Statin therapy is a highly successful and cost-effective strategy for the prevention and treatment of cardiovascular diseases (CVD). Adjusting for statin usage is crucial when exploring the association of the lipidome with CVD to avoid erroneous conclusions. However, practical challenges arise in real-world scenarios due to the frequent absence of statin usage information. To address this limitation, we demonstrate that statin usage can be accurately predicted using lipidomic data. Using three large population datasets and a longitudinal clinical study, we show that lipidomic-based statin prediction models exhibit high prediction accuracy in external validation. Furthermore, we introduce a re-weighted model, designed to overcome a ubiquitous limitation of prediction models, namely the need for predictor alignment between training and target data. We demonstrated that the re-weighted model achieves comparable prediction accuracy to ad hoc models which use the aligned predictor between training and target data. This innovation holds promise for significantly enhancing the transferability of 'omics prediction models, especially in situations where predictor alignment is challenging.