Multivariate Statistical Analysis of Dioxin Profiles to Explain Source Contributions to Serum Dioxins

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University of Michigan, Ann Arbor, MI, USA School of ublic Health Institute for Social

1 Multivariate Statistical Analysis of ioxin rofiles to Explain Source Contributions to Serum ioxins eter Adriaens, h, E The University of Michigan ioxin Exposure Study The University of Michigan, Ann Arbor, MI, USA School of ublic Health Institute for Social Research College of Engineering Center for Statistical Consultation & Research September 3, 27

2 Study Team School of ublic Health avid Garabrant, M, MH Alfred ranzblau, M Olivier Jolliet, h Lynn Zwica, MS Kristine Knutson, MH Elizabeth Hedgeman, MS, MH Qixuan Chen, MS Biling Hong, MA Shih-Yuan Lee, MS Chiung-Wen Chang, MS Xiaohui Jiang, MS Jinkyung Ha Meghan Milbrath Yvan Wenger, MS Sharyn Vantine LimnoTech Tim Towey, MS Noémi Barabas, h Center for Statistical Consulting and Research Brenda W. Gillespie, h Camelia Sima, MS Scott Swan, MS anielle Gwinn College of Engineering eter Adriaens, h, E Avery emond, h, E Shu-Chi Chang, h Hoa Trinh, MS Institute for Social Research James Lepkowski, h, MH Barbara Lohr Ward, MBA Kathy Ladronka Kristen Olson, h Jennifer Sinibaldi, MS inancial support for the study comes from The ow Chemical Company through an unrestricted grant to the University of Michigan. University of Michigan ioxin Exposure Study Slide 2 September 3, 27

3 resentation Overview In this presentation we will: rovide a brief overview of statistical fingerprinting concepts resent the fingerprints found in soil, household dust, and serum samples collected as part of the UMES. Offer interpretation of the signatures found in each matrix. University of Michigan ioxin Exposure Study Slide 3 September 3, 27

4 The Concept of Statistical ingerprinting Each source emits a unique composition of chemicals (dioxin and furan congeners in this case) = fingerprint Once emitted into environment, individual source compositions mix leading to different patterns measured in sediment samples. Correlations among the dioxin congeners help establish which congeners occur together in stable compositions, and each sample is decomposed into these stable patterns. This leads to the fingerprints that have contributed to the mix in each sample. SOURCES: linear mixing 2 + Mixed source pattern.5 Hx4Hx6Hx9 Hp e T Statistical analysis C/ atterns 2 Statistical Analysis.5 Sample Hx4Hx6Hx9 Hp e T University of Michigan ioxin Exposure Study Slide 4 September 3, 27

5 olytopic Vector Analysis (VA) X = A How many end-members (k)? What is their pattern? % How important is each in each sample? 4.67% k - 9.% - -.9% -.42% - Hx4 Hx6 Hx9 Hp e T A A University of Michigan ioxin Exposure Study Slide 5 September 3, 27

UMES Study opulations our study populations were located in counties surrounding the ow Chemical lant: (Tittabawassee River) loodplain Near loodplain Other Midland/Saginaw

6 UMES Study opulations our study populations were located in counties surrounding the ow Chemical lant: (Tittabawassee River) loodplain Near loodplain Other Midland/Saginaw Midland lume One study population, the referent group - Jackson/Calhoun, was far removed from the ow Chemical lant University of Michigan ioxin Exposure Study Slide 6 September 3, 27

7 ata and data treatment ata: 28 soil samples collected from 766 properties 764 household dust samples 946 serum samples Treatment: Non-detects: Any congeners with greater than 5% of samples below detection limits were eliminated. Any samples with greater than 5% of congeners below detection limits were eliminated. Congener concentrations were transformed using a constant row sum and range transformation. or most of the analysis presented today, we will be looking only at dioxins and furans. University of Michigan ioxin Exposure Study Slide 7 September 3, 27

Soil Correlation Matrix Correlation coefficient.75 -.

5 Each family of compounds (dioxins, furans, and CBs)

The higher chlorinated furans tend to also occur with the

8 Soil Correlation Matrix Correlation coefficient Each family of compounds (dioxins, furans, and CBs) tend to occur together. The higher chlorinated furans tend to also occur with the dioxins. CB 69 occurs more frequently with dioxins and furans than with other CBs. University of Michigan ioxin Exposure Study Slide 8 September 3, 27

9 Criteria for End Member Retention Stability of End Members Interpretability of End Members ercent of variance explained Coefficient of determination (reproducibility of each congener) Communality (reproducibility of each sample) University of Michigan ioxin Exposure Study Slide 9 September 3, 27

10 Soil ingerprint Model 5 EM Model for soil dioxins and furans explains 98.9% of variance. End members are stable and interpretable. On following slide: rofiles represent the congener composition of each end member. Congeners in each family listed in order of increasing chlorination. Box and whisker plots present distribution of loadings by study population EM3 has a noticeable regional differences. otential matching industrial emission profiles from USEA source inventory. University of Michigan ioxin Exposure Study Slide September 3, 27

Soil - 5 End Member Model Interpretation EM EM2

2.2 s s s s s s s s s s Variance explained: 39.7% 8.8% 5.7% 5.%.8%.6.5.4.

L JC N MS L JC N MS L JC Automobiles burning unleaded gasoline Oil-fired

25 Graphite electrode sludge Municipal waste incinerator.2.5..5 s s s s s s s s.

11 Soil - 5 End Member Model Interpretation EM EM2 EM3 EM4 EM s s s s s s s s s s Variance explained: 39.7% 8.8% 5.7% 5.%.8% y N MS L JC N MS L JC N MS L JC N MS L JC N MS L JC Automobiles burning unleaded gasoline Oil-fired electrical generation facility.3.25 Graphite electrode sludge Municipal waste incinerator s s s s s s s s s s entachlorophenol s s iesel truck exhaust Common component of several incineration patterns University of Michigan ioxin Exposure Study Slide September 3, 27

12 Comparison of Model With and Without CBs 5 EM model without CBs s s CBs s s CBs s s CBs s s CBs s s CBs 8 EM model with CBs s s CBs s s CBs s s CBs s s CBs s s CBs s s CBs s s CBs s s CBs CBs tend to form distinct end members, while the end members are very similar to those obtained when excluding CBs. University of Michigan ioxin Exposure Study Slide 2 September 3, 27

13 ust Correlation Matrix Correlation coefficient The CBs tend to occur together. Compounds from each family with similar chlorination levels tend to occur together. University of Michigan ioxin Exposure Study Slide 3 September 3, 27

14 ust and Soil Comparison 6 EM dust model s s s s s s s s s s s s 5 EM soil model s s s s s s s s s s ust end members are very similar to soil end members, with one additional pattern consisting mainly of HpC. University of Michigan ioxin Exposure Study Slide 4 September 3, 27

15 ust - 6 End Member Model Interpretation EM EM2 EM3 EM4 EM5 EM s s s s s s s s s s s s Variance explained: 43.%.4% 9.5%.9% 2.4%.2% N MS L JC N MS L JC N MS L JC N MS L JC N MS L JC N MS L JC 6 EM Model explains 99.4% of variance. Box and whisker plots present distribution of loadings by study population EM4 and EM6 have noticeable regional differences. Signatures likely represent similar sources as those for soil. University of Michigan ioxin Exposure Study Slide 5 September 3, 27

Blood Correlation Matrix Correlation coefficient.75 -.

16 Blood Correlation Matrix Correlation coefficient The CBs occur together. Most of the dioxin and furan congeners occur together HpC and CB-77 occur independently. University of Michigan ioxin Exposure Study Slide 6 September 3, 27

17 Serum - 5 EM Model Interpretation 5 EM model explains 98.% of variance Contributions of: EM (HxC/OC dominated) increases with age (t /2 HxC: 8-6 yrs; t /2 OC: 6.7 yrs) EM2 (OC dominated) stable across all age groups EM3 (HpC dominated) decreases with age (t /2 HpC: 3.7 yr) EM4 (dominated by dioxins, but including TC) increases with age (t /2 TC: 7.2 years) EM5 (furan-dominated) decreases with age (t /2 furans: yrs) Note: Median estimated half lives from lesh-janys et al., 996) University of Michigan ioxin Exposure Study Slide 7 September 3, 27

18 Regional Serum End Member Trends loodplain Near loodplain Midland-Saginaw lume Jackson-Calhoun lots present linear trend-lines of end member loading as a function of age for each of the study populations. EM2 and EM4 (and possibly EM) exhibit regional differences: The lume shows an increase in EM2 loading (OC) with age. The loodplain, Near loodplain, and the Other Midland/Saginaw populations show a larger increase in loading with age than do the lume and Jackson/Calhoun populations. EM3 and EM5 do not exhibit any regional differences University of Michigan ioxin Exposure Study Slide 8 September 3, 27

19 Conclusions Soil and dust end members are similar, and they are interpretable as source signatures. Serum end members are age-correlated and may be more representative of differential elimination, rather than exposure source signatures. Correlations of blood EMs blood with exposure source signatures (food, environmental) will need to be informed by pharmaco-kinetic modeling. University of Michigan ioxin Exposure Study Slide 9 September 3, 27

Measurements of Household Dust Concentrations of PCDDs, PCDFs, and PCBs from a Community in Michigan, USA. Lynn Zwica, MS

Measurements of Household Dust Concentrations of PCDDs, PCDFs, and PCBs from a Community in Michigan, USA Lynn Zwica, MS The University of Michigan Dioxin Exposure Study The University of Michigan, Ann