ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes.

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

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.

Assessment of Prevalence, Clinical Characteristics, and Risk Factors Associated With "Low Flow State" Using Cardiac Magnetic Resonance.

Objective: To assess prevalence, clinical characteristics, and risk factors associated with low flow state (LFS) in a multiethnic population with normal left ventricular ejection fraction (LVEF). Patients and Methods: The study included 4398 asymptomatic participants undergoing cardiac magnetic resonance from July 17, 2000, to August 29, 2002. Left ventricular (LV) mass, volume, and myocardial contraction fraction were assessed. Low flow state was defined as stroke volume index (SVi of <35 mL/m2). Clinical characteristics, cardiac risk factors, and cardiac magnetic resonance findings were compared between LFS and normal flow state (NFS) groups (NFS: SVi of ≥35 mL/m2). Results: There were significant differences in the prevalence of LFS in different ethnic groups. Individuals with LFS were older (66±9.6 vs 61±10 years; P<.0001). The prevalence of LFS was 19% in the group aged older than 70 years. The logistic multivariable regression analysis found that age was independently associated with LFS. The LFS group had significantly higher prevalence of diabetes (30% vs 24%; P=.001), LV mass-volume ratio (1.13±0.22 vs 0.91±0.15; P<.0001), inflammatory markers, a lower LV mass index (59±10 vs 65±11 kg/m2; P<.001), lower myocardial contraction fraction (58.1±10.6% vs 75.7±13%; P<.001), and a lower left atrial size index (32.2±4.6 vs 36.7±5.9 mm/m2; P<.0001) than NFS. Conclusion: Low flow state may be considered an under-recognized clinical entity associated with increasing age, multiple risk factors, increased inflammatory markers, a lower LV mass index, and suboptimal myocardial performance despite the presence of normal LVEF and absence of valvular disease.

Untargeted metabolomic analysis investigating links between unprocessed red meat intake and markers of inflammation.

BACKGROUND: Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships. OBJECTIVE: To identify whether any metabolites associated with red meat intake are also associated with inflammation. METHODS: A cross-sectional analysis of observational data from older adults (52.84% women, mean age 63 ± 0.3 y) participating in the Multi-Ethnic Study of Atherosclerosis (MESA). Dietary intake was assessed by food-frequency questionnaire, alongside C-reactive protein (CRP), interleukin-2, interleukin-6, fibrinogen, homocysteine, and tumor necrosis factor alpha, and untargeted proton nuclear magnetic resonance (1H NMR) metabolomic features. Associations between these variables were examined using linear regression models, adjusted for demographic factors, lifestyle behaviors, and body mass index (BMI). RESULTS: In analyses that adjust for BMI, neither processed nor unprocessed forms of red meat were associated with any markers of inflammation (all P > 0.01). However, when adjusting for BMI, unprocessed red meat was inversely associated with spectral features representing the metabolite glutamine (sentinel hit: ß = -0.09 ± 0.02, P = 2.0 × 10-5), an amino acid which was also inversely associated with CRP level (ß = -0.11 ± 0.01, P = 3.3 × 10-10). CONCLUSIONS: Our analyses were unable to support a relationship between either processed or unprocessed red meat and inflammation, over and above any confounding by BMI. Glutamine, a plasma correlate of lower unprocessed red meat intake, was associated with lower CRP levels. The differences in diet-inflammation associations, compared with diet metabolite-inflammation associations, warrant further investigation to understand the extent that these arise from the following: 1) a reduction in measurement error with metabolite measures; 2) the extent that which factors other than unprocessed red meat intake contribute to glutamine levels; and 3) the ability of plasma metabolites to capture individual differences in how food intake is metabolized.

CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.

Clinical heterogeneity remains a challenge in the practice of medicine and is an underlying motivation for much of biomedical research. Unfortunately, despite an abundance of technologies capable of producing millions of discrete data elements with information about a patient's health status or disease prognosis, our ability to translate those data into meaningful improvements in understanding of clinical heterogeneity is limited. To address this gap, we have applied newer approaches to manifold learning and developed additional and complementary techniques to interrogate and interpret complex, high dimensional omics data. The central premise is that there exist manifolds embedded in high dimensional data that represent fundamental biologic processes that may help address the challenges of clinical heterogeneity. Preliminary evidence from several real-world data sets suggests that these techniques can identify coherent and reproducible manifolds embedded in higher dimensional omics data. Work is currently ongoing to determine the clinical informativeness of these novel data structures.

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.

Metabolomic Profiling of Cholesterol Efflux Capacity in a Multiethnic Population: Insights From MESA.

BACKGROUND: Impaired cholesterol efflux capacity (CEC) is a novel lipid metabolism trait associated with atherosclerotic cardiovascular disease. Mechanisms underlying CEC variation are unknown. We evaluated associations of circulating metabolites with CEC to advance understanding of metabolic pathways involved in cholesterol efflux regulation. METHODS: Participants enrolled in the MESA (Multi-Ethnic Study of Atherosclerosis) who underwent nuclear magnetic resonance metabolome profiling and CEC measurement (N=3543) at baseline were included. Metabolite associations with CEC were evaluated using standard linear regression analyses. Repeated ElasticNet and multilayer perceptron regression were used to assess metabolite profile predictive performance for CEC. Features important for CEC prediction were identified using Shapley Additive Explanations values. RESULTS: Greater CEC was significantly associated with metabolite clusters composed of the largest-sized particle subclasses of VLDL (very-low-density lipoprotein) and HDL (high-density lipoprotein), as well as their constituent apo A1, apo A2, phospholipid, and cholesterol components (ß=0.072-0.081; P<0.001). Metabolite profiles had poor accuracy for predicting in vitro CEC in linear and nonlinear analyses (R2<0.02; Spearman ρ<0.18). The most important feature for CEC prediction was race, with Black participants having significantly lower CEC compared with other races. CONCLUSIONS: We identified independent associations among CEC, the largest-sized particle subclasses of VLDL and HDL, and their constituent apolipoproteins and lipids. A large proportion of variation in CEC remained unexplained by metabolites and traditional clinical risk factors, supporting further investigation into genomic, proteomic, and phospholipidomic determinants of CEC.

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.

Implications of Social Disadvantage Score in Cardiovascular Outcomes and Risk Assessment: Findings From the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Social determinants of health contribute to disparate cardiovascular outcomes, yet they have not been operationalized into the current paradigm of cardiovascular risk assessment. METHODS: Data from the Multi-Ethnic Study of Atherosclerosis, which includes participants from 6 US field centers, were used to create an index of baseline Social Disadvantage Score (SDS) to explore its association with incident atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality and impact on ASCVD risk prediction. SDS, which ranges from 0 to 4, was calculated by tallying the following social factors: (1) household income less than the federal poverty level; (2) educational attainment less than a high school diploma; (3) single-living status; and (4) experience of lifetime discrimination. Cox models were used to examine the association between SDS and each outcome with adjustment for traditional cardiovascular risk factors. Changes in the discrimination and reclassification of ASCVD risk by incorporating SDS into the pooled cohort equations were examined. RESULTS: A total of 6434 participants (mean age, 61.9±10.2 years; female 52.8%; non-white 60.9%) had available SDS 1733 (26.9%) with SDS 0; 2614 (40.6%) with SDS 1; 1515 (23.5%) with SDS 2; and 572 (8.9%) with SDS ≥3. In total, 775 incident ASCVD events and 1573 deaths were observed over a median follow-up of 17.0 years. Increasing SDS was significantly associated with incident ASCVD and all-cause mortality after adjusting for traditional risk factors (ASCVD: per unit increase in SDS hazard ratio, 1.15 [95% CI, 1.07-1.24]; mortality: per unit increase in SDS hazard ratio, 1.13 [95% CI, 1.08-1.19]). Adding SDS to pooled cohort equations components in a Cox model for 10-year ASCVD risk prediction did not significantly improve discrimination (P=0.208) or reclassification (P=0.112). CONCLUSIONS: Although SDS is independently associated with incident ASCVD and all-cause mortality, it does not improve 10-year ASCVD risk prediction beyond pooled cohort equations.

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.

COVID-19 Symptoms by Variant Period in the North Carolina COVID-19 Community Research Partnership, North Carolina, USA.

In North Carolina, USA, the SARS-CoV-2 Omicron variant was associated with changing symptomology in daily surveys, including increasing rates of self-reported cough and sore throat and decreased rates of loss of taste and smell. Compared with the pre-Delta period, Delta and Omicron (pre-BA.4/BA.5) variant periods were associated with shorter symptom duration.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification.

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific 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.

Breakthrough SARS-CoV-2 Infections after Vaccination in North Carolina.

We characterize the overall incidence and risk factors for breakthrough infection among fully vaccinated participants in the North Carolina COVID-19 Community Research Partnership cohort. Among 15,808 eligible participants, 638 reported a positive SARS-CoV-2 test after vaccination. Factors associated with a lower risk of breakthrough in the time-to-event analysis included older age, prior SARS-CovV-2 infection, higher rates of face mask use, and receipt of a booster vaccination. Higher rates of breakthrough were reported by participants vaccinated with BNT162b2 or Ad26.COV2.S compared to mRNA-1273, in suburban or rural counties compared to urban counties, and during circulation of the Delta and Omicron variants.