Genetic variants in PPARGC1B and CNTN4 are associated with thromboxane A2 formation and with cardiovascular event free survival in the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT).

BACKGROUND AND AIMS: Elevated urinary 11-dehydro thromboxane B2 (TxB2), a measure of thromboxane A2 formation in vivo, predicts future atherothrombotic events. To further understand this relationship, the genetic determinants of 11-dehydro TxB2 and their associations with cardiovascular morbidity were investigated in this study. METHODS: Genome-wide and targeted genetic association studies of urinary 11-dehydro TxB2 were conducted in 806 Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) participants. RESULTS: The strongest associations were in PPARGC1B (rs4235745, rs32582, rs10515638) and CNTN4 (rs10510230, rs4684343), these 5 single nucleotide polymorphisms (SNPs) were independently associated with 11-dehydro TxB2 formation. Haplotypes of 11-dehydro TxB2 increasing alleles for both PPARGC1B and CNTN4 were significantly associated with 11-dehydro TxB2, explaining 5.2% and 4.5% of the variation in the whole cohort, and 8.8% and 7.9% in participants not taking aspirin, respectively. In a second ASCOT population (n = 6199), addition of these 5 SNPs significantly improved the covariate-only Cox proportional hazards model for cardiovascular events (chisq = 14.7, p=0.01). Two of the risk alleles associated with increased urinary 11-dehydro TxB2 were individually associated with greater incidences of cardiovascular events - rs10515638 (HR = 1.31, p=0.01) and rs10510230 (HR = 1.25, p=0.007); effect sizes were larger in those not taking aspirin. CONCLUSIONS: PPARGC1B and CNTN4 genotypes are associated with elevated thromboxane A2 formation and with an excess of cardiovascular events. Aspirin appears to blunt these associations. If specific protection of PPARGC1B and CNTN4 variant carriers by aspirin is confirmed by additional studies, PPARGC1B and CNTN4 genotyping could potentially assist in clinical decision making regarding the use of aspirin in primary prevention

A Role for TLR4 in Clostridium difficile Infection and the Recognition of Surface Layer Proteins

Clostridium difficile is the etiological agent of antibiotic-associated diarrhoea (AAD) and pseudomembranous colitis in humans. The role of the surface layer proteins (SLPs) in this disease has not yet been fully explored. The aim of this study was to investigate a role for SLPs in the recognition of C. difficile and the subsequent activation of the immune system. Bone marrow derived dendritic cells (DCs) exposed to SLPs were assessed for production of inflammatory cytokines, expression of cell surface markers and their ability to generate T helper (Th) cell responses. DCs isolated from C3H/HeN and C3H/HeJ mice were used in order to examine whether SLPs are recognised by TLR4. The role of TLR4 in infection was examined in TLR4-deficient mice. SLPs induced maturation of DCs characterised by production of IL-12, TNFα and IL-10 and expression of MHC class II, CD40, CD80 and CD86. Furthermore, SLP-activated DCs generated Th cells producing IFNγ and IL-17. SLPs were unable to activate DCs isolated from TLR4-mutant C3H/HeJ mice and failed to induce a subsequent Th cell response. TLR4−/− and Myd88−/−, but not TRIF−/− mice were more susceptible than wild-type mice to C. difficile infection. Furthermore, SLPs activated NFκB, but not IRF3, downstream of TLR4. Our results indicate that SLPs isolated from C. difficile can activate innate and adaptive immunity and that these effects are mediated by TLR4, with TLR4 having a functional role in experimental C. difficile infection. This suggests an important role for SLPs in the recognition of C. difficile by the immune system

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

Background: Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. Methods: We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. Results: All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Conclusions: Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings

The impact of COVID-19 vaccination in prisons in England and Wales : a metapopulation model

Background: High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community. Methods: We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths. Results: Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible. Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points. Conclusions: The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey : a repeated cross-sectional study

Background During: the Coronavirus Disease 2019 (CAU OVID-19): pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies. Methods and findings The repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study. The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants’ age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or over-report the number of contacts they have made

Using high-resolution contact networks to evaluate SARS-CoV-2 transmission and control in large-scale multi-day events

The emergence of highly transmissible SARS-CoV-2 variants has created a need to reassess the risk posed by increasing social contacts as countries resume pre-pandemic activities, particularly in the context of resuming large-scale events over multiple days. To examine how social contacts formed in different activity settings influences interventions required to control Delta variant outbreaks, we collected high-resolution data on contacts among passengers and crew on cruise ships and combined the data with network transmission models. We found passengers had a median of 20 (IQR 10–36) unique close contacts per day, and over 60% of their contact episodes were made in dining or sports areas where mask wearing is typically limited. In simulated outbreaks, we found that vaccination coverage and rapid antigen tests had a larger effect than mask mandates alone, indicating the importance of combined interventions against Delta to reduce event risk in the vaccine era

Modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era

England has experienced a heavy burden of COVID-19, with multiple waves of SARS-CoV-2 transmission since early 2020 and high infection levels following the emergence and spread of Omicron variants since late 2021. In response to rising Omicron cases, booster vaccinations were accelerated and offered to all adults in England. Using a model fitted to more than 2 years of epidemiological data, we project potential dynamics of SARS-CoV-2 infections, hospital admissions and deaths in England to December 2022. We consider key uncertainties including future behavioural change and waning immunity and assess the effectiveness of booster vaccinations in mitigating SARS-CoV-2 disease burden between October 2021 and December 2022. If no new variants emerge, SARS-CoV-2 transmission is expected to decline, with low levels remaining in the coming months. The extent to which projected SARS-CoV-2 transmission resurges later in 2022 depends largely on assumptions around waning immunity and to some extent, behaviour, and seasonality

The impact of COVID-19 vaccination in prisons in England and Wales: a metapopulation model.

BACKGROUND: High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community. METHODS: We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths. RESULTS: Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible. Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points. CONCLUSIONS: The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings

Two further blood pressure loci identified in ion channel genes with a gene-centric approach.

Background— Blood pressure (BP) is highly heritable, but our understanding of the genetic causes underlying variations in BP is incomplete. In this study, we explored whether novel loci associated with BP could be identified using a genecentric approach in 3 community-based cohorts with accurate BP measurements. Methods and Results— Genotyping of 1857 single nucleotide polymorphisms (SNPs) in 91 ion channel genes was performed in a discovery cohort (n=358). Thirty-four SNPs associated with BP traits ( P ≤0.01) were followed up in an independent population (n=387); significant SNPs from this analysis were looked up in another independent population (n=1010) and meta-analyzed. Repeated clinic and ambulatory measurements were available for all but the discovery cohort (clinic only). Association analyses were performed, with systolic, diastolic, and pulse pressures as quantitative traits, adjusting for age and sex. Quantile–quantile plots indicated that the genecentric approach resulted in an inflation of association signals. Of the 29 SNPs taken forward from the discovery cohort, 2 SNPs were associated with BP phenotypes with the same direction of effect, with experiment-wide significance, in follow-up cohort I. These were rs2228291, in the chloride channel gene CLCN2 , and rs10513488, in the potassium channel gene KCNAB1 . Both associations were subsequently replicated in follow-up cohort II. Conclusions— Using a genecentric design and 3 well-phenotyped populations, this study identified 2 previously unreported, biologically plausible, genetic associations with BP. These results suggest that dense genotyping of genes, in pathways known to influence BP, could add to candidate-gene and Genome Wide Association studies in further explaining BP heritability. </jats:sec