Long-Term Cognitive Decline After Newly Diagnosed Heart Failure: Longitudinal Analysis in the CHS (Cardiovascular Health Study).

BACKGROUND: Heart failure (HF) is associated with cognitive impairment. However, we know little about the time course of cognitive change after HF diagnosis, the importance of comorbid atrial fibrillation, or the role of ejection fraction. We sought to determine the associations of incident HF with rates of cognitive decline and whether these differed by atrial fibrillation status or reduced versus preserved ejection fraction. METHODS AND RESULTS: Participants were 4864 men and women aged ≥65 years without a history of HF and free of clinical stroke in the CHS (Cardiovascular Health Study)-a community-based prospective cohort study in the United States, with cognition assessed annually from 1989/1990 through 1998/1999. We identified 496 participants with incident HF by review of hospital discharge summaries and Medicare claims data, with adjudication according to standard criteria. Global cognitive ability was measured by the Modified Mini-Mental State Examination. In adjusted models, 5-year decline in model-predicted mean Modified Mini-Mental State Examination score was 10.2 points (95% confidence interval, 8.6-11.8) after incident HF diagnosed at 80 years of age, compared with a mean 5-year decline of 5.8 points (95% confidence interval, 5.3-6.2) from 80 to 85 years of age without HF. The association was stronger at older ages than at younger ages, did not vary significantly in the presence versus absence of atrial fibrillation (P=0.084), and did not vary significantly by reduced versus preserved ejection fraction (P=0.734). CONCLUSIONS: Decline in global cognitive ability tends to be faster after HF diagnosis than without HF. Clinical and public health implications of this finding warrant further attention.

A review of orbital and intracranial magnetic resonance imaging in 79 canine and 13 feline patients (2004-2010).

OBJECTIVE: To review the distribution of orbital and intracranial disease in canine and feline patients undergoing magnetic resonance imaging (MRI) following referral to a veterinary ophthalmologist and to correlate results of MRI with pathologic conditions including neoplasia, suspected optic neuritis (ON) and orbital cellulitis. Recognized and emerging imaging techniques are reviewed. PROCEDURE: Medical records of 79 canine and 13 feline patients were reviewed. RESULTS: Neoplasia was diagnosed in 53/92 (57.6%) of patients. The most prevalent types of neoplasia were carcinoma (16/53, 30.1%), sarcoma (11/53, 20.8%), lymphoma (8/53, 15.1%) and presumptive meningioma (9/53, 17.0%). Carcinomas and sarcomas were characterized by bony lysis and intracranial/sinonasal extension. Lymphoma was generally unilateral, less invasive and originated from the ventromedial orbit. Intracranial masses representing presumptive meningiomas frequently exhibited a 'dural tail' sign. Diagnosis of suspected ON was made in 13 of 92 (14.1%) patients. Results of MRI in patients with suspected ON included unilateral optic nerve hyperintensity (3/13, 23.0%), bilateral optic nerve hyperintensity (1/13, 7.7%) and optic chiasmal hyperintensity (3/13, 23.0%). Seven suspected ON patients demonstrated intracranial multifocal patchy contrast enhancement (7/13, 53.8%). Diagnosis of orbital cellulitis was made in 12/92 (13.0%) patients. CONCLUSIONS: Orbital neoplasia was the most common pathologic condition detected. Essential Roentgen characteristics are helpful when diagnosing pathologic processes and providing prognoses in cases of orbital or intracranial disease. Magnetic resonance imaging comprises an important diagnostic component in cases of suspected ON. Emerging contrast and functional MRI techniques as well as SI data may increase our ability to characterize disease processes.

A simulation-based approach for evaluating microarray analyses.

The biological complexity of gene expression makes simulation of gene expression data difficult. We propose a spike-in simulation that adds a single simulated gene to the data set of interest. Features of this spike-in gene may be manipulated to observe how often the spiked-in gene appears in the list of differentially expressed genes. This approach provides insight into the data analysis method, the observed data, and the manner in which the method and data interact without relying on indefensible assumptions regarding gene coexpression.

Improved statistical tests for differential gene expression by shrinking variance components estimates.

Combining information across genes in the statistical analysis of microarray data is desirable because of the relatively small number of data points obtained for each individual gene. Here we develop an estimator of the error variance that can borrow information across genes using the James-Stein shrinkage concept. A new test statistic (FS) is constructed using this estimator. The new statistic is compared with other statistics used to test for differential expression: the gene-specific F test (F1), the pooled-variance F statistic (F3), a hybrid statistic (F2) that uses the average of the individual and pooled variances, the regularized t-statistic, the posterior odds statistic B, and the SAM t-test. The FS-test shows best or nearly best power for detecting differentially expressed genes over a wide range of simulated data in which the variance components associated with individual genes are either homogeneous or heterogeneous. Thus FS provides a powerful and robust approach to test differential expression of genes that utilizes information not available in individual gene testing approaches and does not suffer from biases of the pooled variance approach.

A postgenomic method for predicting essential genes at subsaturation levels of mutagenesis: application to Mycobacterium tuberculosis.

We describe a postgenomic in silico approach for identifying genes that are likely to be essential and estimate their proportion in haploid genomes. With the knowledge of all sites eligible for mutagenesis and an experimentally determined partial list of nonessential genes from genome mutagenesis, a Bayesian statistical method provides reasonable predictions of essential genes with a subsaturation level of random mutagenesis. For mutagenesis, a transposon such as Himar1 is suitable as it inserts randomly into TA sites. All of the possible insertion sites may be determined a priori from the genome sequence and with this information, data on experimentally hit TA sites may be used to predict the proportion of genes that cannot be mutated. As a model, we used the Mycobacterium tuberculosis genome. Using the Himar1 transposon, we created a genetically defined collection of 1,425 insertion mutants. Based on our Bayesian statistical analysis using Markov chain Monte Carlo and the observed frequencies of transposon insertions in all of the genes, we estimated that the M. tuberculosis genome contains 35% (95% confidence interval, 28%-41%) essential genes. This analysis further revealed seven functional groups with high probabilities of being enriched in essential genes. The PE-PGRS (Pro-Glu polymorphic GC-rich repetitive sequence) family of genes, which are unique to mycobacteria, the polyketide/nonribosomal peptide synthase family, and mycolic and fatty acid biosynthesis gene families were disproportionately enriched in essential genes. At subsaturation levels of mutagenesis with a random transposon such as Himar1, this approach permits a statistical prediction of both the proportion and identities of essential genes of sequenced genomes.

Relationships and differentially expressed genes among pancreatic cancers examined by large-scale serial analysis of gene expression.

Pancreatic adenocarcinoma is among the most fatal of cancers, in part because of late diagnosis and a lack of effective therapies. Comprehensive studies are needed to better understand and address the cellular mechanisms and pathways of tumorigenesis. Serial analysis of gene expression was used to analyze gene expression profiles of pancreatic cancer cell lines, short-term cultures of normal pancreatic ductal epithelium, and primary pancreatic cancer tissue. A total of 294,920 tags representing 77,746 genes in 10 serial analysis of gene expression libraries were analyzed. A pancreatic cancer cell line (Hs766T) that exhibited a "normoid" profile of gene expression was identified. Several genes that may be involved in the fundamental nature of malignant changes in pancreatic ductal epithelium were suggested from those differentially and highly expressed in pancreatic cancer cells as compared with normal epithelium. Some overexpressed genes, such as S100A4, prostate stem cell antigen, carcinoembryonic antigen-related cell adhesion molecule 6, and mesothelin, suggest potential use as diagnostic markers. Others suggest potential novel therapeutic targets.