Objectives: (i) To provide baseline knowledge of gene expression in macroscopically normal articular cartilage, (ii) to test the hypothesis that age, body-mass-index (BMI), and sex are associated with cartilage RNA transcriptome, and (iii) to predict individuals at potential risk for developing "pre-osteoarthritis" (OA) based on screening of genetic risk-alleles associated with OA and gene transcripts differentially expressed between normal and OA cartilage. Design: Healthy-appearing cartilage was obtained from the medial femoral notch of 12 knees with a meniscus tear undergoing arthroscopic partial meniscectomy. Cartilage had no radiographic, magnetic-resonance-imaging or arthroscopic evidence for degeneration. RNA was subjected to Affymetrix microarrays followed by validation of selected transcripts by microfluidic digital polymerase-chain-reaction. The underlying biological processes were explored computationally. Transcriptome-wide gene expression was probed for association with known OA genetic risk-alleles assembled from published literature and for comparison with gene transcripts differentially expressed between healthy and OA cartilage from other studies. Results: We generated a list of 27,641 gene transcripts in healthy cartilage. Several gene transcripts representing numerous biological processes were correlated with age and BMI and differentially expressed by sex. Based on disease-specific Ingenuity Pathways Analysis, gene transcripts associated with aging were enriched for bone/cartilage disease while the gene expression profile associated with BMI was enriched for growth-plate calcification and OA. When segregated by genetic risk-alleles, two clusters of study patients emerged, one cluster containing transcripts predicted by risk studies. When segregated by OA-associated gene transcripts, three clusters of study patients emerged, one of which is remarkably similar to gene expression pattern in OA. Conclusions: Our study provides a list of gene transcripts in healthy-appearing cartilage. Preliminary analysis into groupings based on OA risk-alleles and OA-associated gene transcripts reveals a subset of patients expressing OA transcripts. Prospective studies in larger cohorts are needed to assess whether these patterns are predictive for OA.