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Enregistrement W2935967081 · doi:10.1113/jp277876

The role of oestrogen in left ventricle (re)modelling in the context of heart failure with preserved ejection fraction

2019· letter· en· W2935967081 sur OpenAlexaff
Sina Hadipour‐Lakmehsari, Sara Al Mouaswas

Notice bibliographique

RevueThe Journal of Physiology · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueCardiovascular Function and Risk Factors
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésCardiologyInternal medicineMedicineHeart failureHeart failure with preserved ejection fractionEjection fractionVentricleDiastoleContext (archaeology)Coronary artery diseaseDiabetes mellitusAtrial fibrillationBlood pressureEndocrinology

Résumé

récupéré en direct d'OpenAlex

Heart failure (HF) is a debilitating clinical condition caused by the impairment of the heart's functional ability to meet the demands of the body. HF usually presents as fatigue, dyspnoea and, in more severe cases, pulmonary and peripheral oedema. There are two major mechanisms of HF: systolic dysfunction and diastolic dysfunction. Systolic dysfunction usually leads to a variant known as HF with reduced ejection fraction (HFrEF). This is mainly a result of the inability of the myocardium to contract with sufficient force to drive adequate amounts of blood to the tissues. Here, the ejection fraction (EF) is significantly reduced to less than 40%. Diastolic dysfunction generally presents as HF with preserved ejection fraction (HfpEF). In this variant, although EF is maintained at greater than 50%, there is evidence of concentric (re)modelling, altered cardiomyocyte morphology and increased deposition of interstitial fibrous tissue. The prevalence of HFpEF is increasing and studies have shown that one-half of HF cases classify as HFpEF. HFpEF has been strongly associated with increased age, obesity, hypertension and female sex. It has been shown that older women with a history of hypertension, diabetes mellitus, atrial fibrillation and coronary artery disease represent the largest group of patients with HFpEF. Despite its increasing prevalence, the pathophysiological mechanisms behind HFpEF are not well understood and no improvements in therapeutic strategies have been established, exemplifying the importance of studies investigating this. Bustamante et al. (2019) published a study in the Journal of Physiology investigating the role of age, oestrogen and obesity with respect to cardiac structure and function, aiming to further advance our understanding of how these factors play a role in early HFpEF pathogenesis. The study used Fisher F344 female rat models, which were assigned to three groups: aged rats, aged ovariectomized rats (OVX) and ovariectomized rats fed 10% fructose to induce obesity (OVF). Echocardiography was used to monitor left ventricular (LV) structure and function, and catheter-based measurements were used to analyse in vivo haemodynamic properties, such as cardiac output (CO), end-diastolic and end-systolic volumes, and isovolumic relaxation time. Additionally, Langendorff's based ex vivo analysis yielded pressure–volume loops and circumferential and longitudinal strain. Lastly, histological-based analysis was used to measure the amount of fibrosis in the left ventricle. OVF and OVX rats were shown to have a significant decrease in CO compared to aged rats, demonstrating that weight and oestrogen played a role in the functional changes in the heart. Although differences were found between aged vs. young rats, no significant differences were found in ventricular wall thickness, ejection fraction, arterial elastance or the amount of fibrosis between aged, OVX and OVF conditions. An increase in chamber diameter, isovolumic relaxation time constant and arterial elastance was seen across the three conditions. No differences were seen in the LV pressure–volume curves across conditions, although a global right-shift was seen compared to young rats. Areas of fibrosis in papillary muscles that indicated lesions were seen across aged animals across all three conditions. Finally, a higher LV tissue area and LV collagen area were seen in aged rats, with no difference in endocardial/epicardial ratio. The results of the study by Bustamante et al. (2019) contribute significantly to the scientific and medical fields. Their study addresses a relatively less understood topic in medicine, which concerns the differences in the presentation and pathogenesis of diseases between males and females. In general, the natural history of diseases, reference values and information about pathogenesis have been explored using mostly pre-clinical male models. However, differences between men and women with respect to diseases are now beginning to be studied and understood. Without a further understanding of these differences, the medical field will be limited in its ability to provide the most effective treatment for diseases such as HFpEF. Ultimately, investigating such differences can lead to changes in the screening, work-up, diagnosis and treatment of diseases between men and women. At one point in medical history, paediatric patients were seen as ‘little adults’. Years of studies and observations have now changed that. Currently, paediatric patients are seen as their own subset, with unique disease presentations, pathology and treatments. The same will eventually become true for gender-based medicine, which will lead to a more precise and personalized care for patients. Additionally, the study by Bustamante et al. (2019) was well designed, thorough and used gold standard techniques to measure a myriad of properties so as to provide a more thorough understanding of the impact of these conditions on the structure and function of the heart. Although the study by Bustamante et al. (2019) provides useful insights, there are a number of limitations worth noting. First, additional experimental groups should have been included to support the theory that oestrogen, age and weight are risk factors for HFpEF. For example, using a young female rat with ovariectomy and an aged male rat would have provided further support for significant differences in cardiac properties being attributed to reduced oestrogen independent of old age. If more rapid (re)modelling in a young female rat with ovariectomy or blunted (re)modelling in an aged male rat had been observed, this would have augmented the strength of the evidence. To continue, the study could have benefitted from the inclusion of a sham surgery group in the young and aged rat groups. Although sham surgery would probably not cause significant cardiac (re)modelling, this would have provided higher quality standardization of the data and ensured that the differences in cardiac (re)modelling were themselves not a result of the surgery. Another example of more robust standardization that would have improved the study quality was the length of the acclimatization period for young rats; the aged rats were stored for 3 months with their condition, whereas the young mice were only at the facility for 1 week. Such standardization would have eliminated the possibility of an environmental effect being responsible for cardiac differences. Furthermore, epidemiological studies have shown that an equal prevalence of HFpEF exists between men and women (Dunlay et al. 2017). The previous imbalance in the prevalence favouring women was a result of women living longer and HFpEF classically being a disease of ageing. Despite this, a study investigating the role of oestrogen would be indispensable in this field and would advance knowledge with respect to gender-based medicine. This is where the inclusion of a male model would have provided validation of differences in functional and structural properties of the heart with respect to the onset of HFpEF between genders. Lastly, there are differences between using fat and fructose for inducing obesity (Barrett et al. 2016). Although all pre-clinical models of obesity have limitations, it would be interesting to investigate whether the study by Bustamante et al. (2019) would have benefitted from using a high-fat model of obesity because, theoretically, this would be more similar to the dietary patterns of those individuals in developed nations who have HFpEF. To advance and validate the risk model described by Bustamante et al. (2019), there are certain additional investigations that would prove beneficial. As previously noted, young female rats with ovariectomy and male aged rats would have added further depth to the results that were obtained. To continue, the effects of HFpEF are best observed during a period of exertion. In the future, a stress experiment using exercise or dobutamine should be conducted to accentuate the deficiencies in CO and increase the relevance of the study with respect to human patients. Measurements of brain natriuretic peptide levels would again validate myocardial disease and increase the solidity of this risk model (Redfield, 2016). Lastly, to expand the impact of the described risk model, mass spectrometry-based proteomic studies can help Illuminate protein differences during the pathogenesis of HFpEF and would highlight molecular differences with and without oestrogen. Pursuing an experiment such as this would add greater depth and could possibly shed light on unique, specific and novel drug targets. In summary, Bustamante et al. (2019) investigated oestrogen-induced differences in cardiac function in aged, female rats. A significant reduction in CO was demonstrated with preserved EF upon ovariectomy, thus confirming the role of oestrogen in cardiac function in a pre-disease model of HFpEF. These findings shed light on sex-based differences in cardiac disease. In addition to determining the underlying pathogenic mechanisms, their study has laid the foundation for future in-depth studies that can ultimately lead to changes in the clinical screening, diagnosis and management of HFpEF in females. None. SH-L and SAL were both involved in the conception, writing, and editing of this manuscript. None.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,013
Tête enseignante GPT0,232
Écart entre enseignants0,219 · 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
GenreCommentaire

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
Publié2019
Routes d'admission1
Résumé présentoui

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