Title: PLASMA PROTEOMIC ASSOCIATIONS OF ARTERIAL STIFFNESS: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS

Authors: BIANCA POURMUSSA1, BA; Hamed Tavolinejad1,2, MD; Marie-Joe Dib, PhD1,2; Cameron Beeche, BS1,3; Karim Kohansal Vajargah, MD11; Joe-David Azzo, MD1,2; Jerome I. Rotter, MD4; Stephen S. Rich, PhD7; Xiuqing Guo, PhD4; Sanjiv J. Shah, MD8; Susan R. Heckbert, MD, PhD5; Alain G. Bertoni, MD, MPH6; Joao A.C. Lima, MD9; Usman A. Tahir, MD10; Julio A. Chirinos, MD, PhD1,2

Affiliations:
1 Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA
2 University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
3 Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
4 Lundquist Institute at Harbor UCLA, Torrance, CA
5 Department of Epidemiology, University of Washington, Seattle, WA
6 Division of Public Health Sciences, Wake Forest University School of Medicine, Winstom-Salem, NC
7 Department of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA
8 Northwestern University Feinberg School of Medicine, Chicago, IL
9 Johns Hopkins University School of Medicine, Baltimore, MD
10 Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
11 Prevention of Metabolic Disorders Research Center, Research Institute for Metabolic and Obesity Disorders, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Introduction: Arterial Stiffness (ARTS), results from advanced age, disease, and genetic factors. ARTS leads to detrimental hemodynamics and end organ damage. Quantification of ARTS therefore informs our understanding about susceptibility to disease. Additionally, identifying the biological pathways that contribute to ARTS may reveal targets for intervention. We aimed to assess the proteomic associations of ARTS with different ARTS phenotypic measures: pulse pressure (PP), total arterial compliance (TAC), cardio-ankle vascular index (CAVI), and cardio-femoral vascular index (CAFVI).
Methods/Design: The proteomic correlates of ARTS metrics were assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) using the Olink-3k panel, which measures 2,923 proteins. We used linear regression models adjusted for cardiovascular risk and demographics factors to assess the association of each protein with ARTS metrics. The false discovery rate method was applied for multiplicity correction. Overrepresentation analysis using the Reactome database was performed in R studio using the ReactomePA package.
Results: The study population included 3,135 participants (mean age 74 years, 53% females). In models adjusted for the Framingham Risk Score, prevalent cardiovascular disease, and race, we identified 807 proteins significantly associated with PP, 693 significantly associated with TAC, 346 significantly associated with CAVI, and 1,101 significantly associated with CAFVI. Significant associations of all four outcomes included RSPO3, TNFRSF11B, and MMP12. Several significantly enriched pathways were identified in this model, including extracellular matrix organization (CAVI), membrane trafficking (CAFVI), and post-translational protein phosphorylation (PP and TAC). In analyses adjusted for age, sex, and race, we identified 5 proteins associated with TAC, 410 proteins associated with PP, with no significant associations for either CAFVI or CAVI.
Conclusions: These findings reveal multiple proteins and biologic pathways associated with ARTS, improving upon current understanding of potential underlying molecular mechanisms. This analysis also demonstrates important differences in proteomic associations of various metrics related to ARTS.

Background: Individuals with chronic obstructive pulmonary disease (COPD) demonstrate marked heterogeneity with respect to lung function decline, emphysema, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. In this study, we aimed to assess the predictive value of ORS for COPD-related traits in both smoking-enriched and general population cohorts.
Methods: We developed and tested ORS in n=3,339 participants of the Genetic Epidemiology of COPD (COPDGene) study with blood RNA-sequencing, proteomic, and metabolomic data collected at the second study visit. On 80% of the data, we trained single- and multi-omic risk scores on a variety of traits using elastic net penalized regression with 10-fold cross-validation. We included 24 cross-sectional and 5 longitudinal traits (where the trait was measured approximately 5 years apart), enriched for measures of disease severity, exacerbations, and traits derived from spirometry and computed tomography scans. We used multivariable models to test association of ORS with outcomes in a held-out COPDGene testing set and externally validated findings in participants of SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) (n= 2,177) and Multi-Ethnic Study of Atherosclerosis (MESA) (n=1,000), adjusting for potential confounders and multiple testing.
Results: Among the 24 cross-sectional traits in the COPDGene testing set, there were significantly associations with 70 of 72 single-omic ORS (false discovery rate adjusted p-value < 0.05). Significant associations were found in 5 of 15 longitudinal ORS with changes in trait values between COPDGene visits, including with forced expiratory volume at one second (FEV1) decline over 5 years and annually. We observed significant association with the relevant traits for all 38 cross-sectional ORS tested in SPIROMICS and for 15 of 24 in MESA. Generally, proteomic and metabolomic risk scores displayed stronger trait associations than transcriptomic risk scores, and multi-omic risk scores had higher predictive capacity than single-omic risk scores.
Conclusions: ORS constructed from blood-based omics can be leveraged to predict cross-sectional and future COPD-related traits in both smoking-enriched and general population cohorts. ORS for clinical use would require phenotype-focused risk score construction and replication.

Introduction
Through our previously developed cellSTAAR method, we demonstrated that integration of single-cell-sequencing-based epigenetic data can boost the power of gene-centric Rare Variant (RV) association tests (RVATs) to detect associations of candidate Cis-Regulatory Elements (cCREs) in complex human diseases. Integrating additional kinds of multi-omics data to capture additional sources of functional variability that exists in the non-coding genome may further increase power.
Methods
We propose omicsSTAAR as a new method to robustly integrate several kinds of multi-omics data into gene-centric RVATs of non-coding regions. First, omicsSTAAR can integrate variant- level multi-omics datasets, such as from methylation studies or eQTL summary statistics, to create custom variant sets of the most likely causal variants weighted with corresponding functional annotations. Association p-values from each variant set are aggregated using the Cauchy Combination Test to create an omnibus p-value summarizing evidence across different categories of multi-omics data. Second, omicsSTAAR can integrate gene-level multi-omics datasets, such as RNA-seq and proteomics experiments, to weight omnibus gene-centric association p-values using “side-information” approaches such as Independent Hypothesis Weighting (IHW). Using such approaches, omicsSTAAR can account for the biological relevance of each gene as measured by expression or protein abundance in relevant tissues.
Results
We applied omicsSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data of four hematological traits: hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), and white blood cell count (WBC). To demonstrate omicsSTAAR, we collected single-cell ATAC-seq data and two TOPMed blood-based datasets: RNA-seq from the WHI and FHS TOPMed cohorts (N = 2,072) and eQTL summary statistics based on 5,007 TOPMed participants. Our analysis reveals associations in several known genes for hematological traits, including HBQ1 and CD84, while showing variability in the which kinds of omics data detect each association. We also demonstrate a substantial increase in the number of discoveries at a reduced significance threshold when combining the variant-level multi-omics data (scATAC-seq and eQTL summary statistics) association results into an omnibus association p-value and when using gene-level multi-omics data (RNA-seq) to weight the gene-centric omnibus p-values.

Background: The identification of novel blood-based biomarkers of small vessel disease of the brain (SVD) may improve pathophysiologic understanding and inform the development of new therapeutic strategies for prevention. We evaluated plasma proteomic associations of white matter fractional anisotropy (WMFA), white matter hyperintensity (WMH) volume, enlarged perivascular space (ePVS) volume, and the presence of microbleeds (MB) on brain magnetic resonance imaging (MRI) in the population-based Multi-Ethnic Study of Atherosclerosis (MESA).

Methods: Eligible MESA participants underwent measurement of 2941 plasma proteins with the antibody-based Olink proteomics platform from blood samples collected in 2016-2018 and completed brain MRI scans in 2018-2019. Participants with quality control exclusion of protein measurements, missing covariate data, and poor quality or missing MRI outcome variables were excluded. The cross-sectional association between the abundance of each plasma protein (normalized protein expression – a relative protein quantification unit measured on a log2 scale) was modeled separately with WMFA, WMH volume, total ePVS volume, and the presence of MBs using multivariable linear or modified Poisson regression, adjusting for demographic variables, estimated glomerular filtration rate (eGFR), and SVD risk factors. For proteins independently associated with the SVD markers on MRI, penalized regression with least absolute shrinkage and selection operator (LASSO) was used to create a parsimonious proteomic model. The Benjamini-Hochberg procedure was used to control the false discovery rate <0.05 to account for multiple hypothesis testing.

Results: Eligible participants (total N=709) had a mean age of 73 years, 53% were women, 25% were Black, 17% were Chinese, 19% were Hispanic or Latino, and 39% were White (Table 1). After adjustment for demographics, eGFR, and SVD risk factors, 769 plasma proteins were associated with WMFA (Figure). LASSO regression identified a 37-protein model predictive of WMFA (Table 2). We did not find plasma proteins to be independently associated with WMH volume, ePVS volume, or the presence of MBs.

Conclusion: Multiple circulating proteins – implicated in central nervous system myelination, lipid metabolism, angiogenesis, coagulation, cellular adhesion and migration, appetite regulation, energy homeostasis, systemic inflammation, and immune regulation – were independently associated with WMFA in a multi-ethnic cohort of older adults.

Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analysis using TOPMed whole genome sequencing-derived genotype data and RNA sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate (FDR) of 5%, we identified 4,798,604 significant eQTL-gene pairs, covering 16,538 unique genes; and 5,921,368 sQTL-gene-cluster pairs, covering 9,605 unique genes. About 31% of detected eQTL and sQTL variants with a minor allele frequency (MAF) > 1% in JHS were rare (MAF < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. We also generated 17,630 eQTL credible sets and 24,525 sQTL credible sets for genes (gene-clusters) with lead QTL p < 5e-8. Finally, we created an open database, which is freely available online (http://jhsqtl.genetics.unc.edu/), allowing fast query and bulk download of our QTL results.