Association between fine particle exposure and common test items in clinical laboratory: A time-series analysis in Changsha, China

• Published in: The Science of the total environment Vol. 723; p. 137955
• Main Authors: Deng, Zhonghua, Tan, Chaochao, Xiang, Yangen, Pan, Jianhua, Shi, Guomin, Huang, Yue, Xiong, Yican, Xu, Keqian
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 25.06.2020
• Subjects: air pollutants; air pollution; alanine transaminase; aspartate transaminase; biomarkers; calcium; carbon monoxide; China; Clinical laboratory; Common test items; disease diagnosis; environmental monitoring; Fine particle; Generalized additive model; health effects assessments; hospitals; humidity; leukocytes; low density lipoprotein; mortality; nitrogen dioxide; ozone; particulates; sulfur dioxide; temperature; Time-series analysis; urea; China; TT; PT; ALT; Cl; UA; TNF; NO2; GLU; RBC; AQI; CEA; IFCC; IL; TEOM; AQHI; ET; VCAM; GAM; Clinical laboratory; TBIL; Time-series analysis; Ig; DBIL; CRP; RR; Common test items; vWF; O3; K; HDL; Na; CREA; LDL; Fine particle; PM2.5; SO2; WBC; UREA; FIB; CLSI; TNFR; CD; AST; CKMB; DEC; ICAM; ESR; CK; CO; TC; APTT; TG; DNA; ALB; PAI; TP; PM; Generalized additive model; Ca
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2020.137955

Most studies on the health effects of PM2.5 (fine particulate matter with diameter smaller than 2.5 μm) use indirect indicators, such as mortality and number of hospital visits. Recent research shows that biomarkers can also be used to evaluate the health effects of PM2.5; however, these biomarkers are not very common. Clinical laboratories can provide a significant amount of test data that have been proven to have important diagnostic value. Therefore, we use big data analysis methods to find the associations between clinical laboratory common test items and PM2.5 exposure. Data related to air pollution and meteorological information between 2014 and 2016 were obtained from the China National Environmental Monitoring Centre and the China National Meteorological Information Center. Additionally, data of 27 common test items from the same period were collected from Changsha Central Hospital. Primary analyses included a generalized additive model to analyze the associations between PM2.5 concentration and common test items; the model was adjusted for time trends, weather conditions (temperature and humidity), and days of the week. Furthermore, we adjusted the effects of other air pollutants, such as PM10, SO2, NO2, CO, and O3. 17 items such as TP, ALB, ALT, AST, TBIL, DBIL, UREA, CREA, UA, GLU, LDL, WBC, K, Cl, Ca, TT, and FIB were significantly positively associated with PM2.5 concentration (P< 0.05) and have concentration-response relationship. After adjusting the effect of PM10+SO2+NO2+CO+O3, TP, ALB, ALT, AST, TBIL, DBIL, UREA, CREA, UA, GLU, WBC, Cl, and Ca were still significantly associated with PM2.5 concentration (P< 0.05). This current study suggested that clinical laboratory common test items may be used to assess and predict the health effects of PM2.5 on the population. [Display omitted] •Association between fine particulate air pollution exposure and clinical laboratory common test items for the first time using big data analysis.•17 clinical laboratory common test items were changeed significantly associated with PM2.5 exposure.•PM2.5 is an independent influencing factor that may causes changes in some clinical laboratory common test items.•Clinical laboratory common test items may assess and predict the health effects of fine particulate air pollution exposure