Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin

Structure and Mobility of Lactose in Lactose/Sodium Montmorillonite Nanocomposites

This study aims at investigating the molecular level organization and molecular mobility in montmorillonite nanocomposites with the uncharged organic low-molecular-weight compound lactose commonly used in pharmaceutical drug delivery, food technology, and flavoring. Nanocomposites were prepared under slow and fast drying conditions, attained by drying at ambient conditions and by spray-drying, respectively. A detailed structural investigation was performed with modulated differential scanning calorimetry, powder X-ray diffraction, solid-state nuclear magnetic resonance, scanning electron microscopy, microcalorimetry, and molecular dynamic simulations. The lactose was intercalated in the sodium montmorillonite interlayer space regardless of the clay content, drying rate, or humidity exposure. Although, the spray-drying resulted in higher proportion of intercalated lactose compared with the drying under ambient conditions, non-intercalated lactose was present at 20 wt% lactose content. This indicates limitations in maximum load capacity of nonionic organic substances into the montmorillonite interlayer space. Furthermore, a fraction of the intercalated lactose in the co-spray-dried nanocomposites diffused out from the clay interlayer space upon humidity exposure. Also, the lactose in the nanocomposites demonstrated higher molecular mobility than that of neat amorphous lactose. This study provides a foundation for understanding functional properties of nanocomposites, such as loading capacity and physical stability

Distinct hippocampal engrams control extinction and relapse of fear memory

Learned fear often relapses after extinction, suggesting that extinction training generates a new memory that coexists with the original fear memory; however, the mechanisms governing the expression of competing fear and extinction memories remain unclear. We used activity-dependent neural tagging to investigate representations of fear and extinction memories in the dentate gyrus. We demonstrate that extinction training suppresses reactivation of contextual fear engram cells while activating a second ensemble, a putative extinction engram. Optogenetic inhibition of neurons that were active during extinction training increased fear after extinction training, whereas silencing neurons that were active during fear training reduced spontaneous recovery of fear. Optogenetic stimulation of fear acquisition neurons increased fear, while stimulation of extinction neurons suppressed fear and prevented spontaneous recovery. Our results indicate that the hippocampus generates a fear extinction representation and that interactions between hippocampal fear and extinction representations govern the suppression and relapse of fear after extinction.We thank J. Dunsmoor for comments on the manuscript. A.F.L. was supported by NIH F31 MH111243 and NIH T32 MH106454. S.L.S. was supported by PD/BD/128076/2016 from the Portuguese Foundation for Science and Technology. Research supported by NIH DP5 OD017908 and New York Stem Cell Science (NYSTEM) C-029157 to C.A.D., NIH R01 MH102595 and NIH R21 EY026446 to M.R.

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Towards the clinical implementation of pharmacogenetics in bipolar disorder.

BackgroundBipolar disorder (BD) is a psychiatric illness defined by pathological alterations between the mood states of mania and depression, causing disability, imposing healthcare costs and elevating the risk of suicide. Although effective treatments for BD exist, variability in outcomes leads to a large number of treatment failures, typically followed by a trial and error process of medication switches that can take years. Pharmacogenetic testing (PGT), by tailoring drug choice to an individual, may personalize and expedite treatment so as to identify more rapidly medications well suited to individual BD patients.DiscussionA number of associations have been made in BD between medication response phenotypes and specific genetic markers. However, to date clinical adoption of PGT has been limited, often citing questions that must be answered before it can be widely utilized. These include: What are the requirements of supporting evidence? How large is a clinically relevant effect? What degree of specificity and sensitivity are required? Does a given marker influence decision making and have clinical utility? In many cases, the answers to these questions remain unknown, and ultimately, the question of whether PGT is valid and useful must be determined empirically. Towards this aim, we have reviewed the literature and selected drug-genotype associations with the strongest evidence for utility in BD.SummaryBased upon these findings, we propose a preliminary panel for use in PGT, and a method by which the results of a PGT panel can be integrated for clinical interpretation. Finally, we argue that based on the sufficiency of accumulated evidence, PGT implementation studies are now warranted. We propose and discuss the design for a randomized clinical trial to test the use of PGT in the treatment of BD

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Quantifying resilience of humans and other animals

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of micro-recoveries observed in natural time series. Such dynamic indicators of resilience (DIORs) may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethink of our approach to the adaptive management of health and resilience

Changes in smoking prevalence among U.S. adults by state and region: Estimates from the Tobacco Use Supplement to the Current Population Survey, 1992-2007

<p>Abstract</p> <p>Background</p> <p>Tobacco control policies at the state level have been a critical impetus for reduction in smoking prevalence. We examine the association between recent changes in smoking prevalence and state-specific tobacco control policies and activities in the entire U.S.</p> <p>Methods</p> <p>We analyzed the 1992-93, 1998-99, and 2006-07 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) by state and two indices of state tobacco control policies or activities [initial outcome index (IOI) and the strength of tobacco control (SOTC) index] measured in 1998-1999. The IOI reflects cigarette excise taxes and indoor air legislation, whereas the SOTC reflects tobacco control program resources and capacity. Pearson Correlation coefficient between the proportionate change in smoking prevalence from 1992-93 to 2006-07 and indices of tobacco control activities or programs was the main outcome measure.</p> <p>Results</p> <p>Smoking prevalence decreased from 1992-93 to 2006-07 in both men and women in all states except Wyoming, where no reduction was observed among men, and only a 6.9% relative reduction among women. The percentage reductions in smoking in men and women respectively were the largest in the West (average decrease of 28.5% and 33.3%) and the smallest in the Midwest (18.6% and 20.3%), although there were notable exceptions to this pattern. The decline in smoking prevalence by state was correlated with the state's IOI in both women and men (r = -0.49, p < 0.001; r = -0.31, p = 0.03; respectively) and with state's SOTC index in women(r = -0.30, p = 0.03 0), but not men (r = -0.21, p = 0.14).</p> <p>Conclusion</p> <p>State level policies on cigarette excise taxes and indoor air legislation correlate strongly with reductions in smoking prevalence since 1992. Strengthening and systematically implementing these policies could greatly accelerate further reductions in smoking.</p