney Diseases and Conditions - Chronic Kidney Disease is the subject of a report. According to news reporting o riginating in Beijing, People's Republic of China, by NewsRx journalists,resear ch stated, "Previous studies have identified several genetic and environmental r isk factors for chronic kidney disease (CKD). However, little is known about the relationship between serum metals and CKD risk." The news reporters obtained a quote from the research from Air Force Medical Cen ter, "We investigated associations between serum metals levels and CKD risk amon g 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyz ed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Me tal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to asses s associations and predict CKD risk based on serum metals. A chained mediation m odel was applied to investigate how interventions with different heavy metals in fluence renal function indicators (creatinine and cystatin C) and their impact o n diagnosing and treating renal impairment. Serum potassium (K), sodium (Na), an d calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybden um (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30 to 45 percentiles compared to the m edian, but the opposite was observed for the 55 to 60 percentiles. For example, a change in serum K concentration from the 25 to the 75 percentile was associate d with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25, 50 and 75 percent iles, respectively. Cumulative metal exposures, especially double-exposure to se rum K and Se may impact CKD risk. Machine learning methods validated the externa l relevance of the metal factors."
BeijingPeople's Republic of ChinaAsi aChronic Kidney DiseaseCyborgsEmerging TechnologiesHealth and MedicineKidney Diseases and ConditionsMachine LearningMathematicsRisk and Preventi on
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