Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip

Background: DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilising precious or limited DNA sources. Materials and methods: We use a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. Results: Data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; however samples with a total input as low as 40ng pass standard quality control tests, and we observe little evidence that low input DNA obscures the associations between DNAm and two phenotypes, age and smoking status. Conclusions: DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

Fat metabolism is associated with telomere length in six population-based studies

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer, and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold pmeta = 6.5x10−4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-ce %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-ce) became significantly associated with LTL (p = 3.6x10−4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (p = 1.9x10−4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.

589 External validation of the increased wall thickness score for the diagnosis of cardiac amyloidosis

Aims This study aimed to validate the increased wall thickness (IWT) score, a multiparametric echocardiographic score to facilitate diagnosis of cardiac amyloidosis (CA), in an independent population of patients with increased LV wall thickness suspicious for CA. Methods and results Between January 2019 and December 2020, 152 consecutive patients with increased LV wall thickness suspicious for CA were included. For all patient, the multiparametric echocardiographic score (IWT score) was calculated. To validate the diagnostic accuracy of an IWT score ≥8 to predict the diagnosis of CA, sensibility (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and predictive accuracy (PA) were calculated. Among the 152 patients included in the study, 50 (33%) were diagnosed as CA, 25 (16%) had severe aortic stenosis, 25 (16%) had hypertensive remodelling, and 52 (34%) had hypertrophic cardiomyopathy. Among the 50 and 102 patients with and without CA, 19 (38%) and 1 (1%) showed an IWT score ≥8, respectively. Overall, the diagnostic accuracy of an IWT score ≥8 for the diagnosis of CA in our population was the following: Se 38% (95% CI: 25–53%); Sp 99% (95% CI: 95–100%); PPV 95% (95% CI: 72–99%); NPV 77% (95% CI: 73–80%); PA 79% (95% CI: 72–85%). Conclusions This study reports the first external validation of the IWT score for the diagnosis of CA in patients with increased LV wall thickness. A score ≥8 showed a high Sp, PPV and PA, suggesting that the IWT score can be used to identify CA patients in those with increased LV wall thickness.

A polygenic risk score predicts mosaic loss of chromosome Y in circulating blood cells

Background Mosaic loss of Y chromosome (LOY) is the most common somatic change that occurs in circulating white blood cells of older men. LOY in leukocytes is associated with increased risk for all-cause mortality and a range of common disease such as hematological and non-hematological cancer, Alzheimer’s disease, and cardiovascular events. Recent genome-wide association studies identified up to 156 germline variants associated with risk of LOY. The objective of this study was to use these variants to calculate a novel polygenic risk score (PRS) for LOY, and to assess the predictive performance of this score in a large independent population of older men. Results We calculated a PRS for LOY in 5131 men aged 70 years and older. Levels of LOY were estimated using microarrays and validated by whole genome sequencing. After adjusting for covariates, the PRS was a significant predictor of LOY (odds ratio [OR] = 1.74 per standard deviation of the PRS, 95% confidence intervals [CI] 1.62–1.86, p < 0.001). Men in the highest quintile of the PRS distribution had > fivefold higher risk of LOY than the lowest (OR = 5.05, 95% CI 4.05–6.32, p < 0.001). Adding the PRS to a LOY prediction model comprised of age, smoking and alcohol consumption significantly improved prediction (AUC = 0.628 [CI 0.61–0.64] to 0.695 [CI 0.67–0.71], p < 0.001). Conclusions Our results suggest that a PRS for LOY could become a useful tool for risk prediction and targeted intervention for common disease in men.

Relating psychiatric symptoms and self-regulation during the COVID-19 crisis

Disruptions of self-regulation are a hallmark of numerous psychiatric disorders. Here, we examine the relationship between transdiagnostic dimensions of psychopathology and changes in self-regulation in the early phase of the COVID-19 pandemic. We used data-driven approach on a large number of cognitive tasks and self-reported surveys in training datasets. Then we derived measures of self-regulation and psychiatric functioning in an independent population sample (N = 102) tested both before and after the onset of the COVID-19 pandemic, when the restrictions in place represented a threat to mental health and forced people to flexibly adjust to modifications of daily routines. We found independent relationships between transdiagnostic dimensions of psychopathology and longitudinal alterations in specific domains of self-regulation defined using the drift diffusion model. Compared to the period preceding the onset of the pandemic, a symptom dimension related to anxiety and depression was characterized by a more cautious behavior, indexed by the need to accumulate more evidence before making a decision. Instead, social-withdrawal related to faster non-decision processes. Self-reported measures of self-regulation predicted variance in psychiatric symptoms both concurrently and prospectively, revealing the psychological dimensions relevant for separate transdiagnostic dimensions of psychiatry, but tasks did not. Taken together, our study shows that self-regulation can be affected depending on the interaction between external events and trait-like vulnerabilities and suggests that the study of cognition needs to take into account the dynamic nature of real-world events as well as within-subject variability over time.

Smartphone Addiction in Mexican Engineering Students

Students academic performance could be affected by excessive use of the smartphone. This study focuses on analyzing the level of cell phone addiction in engineering university students. It also seeks to determine if there is a difference by gender in this behavior. 306 engineering students from a Technological Institute in Veracruz, Mexico participated in the study. The instrument used to obtain data was SAS-SV (Smartphone addiction scale-short version) designed by [1]. In order to identify the set of indicators with the highest factor loadings, an exploratory factor analysis was carried out with extraction of components and orthogonal rotation with the Varimax method. To identify if there is a difference by gender, the t test is used to contrast the hypothesis about two independent population means. The findings demonstrated the extraction of two components, which differs from the one-dimensional model proposed by [1], and no difference was found between the groups of male and female students.

COVID-19 Likelihood Meter: a machine learning approach to COVID-19 screening for Indonesian health workers

The COVID-19 pandemic poses a heightened risk to health workers, especially in low- and middle-income countries such as Indonesia. Due to the limitations to implementing mass RT-PCR testing for health workers, high-performing and cost-effective methodologies must be developed to help identify COVID-19 positive health workers and protect the spearhead of the battle against the pandemic. This study aimed to investigate the application of machine learning classifiers to predict the risk of COVID-19 positivity (by RT-PCR) using data obtained from a survey specific to health workers. Machine learning tools can enhance COVID-19 screening capacity in high-risk populations such as health workers in environments where cost is a barrier to accessibility of adequate testing and screening supplies. We built two sets of COVID-19 Likelihood Meter (CLM) models: one trained on data from a broad population of health workers in Jakarta and Semarang (full model) and tested on the same, and one trained on health workers from Jakarta only (Jakarta model) and tested on an independent population of Semarang health workers. The area under the receiver-operating-characteristic curve (AUC), average precision (AP), and the Brier score (BS) were used to assess model performance. Shapley additive explanations (SHAP) were used to analyze feature importance. The final dataset for the study included 3979 health workers. For the full model, the random forest was selected as the algorithm of choice. It achieved cross-validation mean AUC of 0.818 ± 0.022 and AP of 0.449 ± 0.028 and was high performing during testing with AUC and AP of 0.831 and 0.428 respectively. The random forest model was well-calibrated with a low mean brier score of 0.122 ± 0.004. A random forest classifier was the best performing model during cross-validation for the Jakarta dataset, with AUC of 0.824 ± 0.008, AP of 0.397 ± 0.019, and BS of 0.102 ± 0.007, but the extra trees classifier was selected as the model of choice due to better generalizability to the test set. The performance of the extra trees model, when tested on the independent set of Semarang health workers, was AUC of 0.672 and AP of 0.508. Our models yielded high predictive performance and may have the potential to be utilized as both a COVID-19 screening tool and a method to identify health workers at greatest risk of COVID-19 positivity, and therefore most in need of testing.

Statin Use and COVID-19 Infectivity and Severity in South Korea: Two Population-Based Nationwide Cohort Studies

Background Basic studies suggest that statins as add-on therapy may benefit patients with COVID-19; however, real-world evidence of such a beneficial association is lacking. Objective We investigated differences in SARS-CoV-2 test positivity and clinical outcomes of COVID-19 (composite endpoint: admission to intensive care unit, invasive ventilation, or death) between statin users and nonusers. Methods Two independent population-based cohorts were analyzed, and we investigated the differences in SARS-CoV-2 test positivity and severe clinical outcomes of COVID-19, such as admission to the intensive care unit, invasive ventilation, or death, between statin users and nonusers. One group comprised an unmatched cohort of 214,207 patients who underwent SARS-CoV-2 testing from the Global Research Collaboration Project (GRCP)-COVID cohort, and the other group comprised an unmatched cohort of 74,866 patients who underwent SARS-CoV-2 testing from the National Health Insurance Service (NHIS)-COVID cohort. Results The GRCP-COVID cohort with propensity score matching had 29,701 statin users and 29,701 matched nonusers. The SARS-CoV-2 test positivity rate was not associated with statin use (statin users, 2.82% [837/29,701]; nonusers, 2.65% [787/29,701]; adjusted relative risk [aRR] 0.97; 95% CI 0.88-1.07). Among patients with confirmed COVID-19 in the GRCP-COVID cohort, 804 were statin users and 1573 were matched nonusers. Statin users were associated with a decreased likelihood of severe clinical outcomes (statin users, 3.98% [32/804]; nonusers, 5.40% [85/1573]; aRR 0.62; 95% CI 0.41-0.91) and length of hospital stay (statin users, 23.8 days; nonusers, 26.3 days; adjusted mean difference –2.87; 95% CI –5.68 to –0.93) than nonusers. The results of the NHIS-COVID cohort were similar to the primary results of the GRCP-COVID cohort. Conclusions Our findings indicate that prior statin use is related to a decreased risk of worsening clinical outcomes of COVID-19 and length of hospital stay but not to that of SARS-CoV-2 infection.

Introduction to Quantitative Ecology

Modern practice of ecology, conservation, and resource management demands unprecedented levels of quantitative proficiency in mathematical modeling and statistics. This text provides foundational training in the concepts and methods of mathematical and statistical modeling used in ecology, for readers with all levels of quantitative proficiency and confidence. The first chapter presents a generalized approach to develop ecological models and introduces the “describe, explain, and interpret” framework for linking the model world to the real world. Detailed treatment of population models illustrates the myriad ways in which one can develop a model, shows how modeling choices are informed by the ecological question at hand, and emphasizes the epistemology of quantitative techniques. The second part of the book illustrates how to estimate parameters of models from data, and how to use mathematical models combined with statistics to test hypotheses. The third part of the book is devoted to an in-depth development of technical skills to implement models in two common platforms: spreadsheets and the R programming language. The book concludes by demonstrating a quantitative approach to addressing a question that spans density-dependent versus density-independent population models, fitting models to data, evaluating the strength for density dependence using model selection, and evaluating the types of dynamic behaviors that the population might exhibit.