Acute oxygen sensing in heme oxygenase-2 null mice.

Hemeoxygenase-2 (HO-2) is an antioxidant enzyme that can modulate recombinant maxi-K(+) channels and has been proposed to be the acute O(2) sensor in the carotid body (CB). We have tested the physiological contribution of this enzyme to O(2) sensing using HO-2 null mice. HO-2 deficiency leads to a CB phenotype characterized by organ growth and alteration in the expression of stress-dependent genes, including the maxi-K(+) channel alpha-subunit. However, sensitivity to hypoxia of CB is remarkably similar in HO-2 null animals and their control littermates. Moreover, the response to hypoxia in mouse and rat CB cells was maintained after blockade of maxi-K(+) channels with iberiotoxin. Hypoxia responsiveness of the adrenal medulla (AM) (another acutely responding O(2)-sensitive organ) was also unaltered by HO-2 deficiency. Our data suggest that redox disregulation resulting from HO-2 deficiency affects maxi-K(+) channel gene expression but it does not alter the intrinsic O(2) sensitivity of CB or AM cells. Therefore, HO-2 is not a universally used acute O(2) sensor.

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.