Genetic susceptibility to adverse drug effects - Epidemiology ...

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Common ABCB1 variants are associated with increased digoxin serum concentration Table 1 Primer sequences. Assay Primer/probe Sequence ABCB1 C1236T Forward primer TCTCACTCGTCCTGGTAGATCTTG Reverse primer CACCGTCTGCCCACTCT VIC probe TCAGGTTCAGGCCCTT FAM probe TCAGGTTCAGACCCTT ABCB1 G2677A Forward primer AATACTTTACTCTACTTAATTAATCAATCAT- ATTTAGTTTGACTCA Reverse primer GTCTGGACAAGCACTGAAAGATAAGA VIC probe TTCCCAGCACCTTC FAM probe CTTCCCAGTACCTTC ABCB1 G2677T Forward primer CTTAGAGCATAGTAAGCAGTAGGGAGT Reverse primer GAAATGAAAATGTTGTCTGGACAAGCA VIC probe TTCCCAGCACCTTC FAM probe TTCCCAGAACCTTC ABCB1 C3435T Forward primer ATGTATGTTGGCCTCCTTTGCT Reverse primer GCCGGGTGGTGTCACA VIC probe CCCTCACGATCTCTT FAM probe CCCTCACAATCTCTT The positions of the SNPs are underlined August 30, 2005, digoxin serum concentration assessment, patient death or loss to follow-up, whichever came first. Genotyping. All participants of the Rotterdam Study for whom DNA was available were genotyped for the ABCB1 C1236T, G2677T/A and C3435T polymorphisms. Genotyping was done using Taqman allelic discrimination assays on the ABI Prism 7900 HT Sequence detection system (Applied Biosystems, Foster City, Ca., USA) on 1 ng of genomic DNA extracted from leukocytes, as previously reported. 12 . The primer and probe sequences, designed by Applied Biosystems (Assay-by-Design service) are listed in Table 1. For the tri-allelic variant G2677T/A , two separate assays were designed, one detecting G2677T and one detecting G2677A. Haplotypes were estimated using the estimation maximization algorithm and software as described in the statistical analyses section. Outcome. All available serum digoxin concentrations of subjects from the study population were gathered from 3 hospital laboratories and 1 general practitioner’s laboratory serving the area of the Rotterdam Study. To limit the effect of digoxin dose titration, only the first available digoxin serum concentration assessment of each subject was used. A potentially toxic digoxin concentration was defined as a serum concentration above the upper limit of the therapeutic range (i.e., above 2.00 μg/l). 33
Chapter 3.1 Co-variables. Covariates were gathered at the baseline examination, and included height (in meters), weight (in kilograms) and serum creatinine. Lean body mass (LBM) was computed using Devines formula: LBM = (height (cm) -152) x 0.9 + (50 kg (men)) or (45.5 kg (women)). Body mass index (BMI) was calculated as: weight (kg) / height (m) squared. Renal clearance was estimated using Cockroft and Gault’s formula. Information on daily digoxin dose was expressed as the number of defined daily dosages (DDD, 1 DDD is 0.25 mg) as gathered from the automated pharmacy records. Dose per unit LBM was calculated dividing the dose of digoxin (in DDD) by the LBM. In addition, we identified exposure to inhibitors or inducers of ABCB17 within 7 days of blood sampling. Statistical analyses. Differences in baseline characteristics between men and women were tested by using a Chi-square test for binary variables and a t-test for normally distributed continuous variables. To ensure comparability of serum digoxin concentration assessments across the different laboratories we compared average digoxin concentrations by one-way ANOVA. Exact Hardy Weinberg equilibrium p-values were computed for bi-allelic SNPs and a simulated p-value for the tri-allelic SNP were computed using the Genetics 1.2.1 (G.Warns and F. Leisch) package for R 2.5.0 software (http://www.r-project.org/). Additionally, we compared genotype frequencies with those of the whole Rotterdam Study cohort. D’ was calculated using the ldmax command from GOLD software. 13 All tests for differences in baseline characteristics and linear and logistic regression analyses for the individual SNPs were performed using SPSS for windows software, version 11.1 (Chicago, Illinois, USA). Expectation maximization and linear and logistic regression analyses for the haplotypes were performed with the HaploStats 1.3.0 package for R, using haplo.em and haplo.glm respectively. 14, 15 Inferred haplotypes with a frequency below 1.5% were pooled into one ‘rare haplotype’ group, since estimates become unreliable for rare haplotypes in HaploStats. Both allelic and general genotype models were tested for the three SNPs and haplotypes, although the allelic model was considered primary. In haplotype analyses, the haplotype with C-G-C at positions 1236-2677-3435, respectively, was considered the referent, to which the other haplotypes were compared. Serum digoxin concentration was tested for association with genotype or haplotype as the sole predictor (crude) and adjusted for age, gender, daily digoxin dose, LBM and renal clearance (multivariate) in a linear regression model. Additionally, we accounted for the influence of digoxin dose by repeating the analyses with digoxin concentration/digoxin dose (L/D) ratio as the outcome. In the regression analyses, the 2677A variant carriers were excluded because the frequency of this variant (1.5%) was too low for a reliable effect estimation. To quantify the role of ABCB1 variants in digoxin kinetics, the partial r 2 was calculated as a measure of the variability of serum digoxin concentrations explained by 2 ABCB1 variants, using the formula: partial r = (T k / √(T k + (N - K - 1)), in which T k is the T value for the variable of interest, N is the number of subjects and K is the number of variables in the model. Finally, two sensitivity analyses were carried out. Because for some of the laborato- 34
Page 1 and 2: Genetic susceptibility to adverse d
Page 3 and 4: The work presented in this thesis w
Page 5: Promotiecommissie Promotoren: Prof.
Page 10: 1 General Introduction
Page 13 and 14: Chapter 1 genome. Every SNP represe
Page 15 and 16: Chapter 1 References 1. 2. 3. 4. 5.
Page 18: 2 Quantifying the risk of adverse d
Page 21 and 22: Chapter 2 Introduction The cardiac
Page 23 and 24: Chapter 2 Table 1 Characteristics o
Page 25 and 26: Chapter 2 digoxin treatment appears
Page 27 and 28: Chapter 2 References 1. 2. 3. 4. 5.
Page 30: 3 Common variants in the ABCB1 gene
Page 33: Chapter 3.1 Introduction The ATP-Bi
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Page 39 and 40: Chapter 3.1 Table 5 Risk of serum d
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Chapter 4.1 ditional GG haplotype c
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Chapter 4.1 References 1. 2. 3. 4.
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4.2 Common NOS1AP variants potentia
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Common NOS1AP variants potentiate d
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Chapter 4.3 Introduction In the pas
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Chapter 4.3 Figure 1 Flowdiagram of
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Chapter 4.3 Table 1 Characteristics
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Chapter 4.3 Table 2 Cardiovascular
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Chapter 4.3 Table 3 Drugs, NOS1AP a
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Chapter 4.3 found indication that t
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Chapter 4.3 References 1. 2. 3. 4.
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4.4 Common variation in the NOS1AP
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Common variants in the NOS1AP gene
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5 Effect of ABCB1 and NOS1AP genoty
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Effect of ABCB1 and NOS1AP genotype
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6 General discussion Despite all re
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General discussion ening properties
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General discussion or the excess [C
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General discussion continuously mon
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General discussion cardiovascular d
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General discussion for non-cardiova
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General discussion 25. 26. 27. 28.
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General discussion QTc interval in
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General discussion 169
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7.1 Summary Despite all research ef
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Summary reduced mortality in patien
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Chapter 7.2 steeds aanzienlijk is e
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8 Dankwoord Een proefschrift schrij
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Dankwoord Bijzondere dank aan alle
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10 Bibliography Manuscripts based o
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Bibliography Van der Hooft CS, Diel
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