Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

Researchers in the field of hair analysis have known for at least two decades that test results for many chemical compounds remain positive for a considerable period of time after subjects have reported cessation of use. These findings were generally based on small sample populations or individual case studies. Within the last decade, hair analyses of larger populations have investigated the phenomenon of residual positives in abstinent individuals in order to determine the period of time required for various compounds to present negative hair test results at internationally accepted cutoff levels. Such data has primarily been used to establish guidelines for retesting former abusers of illicit drugs in order to evaluate claims of abstinence. To date, research has focused on cocaine and opiates. The present study is the first to examine the duration of detection of methamphetamine (MA) and its metabolite amphetamine (AP) in the hair of chronic MA users who recently ceased their consumption of the drug. The study population (n=63) consisted of inpatients at a hospital drug rehabilitation program in Chiang Mai, Thailand. Drug taking behavior was collected by personal interview at the time of enrollment. Subjects provided hair samples at approximately monthly intervals for MA and AP analysis by gas chromatography-mass spectrometry at 0.2ng/mg cutoff levels. The correlation of baseline MA and AP concentrations in hair at the beginning of abstinence with corresponding duration of detection indicated great individual variability for the rate of clearance of MA and AP from hair. In regard to duration of detection, the majority of chronic MA users remained MA positive for up to about 90 days of reported abstinence, but by 120 days, the detection rate had fallen to about 16%. All subjects tested negative for MA after 153 days of abstinence. For AP, the limit of the duration of detection was reached at 106 days. With the adoption of a margin of safety to compensate for outlier individual variability, the present study affirmed that hair analysis of chronic MA abusers should test negative for MA after 6 months of claimed abstinence.

In gene-gene interaction analysis using single nucleotide polymorphism (SNP) data, empty cells arise in the genotype contingency table more frequently than in single SNP association studies. Empty cells lead to unidentifiable regression coefficients in regression model fitting. It is unclear whether the degrees of freedom (d.f.) for testing interactions are reduced for such sparse contingency tables. Boolean Operation based Screening and Testing is an exhaustive gene-gene interaction search method in which a fixed d.f. of four (the most conservative choice) is used in the chi-squared null distribution for the likelihood ratio test for gene-gene interactions under a logistic regression model. In this paper, the choice of d.f. is investigated theoretically by introducing a decomposition of type I error. An adaptive method using the observed d.f. can be less conservative than the fixed d.f. method, thereby enhancing power. In simulated data, type I error rates for the adaptive method were usually better controlled under various scenarios for Gaussian linear regression and logistic regression, including prospective and retrospective sampling designs, as well as for artificial data that mimic actual genome-wide SNPs. When the adaptive method was applied to public datasets generated from simulations, it exhibited an improvement in power over the fixed method.