Charting the Molecular Terrain of Exercise: The Power of Multi-Omic Mapping.

Physical activity plays a fundamental role in human health and disease. Exercise has been shown to improve a wide variety of disease states, and the scientific community is committed to understanding the precise molecular mechanisms that underlie the exquisite benefits. This review provides an overview of molecular responses to acute exercise and chronic training, particularly energy mobilization and generation, structural adaptation, inflammation, and immune regulation. Further it offers a detailed discussion on known molecular signals and systemic regulators activated during various forms of exercise and their role in orchestrating health benefits. Critically, the increasing use of multi-omic technologies is explored with an emphasis on how multi-omic and multi-tissue studies contribute to a more profound understanding of exercise biology. These data inform anticipated future advancement in the field and highlight the prospect of integrating exercise with pharmacology for personalized disease prevention and treatment.

Omics-driven investigation of the biology underlying intrinsic submaximal working capacity and its trainability.

Submaximal exercise capacity is an indicator of cardiorespiratory fitness with clinical and public health implications. Submaximal exercise capacity and its response to exercise programs are characterized by heritability levels of about 40%. Using physical working capacity (power output) at a heart rate of 150 beats/min (PWC150) as an indicator of submaximal exercise capacity in subjects of the HERITAGE Family Study, we have undertaken multi-omics and in silico explorations of the underlying biology of PWC150 and its response to 20 wk of endurance training. Our goal was to illuminate the biological processes and identify panels of genes associated with human variability in intrinsic PWC150 (iPWC150) and its trainability (dPWC150). Our bioinformatics approach was based on a combination of genome-wide association, skeletal muscle gene expression, and plasma proteomics and metabolomics experiments. Genes, proteins, and metabolites showing significant associations with iPWC150 or dPWC150 were further queried for the enrichment of biological pathways. We compared genotype-phenotype associations of emerging candidate genes with reported functional consequences of gene knockouts in mouse models. We investigated the associations between DNA variants and multiple muscle and cardiovascular phenotypes measured in HERITAGE subjects. Two panels of prioritized genes of biological relevance to iPWC150 (13 genes) and dPWC150 (6 genes) were identified, supporting the hypothesis that genes and pathways associated with iPWC150 are different from those underlying dPWC150. Finally, the functions of these genes and pathways suggested that human variation in submaximal exercise capacity is mainly driven by skeletal muscle morphology and metabolism and red blood cell oxygen-carrying capacity.NEW & NOTEWORTHY Multi-omics and in silico explorations of the genes and underlying biology of submaximal exercise capacity and its response to 20 wk of endurance training were undertaken. Prioritized genes were identified: 13 genes for variation in submaximal exercise capacity in the sedentary state and 5 genes for the response level to endurance training, with no overlap between them. Genes and pathways associated with submaximal exercise capacity in the sedentary state are different from those underlying trainability.

Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease.

BACKGROUND: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. METHODS: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). RESULTS: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, ß=0.61±0.05, P=3.27×10-30) and MMP-3 (ß=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, ß=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. CONCLUSIONS: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing.

BACKGROUND: In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology. METHODS AND RESULTS: In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations. CONCLUSION: The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.

Phenomapping for novel classification of heart failure with preserved ejection fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories. METHODS AND RESULTS: We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). CONCLUSIONS: Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

Archeological Echocardiography: Digitization and Speckle Tracking Analysis of Archival Echocardiograms in the HyperGEN Study.

BACKGROUND: Several large epidemiologic studies and clinical trials have included echocardiography, but images were stored in analog format and these studies predated tissue Doppler imaging (TDI) and speckle tracking echocardiography (STE). We hypothesized that digitization of analog echocardiograms, with subsequent quantification of cardiac mechanics using STE, is feasible, reproducible, accurate, and produces clinically valid results. METHODS: In the NHLBI HyperGEN study (N = 2234), archived analog echocardiograms were digitized and subsequently analyzed using STE to obtain tissue velocities/strain. Echocardiograms were assigned quality scores and inter-/intra-observer agreement was calculated. Accuracy was evaluated in: (1) a separate second study (N = 50) comparing prospective digital strain versus post hoc analog-to-digital strain, and (2) in a third study (N = 95) comparing prospectively obtained TDI e' velocities with post hoc STE e' velocities. Finally, we replicated previously known associations between tissue velocities/strain, conventional echocardiographic measurements, and clinical data. RESULTS: Of the 2234 HyperGEN echocardiograms, 2150 (96.2%) underwent successful digitization and STE analysis. Inter/intra-observer agreement was high for all STE parameters, especially longitudinal strain (LS). In accuracy studies, LS performed best when comparing post hoc STE to prospective digital STE for strain analysis. STE-derived e' velocities correlated with, but systematically underestimated, TDI e' velocity. Several known associations between clinical variables and cardiac mechanics were replicated in HyperGEN. We also found a novel independent inverse association between fasting glucose and LS (adjusted ß = -2.4 [95% CI -3.6, -1.2]% per 1-SD increase in fasting glucose; P < 0.001). CONCLUSIONS: Archeological echocardiography, the digitization and speckle tracking analysis of archival echocardiograms, is feasible and generates indices of cardiac mechanics similar to contemporary studies.

Association of low-grade albuminuria with adverse cardiac mechanics: findings from the hypertension genetic epidemiology network (HyperGEN) study.

BACKGROUND: Albuminuria is a marker of endothelial dysfunction and has been associated with adverse cardiovascular outcomes. The reasons for this association are unclear but may be attributable to the relationship between endothelial dysfunction and intrinsic myocardial dysfunction. METHODS AND RESULTS: In the Hypertension Genetic Epidemiology Network (HyperGEN) Study, a population- and family-based study of hypertension, we examined the relationship between urine albumin-to-creatinine ratio (UACR) and cardiac mechanics (n=1894, all of whom had normal left ventricular ejection fraction and wall motion). We performed speckle-tracking echocardiographic analysis to quantify global longitudinal, circumferential, and radial strain, and early diastolic (e') tissue velocities. We used E/e' ratio as a marker of increased left ventricular filling pressures. We used multivariable-adjusted linear mixed effect models to determine independent associations between UACR and cardiac mechanics. The mean age was 50±14 years, 59% were female, and 46% were black. Comorbidities were increasingly prevalent among higher UACR quartiles. Albuminuria was associated with global longitudinal strain, global circumferential strain, global radial strain, e' velocity, and E/e' ratio on unadjusted analyses. After adjustment for covariates, UACR was independently associated with lower absolute global longitudinal strain (multivariable-adjusted mean global longitudinal strain [95% confidence interval] for UACR Quartile 1 = 15.3 [15.0-15.5]% versus UACR Q4 = 14.6 [14.3-14.9]%, P for trend <0.001) and increased E/e' ratio (Q1 = 25.3 [23.5-27.1] versus Q4 = 29.0 [27.0-31.0], P=0.003). The association between UACR and global longitudinal strain was present even in participants with UACR < 30 mg/g (P<0.001 after multivariable adjustment). CONCLUSIONS: Albuminuria, even at low levels, is associated with adverse cardiac mechanics and higher E/e' ratio.

Phenotypic spectrum of heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome, with several underlying etiologic and pathophysiologic factors. The heterogeneity of the HFpEF syndrome may explain why (1) diagnosing and treating HFpEF is so challenging and (2) clinical trials in HFpEF have failed thus far. Here we describe 4 ways of categorizing HFpEF based on pathophysiology, clinical/etiologic subtype, type of clinical presentation, and quantitative phenomics (phenomapping analysis). Regardless of the classification method used, improved phenotypic characterization of HFpEF, and matching targeted therapies with specific HFpEF subtypes, will be a critical step towards improving outcomes in this increasingly prevalent syndrome.

Immediate vs. multistage revascularization of non-infarct coronary artery(-ies) in patients with hemodynamically stable multivessel disease acute myocardial infarction: a systematic review and meta-analysis.

BACKGROUND: Untreated multivessel disease (MVD) in acute myocardial infarction (AMI) has been linked to a higher risk of recurrent ischemia and death within one year . Current guidelines recommend percutaneous coronary intervention (PCI) for significant non-infarct artery (-ies) (non-IRA) stenosis in hemodynamically stable AMI patients with MVD, either during or after successful primary PCI, within 45-days. However, deciding the timing of revascularization for non-IRA in cases of MVD is uncertain. METHODS: This meta-analysis was performed based on PRISMA guidelines after registering in PROSPERO (CRD42023472652). Databases were searched for relevant articles published before 10 November 2023. Pertinent data from the included studies were extracted and analyzed using RevMan v5.4. RESULTS: Out of 640 studies evaluated, there were 13 RCTs with 5144 patients with AMI with MVD. The immediate non-IRA PCI is associated with a significantly lower occurrence of unplanned ischemia-driven PCI (OR 0.60; confidence interval [CI] 0.44-0.83) and target-vessel revascularization (OR 0.72; CI 0.53-0.97) . Although there is a favorable trend for major adverse cardiovascular and cerebrovascular events (MACCE), nonfatal AMI, cerebrovascular events, and major bleeding in the immediate non-culprit artery (-ies) PCI, those were statistically non-significant. Similarly, all-cause mortality, cardiovascular mortality, stent thrombosis, and acute renal insufficiency did not show significant differences between two groups. CONCLUSION: Among hemodynamically stable patients with multivessel AMI, the immediate PCI strategy was superior to the multistage PCI strategy for the unplanned ischemia-driven PCI and target-vessel revascularization while odds are favorable in terms of MACCE, nonfatal AMI, cerebrovascular events, and major bleeding at longest follow-up.

Transcarotid versus trans-axillary/subclavian transcatheter aortic valve replacement (TAVR): A systematic review and meta-analysis.

BACKGROUND: Transcatheter Aortic Valve Replacement (TAVR) is the treatment of choice in patients with severe aortic stenosis. Transcarotid (TCa) or Trans-axillary/subclavian (TAx/Sc) are safer and less invasive non-femoral approaches, where transfemoral access is difficult or impossible to obtain. METHODS: This meta-analysis was performed based on PRISMA guidelines after registering in PROSPERO (CRD42023482842). This meta-analysis was performed to compare the safety of the transcarotid and trans-axillary/subclavian approach for TAVR including studies from inception to October 2023. RESULTS: Seven studies with a total of 6227 patients were included in the analysis (TCa: 2566; TAx/Sc: 3661). Transcarotid TAVR approach had a favorable trend for composite of stroke and all-cause mortality (OR 0.79, CI 0.60-1.04), all-cause mortality, stroke, major vascular complication, and new requirement of permanent pacemaker though those were statistically insignificant. On sub-analysis of the results of the studies based on the territory (USA vs French), composite outcome of all cause mortality, stroke and major bleeding (OR 0.54, CI 0.54-0.81), composite of stroke and all cause mortality (OR 0.64, CI 0.50-0.81), and stroke/TIA (OR 0.53, CI 0.39-0.73) showed lower odds of occurrence among patient managed with TCa approach in the American cohort. CONCLUSION: Overall, transcarotid approach had favorable though statistically insignificant odds for composite (stroke and all-cause mortality) and individual outcomes (stroke, all-cause mortality, etc.). There are significant variations in observed outcomes based on study's geographic location. Large prospective randomized clinical trials comparing the two approaches with representative samples are necessary to guide the clinicians in choosing among these approaches.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans.

BACKGROUND: Most genome wide association studies (GWAS) of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry enriched protein quantitative loci (pQTL). METHODS: We conducted a discovery GWAS of ~3,000 plasma proteins measured by the antibody based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS), and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs were further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome wide association study (PheWAS) across two large multi-ethnic electronic health record (EHR) systems in All of Us and BioMe. RESULTS: We identified 1002 pQTLs for 925 proteins. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for Cathepsin L (CTSL) and Siglec-9 that were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, white blood cell count, and multiple sclerosis. CONCLUSIONS: Our findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.

Plasma proteomic changes in response to exercise training are associated with cardiorespiratory fitness adaptations.

Regular exercise leads to widespread salutary effects, and there is increasing recognition that exercise-stimulated circulating proteins can impart health benefits. Despite this, limited data exist regarding the plasma proteomic changes that occur in response to regular exercise. Here, we perform large-scale plasma proteomic profiling in 654 healthy human study participants before and after a supervised, 20-week endurance exercise training intervention. We identify hundreds of circulating proteins that are modulated, many of which are known to be secreted. We highlight proteins involved in angiogenesis, iron homeostasis, and the extracellular matrix, many of which are novel, including training-induced increases in fibroblast activation protein (FAP), a membrane-bound and circulating protein relevant in body-composition homeostasis. We relate protein changes to training-induced maximal oxygen uptake adaptations and validate our top findings in an external exercise cohort. Furthermore, we show that FAP is positively associated with survival in 3 separate, population-based cohorts.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma.

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events.

There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93-2.77, p < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 (p = 1.5 × 10-4). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality (p < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.

Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods.

High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans.

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals.

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.