Objective To estimate the prevalence, types and sociodemographic and biobehavioral correlates of antinuclear antibodies (ANA) in the United States (U.S.). Methods Cross-sectional analysis of 4,754 individuals from the National Health and Nutrition Examination Survey (NHANES) 1999–2004. ANA by indirect immunofluorescence, including cellular staining patterns and specific autoantibody reactivities by immunoprecipitation in those with ANA. Results ANA prevalence in the U.S. population ages 12 years and older was 13.8% (95% CI, 12.2% to 15.5%). ANA increased with age (P = 0.01) and were more prevalent among females than males (17.8% vs. 9.6%, P < 0.001), with the female to male ratio peaking at 40–49 years of age. ANA prevalence was modestly higher in African Americans than whites (adjusted prevalence odds ratio [POR], 1.30; 95% CI, 1.00 to 1.70). Remarkably, ANA were less common in overweight and obese (adjusted POR, 0.74; 95% CI, 0.59 to 0.94) individuals than persons of normal weight. No significant associations were seen with education, family income, alcohol use, smoking history, serum levels of cotinine or C-reactive protein. In ANA-positive individuals, nuclear patterns were seen in 84.6%, cytoplasmic patterns in 21.8%, and nucleolar patterns in 6.1%, and the most common specific autoantibodies were anti-Ro (3.9%) and anti-Su (2.4%). Conclusion These findings suggest that over 32 million persons in the U.S. have ANA and the prevalence is higher among females, older individuals, African Americans and those with normal weight. These data will serve as a useful baseline for future investigations of predictors and changes in ANA prevalence over time.

Background-The National Survey of Lead and Allergens in Housing was the first populationbased study to measure indoor allergen levels in US homes.

Use of the dioxin toxic equivalency factor (TEF) approach in human risk assessments assumes that the combined effects of dioxin-like compounds in a mixture can be predicted based on a potency-adjusted dose-additive combination of constituents of the mixture. In this study, we evaluated the TEF approach in experimental 2-year rodent cancer bioassays with female Harlan Sprague-Dawley rats receiving 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 3,3′,4,4′,5-pentachlorobiphenyl (PCB-126), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF), or a mixture of the three compounds. Statistically based dose–response modeling indicated that the shape of the dose–response curves for hepatic, lung, and oral mucosal neoplasms was the same in studies of the three individual chemicals and the mixture. In addition, the dose response for the mixture could be predicted from a combination of the potency-adjusted doses of the individual compounds. Finally, we showed that use of the current World Health Organization dioxin TEF values adequately predicted the increased incidence of liver tumors (hepatocellular adenoma and cholangiocarcinoma) induced by exposure to the mixture. These data support the use of the TEF approach for dioxin cancer risk assessments.

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We propose a nonlinear regression model for quantitatively analyzing periodic gene expression in studies of experimentally synchronized cells. Our model accounts for the observed attenuation in cycle amplitude by a simple and biologically plausible mechanism. We represent the expression level for each gene as an average across a large number of cells. For a given cell-cycle gene, we model its expression in each cell in the culture as following the same sinusoidal function except that the period, which in any individual cell must be the same for all cell-cycle genes, varies randomly across cells. We model these random periods by using a lognormal distribution. The variability in period causes the measured amplitude of the cyclic expression trajectory to attenuate over time as cells fall increasingly out of synchrony. Gene-specific parameters include initial amplitude and phase angle. Applying the model to data from Whitfield et al. bootstrap test ͉ gene expression ͉ microarray ͉ nonlinear regression E xperimental protocols that arrest cells in vitro at a particular phase of the cell cycle and then release them in a synchronized way allow detailed study of the cycling process. In conjunction with such experiments, cDNA microarray technology allows investigators to assess the temporal expression patterns of thousands of genes simultaneously. Gene-expression studies in yeast (1-3) and in cultured human cells (4, 5) have revealed that expression levels for cell-cycle genes vary periodically and have amplitudes that attenuate through time (Fig. 1). The observed attenuation is generally attributed to cells in the cultures falling increasingly out of synchrony through time.In cultures of homogeneous cells released from a block on cycling, asynchrony can arise from at least two mechanisms. Cells throughout the culture can differ slightly in the exact timing of their arrest or release, or cells can differ slightly in the duration of their individual cycles. The former mechanism leaves asynchrony constant through time, so only the latter mechanism, where asynchrony increases, produces the characteristic attenuation. Such attenuation may hold biologic interest in itself. The rate of attenuation (i.e., the variation in the duration of the cell cycle across cells) may vary by strain of organism or by cell type (e.g., across tumors with differing metastatic potential). Some influences of cell characteristics on the scheduling of the cell cycle are already known. For instance, cell-size distribution in yeast mutants is related to time spent in the late G 1 phase (6).Few methods proposed for the analysis of cell-cycle expression data address attenuation explicitly. For example, several investigators used a sinusoidal template to identify periodically expressed genes but did not explicitly account for attenuation (2, 5). The basic single-pulse model (SPM) proposed by Zhao et al. (7) addresses asynchrony by assuming that the cell-specific timings with which individual cells in a culture reach a given observation point have a norma...