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Vol. 58. Issue 4.
Pages T323-T333 (April 2022)
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Vol. 58. Issue 4.
Pages T323-T333 (April 2022)
Special Article
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[Translated article] Biological Biomarkers in Respiratory Diseases
Biomarcadores biológicos en las enfermedades respiratorias
Francisco García-Ríoa,b, Bernardino Alcázar-Navarreteb,c, Diego Castillo-Villegasd, Catia Cillonizb,e, Alberto García-Ortegaf, Virginia Leiro-Fernándezg, Irene Lojo-Rodriguezg, Alicia Padilla-Galoh, Carlos A. Quezada-Loaizab,i, Jose Antonio Rodriguez-Portalb,j, Manuel Sánchez-de-la-Torreb,k, Oriol Sibilab,l, Miguel A. Martínez-Garcíab,f,
Corresponding author

Corresponding author.
a Servicio de Neumología, Hospital Universitario la Paz-IdiPAZ, Madrid, Spain
b CIBER de enfermedades respiratorias, ISCIII, Madrid, Spain
c Servicio de Neumología, Hospital Virgen de las Nieves, Granada, Spain
d Servicio de Neumología, Hospital Santa Creu i Sant Pau, Barcelona, Spain
e Servicio de Neumología, Hospital Clínic, Barcelona, Spain
f Servicio de Neumología, Hospital Universitario y Politécnico La Fe, Valencia, Spain
g Servicio de Neumología, Hospital Álvaro Cunqueiro, Complexo Hospitalario Universitario de Vigo, Grupo de Investigación NeumovigoI+i, IIS Galicia Sur, Vigo, Pontevedra, Spain
h Servicio de Neumología, Hospital Costa del Sol, Marbella, Málaga, Spain
i Unidad de Trasplante Pulmonar, Servicio de Neumología, Hospital Universitario 12 de Octubre, Madrid, Spain
j Servicio de Neumología, Hospital Virgen del Rocío, Sevilla, Spain
k Grupo de Medicina de Precisión en enfermedades crónicas, Departamento de Respiratorio, Hospital Universitario Arnau de Vilanova y Santa María, Departamento de Enfermería y Fisioterapia, Facultad de Enfermería y Fisioterapia, Universidad de Lleida, IRBLleida, Lleida, Spain
l Servicio de Neumología, Instituto Clínico de Respiratorio, IDIBAPS, Hospital Clínic, Barcelona, Spain
Related content
Arch Bronconeumol. 2022;58:323-3310.1016/j.arbres.2022.01.003
Francisco García-Río, Bernardino Alcázar-Navarrete, Diego Castillo-Villegas, Catia Cilloniz, Alberto García-Ortega, Virginia Leiro-Fernández, Irene Lojo-Rodriguez, Alicia Padilla-Galo, Carlos A. Quezada-Loaiza, Jose Antonio Rodriguez-Portal, Manuel Sánchez-de-la-Torre, Oriol Sibila, Miguel A. Martínez-García
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Figures (2)
Tables (5)
Table 1. Advantages and limitations of different biomarker sampling methods in asthma.
Table 2. Main biomarkers in bronchiectasis and cystic fibrosis and detection method.
Table 3. Main serological biomarkers in IPF and other ILDs.
Table 4. Usefulness of some biomarkers in the diagnosis, treatment and follow-up of patients with pulmonary hypertension.
Table 5. Possible serum biomarkers related to progression in ILD associated with systemic diseases.
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In recent years, personalized or precision medicine has made effective inroads into the management of diseases, including respiratory diseases. The route to implementing this approach must invariably start with the identification and validation of biological biomarkers that are closely related to the diagnosis, treatment, and prognosis of respiratory patients. In this respect, biological biomarkers of greater or lesser reliability have been identified for most respiratory diseases and disease classes, and a large number of studies are being conducted in the search for new indicators. The aim of this review is to update the reader and to analyze the existing scientific literature on the existence and diagnostic, therapeutic, and prognostic validity of the most important biological biomarkers in the main respiratory diseases, and to identify future challenges in this area.

Respiratory disorders
Chronic obstructive pulmonary disease
Idiopathic pulmonary fibrosis
Pleural effusion
Lung cancer
Sleep apnea
Pulmonary embolism
Pulmonary arterial hypertension
Cystic fibrosis
Systemic disorders

En los últimos años la llamada «medicina personalizada o de precisión» ha irrumpido con fuerza en el manejo de las enfermedades, entre ellas las respiratorias. La posibilidad de implantar esta forma de trabajar pasa indefectiblemente por el hallazgo y validación de biomarcadores biológicos que se relacionen bien con el diagnóstico, tratamiento o pronóstico de los pacientes respiratorios. En este sentido, la mayoría de enfermedades respiratorias o grupo de las mismas ya cuentan con biomarcadores biológicos de mayor o menor fiabilidad, y se están realizando un gran número de estudios en busca de nuevos de estos indicadores. El objetivo de la presente revisión es poner al día al lector y analizar la literatura científica existente sobre la existencia y validez diagnóstica, terapéutica o pronóstica de los biomarcadores biológicos más importantes en la actualidad en las principales enfermedades respiratorias, así como sobre los retos futuros en este sentido.

Palabras clave:
Trastornos respiratorios
Enfermedad pulmonar obstructiva crónica
Fibrosis pulmonar idiopática
Derrame pleural
Cáncer de pulmón
Apnea del sueño
Embolia pulmonar
Hipertensión arterial pulmonar
Fibrosis quística
Trastornos sistémicos
Full Text

Biomarkers, defined as a measurable characteristic that constitutes an indicator of a normal or pathogenic biological process or response to an exposure or intervention,1,2 have acquired a key role in respiratory medicine in the development of a patient-based, as opposed to disease-based, therapeutic approach.3 Biomarkers need to have high specificity for the disease or event to be evaluated, they must be easy and inexpensive to measure, show good discriminative capacity, and be more cost effective than indicators currently used in conventional clinical practice.4,5

Biomarkers are generally classified according to their field of application or their nature.4,5 In terms of their application, they are defined as diagnostic, monitoring, pharmacodynamic/response, predictive, prognostic, safety, and susceptibility/risk markers.4

Although imaging or pulmonary function tests can provide proven respiratory biomarkers, most have been developed from “omic-” based procedures, giving rise to genomic, transcriptomic, proteomic, metabolomic and epigenetic biomarkers.6–9 However, digital biomarkers collected from electronic devices10 that provide continuous, real-time information on complex parameters related to respiratory health must also be taken into consideration.

The expansion of biomarkers and their growing importance in the management of respiratory diseases has prompted us to critically evaluate the characteristics, conditions and applicability of the main biomarkers currently available for the management of patients with respiratory diseases.

Biomarkers in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a condition associated with considerable morbidity and mortality.11 The heterogeneous nature of this disease makes it difficult for clinicians to predict prognosis and response to treatment exclusively on the basis of clinical or functional data. For this reason, the search for diagnostic, prognostic and treatment response biomarkers in COPD has been one of the most important and fruitful fields of research in recent decades.

In the stable phase, biomarkers associated with interleukin (IL)-6-mediated inflammation, such as C-reactive protein (CRP) and fibrinogen, are known to be associated with an increased risk of death from COPD,12 and also define a pattern of increased risk of moderate and severe exacerbations.13 Studies investigating other biomarkers have shown that CC16 (club cell protein 16) and sRAGE (soluble receptor for advanced glycation end products) are associated with decreased lung function and emphysema progression, although the association is somewhat weak.14–16

Blood levels of miR-320c, which inhibits SERPINA1 expression in liver cells, are associated with the presence of lung disease in patients with different serum levels of alpha-1 antitrypsin.17 There is also evidence that interstitial expression of SOD3 and fibulin-5 in COPD patients is diminished, and that methylated miR-7 levels are elevated in patients with emphysema.18,19

The most interesting development is probably the use of peripheral blood eosinophil counts as a biomarker of response to treatment with inhaled corticosteroids (ICS). Eosinophil counts of over 300cells/mm3 indicate that adding an ICS to the patient's treatment will reduce the risk of COPD exacerbation. This biomarker has been extensively studied in population samples and clinical trials,20–26 and is a first step towards precision medicine in COPD.27,28

Biomarkers in asthma

Asthma, like COPD, is also a heterogeneous syndrome29 with a broad pheno-endotypic spectrum involving many different mediators. This variability makes it difficult to pinpoint a single biomarker that can help predict severity, evolution, and response to treatment.

Inflammatory mediators of asthma can be measured in various body samples, including the upper and lower airway, saliva, urine, and peripheral blood,30 although each type of sample has its advantages and limitations (Table 1).31

Table 1.

Advantages and limitations of different biomarker sampling methods in asthma.

Methods  Biomarker  Cut-off point  Advantages  Limitations 
Bronchoscopy:-Biopsy-Broncho-alveolar lavage (BAL)-Bronchial brushing  - Eosinophils- Neutrophils- Total inflammatory cell counts- Cytokines- Leakage of markers and mediators- Airway remodeling  No clear cut-off points  Semi-direct read-out  - Invasive- Requires expert staff- Not feasible in very severe disease with compromised lung function- Potential sampling site bias- Dilution (BAL) 
Induced sputum  - Eosinophils- Neutrophils- Total inflammatory cell count- Cytokines- Cell activation markers- No mediators  In general, a cut-off point of ≥3% is used to indicate sputum eosinophilia, and ≥61% to indicate sputum neutrophilia.However, adapting treatment on the basis of sputum eosinophils has established various sputum eosinophil cutoffs, ranging from 2% to 8%.  - Semi-direct read-out- Multiple biomarkers- Reproducible read-out- Appropriate method for disease phenotyping and follow-up in specialized centers  - Semi-invasive- Analyzable samples obtained from approx. 80%––90% of subjects- Adapted protocol needed for very severe disease with compromised lung function(contraindicated if FEV1<1L and/or with concomitant heart disease- Technically complex, time-consuming procedure, restricted to specialized centers 
Peripheral blood  - Eosinophils- Cell activation markers- IgE (total/specific)- Cytokines andmediators  Various cut-off values, mostly ranging 150–500cells/μL are used for blood eosinophils  Easy to collect  - Semi-invasive- Indirect read-out- High intra-subject diurnal variability- Blood eosinophils do not adequately reflect airway eosinophilia during treatment with systemic corticosteroids 
Exhaled breath  - FeNO-Volatile organic compounds (VOC)  Low:FeNO<25 ppb (≥12 years), <20 (<12 years), high FeNO> 50 (≥12 years), <35 (<12 years)  - Non-invasive- Simple method that allows repeat measurements- Appropriate method for phenotyping and monitoring- Direct read-out  - Various factors alter FeNO levels- No standardized VOC collection and analysis methods 
Exhaled breath condensate  - pH- Markers of oxidative stress- Leukotrienes- Cytokines  No clear cut-off points.Some studies show that pH ≤7.20 is related to poorly controlled asthma  - Non-invasive- Allows serial measurements  - Requires a specialized laboratory- Expensive- Variable results due to technical issues- Requires further developmentand validation 

Taken and adapted from: Diamant, et al.31.

Various molecular mechanisms related to asthma clinical phenotypes, particularly T2 asthma, have recently been identified.32 Sputum eosinophils is probably the best characterized and most useful T2 asthma biomarker identified so far. The analysis of induced sputum to determine central airway inflammation is a reproducible sampling method that is less invasive than bronchoscopy; however, it is time-consuming and must be performed in a specialized center.33 The usefulness of other biomarkers, such as immunoglobulin E (IgE), blood eosinophils, measurement of fractional exhaled nitric oxide (FeNO), or periostin, is still unclear.34,35 Therefore, although serum eosinophils do not always correlate with sputum eosinophils,36 they predict response to anti-IL5-IL-5Rα5 biologics,30 and several studies appear to support their usefulness as a predictor of exacerbations.37,38 There is also solid evidence of the association between serum vitamin D levels, asthma control, and the incidence of exacerbations.39 Serum IgE is used to decide the omalizumab regimen, but does not predict therapeutic response.40 FeNO is associated with eosinophilic airway inflammation, which can help diagnose asthma41 and could even identify dupilumab responders.42

Studies in other identifiable serum markers, such as CD26 (a marker of T cell activation), or CD14 (a monocyte-associated marker), insulin-like growth factors, or identifiable factors in exhaled breath condensate, such as mitochondrial or nuclear DNA, suggest they may be useful in establishing asthma pheno-endotypes, since they appear to be capable of differentiating allergic from non-allergic asthma, and have been associated with asthma severity and airway remodeling.43–47

These mediators in isolation do not fulfill the criteria for an ideal biomarker, so the use of combined panels will probably improve the identification of asthma endotypes.

Biomarkers in pneumonia

Biomarkers can be used in both the diagnosis48 and treatment of community-acquired and nosocomial pneumonia, since they help differentiate between bacterial and viral infection,49–56 identify and stratify patients with severe pneumonia,57–62 identify pneumonia-related complications,63,64 and indicate when to start and end antibiotic treatment53,65–69 (Fig. 1). Biomarkers provide reliable information about host response to infection as well as pathogenic activity within the host. These factors can support clinical parameters and aid in decision-making.

Fig. 1.

Biomarkers in pneumonia.

CRP: C-reactive protein, CT-pro-AVP: C-terminal portion of pro-arginine-vasopressin, ET-1: endothelin-1, PCT: procalcitonin, NT-proBNP: N-terminal pro-B-type natriuretic peptide, proADM: proadrenomedullin, PTX-3: pentraxin-3, SP-D: surfactant protein D.


Procalcitonin and CRP are still the most commonly used biomarkers in pneumonia.52,67,70–72 Other biomarkers, such as proadrenomedullin (pro-ADM),73,74 IL-6,75–77 IL-8, N-terminal pro b-type natriuretic peptide (NT-proBNP),61,78,79 C-terminal portion of pro-arginine-vasopressin (CT-pro-AVP),80 pentraxin 3 (PTX3),81,82 fibroblast growth factor 21 (FGF21),83 serum amyloid A (SAA)78 and surfactant protein D (SP-D)84 have recently been evaluated, although further studies are needed to determine their role as pneumonia markers. Biomarker levels in pneumonia can vary considerably, since they can be influenced by factors such as immune status, immunomodulatory therapy, the pathogen itself, disease severity, and the timing of biomarker determination with respect to the start of infection.85 This is one of their main drawbacks in clinical practice. There is a high level of evidence that biomarkers such as CRP and PCT should be considered decision support tools, and that they are most useful when used together with clinical parameters and severity scoring systems.86–89 Despite the many challenges yet to be surmounted in biomarker research, these parameters can substantially improve the management of patients with pneumonia.

Biomarkers in bronchiectasis and cystic fibrosis

The clinical and biological manifestations of cystic fibrosis and bronchiectasis are highly heterogeneous and complex due to the different pathophysiological mechanisms that determine their severity and prognosis, hence the importance of biomarkers that can identify clinical phenotypes and molecular endotypes in these patients, making it possible to administer personalized and targeted treatments.90,91

For many years, sputum color, which reflects pulmonary inflammation, mainly neutrophilic, has been the most widely used and affordable biomarker of poor prognosis in these patients.92 Nowadays, however, major advances in research have led to the identification of quantifiable blood-based and lung biomarkers that play a diagnostic, prognostic and even therapeutic role (Table 2).

Table 2.

Main biomarkers in bronchiectasis and cystic fibrosis and detection method.

Category  Biomarkers  Detection method 
ProteasesNeutrophil elastase  ELISA, semi-quantitative ELISA neutrophil elastase airway test stick – NEAT stick© 
Metalloproteases  ELISA 
Mucins  MUC5AC and MUC5AB  ELISA, chromatography 
Antimicrobial proteins and peptides  LL-37, SLPI, Lactoferrin, Lysozyme  ELISA 
MicrobiologyLung bacterial load  Microbiological culture (semi-quantitative), qPCR (quantitative) 
Pulmonary dysbiosis  Microbiome (16s RNA) 
Systemic inflammationWhite blood cells, neutrophils, platelets and erythrocyte sedimentation rate  Complete blood count, flow cytometry 

CRP: C-reactive protein; ELISA: enzyme-linked immunosorbent assay; qPCR: quantitative polymerase chain reaction; SLPI: leukocyte protease inhibitor; TNF-α: tumor necrosis factor alpha.

Lung proteases have been the most widely studied biomarkers in both diseases due to their key role in perpetuating inflammation and lung damage in these patients.93 One of the most important is neutrophil elastase, which has been shown to be a powerful marker of prognosis and severity and a good therapeutic target in patients with bronchiectasis.94,95 It is important to note that the biomarkers studied are derived not only from the host response to inflammation or bronchial infection, but also from certain characteristics of the infection, such as bacterial load and pulmonary dysbiosis, which have also been associated with severity and response to treatment.96,97

Finally, although the inflammatory response in these patients manifests predominantly in the lung, various blood-based biomarkers that are easily measured in clinical practice, such as CRP or TNF-α, also have a prognostic value.98

Biomarkers in idiopathic pulmonary fibrosis and other interstitial lung diseases

Most research in recent years into new biomarkers in these diseases has focused on idiopathic pulmonary fibrosis and other interstitial lung diseases.99–103 The only biomarkers currently recommended in clinical practice are lung function tests (LFT), radiological findings in chest high resolution computed tomography (HRCT), or histological analysis.104,105 The greatest challenge for the future lies in identifying diagnostic and prognostic biological biomarkers for these diseases, since their course is highly variable and arriving at a firm diagnosis can be difficult without resorting to invasive tests.106 The emergence of new therapies for IPF107 raises the need for new biomarkers that can help evaluate the response to these treatments.108,109

Four areas of research into the pathogenesis of pulmonary fibrosis are currently being explored: epithelial damage/dysfunction; extracellular matrix expression; regulation of the immune system; and genetics. Table 3 shows the most important biomarkers currently being investigated in the field of interstitial lung diseases. None of these promising biomarkers has yet shown significant diagnostic value, as they are not capable of differentiating between different interstitial lung diseases and their prognostic value is similar to that of LFTs. However, future studies could well reveal their true value in the diagnosis of interstitial lung diseases.

Table 3.

Main serological biomarkers in IPF and other ILDs.

Biomarkers of lung fibrosis
Epithelial damage  Extracellular matrix  Immune system  Genetics 
KL6, SP-A, SP-D, CC16, YKL40  MMP1, MMP7, LOXL2  CCL18, IL-6, Osteopontin  MUC5B polymorphisms, Telomere abnormalities 

KL6: Krebs von den Lungen-6; SP-A: surfactant protein A; SP-D: surfactant protein D), CL16: Clara cell protein 16; YKL-40: chitinase-3-Like Protein 1; MMP-1: matrix metalloproteinase 1; MMP-7: matrix metalloproteinase 7; LOXLX2: lysyl oxidase-like 2; CCL18: chemokine (C-C motif) ligand 18; IL-6: interleukin 6; MUC5B: mucin 5B.

Biomarkers in pulmonary embolism

Blood-based biomarkers optimize the diagnosis and treatment of pulmonary embolism (PE) (Fig. 2).

Fig. 2.

Biomarkers in the pathophysiology of acute pulmonary embolism. The biomarkers are positioned according to the pathophysiological mechanism they express and their diagnostic (in red) or prognosis (in purple) value.

BNP: B-type natriuretic peptide; CTEPH: chronic thromboembolic pulmonary hypertension; DVT: deep vein thrombosis; GDF-15: growth differentiation factor 15; H-FABP: heart-type fatty acid-binding protein; LMR: lymphocyte-monocyte ratio; LV: left ventricle; MR-proADM: mid-regional proadrenomedullin; NGAL: neutrophil gelatinase-associated lipocalin; NLR: neutrophil-lymphocyte ratio; NT-proBNP: amino-terminal fragment of proBNP; O2: oxygen; PA: pulmonary artery; PE: pulmonary embolism; PLR: platelet-lymphocyte ratio; pO2: partial pressure of oxygen; RV: right ventricle.


d-Dimer has a high negative predictive value for the diagnosis of PE, and can rule out PE in patients with low or intermediate clinical probability of PE, or those classified as PE-unlikely.110–114 Furthermore, elevated levels of D-dimer during follow-up are associated with a higher risk of thrombotic recurrence after stopping anticoagulation.115–117 D-dimer has also been useful in excluding PE in patients with COVID-19 pneumonia, although in these cases D-dimer cut-off points differ from those used in routine clinical practice in patients without SARS-CoV-2.118

Various blood-based biomarkers have prognostic value and can be used to stratify risk when combined with clinical and imaging parameters.110,119 The harmful effects of PE on the right ventricle (RV) determine prognosis during the acute phase. The most important markers of myocardial damage are cardiac troponins and heart-type fatty acid-binding protein (H-FABP).120–123 The main blood-based biomarkers of RV dysfunction are B-type natriuretic peptide (BNP) and its amino-terminal fragment (nT-proBNP). These cardiac biomarkers are particularly useful because they can rule out an unfavorable early course.124,125 The addition of other prognostic biomarkers, such as copeptin,126 lactate,127 serum creatinine,128 plasma sodium,129 cystatin C, and neutrophil gelatinase-associated lipocalin,130 could help determine the prognosis in patients with acute PE. Some routine analytical parameters have been associated with an increased risk of occult malignancy at the time of PE diagnosis, including anemia, high platelet and leukocyte counts, and d-dimer levels of more than 4000ng/mL.131–134

Finally, other blood-based biomarkers under investigation could be useful in PE: certain inflammatory markers (IL-6),135 neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio,136 and lymphocyte-to-monocyte ratio,137 growth differentiation factor 15 (GDF-15),138 mid-region proadrenomedullin,139 certain circulating microRNAs,140–142 and the microbiome.143

Biomarkers in pulmonary hypertension

In pulmonary arterial hypertension (PAH), prognosis is determined by the pathophysiological interaction between the rate of progression of obstructive changes in the pulmonary microcirculation and the adaptive response of the right ventricle (RV). The pathophysiological mechanisms of PAH include vasoconstriction, smooth muscle proliferation, inflammation, endothelial apoptosis, apoptosis-resistant endothelial proliferation, fibrosis, in situ thrombosis, and finally, plexiform lesions, which are a proliferation of what appear to be monoclonal endothelial cells144,145

A large number of PAH biomarkers have been identified, including markers of myocardial dysfunction and injury, inflammation, vascular dysfunction and proliferation, coagulation and platelet activity, hypoxia, and tissue damage,146–151 all of which can be useful in establishing a prognosis (Table 4).

Table 4.

Usefulness of some biomarkers in the diagnosis, treatment and follow-up of patients with pulmonary hypertension.

1.  Identify patient population at risk of pulmonary arterial hypertension (PE, systemic sclerosis): NT-proBNP, UA, PIM-147. 
2.  Disease progression and response to treatment: BNP, NT-proBNP, ET-1, Ang-2, ADM, PaCO2. 
3.  Identify patients with right heart failure: BNP, NT-proBNP, TnI, OPN. 
4.  PAH prognosis: BNP, NT-proBNP, TnT, IL-6, IL-8, IL-10, IL-12p70, PCR, OPN, ADMA, vWF, PaCO2, UA, kidney function, Na, copeptin, bilirubin. 

ADM: adrenomedullin; ADMA: asymmetric dimethylarginine; Ang: angiopoietin; ANP: atrial natriuretic peptide; BNP: brain natriuretic peptide; BUN: Blood Urea Nitrogen; CRP: C-reactive protein; CysC: cystatin C; ET-1; endothelin 1; GDF-15: growth differentiation factor 15; H-FABP: heart-type fatty binding protein; IL: interleukin; miRNA: microRNA; MPV: mean platelet volume; Na: sodium; NT-proBNP: N-terminal propeptide brain natriuretic; OPN: osteopontin; PIM-1: provirus integration site for Moloney murine leukemia virus; PLC: platelet count; TnI: troponin I; TnT: troponin T; UA: uric acid; VSMCs: vascular smooth muscle cells; vWF: von Willebrand factor.

Biomarkers in lung cancer

Lung cancer (LC) is the most common cause of death from cancer.152 The biomarkers identified so far range from readily available parameters, such as serum albumin or platelet count,153,154 to others that are more complex to measure, such as genetic mutations or biomarkers associated with the airway microbiome.155–157

In non-small cell LC patients with specific genetic lesions, appropriately targeted therapy improves treatment outcomes compared with standard chemotherapy. It is important to determine target molecular alterations in epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), proto-oncogene 1 (ROS 1), B-Raf proto-oncogene (BRAF), neurotrophic tyrosine receptor kinase (NTRK) and programmed cell death-1 (PD-1) and/or programmed cell death ligand-1 (PD-L1) immune checkpoints, as well as tumor mutational burden.158,159 Biomarkers guiding treatment in advanced LC, which have increased in recent years, now include both approved and investigational drugs. It is important to bear in mind that next generation sequencing (NGS) data can show changes and reveal new therapeutic and prognostic biomarkers. Considerable advances have been made in the use of biomarkers in patient selection protocols for screening programs and in the management of incidental pulmonary nodules. Promising results have been obtained from various biomarkers identified in blood and other fluids.160–168 Several multiple biomarker panels have been developed to help clinicians classify indeterminate pulmonary nodules.165–168 However, these panels must be used in an appropriate clinical setting, and require further validation.

Biomarkers in pleural effusion

Measuring biomarkers in pleural fluid (PF) is a rapid, non-invasive method of determining the etiology of pleural effusion (PE). In patients with heart failure, NT-proBNP169 in PF has high sensitivity for determining the cause of the disease, particularly in patients that do not satisfy any Light's criteria. pH and glucose are the most effective biomarkers in guiding decision-making in the management of parapneumonic pleural effusion (PNPE).170 Although many biomarkers of tuberculous pleural effusion (TPE) have been studied, >35IU/L adenosine deaminase (ADA)171 still has the highest diagnostic sensitivity. Many biomarkers of malignant pleural effusion (MPE) have also been studied, although a malignant etiology of PE must still be confirmed by cytohistology. Some of these biomarkers can classify PE according to its etiology; among them, calprotectin172 is the most effective in distinguishing malignant from benign PE. Other validated plasma tumor markers, such as CEA or CA15.3, can indicate the origin of MPE. New molecular biomarkers that can be used to define therapeutic targets and individualize the treatment of LC, such as EGFR, PDL1, ROS1 or ALK, deserve special mention.173 These markers can be measured effectively not only in tumor tissue but also in PE, thus avoiding invasive sampling techniques.

Biomarkers in sleep apnea

Obstructive sleep apnea (OSA) is a respiratory disorder characterized by total or partial occlusion of the airway during sleep. OSA causes sleep fragmentation, changes in intrathoracic pressure, and episodes of hypoxia-reoxygenation. These events occur repeatedly during sleep, and trigger intermediate mechanisms related to the pathophysiological consequences of OSA, such as sympathetic activation, endothelial dysfunction, hypercoagulability, oxidative stress, inflammation, and metabolic dysregulation. Estimates suggest that between 35% and 40% of variance in the apnea hypopnea index may be explained by familial factors.174 Some experts have suggested that genetic variants in craniofacial structure, body fat distribution, and neural control of upper airway muscles may contribute to the manifestation of different OSA phenotypes.

Numerous studies have explored the usefulness of different diagnostic and prognostic biomarkers for OSA. In adults, the combined analysis of glycosylated hemoglobin (HbA1c), CRP, and erythropoietin (EPO) are useful for OSA screening.175 Furthermore, IL-6 and IL-10 detected in blood have been shown to be robust biomarkers for OSA diagnosis,176 and microRNAs have recently emerged as potential biomarkers for diagnosis177,178 and response to CPAP treatment in patients with resistant hypertension and OSA.179 In children, the combination of kallikrein-1, uromodulin, urocortin-3, and orosomucoid-1 in urine samples has shown excellent diagnostic accuracy,180 and changes in urinary neurotransmitters are good biomarkers for OSA.181

Biomarkers related to the pathophysiological processes of OSA have been identified, mainly associated with sympathetic activation (catecholamines),181 endothelial dysfunction (nitric oxide and adhesion molecules such as vascular cell adhesion and intercellular adhesion proteins182,183), hypercoagulability,184,185 oxidative stress (ROS, isoprostane,186 malondialdehyde),187 inflammation188 (HIF-1α, NF-κß, IL-6 and TNF-α), and metabolic dysregulation. Recent evidence has shown the association between OSA and elevated circulating levels of VCAM-1, which would contribute to tumorigenesis through integrin-based adhesion, and could therefore increase cancer prevalence, incidence, and mortality in individuals with OSA.189 Soluble PD-L1 has also been suggested as a potential biomarker of aggressiveness and metastasis in patients with cutaneous melanoma and OSA.190

Biomarkers in systemic diseases with pulmonary involvement

Diffuse interstitial lung disease (ILD) is a leading cause of morbidity and mortality in patients with systemic autoimmune diseases (SAD), and is a particularly common manifestation in rheumatoid arthritis (RA), systemic sclerosis (SSc), and myopathy.191

Serum autoantibodies are currently the only biomarkers available in clinical practice for the diagnosis and classification of SADs.192 Anti-Scl-70 (also known as anti-topoisomerase I), anti-U3-RNP and anti-Th/To antibodies can identify patients at risk of developing SSc-associated ILD.193

In RA, older age, male gender, a history of smoking, and seropositivity for rheumatoid factor (RF) or cyclic citrullinated peptide antibody (CCPA) are risk factors for ILD. Other antibodies against carbamylated proteins and antibodies against peptidyl arginine deaminases (anti-PAD) are also linked to RA.194

Silicosis has also been associated with a higher incidence of systemic autoimmune rheumatic diseases.195

Several antibodies that increase the risk of developing ILD, such as anti-aminoacyl-tRNA synthetase (anti-synthetases) and anti-CADM-140 (MDA5/IFIH1) can be detected in patients with myopathy. ILD is sub-acute in anti-synthetase positive patients, and is particularly aggressive in the case of MDA5 myopathy.196

Progress has been made in recent years in the search for biomarkers other than autoantibodies, such as proteins secreted by alveolar epithelial cells, inflammatory cytokines, and chemokines. IL-6, IL-8, IL-10, CCL2, CXCL10, CX3CL1, fibroblast growth factor 2 (FGF-2), and vascular endothelial growth factor, KL-6, and SP-D have been associated with the presence or progression of ILD in patients with various types of SAD197 (Table 5).

Table 5.

Possible serum biomarkers related to progression in ILD associated with systemic diseases.

Systemic disease  Biomarkers 
Systemic sclerosis  - Anti-topoisomerase-1 Ab (Scl-70)- Anti-U11/U12 RNP Ab- Antinuclear antibody Ab staining pattern (indicates anti-Th/To, U3 RNP)- C-reactive protein- IL-6 and IL-10- CCL2 (MCP-1), CXCL4, CCL18- KL-6- SP-D 
Rheumatoid arthritis  - Rheumatoid factor- ACPA, anti-PAD and anti-carbamylated proteins- HLA-DRB1- MMP-7- KL6- PARC- SP-D- Interferon-γ inducible protein 10 (IP-10 CXCL10) 
Dermato/polymyositis  - Anti-aminoacyl-tRNA synthetase (anti-synthetases)- Anti-CADM-140 (MDA5/IFIH1).- Ferritin- CRP- KL6 
Sjogren's syndrome  -Anti-Ro 52/SSA antibodies-KL6-Angiopoietin-2 protein (Angptl2) 
Systemic lupus erythematosus  -Extractable nuclear antigens (ENA) 

ACPA: anti-citrulline antibodies; CADM: clinically amyopathic dermatomyositis; CCL: chemokine ligand; CRP: C-reactive protein; CXCL4: chemokine ligand 4; IFIH1: interferon induced with helicase C domain 1; IL-6: interleukin 6; IL-10: interleukin 10; KL6: Krebs von den Lungen-6; MCP1: monocyte chemoattractant protein 1; MDA5: melanoma differentiation-associated protein 5; MMP-7: matrix metalloproteinase 7; PAD: peptidyl arginine deaminase; PARC: pulmonary and activation-regulated chemokine; RNA: ribonucleic acid; SP-D: surfactant protein D.


The practice of precision medicine, in which treatment is tailored for each patient, has been made possible by the discovery of markers, particularly biological markers. Ideally, these markers should be simple and inexpensive to measure in clinical practice, easy to interpret, sensitive and specific for a certain disease (in this case, a respiratory disease), and should be diagnostic, prognostic and/or predict response to treatment. Although research has been able to link some lung diseases with more biomarkers than others, considerable interest in this field in recent years suggests that in the not too distant future researchers will identify biological biomarkers that will help us find homogeneity in the predominantly heterogeneous field of respiratory diseases.


This manuscript has not received any funding.

Conflicts of interest

The authors declare that they have no conflicts of interest.

G.A. FitzGerald.
Measure for measure: biomarker standards and transparency.
Sci Transl Med, 8 (2016), pp. 343fs10
FDA-NIH Biomarker Working Group.
BEST (biomarkers endpoints, and other tools) resource [Internet].
Food and Drug Administration, (2016),
J.M. Anaya, C. Duarte-Rey, J.C. Sarmiento-Monroy, D. Bardey, J. Castiblanco, A. Rojas-Villarraga.
Personalized medicine. Closing the gap between knowledge and clinical practice.
Autoimmun Rev, 15 (2016), pp. 833-842
R.M. Califf.
Biomarker definitions and their applications.
Exp Biol Med (Maywood), 243 (2018), pp. 213-221
J.K. Aronson, R.E. Ferner.
Biomarkers – a general review.
Curr Protoc Pharmacol, 76 (2017), pp. 9.23.1-9.23.17
G. Novelli, C. Ciccacci, P. Borgiani, M. Papaluca Amati, E. Abadie.
Genetic tests and genomic biomarkers: regulation, qualification and validation.
Clin Cases Miner Bone Metab, 5 (2008), pp. 149-154
M. Frantzi, A. Bhat, A. Latosinska.
Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development.
Clin Transl Med, 3 (2014), pp. 7
C.R. Marchand, F. Farshidfar, J. Rattner, O.F. Bathe.
A framework for development of useful metabolomic biomarkers and their effective knowledge translation.
Metabolites, 8 (2018), pp. 59
J.L. García-Giménez, M. Seco-Cervera, T.O. Tollefsbol, C. Romá-Mateo, L. Peiró-Chova, P. Lapunzina, et al.
Epigenetic biomarkers: current strategies and future challenges for their use in the clinical laboratory.
Crit Rev Clin Lab Sci, 54 (2017), pp. 529-550
T.R. Insel.
Digital phenotyping: technology for a new science of behavior.
JAMA, 318 (2017), pp. 1215-1216
J.B. Soriano, I. Alfageme, M. Miravitlles, P. de Lucas, J.J. Soler-Cataluña, F. García-Río, et al.
Prevalence and determinants of COPD in Spain: EPISCAN II.
Arch Bronconeumol, 57 (2021), pp. 61-69
J.M. Fermont, K.L. Masconi, M.T. Jensen, R. Ferrari, V.A.P. di Lorenzo, J.M. Marott, et al.
Biomarkers and clinical outcomes in COPD: a systematic review and meta-analysis.
M. Thomsen, T.S. Ingebrigtsen, J.L. Marott, M. Dahl, P. Lange, J. Vestbo, et al.
Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease.
H.Y. Park, A. Churg, J.L. Wright, Y. Li, S. Tam, S.F.P. Man, et al.
Club cell protein 16 and disease progression in chronic obstructive pulmonary disease.
Am J Respir Crit Care Med, 188 (2013), pp. 1413-1419
S. Guerra, M. Halonen, M.M. Vasquez, A. Spangenberg, D.A. Stern, W.J. Morgan, et al.
Relation between circulating CC16 concentrations, lung function, and development of chronic obstructive pulmonary disease across the lifespan: a prospective study.
Lancet Respir Med, 3 (2015), pp. 613-620
R.A. Stockley, D.M.G. Halpin, B.R. Celli, D. Singh.
Chronic obstructive pulmonary disease biomarkers and their interpretation.
Am J Respir Crit Care Med, 199 (2019), pp. 1195-1204
S. Martínez-Gestoso, M.T. García-Sanz, U. Calvo-Álvarez, L. Doval-Oubiña, S. Camba-Matos, F.J. Salgado, et al.
Variability of blood eosinophil count and prognosis of COPD exacerbations.
Ann Med, 53 (2021), pp. 1152-1158
N. Matamala, B. Lara, G. Gómez-Mariano, S. Martínez, I. Vázquez-Domínguez, Á. Otero-Sobrino, et al.
miR-320c regulates SERPINA1 expression and is induced in patients with pulmonary disease.
Arch Bronconeumol (Engl Ed), (2020),
J. García-Valero, J. Olloquequi, E. Rodríguez, M. Martín-Satué, L. Texidó, J. Ferrer.
Decreased expression of EC-SOD and fibulin-5 in alveolar walls of lungs from COPD patients.
Arch Bronconeumol (Engl Ed), (2021),
R. Rosas-Alonso, R. Galera, J.J. Sánchez-Pascuala, R. Casitas, M. Burdiel, E. Martínez-Cerón, et al.
Hypermethylation of anti-oncogenic microRNA 7 is increased in emphysema patients.
Arch Bronconeumol (Engl Ed), 56 (2020), pp. 506-513
M. Bafadhel, S. Peterson, M.A. de Blas, P.M. Calverley, S.I. Rennard, K. Richter, et al.
Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials.
Lancet Respir Med, 6 (2018), pp. 117-126
T.H. Harries, V. Rowland, C.J. Corrigan, I.J. Marshall, L. McDonnell, V. Prasad, et al.
Blood eosinophil count, a marker of inhaled corticosteroid effectiveness in preventing COPD exacerbations in post-hoc RCT and observational studies: systematic review and meta-analysis.
M. Miravitlles, M. Monteagudo, I. Solntseva, B. Alcázar.
Blood eosinophil counts and their variability and risk of exacerbations in COPD: a population-based study.
Arch Bronconeumol, 57 (2021), pp. 13-20
R. Golpe, D. Dacal, P. Sanjuán-López, I. Martín-Robles, L.A. Pérez-de-Llano.
Plasma eosinophil count and patient-centered events in chronic obstructive pulmonary disease in real-life clinical practice.
Arch Bronconeumol, 56 (2020), pp. 129-130
R. Golpe, D. Dacal, P. Sanjuán-López, I. Martín-Robles, L.A. Pérez-de-Llano.
Plasma eosinophil count and patient-centered events in chronic obstructive pulmonary disease in real-life clinical practice.
Arch Bronconeumol, 56 (2020), pp. 129-130
J.J. Soler-Cataluña, L. Novella, C. Soler, M.L. Nieto, V. Esteban, F. Sánchez-Toril, et al.
Clinical characteristics and risk of exacerbations associated with different diagnostic criteria of asthma-COPD overlap.
Arch Bronconeumol, 56 (2020), pp. 282-290
J.M. Díaz López, B. Giran González, B. Alcázar-Navarrete.
Medicina personalizada en la enfermedad pulmonar obstructiva crónica: ¿cómo de cerca estamos?.
Arch Bronconeumol, 56 (2020), pp. 420-421
M. Miravitlles, M. Calle, J.J. Soler-Cataluña.
GesEPOC 2021: one more step towards personalized treatment of COPD.
Arch Bronconeumol, 57 (2021), pp. 9-10
V. Plaza, M. Blanco, G. García, J. Korta, J. Molina, S. Quirce.
Highlights of the Spanish Asthma Guidelines (GEMA), version 5.0.
Arch Bronconeumol (Engl Ed), 57 (2021), pp. 11-12
N.E. Alexis.
Biomarker sampling of the airways in asthma.
Curr Opin Pulm Med, 20 (2014), pp. 46-52
Z. Diamant, S. Vijverberg, K. Alving, A. Bakirtas, L. Bjermer, A. Custovic, et al.
Toward clinically applicable biomarkers for asthma: an EAACI position paper.
Allergy, 74 (2019), pp. 1835-1851
M.M. García Ródenas, C. Fernández-Aracil, F.M. Marco de la Calle.
Might basophils be a reliable biomarker in severe asthma?.
Arch Bronconeumol, 57 (2021), pp. 79-80
S.F. Seys.
Role of sputum biomarkers in the management of asthma.
Curr Opin Pulm Med, 23 (2017), pp. 34-40
A.H. Wagener, S.B. de Nijs, R. Lutter, A.R. Sousa, E.J.M. Weersink, E.H. Bel, et al.
External validation of blood eosinophils FE(NO) and serum periostin as surrogates for sputum eosinophils in asthma.
E. Arismendi, C. Picado Vallés.
Current role of biomarkers in severe uncontrolled asthma.
Arch Bronconeumol, 56 (2020), pp. 347-348
J.M. FitzGerald, E.R. Bleecker, A. Menzies-Gow, J.G. Zangrilli, I. Hirsch, P. Metcalfe, et al.
Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies.
Lancet Respir Med, 6 (2018), pp. 51-64
N. Mallah, S. Rodriguez-Segade, F.J. Gonzalez-Barcala, B. Takkouche.
Blood eosinophil count as predictor of asthma exacerbation. A meta-analysis.
Pediatr Allergy Immunol, 32 (2021), pp. 465-478
F.J. Gonzalez-Barcala, M.E. San-Jose, J.J. Nieto-Fontarigo, J.M. Carreira, U. Calvo-Alvarez, M.J. Cruz, et al.
Association between blood eosinophil count with asthma hospital readmissions.
Eur J Intern Med, 53 (2018), pp. 34-39
R. Andújar-Espinosa, L. Salinero-González.
Vitamin D supplementation: a treatment with possible benefits in asthma.
Arch Bronconeumol (Engl Ed), (2021),
S. Korn, I. Haasler, F. Fliedner, G. Becher, P. Strohner, A. Staatz, et al.
Monitoring free serum IgE in severe asthma patients treated with omalizumab.
Respir Med, 106 (2012), pp. 1494-1500
I. Ojanguren, V.V. Plaza.
FeNO for asthma diagnosis in adults: more lights than shadows.
Arch Bronconeumol, 57 (2021), pp. 85-86
D. Yang, T. Huang, B. Liu, Z. Du, C. Liu.
Dupilumab in patients with uncontrolled asthma: type 2 biomarkers might be predictors of therapeutic efficacy.
S. Vázquez-Mera, J.G. Pichel, F.J. Salgado.
Involvement of IGF proteins in severe allergic asthma: new roles for old players.
Arch Bronconeumol (Engl Ed), (2021),
J.J. Nieto-Fontarigo, F.J. Salgado, M.E. San-José, M.J. Cruz, A. Casas-Fernández, M.J. Gómez-Conde, et al.
The CD14 (-159 C/T) SNP is associated with sCD14 levels and allergic asthma, but not with CD14 expression on monocytes.
J.J. Nieto-Fontarigo, F.J. Salgado, M.E. San-José, M.J. Cruz, L. Valdés, A. Pérez-Díaz, et al.
Expansion of different subpopulations of CD26-/low T cells in allergic and non-allergic asthmatics.
J.J. Nieto-Fontarigo, F.J. González-Barcala, L.J. Andrade-Bulos, M.E. San-José, M.J. Cruz, L. Valdés-Cuadrado, et al.
iTRAQ-based proteomic analysis reveals potential serum biomarkers of allergic and nonallergic asthma.
Allergy, 75 (2020), pp. 3171-3180
G.E. Carpagnano, G. Scioscia, D. Lacedonia, P. Soccio, C.M.I. Quarato, G. Cotugno, et al.
Searching for inflammatory and oxidative stress markers capable of clustering severe asthma.
Arch Bronconeumol (Engl Ed), 57 (2021), pp. 338-344
S. Spoto, J.M. Legramante, M. Minieri, M. Fogolari, A. Terrinoni, E. Valeriani, et al.
How biomarkers can improve pneumonia diagnosis and prognosis: procalcitonin and mid-regional-pro-adrenomedullin.
Biomark Med, 14 (2020), pp. 549-562
A. Julián-Jiménez, J. González Del Castillo, F.J. Candel.
Usefulness and prognostic value of biomarkers in patients with community-acquired pneumonia in the emergency department.
Med Clin (Barc), 148 (2017), pp. 501-510
J. Almirall, I. Bolíbar, P. Toran, G. Pera, X. Boquet, X. Balanzó, et al.
Contribution of C-reactive protein to the diagnosis and assessment of severity of community-acquired pneumonia.
Chest, 125 (2004), pp. 1335-1342
S.A. Flanders, J. Stein, G. Shochat, K. Sellers, M. Holland, J. Maselli, et al.
Performance of a bedside C-reactive protein test in the diagnosis of community-acquired pneumonia in adults with acute cough.
Am J Med, 116 (2004), pp. 529-535
W.H. Self, R.A. Balk, C.G. Grijalva, D.J. Williams, Y. Zhu, E.J. Anderson, et al.
Procalcitonin as a marker of etiology in adults hospitalized with community-acquired pneumonia.
Clin Infect Dis, 65 (2017), pp. 183-190
I.S. Kamat, V. Ramachandran, H. Eswaran, D. Guffey, D.M. Musher.
Procalcitonin to distinguish viral from bacterial pneumonia: a systematic review and meta-analysis.
Clin Infect Dis, 70 (2019), pp. 538-542
M.H. Wu, C.C. Lin, S.L. Huang, H.M. Shih, C.C. Wang, C.C. Lee, et al.
Can procalcitonin tests aid in identifying bacterial infections associated with influenza pneumonia? A systematic review and meta-analysis.
Influenza Other Respir Viruses, 7 (2013), pp. 349-355
J.S. Brown.
Biomarkers and community-acquired pneumonia.
Thorax, 64 (2009), pp. 556-558
S.B. Andersen, G. Baunbæk Egelund, A.V. Jensen, P.T. Petersen, G. Rohde, P. Ravn.
Failure of CRP decline within three days of hospitalization is associated with poor prognosis of community-acquired pneumonia.
Infect Dis (London), 49 (2017), pp. 251-260
M. Masiá, F. Gutiérrez, C. Shum, S. Padilla, J.C. Navarro, E. Flores, et al.
Usefulness of procalcitonin levels in community-acquired pneumonia according to the patients outcome research team pneumonia severity index.
Chest, 128 (2005), pp. 2223-2229
S. Krüger, S. Ewig, R. Marre, J. Papassotiriou, K. Richter, H. von Baum, et al.
Procalcitonin predicts patients at low risk of death from community-acquired pneumonia across all CRB-65 classes.
Eur Respir J, 31 (2008), pp. 349-355
P. Ramírez, M. Ferrer, V. Martí, S. Reyes, R. Martínez, R. Menéndez, et al.
Inflammatory biomarkers and prediction for intensive care unit admission in severe community-acquired pneumonia.
Crit Care Med, 39 (2011), pp. 2211-2217
W.H. Self, C.G. Grijalva, D.J. Williams, A. Woodworth, R.A. Balk, S. Fakhran, et al.
Procalcitonin as an early marker of the need for invasive respiratory or vasopressor support in adults with community-acquired pneumonia.
H. Seo, S.-I. Cha, K.-M. Shin, J.-K. Lim, S.-H. Choi, Y.-H. Lee, et al.
Clinical impact of N-terminal prohormone of brain natriuretic peptide on patients hospitalized with community-acquired pneumonia.
Am J Med Sci, 360 (2020), pp. 383-391
A. Ceccato, M. Panagiotarakou, O.T. Ranzani, M. Martin-Fernandez, R. Almansa-Mora, A. Gabarrus, et al.
Lymphocytopenia as a predictor of mortality in patients with icu-acquired pneumonia.
J Clin Med, 8 (2019), pp. 843
R. Menéndez, R. Méndez, I. Aldás, S. Reyes, P. Gonzalez-Jimenez, P.P. España, et al.
Community-acquired pneumonia patients at risk for early and long-term cardiovascular events are identified by cardiac biomarkers.
Chest, 156 (2019), pp. 1080-1091
A. Putot, E. Bouhey, J. Tetu, J. Barben, E. Timsit, S. Putot, et al.
Troponin elevation in older patients with acute pneumonia: frequency and prognostic value.
J Clin Med, 9 (2020), pp. 3623
P. Schuetz, Y. Wirz, R. Sager, M. Christ-Crain, D. Stolz, M. Tamm, et al.
Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections.
Cochrane Database Syst Rev, 10 (2017), pp. CD007498
P. Schuetz, M. Christ-Crain, M. Wolbers, U. Schild, R. Thomann, C. Falconnier, et al.
Procalcitonin guided antibiotic therapy and hospitalization in patients with lower respiratory tract infections: a prospective, multicenter, randomized controlled trial.
BMC Health Serv Res, 7 (2007), pp. 102
I. Pink, D. Raupach, J. Fuge, R.-P. Vonberg, M.M. Hoeper, T. Welte, et al.
C-reactive protein and procalcitonin for antimicrobial stewardship in COVID-19.
Infection, 49 (2021), pp. 935-943
A. Branche, O. Neeser, B. Mueller, P. Schuetz.
Procalcitonin to guide antibiotic decision making.
Curr Opin Infect Dis, 32 (2019), pp. 130-135
M. Bartoletti, M. Antonelli, F.A. Bruno Blasi, I. Casagranda, A. Chieregato, R. Fumagalli, et al.
Procalcitonin-guided antibiotic therapy: an expert consensus.
Clin Chem Lab Med, 56 (2018), pp. 1223-1229
R. Pfister, M. Kochanek, T. Leygeber, C. Brun-Buisson, E. Cuquemelle, M. Benevides Machado, et al.
Procalcitonin for diagnosis of bacterial pneumonia in critically ill patients during 2009 H1N1 influenza pandemic: a prospective cohort study, systematic review and individual patient data meta-analysis.
Crit Care, 18 (2014), pp. R44
T. Lisboa, R. Seligman, E. Diaz, A. Rodriguez, P.J.Z. Teixeira, J. Rello.
C-reactive protein correlates with bacterial load and appropriate antibiotic therapy in suspected ventilator-associated pneumonia.
Crit Care Med, 36 (2008), pp. 166-171
C. Cillóniz, A. Torres, C. Garcia-Vidal, E. Moreno-Garcia, R. Amaro, N. Soler, et al.
The value of C-reactive protein-to-lymphocyte ratio in predicting the severity of SARS-CoV-2 pneumonia.
Arch Bronconeumol, 57 (2021), pp. 79-82
P.P. España, A. Capelastegui, C. Mar, A. Bilbao, J.M. Quintana, R. Diez, et al.
Performance of pro-adrenomedullin for identifying adverse outcomes in community-acquired pneumonia.
J Infect, 70 (2015), pp. 457-466
J.M. Legramante, M. Mastropasqua, B. Susi, O. Porzio, M. Mazza, G. Miranda Agrippino, et al.
Prognostic performance of MR-pro-adrenomedullin in patients with community acquired pneumonia in the Emergency Department compared to clinical severity scores PSI and CURB.
PLOS ONE, 12 (2017), pp. e0187702
E.H. Burgmeijer, R. Duijkers, R. Lutter, M.J.M. Bonten, V.A. Schweitzer, W.G. Boersma.
Plasma cytokine profile on admission related to aetiology in community-acquired pneumonia.
Clin Respir J, 13 (2019), pp. 605-613
I. Andrijevic, J. Matijasevic, L. Andrijevic, T. Kovacevic, B. Zaric.
Interleukin-6 and procalcitonin as biomarkers in mortality prediction of hospitalized patients with community acquired pneumonia.
Ann Thorac Med, 9 (2014), pp. 162-167
C.D. Fernandes, M.B. Arriaga, M.C.M. Costa, M.C.M. Costa, M.H.M. Costa, C.L. Vinhaes, et al.
Host inflammatory biomarkers of disease severity in pediatric community-acquired pneumonia: a systematic review and meta-analysis.
Open Forum Infect Dis, 6 (2019), pp. ofz520
M. Guo, X. Cao, B. Shen, X. Geng, R. Chen, S. Gong, et al.
The predictive value of NT-pro-brain natriuretic peptide for risk of pneumonia in patients on maintenance hemodialysis.
Blood Purif, 49 (2020), pp. 348-355
M. Kolditz, M. Halank, C.S. Schiemanck, A. Schmeisser, G. Höffken.
High diagnostic accuracy of NT-proBNP for cardiac origin of pleural effusions.
Eur Respir J, 28 (2006), pp. 144-150
S. Krüger, S. Ewig, J. Kunde, O. Hartmann, N. Suttorp, T. Welte, et al.
Pro-atrial natriuretic peptide and pro-vasopressin for predicting short-term and long-term survival in community-acquired pneumonia: results from the German Competence Network CAPNETZ.
Thorax, 65 (2010), pp. 208-214
N.U. Tekerek, B.N. Akyildiz, B.D. Ercal, S. Muhtaroglu.
New biomarkers to diagnose ventilator associated pneumonia: pentraxin 3 and surfactant protein D.
Indian J Pediatr, 85 (2018), pp. 426-432
J. Song, D.W. Park, S. Moon, H.-J. Cho, J.H. Park, H. Seok, et al.
Diagnostic and prognostic value of interleukin-6, pentraxin 3, and procalcitonin levels among sepsis and septic shock patients: a prospective controlled study according to the Sepsis-3 definitions.
BMC Infect Dis, 19 (2019), pp. 968
F. Ebrahimi, C. Wolffenbuttel, C.A. Blum, C. Baumgartner, B. Mueller, P. Schuetz, et al.
Fibroblast growth factor 21 predicts outcome in community-acquired pneumonia: secondary analysis of two randomised controlled trials.
Eur Respir J, 53 (2019), pp. 1800973
V. Prendki, A. Malézieux-Picard, L. Azurmendi, J.-C. Sanchez, N. Vuilleumier, S. Carballo, et al.
Accuracy of C-reactive protein, procalcitonin, serum amyloid A and neopterin for low-dose CT-scan confirmed pneumonia in elderly patients: a prospective cohort study.
PLOS ONE, 15 (2020), pp. e0239606
N. Raess, P. Schuetz, N. Cesana-Nigro, B. Winzeler, S.A. Urwyler, S. Schaedelin, et al.
Influence of prednisone on inflammatory biomarkers in community-acquired pneumonia: secondary analysis of a randomized trial.
J Clin Pharmacol, 61 (2021), pp. 1406-1414
M.W. Kim, J.Y. Lim, S.H. Oh.
Mortality prediction using serum biomarkers and various clinical risk scales in community-acquired pneumonia.
Scand J Clin Lab Invest, 77 (2017), pp. 486-492
H. Zhou, S. Guo, T. Lan, S. Ma, F. Zhang, Z. Zhao.
Risk stratification and prediction value of procalcitonin and clinical severity scores for community-acquired pneumonia in ED.
Am J Emerg Med, 36 (2018), pp. 2155-2160
V.F. Cury, L.Q. Antoniazzi, P.H.K. de Oliveira, W.V. Borelli, S.V.da. Cunha, G.C. Frison, et al.
Developing the pneumonia-optimized ratio for community-acquired pneumonia: an easy, inexpensive and accurate prognostic biomarker.
PLOS ONE, 16 (2021), pp. e0248897
M. Alan, E. Grolimund, A. Kutz, M. Christ-Crain, R. Thomann, C. Falconnier, et al.
Clinical risk scores and blood biomarkers as predictors of long-term outcome in patients with community-acquired pneumonia: a 6-year prospective follow-up study.
J Intern Med, 278 (2015), pp. 174-184
M.A. Martínez-García, C. Oliveira, L. Máiz, R.M. Girón, C. Prados, D. De la Rosa, et al.
Bronchiectasis: a complex heterogeneous disease.
Arch Bronconeumol, 55 (2019), pp. 427-433
A. Agustí, M. Bafadhel, R. Beasley, E.H. Bel, R. Faner, P.G. Gibson, et al.
Precision medicine in airway diseases: moving to clinical practice.
Eur Respir J, 50 (2017), pp. 1701655
R.A. Stockley, D. Bayley, S.L. Hill, A.T. Hill, S. Crooks, E.J. Campbell.
Assessment of airway neutrophils by sputum colour: correlation with airways inflammation.
Thorax, 56 (2001), pp. 366-372
M.C. McKelvey, R. Brown, S. Ryan, M.A. Mall, S. Weldon, C.C. Taggart.
Proteases mucus, and mucosal immunity in chronic lung disease.
Int J Mol Sci, 22 (2021), pp. 5018
J.D. Chalmers, K.L. Moffitt, G. Suarez-Cuartin, O. Sibila, S. Finch, E. Furrie, et al.
Neutrophil elastase activity is associated with exacerbations and lung function decline in bronchiectasis.
Am J Respir Crit Care Med, 195 (2017), pp. 1384-1393
J.D. Chalmers, C.S. Haworth, M.L. Metersky, M.R. Loebinger, F. Blasi, O. Sibila, et al.
Phase 2 trial of the DPP-1 inhibitor brensocatib in bronchiectasis.
N Engl J Med, 383 (2020), pp. 2127-2137
O. Sibila, E. Laserna, A. Shoemark, H.R. Keir, S. Finch, A. Rodrigo-Troyano, et al.
Airway bacterial load and inhaled antibiotic response in bronchiectasis.
Am J Respir Crit Care Med, (2019), pp. 33-41
R. Faner, O. Sibila, A. Agustí, E. Bernasconi, J.D. Chalmers, G.B. Huffnagle, et al.
The microbiome in respiratory medicine: current challenges and future perspectives.
Eur Respir J, 49 (2017), pp. 1602086
T. Posadas, G. Oscullo, E. Zaldivar, C. Villa, Y. Dobarganes, R. Girón, et al.
C-reactive protein concentration in steady-state bronchiectasis: prognostic value of future severe exacerbations data from the Spanish Registry of Bronchiectasis (RIBRON).
Arch Bronconeumol, 57 (2021), pp. 21-27
B. Ley, K.K. Brown, H.R. Collard.
Molecular biomarkers in idiopathic pulmonary fibrosis.
Am J Physiol Cell Mol Physiol, 307 (2014), pp. L681-L691
J. Sellarés, M. Molina-Molina.
Biomarcadores séricos en las enfermedades pulmonares intersticiales difusas.
Arch Bronconeumol, 56 (2020), pp. 349-350
P. Spagnolo, A. Tzouvelekis, T.M. Maher.
Personalized medicine in idiopathic pulmonary fibrosis: facts and promises.
Curr Opin Pulm Med, 21 (2015), pp. 470-478
A. Adegunsoye, V. Rekha, I. Noth.
Integrating genomics into management of fibrotic interstitial lung disease.
Chest, 155 (2019), pp. 1026-1040
L. Guo, Y. Yang, F. Liu, C. Jiang, Y. Yang, H. Pu, et al.
Clinical research on prognostic evaluation of subjects with IPF by peripheral blood biomarkers quantitative imaging characteristics and pulmonary function parameters.
Arch Bronconeumol, 56 (2020), pp. 365-372
G. Bermudo, G. Suarez-Cuartin, P. Rivera-Ortega, J.A. Rodriguez-Portal, J. Sauleda, B. Nuñez, et al.
Different faces of idiopathic pulmonary fibrosis with preserved forced vital capacity.
D. Castillo Villegas, S. Barril, J. Giner, P. Millan-Billi, A. Rodrigo-Troyano, J.L. Merino, et al.
Study of diffuse interstitial lung disease with the analysis of volatile particles in exhaled air.
Arch Bronconeumol (Engl Ed), (2021),
D. Castillo, A. Sánchez-Font, V. Pajares, T. Franquet, R. Llatjós, I. Sansano, et al.
A multidisciplinary proposal for a diagnostic algorithm in idiopathic pulmonary fibrosis: the role of transbronchial cryobiopsy.
Arch Bronconeumol, 56 (2020), pp. 99-105
M. Molina-Molina.
The future of pharmacological treatment in idiopathic pulmonary fibrosis.
Arch Bronconeumol, 55 (2019), pp. 642-647
I. Salvador-Corres, B. Quirant-Sanchez, A. Teniente-Serra, C. Centeno, A. Moreno, L. Rodríguez-Pons, et al.
Detection of autoantibodies in bronchoalveolar lavage in patients with diffuse interstitial lung disease.
Arch Bronconeumol (Engl Ed), 57 (2021), pp. 351-358
L. Lopez-Lopez, E. Cabrera Cesar, E. Lara, M.V. Hidalgo-San Juan, C. Parrado, E. Martín-Montañez, et al.
Pro-fibrotic factors as potential biomarkers of anti-fibrotic drug therapy in patients with idiopathic pulmonary fibrosis.
Arch Bronconeumol, 57 (2021), pp. 231-233
S.V. Konstantinides, G. Meyer, C. Becattini, H. Bueno, G.J. Geersing, V.P. Harjola, et al.
2019 ESC guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS): the task force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC).
Eur Respir J, 54 (2019), pp. 1901647
E. Ceriani, C. Combescure, G. Le Gal, M. Nendaz, T. Perneger, H. Bounameaux, et al.
Clinical prediction rules for pulmonary embolism: a systematic review and meta-analysis.
J Thromb Haemost, 8 (2010), pp. 957-970
J.L. Lobo, S. Alonso, J. Arenas, P. Domènech, P. Escribano, C. Fernández-Capitán, et al.
Multidisciplinary consensus for the management of pulmonary thromboembolism.
M. Carrier, M. Righini, R.K. Djurabi, M.V. Huisman, A. Perrier, P.S. Wells, et al.
VIDAS D-dimer in combination with clinical pre-test probability to rule out pulmonary embolism. A systematic review of management outcome studies.
Thromb Haemost, 101 (2009), pp. 886-892
M. Righini, J. Van Es, P.L. Den Exter, P.M. Roy, F. Verschuren, A. Ghuysen, et al.
Age-adjusted d-dimer cutoff levels to rule out pulmonary embolism: the ADJUST-PE study.
JAMA, 311 (2014), pp. 1117-1124
S. Eichinger, G. Heinze, L.M. Jandeck, P.A. Kyrle.
Risk assessment of recurrence in patients with unprovoked deep vein thrombosis or pulmonary embolism: the Vienna prediction model.
Circulation, 121 (2010), pp. 1630-1636
A. Tosetto, A. Iorio, M. Marcucci, T. Baglin, M. Cushman, S. Eichinger, et al.
Predicting disease recurrence in patients with previous unprovoked venous thromboembolism: a proposed prediction score (DASH).
J Thromb Haemost, 10 (2012), pp. 1019-1025
M.A. Rodger, G. Le Gal, D.R. Anderson, J. Schmidt, G. Pernod, S.R. Kahn, et al.
Validating the HERDOO2 rule to guide treatment duration for women with unprovoked venous thrombosis: multinational prospective cohort management study.
BMJ, 356 (2017), pp. j1065
J.J. Rodriguez-Sevilla, A. Rodó-Pin, I. Espallargas, J. Villar-García, L. Molina, P. Pérez Terán, et al.
Pulmonary embolism in patients with COVID-19 pneumonia: the utility of d-dimer.
Arch Bronconeumol (Engl Ed), 56 (2020), pp. 758-759
A. Bajaj, M. Saleeb, P. Rathor, V. Sehgal, B. Kabak, S. Hosur.
Prognostic value of troponins in acute nonmassive pulmonary embolism: a meta-analysis.
Heart Lung, 44 (2015), pp. 327-334
A. Kaeberich, V. Seeber, D. Jiménez, M. Kostrubiec, C. Dellas, G. Hasenfuß, et al.
Age-adjusted high-sensitivity troponin T cut-off value for risk stratification of pulmonary embolism.
Eur Respir J, 45 (2015), pp. 1323-1331
C. Becattini, M.C. Vedovati, G. Agnelli.
Prognostic value of troponins in acute pulmonary embolism: a meta-analysis.
Circulation, 116 (2007), pp. 427-433
C. Dellas, M. Puls, M. Lankeit, K. Schäfer, M. Cuny, M. Berner, et al.
Elevated heart-type fatty acid-binding protein levels on admission predict an adverse outcome in normotensive patients with acute pulmonary embolism.
J Am Coll Cardiol, 55 (2010), pp. 2150-2157
N. Kucher, G. Printzen, T. Doernhoefer, S. Windecker, B. Meier, O.M. Hess.
Low pro-brain natriuretic peptide levels predict benign clinical outcome in acute pulmonary embolism.
Circulation, 107 (2003), pp. 1576-1578
F.A. Klok, I.C. Mos, M.V. Huisman.
Brain-type natriuretic peptide levels in the prediction of adverse outcome in patients with pulmonary embolism: a systematic review and meta-analysis.
Am J Respir Crit Care Med, 178 (2008), pp. 425-430
K. Hellenkamp, P. Pruszczyk, D. Jiménez, A. Wyzgał, D. Barrios, M. Ciurzyński, et al.
Prognostic impact of copeptin in pulmonary embolism: a multicentre validation study.
Eur Respir J, 51 (2018), pp. 1702037
N.I. Shapiro, S. Trzeciak, J.E. Hollander, R. Birkhahn, R. Otero, T.M. Osborn, et al.
A prospective, multicenter derivation of a biomarker panel to assess risk of organ dysfunction, shock, and death in emergency department patients with suspected sepsis.
Crit Care Med, 37 (2009), pp. 96-104
M. Kostrubiec, M. Pływaczewska, D. Jiménez, M. Lankeit, M. Ciurzynski, S. Konstantinides, et al.
The prognostic value of renal function in acute pulmonary embolism – a multi-centre cohort study.
Thromb Haemost, 119 (2019), pp. 140-148
X.Y. Zhou, H.L. Chen, S.S. Ni.
Hyponatremia and short-term prognosis of patients with acute pulmonary embolism: a meta-analysis.
Int J Cardiol, 227 (2017), pp. 251-256
M. Kostrubiec, A. Łabyk, J. Pedowska-Włoszek, O. Dzikowska-Diduch, A. Wojciechowski, M. Garlińska, et al.
Neutrophil gelatinase-associated lipocalin, cystatin C and eGFR indicate acute kidney injury and predict prognosis of patients with acute pulmonary embolism.
P. Robin, P.Y. Le Roux, C. Tromeur, B. Planquette, N. Prévot-Bitot, C. Lavigne, et al.
Risk factors of occult malignancy in patients with unprovoked venous thromboembolism.
Thromb Res, 159 (2017), pp. 48-51
D. Han, B. óHartaigh, J.H. Lee, I.J. Cho, C.Y. Shim, H.J. Chang, et al.
Impact of d-dimer for prediction of incident occult cancer in patients with unprovoked venous thromboembolism.
PLOS ONE, 11 (2016), pp. e0153514
L. Bertoletti, P. Robin, L. Jara-Palomares, C. Tromeur, J. Pastre, N. Prevot-Bitot, et al.
Predicting the risk of cancer after unprovoked venous thromboembolism: external validation of the RIETE score.
J Thromb Haemost, 15 (2017), pp. 2184-2187
L. Jara-Palomares, R. Otero, D. Jimenez, J.M. Praena-Fernandez, C. Font, C. Falga, et al.
Validation of a prognostic score for hidden cancer in unprovoked venous thromboembolism.
PLOS ONE, 13 (2018), pp. e0194673
P.J. Marchena Yglesias, J.A. Nieto Rodríguez, S. Serrano Martínez, O. Belinchón Moya, A. Cortés Carmona, A. Díaz de Tuesta, et al.
Acute-phase reactants and markers of inflammation in venous thromboembolic disease: correlation with clinical and evolution parameters.
An Med Intern, 23 (2006), pp. 105-110
Q. Wang, J. Ma, Z. Jiang, L. Ming.
Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute pulmonary embolism: a systematic review and meta-analysis.
A.G. Ertem, C. Yayla, B. Acar, O. Kirbas, S. Unal, M. Uzel Sener, et al.
Relation between lymphocyte to monocyte ratio and short-term mortality in patients with acute pulmonary embolism.
Clin Respir J, 12 (2018), pp. 580-586
M. Lankeit, T. Kempf, C. Dellas, M. Cuny, H. Tapken, T. Peter, et al.
Growth differentiation factor-15 for prognostic assessment of patients with acute pulmonary embolism.
Am J Respir Crit Care Med, 177 (2008), pp. 1018-1025
A. Maisel, C. Mueller, R.M. Nowak, W.F. Peacock, P. Ponikowski, M. Mockel, et al.
Midregion prohormone adrenomedullin and prognosis in patients presenting with acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial.
J Am Coll Cardiol, 58 (2011), pp. 1057-1067
J. Qin, H. Liang, D. Shi, J. Dai, Z. Xu, D. Chen, et al.
A panel of microRNAs as a new biomarkers for the detection of deep vein thrombosis.
J Thromb Thrombol, 39 (2015), pp. 215-221
J. Xiao, Z.C. Jing, P.T. Ellinor, D. Liang, H. Zhang, Y. Liu, et al.
MicroRNA-134 as a potential plasma biomarker for the diagnosis of acute pulmonary embolism.
J Transl Med, 9 (2011), pp. 159
X. Zhou, W. Wen, X. Shan, J. Qian, H. Li, T. Jiang, et al.
MiR-28-3p as a potential plasma marker in diagnosis of pulmonary embolism.
Thromb Res, 138 (2016), pp. 91-95
B.B. Finlay.
Are noncommunicable diseases communicable?.
Science, 367 (2020), pp. 250-251
N. Galiè, M. Humbert, J.-L. Vachiery, S. Gibbs, I. Lang, A. Torbicki, et al.
2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC) International Society for Heart and Lung Transplantation (ISHLT).
Eur Respir J, 46 (2015), pp. 903-975
C.A. Quezada Loaiza, M.T. Velázquez Martín, C. Jiménez López-Guarch, M.J. Ruiz Cano, P. Navas Tejedor, P.E. Carreira, et al.
Trends in pulmonary hypertension over a period of 30 years: experience from a single referral centre.
Rev Esp Cardiol (Engl Ed), 70 (2017), pp. 915-923
H.K. Gaggin, J.L. Januzzi Jr.
Natriuretic peptides in heart failure and acute coronary syndrome.
Clin Lab Med, 34 (2014), pp. 43-58
G. Warwick, P.S. Thomas, D.H. Yates.
Biomarkers in pulmonary hypertension.
Eur Respir J, 32 (2008), pp. 503-512
R. Souza, H.B. Bogossian, M. Humbert, C. Jardim, R. Rabelo, M.B. Amato, et al.
N-terminal-pro-brain natriuretic peptide as a haemodynamic marker in idiopathic pulmonary arterial hypertension.
Eur Respir J, 25 (2005), pp. 509-513
B. Pezzuto, R. Badagliacca, R. Poscia, S. Ghio, M. D’Alto, P. Vitulo, et al.
Circulating biomarkers in pulmonary arterial hypertension: update and future direction.
J Heart Lung Transplant, 34 (2015), pp. 282-305
E. Soon, A.M. Holmes, C.M. Treacy, N.J. Doughty, L. Southgate, R.D. Machado, et al.
Elevated levels of inflammatory cytokines predict survival in idiopathic and familial pulmonary arterial hypertension.
Circulation, 122 (2010), pp. 920-927
J. Weatherald, A. Boucly, D. Montani, X. Jaïs, L. Savale, M. Humbert, et al.
Gas exchange and ventilatory efficiency during exercise in pulmonary vascular diseases.
Arch Bronconeumol (Engl Ed), 56 (2020), pp. 578-585
P.M. de Groot, C.C. Wu, B.W. Carter, R.F. Munden.
The epidemiology of lung cancer.
Transl Lung Cancer Res, 7 (2018), pp. 220-233
J. Tang, V. Curull, D. Ramis-Cabrer, X. Duran, A. Rodríguez-Fuster, R. Aguiló, et al.
Preoperative body weight and albumin predict survival in patients with resectable lung neoplasms: role of COPD.
Arch Bronconeumol (Engl Ed), 57 (2021), pp. 51-60
F.J. Gonzalez Barcala, J.M. Garcia Prim, M. Moldes Rodriguez, J. Alvarez Fernandez, M.J. Rey Rey, A. Pose Reino, et al.
Platelet count: association with prognosis in lung cancer.
Med Oncol, 27 (2010), pp. 357-362
M. Yamasaki, N. Matsumoto, S. Nakano, K. Kawamoto, M. Taniwaki, Y. Izumi, et al.
Osimertinib for the treatment of EGFR mutation-positive lung adenocarcinoma complicated with dermatomyositis.
Arch Bronconeumol (Engl Ed), 56 (2020), pp. 822-823
E. García-Pachón, I. Padilla-Navas.
Microbioma de la vía aérea inferior y cáncer de pulmón.
Arch Bronconeumol, 56 (2020), pp. 410
E.M. Garrido-Martín, L. Paz-Ares.
Lung cancer and microbiome.
Arch Bronconeumol (Engl Ed), 56 (2020), pp. 3-4
K.M. Kerr, F. Bibeau, E. Thunnissen, J. Botling, A. Ryška, J. Wolf, et al.
The evolving landscape of biomarker testing for non-small cell lung cancer in Europe.
Lung Cancer, 154 (2021), pp. 161-175
Á. Taus, L. Camacho, P. Rocha, A. Hernández, R. Longarón, S. Clavé, et al.
Plasmatic KRAS kinetics for the prediction of treatment response and progression in patients with KRAS-mutant lung adenocarcinoma.
Arch Bronconeumol (Engl Ed), 57 (2021), pp. 323-329
E.J. Ostrin, D. Sidransky, A. Spira, S.M. Hanash.
Biomarkers for lung cancer screening and detection.
Cancer Epidemiol Biomarkers Prev, 29 (2020), pp. 2411-2415
S. Lam, P. Boyle, G.F. Healey, P. Maddison, L. Peek, A. Murray, et al.
EarlyCDT-Lung: an immunobiomarker test as an aid to early detection of lung cancer.
Cancer Prev Res (Phila), 4 (2011), pp. 1126-1134
P.P. Massion, G.F. Healey, L.J. Peek, L. Fredericks, H.F. Sewell, A. Murray, et al.
Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer.
J Thorac Oncol, 12 (2017), pp. 578-584
J. Edelsberg, D. Weycker, M. Atwood, G. Hamilton-Fairley, J.R. Jett.
Cost-effectiveness of an autoantibody test (EarlyCDT-Lung) as an aid to early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules.
PLOS ONE, 13 (2018), pp. e0197826
C.J. Chapman, G.F. Healey, A. Murray, P. Boyle, C. Robertson, L.J. Peek, et al.
EarlyCDT®-Lung test: improved clinical utility through additional autoantibody assays.
Tumour Biol, 33 (2012), pp. 1319-1326
J.R. Jett, L.J. Peek, L. Fredericks, W. Jewell, W.W. Pingleton, J.F.R. Robertson.
Audit of the autoantibody testEarlyCDT®-lung, in 1600 patients: an evaluation of its performance in routine clinical practice.
Lung Cancer, 83 (2014), pp. 51-55
F.M. Sullivan, F.S. Mair, W. Anderson, P. Armory, A. Briggs, C. Chew, et al.
Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging.
Eur Respir J, 57 (2021), pp. 2000670
G.A. Silvestri, N.T. Tanner, P. Kearney, A. Vachani, P.P. Massion, A. Porter, et al.
Assessment of plasma proteomics biomarker's ability to distinguish benign from malignant lung nodules: results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial.
N.T. Tanner, S.C. Springmeyer, A. Porter, J.R. Jett, P. Mazzone, A. Vachani, et al.
Assessment of integrated classifier's ability to distinguish benign from malignant lung nodules: extended analyses and 2-year follow-up results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial.
Chest, 101 (2020), pp. 1283-1287
Z.J. Han, X.D. Wu, J.J. Cheng, S.D. Zhao, M.Z. Gao, H.Y. Huang, et al.
Diagnostic accuracy of natriuretic peptides for heart failure in patients with pleural effusion: a systematic review and updated meta-analysis.
PLOS ONE, 10 (2015), pp. e0134376
J.M. Porcel, M. Vives, G. Cao, S. Bielsa, A. Ruiz-González, A. Martínez-Irribarren, et al.
Biomarkers of infection for the differential diagnosis of pleural effusions.
Eur Respir J, 34 (2009), pp. 1383-1389
R.M. Palma, S. Bielsa, A. Esquerda, M. Martínez-Alonso, J.M. Porcel.
Diagnostic accuracy of pleural fluid adenosine deaminase for diagnosing tuberculosis meta-analysis of Spanish studies.
Arch Bronconeumol, 55 (2019), pp. 23-30
M.I. Botana-Rial, L. Vázquez-Iglesias, P. Casado-Rey, M. Páez de la Cadena, M.A. Andrade-Olivié, J. Abal-Arca, et al.
Validation of calprotectin as a novel biomarker for the diagnosis of pleural effusion: a multicentre trial.
Sci Rep, 10 (2020), pp. 5679
Y. Yao, M. Peng, Q. Shen, Q. Hu, H. Gong, Q. Li, et al.
Detecting EGFR mutations and ALK/ROS1 rearrangements in non-small cell lung cancer using malignant pleural effusion samples.
Thorac Cancer, 10 (2019), pp. 193-202
S. Redline, P. Tishler.
The genetics of sleep apnea.
Sleep Med Rev, 4 (2000), pp. 583-602
W.E. Fleming, Holty J-EC, R.K. Bogan, D. Hwang, A.S. Ferouz-Colborn, R. Budhiraja, et al.
Use of blood biomarkers to screen for obstructive sleep apnea.
Nat Sci Sleep, 10 (2018), pp. 159-167
G.D.L. Canto, C. Pachêco-Pereira, S. Aydinoz, P.W. Major, C. Flores-Mir, D. Gozal.
Biomarkers associated with obstructive sleep apnea: a scoping review.
Sleep Med Rev, 23 (2015), pp. 28-45
F. Santamaría-Martos, I. Benítez, A. Zapater, C. Girón, L. Pinilla, J.M. Fernandez-Real, et al.
Identification and validation of circulating miRNAs as endogenous controls in obstructive sleep apnea.
PLOS ONE, 14 (2019), pp. e0213622
F. Santamaría-Martos, I. Benítez, F. Ortega, A. Zapater, C. Girón, L. Pinilla, et al.
Circulating microRNA profile as a potential biomarker for obstructive sleep apnea diagnosis.
M. Sánchez-de-la-Torre, A. Khalyfa, A. Sánchez-de-la-Torre, M. Martinez-Alonso, M.A. Martinez-García, A. Barceló, et al.
Precision medicine in patients with resistant hypertension and obstructive sleep apnea: blood pressure response to continuous positive airway pressure treatment.
J Am Coll Cardiol, 66 (2015), pp. 1023-1032
D. Gozal, S. Jortani, A.B. Snow, L. Kheirandish-Gozal, R. Bhattacharjee, J. Kim, et al.
Two-dimensional differential in-gel electrophoresis proteomic approaches reveal urine candidate biomarkers in pediatric obstructive sleep apnea.
Am J Respir Crit Care Med, 180 (2009), pp. 1253-1261
L. Kheirandish-Gozal, C.J.T. McManus, G.H. Kellermann, A. Samiei, D. Gozal.
Urinary neurotransmitters are selectively altered in children with obstructive sleep apnea and predict cognitive morbidity.
Chest, 143 (2013), pp. 1576-1583
V. Bisogni, M.F. Pengo, G. Maiolino, G.P. Rossi.
The sympathetic nervous system and catecholamines metabolism in obstructive sleep apnoea.
L. Dyugovskaya, P. Lavie, L. Lavie.
Increased adhesion molecules expression and production of reactive oxygens pecies in leukocytes of sleep apnea patients.
Am J Respir Crit Care Med, 165 (2002), pp. 934-939
I. Fernández-Bello, E. Monzón Manzano, F. García Río, R. Justo Sanz, C. Cubillos- Zapata, R. Casitas, et al.
Procoagulant state of sleep apnea depends on systemic inflammation and endothelial damage.
Arch Bronconeumol (Engl Ed), (2020),
C. Rångemark, J.A. Hedner, J.T. Carlson, G. Gleerup, K. Winther.
Platelet function and fibrinolytic activity in hypertensive and normotensive sleep apnea patients.
Sleep, 18 (1995), pp. 188-194
G.E. Carpagnano, S.A. Kharitonov, O. Resta, M.P. Foschino-Barbaro, E. Gramiccioni, P.J. Barnes.
8-Isoprostane, a marker of oxidative stress, is increased in exhaled breath condensate of patients with obstructive sleep apnea after night and is reduced by continuous positive airway pressure therapy.
Chest, 124 (2003), pp. 1386-1392
A. Barceló, C. Miralles, F. Barbe, M. Vila, S. Pons, A.G. Agustí.
Abnormal lipid peroxidation in patients with sleep apnoea.
Eur Respir J, 16 (2000), pp. 644-647
M. Yamauchi, S. Tamaki, K. Tomoda, M. Yoshikawa, A. Fukuoka, K. Makinodan, et al.
Evidence for activation of nuclear factor kappaB in obstructive sleep apnoea.
Sleep Breath, 10 (2006), pp. 189-193
F. Santamaria-Martos, I. Benítez, C. Girón, F. Barbé, M.A. Martínez-García, L. Hernández, et al.
Biomarkers of carcinogenesis and tumour growth in patients with cutaneous melanoma and obstructive sleep apnoea.
Eur Respir J, 51 (2018), pp. 1701885
C. Cubillos-Zapata, M.A. Martínez-García, F. Campos-Rodriguez, M. Sánchez-de-la-Torre, E. Nagore, A. Martorell-Calatayud, et al.
Soluble PD-L1 is a potential biomarker of cutaneous melanoma aggressiveness and metastasis in obstructive sleep apnoea patients.
Eur Respir J, 53 (2019), pp. 1801298
C. Strange, K.B. Highland.
Interstitial lung disease in the patient who has connective tissue disease.
Clin Chest Med, 25 (2004), pp. 549-559
A. De Lauretis, E.A. Renzoni.
Molecular biomarkers in interstitial lung diseases.
Mol Diagn Ther, 18 (2014), pp. 505-522
C.P. Denton, D. Khanna.
Systemic sclerosis.
Lancet, 390 (2017), pp. 1685-1699
J. Karami, S. Aslani, A. Jamshidi, M. Garshasbi, M. Mahmoudi.
Genetic implications in the pathogenesis of rheumatoid arthritis; an updated review.
Gene, 702 (2019), pp. 8-16
J.J. Blanco Pérez, V. Arnalich Montiel, Á. Salgado-Barreira, M.A. Alvarez Moure, A.C. Caldera Díaz, R. Melero Gonzalez, et al.
Prevalence and clinical impact of systemic autoimmune rheumatic disease in patients with silicosis.
Arch Bronconeumol (Engl Ed), (2020),
K. Long, S.K. Danoff.
Interstitial lung disease in polymyositis and dermatomyositis.
Clin Chest Med, 40 (2019), pp. 561-572
M. Elhai, A.M. Hoffmann-Vold, J. Avouac, S. Pezet, A. Cauvet, A. Leblond, et al.
Performance of candidate serum biomarkers for systemic sclerosis-interstitial lung disease.
Arthritis Rheumatol, 71 (2019), pp. 972-982
K. Yanaba, M. Hasegawa, K. Takehara, S. Sato.
Comparative study of serum surfactant protein-D and KL-6 concentrations in patients with systemic sclerosis as markers for monitoring the activity of pulmonary fibrosis.
J Rheumatol, 31 (2004), pp. 1112-1120
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