Skip main navigation

Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults

Originally publishedhttps://doi.org/10.1161/CIRCGEN.120.003191Circulation: Genomic and Precision Medicine. 2021;14:e003191

Abstract

Background:

Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand the underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a White cohort.

Methods:

We measured 1305 plasma proteins using the SomaScan platform in 1772 Black participants (mean age, 56 years; 62% women) in JHS (Jackson Heart Study) with LV mass assessed by 2-dimensional echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in White participants from FHS (Framingham Heart Study; mean age, 54 years; 56% women).

Results:

In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04; P=2×10−8; hazard ratio, 1.48; P=0.0001). The strongest association with LV mass was novel: LKHA4 (leukotriene-A4 hydrolase; β=0.05; P=5×10−15). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) showed a novel association with incident HF (hazard ratio, 1.32; P=0.0002). While established biomarkers such as cystatin C and NT-proBNP showed consistent associations in Black and White individuals, LKHA4 and fractalkine were significantly different between the two groups.

Conclusions:

We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.

Introduction

Left ventricular hypertrophy (LVH) represents a major worldwide disease burden: it complicates 18% to 41% of the estimated 65 million cases of hypertension.1 Despite the fact that hypertension is a notable LVH risk factor, LVH represents an independent mortality risk beyond blood pressure alone, raising mortality risk 6-fold to 8-fold, even in the presence of a normal ejection fraction.2,3 LVH has a diverse range of risk factors in addition to hypertension including age, weight, diabetes, obesity, hyperlipidemia, and smoking. Race is also associated with risk; Black individuals in particular experience an increased burden of LVH. Findings from HyperGEN (Hypertension Genetic Epidemiology Network) and the Dallas Heart Study suggest that the prevalence of LVH is at least 2-fold higher in Black individuals compared with White individuals.4

Specific cardiovascular outcomes are similarly heterogeneous. Namely, LVH is an independent risk factor for myocardial ischemia, stroke, heart failure (HF), and sudden cardiac death. However, not all individuals with LVH experience all of these complications.5 Furthermore, the mechanisms by which LVH progresses to HF remain unclear. Treatment response is also diverse; while it has been shown that antihypertensive treatment can reduce LVH over time, and that this reduction improves mortality, in the LIFE trial (Losartan Intervention for Endpoint Reduction in Hypertension), 23% of participants still had LVH after 5 years.6 The heterogeneity of outcomes and treatment effects likely stems from complex interplay between environmental and genetic effects, particularly given the fact that the pathophysiologic mechanisms of LVH involve multiple molecular pathways at the level of the myocytes, kidneys, and the sympathetic nervous system.7–9

Given the heterogeneity of risks, outcomes, and treatment effects, particularly when comparing Black and White individuals, it is essential that we explore the underlying pathophysiologic processes of LVH in more depth specifically in a Black population. Proteomic screening provides a way to interrogate multiple biologic pathways simultaneously. Recent advances in aptamer-based proteomic technologies enable high-throughput profiling of low-abundance analytes in large epidemiological cohorts.10–12 While some proteomic screens have been performed in mouse and rat models of LVH and in White human populations,13–16 there is a paucity of data describing the associations between the human proteome and LVH in Black populations, despite their increased burden of disease.

We, therefore, undertook a proteomic screen of JHS (Jackson Heart Study)—a large population-based cohort comprised of Black participants from the Jackson, MS community to better understand the pathophysiology of LVH and incident HF. We then compared our findings to those from FHS (Framingham Heart Study) to test for potential differences. Finally, we leveraged genetic and metabolomic data from JHS to corroborate these findings.

Methods

Data Availability

All proteomics data are available through dbGaP: JHS accession phs000964/phs002256.v1.p1 and FHS accession phs000007.v31.p12, requiring access request. JHS metabolomics data have been submitted to the JHS Data Coordinating Center and to dbGaP; until posted in dbGaP, all JHS data are available from the JHS Data Coordinating Center on request.

Study Approval

The human study protocols were approved by the Institutional Review Boards of the Beth Israel Deaconess Medical Center, Boston University Medical Center, and University of Mississippi Medical Center, and all participants provided written informed consent.

Full Methods are available as a Data Supplement.

Results

Baseline Characteristics in JHS

A total of 2145 individuals from JHS had proteomic profiling (Figure 1). These individuals were clinically similar to JHS as a whole (n=5306; Table I in the Data Supplement). Of this group with profiling, 1772 had echocardiography. After indexing to height, 301 (17%) had LVH by the American Society of Echocardiography criteria (Table 1). Individuals with LVH were more likely to be older, women, hypertensive, overweight, have diabetes, have a history consistent with coronary heart disease, and have lower estimated glomerular filtration rate. Ejection fraction and low-density lipoprotein cholesterol levels were similar.

Table 1. Cohort Characteristics by LVH Status and Incident HF Status

Characteristic*JHSFHS
No LVH (n=1471)LVH (n=301)No HF (n=1448)HF (n=152)No LVH (n=925)LVH (n=280)No HF (n=1437)HF (n=160)
Age, y55 (12)62 (11)55 (12)65 (11)53 (10)57 (10)54 (10)63 (8)
Women, n868 (59%)238 (79%)900 (62%)101 (66%)492 (53%)185 (66%)774 (54%)71 (44%)
Hypertension, n876 (60%)256 (85%)866 (60%)123 (81%)237 (26%)138 (49%)456 (32%)106 (66%)
Systolic blood pressure, mm Hg126 (17)135 (21)126 (18)134 (19)123 (18)132 (20)125 (18)140 (20)
Hypertensive medication, n730 (50%)239 (79%)731 (50%)112 (74%)126 (14%)70 (25%)239 (17%)75 (47%)
Diabetes, n275 (19%)93 (31%)246 (17%)66 (43%)33 (3.6%)27 (9.6%)75 (5.2%)46 (29%)
Hemoglobin A1c, %6.0 (1.2)6.3 (1.5)5.9 (1.2)6.5 (1.7)5.3 (0.8)5.6 (1.0)5.4 (0.9)6.3 (1.7)
Body mass index, kg/m231 (7)35 (8)32 (7)33 (8)26 (4)29 (5)27 (5)30 (6)
Estimated glomerular filtration rate, mL/min per 1.73 m284 (19)78 (23)85 (18)74 (24)91 (19)87 (22)90 (19)81 (23)
LDL cholesterol, mg/dL127 (37)126 (37)127 (37)129 (42)125 (33)127 (34)126 (33)128 (35)
History of CAD, n74 (5.0%)37 (12%)57 (3.9%)15 (9.9%)13 (1.4%)11 (3.9%)18 (1.3%)17 (11%)
Ejection fraction, %62 (7)63 (10)62 (7)62 (8)67 (7)67 (9)67 (7)62 (13)
LV mass index, g/m2.732.9 (6.0)54.2 (9.9)35.4 (9.5)42.0 (12.1)36.1 (6.2)51.9 (6.7)39.3 (8.8)44.8 (10.5)
Current smoker, n183 (12%)37 (12%)160 (11%)24 (16%)165 (18%)50 (18%)286 (20%)25 (16%)

CAD indicates coronary artery disease; FHS, Framingham Heart Study; HF, heart failure; JHS, Jackson Heart Study; LDL, low-density lipoprotein; LV, left ventricle; and LVH, left ventricular hypertrophy.

* Statistics presented: mean (SD) and n (%).

Figure 1.

Figure 1. Flowchart of analytical approach to proteomic data. A subset of participants in each study had proteomic profiling. Individuals with a complete complement of clinical data and available echocardiography were included in models of left ventricular (LV) mass. Individuals with a complete complement of clinical data, free of heart failure (HF) at baseline, and able to be followed for incident HF were included in models of incident HF. FHS indicates Framingham Heart Study; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; and JHS, Jackson Heart Study. *As samples from Framingham were run on 2 separate platforms, some proteins are available in fewer individuals: n=694 for some proteins in LV mass analyses and n=891 (72 cases) for some proteins in incident HF analyses.

Of 1600 individuals with proteomic profiling, clinical covariates, and without prevalent HF at baseline, 152 (10%) were hospitalized for incident HF during a median of 11 years of follow-up. The pattern of comorbidity burden in the HF group was similar to that of the LVH group (Table 1).

Protein Associations With Left Ventricular Mass

We used linear models to assess the association between 1305 proteins and left ventricular (LV) mass. In an age- and sex-adjusted model, 381 proteins were associated with LV mass with false discovery rate (FDR) <5% (Table II in the Data Supplement). After adjustment for age, sex, body mass index, estimated glomerular filtration rate, systolic blood pressure, presence of hypertension, presence of diabetes, total/high-density lipoprotein cholesterol, history of myocardial infarction, and current smoking status, 110 proteins were associated with LV mass with FDR <5% (Figure 2; Table III in the Data Supplement). As expected, NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04 [0.02–0.05]; P=2×10−8) was strongly associated with LV mass. This association held true among obese individuals (data not shown). Many of the protein-LVH associations were novel, including LKHA4 (leukotriene-A4 hydrolase; change in ln[LV mass] per SD change in protein level, β=0.05 [0.03–0.06]; P=5×10−15) and PRKACA (cAMP-dependent protein kinase catalytic subunit alpha; β=0.03 [0.02–0.04]; P=6×10−8). Proteins with the strongest inverse association with LV mass included carbonic anhydrase (β=−0.03 [−0.04 to −0.02]; P=8×10−8) and 6-phosphogluconate dehydrogenase (β=−0.03 [−0.04 to −0.02]; P=4×10−7). Modeling LV mass indexed to height2.7 did not significantly affect the results (Table IV in the Data Supplement).

Figure 2.

Figure 2. Volcano plot of proteins associated with left ventricular mass in the Jackson Heart Study. β-Estimates and their P are shown for 1305 proteins evaluated by multivariable adjusted linear models predicting ln(left ventricular mass). AMPM2 indicates methionine aminopeptidase 2; Apo B, apolipoprotein B; ASGR1, asialoglycoprotein receptor 1; BGN, biglycan; BNP, B-type natriuretic peptide; BPI, bactericidal permeability-increasing protein; DAN, neuroblastoma suppressor of tumorigenicity 1; FDR, false discovery rate; HMG-1, high mobility group protein B1; HSP 70, heat shock 70 kDa protein 1A; iC3b, complement C3b, inactivated; LKHA4, leukotriene A-4 hydrolase; LSAMP, limbic system-associated membrane protein; NS, nonsignificant; PGCB, brevican core protein; PPID, peptidyl-prolyl cis-trans isomerase D; PRKACA, cyclic adenosine monophosphate(cAMP)-dependent protein kinase catalytic subunit alpha; RAN, guanosine triphosphate(GTP)-binding nuclear protein Ran; S100A6, protein S100-A6; SCF sR, mast/stem cell growth factor receptor Kit; SLAF5, SLAM family member 5; TF, tissue factor; TFF1, trefoil factor 1; and TNFSF15, tumor necrosis factor ligand superfamily member 15.

Protein Associations With Incident HF

The association between protein levels and incident HF was assessed using Cox-proportional hazards models. In an age- and sex-adjusted model, 111 proteins were associated with incident HF with FDR <5%. In a multivariable adjusted model (same covariates as above, as well as interim myocardial infarction), 13 proteins were associated with FDR <5% (Figure 3; Tables V and VI in the Data Supplement). These included well-described biomarkers such as cystatin C (hazard ratio [HR], 1.66 [1.35–2.04]; P=1×10−6) and NT-proBNP (HR, 1.48 [1.22–1.81]; P=0.0001). Cardiotrophin-1, angiopoeitin-2, troponin I, and troponin T were all associated with incident HF as well, as expected, though at levels of significance below the stated threshold in adjusted models (P=0.001, P=0.001, P=0.004, P=0.006, respectively). Growth hormone receptor (HR, 0.68 [0.56–0.83]; P=0.0001) and galectin-3 (HR, 0.74 [0.62–0.87]; P=0.0004) showed negative associations with incident HF. More novel findings included fractalkine (HR, 1.32 [1.14–1.53]; P=0.0002). Only NT-proBNP, galectin-3, cystatin C, and β2 microglobulin had an association with both LV mass and incident HF in adjusted models at an FDR <5%.

Figure 3.

Figure 3. Volcano plot of proteins associated with incident heart failure (HF) in the Jackson Heart Study. Hazard ratios for incident HF and their P for 1305 proteins are shown for multivariable adjusted Cox-proportional hazard models. ApoM indicates apolipoprotein M; ATS13, A disintegrin and metalloproteinase with thrombospondin motifs 13; BNP, B-type natriuretic peptide; CD109, CD109 antigen; CLM6, CMRF35-like molecule 6; CRDL1, chordin-like protein 1; FDR, false discovery rate; GAS1, growth arrest-specific protein 1; G-CSF, granulocyte colony-stimulating factor; GFRa-2, glial cell line-derived neurotrophic factor family receptor alpha-2; HCC-1, C-C motif chemokine 14; IGFBP-2, insulin-like growth factor-binding protein 2; IL-15 Ra, interleukin-15 receptor subunit alpha; IL-1F6, interleukin-36 alpha; JAM-B, junctional adhesion molecule B; LRRK2, leucine-rich repeat serine/threonine-protein kinase 2; MIP-5, macrophage Inflammatory protein-5/C-C motif chemokine 15; NS, nonsignificant; RELT, tumor necrosis factor receptor superfamily member 19L; TIMD3, hepatitis A virus cellular receptor 2; TNF sR-I, tumor necrosis factor receptor superfamily member 1A; UCRP, ubiquitin-like protein interferon-stimulated gene 15; and vWF, von Willebrand factor.

Comparing Protein Associations Between JHS and FHS

To understand differences in protein associations between Black and White cohorts, we compared the effect sizes of those proteins associated with LV mass or incident HF in JHS (at FDR <5% in multivariable adjusted models) to FHS. In the subset of FHS patients that could be evaluated for incident HF (n=1597; Figure 1), subjects had notably less hypertension and hypertension treatment, less diabetes, and less obesity but overall more coronary heart disease (Table 1). Over a median of 21 years, 160 patients developed HF.

For proteins associated with LV mass in JHS, the majority showed directionally consistent associations in FHS (Figure I in the Data Supplement). Figure 4 shows proteins associated with LV mass in JHS with a Bonferroni-adjusted difference in effect size when compared with FHS. LKHA4, alanine aminotransferase 1, HSP70 (heat shock 70 kDa protein 1A), cysteine and glycine-rich protein 3, and neurexophilin-1 were associated with increased LV mass and had effect sizes that differed from FHS at a Bonferroni-adjusted level of significance. Serine/threonine protein kinase 17B, MMP7 (matrilysin), matrix MMP9 (metalloproteinase-9), bactericidal/permeability-increasing protein, peptidyl-prolyl cis-trans isomerase D, and high mobility group protein B1 also had significant differences from FHS but were inversely associated with LV mass. For comparison, renin and NT-proBNP showed no significant differences: renin was inversely associated with LV mass in both cohorts, whereas NT-proBNP was directly associated with LV mass.

Figure 4.

Figure 4. Proteins that associated with left ventricular mass in multivariable adjusted models in JHS (Jackson Heart Study) at a false discovery rate <5% are compared with FHS (Framingham Heart Study). Any proteins below Bonferroni-adjusted P for difference are shown above, as well as renin and NT-proBNP (N-terminal pro-B-type natriuretic peptide), which did not differ between cohorts, for comparison. *Proteins available only on the 1.3K SomaScan platform, which was not run in batch 1 of FHS; therefore, n=694 rather than n=1205.

Given the significant differences, we leveraged data from an alternate, antibody-based proteomics platform to support these findings. Of the 11 proteins that differed from FHS, 5 are measured by the Olink platform and were available in a subset of 458 individuals from JHS. Despite reduced power (due to one-quarter the samples), LKHA4, MMP7, and HSP70 showed significant and consistent associations with LV mass while MMP9 was directionally consistent; neurexophilin-1’s association was inconsistent with the aptamer-based data (Table VII in the Data Supplement).

Fewer differences between the cohorts were seen for proteins associated with incident HF (Figure 5). Growth arrest-specific protein 1 and fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) levels were associated with a higher risk of incident HF in JHS compared with FHS, while growth hormone receptor was associated with a lower risk of incident HF in JHS compared with FHS. However, only growth arrest-specific protein 1 met the Bonferroni-adjusted significance threshold. These relationships were not evaluated using Olink given the smaller sample size and reduced HF events, limiting power.

Figure 5.

Figure 5. Proteins that associated with incident heart failure in multivariable adjusted models in JHS (Jackson Heart Study) at a false discovery rate <5% are compared with FHS (Framingham Heart Study) and shown above. BNP indicates B-type natriuretic peptide; and ISG15, interferon-stimulated gene 15. *Proteins available only on the 1.3K SomaScan platform, which was not run in batch 1; therefore, n=891 with cases=72 rather than n=1597 with cases=160.

HF With Preserved Ejection Fraction Versus Reduced Ejection Fraction

We compared HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), to explore proteins more strongly associated with specific subtypes of HF. Of the 1836 individuals without prevalent HF in whom HF status could be followed, there were 78 cases of HFrEF and 88 cases of HFpEF (Figure 1). Fourteen cases of HF had an indeterminate type and were excluded. In competing risk analysis, 324 proteins were associated with at least 1 subtype of HF at P<0.05 (Table VIII in the Data Supplement). When compared, 41 proteins showed a difference in risk estimate between HFpEF and HFrEF at FDR <10% (Figure 6). Hexokinase-2 levels were associated with a decreased risk of HFpEF but with increased risk of HFrEF (HR for HFrEF, 1.22 [1.07–1.38] per SD change in protein level; P=0.003; HR for HFpEF, 0.80 [0.67–0.95]; P=0.01; P for difference, 6×10−4). Alternatively, killer cell immunoglobulin-like receptor 3DS1 showed the opposite pattern of association (HR for HFpEF, 1.24 [1.07–1.43]; P=0.004; HR for HFrEF, 0.73 [0.56–0.96]; P=0.02; P for difference, 8×10−4). Proteins including cystatin C and NT-proBNP were associated with increased risk for both types of HF and, therefore, were not significantly different between the two groups (Figure 6).

Figure 6.

Figure 6. Proteins that predicted either heart failure with preserved ejection fraction (HFpEF) or heart failure with reduced ejection fraction (HFrEF) in age, sex, and batch adjusted competing risk models (number of aptamers, 324) were then compared with one another. Displayed are proteins whose competing risk estimate significantly differed between the two subtypes at a false discovery rate <10%, as well as cystatin C and NT-proBNP (N-terminal pro-B-type natriuretic peptide), which do not differ between subtypes, for comparison. BNP indicates B-type natriuretic peptide; ISG15, interferon-stimulated gene 15; FLRT1, fibronectin leucine rich transmembrane protein 1; SUMO, small ubiquitin-like modifier; CD5, cluster of differentiation 5; NKG2D, natural killer group 2D; SPARC, secreted protein acidic and cysteine rich.

Associations Between Arachidonic Acid Oxidation Products and Cardiac Phenotypes in JHS

LKHA4 catalyzes the conversion of leukotriene A4 to leukotriene B4 in a reaction which lies downstream of the conversion of arachidonic acid to multiple signaling molecules. Using available metabolomics profiling in JHS, we identified 6 metabolites downstream of arachidonic acid metabolism. While leukotriene A4 and B4 could not be measured due to low abundance, multiple eicosanoids were measured. We determined each metabolite’s partial correlation with LKHA4 after adjustment for age and sex, as well as their association with both LV mass and incident HF after adjustment for all of the previously noted clinical covariates. With the exception of Arachidonic acid, all metabolites were significantly correlated with LKHA4. Much like LKHA4, each metabolite was directly associated with LV mass, but none were associated with incident HF (Table 2). The strongest association was that of the mono-HETEs, which, during profiling, group together as a single peak (Pearson correlation with LKHA4, 0.27; P=1×10−23; β for association with ln(LV mass), 0.03; P=5×10−5; HR for incident HF, 1.17; P=0.20).

Table 2. Arachidonic Acid Oxidation Products and Their Associations With LKHA4, LV Mass, and Incident HF

MetabolitePearson correlation with LKHA4 (95% CI)P valueβ for association with ln(LV mass) (95% CI)P valueHR for incident HF (95% CI)P value
Arachidonic acid0.02 (−0.03 to 0.07)0.440.02 (0.01 to 0.04)5.0×10−40.98 (0.76 to 1.26)0.89
5-Oxo-ETE0.18 (0.13 to 0.23)6.4×10−110.02 (0.00 to 0.03)0.021.07 (0.82 to 1.39)0.62
12-Oxo-ETE0.20 (0.15 to 0.25)2.4×10−130.02 (0.01 to 0.03)0.0061.11 (0.85 to 1.45)0.44
15-Oxo-ETE0.21 (0.16 to 0.26)1.3×10−140.02 (0.01 to 0.03)0.0031.14 (0.87 to 1.49)0.35
14(15)-EET0.26 (0.20 to 0.30)4.2×10−210.02 (0.00 to 0.03)0.011.15 (0.89 to 1.49)0.29
11(12)-EET0.27 (0.22 to 0.32)1.8×10−230.02 (0.01 to 0.03)0.0051.15 (0.89 to 1.48)0.28
HETE0.27 (0.22 to 0.32)1.3×10−230.03 (0.01 to 0.04)4.6×10−51.17 (0.92 to 1.49)0.20

EET indicates epoxyeicosatrienoic acid; ETE, eicosatetraenoic acid; HF heart failure; HR, hazard ratio; LKHA4, leukotriene-A4 hydrolase; and LV, left ventricle.

Discussion

Both LVH and incident HF are associated with a myriad of upstream risk factors and downstream complications. Increased risk is associated with race,4 though the combined impact of social/structural, environmental, and genetic factors on pathobiology remains unclear.17 While proteomic profiling of HF has been performed in White populations,15,16,18 similar work is lacking in Black populations. Comparative profiling is critical to our understanding of the disease and its underlying pathways. To this end, our study utilizes the largest proteomic profiling of a Black population both in number of subjects and number of proteins profiled, 1305 proteins in 2145 individuals, and compares it to an equally large and well profiled White population. These individuals had high-quality echocardiographic phenotyping and rigorous adjudication of HF events, as well as detailed profiling of their comorbidities allowing for statistical adjustment in these heterogeneous disease states. Taken together, these results can form the basis for deeper exploration into the biology of LVH and incident HF.

We observed 110 proteins to be associated with LV mass, even after adjustment for multiple comorbidities and multiple comparisons, and 13 proteins associated with incident HF. Validating the platform, NT-proBNP was strongly associated with increased LV mass, as well as incident HF. Two other well-described renal markers showed a similar pattern, cystatin C and β2 microglobulin. These markers are known to be associated with LV mass in patients with chronic kidney disease,19–21 though the association has not previously been shown in community dwelling adults. By contrast, cystatin C is well known to be associated with incident HF and HF prognosis.22 Similar data for a relationship between β2 microglobulin and incident HF is weaker, though it does appear to be elevated once HF has developed.23,24 Interestingly, galectin-3—a known biomarker in HF and renal disease—showed an inverse association with LV mass and incident HF in both JHS and FHS, contrary to existing literature, which include data from FHS.25,26 This suggests the measurement of galectin-3 by the aptamer may differ from that of the ELISA. The aptamer’s specificity for galectin-3 is supported by genetic and mass spectroscopy data (Table IX in the Data Supplement); however, there are 4 known splice variants of galectin-3, so the specific variant detected by the aptamer may differ from ELISA. If signaling in HF is related to an increase in one variant relative to another, this may explain the result. Further investigation is certainly warranted.

Several proteins showed a strong association with LV mass but not with incident HF. The strongest association between any protein and LV mass was LKHA4. Specifically, this association was unique to the Black population. LKHA4 hydrolyzes leukotriene A4 to leukotriene B4, which exerts proinflammatory effects via the G-protein–coupled receptor BLT1 on leukocytes. This pathway has been implicated in vascular inflammation, and LKHA4 activity has been targeted in drug trials for atherosclerotic disease.27,28 These drugs proved ineffective, possibly due to concomitant inhibition of the enzyme’s anti-inflammatory capabilities.28 Despite a strong association with LV mass, it is not clear why LKHA4 was not associated with incident HF, nor a specific subtype of HF in our analyses. However, previous work shows an association with prevalent HF in a small cohort that included ≈25% Black participants.29 To explore the relationship more deeply, we utilized available profiling of metabolites downstream of arachidonic acid. Leukotriene A4 and leukotriene B4 are such metabolites but are low abundance and not well measured in the blood by liquid chromatography and mass spectrometry. However, arachidonic acid (a precursor to leukotriene A4) and other oxidation products of arachidonic acid are available, including mono-HETEs, epoxyeicosatrienoic acids, and oxoeicosatetraenoic acids. Interestingly, these oxidation products were both correlated with LKHA4 levels and associated with LV mass, but not associated with incident HF, a similar pattern to LKHA4 itself. Furthermore, phospholipase A2, which produces arachidonic acid, was not associated with either LV mass or incident HF after adjustment for clinical covariates. These data highlight pathways downstream of arachidonic acid as areas for further investigation rather than arachidonic acid metabolism as a whole. It may be that LKHA4 and related pathways are responsive to these disease states, rather than causative. Detailed profiling of the eicosanoid lipidome is of critical importance in future studies and, given the observed data, should ideally be performed in diverse populations.

Conversely, fractalkine/CX3CL1 was found to be associated with incident HF but not LV mass—an association that was notably stronger in JHS compared with FHS. Fractalkine is observed in both a membrane-bound and soluble form and is involved in chemoattraction and binding of peripheral inflammatory cells.30 It has been implicated in atherosclerosis, ischemia-reperfusion injury, and cardiomyopathy.30–32 Our data suggest that it has an independent association with incident HF hospitalization in an ambulatory Black population. The fact that many proteins associated with either LV mass or incident HF but not both fits with data suggesting the pathobiology of each may differ: drugs that improve LVH have not been shown to have benefit in HFpEF.33,34

Comparing subtypes of HF (HFpEF versus HFrEF), while exploratory given samples sizes, highlighted a substantial difference between the proteins associated with the HF syndrome as a whole. Those proteins associated most strongly with incident HF overall did not show a predilection for either type, highlighting the syndromic similarities between each subtype. Conversely, the proteins with significant difference between the two subtypes highlight the varying pathobiology between the two phenotypes. Hexokinase 2 and GDF (growth differentiation factor) 11 both showed a stronger association with HFrEF compared with HFpEF. Hexokinase 2 is known to attenuate cardiac hypertrophy when overexpressed in mice by modulating flux through the pentose phosphate pathway, which in turn modulates the reactive oxygen species, which contribute to hypertrophy.35 Similarly, GDF11, which has significant homology to GDF8/myostatin, is thought to modulate cardiac muscle mass. Recent data from experimental studies in mice suggest that GDF8/11 levels decline with age, and injection of GDF11 was able to reduce cardiac mass.36 Our data correlate with these findings, as levels of these proteins appeared to be protective of HFpEF. However, we also show the levels of these proteins associated with a risk of HFrEF—a consideration for any therapeutic targeting of these pathways. The fact that these proteins did not associate with LV mass in our data suggest that their role in humans may have less of an effect on actual cardiac mass and a greater effect on myocardial function.

Limitations

There are several important limitations of our work. While the proteomic profiling discussed here is the most extensive in any Black cohort to date, it does not contain the entire proteome, and important protein associations may be missed as a result. LV mass was estimated by echocardiographic measurements, which are less accurate than newer methods such as cardiac magnetic resonance imaging. However, one would expect this to bias results to the null. In JHS, HF hospitalizations were not adjudicated until 2005, so there is a chance incident HF hospitalizations were not captured (in the interval between baseline and 2005). While 152 cases of incident HF is of acceptable size, we lacked power for anything more than an exploratory analysis of HFpEF and HFrEF. The mean follow-up time required to achieve comparable numbers of HF events between JHS and FHS is significant (11 versus 21 years), which could influence the difference in protein associations, though age adjustment should account for much of that influence. These event rates illustrate the greater risk in Black individuals. Our results cannot yet be externally validated owing to the unique nature of such extensive profiling in a cohort of Black individuals. However, the numerous positive controls, evidence of aptamer specificity, and multiomic associations we observed lend significant support to the validity of the platform and analytic approach.

Conclusions

Our results highlight several biological pathways hypothesized to contribute to the pathophysiologic cascade of LVH and incident HF. Elements of endovascular dysfunction and inflammation—manifested in these data by associations between LKHA4 and LV mass, as well as fractalkine and incident HF—may precede overt disease. Further studies are needed to validate these results and elucidate the detailed underlying mechanisms.

Nonstandard Abbreviations and Acronyms

FDR

false discovery rate

FHS

Framingham Heart Study

GDF

growth differentiation factor

HF

heart failure

HFpEF

heart failure with preserved ejection fraction

HFrEF

heart failure with reduced ejection fraction

HR

hazard ratio

HSP70

heat shock 70 kDa protein 1A

HyperGEN

Hypertension Genetic Epidemiology Network

JHS

Jackson Heart Study

LIFE

Losartan Intervention for Endpoint Reduction in Hypertension

LKHA4

leukotriene-A4 hydrolase

LV

left ventricle

LVH

left ventricular hypertrophy

MMP7

matrilysin

MMP9

metalloproteinase-9

NT-proBNP

N-terminal pro-B-type natriuretic peptide

PRKACA

cAMP-dependent protein kinase catalytic subunit alpha

Acknowledgments

We wish to thank the staffs and participants of the JHS (Jackson Heart Study). The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the US Department of Health and Human Services.

Supplemental Materials

Expanded Methods

Supplemental Tables I–IX

Supplemental Figure I

References 37–55

Disclosures None.

Footnotes

*D.H. Katz and U.A. Tahir contributed equally.

The Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.120.003191.

For Sources of Funding and Disclosures, see page 357.

Correspondence to: Robert E. Gerszten, MD, Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, 185 Pilgrim Rd, Baker 408, Boston, MA 02215. Email

References

  • 1. Cuspidi C, Rescaldani M, Sala C, Grassi G. Left-ventricular hypertrophy and obesity: a systematic review and meta-analysis of echocardiographic studies.J Hypertens. 2014; 32:16–25. doi: 10.1097/HJH.0b013e328364fb58CrossrefMedlineGoogle Scholar
  • 2. Messerli FH, Ketelhut R. Left ventricular hypertrophy: an independent risk factor.J Cardiovasc Pharmacol. 1991; 17(suppl 4):S59–S66.CrossrefMedlineGoogle Scholar
  • 3. Milani RV, Lavie CJ, Mehra MR, Ventura HO, Kurtz JD, Messerli FH. Left ventricular geometry and survival in patients with normal left ventricular ejection fraction.Am J Cardiol. 2006; 97:959–963. doi: 10.1016/j.amjcard.2005.10.030CrossrefMedlineGoogle Scholar
  • 4. Kamath S, Markham D, Drazner MH. Increased prevalence of concentric left ventricular hypertrophy in African-Americans: will an epidemic of heart failure follow?Heart Fail Rev. 2006; 11:271–277. doi: 10.1007/s10741-006-0228-8CrossrefMedlineGoogle Scholar
  • 5. Artham SM, Lavie CJ, Milani RV, Patel DA, Verma A, Ventura HO. Clinical impact of left ventricular hypertrophy and implications for regression.Prog Cardiovasc Dis. 2009; 52:153–167. doi: 10.1016/j.pcad.2009.05.002CrossrefMedlineGoogle Scholar
  • 6. Devereux RB, Wachtell K, Gerdts E, Boman K, Nieminen MS, Papademetriou V, Rokkedal J, Harris K, Aurup P, Dahlöf B. Prognostic significance of left ventricular mass change during treatment of hypertension.JAMA. 2004; 292:2350–2356. doi: 10.1001/jama.292.19.2350CrossrefMedlineGoogle Scholar
  • 7. Greenwood JP, Scott EM, Stoker JB, Mary DA. Hypertensive left ventricular hypertrophy: relation to peripheral sympathetic drive.J Am Coll Cardiol. 2001; 38:1711–1717. doi: 10.1016/s0735-1097(01)01600-xCrossrefMedlineGoogle Scholar
  • 8. Harrap SB, Dominiczak AF, Fraser R, Lever AF, Morton JJ, Foy CJ, Watt GC. Plasma angiotensin II, predisposition to hypertension, and left ventricular size in healthy young adults.Circulation. 1996; 93:1148–1154. doi: 10.1161/01.cir.93.6.1148LinkGoogle Scholar
  • 9. Takefuji M, Wirth A, Lukasova M, Takefuji S, Boettger T, Braun T, Althoff T, Offermanns S, Wettschureck N. G(13)-mediated signaling pathway is required for pressure overload-induced cardiac remodeling and heart failure.Circulation. 2012; 126:1972–1982. doi: 10.1161/CIRCULATIONAHA.112.109256LinkGoogle Scholar
  • 10. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, et al. Genomic atlas of the human plasma proteome.Nature. 2018; 558:73–79. doi: 10.1038/s41586-018-0175-2CrossrefMedlineGoogle Scholar
  • 11. Ngo D, Sinha S, Shen D, Kuhn EW, Keyes MJ, Shi X, Benson MD, O’Sullivan JF, Keshishian H, Farrell LA, et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease.Circulation. 2016; 134:270–285. doi: 10.1161/CIRCULATIONAHA.116.021803LinkGoogle Scholar
  • 12. Emilsson V, Ilkov M, Lamb JR, Finkel N, Gudmundsson EF, Pitts R, Hoover H, Gudmundsdottir V, Horman SR, Aspelund T, et al. Co-regulatory networks of human serum proteins link genetics to disease.Science. 2018; 361:769–773. doi: 10.1126/science.aaq1327CrossrefMedlineGoogle Scholar
  • 13. Guan D, Zhao Y, Zhang Y, Tang D, Wu Q. Proteomics analysis revealed an altered left ventricle protein profile in a mouse model of transverse aortic constriction.Protein Pept Lett. 2016; 23:125–131. doi: 10.2174/0929866523666151125231747CrossrefMedlineGoogle Scholar
  • 14. Prévilon M, Le Gall M, Chafey P, Federeci C, Pezet M, Clary G, Broussard C, François G, Mercadier JJ, Rouet-Benzineb P. Comparative differential proteomic profiles of nonfailing and failing hearts after in vivo thoracic aortic constriction in mice overexpressing FKBP12.6.Physiol Rep. 2013; 1:e00039. doi: 10.1002/phy2.39CrossrefMedlineGoogle Scholar
  • 15. Egerstedt A, Berntsson J, Smith ML, Gidlöf O, Nilsson R, Benson M, Wells QS, Celik S, Lejonberg C, Farrell L, et al. Profiling of the plasma proteome across different stages of human heart failure.Nat Commun. 2019; 10:5830. doi: 10.1038/s41467-019-13306-yCrossrefMedlineGoogle Scholar
  • 16. Nayor M, Short MI, Rasheed H, Lin H, Jonasson C, Yang Q, Hveem K, Felix JF, Morrison AC, Wild PS, et al; CHARGE-Heart Failure Working Group; CHARGE-EchoGen Consortium. Aptamer-based proteomic platform identifies novel protein predictors of incident heart failure and echocardiographic traits.Circ Heart Fail. 2020; 13:e006749. doi: 10.1161/CIRCHEARTFAILURE.119.006749LinkGoogle Scholar
  • 17. Deere B, Griswold M, Lirette S, Fox E, Sims M. Life course socioeconomic position and subclinical disease: The Jackson Heart Study.Ethn Dis. 2016; 26:355–362. doi: 10.18865/ed.26.3.355CrossrefMedlineGoogle Scholar
  • 18. Ferreira JP, Verdonschot J, Collier T, Wang P, Pizard A, Bär C, Björkman J, Boccanelli A, Butler J, Clark A, et al. Proteomic bioprofiles and mechanistic pathways of progression to heart failure.Circ Heart Fail. 2019; 12:e005897. doi: 10.1161/CIRCHEARTFAILURE.118.005897LinkGoogle Scholar
  • 19. Sakuragi S, Ichikawa K, Yamada K, Tanimoto M, Miki T, Otsuka H, Yamamoto K, Kawamoto K, Katayama Y, Tanakaya M, et al. Serum cystatin C level is associated with left atrial enlargement, left ventricular hypertrophy and impaired left ventricular relaxation in patients with stage 2 or 3 chronic kidney disease.Int J Cardiol. 2015; 190:287–292. doi: 10.1016/j.ijcard.2015.04.189CrossrefMedlineGoogle Scholar
  • 20. Masuda M, Ishimura E, Ochi A, Tsujimoto Y, Tahahra H, Okuno S, Tabata T, Nishizawa Y, Inaba M. Serum β2-microglobulin correlates positively with left ventricular hypertrophy in long-term hemodialysis patients.Nephron Clin Pract. 2014; 128:101–106. doi: 10.1159/000365447CrossrefMedlineGoogle Scholar
  • 21. Yilmaz A, Yilmaz B, Küçükseymen S. ß-2 microglobulin level is negatively associated with global left ventricular longitudinal peak systolic strain and left atrial volume index in patients with chronic kidney disease not on dialysis.Anatol J Cardiol. 2016; 16:844–849. doi: 10.14744/AnatolJCardiol.2015.6691MedlineGoogle Scholar
  • 22. Chow SL, Maisel AS, Anand I, Bozkurt B, de Boer RA, Felker GM, Fonarow GC, Greenberg B, Januzzi JL, Kiernan MS, et al; American Heart Association Clinical Pharmacology Committee of the Council on Clinical Cardiology; Council on Basic Cardiovascular Sciences; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Epidemiology and Prevention; Council on Functional Genomics and Translational Biology; and Council on Quality of Care and Outcomes Research. Role of biomarkers for the prevention, assessment, and management of heart failure: a scientific statement From the American Heart Association.Circulation. 2017; 135:e1054–e1091. doi: 10.1161/CIR.0000000000000490LinkGoogle Scholar
  • 23. Matsushita K, Ballew SH, Coresh J. Cardiovascular risk prediction in people with chronic kidney disease.Curr Opin Nephrol Hypertens. 2016; 25:518–523. doi: 10.1097/MNH.0000000000000265CrossrefMedlineGoogle Scholar
  • 24. Vianello A, Caponi L, Galetta F, Franzoni F, Taddei M, Rossi M, Pietrini P, Santoro G. β2-Microglobulin and TIMP1 Are Linked Together in Cardiorenal Remodeling and Failure.Cardiorenal Med. 2015; 5:1–11. doi: 10.1159/000369260CrossrefMedlineGoogle Scholar
  • 25. Ho JE, Liu C, Lyass A, Courchesne P, Pencina MJ, Vasan RS, Larson MG, Levy D. Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community.J Am Coll Cardiol. 2012; 60:1249–1256. doi: 10.1016/j.jacc.2012.04.053CrossrefMedlineGoogle Scholar
  • 26. Yao Y, Shen D, Chen R, Ying C, Wang C, Guo J, Zhang G. Galectin-3 predicts left ventricular remodeling of hypertension.J Clin Hypertens (Greenwich). 2016; 18:506–511. doi: 10.1111/jch.12757CrossrefMedlineGoogle Scholar
  • 27. Kristo F, Hardy GJ, Anderson TJ, Sinha S, Ahluwalia N, Lin AY, Passeri J, Scherrer-Crosbie M, Gerszten RE. Pharmacological inhibition of BLT1 diminishes early abdominal aneurysm formation.Atherosclerosis. 2010; 210:107–113. doi: 10.1016/j.atherosclerosis.2009.11.031CrossrefMedlineGoogle Scholar
  • 28. Low CM, Akthar S, Patel DF, Löser S, Wong CT, Jackson PL, Blalock JE, Hare SA, Lloyd CM, Snelgrove RJ. The development of novel LTA4H modulators to selectively target LTB4 generation.Sci Rep. 2017; 7:44449. doi: 10.1038/srep44449CrossrefMedlineGoogle Scholar
  • 29. Wells QS, Gupta DK, Smith JG, Collins SP, Storrow AB, Ferguson J, Smith ML, Pulley JM, Collier S, Wang X, et al. Accelerating biomarker discovery through electronic health records, automated biobanking, and proteomics.J Am Coll Cardiol. 2019; 73:2195–2205. doi: 10.1016/j.jacc.2019.01.074CrossrefMedlineGoogle Scholar
  • 30. Apostolakis S, Spandidos D. Chemokines and atherosclerosis: focus on the CX3CL1/CX3CR1 pathway.Acta Pharmacol Sin. 2013; 34:1251–1256. doi: 10.1038/aps.2013.92CrossrefMedlineGoogle Scholar
  • 31. Boag SE, Das R, Shmeleva EV, Bagnall A, Egred M, Howard N, Bennaceur K, Zaman A, Keavney B, Spyridopoulos I. T lymphocytes and fractalkine contribute to myocardial ischemia/reperfusion injury in patients.J Clin Invest. 2015; 125:3063–3076. doi: 10.1172/JCI80055CrossrefMedlineGoogle Scholar
  • 32. Escher F, Vetter R, Kühl U, Westermann D, Schultheiss HP, Tschöpe C. Fractalkine in human inflammatory cardiomyopathy.Heart. 2011; 97:733–739. doi: 10.1136/hrt.2010.205716CrossrefMedlineGoogle Scholar
  • 33. Wachtell K, Bella JN, Rokkedal J, Palmieri V, Papademetriou V, Dahlöf B, Aalto T, Gerdts E, Devereux RB. Change in diastolic left ventricular filling after one year of antihypertensive treatment: the Losartan Intervention For Endpoint Reduction in Hypertension (LIFE) study.Circulation. 2002; 105:1071–1076. doi: 10.1161/hc0902.104599LinkGoogle Scholar
  • 34. Yusuf S, Pfeffer MA, Swedberg K, Granger CB, Held P, McMurray JJ, Michelson EL, Olofsson B, Ostergren J; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and preserved left-ventricular ejection fraction: the CHARM-preserved trial.Lancet. 2003; 362:777–781. doi: 10.1016/S0140-6736(03)14285-7CrossrefMedlineGoogle Scholar
  • 35. McCommis KS, Douglas DL, Krenz M, Baines CP. Cardiac-specific hexokinase 2 overexpression attenuates hypertrophy by increasing pentose phosphate pathway flux.J Am Heart Assoc. 2013; 2:e000355. doi: 10.1161/JAHA.113.000355LinkGoogle Scholar
  • 36. Poggioli T, Vujic A, Yang P, Macias-Trevino C, Uygur A, Loffredo FS, Pancoast JR, Cho M, Goldstein J, Tandias RM, et al. Circulating growth differentiation factor 11/8 levels decline with age.Circ Res. 2016; 118:29–37. doi: 10.1161/CIRCRESAHA.115.307521LinkGoogle Scholar
  • 37. Taylor HA, Wilson JG, Jones DW, Sarpong DF, Srinivasan A, Garrison RJ, Nelson C, Wyatt SB. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.Ethn Dis. 2005; 15(4 suppl 6):S6–S4.MedlineGoogle Scholar
  • 38. Carpenter MA, Crow R, Steffes M, Rock W, Heilbraun J, Evans G, Skelton T, Jensen R, Sarpong D. Laboratory, reading center, and coordinating center data management methods in the Jackson Heart Study.Am J Med Sci. 2004; 328:131–144. doi: 10.1097/00000441-200409000-00001CrossrefMedlineGoogle Scholar
  • 39. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150:604–612. doi: 10.7326/0003-4819-150-9-200905050-00006CrossrefMedlineGoogle Scholar
  • 40. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study.Am J Epidemiol. 1979; 110:281–290. doi: 10.1093/oxfordjournals.aje.a112813CrossrefMedlineGoogle Scholar
  • 41. Candia J, Cheung F, Kotliarov Y, Fantoni G, Sellers B, Griesman T, Huang J, Stuccio S, Zingone A, Ryan BM, et al. Assessment of variability in the SOMAscan assay.Sci Rep. 2017; 7:14248. doi:10.1038/s41598-017-14755-5CrossrefMedlineGoogle Scholar
  • 42. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, Eriksson A, Rennel Dickens E, Ohlsson S, Edfeldt G, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability.PLoS One. 2014; 9:e95192. doi: 10.1371/journal.pone.0095192CrossrefMedlineGoogle Scholar
  • 43. Smith JG, Gerszten RE. Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease.Circulation. 2017; 135:1651–1664. doi: 10.1161/CIRCULATIONAHA.116.025446LinkGoogle Scholar
  • 44. Raffield LM, Zakai NA, Duan Q, Laurie C, Smith JD, Irvin MR, Doyle MF, Naik RP, Song C, Manichaikul AW, et al; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Hematology & Hemostasis TOPMed Working Group*. D-Dimer in African Americans: whole genome sequence analysis and relationship to cardiovascular disease risk in the Jackson Heart Study.Arterioscler Thromb Vasc Biol. 2017; 37:2220–2227. doi: 10.1161/ATVBAHA.117.310073LinkGoogle Scholar
  • 45. Jiang L, Zheng Z, Qi T, Kemper KE, Wray NR, Visscher PM, Yang J. A resource-efficient tool for mixed model association analysis of large-scale data.Nat Genet. 2019; 51:1749–1755. doi: 10.1038/s41588-019-0530-8CrossrefMedlineGoogle Scholar
  • 46. Berglund G, Elmstähl S, Janzon L, Larsson SA. The Malmo diet and cancer study. Design and feasibility.J Intern Med. 1993; 233:45–51. doi: 10.1111/j.1365-2796.1993.tb00647.xCrossrefMedlineGoogle Scholar
  • 47. Benson MD, Yang Q, Ngo D, Zhu Y, Shen D, Farrell LA, Sinha S, Keyes MJ, Vasan RS, Larson MG, et al. Genetic architecture of the cardiovascular risk proteome.Circulation. 2018; 137:1158–1172. doi: 10.1161/CIRCULATIONAHA.117.029536LinkGoogle Scholar
  • 48. Wilk JB, Chen TH, Gottlieb DJ, Walter RE, Nagle MW, Brandler BJ, Myers RH, Borecki IB, Silverman EK, Weiss ST, et al. A genome-wide association study of pulmonary function measures in the Framingham Heart Study.PLoS Genet. 2009; 5:e1000429. doi: 10.1371/journal.pgen.1000429CrossrefMedlineGoogle Scholar
  • 49. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.Genet Epidemiol. 2010; 34:816–834. doi: 10.1002/gepi.20533CrossrefMedlineGoogle Scholar
  • 50. Chen MH, Yang Q. GWAF: an R package for genome-wide association analyses with family data.Bioinformatics. 2010; 26:580–581. doi: 10.1093/bioinformatics/btp710CrossrefMedlineGoogle Scholar
  • 51. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, Sarwath H, Thareja G, Wahl A, DeLisle RK, et al. Connecting genetic risk to disease end points through the human blood plasma proteome.Nat Commun. 2017; 8:14357. doi: 10.1038/ncomms14357CrossrefMedlineGoogle Scholar
  • 52. Pandey A, Keshvani N, Ayers C, Correa A, Drazner MH, Lewis A, Rodriguez CJ, Hall ME, Fox ER, Mentz RJ, et al. Association of cardiac injury and malignant left ventricular hypertrophy with risk of heart failure in African Americans: The Jackson Heart Study.JAMA Cardiol. 2019; 4:51–58. doi: 10.1001/jamacardio.2018.4300CrossrefMedlineGoogle Scholar
  • 53. Marwick TH, Gillebert TC, Aurigemma G, Chirinos J, Derumeaux G, Galderisi M, Gottdiener J, Haluska B, Ofili E, Segers P, et al. Recommendations on the use of echocardiography in adult hypertension: a report from the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE).J Am Soc Echocardiogr. 2015; 28:727–754. doi: 10.1016/j.echo.2015.05.002CrossrefMedlineGoogle Scholar
  • 54. Keku E, Rosamond W, Taylor HA, Garrison R, Wyatt SB, Richard M, Jenkins B, Reeves L, Sarpong D. Cardiovascular disease event classification in the Jackson Heart Study: methods and procedures.Ethn Dis. 2005; 15(4 suppl 6):S6–62.MedlineGoogle Scholar
  • 55. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk.J Am Stat Assoc. 1999; 94:496–509.CrossrefGoogle Scholar

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.