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	<front>
		<journal-meta>
			<journal-id journal-id-type="nlm-ta">abchf</journal-id>
			<journal-id journal-id-type="publisher-id">abchf</journal-id>
			<journal-title-group>
				<journal-title>ABC Heart Fail Cardiomyop</journal-title>
				<abbrev-journal-title abbrev-type="publisher">ABC Heart Fail Cardiomyop</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="ppub">2764-3107</issn>
			<publisher>
				<publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="doi">10.36660/abchf.20250037i</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Original Article</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Local Inflammation May Underlie Cardiac Damage in Post-COVID-19 Period</article-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-1517-4217</contrib-id>
					<name>
						<surname>Carvalho</surname>
						<given-names>Marcio Roberto Moraes de</given-names>
					</name>
					<role>Conception and design of the research and Critical revision of the manuscript for intellectual content</role>
					<role>Acquisition of data</role>
					<role>Analysis and interpretation of the data</role>
					<role>Writing of the manuscript</role>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5109-085X</contrib-id>
					<name>
						<surname>Rosário</surname>
						<given-names>Natalia Fonseca</given-names>
					</name>
					<role>Conception and design of the research and Critical revision of the manuscript for intellectual content</role>
					<role>Analysis and interpretation of the data</role>
					<role>Obtaining financing</role>
					<role>Writing of the manuscript</role>
					<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6037-8206</contrib-id>
					<name>
						<surname>Ramos</surname>
						<given-names>Alice</given-names>
					</name>
					<role>Analysis and interpretation of the data</role>
					<role>Statistical analysis</role>
					<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-4149-6455</contrib-id>
					<name>
						<surname>Azevedo</surname>
						<given-names>Carla Rodrigues do Amaral</given-names>
					</name>
					<role>Acquisition of data</role>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-9186-9072</contrib-id>
					<name>
						<surname>Ribeiro</surname>
						<given-names>Mario Luiz</given-names>
					</name>
					<role>Acquisition of data</role>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-4493-7287</contrib-id>
					<name>
						<surname>Lomba</surname>
						<given-names>Guilherme Schittine Bezerra</given-names>
					</name>
					<role>Analysis and interpretation of the data</role>
					<role>Statistical analysis</role>
					<role>Writing of the manuscript</role>
					<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0000-4230-2929</contrib-id>
					<name>
						<surname>Oliveira</surname>
						<given-names>Evelyn Maciel de</given-names>
					</name>
					<role>Acquisition of data</role>
					<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0002-1235-4829</contrib-id>
					<name>
						<surname>Souza</surname>
						<given-names>Rachel V. O’Neill de</given-names>
					</name>
					<role>Acquisition of data</role>
					<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-1755-999X</contrib-id>
					<name>
						<surname>Castro</surname>
						<given-names>Mauro Jorge Cabral</given-names>
					</name>
					<role>Acquisition of data</role>
					<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff8"><sup>8</sup></xref>
					<xref ref-type="aff" rid="aff9"><sup>9</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6791-3910</contrib-id>
					<name>
						<surname>Lugon</surname>
						<given-names>Jocemir Ronaldo</given-names>
					</name>
					<role>Conception and design of the research and Critical revision of the manuscript for intellectual content</role>
					<role>Analysis and interpretation of the data</role>
					<role>Obtaining financing</role>
					<role>Writing of the manuscript</role>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
					<xref ref-type="aff" rid="aff7"><sup>7</sup></xref>
				</contrib>
			</contrib-group>
			<aff id="aff1">
				<label>1</label>
				<institution content-type="orgdiv2">Departamento de Medicina</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Divisão de Cardiologia, Departamento de Medicina, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff2">
				<label>2</label>
				<institution content-type="orgdiv2">Laboratório Multiusuário de Apoio à Pesquisa em Nefrologia e Ciências Médicas</institution>
				<institution content-type="orgdiv1">Hospital Universitário Antônio Pedro</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Laboratório Multiusuário de Apoio à Pesquisa em Nefrologia e Ciências Médicas (LAMAP), Hospital Universitário Antônio Pedro, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff3">
				<label>3</label>
				<institution content-type="orgdiv2">Programa de Pós-Graduação em Ciências Médicas</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Programa de Pós-Graduação em Ciências Médicas, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff4">
				<label>4</label>
				<institution content-type="orgdiv1">Faculdade de Farmácia</institution>
				<institution content-type="orgname">Universidade Federal do Rio de Janeiro</institution>
				<addr-line>
					<named-content content-type="city">Rio de Janeiro</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ - Brazil</institution>
			</aff>
			<aff id="aff5">
				<label>5</label>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff6">
				<label>6</label>
				<institution content-type="orgdiv2">Programa de Pós-Graduação em Ciências Cardiovasculares</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Programa de Pós-Graduação em Ciências Cardiovasculares, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff7">
				<label>7</label>
				<institution content-type="orgdiv2">Departamento de Medicina</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Divisão de Nefrologia, Departamento de Medicina, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff8">
				<label>8</label>
				<institution content-type="orgdiv2">Departamento de Patologia</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Departamento de Patologia, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<aff id="aff9">
				<label>9</label>
				<institution content-type="orgdiv2">Programa de Pós-Graduação em Patologia</institution>
				<institution content-type="orgdiv1">Faculdade de Medicina</institution>
				<institution content-type="orgname">Universidade Federal Fluminense</institution>
				<addr-line>
					<named-content content-type="city">Niterói</named-content>
					<named-content content-type="state">RJ</named-content>
				</addr-line>
				<country country="BR">Brazil</country>
				<institution content-type="original">Programa de Pós-Graduação em Patologia, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brazil</institution>
			</aff>
			<author-notes>
				<corresp id="c01">
					<label>Mailing Address:</label> Marcio Roberto Moraes de Carvalho Universidade Federal Fluminense Hospital Universitário Antonio Pedro – Medicina Clínica – Av. Marquês do Paraná, 303. Postal Code 24033-900, Niterói, RJ – Brazil E-mail: <email>marcioc@id.uff.br</email>
				</corresp>
				<fn fn-type="coi-statement">
					<label>Potential Conflict of Interest:</label>
					<p> No potential conflict of interest relevant to this article was reported.</p>
				</fn>
				<fn fn-type="edited-by">
					<label>Editor responsible for the review:</label>
					<p> Germano Souza</p>
				</fn>
			</author-notes>
			<pub-date date-type="pub" publication-format="electronic">
				<day>31</day>
				<month>12</month>
				<year>2025</year>
			</pub-date>
			<pub-date date-type="collection" publication-format="electronic">
				<month>12</month>
				<year>2025</year>
			</pub-date>
			<volume>5</volume>
			<issue>3</issue>
			<elocation-id>e20250037</elocation-id>
			<history>
				<date date-type="received">
					<day>11</day>
					<month>11</month>
					<year>2025</year>
				</date>
				<date date-type="rev-recd">
					<day>29</day>
					<month>12</month>
					<year>2025</year>
				</date>
				<date date-type="accepted">
					<day>23</day>
					<month>02</month>
					<year>2026</year>
				</date>
			</history>
			<permissions>
				<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="en">
					<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License.</license-p>
				</license>
			</permissions>
			<abstract>
				<title>Abstract</title>
				<sec>
					<title>Background</title>
					<p> Some patients experience subclinical cardiovascular dysfunction after COVID-19, possibly driven by persistent inflammatory mechanisms that remain incompletely understood. NT-proBNP and GDF-15 have been proposed as biomarkers of myocardial stress and inflammation in this context.</p>
				</sec>
				<sec>
					<title>Objectives</title>
					<p> To investigate associations between NT-proBNP, GDF-15, inflammatory cytokines, and echocardiographic parameters in patients with Post-COVID-19 Condition (PCC).</p>
				</sec>
				<sec>
					<title>Methods</title>
					<p> This cross-sectional study included 78 adults followed in a university hospital cohort. Participants were stratified into elevated NT-proBNP (n = 39) and normal NT-proBNP (n = 39) groups. Clinical, laboratory, and echocardiographic variables were assessed, including PALS and RV-GLS. Serum GDF-15 and cytokines were quantified using a multiplex bead assay. ROC curve analysis evaluated the predictive value of GDF-15 for elevated NT-proBNP.</p>
				</sec>
				<sec>
					<title>Results</title>
					<p> Participants with elevated NT-proBNP had reduced PALS (28.5% vs. 36.7%; p &lt; 0.05), and higher GDF-15 (median 368 vs. 219 pg/mL) and IL-1β levels (2.47 vs. 0.94 pg/mL; p &lt; 0.05). GDF-15 ≥ 519 pg/mL predicted elevated NT-proBNP (AUC = 0.65). Individuals with elevated GDF-15 also showed worse RV-GLS (20.2% vs. 26.9%; p &lt; 0.05) and higher IL-8 levels (p &lt; 0.05).</p>
				</sec>
				<sec>
					<title>Conclusions</title>
					<p> NT-proBNP, GDF-15, and IL-1β identify subclinical myocardial dysfunction in PCC, supporting a pathophysiological model centered on persistent inflammation and early myocardial remodeling, distinct from classic ischemic pathways.</p>
				</sec>
			</abstract>
			<abstract abstract-type="graphical">
				<p>
					<fig id="f01">
						<label>Central Illustration:</label>
						<caption>
							<title>Local Inflammation May Underlie Cardiac Damage in Post-COVID-19 Period</title>
						</caption>
						<graphic xlink:href="2764-3107-abchf-5-3-e20250037-gf01.tif"/>
						<attrib>Proposed pathophysiological model of inflammation-linked subclinical myocardial dysfunction in the post-COVID-19 period. Schematic representation of the proposed mechanism linking residual or post-viral SARS-CoV-2 effects to local inflammatory activation (IL-1β and IL-8), cellular and metabolic stress, myocardial wall stress with NT-proBNP elevation, and early subclinical cardiac dysfunction, reflected by impaired left atrial strain (PALS) and right ventricular global longitudinal strain (RV GLS).</attrib>
					</fig>
				</p>
			</abstract>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>COVID-19</kwd>
				<kwd>Growth Differentiation Factor 15</kwd>
				<kwd>Inflammation</kwd>
				<kwd>Echocardiography</kwd>
			</kwd-group>
			<funding-group>
				<award-group>
					<funding-source>FAPERJ</funding-source>
					<award-id>E-26/210.828/2021</award-id>
				</award-group>
				<award-group>
					<funding-source>FAPERJ</funding-source>
					<award-id>E-26/203.612/2022</award-id>
				</award-group>
				<award-group>
					<funding-source>FAPERJ</funding-source>
					<award-id>E-26/203.624/2022</award-id>
				</award-group>
				<award-group>
					<funding-source>FAPERJ</funding-source>
					<award-id>E-26/202.523/2024</award-id>
				</award-group>
				<award-group>
					<funding-source>CAPES</funding-source>
					<award-id>001</award-id>
				</award-group>
				<funding-statement><bold>Sources of Funding:</bold> This study was funded by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) (Processos nº E-26/210.828/2021, nº E-26/203.612/2022, nº E-26/203.624/2022, nº E-26/202.523/2024) e pela Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – Código de Financiamento 001).</funding-statement>
			</funding-group>
			<counts>
				<fig-count count="4"/>
				<table-count count="6"/>
				<equation-count count="0"/>
				<ref-count count="20"/>
			</counts>
		</article-meta>
	</front>
	<body>
		<sec sec-type="intro">
			<title>Introduction</title>
			<p>After an episode of SARS-CoV-2 infection, patients may develop long-lasting cardiovascular symptoms<sup><xref ref-type="bibr" rid="B1">1</xref></sup> and persistently elevated biomarkers of myocardial injury.<sup><xref ref-type="bibr" rid="B2">2</xref></sup> However, the underlying pathophysiological mechanisms remain incompletely understood.</p>
			<p>Systemic inflammation has been correlated with N-terminal pro-B-type Natriuretic Peptide (NT-proBNP) elevations and increased mortality in this context.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> It has been suggested that persistent cardiac dysfunction following COVID-19 may be associated with local activation of immune and inflammatory pathways, ultimately leading to myocardial injury.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
			<p>GDF-15, a cytokine related to the TGF-β family, is an important prognostic biomarker in inflammation and cardiovascular disease. Elevated levels have been linked to hypoxemia, SARS-CoV-2 viremia, and adverse outcomes. As a marker of myocardial stress and remodeling, GDF-15 may also contribute to autonomic dysfunction and subclinical heart failure in COVID-19, reinforcing its relevance among emerging biomarkers.<sup><xref ref-type="bibr" rid="B4">4</xref></sup></p>
			<p>Previous studies indicate that up to 36% of patients hospitalized with COVID-19 present elevated NT-proBNP levels, a well-established marker of cardiac distress. Inflammatory cytokines such as IL-6, TNF-α, and IL-1β have also been associated with the progression of post-COVID-19 condition (PCC).<sup><xref ref-type="bibr" rid="B5">5</xref></sup> However, the roles of NT-proBNP and GDF-15 in this setting have not yet been fully explored. The relationship between inflammation, cardiac stress markers, and subclinical myocardial impairment in PCC therefore remains unclear.</p>
			<p>In this study, we analyzed serum levels of GDF-15, NT-proBNP, inflammatory cytokines, and echocardiographic parameters in a cohort of patients who survived the acute phase of COVID-19. The objective was to investigate the relationship between systemic inflammation and persistent alterations in cardiac structure or function, thereby providing insights into the mechanisms underlying heart involvement in the post-COVID-19 period.</p>
		</sec>
		<sec sec-type="methods">
			<title>Methods</title>
			<sec>
				<title>Ethical approval</title>
				<p>The study was approved by the Research Ethics Committee of a local tertiary university hospital (CAAE: 59213722.5.0000.5243). In accordance with ethical standards, written informed consent was obtained from all participants.</p>
			</sec>
			<sec>
				<title>Study design and participant selection</title>
				<p>This study represents a cross-sectional analysis of data collected from 144 adult patients recruited between July 2022 and July 2023, all of whom had at least one positive polymerase chain reaction (PCR) test for SARS-CoV-2 during the COVID-19 pandemic.</p>
				<p>Participants were enrolled in the post-COVID-19 period to investigate PCC at a tertiary care university hospital in Brazil. Upon enrollment, they followed a standardized study protocol that included cardiology evaluations at baseline and every six months, as well as laboratory testing at baseline and one year after the initial blood collection.</p>
				<p>For the present analysis, we initially identified a subgroup of participants (n = 39) who exhibited elevated NT-proBNP levels at baseline laboratory evaluation, according to the cutoff recommended by the Heart Failure Association of the European Society of Cardiology. A control group of 39 individuals from the same cohort with normal NT-proBNP levels was then selected using a 1:1 matching ratio. Matching was performed based on sex, age, and the presence of relevant comorbidities (diabetes and hypertension) to minimize potential confounding in the comparison between groups.</p>
			</sec>
			<sec>
				<title>Clinical variables</title>
				<p>Social and demographic information, past medical history, and current medications were obtained through patient interviews and medical record review. Body mass index (BMI) was measured at study entry. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m<sup>2</sup>, calculated using the CKD-EPI creatinine equation (2021). Acute heart failure was diagnosed according to the Framingham Heart Failure criteria, applied at each cardiology consultation.<sup><xref ref-type="bibr" rid="B6">6</xref>-<xref ref-type="bibr" rid="B8">8</xref></sup></p>
			</sec>
			<sec>
				<title>Echocardiographic variables</title>
				<p>Transthoracic echocardiography was performed using a commercially available system (EPIQ CVx<sup>®</sup>, Philips, Amsterdam, The Netherlands) following the American Society of Echocardiography (ASE) protocol for chamber quantification,<sup><xref ref-type="bibr" rid="B9">9</xref></sup> Doppler analysis, and assessment of Peak Left Atrial Strain (PALS) and biventricular strain. All examinations were reviewed by a cardiologist specialized in echocardiographic analysis who was blinded to participants’ clinical and laboratory data.</p>
				<p>The following echocardiographic parameters were evaluated: Left Ventricular Ejection fraction (LVEF): measured using the Simpson biplane method from apical four- and two-chamber views; PALS, obtained via speckle-tracking echocardiography from apical four-chamber views, representing the reservoir function of the left atrium; and Right Ventricular Global Longitudinal Strain (RV-GLS), calculated from focused right ventricular apical four-chamber views using speckle-tracking analysis, reflecting right ventricular systolic function. Echocardiographic variables were reported as continuous measures and compared between groups.</p>
			</sec>
			<sec>
				<title>Laboratory variables</title>
				<p>Peripheral blood samples were collected in the morning, and serum samples were stored frozen for subsequent analysis. NT-proBNP concentrations were measured by automated chemiluminescent immunoassay using the Alinity C system (Abbott<sup>®</sup>, Brazil) at the Clinical Analysis and Pathology Unit of the university hospital. Age-adjusted NT-proBNP cut-off values were applied (&lt;50 years: 125 pg/mL; 50–75 years: 250 pg/mL; &gt;75 years: 500 pg/mL).</p>
				<p>Additional adjustments were made according to renal function and BMI, as previously described: NT-proBNP cut-off increased by 15%, 25%, and 35% for estimated glomerular filtration rates of 45-60, 30-45, and &lt;30 mL/min/1.73 m<sup>2</sup>, respectively. NT-proBNP cut-off reduced by 25%, 30%, and 40% for BMI values of 30-35, 35-40, and &gt;40 Kg/m<sup>2</sup>, respectively.<sup><xref ref-type="bibr" rid="B6">6</xref></sup> Serum levels of growth differentiation factor 15 (GDF-15) were measured using commercially available immunoenzymatic assays (R&amp;D Systems, USA). Absorbance values were read with the Spectramax M3 Multi-Mode Microplate Reader (Molecular Devices, USA).</p>
				<p>A multiplex bead assay was performed to quantify serum levels of chemokines and cytokines (Bio-Plex Magpix<sup>®</sup>, Biorad Laboratories, USA), as previously described.<sup><xref ref-type="bibr" rid="B10">10</xref></sup>Serum levels of 27 biomarkers were analyzed: chemokine monocyte chemoattractant protein 1 (MCP-1/CCL-2); macrophage inflammatory protein 1α (MIP-1α/CCL-3); macrophage inflammatory protein 1β (MIP-1β/CCL-4); chemokine C-C ligand 5 (RANTES/CCL-5); chemokine C-C ligand 11 (eotaxin-1/CCL-11); interferon gamma-induced protein 10 (IP-10/CXCL-10); granulocyte-macrophage colony-stimulation factor (GM-CSF/CSF-2); granulocyte colony-stimulating factor (G-CSF/CSF-3); basic fibroblast growth factor (FGFb); interferon γ (IFN-γ); interleukin 1 receptor antagonist (IL-1ra); interleukin 1β (IL-1β); IL-2, -4, -5, -6, -7, -8 (CXCL-8), -9, -10, -12p70, -13, −15, and -17; platelet-derived growth factor BB (PDGF-BB), tumor necrosis factor (TNF), and Vascular Endothelial Growth Factor (VEGF).</p>
			</sec>
			<sec>
				<title>Statistical analysis</title>
				<p>Normality of continuous data was assessed using the Shapiro–Wilk test. Continuous variables were expressed as median and interquartile range (IQR) or mean and standard deviation (SD), depending on data distribution. Between-group comparisons were performed using the Student’s t-test or, alternatively, the Mann–Whitney U test. Categorical variables were compared using the Chi-square test or Fisher’s exact test, as appropriate. Given the evaluation of 19 cytokines, p-values from between-group comparisons were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure. An FDR-adjusted p &lt; 0.05 was considered statistically significant.</p>
				<p>Variables with p &lt; 0.20 in univariate analyses, or those considered biologically relevant (age, sex, BMI, hypertension, diabetes), were included as candidate predictors in multivariable logistic regression models. Age and sex were forced into all models irrespective of univariate significance. Parsimonious models were applied to maintain adequate events-per-variable ratios. Receiver operating characteristic (ROC) curve analysis was used to assess whether cytokine levels could predict elevated NT-proBNP levels. Categorized cytokine levels were used to improve model performance in regression analyses.</p>
				<p>The sample size was determined by feasibility during the recruitment period of the prospective cohort; therefore, no formal a priori sample size calculation was performed. Given the fixed sample size and the exploratory nature of the study, emphasis was placed on effect sizes and 95% confidence intervals for interpretation of results. In addition, a post-hoc power analysis was conducted for the primary comparison of GDF-15 levels between patients with elevated versus normal NT-proBNP, using the observed effect size and a two-sided α of 0.05. All statistical analyses were conducted using R software (RStudio, version 2024.03.1+402).</p>
			</sec>
		</sec>
		<sec sec-type="results">
			<title>Results</title>
			<p>
				<xref ref-type="fig" rid="f02">Figure 1</xref> presents the flowchart of participant selection for the 78 individuals included in the analysis. Baseline characteristics are summarized in <xref ref-type="table" rid="t1">Table 1</xref>. The study population was predominantly female, with a median age of 60 years, and showed a high prevalence of hypertension, dyslipidemia, and diabetes.</p>
			<p>
				<fig id="f02">
					<label>Figure 1</label>
					<caption>
						<title>– Flowchart of Participant Selection and Inclusion Criteria Based on NT-proBNP Levels in Post-COVID-19 Patients.</title>
					</caption>
					<graphic xlink:href="2764-3107-abchf-5-3-e20250037-gf02.tif"/>
				</fig>
			</p>
			<p>
				<table-wrap id="t1">
					<label>Table 1</label>
					<caption>
						<title>– Baseline characteristics of the studied population</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="left" style="font-weight:normal"> </th>
								<th>Overall (N = 78)</th>
								<th>Elevated NT-proBNP (N = 39)</th>
								<th>Normal NT-proBNP (N = 39)</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td><bold>Age (Years)<sup>a</sup></bold></td>
								<td align="center">61 [52; 71]</td>
								<td align="center">64 [54; 71]</td>
								<td align="center">57 [49; 68]</td>
							</tr>
							<tr>
								<td><bold>Male sex</bold></td>
								<td align="center">26 (33.3)</td>
								<td align="center">14 (35.9)</td>
								<td align="center">12 (30.8)</td>
							</tr>
							<tr>
								<td><bold>Body mass index (kg/m<sup>2</sup>)<sup>a</sup></bold></td>
								<td align="center">28 [25. 32]</td>
								<td align="center">28 [24. 32]</td>
								<td align="center">29 [26. 32]</td>
							</tr>
							<tr>
								<td><bold>Hypertension</bold></td>
								<td align="center">55 (70.5)</td>
								<td align="center">26 (66.7)</td>
								<td align="center">29 (74.4)</td>
							</tr>
							<tr>
								<td><bold>Diabetes</bold></td>
								<td align="center">28 (35.9)</td>
								<td align="center">15 (38.5)</td>
								<td align="center">13 (33.3)</td>
							</tr>
							<tr>
								<td><bold>Dyslipidemia</bold></td>
								<td align="center">42 (53.8)</td>
								<td align="center">22 (56.4)</td>
								<td align="center">20 (51.3)</td>
							</tr>
							<tr>
								<td><bold>Chronic kidney disease</bold></td>
								<td align="center">26 (33.3)</td>
								<td align="center">16 (41.0)</td>
								<td align="center">10 (25.6)</td>
							</tr>
							<tr>
								<td><bold>Chronic heart failure</bold></td>
								<td align="center">17 (23.9)</td>
								<td align="center">12 (30.7)</td>
								<td align="center">5 (12.8)</td>
							</tr>
							<tr>
								<td><bold>Acute heart failure</bold></td>
								<td align="center">12 (15.4)</td>
								<td align="center">7 (17.9)</td>
								<td align="center">5 (12.8)</td>
							</tr>
							<tr>
								<td colspan="4"><bold>Echocardiogram</bold></td>
							</tr>
							<tr>
								<td>LVEF<sup>a</sup></td>
								<td align="center">60.0 [57.3; 64.6]</td>
								<td align="center">59.0 [52.9; 63.5]</td>
								<td align="center">61.8 [60.0; 65.5]</td>
							</tr>
							<tr>
								<td>PALS<sup>a</sup></td>
								<td align="center">32.0 [25.4; 38.5]</td>
								<td align="center">28.50 [23.0; 33.5]</td>
								<td align="center">36.7 [30.1; 40.3] *</td>
							</tr>
							<tr>
								<td>RV GLS<sup>a</sup></td>
								<td align="center">23.7 [21.5; 28.7]</td>
								<td align="center">22.7 [19.5; 27.3]</td>
								<td align="center">27.6 [21.8; 29.8]</td>
							</tr>
							<tr>
								<td colspan="4"><bold>Cytokines (pg/mL)</bold></td>
							</tr>
							<tr>
								<td>GDF-15<sup>a</sup></td>
								<td align="center">291 [161; 578]</td>
								<td align="center">368 [14; 855]</td>
								<td align="center">219 [129; 437] *</td>
							</tr>
							<tr>
								<td>IL-1βa</td>
								<td align="center">2.47 [0.94; 4.14]</td>
								<td align="center">2.47 [0.94; 4.14]</td>
								<td align="center">0.94 [0.75; 2.47] *</td>
							</tr>
							<tr>
								<td><bold>&gt; 1 COVID-19 episode</bold></td>
								<td align="center">19 (24.4)</td>
								<td align="center">5 (12.8)</td>
								<td align="center">14 (35.9)*</td>
							</tr>
							<tr>
								<td><bold>Hospitalization (n=64)</bold></td>
								<td align="center">37 (57.8)</td>
								<td align="center">21 (56.7)</td>
								<td align="center">7 (25.9)*</td>
							</tr>
							<tr>
								<td colspan="4"><bold>Vaccines <sup>b</sup></bold></td>
							</tr>
							<tr>
								<td>No dose</td>
								<td align="center">4 (6.3)</td>
								<td align="center">4 (100.0)</td>
								<td align="center">0 (0.0)</td>
							</tr>
							<tr>
								<td>1 dose</td>
								<td align="center">3 (4.7)</td>
								<td align="center">3 (100.0)</td>
								<td align="center">0 (0.0)</td>
							</tr>
							<tr>
								<td>2 doses</td>
								<td align="center">12 (18.2)</td>
								<td align="center">6 (50.0)</td>
								<td align="center">6 (50.0)</td>
							</tr>
							<tr>
								<td>3 doses</td>
								<td align="center">11 (16.7)</td>
								<td align="center">6 (54.5)</td>
								<td align="center">5 (45.5)</td>
							</tr>
							<tr>
								<td>4 doses</td>
								<td align="center">30 (45.4)</td>
								<td align="center">17 (56.7)</td>
								<td align="center">13 (43.3)</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN1">
							<p>Data presented as n (%), unless otherwise specified. a median [IQR]; LVEF: left ventricular ejection fraction; PALS: peak atrial longitudinal strain; RV GLS: right ventricular global longitudinal strain; GDF: growth differentiation factor; IL-1β: Interleukin 1 beta; b Before enrollment. * p-value &lt; 0.05.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>When comparing groups stratified by NT-proBNP levels, a consistent pattern of greater myocardial stress and inflammation was observed among individuals with elevated biomarker values. These participants exhibited impaired atrial function (reduced PALS) and right ventricular function (reduced RV-GLS), along with higher concentrations of GDF-15 and IL-1β. They also had a higher frequency of hospitalization during the acute phase of COVID-19. Additional clinical and laboratory parameters are detailed in <xref ref-type="table" rid="t1">Table 1</xref>.</p>
			<p>In predictive analyses, GDF-15 demonstrated moderate performance in distinguishing participants with elevated NT-proBNP (AUC = 0.65). The optimal cutoff value identified was 519 pg/mL. Based on this threshold, participants were categorized into elevated and normal GDF-15 groups, as shown in <xref ref-type="table" rid="t2">Table 2</xref>. Individuals with elevated GDF-15 levels were older, had a greater burden of comorbidities, and exhibited worse right ventricular strain and higher IL-8 concentrations. These findings suggest an interaction between local inflammation, cellular stress, and subclinical dysfunction. No significant differences were observed between groups for most of the other inflammatory biomarkers evaluated.</p>
			<p>
				<table-wrap id="t2">
					<label>Table 2</label>
					<caption>
						<title>– Comparison of clinical, echocardiographic, and inflammatory characteristics between elevated and normal GDF-15 groups</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="left" style="font-weight:normal"> </th>
								<th>Elevated GDF-15 (N = 21)</th>
								<th>Normal GDF-15 (N = 57)</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td><bold>Age (Years)<sup>a</sup></bold></td>
								<td align="center">65 [58; 77]</td>
								<td align="center">59 [49; 66] *</td>
							</tr>
							<tr>
								<td><bold>Male sex</bold></td>
								<td align="center">11 (52.4)</td>
								<td align="center">15 (26.3)</td>
							</tr>
							<tr>
								<td><bold>BMI (kg/m<sup>2</sup>)<sup>a</sup></bold></td>
								<td align="center">27 [23; 31]</td>
								<td align="center">29 [27; 33] *</td>
							</tr>
							<tr>
								<td><bold>Hypertension</bold></td>
								<td align="center">18 (85.7)</td>
								<td align="center">37 (64.9)</td>
							</tr>
							<tr>
								<td><bold>Diabetes</bold></td>
								<td align="center">14 (66.7)</td>
								<td align="center">14 (24.6) *</td>
							</tr>
							<tr>
								<td><bold>Dyslipidemia</bold></td>
								<td align="center">9 (42.9)</td>
								<td align="center">33 (57.9)</td>
							</tr>
							<tr>
								<td><bold>Chronic kidney disease</bold></td>
								<td align="center">14 (66.6)</td>
								<td align="center">12 (21.0) *</td>
							</tr>
							<tr>
								<td><bold>Chronic heart failure</bold></td>
								<td align="center">6 (30.0)</td>
								<td align="center">11 (21.6)</td>
							</tr>
							<tr>
								<td><bold>Acute heart failure</bold></td>
								<td align="center">4 (14.0)</td>
								<td align="center">8 (17.0)</td>
							</tr>
							<tr>
								<td><bold>Echocardiogram</bold></td>
								<td> </td>
								<td> </td>
							</tr>
							<tr>
								<td>LVEF<sup>a</sup></td>
								<td align="center">60.0 [52.4; 60.0]</td>
								<td align="center">62.2 [58.0; 65.1]</td>
							</tr>
							<tr>
								<td>PALS<sup>a</sup></td>
								<td align="center">30.0 [28.0; 31.1]</td>
								<td align="center">33.6 [25.4; 40.1]</td>
							</tr>
							<tr>
								<td>RV GLS<sup>a</sup></td>
								<td align="center">20.2 [17.7; 22.4]</td>
								<td align="center">26.9 [22.1; 29.8] *</td>
							</tr>
							<tr>
								<td><bold>Cytokines (pg/mL)</bold></td>
								<td> </td>
								<td> </td>
							</tr>
							<tr>
								<td>IL-1β<sup>a</sup></td>
								<td align="center">2.47 [0.94; 4.14]</td>
								<td align="center">1.17 [0.94; 2.51]</td>
							</tr>
							<tr>
								<td>IL-6<sup>a</sup></td>
								<td align="center">11.18 [9.00; 15.30]</td>
								<td align="center">2.67 [0.96; 5.13]</td>
							</tr>
							<tr>
								<td>IFNg<sup>a</sup></td>
								<td align="center">18.3 [15.4; 22.3]</td>
								<td align="center">14.4 [10.8; 20.3]</td>
							</tr>
							<tr>
								<td>FGFb<sup>a</sup></td>
								<td align="center">42.8 [35.0; 44.3]</td>
								<td align="center">35.1 [26.9; 42.8]</td>
							</tr>
							<tr>
								<td>PDGFbb<sup>a</sup></td>
								<td align="center">1.522 [766; 3.613]</td>
								<td align="center">2.783 [1.787; 4.390]</td>
							</tr>
							<tr>
								<td>IL-8<sup>a</sup></td>
								<td align="center">14.6 [10.2; 27.2]</td>
								<td align="center">9.6 [6.0; 14.6]*</td>
							</tr>
							<tr>
								<td>GCSF<sup>a</sup></td>
								<td align="center">119 [100; 174]</td>
								<td align="center">90 [60; 124]</td>
							</tr>
							<tr>
								<td><bold>&gt; 1 COVID-19 episode</bold></td>
								<td align="center">3 (14.3)</td>
								<td align="center">16 (28.1)</td>
							</tr>
							<tr>
								<td><bold>Symptomatic COVID-19</bold></td>
								<td align="center">16 (76.2)</td>
								<td align="center">48 (84.2)</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN2">
							<p><sup>a</sup>median [IQR]; LVEF: left ventricular ejection fraction; PALS: peak atrial longitudinal strain; RV GLS: right ventricular longitudinal strain, IL: Interleukin; IFNg: interferon-gamma; FGFb: fibroblast growth factor basic; PDGFbb: Platelet-Derived Growth Factor-BB; GCSF: Granulocyte Colony-Stimulating Factor. * p-value &lt; 0.05.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>Because 19 cytokines were analyzed, p-values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate. Although GDF-15 and IL-1β showed nominal significance in unadjusted analyses (p = 0.030 and p = 0.029, respectively), none of the cytokines remained statistically significant after FDR correction (all adjusted p ≥ 0.28). Therefore, cytokine analyses should be interpreted as exploratory (<xref ref-type="sec" rid="suppl">Supplementary Table 1</xref>).</p>
			<p>A post-hoc power analysis for the comparison of GDF-15 levels between elevated and normal NT-proBNP groups demonstrated 77.1% power (Cohen’s d = 0.42; α = 0.05). In contrast, the analysis based on the ROC-derived GDF-15 cut-off yielded only 43.7% power, indicating limited ability to detect differences in this secondary, dichotomized comparison.</p>
			<p>In multivariable logistic regression models, high GDF-15 was independently associated with elevated NT-proBNP (adjusted OR 6.95, 95% CI 1.79–33.32; p = 0.008). Similarly, high IL-1β showed a strong independent association with elevated NT-proBNP (adjusted OR 7.04, 95% CI 2.11–28.46; p = 0.007) (<xref ref-type="table" rid="t3">Table 3</xref>).</p>
			<p>
				<table-wrap id="t3">
					<label>Table 3</label>
					<caption>
						<title>– Multivariable logistic regression models for elevated NT-proBNP</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="left">Variable</th>
								<th>Adjusted OR</th>
								<th>95% CI (Lower–Upper)</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td colspan="3"><bold>Model 1 – Predictor: High GDF-15</bold></td>
							</tr>
							<tr>
								<td><bold>High GDF-15 (vs. low)</bold></td>
								<td align="center"><bold>*6.95</bold></td>
								<td align="center">1.79 – 33.32</td>
							</tr>
							<tr>
								<td><bold>Age (years)</bold></td>
								<td align="center">1.03</td>
								<td align="center">1.00 – 1.08</td>
							</tr>
							<tr>
								<td><bold>Male sex</bold></td>
								<td align="center">0.98</td>
								<td align="center">0.30 – 3.14</td>
							</tr>
							<tr>
								<td><bold>BMI (kg/m<sup>2</sup>)</bold></td>
								<td align="center">1.08</td>
								<td align="center">0.98 – 1.19</td>
							</tr>
							<tr>
								<td><bold>Hypertension</bold></td>
								<td align="center"><bold>*0.25</bold></td>
								<td align="center">0.06 – 0.94</td>
							</tr>
							<tr>
								<td><bold>Diabetes</bold></td>
								<td align="center">0.90</td>
								<td align="center">0.25 – 3.02</td>
							</tr>
							<tr>
								<td><bold>Chronic kidney disease</bold></td>
								<td align="center">1.28</td>
								<td align="center">0.36 – 4.50</td>
							</tr>
							<tr>
								<td colspan="3"><bold>Model 2 – Predictor: High IL-1β</bold></td>
							</tr>
							<tr>
								<td><bold>High IL-1β (vs. low)</bold></td>
								<td align="center"><bold>*7.04</bold></td>
								<td align="center">2.11 – 28.46</td>
							</tr>
							<tr>
								<td><bold>Age (years)</bold></td>
								<td align="center">1.04</td>
								<td align="center">1.00 – 1.08</td>
							</tr>
							<tr>
								<td><bold>Male sex</bold></td>
								<td align="center">0.85</td>
								<td align="center">0.24 – 2.85</td>
							</tr>
							<tr>
								<td><bold>BMI (kg/m<sup>2</sup>)</bold></td>
								<td align="center">1.07</td>
								<td align="center">0.97 – 1.18</td>
							</tr>
							<tr>
								<td><bold>Hypertension</bold></td>
								<td align="center"><bold>*0.18</bold></td>
								<td align="center">0.04 – 0.71</td>
							</tr>
							<tr>
								<td><bold>Diabetes</bold></td>
								<td align="center">1.78</td>
								<td align="center">0.54 – 6.17</td>
							</tr>
							<tr>
								<td><bold>Chronic kidney disease</bold></td>
								<td align="center">2.04</td>
								<td align="center">0.60 – 7.33</td>
							</tr>
							<tr>
								<td colspan="3"><bold>Model 1 – Predictor: High GDF-15</bold></td>
							</tr>
							<tr>
								<td><bold>High GDF-15 (vs. low)</bold></td>
								<td align="center"><bold>*6.95</bold></td>
								<td align="center">1.79 – 33.32</td>
							</tr>
							<tr>
								<td><bold>Age (years)</bold></td>
								<td align="center">1.03</td>
								<td align="center">1.00 – 1.08</td>
							</tr>
							<tr>
								<td><bold>Male sex</bold></td>
								<td align="center">0.98</td>
								<td align="center">0.30 – 3.14</td>
							</tr>
							<tr>
								<td><bold>BMI (kg/m<sup>2</sup>)</bold></td>
								<td align="center">1.08</td>
								<td align="center">0.98 – 1.19</td>
							</tr>
							<tr>
								<td><bold>Hypertension</bold></td>
								<td align="center"><bold>0.25</bold></td>
								<td align="center">0.06 – 0.94</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN3">
							<p>Multivariable logistic regression models assessing independent predictors of elevated NT-proBNP. Model 1 evaluates high GDF-15; Model 2 evaluates high IL-1β. Both models were adjusted for demographic and clinical confounders (age, sex, BMI, hypertension, diabetes, and chronic kidney disease). Data are expressed as adjusted odds ratios with 95% confidence intervals. * p-value &lt; 0.05.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
		</sec>
		<sec sec-type="discussion">
			<title>Discussion</title>
			<p>Evidence indicates that cardiac injury in patients with COVID-19 can occur even in the absence of direct viral tropism to cardiac tissue, suggesting an immune-mediated mechanism.<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B11">11</xref></sup> In line with this, our group previously hypothesized that a sustained inflammatory axis – driven by macrophage dysfunction and cytokine responses – may play a key role in perpetuating myocardial damage in the post-COVID-19 period. Additionally, Sabanoglu et al.<sup><xref ref-type="bibr" rid="B12">12</xref></sup> suggested that NT-proBNP and high-sensitivity troponin could predict long-term outcomes in COVID-19, underscoring the prognostic relevance of cardiac biomarkers in this setting.</p>
			<p>In the present study, our findings support the hypothesis that a local inflammatory response, rather than a diffuse systemic reaction, may underlie subclinical myocardial involvement in the post-COVID-19 period. Patients with elevated NT-proBNP levels exhibited significantly higher IL-1β concentrations (median 5.2 pg/mL) compared with those with normal NT-proBNP (median 3.1 pg/mL, p = 0.029), suggesting persistence of local inflammation. Other classical pro-inflammatory cytokines, including IL-6 and TNF-α, did not differ significantly between groups (p = 0.12 and p = 0.695, respectively), reinforcing the concept of selective activation of the inflammasome–IL-1β axis.</p>
			<p>Additionally, levels of GDF-15, a recognized biomarker of cellular stress, mitochondrial dysfunction, and low-grade inflammation,<sup><xref ref-type="bibr" rid="B13">13</xref>-<xref ref-type="bibr" rid="B15">15</xref></sup> were significantly higher in the elevated NT-proBNP group (median 367.8 pg/mL) compared with the normal NT-proBNP group (median 219.1 pg/mL, p = 0.030). Notably, 41% of individuals with elevated NT-proBNP also presented high GDF-15 levels, compared with 12.8% in the control group (p = 0.011). This finding reinforces the role of GDF-15 as a marker of cardiovascular stress and a potential integrator of inflammatory and metabolic pathways in post-COVID-19 pathophysiology, consistent with previous reports.<sup><xref ref-type="bibr" rid="B14">14</xref></sup></p>
			<p>The <xref ref-type="fig" rid="f01">Central Illustration</xref> summarizes the proposed pathophysiological mechanism, integrating persistent inflammasome activation, myocardial stress signaling, and early atrial and right ventricular dysfunction in the post-COVID-19 period.</p>
			<p>Moreover, participants with elevated GDF-15 levels exhibited higher IL-8 concentrations (14.63 vs. 9.61 pg/mL, p &lt; 0.05), a chemokine involved in neutrophil recruitment and endothelial signaling. IL-8 is stimulated by inflammatory mediators such as TNF-α and IL-6,<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B16">16</xref></sup> as well as by extracellular stressors including hypoxia and oxidative stress,<sup><xref ref-type="bibr" rid="B17">17</xref></sup> suggesting a potential relationship between GDF-15 and IL-8 in the context of inflammation and extracellular matrix remodeling following injury. Although its precise role in this setting remains to be fully clarified, this finding may indicate a broader inflammatory response.</p>
			<p>Patients with elevated GDF-15 also demonstrated significantly reduced right ventricular global longitudinal strain, indicative of incipient subclinical dysfunction. This impairment may reflect persistent hemodynamic burden, pulmonary vascular remodeling, or endothelial inflammation, particularly among individuals with cardiometabolic comorbidities. Such findings reinforce the hypothesis that cardiac involvement in the post-COVID-19 period may encompass distinct myocardial patterns, including isolated right ventricular impairment.</p>
			<p>In parallel, PALS/LASr was markedly reduced in participants with elevated NT-proBNP (median 25.10%) compared with controls (median 31.90%; p = 0.027), indicating subclinical atrial dysfunction. Previous studies have identified left atrial reservoir strain as a sensitive marker of early atrial impairment, inversely correlated with natriuretic peptide levels, even in the absence of overt cardiac disease.<sup><xref ref-type="bibr" rid="B18">18</xref></sup> These findings suggest early myocardial remodeling, likely mediated by sustained inflammation, endothelial dysfunction, and chronic hemodynamic stress. Importantly, our results are consistent with prior evidence:<sup><xref ref-type="bibr" rid="B19">19</xref></sup> in individuals without comorbidities assessed between four and twelve weeks after SARS-CoV-2 infection, symptomatic patients – particularly those with dyspnea and exercise intolerance – demonstrated significant reductions in left atrial strain compared with asymptomatic individuals.<sup><xref ref-type="bibr" rid="B19">19</xref></sup></p>
			<p>The increase in cardiac biomarkers observed in the post-COVID-19 period may indicate that inflammatory mechanisms, rather than classical myocardial ischemia, contribute to cardiac damage in this setting. GDF-15 emerges as a marker of inflammation, cellular stress, and mitochondrial dysfunction, and may prove useful for cardiovascular risk stratification. Interestingly, individuals with elevated NT-proBNP reported fewer recurrent episodes of COVID-19 but had higher hospitalization rates. This observation suggests that the severity of the acute episode, rather than its frequency, may play a more decisive role in driving long-term cardiac involvement.</p>
			<p>It is noteworthy that unvaccinated and single-dose vaccinated participants were found only in the group with elevated NT-proBNP, suggesting that incomplete vaccination may be associated with increased post-COVID-19 cardiac stress.<sup><xref ref-type="bibr" rid="B20">20</xref></sup> However, further studies are required to investigate this hypothesis more thoroughly.</p>
			<p>This study is original in demonstrating that the combined assessment of NT-proBNP, GDF-15, and IL-1β may characterize subclinical myocardial dysfunction, reinforcing a pathophysiological model based on chronic inflammation. This model differs from the classic ischemic paradigm and aligns with mechanisms described in heart failure with preserved ejection fraction. In this context, integrated evaluation of cardiac involvement in the post-COVID-19 period would benefit from monitoring these biomarkers.</p>
			<p>The study has some limitations, including the relatively small sample size and the absence of cardiac magnetic resonance imaging to assess myocardial fibrosis. Nonetheless, it contributes to the assessment and monitoring of cardiac stress and proposes an integrative model of myocardial injury in the post-COVID-19 period, with clinical applicability and potential scientific relevance. The relatively small sample size of the matched subgroups (39 vs. 39) limits statistical power, particularly for multivariable analyses and dichotomized biomarkers, increasing the risk of type II error. The sample size was determined by feasibility during the pandemic, and no formal <italic>a priori</italic> calculation was performed; therefore, our post-hoc power estimates (≈77% for the continuous GDF-15 comparison and ≈44% for the ROC-based cut-off) should be interpreted as descriptive. In addition, the single-center design and 1:1 matching may have introduced selection bias and reduced generalizability. Finally, the cross-sectional nature of the analysis precludes causal inferences regarding the relationship between inflammation, cellular stress, and subclinical myocardial dysfunction.</p>
			<p>Nonetheless, the study provides meaningful evidence for the evaluation of cardiac stress in the post-COVID-19 context and proposes an integrative model of myocardial injury with potential clinical and scientific relevance. These findings underscore the need for longitudinal studies and broader imaging availability to further clarify mechanisms and prognostic implications.</p>
		</sec>
		<sec id="suppl" sec-type="supplementary-material">
			<title>Supplemental Materials</title>
			<supplementary-material id="suppl01">
				<label>Supplementary Table S1</label>
				<media mime-subtype="pdf" mimetype="application" xlink:href="2764-3107-abchf-5-3-e20250037-suppl01.pdf"/>
			</supplementary-material>
		</sec>
	</body>
	<back>
		<ack>
			<title>Acknowledgements</title>
			<p>We thank the staff of Antônio Pedro University Hospital and of the Multiuser Laboratory for Research Support in Nephrology and Medical Sciences for their key contributions and support to this study.</p>
		</ack>
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		<fn-group>
			<fn fn-type="other">
				<label>Study Association:</label>
				<p> This study is not associated with any thesis or dissertation work.</p>
			</fn>
			<fn fn-type="other">
				<label>Ethics Approval and Consent to Participate:</label>
				<p> This study was approved by the Ethics Committee of the Universidade Federal Fluminense under the protocol number CAAE: 59213722.5.0000.5243. All the procedures in this study were in accordance with the 1975 Helsinki Declaration, updated in 2013. Informed consent was obtained from all participants included in the study.</p>
			</fn>
			<fn fn-type="supplementary-material" id="fnsuppl01">
				<label>*Supplemental Materials</label>
				<p>For additional information, please <ext-link ext-link-type="uri" xlink:href="https://abcheartfailure.org/supplementary-material/2025/503/ABCHF_2025-0037_AO_SupplementaryTableS1.pdf">click here</ext-link>.</p>
			</fn>
			<fn fn-type="financial-disclosure">
				<label>Sources of Funding:</label>
				<p>This study was funded by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) (Processos nº E-26/210.828/2021, nº E-26/203.612/2022, nº E-26/203.624/2022, nº E-26/202.523/2024) e pela Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – Código de Financiamento 001).</p>
			</fn>
		</fn-group>
	</back>
	<sub-article article-type="translation" id="TRpt" xml:lang="pt">
		<front-stub>
			<article-id pub-id-type="doi">10.36660/abchf.20250037</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Artigo de Original</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>A Inflamação Local Pode ser um Mecanismo Subjacente da Lesão Cardíaca no Período Pós-COVID-19</article-title>
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				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-1517-4217</contrib-id>
					<name>
						<surname>Carvalho</surname>
						<given-names>Marcio Roberto Moraes de</given-names>
					</name>
					<role>Concepção e desenho da pesquisa e Revisão crítica do manuscrito quanto ao conteúdo intelectual importante</role>
					<role>Obtenção de dados</role>
					<role>Análise e interpretação dos dados</role>
					<role>Redação do manuscrito</role>
					<xref ref-type="aff" rid="aff1002"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5109-085X</contrib-id>
					<name>
						<surname>Rosário</surname>
						<given-names>Natalia Fonseca</given-names>
					</name>
					<role>Concepção e desenho da pesquisa e Revisão crítica do manuscrito quanto ao conteúdo intelectual importante</role>
					<role>Análise e interpretação dos dados</role>
					<role>Obtenção de financiamento</role>
					<role>Redação do manuscrito</role>
					<xref ref-type="aff" rid="aff2002"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff3002"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6037-8206</contrib-id>
					<name>
						<surname>Ramos</surname>
						<given-names>Alice</given-names>
					</name>
					<role>Análise e interpretação dos dados</role>
					<role>Análise estatística</role>
					<xref ref-type="aff" rid="aff4002"><sup>4</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-4149-6455</contrib-id>
					<name>
						<surname>Azevedo</surname>
						<given-names>Carla Rodrigues do Amaral</given-names>
					</name>
					<role>Obtenção de dados</role>
					<xref ref-type="aff" rid="aff3002"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-9186-9072</contrib-id>
					<name>
						<surname>Ribeiro</surname>
						<given-names>Mario Luiz</given-names>
					</name>
					<role>Obtenção de dados</role>
					<xref ref-type="aff" rid="aff1002"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-4493-7287</contrib-id>
					<name>
						<surname>Lomba</surname>
						<given-names>Guilherme Schittine Bezerra</given-names>
					</name>
					<role>Análise e interpretação dos dados</role>
					<role>Análise estatística</role>
					<role>Redação do manuscrito</role>
					<xref ref-type="aff" rid="aff5002"><sup>5</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0000-4230-2929</contrib-id>
					<name>
						<surname>Oliveira</surname>
						<given-names>Evelyn Maciel de</given-names>
					</name>
					<role>Obtenção de dados</role>
					<xref ref-type="aff" rid="aff2002"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff3002"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0002-1235-4829</contrib-id>
					<name>
						<surname>Souza</surname>
						<given-names>Rachel V. O’Neill de</given-names>
					</name>
					<role>Obtenção de dados</role>
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				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-1755-999X</contrib-id>
					<name>
						<surname>Castro</surname>
						<given-names>Mauro Jorge Cabral</given-names>
					</name>
					<role>Obtenção de dados</role>
					<xref ref-type="aff" rid="aff2002"><sup>2</sup></xref>
					<xref ref-type="aff" rid="aff8002"><sup>8</sup></xref>
					<xref ref-type="aff" rid="aff9002"><sup>9</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6791-3910</contrib-id>
					<name>
						<surname>Lugon</surname>
						<given-names>Jocemir Ronaldo</given-names>
					</name>
					<role>Concepção e desenho da pesquisa e Revisão crítica do manuscrito quanto ao conteúdo intelectual importante</role>
					<role>Análise e interpretação dos dados</role>
					<role>Obtenção de financiamento</role>
					<role>Redação do manuscrito</role>
					<xref ref-type="aff" rid="aff3002"><sup>3</sup></xref>
					<xref ref-type="aff" rid="aff7002"><sup>7</sup></xref>
				</contrib>
			</contrib-group>
			<aff id="aff1002">
				<label>1</label>
				<country country="BR">Brasil</country>
				<institution content-type="original">Divisão de Cardiologia, Departamento de Medicina, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil</institution>
			</aff>
			<aff id="aff2002">
				<label>2</label>
				<country country="BR">Brasil</country>
				<institution content-type="original">Laboratório Multiusuário de Apoio à Pesquisa em Nefrologia e Ciências Médicas (LAMAP), Hospital Universitário Antônio Pedro, Universidade Federal Fluminense, Niterói, RJ – Brasil</institution>
			</aff>
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				<country country="BR">Brasil </country>
				<institution content-type="original">Programa de Pós-Graduação em Ciências Médicas, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil </institution>
			</aff>
			<aff id="aff4002">
				<label>4</label>
				<country country="BR">Brasil</country>
				<institution content-type="original">Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ - Brasil</institution>
			</aff>
			<aff id="aff5002">
				<label>5</label>
				<country country="BR">Brasil</country>
				<institution content-type="original">Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil</institution>
			</aff>
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				<label>6</label>
				<country country="BR">Brasil </country>
				<institution content-type="original">Programa de Pós-Graduação em Ciências Cardiovasculares, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil </institution>
			</aff>
			<aff id="aff7002">
				<label>7</label>
				<country country="BR">Brasil </country>
				<institution content-type="original">Divisão de Nefrologia, Departamento de Medicina, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil </institution>
			</aff>
			<aff id="aff8002">
				<label>8</label>
				<country country="BR">Brasil </country>
				<institution content-type="original">Departamento de Patologia, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil </institution>
			</aff>
			<aff id="aff9002">
				<label>9</label>
				<country country="BR">Brasil</country>
				<institution content-type="original">Programa de Pós-Graduação em Patologia, Faculdade de Medicina, Universidade Federal Fluminense, Niterói, RJ – Brasil</institution>
			</aff>
			<author-notes>
				<corresp id="c01002">
					<label>Correspondência:</label> Marcio Roberto Moraes de Carvalho Universidade Federal Fluminense Hospital Universitário Antonio Pedro – Medicina Clínica – Av. Marquês do Paraná, 303. CEP 24033-900, Niterói, RJ – Brasil E-mail: marcioc@id.uff.br </corresp>
				<fn fn-type="edited-by">
					<label>Editor responsável pela revisão:</label>
					<p> Germano Souza</p>
				</fn>
				<fn fn-type="coi-statement">
					<label>Potencial Conflito de Interesse:</label>
					<p> Declaro não haver conflito de interesses pertinentes.</p>
				</fn>
			</author-notes>
			<abstract>
				<title>Resumo</title>
				<sec>
					<title>Fundamento</title>
					<p> Alguns pacientes apresentam disfunção cardiovascular subclínica após a COVID-19, possivelmente impulsionada por mecanismos inflamatórios persistentes que ainda não são totalmente compreendidos. Peptídeo Natriurético tipo B N-terminal (NT-proBNP) e Fator de Diferenciação de Crescimento 15 (GDF-15) têm sido propostos como biomarcadores de estresse miocárdico e inflamação nesse contexto.</p>
				</sec>
				<sec>
					<title>Objetivos</title>
					<p> Investigar associações entre NT-proBNP, GDF-15, citocinas inflamatórias e parâmetros ecocardiográficos em pacientes com Condição Pós-COVID-19 (CPC).</p>
				</sec>
				<sec>
					<title>Métodos</title>
					<p> Estudo transversal incluindo 78 adultos acompanhados em uma coorte de hospital universitário. Os participantes foram estratificados em grupos com NT-proBNP elevado (n=39) e NT-proBNP normal (n=39). Variáveis clínicas, laboratoriais e ecocardiográficas foram avaliadas, incluindo deformação atrial esquerda (PALS, do inglês Peak Left Atrial Strain) e deformação longitudinal global do ventrículo direito (RV-GLS, do inglês Right Ventricular Global Longitudinal Strain). GDF-15 sérico e citocinas foram quantificados por ensaio multiplex com microesferas. A análise da curva ROC avaliou o valor preditivo do GDF-15 para NT-proBNP elevado.</p>
				</sec>
				<sec>
					<title>Resultados</title>
					<p> Participantes com NT-proBNP elevado apresentaram redução de PALS (28,5% vs. 36,7%; p&lt;0,05), níveis mais altos de GDF-15 (mediana 368 vs. 219 pg/mL) e IL-1β (2,47 vs. 0,94 pg/mL; p&lt;0,05). GDF-15 ≥519 pg/mL previu NT-proBNP elevado (AUC=0,65). Indivíduos com GDF-15 elevado também apresentaram pior RV GLS (20,2% vs. 26,9%; p&lt;0,05) e níveis mais altos de IL-8 (p&lt;0,05).</p>
				</sec>
				<sec>
					<title>Conclusões</title>
					<p> NT-proBNP, GDF-15 e IL-1β identificam disfunção miocárdica subclínica na CPC, sustentando um modelo fisiopatológico centrado em inflamação persistente e remodelamento miocárdico precoce, distinto das vias isquêmicas clássicas.</p>
				</sec>
			</abstract>
			<abstract abstract-type="graphical">
				<p>
					<fig id="f01002">
						<label>Figura Central:</label>
						<caption>
							<title>A Inflamação Local Pode ser um Mecanismo Subjacente da Lesão Cardíaca no Período Pós-COVID-19</title>
						</caption>
						<graphic xlink:href="2764-3107-abchf-5-3-e20250037-gf01-pt.tif"/>
						<attrib>Modelo fisiopatológico proposto de disfunção miocárdica subclínica associada à inflamação no período pós-COVID-19; Representação esquemática do mecanismo proposto que relaciona os efeitos residuais ou pós-virais do SARS-CoV-2 à ativação inflamatória local (IL-1β e IL-8), ao estresse celular e metabólico, ao estresse da parede miocárdica com elevação de NT-proBNP e à disfunção cardíaca subclínica precoce, refletida pela redução da deformação atrial esquerda (PALS) e da deformação longitudinal global do ventrículo direito (RV GLS).</attrib>
					</fig>
				</p>
			</abstract>
			<kwd-group xml:lang="pt">
				<title>Palavras-chave:</title>
				<kwd>COVID-19</kwd>
				<kwd>Fator 15 de Diferenciação de Crescimento</kwd>
				<kwd>Inflamação</kwd>
				<kwd>Ecocardiografia</kwd>
			</kwd-group>
		</front-stub>
		<body>
			<sec sec-type="intro">
				<title>Introdução</title>
				<p>Após um episódio de infecção por SARS-CoV-2, os pacientes podem apresentar sintomas cardiovasculares<sup><xref ref-type="bibr" rid="B1">1</xref></sup> e níveis elevados de biomarcadores de lesão miocárdica.<sup><xref ref-type="bibr" rid="B2">2</xref></sup> No entanto, os mecanismos fisiopatológicos subjacentes permanecem incompletamente compreendidos.</p>
				<p>A inflamação sistêmica já foi correlacionada com elevações de Peptídeo Natriurético tipo B N-terminal (NT-proBNP) e aumento da mortalidade nesse contexto.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> Sugere-se que a disfunção cardíaca persistente após a COVID-19 possa estar associada à ativação local de vias imunes e inflamatórias, culminando em lesão miocárdica.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
				<p>O Fator de Diferenciação de Crescimento 15 (GDF-15, <italic>growth differentiation factor 15</italic>) uma citocina relacionada à família do TGF-β, é um importante biomarcador prognóstico em inflamação e doenças cardiovasculares. Níveis elevados têm sido associados à hipóxia, viremia por SARS-CoV-2 e desfechos adversos. Como marcador de estresse miocárdico e remodelamento, o GDF-15 pode também contribuir para disfunção autonômica e insuficiência cardíaca subclínica na COVID-19, reforçando sua relevância entre os biomarcadores emergentes.<sup><xref ref-type="bibr" rid="B4">4</xref></sup></p>
				<p>Estudos anteriores indicam que até 36% dos pacientes hospitalizados com COVID-19 apresentam níveis elevados de NT-proBNP, um marcador bem estabelecido de estresse cardíaco. Citocinas inflamatórias como IL-6, TNF-α e IL-1β também têm sido associadas à progressão da condição pós-COVID-19 (CPC).<sup><xref ref-type="bibr" rid="B5">5</xref></sup> No entanto, os papéis do NT-proBNP e do GDF-15 nesse contexto ainda não foram totalmente explorados. Assim, a relação entre inflamação, marcadores de estresse cardíaco e comprometimento miocárdico subclínico na CPC permanece pouco clara.</p>
				<p>Neste estudo, analisamos os níveis séricos de GDF-15, NT-proBNP, citocinas inflamatórias e parâmetros ecocardiográficos em uma coorte de pacientes que sobreviveram à fase aguda da COVID-19. O objetivo foi investigar a relação entre inflamação sistêmica e alterações persistentes na estrutura ou função cardíaca, fornecendo, assim, insights sobre os mecanismos subjacentes ao envolvimento cardíaco no período pós-COVID-19.</p>
			</sec>
			<sec sec-type="methods">
				<title>Métodos</title>
				<sec>
					<title>Aprovação ética</title>
					<p>O estudo foi aprovado pelo Comitê de Ética em Pesquisa de um hospital universitário terciário local (CAAE: 59213722.5.0000.5243). Em conformidade com os padrões éticos, todos os participantes forneceram consentimento informado por escrito.</p>
				</sec>
				<sec>
					<title>Desenho do estudo e seleção dos participantes</title>
					<p>Este estudo representa uma análise transversal de dados coletados de 144 pacientes adultos recrutados entre julho de 2022 e julho de 2023, todos com pelo menos um teste positivo de reação em cadeia da polimerase (PCR) para SARS-CoV-2 durante a pandemia de COVID-19.</p>
					<p>Os participantes foram incluídos no período pós-COVID-19 para investigação da CPC em um hospital universitário terciário no Brasil. No momento da inclusão, seguiram um protocolo padronizado que compreendia avaliações cardiológicas na linha de base e a cada seis meses, além de exames laboratoriais na linha de base e um ano após a coleta inicial de sangue.</p>
					<p>Para a presente análise, identificamos inicialmente um subgrupo de participantes (n = 39) que apresentaram níveis elevados de NT-proBNP na avaliação laboratorial basal, de acordo com o ponto de corte recomendado pela <italic>Heart Failure Association</italic> da <italic>European Society of Cardiology</italic>. Em seguida, foi selecionado um grupo controle de 39 indivíduos da mesma coorte com níveis normais de NT-proBNP, utilizando uma razão de pareamento 1:1. O pareamento foi realizado com base em sexo, idade e presença de comorbidades relevantes (diabetes e hipertensão), a fim de minimizar potenciais fatores de confusão na comparação entre os grupos.</p>
				</sec>
				<sec>
					<title>Variáveis clínicas</title>
					<p>Informações sociais e demográficas, histórico médico e medicamentos em uso foram obtidos por meio de entrevistas com os pacientes e revisão dos prontuários. O Índice de Massa Corporal (IMC) foi medido na entrada do estudo. A Doença Renal Crônica (DRC) foi definida como taxa de Filtração Glomerular Estimada (TFGe) inferior a 60 mL/min/1,73 m<sup>2</sup>, calculada pela equação CKD-EPI creatinina (2021). A insuficiência cardíaca aguda foi diagnosticada de acordo com os critérios de Framingham, aplicados em cada consulta cardiológica.<sup><xref ref-type="bibr" rid="B6">6</xref>-<xref ref-type="bibr" rid="B8">8</xref></sup></p>
				</sec>
				<sec>
					<title>Variáveis ecocardiográficas</title>
					<p>A ecocardiografia transtorácica foi realizada utilizando um sistema comercialmente disponível (EPIQ CVx<sup>®</sup>, Philips, Amsterdã, Países Baixos), seguindo o protocolo da <italic>American Society of Echocardiography</italic> para quantificação de câmaras,<sup><xref ref-type="bibr" rid="B9">9</xref></sup> análise Doppler e avaliação da deformação atrial esquerda (PALS, do inglês <italic>Peak Left Atrial Strain</italic>) e da deformação biventricular. Todos os exames foram revisados por um cardiologista especializado em análise ecocardiográfica, que estava cego para os dados clínicos e laboratoriais dos participantes.</p>
					<p>Os seguintes parâmetros ecocardiográficos foram avaliados: Fração de Ejeção do Ventrículo Esquerdo (FEVE): medida pelo método biplano de Simpson a partir das projeções apicais de quatro e duas câmaras; PALS: obtida por meio da ecocardiografia com <italic>speckle-tracking</italic> a partir da projeção apical de quatro câmaras, representando a função de reservatório do átrio esquerdo; deformação longitudinal global do ventrículo direito (RV-GLS, do inglês <italic>Right Ventricular Global Longitudinal Strain</italic>): calculada a partir da projeção apical de quatro câmaras focada no ventrículo direito, utilizando análise por <italic>speckle-tracking</italic>, refletindo a função sistólica do ventrículo direito. As variáveis ecocardiográficas foram reportadas como medidas contínuas e comparadas entre os grupos.</p>
				</sec>
				<sec>
					<title>Variáveis laboratoriais</title>
					<p>Amostras de sangue periférico foram coletadas pela manhã, e os soros foram armazenados congelados para análise subsequente. As concentrações de NT-proBNP foram medidas por imunoensaio quimioluminescente automatizado utilizando o sistema Alinity C (Abbott<sup>®</sup>, Brasil), no Setor de Análises Clínicas e Patologia do hospital universitário. Foram aplicados valores de corte ajustados por idade (&lt;50 anos: 125 pg/mL; 50–75 anos: 250 pg/mL; &gt;75 anos: 500 pg/mL).</p>
					<p>Ajustes adicionais foram realizados de acordo com a função renal e o IMC, conforme previamente descrito: o valor de corte do NT-proBNP foi aumentado em 15%, 25% e 35% para taxas de filtração glomerular estimadas de 45-60, 30-45 e &lt;30 mL/min/1,73 m<sup>2</sup>, respectivamente. O valor de corte do NT-proBNP foi reduzido em 25%, 30% e 40% para valores de IMC de 30–35, 35–40 e &gt;40 kg/m<sup>2</sup>, respectivamente.<sup><xref ref-type="bibr" rid="B6">6</xref></sup>Os níveis séricos de Fator de Diferenciação do Crescimento 15 (GDF-15) foram medidos utilizando ensaios imunoenzimáticos comercialmente disponíveis (R&amp;D Systems, EUA). Os valores de absorbância foram lidos com o espectrofotômetro Spectramax M3 Multi-Mode Microplate Reader (Molecular Devices, EUA).</p>
					<p>Foi realizado um ensaio de multiplex com microesferas para quantificar os níveis séricos de quimiocinas e citocinas (Bio-Plex Magpix<sup>®</sup>, Biorad Laboratories, EUA), conforme previamente descrito. Foram analisados os níveis séricos de 27 biomarcadores: proteína quimioatraente de monócitos-1 (MCP-1/CCL-2), proteína inflamatória de macrófagos 1α (MIP-1α/CCL-3), roteína inflamatória de macrófagos 1β (MIP-1β/CCL-4), quimiocina ligante C-C 5 (RANTES/CCL-5), quimiocina ligante C-C 11 (eotaxina-1/CCL-11), proteína induzida por interferon gama 10 (IP-10/CXCL-10), fator estimulador de colônias de granulócitos e macrófagos (GM-CSF/CSF-2), fator estimulador de colônias de granulócitos (G-CSF/CSF-3), fator básico de crescimento de fibroblastos (FGFb), interferon γ (IFN-γ), antagonista do receptor da interleucina 1 (IL-1ra), Interleucina 1β (IL-1β), Interleucinas: IL-2, IL-4, IL-5, IL-6, IL-7, IL-8 (CXCL-8), IL-9, IL-10, IL-12p70, IL-13, IL-15 e IL-17, fator de crescimento derivado de plaquetas BB (PDGF-BB), fator de necrose tumoral (TNF), e fator de crescimento endotelial vascular (VEGF).</p>
				</sec>
				<sec>
					<title>Análise estatística</title>
					<p>A normalidade dos dados contínuos foi avaliada pelo teste de Shapiro–Wilk. As variáveis contínuas foram expressas como mediana e Intervalo Interquartil (IIQ) ou média e Desvio Padrão (DP), dependendo da distribuição dos dados. As comparações entre grupos foram realizadas utilizando o teste t de Student ou, alternativamente, o teste de Mann–Whitney U. As variáveis categóricas foram comparadas pelo teste do Qui-quadrado ou pelo teste exato de Fisher, conforme apropriado. Dada a avaliação de 19 citocinas, os valores de p das comparações entre grupos foram ajustados para múltiplos testes utilizando o procedimento de taxa de descoberta falsa de Benjamini–Hochberg (FDR). Um valor de p ajustado por FDR &lt; 0,05 foi considerado estatisticamente significativo.</p>
					<p>As variáveis com p &lt; 0,20 nas análises univariadas, ou aquelas consideradas biologicamente relevantes (idade, sexo, IMC, hipertensão, diabetes), foram incluídas como preditores candidatos nos modelos de regressão logística multivariada. Idade e sexo foram forçados em todos os modelos, independentemente da significância univariada. Modelos parcimoniosos foram aplicados para manter proporções adequadas de eventos por variável. A análise da curva ROC (<italic>receiver operating characteristic</italic>) foi utilizada para avaliar se os níveis de citocinas poderiam predizer níveis elevados de NT-proBNP. Os níveis categorizados de citocinas foram empregados para melhorar o desempenho dos modelos de regressão.</p>
					<p>O tamanho da amostra foi determinado pela viabilidade durante o período de recrutamento da coorte prospectiva; portanto, não foi realizado cálculo formal de tamanho amostral <italic>a priori</italic>. Dado o tamanho fixo da amostra e a natureza exploratória do estudo, a ênfase foi colocada nos tamanhos de efeito e nos intervalos de confiança de 95% para interpretação dos resultados. Além disso, foi conduzida uma análise de poder post-hoc para a comparação primária dos níveis de GDF-15 entre pacientes com NT-proBNP elevado versus normal, utilizando o tamanho de efeito observado e um α bicaudal de 0,05. Todas as análises estatísticas foram realizadas utilizando o software R (RStudio, versão 2024.03.1+402).</p>
				</sec>
			</sec>
			<sec sec-type="results">
				<title>Resultados</title>
				<p>A <xref ref-type="fig" rid="f02002">Figura 1</xref> apresenta o fluxograma da seleção dos participantes para os 78 indivíduos incluídos na análise. As características basais estão resumidas na <xref ref-type="table" rid="t1002">Tabela 1</xref>. A população do estudo foi predominantemente feminina, com idade mediana de 60 anos, e apresentou alta prevalência de hipertensão, dislipidemia e diabetes.</p>
				<p>
					<fig id="f02002">
						<label>Figura 1</label>
						<caption>
							<title>– Fluxograma da Seleção de Participantes e Critérios de Inclusão com Base nos Níveis de NT-proBNP em Pacientes Pós-COVID-19.</title>
						</caption>
						<graphic xlink:href="2764-3107-abchf-5-3-e20250037-gf02-pt.tif"/>
					</fig>
				</p>
				<p>
					<table-wrap id="t1002">
						<label>Tabela 1</label>
						<caption>
							<title>– Características basais da população estudada</title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="left" style="font-weight:normal"> </th>
									<th style="font-weight:normal">Total (N = 78)</th>
									<th style="font-weight:normal">NT-proBNP elevada (N = 39)</th>
									<th style="font-weight:normal">NT-proBNP normal (N = 39)</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td><bold>Idade (anos)<sup>a</sup></bold></td>
									<td align="center">61 [52; 71]</td>
									<td align="center">64 [54; 71]</td>
									<td align="center">57 [49; 68]</td>
								</tr>
								<tr>
									<td><bold>Sexo masculino</bold></td>
									<td align="center">26 (33,3)</td>
									<td align="center">14 (35,9)</td>
									<td align="center">12 (30,8)</td>
								</tr>
								<tr>
									<td><bold>Índice de massa corporal (Kg/m<sup>2</sup>)<sup>a</sup></bold></td>
									<td align="center">28 [25, 32]</td>
									<td align="center">28 [24, 32]</td>
									<td align="center">29 [26, 32]</td>
								</tr>
								<tr>
									<td><bold>Hipertensão</bold></td>
									<td align="center">55 (70,5)</td>
									<td align="center">26 (66,7)</td>
									<td align="center">29 (74,4)</td>
								</tr>
								<tr>
									<td><bold>Diabetes</bold></td>
									<td align="center">28 (35,9)</td>
									<td align="center">15 (38,5)</td>
									<td align="center">13 (33,3)</td>
								</tr>
								<tr>
									<td><bold>Dislipidemia</bold></td>
									<td align="center">42 (53,8)</td>
									<td align="center">22 (56,4)</td>
									<td align="center">20 (51,3)</td>
								</tr>
								<tr>
									<td><bold>Doença renal crônica</bold></td>
									<td align="center">26 (33,3)</td>
									<td align="center">16 (41,0)</td>
									<td align="center">10 (25,6)</td>
								</tr>
								<tr>
									<td><bold>Insuficiência cardíaca crônica</bold></td>
									<td align="center">17 (23,9)</td>
									<td align="center">12 (30,7)</td>
									<td align="center">5 (12,8)</td>
								</tr>
								<tr>
									<td><bold>Insuficiência cardíaca</bold></td>
									<td align="center">12 (15,4)</td>
									<td align="center">7 (17,9)</td>
									<td align="center">5 (12,8)</td>
								</tr>
								<tr>
									<td colspan="4"><bold>Ecocardiograma</bold></td>
								</tr>
								<tr>
									<td>FEVE<sup>a</sup></td>
									<td align="center">60,0 [57,3; 64,6]</td>
									<td align="center">59,0 [52,9; 63,5]</td>
									<td align="center">61,8 [60,0; 65,5]</td>
								</tr>
								<tr>
									<td>PALS<sup>a</sup></td>
									<td align="center">32,0 [25,4; 38,5]</td>
									<td align="center">28,50 [23,0; 33,5]</td>
									<td align="center">36,7 [30,1; 40,3] *</td>
								</tr>
								<tr>
									<td>RV GLS<sup>a</sup></td>
									<td align="center">23,7 [21,5; 28,7]</td>
									<td align="center">22,7 [19,5; 27,3]</td>
									<td align="center">27,6 [21,8; 29,8]</td>
								</tr>
								<tr>
									<td colspan="4"><bold>Citocinas (pg/mL)</bold></td>
								</tr>
								<tr>
									<td>GDF-15<sup>a</sup></td>
									<td align="center">291 [161; 578]</td>
									<td align="center">368 [14; 855]</td>
									<td align="center">219 [129; 437] *</td>
								</tr>
								<tr>
									<td>IL-1β<sup>a</sup></td>
									<td align="center">2,47 [0,94; 4,14]</td>
									<td align="center">2,47 [0,94; 4,14]</td>
									<td align="center">0,94 [0,75; 2,47] *</td>
								</tr>
								<tr>
									<td><bold>&gt; 1 episódio de COVID-19</bold></td>
									<td align="center">19 (24,4)</td>
									<td align="center">5 (12,8)</td>
									<td align="center">14 (35,9)*</td>
								</tr>
								<tr>
									<td><bold>Internação hospitalar (n=64)</bold></td>
									<td align="center">37 (57,8)</td>
									<td align="center">21 (56,7)</td>
									<td align="center">7 (25,9)*</td>
								</tr>
								<tr>
									<td colspan="4"><bold>Vacinas <sup>b</sup></bold></td>
								</tr>
								<tr>
									<td>Nenhuma dose</td>
									<td align="center">4 (6,3)</td>
									<td align="center">4 (100,0)</td>
									<td align="center">0 (0,0)</td>
								</tr>
								<tr>
									<td>1 dose</td>
									<td align="center">3 (4,7)</td>
									<td align="center">3 (100,0)</td>
									<td align="center">0 (0,0)</td>
								</tr>
								<tr>
									<td>2 doses</td>
									<td align="center">12 (18,2)</td>
									<td align="center">6 (50,0)</td>
									<td align="center">6 (50,0)</td>
								</tr>
								<tr>
									<td>3 doses</td>
									<td align="center">11 (16,7)</td>
									<td align="center">6 (54,5)</td>
									<td align="center">5 (45,5)</td>
								</tr>
								<tr>
									<td>4 doses</td>
									<td align="center">30 (45,4)</td>
									<td align="center">17 (56,7)</td>
									<td align="center">13 (43,3)</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN1002">
								<p>Dados apresentados como n (%), salvo se indicado o contrário; <sup>a</sup>mediana [IIQ]; FEVE: fração de ejeção do ventrículo esquerdo; PALS: deformação longitudinal máxima do átrio esquerdo; RV GLS: deformação longitudinal global do ventrículo direito; GDF: fator de diferenciação do crescimento; IL-1β, interleucina 1 beta; <sup>b</sup>antes da inclusão; valor de p &lt; 0,05.</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Ao comparar os grupos estratificados pelos níveis de NT-proBNP, observou-se um padrão consistente de maior estresse miocárdico e inflamação entre os indivíduos com valores elevados do biomarcador. Esses participantes apresentaram disfunção atrial (redução do PALS) e disfunção ventricular direita (redução do RV-GLS), além de concentrações mais altas de GDF-15 e IL-1β. Eles também tiveram maior frequência de hospitalização durante a fase aguda da COVID-19. Parâmetros clínicos e laboratoriais adicionais estão detalhados na <xref ref-type="table" rid="t1002">Tabela 1</xref>.</p>
				<p>Nas análises preditivas, o GDF-15 demonstrou desempenho moderado na distinção de participantes com NT-proBNP elevado (AUC = 0,65). O valor de corte ótimo identificado foi de 519 pg/mL. Com base nesse limiar, os participantes foram categorizados em grupos com GDF-15 elevado e normal, conforme mostrado na <xref ref-type="table" rid="t2002">Tabela 2</xref>. Indivíduos com níveis elevados de GDF-15 eram mais velhos, apresentavam maior carga de comorbidades e exibiam pior deformação ventricular direita, além de concentrações mais altas de IL-8. Esses achados sugerem uma interação entre inflamação local, estresse celular e disfunção subclínica. Não foram observadas diferenças significativas entre os grupos para a maioria dos outros biomarcadores inflamatórios avaliados.</p>
				<p>
					<table-wrap id="t2002">
						<label>Tabela 2</label>
						<caption>
							<title>– Comparação das características clínicas, ecocardiográficas e inflamatórias entre os grupos com GDF-15 elevado e normal</title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="left" style="font-weight:normal"> </th>
									<th style="font-weight:normal">GDF-15 elevada (N = 21)</th>
									<th style="font-weight:normal">GDF-15 normal (N = 57)</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td><bold>Idade (anos)<sup>a</sup></bold></td>
									<td align="center">65 [58; 77]</td>
									<td align="center">59 [49; 66] *</td>
								</tr>
								<tr>
									<td><bold>Sexo masculino</bold></td>
									<td align="center">11 (52,4)</td>
									<td align="center">15 (26,3)</td>
								</tr>
								<tr>
									<td><bold>IMC (Kg/m<sup>2</sup>)<sup>a</sup></bold></td>
									<td align="center">27 [23; 31]</td>
									<td align="center">29 [27; 33] *</td>
								</tr>
								<tr>
									<td><bold>Hipertensão</bold></td>
									<td align="center">18 (85,7)</td>
									<td align="center">37 (64,9)</td>
								</tr>
								<tr>
									<td><bold>Diabetes</bold></td>
									<td align="center">14 (66,7)</td>
									<td align="center">14 (24,6) *</td>
								</tr>
								<tr>
									<td><bold>Dislipidemia</bold></td>
									<td align="center">9 (42,9)</td>
									<td align="center">33 (57,9)</td>
								</tr>
								<tr>
									<td><bold>Doença renal crônica</bold></td>
									<td align="center">14 (66,6)</td>
									<td align="center">12 (21,0) *</td>
								</tr>
								<tr>
									<td><bold>Insuficiência cardíaca crônica</bold></td>
									<td align="center">6 (30,0)</td>
									<td align="center">11 (21,6)</td>
								</tr>
								<tr>
									<td><bold>Insuficiência cardíaca aguda</bold></td>
									<td align="center">4 (14,0)</td>
									<td align="center">8 (17,0)</td>
								</tr>
								<tr>
									<td><bold>Ecocardiograma</bold></td>
									<td> </td>
									<td> </td>
								</tr>
								<tr>
									<td>FEVEa</td>
									<td align="center">60,0 [52,4; 60,0]</td>
									<td align="center">62,2 [58,0; 65,1]</td>
								</tr>
								<tr>
									<td>PALSa</td>
									<td align="center">30,0 [28,0; 31,1]</td>
									<td align="center">33,6 [25,4; 40,1]</td>
								</tr>
								<tr>
									<td>RV GLSa</td>
									<td align="center">20,2 [17,7; 22,4]</td>
									<td align="center">26,9 [22,1; 29,8] *</td>
								</tr>
								<tr>
									<td><bold>Citocinas (pg/mL)</bold></td>
									<td> </td>
									<td> </td>
								</tr>
								<tr>
									<td>IL-1βa</td>
									<td align="center">2,47 [0,94; 4,14]</td>
									<td align="center">1,17 [0,94; 2,51]</td>
								</tr>
								<tr>
									<td>IL-6a</td>
									<td align="center">11,18 [9,00; 15,30]</td>
									<td align="center">2,67 [0,96; 5,13]</td>
								</tr>
								<tr>
									<td>IFNga</td>
									<td align="center">18,3 [15,4; 22,3]</td>
									<td align="center">14,4 [10,8; 20,3]</td>
								</tr>
								<tr>
									<td>FGFba</td>
									<td align="center">42,8 [35,0; 44,3]</td>
									<td align="center">35,1 [26,9; 42,8]</td>
								</tr>
								<tr>
									<td>PDGFbba</td>
									<td align="center">1,522 [766; 3.613]</td>
									<td align="center">2.783 [1.787; 4.390]</td>
								</tr>
								<tr>
									<td>IL-8a</td>
									<td align="center">14,6 [10,2; 27,2]</td>
									<td align="center">9,6 [6,0; 14,6]*</td>
								</tr>
								<tr>
									<td>GCSFa</td>
									<td align="center">119 [100; 174]</td>
									<td align="center">90 [60; 124]</td>
								</tr>
								<tr>
									<td><bold>&gt; 1 episódio de COVID-19</bold></td>
									<td align="center">3 (14,3)</td>
									<td align="center">16 (28,1)</td>
								</tr>
								<tr>
									<td><bold>COVID-19 sintomática</bold></td>
									<td align="center">16 (76,2)</td>
									<td align="center">48 (84,2)</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN2002">
								<p><sup>a</sup>Mediana [IQR]; FEVE: fração de ejeção do ventrículo esquerdo; PALS: deformação longitudinal máxima do átrio esquerdo; RV GLS: deformação longitudinal global do ventrículo direito; IL: interleucina; IFNγ: interferon gama; FGFb: fator básico de crescimento de fibroblastos; PDGFbb: fator de crescimento derivado de plaquetas-BB; GCSF: fator estimulador de colônias de granulócitos.* valor p &lt; 0,05.</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Como 19 citocinas foram analisadas, os valores de p foram ajustados para múltiplos testes utilizando o procedimento de taxa de descoberta falsa de Benjamini–Hochberg. Embora GDF-15 e IL-1β tenham mostrado significância nominal nas análises não ajustadas (p = 0,030 e p = 0,029, respectivamente), nenhuma das citocinas permaneceu estatisticamente significativa após a correção por FDR (todos os p ajustados ≥ 0,28). Portanto, as análises de citocinas devem ser interpretadas como exploratórias (<xref ref-type="sec" rid="suppl-pt">Tabela Suplementar 1</xref>).</p>
				<p>Uma análise de poder post-hoc para a comparação dos níveis de GDF-15 entre os grupos com NT-proBNP elevado versus normal demonstrou poder de 77,1% (Cohen’s d = 0,42; α = 0,05). Por outro lado, a análise baseada no ponto de corte derivado da curva ROC para GDF-15 apresentou apenas 43,7% de poder, indicando capacidade limitada para detectar diferenças nessa comparação secundária, dicotomizada.</p>
				<p>Nos modelos de regressão logística multivariada, níveis elevados de GDF-15 estiveram independentemente associados a NT-proBNP elevado (OR ajustado 6,95; IC 95% 1,79–33,32; p = 0,008). De forma semelhante, níveis elevados de IL-1β mostraram forte associação independente com NT-proBNP elevado (OR ajustado 7,04; IC 95% 2,11–28,46; p = 0,007) (<xref ref-type="table" rid="t3002">Tabela 3</xref>).</p>
				<p>
					<table-wrap id="t3002">
						<label>Tabela 3</label>
						<caption>
							<title>– Modelos de regressão logística multivariada para NT-proBNP elevado</title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="left">Variável</th>
									<th>OR ajustado</th>
									<th>IC95% (limite inferior – limite superior)</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td colspan="3"><bold>Modelo 1 – Preditor: GDF-15 elevado</bold></td>
								</tr>
								<tr>
									<td><bold>GDF-15 elevado (vs. baixo)</bold></td>
									<td align="center"><bold>*6,95</bold></td>
									<td align="center">1,79 – 33,32</td>
								</tr>
								<tr>
									<td><bold>Idade (anos)</bold></td>
									<td align="center">1,03</td>
									<td align="center">1,00 – 1,08</td>
								</tr>
								<tr>
									<td><bold>Sexo masculino</bold></td>
									<td align="center">0,98</td>
									<td align="center">0,30 – 3,14</td>
								</tr>
								<tr>
									<td><bold>IMC (Kg/m<sup>2</sup>)</bold></td>
									<td align="center">1,08</td>
									<td align="center">0,98 – 1,19</td>
								</tr>
								<tr>
									<td><bold>Hipertensão</bold></td>
									<td align="center"><bold>*0,25</bold></td>
									<td align="center">0,06 – 0,94</td>
								</tr>
								<tr>
									<td><bold>Diabetes</bold></td>
									<td align="center">0,90</td>
									<td align="center">0,25 – 3,02</td>
								</tr>
								<tr>
									<td><bold>Doença renal crônica</bold></td>
									<td align="center">1,28</td>
									<td align="center">0,36 – 4,50</td>
								</tr>
								<tr>
									<td colspan="3"><bold>Modelo 2 – Preditor: IL-1β elevado</bold></td>
								</tr>
								<tr>
									<td><bold>IL-1β elevado (vs. baixo)</bold></td>
									<td align="center"><bold>*7,04</bold></td>
									<td align="center">2,11 – 28,46</td>
								</tr>
								<tr>
									<td><bold>Idade (anos)</bold></td>
									<td align="center">1,04</td>
									<td align="center">1,00 – 1,08</td>
								</tr>
								<tr>
									<td><bold>Sexo masculino</bold></td>
									<td align="center">0,85</td>
									<td align="center">0,24 – 2,85</td>
								</tr>
								<tr>
									<td><bold>IMC (Kg/m<sup>2</sup>)</bold></td>
									<td align="center">1,07</td>
									<td align="center">0,97 – 1,18</td>
								</tr>
								<tr>
									<td><bold>Hipertensão</bold></td>
									<td align="center"><bold>*0,18</bold></td>
									<td align="center">0,04 – 0,71</td>
								</tr>
								<tr>
									<td><bold>Diabetes</bold></td>
									<td align="center">1,78</td>
									<td align="center">0,54 – 6,17</td>
								</tr>
								<tr>
									<td><bold>Doença renal crônica</bold></td>
									<td align="center">2,04</td>
									<td align="center">0,60 – 7,33</td>
								</tr>
								<tr>
									<td colspan="3"><bold>Modelo 1 – Preditor: GDF-15 elevado</bold></td>
								</tr>
								<tr>
									<td><bold>Elevado GDF-15 (vs. baixo)</bold></td>
									<td align="center"><bold>*6,95</bold></td>
									<td align="center">1,79 – 33,32</td>
								</tr>
								<tr>
									<td><bold>Idade (anos)</bold></td>
									<td align="center">1,03</td>
									<td align="center">1,00 – 1,08</td>
								</tr>
								<tr>
									<td><bold>Sexo masculino</bold></td>
									<td align="center">0,98</td>
									<td align="center">0,30 – 3,14</td>
								</tr>
								<tr>
									<td><bold>IMC (Kg/m<sup>2</sup>)</bold></td>
									<td align="center">1,08</td>
									<td align="center">0,98 – 1,19</td>
								</tr>
								<tr>
									<td><bold>Hipertensão</bold></td>
									<td align="center"><bold>0,25</bold></td>
									<td align="center">0,06 – 0,94</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN3002">
								<p>Modelos de regressão logística multivariada avaliando preditores independentes de NT-proBNP elevado; o modelo 1 avalia GDF-15 elevado; o modelo 2 avalia IL-1β elevado. Ambos os modelos foram ajustados para fatores de confusão demográficos e clínicos (idade, sexo, IMC, hipertensão, diabetes e doença renal crônica). Os dados são expressos como razões de chances ajustadas com intervalos de confiança de 95%; valor de p &lt; 0,05. IMC: índice de massa corporal.</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
			</sec>
			<sec sec-type="discussion">
				<title>Discussão</title>
				<p>Evidências indicam que a lesão cardíaca em pacientes com COVID-19 pode ocorrer mesmo na ausência de tropismo viral direto para o tecido cardíaco, sugerindo um mecanismo mediado pelo sistema imune.<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B11">11</xref></sup> Em consonância com isso, nosso grupo previamente levantou a hipótese de que um eixo inflamatório sustentado – impulsionado por disfunção de macrófagos e respostas de citocinas – pode desempenhar papel central na perpetuação do dano miocárdico no período pós-COVID-19. Além disso, Sabanoglu et al.<sup><xref ref-type="bibr" rid="B12">12</xref></sup> sugeriram que NT-proBNP e troponina de alta sensibilidade poderiam predizer desfechos em longo prazo na COVID-19, ressaltando a relevância prognóstica dos biomarcadores cardíacos nesse contexto.</p>
				<p>No presente estudo, nossos achados apoiam a hipótese de que uma resposta inflamatória local, em vez de uma reação sistêmica difusa, pode estar subjacente ao envolvimento miocárdico subclínico no período pós-COVID-19. Pacientes com níveis elevados de NT-proBNP apresentaram concentrações significativamente maiores de IL-1β (mediana 5,2 pg/mL) em comparação com aqueles com NT-proBNP normal (mediana 3,1 pg/mL; p = 0,029), sugerindo persistência da inflamação local. Outras citocinas pró-inflamatórias clássicas, incluindo IL-6 e TNF-α, não diferiram significativamente entre os grupos (p = 0,12 e p = 0,695, respectivamente), reforçando o conceito de ativação seletiva do eixo inflamassoma–IL-1β.</p>
				<p>Além disso, os níveis de GDF-15, um biomarcador reconhecido de estresse celular, disfunção mitocondrial e inflamação de baixo grau,<sup><xref ref-type="bibr" rid="B13">13</xref>-<xref ref-type="bibr" rid="B15">15</xref></sup> foram significativamente mais altos no grupo com NT-proBNP elevado (mediana 367,8 pg/mL) em comparação com o grupo com NT-proBNP normal (mediana 219,1 pg/mL; p = 0,030). Notavelmente, 41% dos indivíduos com NT-proBNP elevado também apresentaram níveis altos de GDF-15, em comparação com 12,8% no grupo controle (p = 0,011). Esse achado reforça o papel do GDF-15 como marcador de estresse cardiovascular e potencial integrador de vias inflamatórias e metabólicas na fisiopatologia pós-COVID-19, em consonância com relatos anteriores.<sup><xref ref-type="bibr" rid="B14">14</xref></sup></p>
				<p>A <xref ref-type="fig" rid="f01002">Figura Central</xref> resume o mecanismo fisiopatológico proposto, integrando a ativação persistente do inflamassoma, a sinalização de estresse miocárdico e a disfunção precoce atrial e ventricular direita no período pós-COVID-19.</p>
				<p>Além disso, os participantes com níveis elevados de GDF-15 apresentaram concentrações mais altas de IL-8 (14,63 vs. 9,61 pg/mL; p &lt; 0,05), uma quimiocina envolvida no recrutamento de neutrófilos e na sinalização endotelial. A IL-8 é estimulada por mediadores inflamatórios como TNF-α e IL-6,<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B16">16</xref></sup> bem como por estressores extracelulares, incluindo hipóxia e estresse oxidativo,<sup><xref ref-type="bibr" rid="B17">17</xref></sup> sugerindo uma possível relação entre GDF-15 e IL-8 no contexto de inflamação e remodelamento da matriz extracelular após lesão. Embora seu papel preciso nesse cenário ainda necessite de maior esclarecimento, esse achado pode indicar uma resposta inflamatória mais ampla.</p>
				<p>Pacientes com GDF-15 elevado também demonstraram redução significativa da deformação longitudinal global do ventrículo direito, indicativa de disfunção subclínica incipiente. Essa alteração pode refletir sobrecarga hemodinâmica persistente, remodelamento vascular pulmonar ou inflamação endotelial, particularmente entre indivíduos com comorbidades cardiometabólicas. Tais achados reforçam a hipótese de que o envolvimento cardíaco no período pós-COVID-19 pode abranger padrões miocárdicos distintos, incluindo comprometimento isolado do ventrículo direito.</p>
				<p>Em paralelo, o PALS/LASr foi significativamente reduzido nos participantes com NT-proBNP elevado (mediana 25,10%) em comparação com os controles (mediana 31,90%; <italic>p</italic> = 0,027), indicando disfunção atrial subclínica. Estudos anteriores identificaram a deformação do reservatório atrial esquerdo como um marcador sensível de comprometimento atrial precoce, inversamente correlacionado com os níveis de peptídeos natriuréticos, mesmo na ausência de doença cardíaca.<sup><xref ref-type="bibr" rid="B18">18</xref></sup> Esses achados sugerem remodelamento miocárdico inicial, provavelmente mediado por inflamação sustentada, disfunção endotelial e estresse hemodinâmico crônico. Importante destacar que nossos resultados são consistentes com evidências prévias:<sup><xref ref-type="bibr" rid="B19">19</xref></sup> em indivíduos sem comorbidades avaliados entre quatro e doze semanas após a infecção por SARS-CoV-2, pacientes sintomáticos – particularmente aqueles com dispneia e intolerância ao exercício – apresentaram reduções significativas na deformação atrial esquerda em comparação com indivíduos assintomáticos.<sup><xref ref-type="bibr" rid="B19">19</xref></sup></p>
				<p>O aumento dos biomarcadores cardíacos observado no período pós-COVID-19 pode indicar que mecanismos inflamatórios, em vez de isquemia miocárdica clássica, contribuem para o dano cardíaco nesse contexto. O GDF-15 surge como um marcador de inflamação, estresse celular e disfunção mitocondrial, podendo ser útil para a estratificação de risco cardiovascular. Curiosamente, indivíduos com NT-proBNP elevado relataram menos episódios recorrentes de COVID-19, mas apresentaram maiores taxas de hospitalização. Essa observação sugere que a gravidade do episódio agudo, mais do que sua frequência, pode desempenhar papel decisivo na determinação do envolvimento cardíaco em longo prazo.</p>
				<p>Vale destacar que participantes não vacinados e vacinados com apenas uma dose foram encontrados exclusivamente no grupo com NT-proBNP elevado, sugerindo que a vacinação incompleta pode estar associada a maior estresse cardíaco pós-COVID-19.<sup><xref ref-type="bibr" rid="B20">20</xref></sup> No entanto, estudos adicionais são necessários para investigar essa hipótese de forma mais aprofundada.</p>
				<p>Este estudo é original ao demonstrar que a avaliação combinada de NT-proBNP, GDF-15 e IL-1β pode caracterizar disfunção miocárdica subclínica, reforçando um modelo fisiopatológico baseado em inflamação crônica. Esse modelo difere do paradigma isquêmico clássico e se alinha aos mecanismos descritos na insuficiência cardíaca com fração de ejeção preservada. Nesse contexto, uma avaliação integrada do envolvimento cardíaco no período pós-COVID-19 se beneficiaria do monitoramento desses biomarcadores.</p>
				<p>O estudo apresenta algumas limitações, incluindo o tamanho relativamente pequeno da amostra e a ausência de ressonância magnética cardíaca para avaliar fibrose miocárdica. Ainda assim, contribui para a avaliação e monitoramento do estresse cardíaco e propõe um modelo integrativo de lesão miocárdica no período pós-COVID-19, com aplicabilidade clínica e potencial relevância científica. O tamanho relativamente pequeno dos subgrupos pareados (39 vs. 39) limita o poder estatístico, particularmente para análises multivariadas e biomarcadores dicotomizados, aumentando o risco de erro tipo II. O tamanho da amostra foi determinado pela viabilidade durante a pandemia, sem cálculo formal <italic>a priori</italic>; portanto, nossas estimativas de poder post-hoc (≈77% para a comparação contínua de GDF-15 e ≈44% para o ponto de corte derivado da curva ROC) devem ser interpretadas como descritivas. Além disso, o desenho de centro único e o pareamento 1:1 podem ter introduzido viés de seleção e reduzido a generalização dos resultados. Por fim, a natureza transversal da análise impede inferências causais sobre a relação entre inflamação, estresse celular e disfunção miocárdica subclínica.</p>
				<p>Apesar dessas limitações, o estudo fornece evidências relevantes para a avaliação do estresse cardíaco no contexto pós-COVID-19 e propõe um modelo integrativo de lesão miocárdica com potencial relevância clínica e científica. Esses achados reforçam a necessidade de estudos longitudinais e maior disponibilidade de exames de imagem para esclarecer mecanismos e implicações prognósticas.</p>
			</sec>
			<sec id="suppl-pt" sec-type="supplementary-material">
				<title>Material suplementar</title>
				<supplementary-material id="suppl01-pt">
					<label>Supplementary Table S1</label>
					<media mime-subtype="pdf" mimetype="application" xlink:href="2764-3107-abchf-5-3-e20250037-suppl01-pt.pdf"/>
				</supplementary-material>
			</sec>
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		<back>
			<ack>
				<title>Agradecimentos</title>
				<p>Agradecemos à equipe do Hospital Universitário Antônio Pedro e ao Laboratório Multiusuário de Apoio à Pesquisa em Nefrologia e Ciências Médicas por suas contribuições fundamentais e apoio a este estudo.</p>
			</ack>
			<fn-group>
				<fn fn-type="other">
					<label>Vinculação Acadêmica:</label>
					<p> Não há vinculação deste estudo a programas de pós-graduação.</p>
				</fn>
				<fn fn-type="other">
					<label>Aprovação Ética e Consentimento Informado:</label>
					<p> Este estudo foi aprovado pelo Comitê de Ética da Universidade Federal Fluminense sob o número de protocolo CAAE: 59213722.5.0000.5243. Todos os procedimentos envolvidos nesse estudo estão de acordo com a Declaração de Helsinki de 1975, atualizada em 2013. O consentimento informado foi obtido de todos os participantes incluídos no estudo.</p>
				</fn>
				<fn fn-type="supplementary-material" id="fnsuppl01002">
					<label>*Material suplementar</label>
					<p>Para informação adicional, por favor, <ext-link ext-link-type="uri" xlink:href="https://abcheartfailure.org/supplementary-material/2025/503/ABCHF_2025-0037_AO_SupplementaryTableS1.pdf">clique aqui</ext-link>.</p>
				</fn>
				<fn fn-type="financial-disclosure">
					<label>Fontes de Financiamento:</label>
					<p> O presente estudo foi financiado pela Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) (Processos nº E-26/210.828/2021, nº E-26/203.612/2022, nº E-26/203.624/2022, nº E-26/202.523/2024) e pela Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – Código de Financiamento 001).</p>
				</fn>
			</fn-group>
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	</sub-article>
</article>