Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

Osteoarticular Expression of Musashi-1 in an Experimental Model of Arthritis.

Background. Collagen-induced arthritis (CIA), a murine experimental disease model induced by immunization with type II collagen (CII), is used to evaluate novel therapeutic strategies for rheumatoid arthritis. Adult stem cell marker Musashi-1 (Msi1) plays an important role in regulating the maintenance and differentiation of stem/precursor cells. The objectives of this investigation were to perform a morphological study of the experimental CIA model, evaluate the effect of TNFα-blocker (etanercept) treatment, and determine the immunohistochemical expression of Msi1 protein. Methods. CIA was induced in 50 male DBA1/J mice for analyses of tissue and serum cytokine; clinical and morphological lesions in limbs; and immunohistochemical expression of Msi1. Results. Clinically, TNFα-blocker treatment attenuated CIA on day 32 after immunization (P < 0.001). Msi1 protein expression was significantly higher in joints damaged by CIA than in those with no lesions (P < 0.0001) and was related to the severity of the lesions (Spearman's rho = 0.775, P = 0.0001). Conclusions. Treatment with etanercept attenuates osteoarticular lesions in the murine CIA model. Osteoarticular expression of Msi1 protein is increased in joints with CIA-induced lesion and absent in nonlesioned joints, suggesting that this protein is expressed when the lesion is produced in order to favor tissue repair.