Blood-based biomarkers for detecting mild osteoarthritis in the human knee

SummaryObjectiveThis study was designed to test the utility of a blood-based approach to identify mild osteoarthritis (OA) of the knee.MethodsBlood samples were drawn from 161 subjects, including 85 subjects with arthroscopically diagnosed mild OA of the knee and 76 controls. Following RNA isolation, an in-house custom cDNA microarray was used to screen for differentially expressed genes. A subset of selected genes was then tested using real-time RT-PCR. Logistic regression analysis was used to evaluate linear combinations of the biomarkers and receiver operating characteristic curve analysis was used to assess the discriminatory power of the combinations.ResultsGenes differentially expressed (3543 genes) between mild knee OA and control samples were identified through microarray analysis. Subsequent real-time RT-PCR verification identified six genes significantly down-regulated in mild OA: heat shock 90kDa protein 1, alpha; inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase complex-associated protein; interleukin 13 receptor, alpha 1; laminin, gamma 1; platelet factor 4 (also known as chemokine (C-X-C motif) ligand 4) and tumor necrosis factor, alpha-induced protein 6. Logistic regression analysis identified linear combinations of nine genes – the above six genes, early growth response 1; alpha glucosidase II alpha subunit; and v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) – as discriminatory between subjects with mild OA and controls, with a sensitivity of 86% and specificity of 83% in a training set of 78 samples. The optimal biomarker combinations were then evaluated using a blind test set (67 subjects) which showed 72% sensitivity and 66% specificity.ConclusionsLinear combinations of blood RNA biomarkers offer a substantial improvement over currently available diagnostic tools for mild OA. Blood-derived RNA biomarkers may be of significant clinical value for the diagnosis of early, asymptomatic OA of the knee

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Sample Grain Influences the Functional Relationship Between Canopy Cover and Gopher Tortoise ( Gopherus polyphemus

Change in vegetation structure alters habitat suitability for the threatened gopher tortoise (Gopherus polyphemus). An understanding of this dynamic is crucial to inform habitat and tortoise management strategies. However, it is not known how the choice of the sample grain (i.e., cell size) at which vegetation structure is measured impacts estimates of tortoise-habitat relationships. We used lidar remote sensing to estimate canopy cover around 1573 gopher tortoise burrows at incrementally larger sample grains (1-707 m2) in 450 ha of longleaf pine (Pinus palustris) savanna. Using an information theoretic approach, we demonstrate that the choice of grain size profoundly influences modeled relationships between canopy cover and burrow abandonment. At the most supported grain size (314 m2), the probability of burrow abandonment increased by 1.7% with each percent increase in canopy cover. Ultimately, detecting the appropriate sample grain can lead to more effective development of functional relationships and improve predictive models to manage gopher tortoise habitats