The clinical and public health significance of nonalcoholic fatty liver disease (NAFLD) is not well established. We investigated the long-term effect of NAFLD on mortality. This analysis utilized the National Health and Nutrition Examination Survey conducted in 1988-1994 and subsequent follow-up data for mortality through December 31, 2006. NAFLD was defined by ultrasonographic detection of hepatic steatosis in the absence of other known liver diseases. The presence and severity of hepatic fibrosis in subjects with NAFLD was determined by the NAFLD fibrosis score (NFS), the aspartate aminotransferase to platelet ratio index (APRI), and FIB-4 score. Of 11,154 participants, 34.0% had NAFLD—the majority (71.7%) had NFS consistent with lack of significant fibrosis (NFS <−1.455), whereas 3.2% had a score indicative of advanced fibrosis (NFS >0.676). After a median follow-up of 14.5 years, NAFLD was not associated with higher mortality (age- and sex-adjusted hazard ratio [HR]: 1.05; 95% confidence interval [CI]: 0.93-1.19). In contrast, there was a progressive increase in mortality with advancing fibrosis scores. Compared to subjects without fibrosis, those with a high probability of advanced fibrosis had a 69% increase in mortality (for NFS: HR, 1.69, 95% CI: 1.09-2.63; for APRI: HR, 1.85, 95% CI: 1.02-3.37; for FIB-4: HR, 1.66, 95% CI: 0.98-2.82) after adjustment for other known predictors of mortality. These increases in mortality were almost entirely from cardiovascular causes (for NFS: HR, 3.46, 95% CI: 1.91-6.25; for APRI: HR, 2.53, 95% CI: 1.33-4.83; for FIB-4: HR, 2.68, 95% CI: 1.44-4.99). Conclusions : Ultrasonography-diagnosed NAFLD is not associated with increased mortality. However, advanced fibrosis, as determined by noninvasive fibrosis marker panels, is a significant predictor of mortality, mainly from cardiovascular causes, independent of other known factors.

Background: Assessing the reliability of experimental replicates (or global alterations corresponding to different experimental conditions) is a critical step in analyzing RNA-Seq data. Pearson’s correlation coefficient r has been widely used in the RNA-Seq field even though its statistical characteristics may be poorly suited to the task. Results: Here we present a single-parameter test procedure for count data, the Simple Error Ratio Estimate (SERE), that can determine whether two RNA-Seq libraries are faithful replicates or globally different. Benchmarking shows that the interpretation of SERE is unambiguous regardless of the total read count or the range of expression differences among bins (exons or genes), a score of 1 indicating faithful replication (i.e., samples are affected only by Poisson variation of individual counts), a score of 0 indicating data duplication, and scores >1 corresponding to true global differences between RNA-Seq libraries. On the contrary the interpretation of Pearson’s r is generally ambiguous and highly dependent on sequencing depth and the range of expression levels inherent to the sample (difference between lowest and highest bin count). Cohen’s simple Kappa results are also ambiguous and are highly dependent on the choice of bins. For quantifying global sample differences SERE performs similarly to a measure based on the negative binomial distribution yet is simpler to compute. Conclusions: SERE can therefore serve as a straightforward and reliable statistical procedure for the global assessment of pairs or large groups of RNA-Seq datasets by a single statistical parameter.

Background: Massive parallel sequencing has the potential to replace microarrays as the method for transcriptome profiling. Currently there are two protocols: full-length RNA sequencing (RNA-SEQ) and 3'-tag digital gene expression (DGE). In this preliminary effort, we evaluated the 3' DGE approach using two reference RNA samples from the MicroArray Quality Control Consortium (MAQC). Results: Using Brain RNA sample from multiple runs, we demonstrated that the transcript profiles from 3' DGE were highly reproducible between technical and biological replicates from libraries constructed by the same lab and even by different labs, and between two generations of Illumina's Genome Analyzers. Approximately 65% of all sequence reads mapped to mitochondrial genes, ribosomal RNAs, and canonical transcripts. The expression profiles of brain RNA and universal human reference RNA were compared which demonstrated that DGE was also highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Furthermore, one lane of 3' DGE sequencing, using the current sequencing chemistry and image processing software, had wider dynamic range for transcriptome profiling and was able to detect lower expressed genes which are normally below the detection threshold of microarrays. Conclusion: 3' tag DGE profiling with massive parallel sequencing achieved high sensitivity and reproducibility for transcriptome profiling. Although it lacks the ability of detecting alternative splicing events compared to RNA-SEQ, it is much more affordable and clearly out-performed microarrays (Affymetrix) in detecting lower abundant transcripts.

Background: Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data. Methods: We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value (QNβ); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated. Results: Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets. Conclusion: Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Finite‐element analysis (FEA) of quantitative computed tomography (QCT) scans can estimate site‐specific whole‐bone strength. However, it is uncertain whether the site‐specific detail included in FEA‐estimated proximal femur (hip) strength can determine fracture risk at sites with different biomechanical characteristics. To address this question, we used FEA of proximal femur QCT scans to estimate hip strength and load‐to‐strength ratio during a simulated sideways fall and measured total hip areal and volumetric bone mineral density (aBMD and vBMD) from QCT images in an age‐stratified random sample of community‐dwelling adults age 35 years or older. Among 314 women (mean age ± SD: 61 ± 15 years; 235 postmenopausal) and 266 men (62 ± 16 years), 139 women and 104 men had any prevalent fracture, whereas 55 Women and 28 men had a prevalent osteoporotic fracture that had occurred at age 35 years or older. Odds ratios by age‐adjusted logistic regression analysis for prevalent overall and osteoporotic fractures each were similar for FEA hip strength and load‐to‐strength ratio, as well as for total hip aBMD and vBMD. C‐statistics (estimated areas under ROC curves) also were similar [eg, 0.84 to 0.85 (women) and 0.75 to 0.78 (men) for osteoporotic fractures]. In women and men, the association with prevalent osteoporotic fractures increased below an estimated hip strength of approximately 3000 N. Despite its site‐specific nature, FEA‐estimated hip strength worked equally well at predicting prevalent overall and osteoporotic fractures. Furthermore, an estimated hip strength below 3000 N may represent a critical level of systemic skeletal fragility in both sexes that warrants further investigation. © 2011 American Society for Bone and Mineral Research.