Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: the REACT-SCOT case-control study.

BACKGROUND: The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing. METHODS: Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days. RESULTS: Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be "medications compromising COVID", all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure. CONCLUSIONS: Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy. REGISTRATION: ENCEPP number EUPAS35558.

Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study.

BACKGROUND: The objectives of this study were to identify risk factors for severe coronavirus disease 2019 (COVID-19) and to lay the basis for risk stratification based on demographic data and health records. METHODS AND FINDINGS: The design was a matched case-control study. Severe COVID-19 was defined as either a positive nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the national database followed by entry to a critical care unit or death within 28 days or a death certificate with COVID-19 as underlying cause. Up to 10 controls per case matched for sex, age, and primary care practice were selected from the national population register. For this analysis-based on ascertainment of positive test results up to 6 June 2020, entry to critical care up to 14 June 2020, and deaths registered up to 14 June 2020-there were 36,948 controls and 4,272 cases, of which 1,894 (44%) were care home residents. All diagnostic codes from the past 5 years of hospitalisation records and all drug codes from prescriptions dispensed during the past 240 days were extracted. Rate ratios for severe COVID-19 were estimated by conditional logistic regression. In a logistic regression using the age-sex distribution of the national population, the odds ratios for severe disease were 2.87 for a 10-year increase in age and 1.63 for male sex. In the case-control analysis, the strongest risk factor was residence in a care home, with rate ratio 21.4 (95% CI 19.1-23.9, p = 8 × 10-644). Univariate rate ratios for conditions listed by public health agencies as conferring high risk were 2.75 (95% CI 1.96-3.88, p = 6 × 10-9) for type 1 diabetes, 1.60 (95% CI 1.48-1.74, p = 8 × 10-30) for type 2 diabetes, 1.49 (95% CI 1.37-1.61, p = 3 × 10-21) for ischemic heart disease, 2.23 (95% CI 2.08-2.39, p = 4 × 10-109) for other heart disease, 1.96 (95% CI 1.83-2.10, p = 2 × 10-78) for chronic lower respiratory tract disease, 4.06 (95% CI 3.15-5.23, p = 3 × 10-27) for chronic kidney disease, 5.4 (95% CI 4.9-5.8, p = 1 × 10-354) for neurological disease, 3.61 (95% CI 2.60-5.00, p = 2 × 10-14) for chronic liver disease, and 2.66 (95% CI 1.86-3.79, p = 7 × 10-8) for immune deficiency or suppression. Seventy-eight percent of cases and 52% of controls had at least one listed condition (51% of cases and 11% of controls under age 40). Severe disease was associated with encashment of at least one prescription in the past 9 months and with at least one hospital admission in the past 5 years (rate ratios 3.10 [95% CI 2.59-3.71] and 2.75 [95% CI 2.53-2.99], respectively) even after adjusting for the listed conditions. In those without listed conditions, significant associations with severe disease were seen across many hospital diagnoses and drug categories. Age and sex provided 2.58 bits of information for discrimination. A model based on demographic variables, listed conditions, hospital diagnoses, and prescriptions provided an additional 1.07 bits (C-statistic 0.804). A limitation of this study is that records from primary care were not available. CONCLUSIONS: We have shown that, along with older age and male sex, severe COVID-19 is strongly associated with past medical history across all age groups. Many comorbidities beyond the risk conditions designated by public health agencies contribute to this. A risk classifier that uses all the information available in health records, rather than only a limited set of conditions, will more accurately discriminate between low-risk and high-risk individuals who may require shielding until the epidemic is over.

VDJ recombinase-mediated TCR ß locus gene usage and coding joint processing in peripheral T cells during perinatal and pediatric development.

The generation of TCR proteins is the result of V(D)J recombinase-mediated genomic rearrangements at recombination signal sequences (RSS) in human lymphocytes. V(D)J recombinase can also mediate rearrangements at nonimmune or "cryptic" RSS in normal and leukemic human peripheral T cells. We previously demonstrated age- and gender-specific developmental differences in V(D)J coding joint processing at cryptic RSS within the HPRT locus in peripheral T cells from healthy children (Murray et al. 2006. J. Immunol. 177: 5393-5404). In this study, we investigated developmentally specific V(D)J recombinase TCRß immune gene rearrangements and coding joint processing at RSS in peripheral T cells in the same pediatric population. This approach provided a unique opportunity to investigate site-specific V(D)J recombinase rearrangements and coding joint processing at immune and nonimmune genes from the same individual T cell population. We determined the genomic sequence of 244 TCRß coding junctions from 112 (63 male, 49 female) subjects from the late stages of fetal development through 9 y of age. We observed both age- and gender-specific V(D)J recombinase-mediated TCRß gene usage and coding joint processing at immune RSS. To the best of our knowledge, these data represent the first description of age- and gender-specific developmental differences in TCR gene usage and coding joint processing that could directly influence TCR diversity and immune specificity. It will be important for future studies to ascertain the mechanistic etiology of these developmental and gender differences in TCR diversity and specificity, as well as their importance with respect to the age and gender risks for infectious and autoimmune diseases in humans.

Risks of and risk factors for COVID-19 disease in people with diabetes: a cohort study of the total population of Scotland.

BACKGROUND: We aimed to ascertain the cumulative risk of fatal or critical care unit-treated COVID-19 in people with diabetes and compare it with that of people without diabetes, and to investigate risk factors for and build a cross-validated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes. METHODS: In this cohort study, we captured the data encompassing the first wave of the pandemic in Scotland, from March 1, 2020, when the first case was identified, to July 31, 2020, when infection rates had dropped sufficiently that shielding measures were officially terminated. The participants were the total population of Scotland, including all people with diabetes who were alive 3 weeks before the start of the pandemic in Scotland (estimated Feb 7, 2020). We ascertained how many people developed fatal or critical care unit-treated COVID-19 in this period from the Electronic Communication of Surveillance in Scotland database (on virology), the RAPID database of daily hospitalisations, the Scottish Morbidity Records-01 of hospital discharges, the National Records of Scotland death registrations data, and the Scottish Intensive Care Society and Audit Group database (on critical care). Among people with fatal or critical care unit-treated COVID-19, diabetes status was ascertained by linkage to the national diabetes register, Scottish Care Information Diabetes. We compared the cumulative incidence of fatal or critical care unit-treated COVID-19 in people with and without diabetes using logistic regression. For people with diabetes, we obtained data on potential risk factors for fatal or critical care unit-treated COVID-19 from the national diabetes register and other linked health administrative databases. We tested the association of these factors with fatal or critical care unit-treated COVID-19 in people with diabetes, and constructed a prediction model using stepwise regression and 20-fold cross-validation. FINDINGS: Of the total Scottish population on March 1, 2020 (n=5 463 300), the population with diabetes was 319 349 (5·8%), 1082 (0·3%) of whom developed fatal or critical care unit-treated COVID-19 by July 31, 2020, of whom 972 (89·8%) were aged 60 years or older. In the population without diabetes, 4081 (0·1%) of 5 143 951 people developed fatal or critical care unit-treated COVID-19. As of July 31, the overall odds ratio (OR) for diabetes, adjusted for age and sex, was 1·395 (95% CI 1·304-1·494; p<0·0001, compared with the risk in those without diabetes. The OR was 2·396 (1·815-3·163; p<0·0001) in type 1 diabetes and 1·369 (1·276-1·468; p<0·0001) in type 2 diabetes. Among people with diabetes, adjusted for age, sex, and diabetes duration and type, those who developed fatal or critical care unit-treated COVID-19 were more likely to be male, live in residential care or a more deprived area, have a COVID-19 risk condition, retinopathy, reduced renal function, or worse glycaemic control, have had a diabetic ketoacidosis or hypoglycaemia hospitalisation in the past 5 years, be on more anti-diabetic and other medication (all p<0·0001), and have been a smoker (p=0·0011). The cross-validated predictive model of fatal or critical care unit-treated COVID-19 in people with diabetes had a C-statistic of 0·85 (0·83-0·86). INTERPRETATION: Overall risks of fatal or critical care unit-treated COVID-19 were substantially elevated in those with type 1 and type 2 diabetes compared with the background population. The risk of fatal or critical care unit-treated COVID-19, and therefore the need for special protective measures, varies widely among those with diabetes but can be predicted reasonably well using previous clinical history. FUNDING: None.

Microarray data and gene expression statistics for Saccharomyces cerevisiae exposed to simulated asbestos mine drainage.

Here we describe microarray expression data (raw and normalized), experimental metadata, and gene-level data with expression statistics from Saccharomyces cerevisiae exposed to simulated asbestos mine drainage from the Vermont Asbestos Group (VAG) Mine on Belvidere Mountain in northern Vermont, USA. For nearly 100 years (between the late 1890s and 1993), chrysotile asbestos fibers were extracted from serpentinized ultramafic rock at the VAG Mine for use in construction and manufacturing industries. Studies have shown that water courses and streambeds nearby have become contaminated with asbestos mine tailings runoff, including elevated levels of magnesium, nickel, chromium, and arsenic, elevated pH, and chrysotile asbestos-laden mine tailings, due to leaching and gradual erosion of massive piles of mine waste covering approximately 9 km2. We exposed yeast to simulated VAG Mine tailings leachate to help gain insight on how eukaryotic cells exposed to VAG Mine drainage may respond in the mine environment. Affymetrix GeneChip® Yeast Genome 2.0 Arrays were utilized to assess gene expression after 24-h exposure to simulated VAG Mine tailings runoff. The chemistry of mine-tailings leachate, mine-tailings leachate plus yeast extract peptone dextrose media, and control yeast extract peptone dextrose media is also reported. To our knowledge this is the first dataset to assess global gene expression patterns in a eukaryotic model system simulating asbestos mine tailings runoff exposure. Raw and normalized gene expression data are accessible through the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) Database Series GSE89875 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89875).

Impact of in-patient research participation on subsequent heroin use patterns: implications for ethics and public health.

AIMS: Research on drug dependence often involves the administration of drugs of abuse to experienced drug users under controlled laboratory conditions. The primary objective of this study was to assess whether participation in such research alters the frequency of heroin use by non-treatment-seeking opioid-dependent volunteers after study completion. DESIGN: Data were examined from four in-patient studies involving controlled opioid administration. SETTING: Substance Use Research Center at Columbia University, New York State Psychiatric Institute. PARTICIPANTS: Sixty-nine heroin-dependent volunteers. MEASUREMENTS: Participants' self-reported heroin use prior to and 1 month after study participation was compared using a Wilcoxon test. Because a number of participants reported that they had stopped using heroin, a logistic regression was used to identify correlates of heroin cessation 1 month after study completion. FINDINGS: One hundred and one participants entered laboratory studies and 69 completed them. Self-reported heroin use significantly decreased 1 month after study participation [1.7 (±2.0) bags per day] compared to baseline [6.8 (±4.2) bags per day], P < 0.001 among the 69 completers. In addition, 42% of the completers were heroin-abstinent 1 month after study completion. Being African American, having a history of opioid dependence treatment, reporting heavier heroin use at baseline and a longer history of heroin use were correlated with cessation of heroin use. CONCLUSIONS: Participation in opioid administration studies does not increase subsequent heroin use and for some individuals leads to accessing opioid dependence treatment and cessation of heroin use in the short term.

Microarray analysis for Saccharomyces cerevisiae.

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent. In the experiment, yeast is grown for 48 hours in 1/2X YPD broth containing 3X glucose. The culture is split into a control and treated group. The experiment culture is treated with 0.5 mM H2O2 in Hanks Buffered Saline (HBSS) for 1 hour. The control culture is treated with HBSS only. Total RNA is extracted from both cultures and is converted to a biotin-labeled cRNA product through a multistep process. The final synthesis product is taken back to the UVM Microarray Core Facility and hybridized to the Affymetrix yeast GeneChips. The resulting gene expression data are uploaded into bioinformatics data analysis software.

V(D)J recombinase-mediated processing of coding junctions at cryptic recombination signal sequences in peripheral T cells during human development.

V(D)J recombinase mediates rearrangements at immune loci and cryptic recombination signal sequences (cRSS), resulting in a variety of genomic rearrangements in normal lymphocytes and leukemic cells from children and adults. The frequency at which these rearrangements occur and their potential pathologic consequences are developmentally dependent. To gain insight into V(D)J recombinase-mediated events during human development, we investigated 265 coding junctions associated with cRSS sites at the hypoxanthine-guanine phosphoribosyltransferase (HPRT) locus in peripheral T cells from 111 children during the late stages of fetal development through early adolescence. We observed a number of specific V(D)J recombinase processing features that were both age and gender dependent. In particular, TdT-mediated nucleotide insertions varied depending on age and gender, including percentage of coding junctions containing N-nucleotide inserts, predominance of GC nucleotides, and presence of inverted repeats (Pr-nucleotides) at processed coding ends. In addition, the extent of exonucleolytic processing of coding ends was inversely related to age. We also observed a coding-partner-dependent difference in exonucleolytic processing and an age-specific difference in the subtypes of V(D)J-mediated events. We investigated these age- and gender-specific differences with recombination signal information content analysis of the cRSS sites in the human HPRT locus to gain insight into the mechanisms mediating these developmentally specific V(D)J recombinase-mediated rearrangements in humans.