JCB J Circ Biomark 2020; 9(1): 8-12 DOI: 10.33393/jcb.2020.2164 ISSN 1849-4544 ORIGINAL RESEARCH ARTICLE Lung ultrasound and biomarkers in primary care: Partners for a better management of patients with heart failure? Mar Domingo1-3, Laura Conangla1-3, Josep Lupón1,4,5, Asunción Wilke2,4, Gladys Juncà1, Elena Revuelta-López1,5,6, Xavier Tejedor7, Antoni Bayes-Genis1,4,5 1 Heart Failure Unit and Cardiology Department, Hospital Universitari Germans Trias i Pujol, Badalona - Spain 2 Primary Care Service Barcelonès Nord i Maresme, Catalan Health Institute, Badalona - Spain 3 Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Barcelona - Spain 4 Department of Medicine, Universitat Autònoma de Barcelona, Barcelona - Spain 5 CIBERCV, Instituto de Salud Carlos III, Madrid - Spain 6 ICREC Research Program, Germans Trias i Pujol Health Science Research Institute, Can Ruti Campus, Badalona - Spain 7 Biochemistry Service, Hospital Universitari Germans Trias i Pujol, Badalona - Spain ABSTRACT Introduction: The association of pulmonary congestion assessed by lung ultrasound (LUS) and biomarkers—other than N-terminal pro-brain natriuretic peptide (NT-proBNP)—is uncertain. Methods: We investigated the relationship between total B-line count by LUS and several biomarkers in outpa- tients with suspicion of heart failure (HF). Primary care patients with suspected new-onset nonacute HF were evaluated both with a 12-scan LUS protocol (8 anterolateral areas plus 4 lower posterior thoracic areas) and 11 inflammatory and cardiovascular biomarkers. A cardiologist blinded to LUS and biomarkers except NT-proBNP confirmed HF diagnosis. After log-transformation of biomarkers’ concentrations, unadjusted and adjusted cor- relations were performed. Results: A total of 170 patients were included (age 76 ± 10 years, 67.6% women). HF diagnosis was confirmed in 38 (22.4%) patients. After adjustment by age, sex, body mass index, and renal function, total B-line sum sig- nificantly correlated with NT-proBNP (R = 0.29, p < 0.001), growth/differentiation factor-15 (GDF-15; R = 0.23, p = 0.003), high-sensitive Troponin T (hsTnT; R = 0.36, p < 0.001), soluble interleukin-1 receptor-like 1 (sST2; R = 0.29, p < 0.001), cancer antigen 125 (CA-125; R = 0.17, p = 0.03), high-sensitivity C-reactive protein (hsCRP; R = 0.20, p = 0.009), and interleukin (IL)-6 (R = 0.23, p = 0.003). In contrast, IL-33 (R = −0.01, p = 0.93), IL-1β (R = −0.10, p = 0.20), soluble neprilysin (sNEP; R = 0.09, p = 0.24), tumor necrosis factor-alpha (TNF-α; R = 0.07, p = 0.39), and TNF-α receptor superfamily member 1A (TNFRSF1A; R = 0.14, p = 0.07) did not. Conclusions: Total B-line sum correlated significantly, although moderately, with congestion and several inflam- mation biomarkers. Unexpectedly, the highest correlation found was with hsTnT. Keywords: Biomarkers, Congestion, Diagnosis, Heart failure, Lung ultrasound, Primary care Introduction Complementary tools such as lung ultrasound (LUS) and car- diac biomarkers might aid in the diagnostic approach. Heart failure (HF) diagnosis is challenging in ambula- LUS is highly sensitive for pulmonary congestion asses- tory patients, since signs and symptoms are mild and can sment in HF (1), since the number and distribution of B-lines be related with other diseases, and even to natural aging. denote the amount of extravascular fluid in the lung. Current guidelines included natriuretic peptides to mini- mize HF diagnosis complexity, especially in the nonacute Received: June 11, 2020 setting when echocardiography is not immediately available. Accepted: September 4, 2020 In recent years, several cardiac biomarkers have been descri- Published online: October 16, 2020 bed, reflecting different active pathogenic pathways in HF (2). The association of B-lines and N-terminal pro-brain natri- Corresponding author: uretic peptide (NT-proBNP) has been characterized in decom- Antoni Bayes-Genis Heart Institute, Hospital Universitari Germans Trias i Pujol pensated acute HF patients. Nevertheless, there are few data Department of Medicine, Universitat Autònoma de Barcelona on outpatients, and no data with other cardiac biomarkers. Carretera del Canyet s/n 08916, Badalona - Spain Accordingly, we investigated the correlation between B-lines abayesgenis@gmail.com and different biomarkers in outpatients with suspicion of HF © 2020 The Authors. This article is published by AboutScience and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Any commercial use is not permitted and is subject to Publisher’s permissions. Full information is available at www.aboutscience.eu Domingo et al 9 in the primary care setting. We hypothesized that biomarkers Electric®) and interpreted bench side. LUS was performed with multiple bio-profiling other than NT-proBNP might be with patient in a seated position; 8 anterolateral thoracic associated with pulmonary congestion by LUS. areas plus 4 posterior lower areas were examined. Each of the 12 areas was classified according to the number of B-lines Methods in the sagittal scan. A thoracic area was considered positive if ≥3 B-lines were observed. Pleural effusion was considered as Study design and patients 10 B-lines. LUS congestion was defined as 2 out of 6 positive scans in each hemithorax. The present study is a biomarker subanalysis of a prospec- tive cohort of ambulatory patients >50 years old, referred by Transthoracic Doppler echocardiography their primary care physician to NT-proBNP test for suspected new-onset non-acute HF (July 2015 to January 2018) (3). We Echocardiographic study was performed using an iE33 excluded patients with established HF diagnosis, pulmonary ultrasound system (Philips Medical Systems; Andover, Massa- fibrosis, or radiological pachypleuritis. The study was perfor- chusetts) with a S5-1 sector transducer (5.1 MHz bandwidth), med in accordance with the Declaration of Helsinki; the local and analyses were performed with an EchoPAC. ethics committee approved the research protocol and infor- med consent was obtained from all subjects. Statistical analysis Procedures Categorical values are described as absolute numbers (percentages) and continuous variables as means (standard All inclusion visits were scheduled in a centralized setting, deviations) or medians [interquartile ranges], depending on where the primary care physician investigator (LC, MD, AW) whether data distribution was normal as assessed by nor- evaluated the patients, focusing on Framingham criteria, and mal Q-Q plots. To assess the relationship of total B-line sum performed LUS. Blood samples were collected for NT-proBNP acquired by LUS with biomarkers’ concentrations, Pearson measurement (XT), and serum aliquots were stored at −80°C correlation was used after logarithmic transformation of prior to assay (ER-L). At a subsequent visit, a cardiologist biomarker levels; afterward, partial correlations adjusted by investigator (GJ) assessed all participants and performed a age and sex, and finally by age, sex, body mass index, and transthoracic Doppler echocardiogram. This physician confir- estimated glomerular filtration rate (eGFR) were performed. med HF diagnosis, following the European Society of Cardio- Analyses were performed using Statistical Package for the logy guidelines. The cardiologist had access to the patients’ Social Sciences (SPSS) 24. A two-sided p < 0.05 was conside- electronic records, including the primary care investigator red significant. visit and NT-proBNP, but was blinded to LUS and other bio- markers. Results Assays Table I shows baseline characteristic and biomarker values of the 170 patients included. They were elderly, pre- Biomarker panel dominantly women, obese or overweight, and mainly in New York Heart Association (NYHA) class II. HF diagnosis was con- NT-proBNP, high-sensitive Troponin T (hsTnT) and growth/ firmed in 38 (22.4%) patients, and only one had left ventricu- differentiation factor-15 (GDF-15) were measured by Cobas lar ejection fraction (LVEF) < 40%. Patients with HF diagnosis Elecsys® kits (Roche Diagnostics). Cancer antigen 125 (CA- had higher levels of all biomarkers except IL-33, IL-1β, and 125) was tested by ARCHITECT CA 125 II assay (Abbott Dia- soluble neprilysin (sNEP). They also had a higher number of gnostic). High-sensitivity C-reactive protein (hsCRP) was total B-line count (p < 0.001). Although 85% of patients had measured by hsCRP reagent (Beckman Coulter). Human solu- exertional dyspnea, only 17.1% had crackles, 9.4% orthop- ble neprilysin (NEP) and soluble interleukin-1 receptor-like nea, and 3.5% paroxysmal nocturnal dyspnea. 1 (sST2) were measured by Human Soluble neprilysin/CD10 Correlations between total B-line sum and studied bio- ELISA kit (Aviscera Bioscience) and Presage® ST2 (Critical Dia- markers are shown in Table II. Unadjusted analyses sho- gnostics) assays, respectively. Interleukin (IL)-1β, IL-33, IL-6, wed that total B-line sum was significantly associated with tumor necrosis factor-alpha (TNF-α), and TNF-α receptor NT-proBNP, GDF-15, hsTnT, sST2, CA-125, hsCRP, IL-6, and superfamily member 1A (TNFRSF1A) were tested by Quanti- TNFRSF1A (R range 0.18-0.35), while IL-33, IL-1β, sNEP, and kine® immunoassay kits (R&D Systems). TNF-α levels were not associated with total B-line sum. After NT-proBNP was analyzed after collection. The rest of the the adjustments for the four covariates, R values tended to biomarkers were analyzed in the first or second freeze-thaw slightly decrease except for hsTnT and TNFRSF1A that lost cycle. statistical significance. Lung ultrasound Discussion LUS was performed with a pocket device (V-scan simple Bedside LUS has appeared as a step forward for HF dia- model with a sectorial phased array transducer; General gnosis, and biomarkers other than NT-proBNP are currently © 2020 The Authors. Published by AboutScience 10 Lung ultrasound and biomarkers for heart failure diagnosis in primary care TABLE I - Demographic, clinical characteristics and biomarker levels of patients Total HF diagnosis No HF diagnosis p-value n = 170 n = 38 n = 132 Age, years 76 ± 10.4 81.2 ± 8.3 74.4 ± 10 <0.001 Female sex, n (%) 115 (67.6) 23 (60.5) 92 (69.7) 0.29 LVEF, % 63 ± 5.8 59.9 ± 7.2 63.8 ± 5 <0.001 Comorbidities, n (%) Hypertension 132 (77.6) 36 (94.7) 96 (72.7) 0.004 Diabetes mellitus 43 (25.3) 12 (31.6) 31 (23.5) 0.31 COPD 19 (11.2) 7 (18.4) 12 (9.1) 0.11 Valvular heart disease 6 (3.5) 3 (7.9) 3 (2.3) 0.10 Myocardial infarction 15 (8.8) 7 (18.4) 8 (6.1) 0.02 Atrial fibrillation 19 (11.2) 16 (42.1) 3 (2.3) <0.001 Obesity (BMI >30 kg/m2) 84 (49.4) 20 (52.6) 64 (48.5) 0.68 eGFR < 60 mL/min/1.72 m2 48 (28.2) 18 (47.4) 30 (22.7) 0.003 Functional class, n (%) <0.001 I 20 (11.8) 2 (6.1) 18 (13.6) II 116 (68.2) 18 (54.5) 98 (74.2) III 34 (20.0) 18 (39.4) 16 (12.1) Exertion dyspnea 145 (85.3) 36 (94.7) 109 (82.6) 0.06 Orthopnea 16 (9.4) 8 (21.1) 8 (6.1) 0.005 Paroxysmal nocturnal dyspnea 6 (3.5) 2 (5.3) 4 (3.0) 0.51 Lung crackles 29 (17.1) 11 (28.9) 18 (13.6) 0.03 Total B-line sum 5.6 ± 10.1 14.1 ± 15.0 3.2 ± 6.4 <0.001 Biomarkers NT-proBNP, ng/L 202 (104-640) 1350 (666-3551) 148 (88-289) <0.001 GDF-15, ng/L 1708 (1175-2511) 3133 (2040-4075) 1470 (1113-2117) <0.001 hsTnT, ng/L 11.9 (6.7-21.7) 24.8 (13.8-39.9) 9.6 (5.9-15.9) <0.001 sST2, ng/mL 27.5 (21.8-37.6) 39.6 (32-56.6) 25.2 (20.7-33.8) <0.001 CA-125, U/mL 13.7 (9.7-22.7) 21.4 (11.4-55.4) 13 (8.8-20) <0.001 hsCRP, mg/L 3.4 (1.9-7.3) 5.3 (2.3-19.2) 3.1 (1.7-5.8) 0.005 IL-33, pg/mL 93.7 (93.7-601.2) 93.7 (93.7-548) 93.7 (93.7-632.3) 0.78 IL-1β, ng/mL 0.34 (0.27-0.44) 0.33 (0.25-0.45) 0.35 (0.28-0.43) 0.60 IL-6, pg/mL 4.4 (2.9-7.2) 6.3 (4.3-14.3) 3.9 (2.8-5.9) <0.001 sNEP, ng/mL 0.209 (0.062-0.605) 0.206 (0.062-0.465) 0.208 (0.062-0.630) 0.62 TNF-α, pg/mL 56.3 (49.9-67.5) 60.8 (52.7-71.1) 55.1 (49.1-66.1) 0.02 TNFRSF1A, ng/mL 1.88 (1.45-2.39) 2.39 (1.72-3.56) 1.75 (1.42-2.21) <0.001 Data are expressed as mean (standard deviation), median (percentiles 25th-75th), or absolute numbers (percentages). BMI = body mass index; CA-125 = cancer antigen 125; COPD = chronic obstructive pulmonary disease; eGFR = estimated glomerular filtration rate; GDF-15 = growth differentiation factor 15; HF = heart failure; hsCRP = high-sensitivity C-reactive protein; hsTnT = high-sensitivity troponin T; IL = interleukin; LVEF = left ventricular ejection fraction; NT-proBNP = N-terminal pro-brain natriuretic peptide; NYHA = New York Heart Association; sNEP = soluble neprilysin; sST2 = soluble interleukin-1 receptor-like 1; TNF-α = tumor necrosis factor α; TNFRSF1A = TNF receptor superfamily member 1A. under investigation. Although there is a growing interest on HF suspicion. Our results showed that (i) total B-line sum both, added value for better patient diagnosis and mana- observed by LUS was significantly—although moderately— gement has scarcely been studied. In our study, we asses- associated with several biomarkers of active pathogenic sed the correlation between B-lines and a biomarker panel pathways in HF, especially with those related to congestion in primary care outpatients with new-onset nonacute and inflammation; and (ii) hsTnT, a biomarker related to © 2020 The Authors. Published by AboutScience Domingo et al 11 TABLE II - Correlations between total B-line sum and studied biomarkers NT-proBNP GDF-15 hsTnT sST2 CA-125 hsCRP IL-6 IL-33 IL-1β sNEP TNF-α TNFRSF1A Unadjusted R 0.32 0.27 0.35 0.32 0.21 0.22 0.27 –0.02 –0.08 0.10 0.09 0.18 p-value <0.001 <0.001 <0.001 <0.001 0.007 0.004 <0.001 0.81 0.28 0.21 0.26 0.02 Adjusted by age and sex R 0.29 0.23 0.34 0.30 0.19 0.21 0.24 –0.01 –0.11 0.09 0.08 0.15 p-value <0.001 0.003 <0.001 <0.001 0.02 0.007 0.002 0.93 0.18 0.24 0.32 0.06 Adjusted by age, sex, BMI, and eGFR* R 0.29 0.23 0.36 0.29 0.17 0.20 0.23 –0.01 –0.10 0.09 0.07 0.14 p-value <0.001 0.003 <0.001 <0.001 0.03 0.009 0.003 0.93 0.20 0.24 0.39 0.07 *estimated by CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). BMI = body mass index; CA-125 = cancer antigen 125; eGFR = estimated glomerular filtration rate; GDF-15 = growth differentiation factor 15; hsCRP = high- sensitivity C-reactive protein; hsTnT = high-sensitivity troponin T; IL = interleukin; NT-proBNP = N-terminal pro-brain natriuretic peptide; sNEP = soluble neprilysin; sST2 = soluble interleukin-1 receptor-like 1; TNF-α = tumor necrosis factor α; TNFRSF1A = TNF receptor superfamily member 1A. myocardial injury, was mainly and unexpectedly associated pathogenic pathways in HF, correlated with total B-line sum with total B-line sum. in a greater degree than the more “pure” inflammatory ones HF diagnosis can be difficult at early stages, especially in (hsCRP, IL-1β, IL-33, IL-6, TNF-α, and TNFRSF1A). women and old patients with comorbidities. Our patients Maybe the more remarkable finding was the high corre- were elderly, mainly women, and not very symptomatic. Less lation of total B-line sum with hsTnT, a biomarker of myocar- than 10% of patients had lung congestion symptoms and only dial injury frequently elevated in patients with HF without 17% crackles. In this context, HF was only confirmed in 22.4% coronary ischemia. Unexpectedly, hsTnT correlation was of patients since Framingham criteria, despite being highly even higher than that observed with NT-proBNP and sST2. specific, have a poor sensitivity for HF diagnosis in ambula- Recently, Myhre et al (6) showed that high-sensitive car- tory patients. diac Troponin T (hs-cTnT) concentrations were associated Hand-held devices could be easily incorporated in pri- with worse diastolic function, suggesting that high levels of mary care and have shown a good correlation with standard hs-cTnT may serve as an early marker of subclinical altera- ultrasound equipment for B-line detection. In our study, we tions in diastolic function that may lead to a predisposition used the 8-zone technique adding 4 posterior zones since to HF. these areas are the first that show signs of congestion and Finally, although there was a statistically significantly cor- could add accuracy in outpatients. relation between total B-line sum and CA-125, we anticipated As expected and according to previous studies in acute a higher correlation, since both are surrogates of pulmonary HF, total B-line sum was significantly associated with NT- and systemic congestion (7), respectively. Congestion plays a proBNP levels. However, we projected a highest correlation, major role in acute HF syndromes; however, it is known that since increased intracardiac filling pressures often precede severity and organ distribution are largely heterogeneous. In lung congestion. Nevertheless, in mildly symptomatic pri- fact, our primary care patients showed low percentages of mary care patients, pulmonary congestion is not always congestion signs or symptoms. present unlike hemodynamic dysfunction. sST2 levels also Limitations of our study include the limited sample size correlated with B-line count in a similar level that NT-proBNP. and the low incidence of HF since our target was primary care ST2 is a member of the IL-1 receptor family linked to myo- patients with mild symptoms and suspicion of HF. These facts cardial fibrosis and adverse remodeling, both related to might have an impact on the external validity of the study. diastolic dysfunction and increased end-diastolic pressures Also HF diagnosis was performed by a single cardiologist. that can contribute to pulmonary congestion. sST2 has been Although larger studies in diverse populations are needed, described as a 3-in-1 biomarker and provides insight into the our data are hypotheses generating correlations between hemodynamic, inflammatory, and pro-fibrotic/remodeling B-lines, a surrogate of pulmonary congestion, and of biomar- burden of the myocardium (4). Total B-line sum also corre- kers in HF patients. lated with GDF-15, a marker of cell injury inflammation, oxi- dative stress, and hypoxia. These results are consistent with Conclusion previous studies where GDF-15 may indicate a greater syste- mic inflammatory response in old patients and those with HF In primary care outpatients with new-onset nonacute and preserved EF (5), as was the population of our study. It is HF suspicion, total B-line sum is significantly—although remarkable that these two biomarkers, being in part inflam- moderately—associated with several biomarkers of conge- matory biomarkers but also associated with other several stion and inflammation, and remarkably with hsTnT. © 2020 The Authors. Published by AboutScience 12 Lung ultrasound and biomarkers for heart failure diagnosis in primary care Acknowledgments 3. Conangla L, Domingo M, Lupón J, et al. Lung ultrasound for heart failure diagnosis in primary care. J Card Fail. 2020 Jun 6:S1071- The authors thank Jaume Barallat and Adriana Cserkóová 9164(19)31822-6. doi: 10.1016/j.cardfail.2020.04.019. Online for their valuable technical support in sample processing. ahead of print. 4. Pascual-Figal DA, Bayes-Genis A, Asensio-Lopez MC, et al. The interleukin-1 axis and risk of death in patients with Disclosures acutely decompensated heart failure. J Am Coll Cardiol. Conflict of interest: The authors declare no conflict of interest. 2019;73:1016-1025. Financial support: This work was supported by La Marató de TV3 5. Wollert KC, Kempf T, Wallentin L. Growth differentiation fac- [PI 201510.10], the Primary Healthcare University Research Institute tor 15 as a biomarker in cardiovascular disease. Clin Chem. IDIAP-Jordi Gol, and the Catalan Society of Family Physicians (CAMFiC). 2017;63:140-151. 6. Myhre PL, Claggett B, Ballantyne CM, et al. Association between circulating troponin concentrations, left ventricular References systolic and diastolic functions, and incident heart failure in older adults. JAMA Cardiol. 2019;4:997-1006. 1. Platz E, Jhund PS, Campbell RT, McMurray JJ. Assessment and 7. Núñez J, Bayés-Genís A, Revuelta-López E, et al. Clinical role prevalence of pulmonary oedema in contemporary acute heart of CA125 in worsening heart failure: a BIOSTAT-CHF study failure trials: a systematic review. Eur J Heart Fail. 2015;17:906-916. subanalysis. JACC Heart Fail. 2020;8:386-397. 2. Berezin AE. Prognostication in different heart failure pheno­types: the role of circulating biomarkers. J Circ Biomark. 2016;5:6. © 2020 The Authors. Published by AboutScience