Login / Signup

Co-exposures to toxic metals cadmium, lead, and mercury and their impact on unhealthy kidney function.

Ram Baboo Jain
Published in: Environmental science and pollution research international (2019)
Cross-sectional data (N = 25427) from the National Health and Nutrition Examination Survey for 2003-2014 for US adults were used to estimate the impact of co-exposure to high levels of cadmium, lead, and mercury on the unhealthy kidney function. If observed concentrations of cadmium, lead, and total mercury were above the 75th percentile of their respective distributions, the exposure to the corresponding metal was considered to be high. Logistic regression models were fitted to estimate the probabilities of an unhealthy kidney function. Two alternate definitions of unhealthy kidney function were used. First, if estimated, glomerular filtration rate (eGFR) was < 60 mL/min/1.73 m2 (KeGFR) and second, if the observed albumin creatinine ratio (ACR) was ≥ 30 mg/g creatinine (KACR). As compared with low exposures, adjusted odds ratios (AOR) for unhealthy kidney function when exposed to high levels of lead and cadmium were observed to be 1.567 (1.346-1.823) and 1.663 (1.376-2.010) respectively for KeGFR. When exposed to high levels of both cadmium and lead, AORs for unhealthy kidney functions KeGFR and KARC were found to be 2.369 (1.868-3.004) and 1.522 (1.216-1.905) respectively. When exposed to high levels of cadmium, lead, and mercury, AORs for unhealthy kidney functions KeGFR and KARC were found to be 2.248 (1.428-3.538) and 1.502 (1.024-2.204) respectively. High exposure to lead along with any level of exposure to cadmium and total mercury was found to adversely affect the health of kidney function. High exposure to mercury does not affect unhealthy kidney function.
Keyphrases
  • heavy metals
  • cross sectional
  • healthcare
  • small cell lung cancer
  • mental health
  • risk assessment
  • uric acid
  • health risk assessment
  • health risk
  • drinking water
  • big data
  • health information
  • deep learning