Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

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)

Neighborhood deprivation and change in BMI among adults with type 2 diabetes: the Diabetes Study of Northern California (DISTANCE).

ObjectiveTo compare associations between neighborhood deprivation and measures of BMI change among adults with type 2 diabetes.Research design and methodsUsing data from the Kaiser Permanente Diabetes Study of Northern California (DISTANCE) survey, we estimated the association between neighborhood deprivation and two measures of BMI change over 3 years: 1) a continuous measure and 2) a categorical measure of clinically substantive BMI loss or gain (≥7% of baseline BMI) versus stable BMI. The sample included 13,609 adults.ResultsOn average, there was little change in BMI (-0.12, SD 3.07); 17.0 and 16.1% had clinically substantive BMI loss or gain, respectively, at follow-up. There was a positive association between neighborhood deprivation and BMI change for adults in the most versus least-deprived quartile of neighborhood deprivation (β = 0.22, P = 0.02) in adjusted models. In addition, relative to the least-deprived quartile (Q1), adults in more-deprived quartiles of neighborhood deprivation were more likely to experience either substantive BMI loss (Q2 relative risk ratio 1.19, 95% CI 1.00-1.41; Q3 1.20, 1.02-1.42; Q4 1.30, 1.08-1.55) or gain (Q2 1.25, 1.04-1.49; Q3 1.24, 1.04-1.49; Q4 1.45, 1.20-1.75).ConclusionsGreater neighborhood deprivation was positively associated with BMI change among adults with diabetes as well as with clinically substantive BMI loss or gain. Findings stress the importance of allowing for simultaneous associations with both gain and loss in future longitudinal studies of neighborhood deprivation and weight change, which may be particularly true for studies of patients with diabetes for whom both weight loss and gain have health implications

An Automated Telephone Nutrition Support System for Spanish-Speaking Patients With Diabetes

In the United States, Spanish-speaking patients with diabetes often receive inadequate dietary counseling. Providing language and culture-concordant dietary counseling on an ongoing basis is critical to diabetes self-care. To determine if automated telephone nutrition support (ATNS) counseling could help patients improve glycemic control by duplicating a successful pilot in Mexico in a Spanish-speaking population in Oakland, California. A prospective randomized open-label trial with blinded endpoint assessment (PROBE) was performed. The participants were seventy-five adult patients with diabetes receiving care at a federally qualified health center in Oakland, California. ATNS, a computerized system that dialed patients on their phones, prompted them in Spanish to enter (via keypad) portions consumed in the prior 24 hours of various cultural-specific dietary items, and then provided dietary feedback based on proportion of high versus low glycemic index foods consumed. The control group received the same ATNS phone calls 14 weeks after enrollment. The primary outcome was hemoglobin A1c % (A1c) 12 weeks following enrollment. Participants had no significant improvement in A1c (–0.3% in the control arm, –0.1% in the intervention arm, P = .41 for any difference) or any secondary parameters. In our study, an ATNS system did not improve diabetes control in a Spanish-speaking population in Oakland

An Automated Telephone Nutrition Support System for Spanish-Speaking Patients With Diabetes

In the United States, Spanish-speaking patients with diabetes often receive inadequate dietary counseling. Providing language and culture-concordant dietary counseling on an ongoing basis is critical to diabetes self-care. To determine if automated telephone nutrition support (ATNS) counseling could help patients improve glycemic control by duplicating a successful pilot in Mexico in a Spanish-speaking population in Oakland, California. A prospective randomized open-label trial with blinded endpoint assessment (PROBE) was performed. The participants were seventy-five adult patients with diabetes receiving care at a federally qualified health center in Oakland, California. ATNS, a computerized system that dialed patients on their phones, prompted them in Spanish to enter (via keypad) portions consumed in the prior 24 hours of various cultural-specific dietary items, and then provided dietary feedback based on proportion of high versus low glycemic index foods consumed. The control group received the same ATNS phone calls 14 weeks after enrollment. The primary outcome was hemoglobin A1c % (A1c) 12 weeks following enrollment. Participants had no significant improvement in A1c (-0.3% in the control arm, -0.1% in the intervention arm, P = .41 for any difference) or any secondary parameters. In our study, an ATNS system did not improve diabetes control in a Spanish-speaking population in Oakland

High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence

ABSTRACT A number of recent retrospective studies have demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater are associated with coronavirus disease 2019 (COVID-19) cases in the corresponding sewersheds. Implementing high-resolution, prospective efforts across multiple plants depends on sensitive measurements that are representative of COVID-19 cases, scalable for high-throughput analysis, and comparable across laboratories. We conducted a prospective study across eight publicly owned treatment works (POTWs). A focus on SARS-CoV-2 RNA in solids enabled us to scale up our measurements with a commercial lab partner. Samples were collected daily, and results were posted to a website within 24 h. SARS-CoV-2 RNA in daily samples correlated with the incidence of COVID-19 cases in the sewersheds; a 1 log10 increase in SARS-CoV-2 RNA in settled solids corresponds to a 0.58 log10 (4×) increase in sewershed incidence rate. SARS-CoV-2 RNA signals measured with the commercial laboratory partner were comparable across plants and comparable to measurements conducted in a university laboratory when normalized by pepper mild mottle virus (PMMoV) RNA. Results suggest that SARS-CoV-2 RNA should be detectable in settled solids for COVID-19 incidence rates of >1/100,000 (range, 0.8 to 2.3 cases per 100,000). These sensitive, representative, scalable, and comparable methods will be valuable for future efforts to scale up wastewater-based epidemiology. IMPORTANCE Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample. In order for a method to be useful for ongoing COVID-19 monitoring, it should be sensitive for detection of low concentrations of SARS-CoV-2, representative of incidence rates in the community, scalable to generate data quickly, and comparable across laboratories. This paper presents a method utilizing wastewater solids to meet these goals, producing measurements of SARS-CoV-2 RNA strongly associated with COVID-19 cases in the sewershed of a publicly owned treatment work. Results, provided within 24 h, can be used to detect incidence rates as low as approximately 1/100,000 cases and can be normalized for comparison across locations generating data using different methods