Two biomarkers for diagnosis and monitoring of atherosclerotic cardiovascular disease

A method for classifying a sample obtained from a mammalian subject, comprising:

• obtaining a dataset associated with said sample, wherein said dataset comprises protein expression levels for at least three protein markers selected from the group consisting of RANTES, TIMP1, MCP-1, MCP-2, MCP-3, MCP-4, eotaxin, IP-10, M-CSF, IL-3, TNFa, Ang-2, IL-5, IL-7, and IGF-1, wherein one of the at least three protein markers is RANTES or TIMP1;
• inputting said dataset into an analytical process that uses said data to classify said sample, wherein said classification is selected from the group consisting of an atherosclerotic cardiovascular disease classification, a healthy classification, a medication exposure classification, a no medication exposure classification, a low coronary calcium score and a high coronary calcium score;
• and
• classifying said sample according to the output of said process.

A method for classifying a sample obtained from a mammalian subject, comprising:

• obtaining a dataset associated with said sample, wherein said dataset comprises protein expression levels for at least three protein markers selected from the group consisting of MCP1, MCP2, MCP3, MCP4, Eotaxin, IP10, MCSF, IL3, TNFα, ANG2, IL5, IL7, IGF1, IL10, INFγ, VEGF, MIP1a, RANTES, IL6, IL8, ICAM-1, TIMP1, CCL19, TCA4/6kine/CCL21, CSF3, TRANCE, IL2, IL4, IL13, Il1b, CXCL1/GRO1, GROalpha, IL12, and Leptin, wherein one of the at least three protein markers is RANTES or TIMP1;
• inputting said data into a predictive model that uses said data to classify said sample, wherein said classification is selected from the group consisting of an atherosclerotic cardiovascular disease classification, a healthy classification, a medication exposure classification, a no medication exposure classification, wherein said predictive model has at least one quality metric of at least 0.7 for classification;
• and
• classifying said sample according to the output of said predictive model.