Supplementary Materials: A Preliminary Link between Hydroxylated Metabolites of Polychlorinated Biphenyls and Free Thyroxin in Humans

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "Supplementary Materials: A Preliminary Link between Hydroxylated Metabolites of Polychlorinated Biphenyls and Free Thyroxin in Humans"

Ονήσιμος Αλεξόπουλος
6 χρόνια πριν
Προβολές:

1 S1 of S11 Supplementary Materials: A Preliminary Link between Hydroxylated Metabolites of Polychlorinated Biphenyls and Free Thyroxin in Humans Eveline Dirinck, Alin C. Dirtu, Govindan Malarvannan, Adrian Covaci, Philippe G. Jorens and Luc F. Van Gaal Table S1. PCB levels in serum (n = 180). Wet Weight Basis (ng/l) Lipid Weight Basis (ng/g Lipid) PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB PCB196/ PCB PBC PCB Results are expressed as median (minimum maximum).

2 S2 of S11 Table S2. PCB metabolite levels in serum (n = 180). Median Range (min max) 4 HO PCB (0.0 52) 4 HO PCB (0.3 5) 3 HO PCB (0.9 49) 4 HO PCB (1.0 77) 3 HO PCB (0.7 23) 4 HO PCB ( ) 4 HO PCB (0.3 11) 3 HO PCB (0.8 51) 4 HO PCB (0.0 2) 4 HO PCB (0.7 12) 4 HO PCB (1.0 81) 4 HO PCB (0.9 17) 4 HO PCB (0.0 3) 3 HO PCB (0.6 19) 4 HO PCB (0.8 29) 4 HO PCB (0.0 4) 4 diho PCB (0.0 4) 4 HO PCB (0.0 5) Results are expressed in ng/l and represented as median (minimum maximum). Table S3. Linear regression for FT4 with lipid adjusted PCBs. PCB PCB PCB Gender ( ) Age ( ) Smoking status ( ) PCB ( ) Gender ( ) Age ( ) Smoking status ( ) PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) 0.264

3 S3 of S11 Table S3. Cont. PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) 0.030

4 S4 of S11 Table S3. Cont. PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) Smoking status ( ) PCB ( ) 0.754

5 S5 of S11 Table S3. Cont. PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) 0.926

6 S6 of S11 Table S3. Cont. PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB196/ Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB196/ ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) PCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) 0.785

7 S7 of S11 Table S3. Cont. PCB SumPCB Gender ( ) Age ( ) BMI ( ) Smoking status ( ) PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoking status ( ) SumPCB ( ) The model included gender, age, BMI, current smoking behavior, and one serum PCB level (lipid adjusted); FT4 was used as a square root transformed variable; All PCB levels were used after log transformation. Table S4. Linear regression for FT4 with hydroxylated PCB metabolites and total lipids. 4HO-PCB HO-PCB HO-PCB Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) 0.016

8 S8 of S11 Table S4. Cont. 4HO-PCB HO-PCB HO-PCB HO-PCB HO-PCB Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) 0.285

9 S9 of S11 Table S4. Cont. 4HO-PCB HO-PCB HO-PCB HO-PCB HO-PCB Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) 0.536

10 S10 of S11 Table S4. Cont. 3HO-PCB HO-PCB HO-PCB diMeO-PCB HO-PCB Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) diMeO-PCB ( ) Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) HO-PCB ( ) 0.412

11 S11 of S11 Table S4. Cont. sum HO-PCBs Gender ( ) Age ( ) BMI ( ) Smoke ( ) Total lipids ( ) Sum HO-PCBs ( ) by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC BY) license (

Σχετικά έγγραφα

Supplemental tables and figures

Supplemental tables and figures Supplemental Table S1. Demographic and clinical parameters by quartiles of white Variables blood cell count. WBC quartiles( 1 9 cells/l) Q1(.6-5.3, n=2587) Q2(5.4-6.3, n=263)