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1463 Pm10 exposure and gene expression modulation in a population of healthy steel workers
  1. A Biggeri1,
  2. F Nordio2,
  3. D Catelan1,
  4. V Bollati3,4,
  5. P Apostoli5,
  6. M Carugno3,
  7. M Bonzini3,4
  1. 1Dept of Statistics, Informatics and Applications ‘G. Parenti’, University of Florence, Florence, Italy
  2. 2Cardiovascular Medicine Division, Brigham and Women’s Hospital, Boston, Massachusetts
  3. 3EPIGET Lab – Dept. Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
  4. 4Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Occupational Medicine Unit, Milan, Italy
  5. 5Dept Experimental and Applied Medicine, University of Brescia, Brescia, Italy


Introduction The association of gene expressions with exposure to ambient particulate matter (PM) concentrations is still mainly explorative. We took advantage of a study designed to evaluate the short-term association between PM exposure in working environment and biological molecular targets to assess the correlation between PM10 exposure and gene expressions of 44 genes.

Methods 60 workers of a steel plant, aged 25–55 years, were enrolled in a panel study with two blood samples at the beginning and the end of a week. Gene expression was quantified by a 44-gene PCR Array and normalised by GAPDH housekeeping. Gene expression and PM10 data were log-transformed. Univariate correlation between each gene expression and PM10 is misleading because does not account for the association structure among genes. We instead focused on the entire association network among genes and the impact of PM10 exposure on it. Since our study is mainly explorative we deliberately choose a simple approach: the minimal BIC forest. This approach, which relies on Linearity, Multivariate Normality and the Adequacy of Linear Scores, is useful as a preliminary step towards understanding the overall dependence structure of high-dimensional discrete and/or continuous data.

Results The minimal BIC forest resulted in the identification of eight subgroups of correlated genes expression. The model including PM10 showed a negative association between PM10 exposure and NFKB1 expression. The sensitivity analysis on the assumptions was conducted as follows:

  1. all possible [n=1892] Student t-statistics for squared terms were calculated;

  2. all possible [n=39 732] choices of cross-product terms in the linear regressions.

No violation regarding the correlation between PM10 and gene expressions was found.

Conclusions The study was explorative and no a priori knowledge on gene expression pathways was considered. In conclusion we found an association of PM10 exposure with NFKB1 expression in a occupational context of high ambient concentrations.

  • PM10
  • gene expression
  • BIC forest

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