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Enregistrement W4283826314 · doi:10.1093/cvr/cvac107

Common disease-promoting signalling pathways in heart failure and atrial fibrillation: putative underlying mechanisms and potential therapeutic consequences

2022· letter· en· W4283826314 sur OpenAlexaff
Joshua A. Keefe, Xander H.T. Wehrens, Dobromir Dobrev

Notice bibliographique

RevueCardiovascular Research · 2022
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAmyloidosis: Diagnosis, Treatment, Outcomes
Établissements canadiensUniversité de MontréalMontreal Heart Institute
Organismes subventionnairesNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNational Institutes of HealthDeutsche Forschungsgemeinschaft
Mots-clésAtrial fibrillationHeart failureMedicineDiseaseBioinformaticsCardiologySignal transductionHeart diseaseSignalling pathwaysInternal medicineBiologyGeneticsReceptor

Résumé

récupéré en direct d'OpenAlex

This editorial refers to ‘Pathophysiological pathways in patients with heart failure and atrial fibrillation’ by B.T. Santema et al., https://doi.org/10.1093/cvr/cvab331. Atrial fibrillation (AF) and heart failure (HF) are common, progressive diseases that often co-exist. Common epidemiological risk factors and structural alterations such as chamber dilatation and fibrosis point toward overlapping pathophysiological mechanisms. Although beta-blockers are indicated for symptomatic HF, there is no mortality benefit in HF patients with concomitant AF.1 Moreover, the diagnostic value of HF biomarkers such as B-type natriuretic peptide (BNP) is limited in patients with AF. Thus, identifying common mechanisms between AF and HF carries significant diagnostic and therapeutic utility. One plausible common pathway between AF and HF is amyloidosis (AL) or the extracellular deposition of insoluble proteins. AL is diagnosed by echocardiography and confirmed with cardiac biopsy. Prior studies2 on cardiac AL in AF have focused on primary AL, hereditary mutated transthyretin-related (ATTRm), and wild-type transthyretin (ATTRwt) AL. These studies have shown that degree of ventricular dysfunction and left atrial dilatation correlate with the duration of amyloid deposition—from least affected in ATTRm to most affected in ATTRwt.2 However, prior studies have not explored amyloid beta (Aβ) as a common pathway in AF and HF. Aβ is produced by proteolytic cleavage of amyloid precursor protein and is known to cause Alzheimer’s dementia (AD). Aβ deposition has also been shown to occur in the heart, particularly in patients with AD.3 In this study, Santema et al.4 provide observational evidence of altered Aβ metabolism as a common pathway in HF and AF (Figure 1). Role of TTR and Aβ amyloid in HF and AF. TTR and Aβ are produced in the liver. Abnormal folding into insoluble beta-pleated sheets predisposes to tissue deposition. Patirisan and Inotersen are Food and Drug Administration (FDA)-approved anti-sense oligonucleotides that target TTR mRNA. Tafamidis is an FDA-approved stabilizer of TTR tetramers. Aducanumab is an FDA-approved anti-Aβ monoclonal antibody for AD. TTR, transthyretin. In an index cohort of 1620 HF patients, Santema et al.4 found 24 upregulated and 3 downregulated biomarkers in HF patients with AF compared to those without AF. Validation in an independent HF cohort demonstrated eight upregulated biomarkers in HF patients with AF, all of which overlapped with those found in the index cohort: insulin-like growth factor binding protein (IGFBP) 7, neurogenic locus notch homologue protein 3, spondin-1, interleukin-1 receptor-like 1, natriuretic peptide 8, matrix metalloproteinase 2, IGFBP1, and growth differentiation factor 15. Pathway analyses revealed enrichment of Aβ-metabolic processes. After adjusting for age, sex, body mass index, baseline heart rate, coronary artery disease, and renal disease, spondin-1, IGFBP1, and IGFBP7 remained upregulated. Analyses across left ventricular ejection fraction (EF) categories did not affect the results. Altogether, Santema et al.4 concluded that upregulation of Aβ-metabolic processes is unique to patients with HF and concomitant AF, regardless of EF. Although the study of Santema et al.4 provide novel insights, several issues need consideration. First, the post hoc nature of the analyses likely diminished the statistical power to detect the hypothesized differences in biomarkers by AF status. Moreover, lack of a study arm of AF patients without HF fails to address the possibility of the upregulated biomarkers being solely due to AF, with HF as a tertiary association. One limitation discussed in the paper—that of misclassifying patients with paroxysmal AF—may have strengthened the study as true biomarkers of AF would more likely be elevated in persistent, as opposed to paroxysmal AF. Another point of consideration is the use of circulating biomarkers as a proxy for a local phenomenon (AL). Lack of cardiac Aβ deposition confirmation and consideration of neurologic phenotype fails to rule out CNS pathology as a confounding driver of the observed circulating biomarker elevations. A final point to consider from this study is the definition of renal disease. AL most commonly affects the kidney and presents as a nephrotic syndrome, which has distinct clinical manifestations and sequalae from chronic kidney disease. While both cohorts of HF patients with AF had significantly higher serum creatinine—indicating glomerular dysfunction—lack of other differentiating laboratory parameters such as proteinuria, serum albumin or cystatin-C, and serum calcium and phosphorus introduces potential confounding by type of renal disease. Renal impairment, specifically a decline in glomerular filtration rate, is an important overlapping disease association between AF and HF and should have been more clearly delineated in this study. While Aβ remains unexplored in relation to AF and HF, other overlapping pathways between AF and HF have been often studied. Atrial natriuretic peptide and BNP, known to portend diagnostic utility in HF, are also elevated in AF, with degree of elevation modified by HF status.5 Inflammation and oxidative stress are implicated in both AF and HF. Galectin-3, a biomarker of oxidative stress, correlates with atrial fibrosis. Its diagnostic utility in AF, however, is confounded by systemic fibrosis.5 C-reactive protein and inflammasome activation are seen in AF6 and HF.7 Similarly, nuclear factor of activated T-cell signalling has been associated with AF in mouse models of AF8 and HF.9 Inflammation is often linked with fibrosis. In AF, fibrosis causes heterogeneous slowing of atrial conduction that predisposes to AF-maintaining reentry. In HF, fibrosis impairs cardiac contractility and diastolic filling. Paracrine calcitonin and BMP-1 signalling have been shown to mediate atrial fibrosis in AF.10 In HF, increased fibrosis and slowed conduction velocity have been demonstrated in atrial samples from HF with reduced EF (HFrEF) patients.11 Fibrosis often co-occurs with electrical remodelling. Indeed, human atrial samples from patients with HFrEF and AF have prolonged action potential duration, L-type calcium channel inactivation time, and greater ryanodine receptor open probability, secondary to CaMKII phosphorylation at Serine2814, compared with HFrEF patients without AF.11 Thus, although many common signalling pathways between HF and AF already exist, therapeutic targeting of these pathways is still lacking. In the present study, Santema et al.4 further extend the list of shared pathways between AF and HF, suggesting Aβ as a potential novel pathway driving both conditions. Again, direct translation of these findings into diagnostic and therapeutic interventions will require further preclinical and clinical validation before therapeutic targeting of cardiac Aβ deposition could be developed. The current treatment approach for cardiac AL involves treatment of concomitant HF and/or AF and treatment of the underlying protein disorder. Treatment of HF involves loop diuretics, and treatment of AF involves beta blockers for rate control; digoxin is cautiously used as binding by amyloid fibrils increases toxicity risk. Anticoagulation is paramount due to amyloid-associated atrial myopathy, which predisposes to atrial thrombus formation. Treatment of the underlying amyloid protein disorder is an active field of research, with three agents currently Food and Drug Administration (FDA)-approved:12 Tafamidis, which stabilizes transthyretin tetramers, and Patisiran and Inotersen, which are anti-sense oligonucleotides that interfere with hepatic transthyretin synthesis (Figure 1). Regarding Aβ, aducanumab, an anti-Aβ monoclonal antibody, was approved by the FDA in July 2021 for AD (Figure 1). Nonetheless, targeting Aβ in the heart will require direct evidence of cardiac Aβ deposition and subsequent demonstration from animal models of the therapeutic utility of targeting Aβ pathways in AF and HF. This work was supported by the German Research Foundation DFG (Do 769/4-1) and the National Institutes of Health (R01-HL131517, R01-HL089598, R01-HL136389, and R01HL163277 to D.D., R01-HL089598 , R01-HL147108, and R01-HL153350 to X.H.T.W.), and the European Union (large-scale integrative project MEASTRIA, No. 965286 to D.D.), and the Robert and Janice MacNair Foundation McNair MD/PhD Scholars Programme (J.A.K.), and the Baylor College of Medicine Medical Scientist Training Programme supposed by T32-GM136611 (J.A.K.)

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0210,017
Charge utile insuffisante (le modèle a refusé de juger)0,0020,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,070
Tête enseignante GPT0,327
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentnon

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