Use of Genotype Frequencies in Medicated Groups to Investigate Prescribing Practice: APOE and Statins as a Proof of Principle

摘要

BACKGROUND: If treatments are used to modify a trait, then patients with high-risk genotypes for the trait should be found at higher frequency in treatment groups than in the general population. The frequency ratio of high- to low-risk genotypes treated should reflect the mean threshold above which the treatment is given in the population. As an example, we hypothesized that because APOE (apolipoprotein E) alleles affect the LDL cholesterol (LDLc) concentration, APOE genotype frequencies in statin takers should act as a proxy for the prevailing treatment threshold of LDLc.METHODS: We used LDLc, statin usage, and APOE genotype data from the British Women's Heart and Health Study (n = 2289; age, 60–79 years) and calculated the genotype ratio treatment index (GRTI) by dividing the proportion of ε3/ε2 or ε3/ε4 participants prescribed a statin by the proportion of ε3/ε3 participants prescribed a statin, both overall and according to socioeconomic class, geographic region, and coronary heart disease (CHD) status. Genotype-specific LDLc distributions were used to calculate the mean LDLc treatment threshold.RESULTS: For genotype ε3/ε2, the GRTI was 0.52 (95% CI, 0.30–0.87) for statin takers overall, 0.22 (95% CI, 0.00–0.56) for those without CHD, and 0.69 (95% CI, 0.31–1.18) for those with CHD. The GRTIs for those without and with CHD backcalculate to LDLc thresholds of 5.65 mmol/L (95% CI, 5.50–5.82 mmol/L) and 4.39 mmol/L (95% CI, 4.21–4.59 mmol/L), respectively. Scotland and North England showed dissimilar GRTIs, which backcalculated to LDLc thresholds of 5.06 mmol/L (95% CI, 4.83–5.28 mmol/L) and 5.44 mmol/L (95% CI, 5.19–5.69 mmol/L), respectively, for all women.CONCLUSIONS: The findings illustrate how genotype frequencies can be a proxy for treatment thresholds used in clinical practice. Genome-wide studies have identified 500 disease-relevant polymorphisms. GRTIs from cost-efficient genotyping, in combination with phenotypic data, may have wide potential in health services research.