Serum Cartilage Oligomeric Matrix Protein Concentration Increases More After Running Than Swimming for Older People.

BACKGROUND: Knee osteoarthritis is common in older people. Serum cartilage oligomeric matrix protein (sCOMP) is a biomarker of knee articular cartilage metabolism. The purpose of this study was 2-fold: to (1) determine acute effects of running and swimming on sCOMP concentration in older people; and (2) investigate relationships between sCOMP concentration change due to running and swimming and measures of knee health in older people. HYPOTHESES: Running would result in greater increase in sCOMP concentration than swimming, and increase in sCOMP concentration due to running and swimming would associate positively with measures of poor knee health. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 3. METHODS: A total of 20 participants ran 5 km and 19 participants swam 1500 m. sCOMP concentration was measured immediately before, immediately after, and 15, 30, and 60 minutes after running or swimming. sCOMP concentration change due to running and swimming was compared. Correlations between sCOMP concentration change due to running and swimming, and other measures of knee health were evaluated, including the Tegner Activity Scale and Knee injury and Osteoarthritis Outcome Score. RESULTS: sCOMP concentration increased 29% immediately after running, relative to baseline, but only 6% immediately after swimming (P < 0.01). No significant relationship was observed between acute sCOMP change due to running and swimming, and observed measures of knee health (P > 0.05). Participants with clinically relevant knee symptoms exhibited greater sCOMP concentration before and after running and swimming (P = 0.03) and had greater body mass (P = 0.04). CONCLUSION: Running results in greater acute articular cartilage metabolism than swimming; however, the chronic effects of this are unclear. Older people with clinically relevant knee symptoms possess greater sCOMP concentration and are heavier, independent of exercise mode and physical activity level. CLINICAL RELEVANCE: These results describe the effects of exercise (running and swimming) for older physically active persons, with and without knee pain.

Spectral adjustment for spatial confounding.

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the exposure to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matérn coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.

Affinity-based measures of biomarker performance evaluation.

We propose new summary measures of biomarker accuracy which can be used as companions to existing diagnostic accuracy measures. Conceptually, our summary measures are tantamount to the so-called Hellinger affinity and we show that they can be regarded as measures of agreement constructed from similar geometrical principles as Pearson correlation. We develop a covariate-specific version of our summary index, which practitioners can use to assess the discrimination performance of a biomarker, conditionally on the value of a predictor. We devise nonparametric Bayes estimators for the proposed indexes, derive theoretical properties of the corresponding priors, and assess the performance of our methods through a simulation study. The proposed methods are illustrated using data from a prostate cancer diagnosis study.

The Construction of Sham Dry Needles and Their Validity.

Appropriate control interventions are necessary to show the treatment effect of dry needling. Different control procedures, such as dry needling of the contralateral side, and sham treatments, such as random and superficial needle insertion, have been utilized in trials. However, those methods might elicit a physiological response and are subsequently not ideal for use as a control. This descriptive study illustrates the construction of low-cost sham dry needles and evaluates their validity. Forty-two healthy asymptomatic subjects received either sham or real dry needling intervention to their right gluteal muscles and reported if they felt that the needle pierced the skin. They also graded the severity and qualified (sharp or dull) the pain associated with the intervention. The results showed that most of the subjects in both groups believed the needle penetrated the skin. The quantity of pain associated with the treatment was similar in both groups, but the quality assigned was different. The authors conclude that sham dry needling can be accomplished and used as a valid control treatment in dry needling research using these low-cost sham needles.

Bayesian Local Contamination Models for Multivariate Outliers.

In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development.