Serum Urate Levels Predict Joint Space Narrowing in Non-Gout Patients With Medial Knee Osteoarthritis.

OBJECTIVE: The pathogenesis of osteoarthritis (OA) includes both mechanical and inflammatory features. Studies have implicated synovial fluid uric acid (UA) as a potential OA biomarker, possibly reflecting chondrocyte damage. Whether serum UA levels reflect/contribute to OA is unknown. We investigated whether serum UA levels predict OA progression in a non-gout knee OA population. METHODS: Eighty-eight patients with medial knee OA (body mass index [BMI] <33 kg/m2 ) but without gout were studied. Baseline serum UA levels were measured in previously banked serum samples. At 0 and 24 months, patients underwent standardized weight-bearing fixed-flexion posteroanterior knee radiography to determine joint space width (JSW) and Kellgren/Lawrence grades. Joint space narrowing (JSN) was calculated as the change in JSW from 0 to 24 months. Twenty-seven patients underwent baseline contrast-enhanced 3T knee magnetic resonance imaging for assessment of synovial volume. RESULTS: Serum UA levels correlated with JSN values in both univariate (r = 0.40, P < 0.01) and multivariate (r = 0.28, P = 0.01) analyses. There was a significant difference in mean JSN after dichotomization at a serum UA cut point of 6.8 mg/dl, the solubility point for serum urate, even after adjustment (JSN of 0.90 mm for a serum UA ≥6.8 mg/dl and 0.31 mm for a serum UA <6.8 mg/dl; P < 0.01). Baseline serum UA levels distinguished progressors (JSN >0.2 mm) and fast progressors (JSN >0.5 mm) from nonprogressors (JSN ≤0.0 mm) in multivariate analyses (area under the receiver operating characteristic curve 0.63 [P = 0.03] and 0.62 [P = 0.05], respectively). Serum UA levels correlated with the synovial volume (r = 0.44, P < 0.01), a possible marker of JSN, although this correlation did not persist after controlling for age, sex, and BMI (r = 0.13, P = 0.56). CONCLUSION: In non-gout patients with knee OA, the serum UA level predicted future JSN and may serve as a biomarker for OA progression.

Sequential mechanisms underlying concentration invariance in biological olfaction.

Concentration invariance-the capacity to recognize a given odorant (analyte) across a range of concentrations-is an unusually difficult problem in the olfactory modality. Nevertheless, humans and other animals are able to recognize known odors across substantial concentration ranges, and this concentration invariance is a highly desirable property for artificial systems as well. Several properties of olfactory systems have been proposed to contribute to concentration invariance, but none of these alone can plausibly achieve full concentration invariance. We here propose that the mammalian olfactory system uses at least six computational mechanisms in series to reduce the concentration-dependent variance in odor representations to a level at which different concentrations of odors evoke reasonably similar representations, while preserving variance arising from differences in odor quality. We suggest that the residual variance then is treated like any other source of stimulus variance, and categorized appropriately into "odors" via perceptual learning. We further show that naïve mice respond to different concentrations of an odorant just as if they were differences in quality, suggesting that, prior to odor categorization, the learning-independent compensatory mechanisms are limited in their capacity to achieve concentration invariance.