CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians.

BACKGROUND: South Asians are at higher risk for type 2 diabetes (T2D) than many other race/ethnic groups. Ectopic adiposity, specifically hepatic steatosis and visceral fat may partially explain this. Our objective was to derive metabolite risk scores for ectopic adiposity and assess associations with incident T2D in South Asians. METHODS: We examined 550 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 years without known cardiovascular disease or T2D and with metabolomic data. Computed tomography scans at baseline assessed hepatic attenuation and visceral fat area, and fasting serum specimens at baseline and after 5 years assessed T2D. LC-MS-based untargeted metabolomic analysis was performed followed by targeted integration and reporting of known signals. Elastic net regularized linear regression analyses was used to derive risk scores for hepatic steatosis and visceral fat using weighted coefficients. Logistic regression models associated metabolite risk score and incident T2D, adjusting for age, gender, study site, BMI, physical activity, diet quality, energy intake and use of cholesterol-lowering medication. RESULTS: Average age of participants was 55 years, 36% women with an average body mass index (BMI) of 25 kg/m2 and 6% prevalence of hepatic steatosis, with 47 cases of incident T2D at 5 years. There were 445 metabolites of known identity. Of these, 313 metabolites were included in the MET-Visc score and 267 in the MET-Liver score. In most fully adjusted models, MET-Liver (OR 2.04 [95% CI 1.38, 3.03]) and MET-Visc (OR 2.80 [1.75, 4.46]) were associated with higher odds of T2D. These associations remained significant after adjustment for measured adiposity. CONCLUSIONS: Metabolite risk scores for intrahepatic fat and visceral fat were strongly related to incident T2D independent of measured adiposity. Use of these biomarkers to target risk stratification may help capture pre-clinical metabolic abnormalities.

Association of lipoprotein subfractions with incidence of type 2 diabetes among five U.S. Race and Ethnic groups: The Mediators of Atherosclerosis in South Asians Living in America (MASALA) and Multi-Ethnic study of Atherosclerosis (MESA).

AIMS: We examined associations between lipoprotein subfractions and prevalent and incident T2D in two race/ethnically diverse cohort studies. METHODS: Adults self-identifying as White, Black, Chinese, Hispanic and South Asian-American without cardiovascular disease, with fasting serum, demographic, and clinical data at enrollment and after 5 years of follow-up were included. Lipoprotein subfractions were measured at enrollment using NMR spectrometry. LASSO regularized logistic regression models adjusted for age, sex, race/ethnicity, lipid-lowering agent use, and waist circumference assessed odds of incident T2D in pooled analyses. RESULTS: There were 4474 participants with lipoprotein subfraction data at enrollment and 3839 participants without prevalent diabetes, mean age 62 years, 51 % women, with 234 incident T2D cases at 5 years. Triglycerides in small, dense LDL-5 [OR 1.26 (95 % CI 1.11,1.43)], VLDL triglycerides 1.30** [1.16,1.46] and phospholipids in VLDL-1 [OR 1.31 (1.17,1.47)] were associated with higher odds of incident T2D, while free cholesterol in large HDL-1 [OR 0.75 (95 % CI 0.63,0.89)] was inversely associated. The results were similar for prevalent diabetes and did not vary by race/ethnic group. CONCLUSIONS: Composition of lipoprotein subfractions is differentially associated with prevalent and incident T2D without difference by race/ethnic group. Assessment of lipoprotein composition may enhance targeted risk reduction for T2D.

Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Avocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes. OBJECTIVES: To identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia. METHODS: Baseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an ∼18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined. RESULTS: Three highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 ∗ 10-7) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (ß = -0.07 +/- 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m2). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (ß = -0.22 +/- 0.02, P < 2.0 ∗ 10-16), lower fasting insulin (ß = -0.17 +/- 0.02, P < 2.0 ∗ 10-16), and a lower incidence of T2D (hazard ratio: 0.68; 0.63-074, P < 2.0 ∗ 10-16), even when adjusting for BMI. CONCLUSIONS: Highly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. Results: We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementation: The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

Metabolomic Profile of the Healthy Eating Index-2015 in the Multiethnic Study of Atherosclerosis.

BACKGROUND: Poor diet quality is a risk factor for type 2 diabetes and cardiovascular disease. However, knowledge of metabolites marking adherence to Dietary Guidelines for Americans (2015 version) are limited. OBJECTIVES: The goal was to determine a pattern of metabolites associated with the Healthy Eating Index (HEI)-2015, which measures adherence to the Dietary Guidelines for Americans. METHODS: The analysis examined 3557 adult men and women from the longitudinal cohort Multiethnic Study of Atherosclerosis (MESA), without known cardiovascular disease and with complete dietary data. Fasting serum specimens and diet and demographic questionnaires were assessed at baseline. Untargeted 1H 1-dimensional nuclei magnetic resonance spectroscopy (600 MHz) was used to generate metabolomics and lipidomics. A metabolome-wide association study specified each spectral feature as outcomes, HEI-2015 score as predictor, adjusting for age, sex, race, and study site in linear regression analyses. Subsequently, hierarchical clustering defined the discrete groups of correlated nuclei magnetic resonance features associated with named metabolites, and the linear regression analysis assessed for associations with HEI-2015 total and component scores. RESULTS: The sample included 50% women with an mean age of 63 years, with 40% identifying as White, 23% as Black, 24% as Hispanic, and 13% as Chinese American. The mean HEI-2015 score was 66. The metabolome-wide association study identified 179 spectral features significantly associated with HEI-2015 score. The cluster analysis identified 7 clusters representing 4 metabolites; HEI-2015 score was significantly associated with all. HEI-2015 score was associated with proline betaine [ß = 0.12 (SE = 0.02); P = 4.70 × 10-13] and was inversely related to proline [ß = -0.13 (SE = 0.02); P = 4.45 × 10-14], 1,5 anhydrosorbitol [ß = -0.08 (SE = 0.02); P = 4.37 × 10-7] and unsaturated fatty acyl chains [ß = 0.08 (SE = 0.02); P = 8.98 × 10-7]. Intake of total fruit, whole grains, and seafood and plant proteins was associated with proline betaine. CONCLUSIONS: Diet quality is significantly associated with unsaturated fatty acyl chains, proline betaine, and proline. Further analysis may clarify the link between diet quality, metabolites, and pathogenesis of cardiometabolic disease.

Venous or arterial thrombosis in COVID-19 cases in the North Carolina COVID-19 Community Research Partnership (NC-CCRP).

Background: Although the incidence of venous and arterial thrombosis after a COVID-19 diagnosis and hospitalization has been well described using data available from electronic health records (EHR), little is known about their incidence after mild infections. Objectives: To characterize the cumulative incidence and risk factors for thrombosis after a COVID-19 diagnosis among those identified through the EHR and those with a self-reported case. Methods: We calculated the cumulative incidence of thromboembolism diagnoses after EHR-identified and self-reported cases in the North Carolina COVID-19 Community Partnership, a prospective, multisite, longitudinal surveillance cohort using a Kaplan-Meier approach. We performed Cox regression to estimate the hazard of a thromboembolism diagnosis after COVID-19 by comorbidities, vaccination status, and dominant SARS-CoV-2 variant. Results: Of a cohort of comprising more than 39,500 participants from 6 North Carolina sites, there were 6271 self-reported or EHR-diagnosed cases of COVID-19 reported between July 1, 2020, and April 30, 2022, of which 46 participants were diagnosed with a new-onset thromboembolism in the 365 days after their reported case. Self-reported cases had a lower estimated cumulative incidence of 0.15% (95% CI, 0.03-0.28) by day 90 and 0.64% (95% CI, 0.30-0.97) by day 365 compared with EHR-based diagnoses that had cumulative incidences of 0.73% (95% CI, 0.36-1.09) and 1.78 (95% CI, 1.14-2.46) by days 90 and 365 (log-rank test P value <.001). Those hospitalized and with pre-existing pulmonary and cardiovascular diseases were associated with the highest risk of a thromboembolism. Conclusion: We observed a higher cumulative incidence of thromboembolism after EHR-identified COVID-19 than self-reported cases.

Association of Lp(a) (Lipoprotein[a]) and Hypertension in Primary Prevention of Cardiovascular Disease: The MESA.

BACKGROUND: This study explored the longitudinal relationship of Lp(a) (lipoprotein[a]) and hypertension to cardiovascular outcomes in a large multiethnic cohort free of baseline cardiovascular disease. METHODS: Individuals from the MESA (Multi-Ethnic Study of Atherosclerosis; N=6674) were grouped as follows: group 1: Lp(a) <50 mg/dL and no hypertension; group 2: Lp(a) ≥50 mg/dL and no hypertension; group 3: Lp(a) <50 mg/dL and hypertension; and group 4: Lp(a) ≥50 mg/dL and hypertension. Kaplan-Meier curves and multivariable Cox proportional hazard models were used to assess the relationship of Lp(a) and hypertension with time to cardiovascular disease events. RESULTS: Mean follow-up time was 13.9 (5.0) years and 809 participants experienced a cardiovascular disease event. A statistically significant interaction was found between Log[Lp(a)] and hypertension status (P=0.091). Compared with the reference group (Lp[a] <50 mg/dL and no hypertension), those with Lp[a] ≥50 mg/dL and no hypertension had no increased risk for cardiovascular disease events (hazard ratio, 1.09 [95% CI, 0.79-1.50]). However, those with Lp(a) <50 mg/dL and hypertension or Lp(a) ≥50 mg/dL and hypertension demonstrated a statistically significant increase in risk compared to the reference group (hazard ratio, 1.66 [95% CI, 1.39-1.98]) and (hazard ratio, 2.07 [95% CI, 1.63-2.62]), respectively. Among those with hypertension, Lp(a) was associated with a significant increase in cardiovascular disease risk (hazard ratio, 1.24 [95% CI, 1.01-1.53]). CONCLUSIONS: Although the major contribution to cardiovascular risk was hypertension, elevated Lp(a) significantly modified the association of hypertension with cardiovascular disease. More research is needed to understand mechanistic links among Lp(a), hypertension, and cardiovascular disease.

Cosbin: cosine score-based iterative normalization of biologically diverse samples.

Motivation: Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant. Results: We report an efficient and accurate data-driven method-Cosine score-based iterative normalization (Cosbin)-to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups. Availability and implementation: The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Vaccine-induced seroconversion in participants in the North Carolina COVID-19 community Research Partnership.

Well-regulated clinical trials have shown FDA-approved COVID-19 vaccines to be immunogenic and highly efficacious. We evaluated seroconversion rates in adults reporting ≥ 1 dose of an mRNA COVID-19 vaccine in a cohort study of nearly 8000 adults residing in North Carolina to validate immunogenicity using a novel approach: at-home, participant administered point-of-care testing. Overall, 91.4% had documented seroconversion within 75 days of first vaccination (median: 31 days). Participants who were older and male participants were less likely to seroconvert (adults aged 41-65: adjusted hazard ratio [aHR] 0.69 [95% confidence interval (CI): 0.64, 0.73], adults aged 66-95: aHR 0.55 [95% CI: 0.50, 0.60], compared to those 18-40; males: aHR 0.92 [95% CI: 0.87, 0.98], compared to females). Participants with evidence of prior infection were more likely to seroconvert than those without (aHR 1.50 [95% CI: 1.19, 1.88]) and those receiving BNT162b2 were less likely to seroconvert compared to those receiving mRNA-1273 (aHR 0.84 [95% CI: 0.79, 0.90]). Reporting at least one new symptom after first vaccination did not affect time to seroconversion, but participants reporting at least one new symptom after second vaccination were more likely to seroconvert (aHR 1.11 [95% CI: 1.05, 1.17]). This data demonstrates the high community-level immunogenicity of COVID-19 vaccines, albeit with notable differences in older adults, and feasibility of using at-home, participant administered point-of-care testing for community cohort monitoring. Trial registration: ClinicalTrials.gov NCT04342884.

COT: an efficient and accurate method for detecting marker genes among many subtypes.

Motivation: Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results: We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods. Availability and implementation: The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Frailty and COVID-19 mRNA Vaccine Antibody Response in the COVID-19 Community Research Partnership.

BACKGROUND: COVID-19 has disproportionately affected older adults. Frailty has been associated with impaired vaccine response in other vaccine types, but the impact of frailty on mRNA vaccine response is undefined. METHODS: Observational study of adults aged 55 and older from 1 U.S. health care system between January 22, 2021 and September 16, 2021 with self-reported Moderna or Pfizer COVID-19 mRNA vaccine and an electronic frailty index (eFI) score from their medical record (n = 1 677). Participants' frailty status was compared with positive antibody detection (seroconversion) following full vaccination and subsequent loss of positive antibody detection (seroreversion) using logistic regression models. RESULTS: Of 1 677 older adults with median (interquartile range) age, 67 (62 and 72) years, and frailty status (nonfrail: 879 [52%], prefrail: 678 [40%], and frail: 120 [7.2%]), seroconversion was not detected in 23 (1.4%) over 60 days following full vaccination. Frail individuals were less likely to seroconvert than nonfrail individuals, adjusted odds ratio (OR) 3.75, 95% confidence interval (CI; 1.04, 13.5). Seroreversion was detected in 50/1 631 individuals (3.1%) over 6 months of median follow-up antibody testing. Frail individuals were more likely to serorevert than nonfrail individuals, adjusted OR 3.02, 95% CI (1.17, 7.33). CONCLUSION: Overall antibody response to COVID-19 mRNA vaccination was high across age and frailty categories. While antibody detection is an incomplete descriptor of vaccine response, the high sensitivity of this antibody combined with health-system data reinforce our conclusions that frailty is an independent predictor of impaired antibody response to the COVID-19 mRNA vaccines. Frailty should be considered in vaccine studies and prevention strategies.

Associations of NAFLD with circulating ceramides and impaired glycemia.

AIM: Determine the association of circulating ceramides with NAFLD and glycemic impairment. METHODS: Sample: 669 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort aged 40-84 years without cardiovascular disease, cirrhosis, or significant alcohol intake. CLINICAL MEASURES: Computed tomography scans at baseline for hepatic attenuation. Fasting serum specimens at baseline and after 5 years. Lipidomics: LC-MS-based analysis of 19 known ceramide signals. STATISTICAL ANALYSIS: Linear and logistic regression models of log-transformed ceramides, hepatic attenuation and glucose adjusted for age, sex, calories, study site, BMI, exercise, diet quality, alcohol, saturated fat, lipid-lowering medications and fasting glucose. RESULTS: Average age was 55 years, 44% were women, mean BMI was 25.9 kg/m2, and 8% had NAFLD. In adjusted models, Cer(d16:1/20:0) and Cer(d18:1/18:0) were associated with lower mean hepatic attenuation (increased liver fat) (ß -4.29; 95% CI [-5.98, -2.59]) and (ß -3.40; 95% CI [-5.11, -1.70]), and LacCer(d18:1/16:0) with higher attenuation (ß 4.44; 95% CI [2.15, 6.73]). All three ceramides partially mediated the relationship between hepatic attenuation and fasting glucose by 16%, 11% and 5%, respectively, after 5-years. CONCLUSIONS: Three circulating ceramides were strongly associated with NAFLD and fasting glucose after 5 years, and partially mediated this association.

Analysis of accumulated SARS-CoV-2 seroconversion in North Carolina: The COVID-19 Community Research Partnership.

INTRODUCTION: The COVID-19 Community Research Partnership is a population-based longitudinal syndromic and sero-surveillance study. The study includes over 17,000 participants from six healthcare systems in North Carolina who submitted over 49,000 serology results. The purpose of this study is to use these serology data to estimate the cumulative proportion of the North Carolina population that has either been infected with SARS-CoV-2 or developed a measurable humoral response to vaccination. METHODS: Adult community residents were invited to participate in the study between April 2020 and February 2021. Demographic information was collected and daily symptom screen was completed using a secure, HIPAA-compliant, online portal. A portion of participants were mailed kits containing a lateral flow assay to be used in-home to test for presence of anti-SARS-CoV-2 IgM or IgG antibodies. The cumulative proportion of participants who tested positive at least once during the study was estimated. A standard Cox proportional hazards model was constructed to illustrate the probability of seroconversion over time up to December 20, 2020 (before vaccines available). A separate analysis was performed to describe the influence of vaccines through February 15, 2021. RESULTS: 17,688 participants contributed at least one serology result. 68.7% of the population were female, and 72.2% were between 18 and 59 years of age. The average number of serology results submitted per participant was 3.0 (±1.9). By December 20, 2020, the overall probability of seropositivity in the CCRP population was 32.6%. By February 15, 2021 the probability among healthcare workers and non-healthcare workers was 83% and 49%, respectively. An inflection upward in the probability of seropositivity was demonstrated around the end of December, suggesting an influence of vaccinations, especially for healthcare workers. Among healthcare workers, those in the oldest age category (60+ years) were 38% less likely to have seroconverted by February 15, 2021. CONCLUSIONS: Results of this study suggest more North Carolina residents may have been infected with SARS-CoV-2 than the number of documented cases as determined by positive RNA or antigen tests. The influence of vaccinations on seropositivity among North Carolina residents is also demonstrated. Additional research is needed to fully characterize the impact of seropositivity on immunity and the ultimate course of the pandemic.

Comparative assessment and novel strategy on methods for imputing proteomics data.

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy-convex analysis of mixtures-for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

Survey of Adherence with COVID-19 Prevention Behaviors During the 2020 Thanksgiving and Winter Holidays Among Members of the COVID-19 Community Research Partnership.

Prevention behaviors represent important public health tools to limit spread of SARS-CoV-2. Adherence with recommended public health prevention behaviors among 20000 + members of a COVID-19 syndromic surveillance cohort from the mid-Atlantic and southeastern United States was assessed via electronic survey following the 2020 Thanksgiving and winter holiday (WH) seasons. Respondents were predominantly non-Hispanic Whites (90%), female (60%), and ≥ 50 years old (59%). Non-household members (NHM) were present at 47.1% of Thanksgiving gatherings and 69.3% of WH gatherings. Women were more likely than men to gather with NHM (p < 0.0001). Attending gatherings with NHM decreased with older age (Thanksgiving: 60.0% of participants aged < 30 years to 36.3% aged ≥ 70 years [p-trend < 0.0001]; WH: 81.6% of those < 30 years to 61.0% of those ≥ 70 years [p-trend < 0.0001]). Non-Hispanic Whites were more likely to gather with NHM than were Hispanics or non-Hispanic Blacks (p < 0.0001). Mask wearing, reported by 37.3% at Thanksgiving and 41.9% during the WH, was more common among older participants, non-Hispanic Blacks, and Hispanics when gatherings included NHM. In this survey, most people did not fully adhere to recommended public health safety behaviors when attending holiday gatherings. It remains unknown to what extent failure to observe these recommendations may have contributed to the COVID-19 surges observed following Thanksgiving and the winter holidays in the United States.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.

MOTIVATION: Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. RESULTS: We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. AVAILABILITY AND IMPLEMENTATION: The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AtheroSpectrum Reveals Novel Macrophage Foam Cell Gene Signatures Associated With Atherosclerotic Cardiovascular Disease Risk.

BACKGROUND: Whereas several interventions can effectively lower lipid levels in people at risk for atherosclerotic cardiovascular disease (ASCVD), cardiovascular event risks remain, suggesting an unmet medical need to identify factors contributing to cardiovascular event risk. Monocytes and macrophages play central roles in atherosclerosis, but studies have yet to provide a detailed view of macrophage populations involved in increased ASCVD risk. METHODS: A novel macrophage foaming analytics tool, AtheroSpectrum, was developed using 2 quantitative indices depicting lipid metabolism and the inflammatory status of macrophages. A machine learning algorithm was developed to analyze gene expression patterns in the peripheral monocyte transcriptome of MESA participants (Multi-Ethnic Study of Atherosclerosis; set 1; n=911). A list of 30 genes was generated and integrated with traditional risk factors to create an ASCVD risk prediction model (30-gene cardiovascular disease risk score [CR-30]), which was subsequently validated in the remaining MESA participants (set 2; n=228); performance of CR-30 was also tested in 2 independent human atherosclerotic tissue transcriptome data sets (GTEx [Genotype-Tissue Expression] and GSE43292). RESULTS: Using single-cell transcriptomic profiles (GSE97310, GSE116240, GSE97941, and FR-FCM-Z23S), AtheroSpectrum detected 2 distinct programs in plaque macrophages-homeostatic foaming and inflammatory pathogenic foaming-the latter of which was positively associated with severity of atherosclerosis in multiple studies. A pool of 2209 pathogenic foaming genes was extracted and screened to select a subset of 30 genes correlated with cardiovascular event in MESA set 1. A cardiovascular disease risk score model (CR-30) was then developed by incorporating this gene set with traditional variables sensitive to cardiovascular event in MESA set 1 after cross-validation generalizability analysis. The performance of CR-30 was then tested in MESA set 2 (P=2.60×10-4; area under the receiver operating characteristic curve, 0.742) and 2 independent data sets (GTEx: P=7.32×10-17; area under the receiver operating characteristic curve, 0.664; GSE43292: P=7.04×10-2; area under the receiver operating characteristic curve, 0.633). Model sensitivity tests confirmed the contribution of the 30-gene panel to the prediction model (likelihood ratio test; df=31, P=0.03). CONCLUSIONS: Our novel computational program (AtheroSpectrum) identified a specific gene expression profile associated with inflammatory macrophage foam cells. A subset of 30 genes expressed in circulating monocytes jointly contributed to prediction of symptomatic atherosclerotic vascular disease. Incorporating a pathogenic foaming gene set with known risk factors can significantly strengthen the power to predict ASCVD risk. Our programs may facilitate both mechanistic investigations and development of therapeutic and prognostic strategies for ASCVD risk.

Monocyte chemoattractant protein-1 is not predictive of cardiac events in patients with non-low-risk chest pain.

BACKGROUND: Prior studies suggest monocyte chemoattractant protein-1 (MCP-1) may be useful for risk stratifying ED patients with chest pain. We hypothesise that MCP-1 will be predictive of 90-day major adverse cardiovascular events (MACEs) in non-low-risk patients. METHODS: A case-control study was nested within a prospective multicentre cohort (STOP-CP), which enrolled adult patients being evaluated for acute coronary syndrome at eight US EDs from 25 January 2017 to 06 September 2018. Patients with a History, ECG, Age, and Risk factor score (HEAR score) ≥4 or coronary artery disease (CAD), a non-ischaemic ECG, and non-elevated contemporary troponins at 0 and 3 hours were included. Cases were patients with 90-day MACE (all-cause death, myocardial infarction or revascularisation). Controls were patients without MACE selected with frequency matching using age, sex, race, and HEAR score or the presence of CAD. Serum MCP-1 was measured. Sensitivity and specificity were determined for cut-off points of 194 pg/mL, 200 pg/mL, 238 pg/mL and 281 pg/mL. Logistic regression adjusting for age, sex, race, and HEAR score/presence of CAD was used to determine the association between MCP-1 and 90-day MACE. A separate logistic model also included high-sensitivity troponin (hs-cTnT). RESULTS: Among 40 cases and 179 controls, there was no difference in age (p=0.90), sex (p=1.00), race (p=0.85), or HEAR score/presence of CAD (p=0.89). MCP-1 was similar in cases (median 191.9 pg/mL, IQR: 161.8-260.1) and controls (median 196.6 pg/mL, IQR: 163.0-261.1) (p=0.48). At a cut-off point of 194 pg/mL, MCP-1 was 50.0% (95% CI 33.8% to 66.2%) sensitive and 46.9% (95% CI 39.4% to 54.5%) specific for 90-day MACE. After adjusting for covariates, MCP-1 was not associated with 90-day MACE at any cut-off point (at 194 pg/mL, OR 0.88 (95% CI 0.43 to 1.79)). When including hs-cTnT in the model, MCP-1 was not associated with 90-day MACE at any cut-off point (at 194 pg/mL, OR 0.85 (95% CI 0.42 to 1.73)). CONCLUSION: MCP-1 is not predictive of 90-day MACE in patients with non-low-risk chest pain.