Association of albumin to creatinine ratio with urinary arsenic and metal exposure: evidence from NHANES 2015-2016

Purpose Urinary metals can be used to identify metal exposure in humans from various sources in the environment. Decreased renal function and cardiovascular dysfunction may occur due to low levels of metal exposure in the general population. The purpose of this study is to assess the association between urinary arsenic and metals and a higher albumin to creatinine ratio (ACR) among adults in the general US population. Methods We conducted a cross sectional analyses using the 2015-2016 National Health and Nutrition Examination Survey (NHANES) dataset. Multiple linear logistic models were used to examine the association between 21 urinary arsenic and metal concentrations (arsenous acid, arsenic acid, arsenobetaine, arsenocholine, dimethylarsinic acid, monomethylarsonic acid, total arsenic, mercury, barium, cadmium, cobalt, cesium, molybdenum, manganese, lead, antinomy, tin, strontium, thallium, tungsten, uranium) and increased ACR (>= 30 mg/g). Results The sample included 4122 adults, of whom approximately 9.4% of males and 10.7% females had increased ACRs. The exposure included urinary arsenic compounds (7) and urinary metal compounds (14) at or above the limit of detection. Urinary dimethylarsinic acid [OR 38.9, 95% CI 3.6-414.6], urinary monomethylarsonic acid [OR 18.6, 95% CI 1.1-308.2], urinary cadmium [OR 11.9, 95% CI 1.2-122.0], urinary cesium [OR 17.0, 95% CI 2.7-105.8], and urinary antimony [OR 10.7, 95% CI 2.2-51.3] were associated with an increased ACR. No other urinary metals were significantly associated with increased ACR. Conclusion Increased ACR was positively associated with urinary dimethylarsinic acid, monomethylarsonic acid, cadmium, cesium, and antimony.

Automated summary

Urinary dimethylarsinic acid, monomethylarsonic acid, cadmium, cesium, and antimony were found to be positively associated with increased ACR among adults in the general US population, based on the 2015-2016 NHANES dataset analysis.