Cardiovascular disease risk: it is complicated, but race and ethnicity are key, a Bayesian network analysis

BackgroundCardiovascular diseases are the leading cause of morbidity and mortality in the United States. Despite the complexity of cardiovascular disease etiology, we do not fully comprehend the interactions between non-modifiable factors (e.g., age, sex, and race) and modifiable risk factors (e.g., health behaviors and occupational exposures).ObjectiveWe examined proximal and distal drivers of cardiovascular disease and elucidated the interactions between modifiable and non-modifiable risk factors.MethodsWe used a machine learning approach on four cohorts (2005–2012) of the National Health and Nutrition Examination Survey data to examine the effects of risk factors on cardiovascular risk quantified by the Framingham Risk Score (FRS) and the Pooled Cohort Equations (PCE). We estimated a network of risk factors, computed their strength centrality, closeness, and betweenness centrality, and computed a Bayesian network embodied in a directed acyclic graph.ResultsIn addition to traditional factors such as body mass index and physical activity, race and ethnicity and exposure to heavy metals are the most adjacent drivers of PCE. In addition to the factors directly affecting PCE, sleep complaints had an immediate adverse effect on FRS. Exposure to heavy metals is the link between race and ethnicity and FRS.ConclusionHeavy metal exposures and race/ethnicity have similar proximal effects on cardiovascular disease risk as traditional clinical and lifestyle risk factors, such as physical activity and body mass. Our findings support the inclusion of diverse racial and ethnic groups in all cardiovascular research and the consideration of the social environment in clinical decision-making

Alcohol and cannabis use among adolescents in Flemish secondary school in Brussels: effects of type of education

<p>Abstract</p> <p>Background</p> <p>Research regarding socio-economic differences in alcohol and drug use in adolescence yields mixed results. This study hypothesizes that (1) when using education type as a proxy of one's social status, clear differences will exist between students from different types of education, regardless of students' familial socio-economic background; (2) and that the effects of education type differ according to their cultural background.</p> <p>Methods</p> <p>Data from the Brussels youth monitor were used, a school survey administered among 1,488 adolescents from the 3rd to 6th year of Flemish secondary education. Data were analyzed using multilevel logistic regression models.</p> <p>Results</p> <p>Controlling for their familial background, the results show that native students in lower educational tracks use alcohol and cannabis more often than students in upper educational tracks. Such a relationship was not found for students from another ethnic background.</p> <p>Conclusion</p> <p>Results from this study indicate that research into health risks should take into account both adolescents' familial background and individual social position as different components of youngsters' socio-economic background.</p

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Endocrine and Physiological Changes in Response to Chronic Corticosterone: A Potential Model of the Metabolic Syndrome in Mouse

Numerous clinical and experimental studies have linked stress to changes in risk factors associated with the development of physiological syndromes, including metabolic disorders. How different mediators of the stress response, such as corticosterone (CORT), influence these changes in risk remains unclear. Although CORT has beneficial short-term effects, long-term CORT exposure can result in damage to the physiological systems it protects acutely. Disruption of this important physiologic signal is observed in numerous disparate disorders, ranging from depression to Cushing’s syndrome. Thus, understanding the effects of chronic high CORT on metabolism and physiology is of key importance. We explored the effects of 4-wk exposure to CORT dissolved in the drinking water on the physiology and behavior of male mice. We used this approach as a noninvasive way of altering plasma CORT levels while retaining some integrity in the diurnal rhythm present in normal animals. This approach has advantages over methods involving constant CORT pellets, CORT injections, or adrenalectomy. We found that high doses of CORT (100 μg/ml) result in rapid and dramatic increases in weight gain, increased adiposity, elevated plasma leptin, insulin and triglyceride levels, hyperphagia, and decreased home-cage locomotion. A lower dose of CORT (25 μg/ml) resulted in an intermediate phenotype in some of these measures but had no effect on others. We propose that the physiological changes observed in the high-CORT animals approximate changes observed in individuals suffering from the metabolic syndrome, and that they potentially serve as a model for hypercortisolemia and stress-related obesity

DNA-based faecal dietary analysis: A comparison of qPCR and high throughput sequencing approaches

The genetic analysis of faecal material represents a relatively non-invasive way to study animal diet and has been widely adopted in ecological research. Due to the heterogeneous nature of faecal material the primary obstacle, common to all genetic approaches, is a means to dissect the constituent DNA sequences. Traditionally, bacterial cloning of PCR amplified products was employed; less common has been the use of species-specific quantitative PCR (qPCR) assays. Currently, with the advent of High-Throughput Sequencing (HTS) technologies and indexed primers it has become possible to conduct genetic audits of faecal material to a much greater depth than previously possible. To date, no studies have systematically compared the estimates obtained by HTS with that of qPCR. What are the relative strengths and weaknesses of each technique and how quantitative are deep-sequencing approaches that employ universal primers? Using the locally threatened Little Penguin (Eudyptula minor) as a model organism, it is shown here that both qPCR and HTS techniques are highly correlated and produce strikingly similar quantitative estimates of fish DNA in faecal material, with no statistical difference. By designing four species-specific fish qPCR assays and comparing the data to the same four fish in the HTS data it was possible to directly compare the strengths and weaknesses of both techniques. To obtain reproducible quantitative data one of the key, and often overlooked, steps common to both approaches is ensuring that efficient DNA isolation methods are employed and that extracts are free of inhibitors. Taken together, the methodology chosen for long-term faecal monitoring programs is largely dependent on the complexity of the prey species present and the level of accuracy that is desired. Importantly, these methods should not be thought of as mutually exclusive, as the use of both HTS and qPCR in tandem will generate datasets with the highest fidelity

Inverse correlation between plasma 2‐arachidonoylglycerol levels and subjective severity of depression

Objective: Endocannabinoids have been implicated in the pathophysiology of Major Depressive Disorder (MDD) and might represent potential targets for therapeutic intervention. Objectives of the study were: (1) to measure plasma levels of endocannabinoids in a group of antidepressant-free depressed outpatients; (2) to explore their relationship with the severity of depressive symptoms as subjectively perceived by the patients; and (3) to investigate the effect of the selective serotonin reuptake inhibitor escitalopram on endocannabinoid levels. Methods: We measured plasma levels of the two major endocannabinoids, 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (anadamide), in 12 drug-free outpatients diagnosed with MDD and in 12 matched healthy controls. In the patient group, endocannabinoids plasma levels were assessed at baseline and after 2 months of treatment with escitalopram. Results: Baseline plasma levels of the two endocannabinoids did not differ between depressed patients and healthy controls. However, there was a significant inverse correlation between 2-arachidonoylglycerol levels and the severity of subjectively perceived depressive symptoms. Treatment with escitalopram did not change endocannabinoid levels in depressed patients, although it caused the expected improvement of depressive symptoms. Conclusions: Our results suggest that 2-arachidonylglycerol, the most abundant endocannabinoid in the central nervous system, might act to mitigate depressive symptoms, and raise the interesting possibility that 2-arachidonylglycerol and anandamide are differentially regulated in patients affected by MDD. Also, our data suggest but do not prove that the endocannabino