In summary, we demonstrate the highly polygenic genetic architecture of lifelong medication-use patterns in hyperlipidemia, hypertension and T2D largely shared with cardiometabolic traits. Our findings highlight the possible utility of using risk-factor-related genetic information for optimizing pharmacological treatment with medicine-use-related genetic information to improve the prediction and prevention of cardiometabolic diseases.
For the analysis of binary phenotypes, we identified risk-factor-specific common drug-use patterns from the purchase registries for hyperlipidemias, hypertension and T2D from the whole dataset of 218,588 individuals alive at the beginning of the follow-up. For medications used in the treatment of hypertension, we analyzed four thresholds of the number of drugs purchased from different hypertension medicine subgroups, requiring at least one purchase during the follow-up: more than one group, more than two groups, more than three groups and all five groups used. Controls were individuals with records of subgroup purchases that are less than in the case-defining groups .
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