Abstract
Background
The determinants of pulmonary artery systolic pressure (PASP) are not fully understood. It is unknown whether racial differences in PASP exist or if other population characteristics are associated with pulmonary pressure in humans. We examined echocardiographically estimated PASP in the Coronary Artery Risk Development in Young Adults (CARDIA) study, a middle‐aged, biracial community‐based cohort.
Methods and Results
At the CARDIA year‐25 examination, 3469 participants underwent echocardiography, including measurement of tricuspid regurgitant jet velocity to estimate PASP. Clinical features, laboratory values, pulmonary function tests, and measurement of adipose depot volume were analyzed for association with PASP. PASP was estimated in 1311 individuals (61% female, 51% white). Older age, higher blood pressure, and higher body mass index were associated with higher PASP. Black race was associated with higher PASP after adjustment for demographics and left and right ventricular function (β 0.94, 95% CI 0.24‐1.64; P=0.009), but this association was no longer significant after further adjustment for lung volume (β 0.42, 95% CI −0.68 to 0.96; P=0.74). Insulin resistance, inflammation (C‐reactive protein and interleukin‐6), and visceral adipose volume were independently associated with higher PASP after adjustment for relevant covariates. PASP rose with worsening diastolic function (ratio of early transmitral Doppler velocity to average mitral annular tissue Doppler velocity [E/e′] and left atrial volume index).
Conclusions
In a large biracial cohort of middle‐aged adults, we identified associations among black race, insulin resistance, and diastolic dysfunction with higher echocardiographically estimated PASP. Further studies are needed to examine racial differences in PASP and whether insulin resistance directly contributes to pulmonary vascular disease in humans.
Keywords: adipose tissue, echocardiography, inflammation, metabolic syndrome, pulmonary hypertension
Subject Categories: Metabolic Syndrome, Pulmonary Hypertension, Echocardiography, Risk Factors
Introduction
Elevated pulmonary artery systolic pressure (PASP) is associated with increased mortality in individuals with and without prevalent cardiovascular disease.1, 2, 3, 4 Prior studies have established a relationship among PASP, older age, and left ventricular function.1, 3 The largest studies to date have been either retrospective in referral populations1, 3, 5 or in racially homogeneous samples,1, 4 which may limit their generalizability. Black populations have a higher incidence of systemic hypertension, more advanced left ventricular (LV) remodeling, and lower lung volumes compared with whites.6, 7, 8, 9, 10 It is unknown if these features are associated with higher pulmonary pressure in the black population.
The impact on PASP of variables not directly related to heart and lung function has not been described. Recent evidence in humans and experimental models suggests that metabolic traits such as insulin resistance and adiposity may directly contribute to pulmonary vascular disease.11, 12, 13, 14 We aimed to address important knowledge gaps with respect to potential determinants of pulmonary artery pressure that have not been fully examined in humans.
The purpose of this study was to describe the relationship between race and cardiometabolic traits with PASP in the Coronary Artery Risk Development in Young Adults (CARDIA) study cohort, a middle‐aged, biracial community‐based population without prevalent cardiovascular disease.
Methods
Study Population
CARDIA is a prospective observational cohort study of the determinants of cardiovascular disease.15 At the baseline examination (1985‐1986), 5115 black or white men and women aged 18 to 30 years were enrolled from 4 sites in the United States. The design and study procedures for the CARDIA cohort have been described in detail.15 Participants who underwent the year‐25 (2010‐2011) echocardiogram exam (n=3469) were included in this analysis. Institutional review boards from each site approved the study, and signed informed consent was obtained from all participants.
Clinical Covariates
Standard protocols were used at each study site for collection of all exam components.15 Race and sex were self‐reported. Systolic and diastolic blood pressures were averaged from the second and third readings measured using an automated, calibrated monitor. Pulmonary function testing was performed during the year‐20 examination, as previously described.16 Obesity was defined as a body mass index (BMI) of >30 kg/m2. Physical activity was self‐reported and quantified using a validated questionnaire.15, 17 Participants were asked about the frequency of engaging in 13 different categories of exercise and scored based on the duration and frequency of activity in each category. Exercise units were calculated based on the frequency and intensity of each activity. Laboratory data (glucose, lipids, insulin, creatinine, and C‐reactive protein) were measured from fasting samples, processed centrally, and obtained on the same study visit as the echocardiogram. Interleukin‐6 assays were performed during the year‐20 examination; thus, analyses with this marker were exploratory, given the interval between measurement and PASP estimation. Prevalent diabetes was defined as a fasting glucose ≥126 mg/dL, a 2‐hour postload glucose value ≥200 mg/dL, hemoglobin A1c ≥6.5%, or the current use of antidiabetic medications. Prevalent hypertension was defined as self‐report, systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or use of antihypertensive medications. Estimated glomerular filtration rate was calculated from the chronic kidney disease epidemiology equation,18 and the homeostatic model assessment of insulin resistance (HOMA‐IR) was calculated according the formula (glucose [mg/dL]×insulin [mIU/mL])/405.19 Metabolic syndrome was defined according to the third report of the National Cholesterol Education Program.20
Echocardiography
The CARDIA year‐25 exam echocardiography protocol has been published previously8 and was performed according to American Society of Echocardiography guidelines.21, 22, 23 Transthoracic echocardiography was performed using an Artida cardiac ultrasound scanner (Toshiba Medical Systems, Otawara, Japan) by registered sonographers using a 1.8‐ to 4.2‐MHz phased‐array transducer according to a standard protocol. Experienced sonographers analyzed images offline using commercially available software (Digisonics Inc, Houston, TX). Experienced readers interpreted digitized images at a core reading center (Johns Hopkins University, Baltimore, MD). Wall motion 2‐dimensional tracking software (Toshiba Medical Systems, Otawara, Japan) was used to perform speckle‐tracking analysis to obtain longitudinal and circumferential left ventricular peak systolic strain. The tricuspid regurgitant (TR) jet velocity was measured in triplicate and averaged using continuous‐wave Doppler echocardiography. PASP was estimated from the TR jet velocity and an assumed right atrial pressure of 10 mm Hg using the modified Bernoulli equation (PASP=4[TR jet velocity]2+right atrial pressure). Our findings did not change in magnitude or significance when TR jet values were analyzed without an assumed right atrial pressure. Therefore, to provide better clinical context, we elected to report values as estimated PASP. Prior studies have made similar assumptions regarding right atrial pressure, which correlate well with invasive values.1, 3 Echocardiographic pulmonary hypertension (PH) was defined as PASP >40 mm Hg.24, 25
Computed Tomography
Returning participants at the year‐25 exam were invited to undergo computed tomography of the chest and abdomen using 64‐channel multidetector computed tomography scanners (GE Healthcare, Milwaukee, WI or Siemens, Erlangen, Germany).26 Subcutaneous, visceral, and pericardial adipose depot volumes were quantified from computed tomography images (n=3174).27 A Hounsfield unit range of −190 to −30 was used to identify adipose tissue in all depots. Pericardial adipose volume was quantified by summing the adipose‐containing pixels in the axial slices within 15 mm above and 30 mm below the superior extent of the left main coronary artery.28, 29
Statistical Analysis
Data are presented as median (interquartile range) or counts (percentage) for continuous and categorical variables, respectively. Comparisons between groups were performed using the Mann‐Whitney U test or Kruskal‐Wallis test, as appropriate. Categorical variables were compared using the chi‐squared test. Correlation between continuous variables was measured using Spearman rank correlation. Multivariable linear regression models were used to examine the associations between PASP and clinical, echocardiographic, and adiposity variables. Covariates included in multivariable models were selected a priori based on clinical relevance. For the association between black race and PASP, we first adjusted for basic demographics (age, sex, BMI) and systolic blood pressure, which differs between races. We next built on this model by adding measures of LV and right ventricular (RV) structure and function, which were previously reported to differ by race in CARDIA.8 Finally, we added forced expiratory volume in 1 second (FEV1) given prior descriptions of racial differences in lung volume and function. In the model exploring HOMA‐IR and PASP, we adjusted for basic demographics and diastolic function because of the association between diabetes and diastolic function.30 For associations with visceral adipose, we adjusted for demographics, HOMA‐IR, and C‐reactive protein (CRP) to account for the possibility that insulin resistance and inflammation may mediate the relationship between adiposity and PASP. Nonlinear relationships between PASP and cardiometabolic variables were assessed using multivariable adjusted restricted cubic splines.
Results
Clinical Characteristics
In total, 3470 individuals participated in the year‐25 echocardiography exam, of whom 1311 (38%) had a measureable TR jet, and these constituted the study cohort. No subject had severe TR or RV dysfunction. Characteristics of subjects with and without a measureable TR jet are presented in Table 1. Individuals without a measureable TR jet had more cardiovascular comorbidities and higher rates of diabetes, obesity, and metabolic syndrome. Clinical characteristics of the 1311 included study participants stratified by quartiles of PASP are shown in Table 2. Increasing PASP was associated with older age, higher systolic and diastolic blood pressure, and higher BMI. The prevalence of smoking was similar across PASP quartiles, but FEV1 and physical activity were lowest in the highest quartile of PASP (P<0.001 and P=0.02, respectively). There was no difference in the distribution of PASP between males and females (Table 2).
Table 1.
Clinical Characteristics of CARDIA Participants With and Without a Measureable TR Jet
| No TR Jet (N=2159) | TR Jet (N=1311) | P Value | |
|---|---|---|---|
| Age, y | 51 (47, 53) | 51 (47, 53) | 0.41 |
| Male | 997 (46) | 507 (51) | <0.001 |
| White | 1184 (55) | 667 (51) | 0.02 |
| BMI, kg/m2 | 29 (25, 35) | 28 (25, 33) | <0.001 |
| Hypertension | 751 (35) | 382 (29) | <0.001 |
| SBP, mm Hg | 118 (110, 129) | 117 (108, 127) | <0.001 |
| DBP, mm Hg | 75 (67, 82) | 74 (67, 81) | 0.001 |
| Diabetes mellitus | 263 (12) | 109 (8) | <0.001 |
| Obesity | 1001 (46) | 510 (39) | <0.001 |
| Hyperlipidemia | 639 (31) | 331 (26) | 0.004 |
| Metabolic syndrome | 607 (28) | 233 (18) | 0.001 |
| Glucose, mg/dL | 94 (88, 103) | 92 (86, 100) | <0.001 |
| Insulin, mIU | 9.3 (5.7, 15.2) | 8.0 (5.0, 12.8) | <0.001 |
| HOMA‐IR | 2.2 (1.3, 3.9) | 1.8 (1.1, 3.1) | <0.001 |
| Smoker, ever | 467 (22) | 277 (22) | 0.59 |
| FEV1, L | 3.0 (2.5, 3.6) | 2.9 (2.5, 3.5) | 0.12 |
| Physical activity, exercise units | 271 (124, 478) | 288 (138, 504) | 0.82 |
| eGFR, mL/(min·1.73 m2) | 94 (83, 108) | 94 (83, 108) | 0.73 |
| Interleukin‐6, ng/mL | 1.7 (1.0, 3.0) | 1.7 (1.0, 2.9) | 0.035 |
| C‐reactive protein, mg/L | 1.5 (0.6, 3.8) | 1.3 (0.6, 3.0) | <0.001 |
Data are presented as median (25th, 75th percentiles) or counts (%). BMI indicates body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FEV1, forced expiratory volume in 1 second; HOMA‐IR, homeostatic model of insulin resistance; SBP, systolic blood pressure; TR, tricuspid regurgitant.
Table 2.
Clinical Characteristics Stratified by Quartiles of Pulmonary Artery Systolic Pressure
| Pulmonary Artery Systolic Pressure, mm Hg | P Value | ||||
|---|---|---|---|---|---|
| <27 (N=328) | 27 to 31 (N=328) | 31 to 34 (N=327) | >34 (N=328) | ||
| Age, y | 50 (47, 53) | 51 (47, 53) | 50 (47, 53) | 52 (48, 53) | 0.006 |
| Male | 130 (40) | 105 (32) | 138 (42) | 134 (41) | <0.035 |
| Black | 144 (43) | 137 (42) | 160 (49) | 206 (63) | <0.001 |
| BMI, kg/m2 | 27 (24, 31) | 28 (24, 32) | 28 (25, 33) | 31 (27, 35) | <0.001 |
| Hypertension | 68 (21) | 88 (27) | 97 (30) | 129 (40) | <0.001 |
| SBP, mm Hg | 113 (105, 122) | 116 (106, 126) | 118 (107, 127) | 122 (113, 132) | <0.001 |
| DBP, mm Hg | 72 (64, 78) | 73 (66, 80) | 74 (65, 80) | 76 (70, 84) | <0.001 |
| Diabetes mellitus | 25 (8) | 19 (6) | 26 (8) | 39 (12) | <0.001 |
| Obesity | 97 (30) | 119 (36) | 119 (37) | 175 (54) | <0.001 |
| Hyperlipidemia | 82 (26) | 80 (25) | 78 (25) | 91 (29) | 0.67 |
| Metabolic syndrome | 30 (9) | 45 (14) | 49 (15) | 66 (21) | <0.001 |
| Glucose, mg/dL | 91 (87, 99) | 91 (85, 98) | 93 (87, 100) | 94 (87, 102) | 0.019 |
| Insulin, mIU | 7.5 (4.8, 11.5) | 7.6 (4.5, 12.2) | 7.5 (4.8, 12.6) | 9.7 (6.0, 14.5) | <0.001 |
| HOMA‐IR | 1.7 (1.1, 2.9) | 1.7 (1.0, 2.9) | 1.8 (1.1, 2.8) | 2.3 (1.3, 3.7) | <0.001 |
| Smoker, ever | 81 (25) | 65 (20) | 63 (20) | 68 (21) | 0.36 |
| FEV1, L | 3.1 (2.6, 3.6) | 2.9 (2.5, 3.5) | 3.0 (2.6, 3.6) | 2.8 (2.3, 3.3) | <0.001 |
| Physical activity, exercise units | 312 (147, 516) | 284 (156, 516) | 303 (136, 548) | 261 (104, 458) | <0.02 |
| eGFR, mL/(min·1.73 m2) | 95 (83, 106) | 94 (81, 107) | 94 (82, 107) | 96 (83, 108) | 0.51 |
| Interleukin‐6, ng/mL | 1.6 (0.9, 2.5) | 1.5 (1.0, 2.6) | 1.7 (1.0, 3.1) | 1.9 (1.0, 3.3) | 0.004 |
| C‐reactive protein, mg/L | 0.9 (0.5, 2.1) | 1.2 (0.5, 3.1) | 1.3 (0.6, 2.7) | 1.8 (0.7, 4.6) | <0.001 |
Data are presented as median (25th, 75th percentiles) or counts (%). BMI indicates body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FEV1, forced expiratory volume in 1 second; HOMA‐IR, homeostatic model of insulin resistance; SBP, systolic blood pressure.
Black individuals accounted for 63% of the highest PASP quartile but only 49% of the study cohort. PASP was higher among black individuals (32±6 mm Hg vs 30±5 mm Hg in whites; P<0.001) overall and across the top 3 quartiles of age (Figure 1). A total of 86 individuals met an echocardiographic definition of PH, of whom 71% were black (Table 3). In general, subjects with PH were characterized by greater cardiometabolic dysfunction, including a higher prevalence of diabetes, obesity, hypertension, and hyperlipidemia and 21% lower reported physical activity (Table 3).
Figure 1.
Comparison of pulmonary artery systolic pressure (PASP) in black and white CARDIA participants stratified by quartiles of age. PASP is significantly higher in black individuals (red bars) compared with whites (blue bars; P<0.001). This relationship was observed across each of the top 3 quartiles of age.
Table 3.
Clinical Characteristics Stratified by Presence or Absence of Echocardiographic Pulmonary Hypertension
| PASP>40 (N=86) | PASP<40 (N=1255) | P Value | |
|---|---|---|---|
| PASP, mm Hg | 42.1 (40.8, 47.2) | 30.4 (27.1, 33.7) | <0.001 |
| Age, y | 52 (48, 54) | 51 (47, 53) | 0.024 |
| Male | 32 (37) | 475 (39) | 0.77 |
| Black | 61 (71) | 583 (48) | <0.001 |
| BMI, kg/m2 | 32 (28, 37) | 28 (24, 32) | <0.001 |
| Hypertension | 44 (51) | 338 (28) | <0.001 |
| SBP, mm Hg | 124 (114, 136) | 117 (107, 127) | <0.001 |
| DBP, mm Hg | 76 (71, 87) | 73 (66, 80) | 0.001 |
| Diabetes mellitus | 54 (64) | 456 (37) | <0.001 |
| Obesity | 1010 (46) | 510 (39) | <0.001 |
| Hyperlipidemia | 32 (38) | 299 (25) | 0.01 |
| Metabolic syndrome | 23 (28) | 167 (14) | <0.001 |
| Glucose, mg/dL | 93 (88, 101) | 92 (86, 100) | 0.77 |
| Insulin, mIU | 10.08 (5.3, 15.7) | 7.8 (5.0, 12.5) | 0.076 |
| HOMA‐IR | 2.4 (1.1, 3.8) | 1.8 (1.1, 3.0) | 0.095 |
| Smoker, ever | 16 (19) | 261 (22) | 0.24 |
| FEV1, L | 2.5 (2.1, 3.1) | 3.0 (2.5, 3.5) | <0.001 |
| Physical activity, exercise units | 231 (61, 405) | 292 (144, 510) | 0.005 |
| eGFR, mL/(min·1.73 m2) | 94 (84, 109) | 94 (82, 108) | 0.89 |
| Interleukin‐6, ng/mL | 2.5 (1.2, 4.0) | 1.6 (1.0, 2.8) | 0.002 |
| C‐reactive protein, mg/L | 2.2 (0.8, 7.1) | 1.2 (0.6, 2.9) | <0.001 |
Data are presented as median (25th, 75th percentiles) or counts (%). BMI indicates body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FEV1, forced expiratory volume in 1 second; HOMA‐IR, homeostatic model of insulin resistance; PASP, pulmonary artery systolic pressure; SBP, systolic blood pressure.
Cardiac Structure and Function
Although cardiac structure and function varied according to quartiles of PASP, most associations were nonlinear (Table 4). For example, LV end‐diastolic volume index and end‐systolic volume index were higher in the second and third PASP quartiles and declined in the highest quartile. Left atrial volume index and E/e′ increased across quartiles, indicative of rising PASP with worse diastolic function. LV ejection fraction (LVEF) increased slightly with PASP, but there was no significant change in cardiac index or longitudinal or circumferential myocardial strain across quartiles. The association of PASP with RV function measurements was conflicting; we found no change in tricuspid annular plane systolic excursion (TAPSE) across quartiles but a significant increase in RV tricuspid annular tissue Doppler velocity (S′) in the highest quartile of PASP.
Table 4.
Echocardiographic Characteristics Stratified by Quartiles of PASP
| PASP, mm Hg | P Value | ||||
|---|---|---|---|---|---|
| <27 (N=328) | 27 to 31 (N=328) | 31 to 34 (N=327) | >34 (N=328) | ||
| LVEDVI, mL/m2 | 52 (46, 61) | 57 (49, 64) | 57 (49, 63) | 56 (48, 65) | <0.001 |
| LVESVI, mL/m2 | 20 (16, 25) | 22 (18, 26) | 22 (17, 26) | 20 (16, 25) | <0.001 |
| LVMI, g/m2 | 78 (68, 93) | 82 (69, 99) | 80 (68, 97) | 85 (70, 104) | 0.049 |
| LAVI, mL/m2 | 23 (19, 28) | 26 (21, 30) | 26 (21, 30) | 26 (22, 32) | <0.001 |
| e′ avg, cm/s | 10.6 (9.0, 12.0) | 10.6 (9.2, 12.3) | 10.7 (9.3, 12.1) | 10.0 (8.6, 11.7) | 0.018 |
| E velocity, cm/s | 76 (65, 86) | 78 (68, 89) | 80 (71, 91) | 83 (74, 94) | <0.001 |
| A velocity, cm/s | 60 (49, 69) | 61 (52, 72) | 62 (51, 75) | 68 (56, 80) | <0.001 |
| E/A | 1.3 (1.1, 1.5) | 1.3 (1.1, 1.5) | 1.3 (1.1, 1.5) | 1.2 (1.1, 1.5) | 0.09 |
| Decel time, ms | 178 (155, 206) | 179 (149, 206) | 178 (148, 201) | 178 (146, 205) | 0.60 |
| E/e′ | 7.1 (6.1, 8.5) | 7.5 (6.2, 8.7) | 7.6 (6.4, 9.1) | 8.3 (6.9, 10.1) | <0.001 |
| LVEF, % | 61 (57, 66) | 61 (56, 65) | 61 (57, 66) | 63 (59, 68) | <0.001 |
| CI, L/(min·m2) | 2.76 (2.32, 3.35) | 2.84 (2.37, 3.45) | 2.69 (2.30, 3.24) | 2.90 (2.35, 3.45) | 0.053 |
| GLS, % | −15.2 (−16.6, −13.6) | −15.3 (−17.0, −13.7) | −15.1 (−16.9, −13.8) | −15.2 (−16.9, −13.7) | 0.84 |
| GCS, % | −15.1 (−16.8, −13.4) | −15.7 (−17.4, −13.6) | −15.2 (−17.1, −13.6) | −15.7 (−17.8, −13.5) | 0.23 |
| TAPSE, cm | 2.5 (2.1, 2.8) | 2.6 (2.2, 2.9) | 2.5 (2.2, 2.9) | 2.6 (2.3, 2.9) | 0.37 |
| RV S′, cm/s | 12.9 (11.6, 14.3) | 13.0 (11.7, 14.6) | 13.3 (12.0, 15.1) | 13.7 (12.4, 15.9) | <0.001 |
Data presented as median (25th, 75th percentiles). A indicates transmitral spectral Doppler late velocity; CI, cardiac index; Decel time, transmitral spectral E‐wave deceleration time; E, transmitral spectral Doppler early velocity; e′ avg, average mitral annulus tissue Doppler early relaxation velocity; GCS, global circumferential systolic strain; GLS, global longitudinal systolic strain; LAVI, left atrial volume index; LVEDVI, left ventricular end‐diastolic volume index; LVEF, left ventricular ejection fraction; LVESVI, left ventricular end‐systolic volume index; LVMI, left ventricular mass index; PASP, Pulmonary Artery Systolic Pressure; S′, tricuspid annular tissue Doppler velocity; TAPSE, tricuspid annular plane systolic excursion.
Determinants of PASP
Univariate and age‐ and sex‐adjusted associations between clinical features and PASP are presented in Table 5. Multivariable models were created to specifically examine associations of PASP with race, metabolic features, and adiposity.
Table 5.
Univariate and Adjusted Associations of Clinical Features With PASP
| Variable | β (95%CI) | P Value | β Adjusted for Age and Sex (95%CI) | P Value |
|---|---|---|---|---|
| Age, y | 0.78 (0.25–1.32) | 0.004 | 0.79 (0.26‐1.32) | 0.004 |
| Male | 0.39 (−0.28 to 1.06) | 0.25 | 0.79 (0.26‐1.32) | 0.004 |
| Black race | 1.84 (1.20‐2.49) | <0.001 | 1.12 (0.58‐1.65) | <0.001 |
| BMI, kg/m2 | 1.46 (1.06‐1.85) | <0.001 | 0.81 (0.29‐1.33) | 0.002 |
| Hypertension | 2.07 (1.36‐2.78) | <0.001 | 0.69 (0.16‐1.23) | 0.01 |
| SBP, mm Hg | 1.38 (0.99‐1.77) | <0.001 | 0.68 (0.15‐1.21) | 0.01 |
| DBP, mm Hg | 1.21 (0.80‐1.61) | <0.001 | 0.82 (0.29‐1.35) | 0.002 |
| Diabetes mellitus | 1.74 (0.55‐2.92) | 0.004 | 0.75 (0.22‐1.29) | 0.006 |
| Obesity | 1.99 (1.33‐2.65) | <0.001 | 0.81 (0.28‐1.34) | 0.003 |
| Hyperlipidemia | 0.56 (−0.20 to 1.32) | 0.15 | 0.68 (0.13‐1.23) | 0.02 |
| Metabolic syndrome | 1.96 (1.07‐2.86) | <0.001 | 0.71 (0.20‐1.23) | 0.007 |
| Glucose, mg/dL | −0.03 (−0.21 to 0.16) | 0.78 | 0.74 (0.21‐1.27) | 0.006 |
| Insulin, mIU | 0.61 (0.24‐0.98) | 0.001 | 0.76 (0.22‐1.29) | 0.005 |
| HOMA‐IR | 0.28 (−0.01 to 0.57) | 0.06 | 0.73 (0.20‐1.27) | 0.007 |
| Smoker, ever | −0.48 (−1.28 to 0.33) | 0.25 | 0.82 (0.28‐1.37) | 0.003 |
| FEV1, L | −1.10 (−1.55 to −0.64) | <0.001 | 0.60 (0.04‐1.17) | 0.035 |
| Physical activity, exercise units | −0.46 (−0.88 to −0.04) | 0.03 | 0.82 (0.29‐1.35) | 0.003 |
| eGFR, mL/(min·1.73 m2) | −0.11 (−0.50 to 0.28) | 0.59 | 0.74 (0.20‐1.28) | 0.007 |
| Interleukin‐6, ng/mL | 0.45 (0.21‐0.69) | <0.001 | 0.85 (0.28‐1.42) | 0.003 |
| C‐reactive protein, mg/L | 0.41 (0.25‐0.57) | <0.001 | 0.78 (0.26‐1.31) | 0.004 |
Beta coefficients (β) for continuous variables represent the effect of moving from a value at the 25th percentile to the 75th percentile. BMI indicates body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FEV1, forced vital capacity in 1 second; HOMA‐IR, homeostatic model of insulin resistance; PASP, pulmonary artery systolic pressure; SPB, systolic blood pressure.
Black race was significantly associated with PASP after adjustment for age, sex, BMI, and systolic blood pressure (Table 6, Model 1; β 1.22, 95%CI 0.53‐1.91; P<0.001). This relationship was attenuated but remained significant after LVEF, LA volume index, E/e′, and RV S′ were added to the model (Table 6, Model 2; β 0.94, 95%CI 0.24‐1.64; P=0.009). The association between black race and PASP was no longer significant after addition of FEV1 to the adjusted model (Table 6, Model 3; β 0.42, 95%CI −0.68 to 0.96; P=0.74). We found no significant interaction between race and BMI or race and sex for the association with PASP (P>0.05 for all).
Table 6.
Multivariate Associations of Black Race and PASP
| β (95%CI) | P Value | |
|---|---|---|
| Model 1: age, sex, BMI, systolic blood pressure | ||
| Black race | 1.22 (0.53‐1.91) | <0.001 |
| Age, y | 0.91 (0.38‐1.43) | <0.001 |
| Male | 0.55 (−0.11 to 1.21) | 0.10 |
| BMI, kg/m2 | 1.09 (0.69‐1.50) | <0.001 |
| SBP, mm Hg | 0.91 (0.50‐1.31) | <0.001 |
| Model 2: age, sex, BMI, SBP, LVEF, LA volume index, RV S′ | ||
| Black race | 0.94 (0.24‐1.64) | 0.009 |
| Age, y | 0.91 (0.38‐1.44 | <0.001 |
| Male | 0.59 (−0.08 to 1.26) | 0.08 |
| BMI, kg/m2 | 0.79 (0.37‐1.21) | <0.001 |
| SBP, mm Hg | 0.72 (0.29‐1.14) | <0.001 |
| LVEF, % | 0.18 (−0.21 to 0.58) | 0.36 |
| LAVI, mL/m2 | 0.96 (0.55‐1.36) | <0.001 |
| E/e′ | 0.57 (0.16‐0.99) | 0.007 |
| RV S′, cm/s | 0.99 (0.61‐1.37) | <0.001 |
| Model 3: age, sex, BMI, SBP, LVEF, LA volume index, RV S′, FEV1 | ||
| Black race | 0.42 (−0.68 to 0.96) | 0.74 |
| Age, y | 0.65 (0.07‐1.22) | 0.03 |
| Male | 1.89 (0.95‐2.82) | <0.001 |
| BMI, kg/m2 | 0.81 (0.35‐1.27) | <0.001 |
| SBP, mm Hg | 0.65 (0.19‐1.10) | 0.006 |
| LVEF, % | 0.23 (−0.20 to 0.66) | 0.29 |
| LAVI, mL/m2 | 0.90 (0.47‐1.34) | <0.001 |
| E/e′ | 0.46 (0.02‐0.96) | 0.04 |
| RV S′, cm/s | 1.09 (0.69‐1.50) | <0.001 |
| FEV1, L | Nonlinear | 0.02 |
Beta coefficients (β) for continuous variables represent the effect of moving from a value at the 25th percentile to the 75th percentile. BMI indicates body mass index; E, transmitral spectral Doppler early velocity; e′ avg, average mitral annulus tissue Doppler early relaxation velocity; FEV1, forced vital capacity in 1 second; LAVI, left atrial volume index; LVEF, left ventricular ejection fraction; PASP, pulmonary artery systolic pressure; RV S′, RV basal tissue Doppler myocardial velocity during systole; SPB, systolic blood pressure.
Metabolism and Inflammation
The prevalence of diabetes and obesity increased across quartiles of PASP (Table 1). HOMA‐IR increased across PASP quartiles and appeared to be driven by increased insulin levels, as fasting glucose was similar across groups. PASP increased with the number of components of the metabolic syndrome (P<0.001; Figure 2). The inflammation markers IL‐6 and CRP increased with PASP, with CRP nearly doubling between the highest and lowest quartiles. The relationship between HOMA‐IR and PASP was nonlinear when adjusted for age, sex, BMI, LA volume index, E/E′, and race (Table 7 and Figure 3; P=0.002). We observed an inflection point of rising PASP at HOMA‐IR values consistent with the development of insulin resistance. PASP appeared to decline at higher HOMA‐IR values; however, this is difficult to interpret given the wide confidence intervals. The association between CRP and increased PASP also remained significant, albeit modestly, after adjustment for age, sex, BMI, and race (β 0.09 per 1‐unit increase, 95%CI 0.02‐0.16; P=0.01). Models for these analyses are presented in Table 7.
Figure 2.
Pulmonary artery systolic pressure (PASP) according to number of prevalent components of the metabolic syndrome. CARDIA participants were categorized according to the number of metabolic syndrome components present. In general, we observed a stepwise increase in PASP as the number of metabolic syndrome components increased (P<0.001).
Table 7.
Multivariate Associations of HOMA‐IR and CRP With PASP
| β (95% CI) | P Value | |
|---|---|---|
| HOMA‐IR model | ||
| HOMA‐IR | Nonlinear | 0.002 |
| Age, y | 0.87 (0.36‐1.40) | <0.001 |
| Male | 0.83 (0.18‐1.48) | 0.01 |
| BMI, kg/m2 | 0.93 (0.46‐1.41) | <0.001 |
| LAVI, mL/m2 | 0.92 (0.52‐1.32) | <0.001 |
| E/E′ | 0.83 (0.44‐1.22) | <0.001 |
| Black race | 1.21 (0.55‐1.87) | <0.001 |
| CRP model | ||
| CRP, mg/L | 0.21 (0.04‐0.39) | 0.014 |
| Age, y | 1.00 (0.47‐1.52) | <0.001 |
| Male | 0.87 (0.21‐1.53) | 0.009 |
| BMI, kg/m2 | 1.03 (0.61‐1.46) | <0.001 |
| Black race | 1.45 (0.78‐2.12) | <0.001 |
Beta coefficients (β) for continuous variables represent the effect of moving from a value at the 25th percentile to the 75th percentile. BMI indicates body mass index; CRP, C‐reactive protein; E, transmitral spectral Doppler early velocity; HOMA‐IR, homeostatic index of insulin resistance; LAVI, left atrial volume index; PASP, pulmonary artery systolic pressure.
Figure 3.
Multivariable adjusted association between homeostatic model of insulin resistance (HOMA‐IR) and pulmonary artery systolic pressure (PASP). When adjusted for age, sex, and BMI, the relationship between HOMA‐IR and PASP in the CARDIA cohort was nonlinear. We observed an inflection point of increasing PASP at a HOMA‐IR of ~2. Subsequent decline in PASP at high values of HOMA‐IR may reflect reduced sample size at these values as suggested by the widening confidence interval.
Adiposity
Adipose volume quantification was performed in 91% (1195/1311) of participants with a measurable TR jet. Higher PASP was associated with greater subcutaneous, visceral, and pericardial adipose volume (Table 8; P<0.001 for all), although the correlation between PASP and each depot was modest (rs=0.12‐0.17; P<0.001 for all). The impact of visceral adiposity (the most metabolically active depot) on PASP was independent of age, sex, BMI, HOMA‐IR, and CRP (Table 9; β 0.01 per cm3, 95%CI 0.003‐0.02; P=0.009). Of note, black race remained significantly associated with PASP in models adjusting for insulin resistance, CRP, and visceral adipose volume.
Table 8.
Measures of Adiposity Stratified by Quartiles of PASP
| PASP, mm Hg | P Value | ||||
|---|---|---|---|---|---|
| <27 (N=328) | 27 to 31 (N=328) | 31 to 34 (N=327) | >34 (N=328) | ||
| Total abdominal adipose, cm3 | 392 (287, 501) | 418 (288, 589) | 423 (314, 589) | 507 (373, 672) | <0.001 |
| Subcutaneous adipose, cm3 | 260 (186, 357) | 281 (191, 412) | 288 (204, 414) | 343 (231, 484) | <0.001 |
| Visceral adipose, cm3 | 95 (62, 145) | 102 (64, 158) | 108 (74, 153) | 124 (89, 183) | <0.001 |
| Pericardial adipose, cm3 | 42 (29, 60) | 44 (30, 62) | 41 (28, 62) | 52 (35, 75) | <0.001 |
Data are presented as median (25th, 75th percentiles). PASP indicates pulmonary artery systolic pressure.
Table 9.
Multivariate Associations of Visceral Adiposity With PASP
| β (95%CI) | P Value | |
|---|---|---|
| Visceral adipose, cm3 | 1.32 (0.71‐1.93) | <0.001 |
| Age, y | 0.92 (0.36‐1.48) | 0.001 |
| Black race | 2.23 (1.49‐2.96) | <0.001 |
| Male | 0.46 (−0.28 to 1.21) | 0.22 |
| HOMA‐IR | Nonlinear | 0.01 |
| CRP, mg/L | 0.71 (−0.002 to 1.41) | 0.03 |
Beta coefficients (β) for continuous variables represent the effect of moving from a value at the 25th percentile to the 75th percentile. CRP indicates C‐reactive protein; HOMA‐IR, homeostatic index of insulin resistance; PASP, pulmonary artery systolic pressure.
Discussion
We examined the clinical, physiologic, and biochemical correlates of PASP in 1311 participants in the CARDIA study, a large biracial cohort of middle‐aged individuals. In addition to confirming previously published relationships with age and LV function, we report newly recognized associations of higher PASP with black race and metabolic traits. Plasma markers of insulin resistance and inflammation and adipose volume were also associated with PASP.
Context With Existing Literature
Prior studies have examined the clinical and echocardiographic correlates of PASP.1, 3, 5 In a large hospital‐based referral cohort with normal echocardiograms, McQuillan et al found that higher age, male sex, BMI, and LVEF were associated with higher PASP.3 Lam et al and Armstrong et al report similar findings with respect to age and BMI but did not find a difference in PASP between males and females,1, 5 as in CARDIA. Similar PASP between males and females does not appear to be related to the disproportionate number of females in CARDIA (61%), which is similar to those in other cohorts (57% to 67%).1, 4, 5 As in other population‐based studies, we found that LV diastolic function was a significant contributor to PASP.1, 5
The CARDIA cohort builds on prior studies in this area with several strengths. The CARDIA study is prospective, and echocardiogram interpretation was performed at a central core lab. No data have been published on PASP from a prospective cohort of middle‐aged individuals. Prior reports have largely involved retrospective cohorts with clinical interpretations. The CARDIA cohort is 51% black, whereas race was not reported in prior reports or is assumed to be almost exclusively white or black. Finally, prior studies were limited to demographic and echocardiographic data. In contrast, the CARDIA study includes detailed information about comorbid conditions, plasma biomarkers, and adipose volume measurements.
Determinants of PASP
To our knowledge, this is the first large study to examine racial differences in PASP in a community‐based population. Choudhary et al demonstrated that elevated PASP is associated with an increased risk of hospitalization for heart failure in the Jackson Heart Study, but this cohort does not include white individuals.4 Ristow et al examined pulmonary pressure in the Heart and Soul Study, but racial differences were not compared directly, and only 105 black individuals were studied.31 Khush et al found a similar TR gradient but higher PA diastolic pressure among black individuals (n=87 compared with 273 white) with coronary artery disease in the Heart and Soul Study.32 In the CARDIA cohort, which is not selected for existing cardiovascular disease, black race was associated with higher PASP. This relationship did not appear to be mediated by BMI, prevalent hypertension, metabolic features, or LV/RV function. Rather, the relationship between race and PASP appeared to be most strongly influenced by lower FEV1 in black individuals. This finding is clinically relevant because even modest increases in PASP are associated with higher cardiovascular risk.33 Data from the Multiethnic Study of Atherosclerosis suggest that there may be racial differences in RV adaptation to cardiopulmonary disease.34 These findings warrant further investigation into racial differences in the development of pulmonary vascular disease, including at subclinical values of PASP.
Literature supporting a relationship between metabolic dysregulation and the development of pulmonary vascular disease is growing. Robbins et al demonstrated that the prevalence of metabolic syndrome is increased in patients with pulmonary venous hypertension and pulmonary arterial hypertension.13 In pulmonary arterial hypertension the prevalence of glucose intolerance approaches 50% and is independent of BMI, suggesting that obesity does not mediate the relationship.35, 36 Gopal et al recently demonstrated that pulmonary vascular disease and right ventricular dysfunction are worse in obese individuals with metabolic syndrome compared with those without metabolic syndrome.14 Animal models support a direct contribution of insulin resistance to pulmonary pressure. For example, in wild‐type mice, hyperinsulinemia, but not hyperglycemia, leads to an increase in systolic pulmonary artery pressure,37 and reversal of insulin resistance with metformin in wild‐type mice fed a high‐fat diet leads to a reduction in pulmonary pressure.38
We found that HOMA‐IR increased with PASP, including in the vast majority of participants whose PASP was within the normal range. The relationship was nonlinear, with the greatest increase in PASP at values of HOMA‐IR consistent with insulin resistance. The decline in PASP at very high values of HOMA‐IR may reflect low sample size, which is supported by the wide confidence intervals. HOMA‐IR was highest in those meeting an echocardiographic definition of PH (>40 mm Hg). Higher PASP also correlated with the number of prevalent components of the metabolic syndrome. These findings suggest that insulin resistance and related metabolic dysregulation may contribute to pulmonary vascular disease, including subclinical values of PASP. Mild elevation in invasive pulmonary pressure is associated with increased all‐cause mortality and hospitalization, highlighting the potential clinical relevance of these findings.33 Additional investigations are needed to better understand this relationship, ideally longitudinal studies that can examine the temporal relationship between insulin resistance and the response of pulmonary pressure.
A relationship between inflammatory markers and pulmonary vascular disease is well described.39, 40, 41, 42 Although observational, our findings appear to support such an association in a largely healthy cohort. As with HOMA‐IR, both CRP and IL‐6 were highest in the subset of individuals meeting an echocardiographic definition of PH; however, this observation could be related to uncaptured comorbid conditions that are associated with both inflammation and PH (eg, connective tissue disease).
Prior studies examining the echocardiographic correlates of PASP have not included quantitative measures of RV function. The relationship between RV function and PASP was conflicting in our study; RV S′ increased across quartiles of PASP whereas TAPSE did not change. A potential explanation for increased RV S′ with rising PASP is a compensatory increase in contractility in response to elevated afterload. RV S′ is a Doppler measurement of myocardial velocity, which may be more sensitive to subtle changes in RV function than TAPSE, a measure of displacement. Given the tight ranges of these parameters in this largely normal cohort, the clinical significance of a modest change in RV S′ is unknown. Of note, RV S′ and TAPSE were not statistically different in subjects with PASP >40 mm Hg, suggesting that increased contractility was not responsible for the increase in pressure.
Limitations
Our study has limitations, which should be considered when interpreting our findings. The CARDIA cohort is not as well suited to evaluate the effects of age across a wide range compared with the studies by McQuillan and Lam; however, our study does fill an important gap in the literature regarding PASP in middle‐aged adults. The cross‐sectional design does not allow any determination regarding causality in the relationship between metabolic and inflammatory markers with PASP. Doppler‐based estimations of PASP do not agree perfectly with invasive measurements and may over‐ or underestimate by up to 10 mm Hg in studies performed in clinical populations.43 However, many studies show strong agreement between Doppler and invasive measurements, and echocardiography remains the mainstay in the evaluation of suspected pulmonary hypertension.21 Right atrial pressure was not estimated based on inferior vena cava diameter and respiratory variation in the CARDIA cohort. We elected to add a constant 10 mm Hg to the TR gradient, an approach adopted by other studies of large cohorts.1, 3 Nonetheless, this approach may lead to over‐ or underestimation of PASP in some subjects. Finally, the prevalence of a measureable TR jet (38%) in CARDIA is lower than other published cohorts.1, 2, 5 This may reflect differences in interpretation between clinical cohorts and centralized interpretation in the CARDIA echocardiography core lab. The high rate of obesity in CARDIA may also contribute to lack of a measureable TR jet.
Conclusions
In a large, prospective, and biracial cohort of middle‐aged adults, we identified new associations between black race and metabolic dysregulation with higher echocardiographically estimated PASP. Further studies are needed to examine racial differences in PASP and whether metabolic dysfunction directly contributes to pulmonary vascular disease in humans.
Sources of Funding
This work was supported by the American Heart Association Fellow to Faculty Grant #13FTF16070002 and Gilead Sciences Research Program in Pulmonary Arterial Hypertension (Brittain), National Heart, Lung, and Blood Institute 1P0 HL108800‐01A1 (Brittain, Hemnes), 1 U01 HL125212‐01 (Brittain, Hemnes), National Heart, Lung, and Blood Institute K12HL109019 (Gupta). The CARDIA study was conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), Johns Hopkins University School of Medicine (HHSN268200900041C), and Vanderbilt University (HL098445). The CARDIA study is also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra‐agency agreement between NIA and NHLBI (AG0005).
Disclosures
None.
Acknowledgments
The authors would like to thank Dr Anderson Armstrong for his thoughtful review of the manuscript.
(J Am Heart Assoc. 2017;6:e005111 DOI: 10.1161/JAHA.116.005111.)28360228
References
- 1. Lam CSP, Borlaug BA, Kane GC, Enders FT, Rodeheffer RJ, Redfield MM. Age‐associated increases in pulmonary artery systolic pressure in the general population. Circulation. 2009;119:2663–2670. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Lam CS, Roger VL, Rodeheffer RJ, Borlaug BA, Enders FT, Redfield MM. Pulmonary hypertension in heart failure with preserved ejection fraction: a community‐based study. J Am Coll Cardiol. 2009;53:1119–1126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. McQuillan BM, Picard MH, Leavitt M, Weyman AE. Clinical correlates and reference intervals for pulmonary artery systolic pressure among echocardiographically normal subjects. Circulation. 2001;104:2797–2802. [DOI] [PubMed] [Google Scholar]
- 4. Choudhary G, Jankowich M, Wu W‐C. Elevated pulmonary artery systolic pressure predicts heart failure admissions in African Americans: Jackson Heart Study. Circ Heart Fail. 2014;7:558–564. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Armstrong DWJ, Tsimiklis G, Matangi MF. Factors influencing the echocardiographic estimate of right ventricular systolic pressure in normal patients and clinically relevant ranges according to age. Can J Cardiol. 2010;26:e35–e39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Wang X, Poole JC, Treiber FA, Harshfield GA, Hanevold CD, Snieder H. Ethnic and sex differences in ambulatory blood pressure trajectories: results from a 15‐year longitudinal study in youth and young adults. Circulation. 2006;114:2780–2787. [DOI] [PubMed] [Google Scholar]
- 7. Kramer H, Han C, Post W, Goff D, Diez‐Roux A, Cooper R, Jinagouda S, Shea S. Racial/ethnic differences in hypertension and hypertension treatment and control in the Multi‐Ethnic Study of Atherosclerosis (MESA). Am J Hypertens. 2004;17:963–970. [DOI] [PubMed] [Google Scholar]
- 8. Kishi S, Reis JP, Venkatesh BA, Gidding SS, Armstrong AC, Jacobs DR, Sidney S, Wu CO, Cook NL, Lewis CE, Schreiner PJ, Isogawa A, Liu K, Lima JAC. Race‐ethnic and sex differences in left ventricular structure and function: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. J Am Heart Assoc. 2015;4:e001264 DOI: 10.1161/JAHA.114.001264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Glindmeyer HW, Lefante JJ, McColloster C, Jones RN, Weill H. Blue‐collar normative spirometric values for Caucasian and African‐American men and women aged 18 to 65. Am J Respir Crit Care Med. 1995;151:412–422. [DOI] [PubMed] [Google Scholar]
- 10. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med. 1999;159:179–187. [DOI] [PubMed] [Google Scholar]
- 11. West J, Niswender KD, Johnson JA, Pugh ME, Gleaves L, Fessel JP, Hemnes AR. A potential role for insulin resistance in experimental pulmonary hypertension. Eur Respir J. 2013;41:861–871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Hemnes AR, Brittain EL, Trammell AW, Fessel JP, Austin ED, Penner N, Maynard KB, Gleaves L, Talati M, Absi T, Disalvo T, West J. Evidence for right ventricular lipotoxicity in heritable pulmonary arterial hypertension. Am J Respir Crit Care Med. 2014;189:325–334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Robbins IM, Newman JH, Johnson RF, Hemnes AR, Fremont RD, Piana RN, Zhao DX, Byrne DW. Association of the metabolic syndrome with pulmonary venous hypertension. Chest. 2009;136:31–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Gopal DM, Santhanakrishnan R, Wang Y‐C, Ayalon N, Donohue C, Rahban Y, Perez AJ, Downing J, Liang C‐S, Gokce N, Colucci WS, Ho JE. Impaired right ventricular hemodynamics indicate preclinical pulmonary hypertension in patients with metabolic syndrome. J Am Heart Assoc. 2015;4:e001597 DOI: 10.1161/JAHA.114.001597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB, Jacobs DR, Liu K, Savage PJ. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41:1105–1116. [DOI] [PubMed] [Google Scholar]
- 16. Pletcher MJ, Vittinghoff E, Kalhan R, Richman J, Safford M, Sidney S, Lin F, Kertesz S. Association between marijuana exposure and pulmonary function over 20 years. JAMA. 2012;307:173–181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Carnethon MR, Evans NS, Church TS, Lewis CE, Schreiner PJ, Jacobs DR, Sternfeld B, Sidney S. Joint associations of physical activity and aerobic fitness on the development of incident hypertension: coronary artery risk development in young adults. Hypertension. 2010;56:49–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD‐EPI (Chronic Kidney Disease Epidemiology Collaboration) . A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wallace TM, Matthews DR. The assessment of insulin resistance in man. Diabet Med. 2002;19:527–534. [DOI] [PubMed] [Google Scholar]
- 20. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) . Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421. [PubMed] [Google Scholar]
- 21. Rudski LG, Lai WW, Afilalo J, Hua L, Handschumacher MD, Chandrasekaran K, Solomon SD, Louie EK, Schiller NB. Guidelines for the echocardiographic assessment of the right heart in adults: a report from the American Society of Echocardiography endorsed by the European Association of Echocardiography, a registered branch of the European Society of Cardiology, and the Canadian Society of Echocardiography. J Am Soc Echocardiogr. 2010;23:685–713; quiz 786–8. [DOI] [PubMed] [Google Scholar]
- 22. Lang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA, Picard MH, Roman MJ, Seward J, Shanewise JS, Solomon SD, Spencer KT, Sutton MS, Stewart WJ. Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. J Am Soc Echocardiogr. 2005;18:1440–1463. [DOI] [PubMed] [Google Scholar]
- 23. Zoghbi WA, Enriquez‐Sarano M, Foster E, Grayburn PA, Kraft CD, Levine RA, Nihoyannopoulos P, Otto CM, Quinones MA, Rakowski H, Stewart WJ, Waggoner A, Weissman NJ. Recommendations for evaluation of the severity of native valvular regurgitation with two‐dimensional and Doppler echocardiography. J Am Soc Echocardiogr. 2003;16:777–802. [DOI] [PubMed] [Google Scholar]
- 24. McLaughlin VV, Archer SL, Badesch DB, Barst RJ, Farber HW, Lindner JR, Mathier MA, McGoon MD, Park MH, Rosenson RS, Rubin LJ, Tapson VF, Varga J. ACCF/AHA 2009 expert consensus document on pulmonary hypertension: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents and the American Heart Association developed in collaboration with the American College of Chest Physicians; American Thoracic Society, Inc.; and the Pulmonary Hypertension Association. J Am Coll Cardiol. 2009;53:1573–1619. [DOI] [PubMed] [Google Scholar]
- 25. Jankowich MD, Wu W‐C, Choudhary G. Association of elevated plasma endothelin‐1 levels with pulmonary hypertension, mortality, and heart failure in African American individuals: the Jackson Heart Study. JAMA Cardiol. 2016;1:461–469. [DOI] [PubMed] [Google Scholar]
- 26. Reis JP, Loria CM, Lewis CE, Powell‐Wiley TM, Wei GS, Carr JJ, Terry JG, Liu K. Association between duration of overall and abdominal obesity beginning in young adulthood and coronary artery calcification in middle age. JAMA. 2013;310:280–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Alman AC, Jacobs DR, Lewis CE, Snell‐Bergeon JK, Carnethon MR, Terry JG, Goff DC, Ding J, Carr JJ. Higher pericardial adiposity is associated with prevalent diabetes: the Coronary Artery Risk Development in Young Adults Study. Nutr Metab Cardiovasc Dis. 2016;26:326–332. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Mueller NT, Pereira MA, Demerath EW, Dreyfus JG, MacLehose RF, Carr JJ, Terry JG, Jacobs DR. Earlier menarche is associated with fatty liver and abdominal ectopic fat in midlife, independent of young adult BMI: the CARDIA study. Obesity (Silver Spring). 2015;23:468–474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. VanWagner LB, Ning H, Lewis CE, Shay CM, Wilkins J, Carr JJ, Terry JG, Lloyd‐Jones DM, Jacobs DR, Carnethon MR. Associations between nonalcoholic fatty liver disease and subclinical atherosclerosis in middle‐aged adults: the Coronary Artery Risk Development in Young Adults Study. Atherosclerosis. 2014;235:599–605. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Rijzewijk LJ, van der Meer RW, Smit JWA, Diamant M, Bax JJ, Hammer S, Romijn JA, de Roos A, Lamb HJ. Myocardial steatosis is an independent predictor of diastolic dysfunction in type 2 diabetes mellitus. J Am Coll Cardiol. 2008;52:1793–1799. [DOI] [PubMed] [Google Scholar]
- 31. Ristow B, Ali S, Ren X, Whooley MA, Schiller NB. Elevated pulmonary artery pressure by Doppler echocardiography predicts hospitalization for heart failure and mortality in ambulatory stable coronary artery disease: the Heart and Soul Study. J Am Coll Cardiol. 2007;49:43–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Khush KK, Shah SJ, Ristow B, De Marco T, Whooley MA, Schiller NB. Association of African American race with elevated pulmonary artery diastolic pressure: data from the Heart and Soul Study. J Am Soc Echocardiogr. 2007;20:1307–1313. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Maron BA, Hess E, Maddox TM, Opotowsky AR, Tedford RJ, Lahm T, Joynt KE, Kass DJ, Stephens T, Stanislawski MA, Swenson ER, Goldstein RH, Leopold JA, Zamanian RT, Elwing JM, Plomondon ME, Grunwald GK, Barón AE, Rumsfeld J, Choudhary G. Association of borderline pulmonary hypertension with mortality and hospitalization in a large patient cohort: insights from the VA‐CART program. Circulation. 2016;133:1240–1248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Kawut SM, Lima JA, Barr RG, Chahal H, Jain A, Tandri H, Praestgaard A, Bagiella E, Kizer JR, Johnson WC, Kronmal RA, Bluemke DA. Sex and race differences in right ventricular structure and function: the Multi‐Ethnic Study of Atherosclerosis‐right ventricle study. Circulation. 2011;123:2542–2551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Pugh ME, Robbins IM, Rice TW, West J, Newman JH, Hemnes AR. Unrecognized glucose intolerance is common in pulmonary arterial hypertension. J Heart Lung Transplant. 2011;30:904–911. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Zamanian RT, Hansmann G, Snook S, Lilienfeld D, Rappaport KM, Reaven GM, Rabinovitch M, Doyle RL. Insulin resistance in pulmonary arterial hypertension. Eur Respir J. 2009;33:318–324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Trammell AW, Penner N, Fessel JP, Niswender KD, Newman JH, West JD, Hemnes AR. Hyperinsulinemia promotes pulmonary hypertension in mice. Am J Respir Crit Care Med. 2014;189:A2620 (abstract). [Google Scholar]
- 38. Brittain EL, Penner N, Trammell AW, Fessel JP, Talati M, Gleaves L, Newman JH, Hemnes AR. Metformin prevents myocardial lipid accumulation and improves right ventricular structure and function in mice fed high fat diet. Am J Respir Crit Care Med. 2014;189:A2621 (abstract). [Google Scholar]
- 39. Humbert M, Monti G, Brenot F, Sitbon O, Portier A, Grangeot‐Keros L, Duroux P, Galanaud P, Simonneau G, Emilie D. Increased interleukin‐1 and interleukin‐6 serum concentrations in severe primary pulmonary hypertension. Am J Respir Crit Care Med. 1995;151:1628–1631. [DOI] [PubMed] [Google Scholar]
- 40. Soon E, Holmes AM, Treacy CM, Doughty NJ, Southgate L, Machado RD, Trembath RC, Jennings S, Barker L, Nicklin P, Walker C, Budd DC, Pepke‐Zaba J, Morrell NW. Elevated levels of inflammatory cytokines predict survival in idiopathic and familial pulmonary arterial hypertension. Circulation. 2010;122:920–927. [DOI] [PubMed] [Google Scholar]
- 41. Quarck R, Nawrot T, Meyns B, Delcroix M. C‐reactive protein: a new predictor of adverse outcome in pulmonary arterial hypertension. J Am Coll Cardiol. 2009;53:1211–1218. [DOI] [PubMed] [Google Scholar]
- 42. Hassoun PM, Mouthon L, Barberà JA, Eddahibi S, Flores SC, Grimminger F, Jones PL, Maitland ML, Michelakis ED, Morrell NW, Newman JH, Rabinovitch M, Schermuly R, Stenmark KR, Voelkel NF, Yuan JX‐J, Humbert M. Inflammation, growth factors, and pulmonary vascular remodeling. J Am Coll Cardiol. 2009;54:S10–S19. [DOI] [PubMed] [Google Scholar]
- 43. Fisher MR, Forfia PR, Chamera E, Housten‐Harris T, Champion HC, Girgis RE, Corretti MC, Hassoun PM. Accuracy of Doppler echocardiography in the hemodynamic assessment of pulmonary hypertension. Am J Respir Crit Care Med. 2009;179:615–621. [DOI] [PMC free article] [PubMed] [Google Scholar]