Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study

<p>Abstract</p> <p>Background</p> <p>The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.</p> <p>Results</p> <p>Three comparative experiments are designed to evaluate the performance of MegaSNPHunter. The first experiment uses synthetic data generated on the basis of epistasis models. The second one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips). The third one chooses the rheumatoid arthritis study from Wellcome Trust Case Control Consortium (WTCCC) using Affymetrix GeneChip 500K Mapping Array Set. MegaSNPHunter outperforms the best solution in this area and reports many potential interactions for the two real studies.</p> <p>Conclusion</p> <p>The experimental results on both synthetic data and two real data sets demonstrate that our proposed approach outperforms the best solution that is currently available in handling large-scale SNP data both in terms of speed and in terms of detection of potential interactions that were not identified before. To our knowledge, MegaSNPHunter is the first approach that is capable of identifying the disease-associated SNP interactions from WTCCC studies and is promising for practical disease prognosis.</p

A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features

Cardiovascular diseases are a public health concern; they remain the leading cause of morbidity and mortality in patients with type 2 diabetes. Phenotypic information available from retinal fundus images and clinical measurements, in addition to genomic data, can identify relevant biomarkers of cardiovascular health. In this study, we assessed whether such biomarkers stratified risks of major adverse cardiac events (MACE). A retrospective analysis was carried out on an extract from the Tayside GoDARTS bioresource of participants with type 2 diabetes (n = 3,891). A total of 519 features were incorporated, summarising morphometric properties of the retinal vasculature, various single nucleotide polymorphisms (SNPs), as well as routine clinical measurements. After imputing missing features, a predictive model was developed on a randomly sampled set (n = 2,918) using L1-regularised logistic regression (lasso). The model was evaluated on an independent set (n = 973) and its performance associated with overall hazard rate after censoring (log-rank p < 0.0001), suggesting that multimodal features were able to capture important knowledge for MACE risk assessment. We further showed through a bootstrap analysis that all three sources of information (retinal, genetic, routine clinical) offer robust signal. Particularly robust features included: tortuousity, width gradient, and branching point retinal groupings; SNPs known to be associated with blood pressure and cardiovascular phenotypic traits; age at imaging; clinical measurements such as blood pressure and high density lipoprotein. This novel approach could be used for fast and sensitive determination of future risks associated with MACE

A markov classification model for metabolic pathways

<p>Abstract</p> <p>Background</p> <p>This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.</p> <p>Results</p> <p>We compared the performance of HME3M with logistic regression and support vector machines (SVM) for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for <it>Arabidopsis thaliana</it>. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of <it>Arabidopsis</it>. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of <it>Arabidopsis</it>.</p> <p>Conclusions</p> <p>This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.</p

Post-infectious headache: a reactive headache?

Post-infectious disease syndrome includes both neurological and non-neurological disorders. However, headache as an isolated or a presenting complaint of post-infectious illness has not been well acknowledged in the literature. In this retrospective observation, patients having daily headache of more than 1 week and <4 weeks duration were included. We divided this group into patients having headache with preceding history of febrile illness in the recent past and patients without such history of febrile illness. We compared clinical features and therapeutic responses of various drugs between the groups. There were no significant differences in demographic features in these groups. However, associated neck pain, nausea, photophobia and meningeal signs were more prevalent in patients having history of preceding infection. A relatively lower proportion of subjects showed complete response to drugs at 3 months in post-infectious group. Good responses were noted to steroids in post-infectious group. In conclusion, a subset of patients with daily headache may be because of post-infectious pathology and treatment in the early stage may prevent it from becoming chronic. Large prospective studies are required to confirm these observations

Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease

Khat Use Is Associated with Impaired Working Memory and Cognitive Flexibility

Rationale Khat consumption has increased during the last decades in Eastern Africa and has become a global phenomenon spreading to ethnic communities in the rest of the world, such as The Netherlands, United Kingdom, Canada, and the United States. Very little is known, however, about the relation between khat use and cognitive control functions in khat users. Objective We studied whether khat use is associated with changes in working memory (WM) and cognitive flexibility, two central cognitive control functions. Methods Khat users and khat-free controls were matched in terms of sex, ethnicity, age, alcohol and cannabis consumption, and IQ (Raven's progressive matrices). Groups were tested on cognitive flexibility, as measured by a Global-Local task, and on WM using an N-back task. Result Khat users performed significantly worse than controls on tasks tapping into cognitive flexibility as well as monitoring of information in WM. Conclusions The present findings suggest that khat use impairs both cognitive flexibility and the updating of information in WM. The inability to monitor information in WM and to adjust behavior rapidly and flexibly may have repercussions for daily life activities

Leptin Contributes to the Adaptive Responses of Mice to High-Fat Diet Intake through Suppressing the Lipogenic Pathway

Background: Leptin is an adipocyte-derived hormone that plays a critical role in energy homeostasis and lipid metabolism. Overnutrition-associated obesity is known to be accompanied by hyperleptinemia. However, the physiological actions of leptin in the metabolic responses to high-fat diet (HFD) intake remain to be completely elucidated. Here we characterized the metabolic features of mice fed high-fat diets and investigated the impact of leptin upon the lipogenic program which was found to be suppressed by HFD feeding through a proteomics approach. Results: When maintained on two types of high-fat diets for up to 16 weeks, mice with a higher fat intake exhibited increased body fat accumulation at a greater pace, developing more severely impaired glucose tolerance. Notably, HFD feeding at 4 weeks elicited the onset of marked hyperleptinemia, prior to the occurrence of apparent insulin resistance and hyperinsulinemia. Proteomic analysis revealed dramatically decreased expression of lipogenic enzymes in the white adipose tissue (WAT) from HFD-fed mice, including ATP-citrate lyase (ACL) and fatty acid synthase (FAS). The expression of ACL and FAS in the liver was similarly suppressed in response to HFD feeding. By contrast, HFD-induced downregulation of hepatic ACL and FAS was significantly attenuated in leptin receptor-deficient db/db mice. Furthermore, in the liver and WAT of wild type animals, intraperitoneal leptin administration was able to directly suppress the expression of these two lipogenic enzymes, accompanied by reduced triglyceride levels both in the liver and serum. Conclusions: These results suggest that leptin contributes to the metabolic responses in adaptation to overnutrition through suppressing the expression of lipogenic enzymes, and that the lipogenic pathway represents a key targeted peripheral component in exerting leptin's liporegulatory actions. © 2009 Jiang et al