Introduction Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profle data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identifed in plasma. Objectives To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict ‘clinical lipid’ concentration from lipid profle data. Methods Lipid profles were generated from plasma (n=777) and DBS (n=835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four ‘clinical lipids’. Results In plasma samples panels of lipid markers were ...