scholarly journals Left Ventricular Hypertrophy and Asymptomatic Cardiac Function Impairment in Chinese Patients with Simple Obesity using Echocardiography

Obesity Facts ◽  
2015 ◽  
Vol 8 (3) ◽  
pp. 210-219 ◽  
Author(s):  
Ting Sun ◽  
Jing Xie ◽  
Lili Zhu ◽  
Zhihua Han ◽  
Yushui Xie
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Cardiac Function ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Chinese Patients ◽  
Function Impairment ◽  
Simple Obesity

scholarly journals Correlations of circulating miR-26b level with left ventricular hypertrophy and cardiac function in elderly patients with hypertension

2021 ◽  
Vol 37 (4) ◽  
Author(s):  
Jian Fu ◽  
Fang Lin ◽  
Zhengxia Pan ◽  
Chun Wu
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Elderly Patients ◽  
Cardiac Function ◽  
Clinical Data ◽  
Ventricular Hypertrophy ◽  
Protective Factor ◽  
Left Ventricular ◽  
Potential Biomarker ◽  
Biochemical Indices ◽  
Level Group

Objectives: To study the correlations of circulating miR-26b level with left ventricular hypertrophy (LVH) and cardiac function in elderly patients with hypertension. Methods: A total of 132 eligible patients were divided into low and high miR-26b level groups. Their baseline clinical data and biochemical indices were compared. The correlations between miR-26b level and echocardiographic parameters were studied by Pearson’s analysis. Factors affecting LVH were explored by multivariate logistic regression analysis. The role of miR-26b in diagnosing LVH was predicted by receiver operating characteristic curve. Results: The relative expression level of miR-26b was 4.56-16.93, with a median of 7.62. The two groups had similar baseline clinical data and biochemical indices (P>0.05). Compared with high miR-26b level group, interventricular septal thickness (IVST), left ventricular posterior wall thickness (LVPWT), left ventricular mass index (LVMI) and number of LVH cases in low miR-26b level group significantly increased (P<0.05), and mitral ratio of peak early to late diastolic filling velocity (E/A) decreased (P<0.05). Circulating miR-26b level was negatively correlated with IVST, LVPWT and LVMI (P<0.0001), and positively correlated with E/A (P<0.0001). The proportion of cardiac hypofunction cases in low miR-26b level group significantly exceeded that of high miR-26b level group (P<0.05). Age and increased IVST, LVPWT and LVMI were independent risk factors for LVH (P<0.05), and elevated miR-26b level was a protective factor (P<0.05). AUC was 0.836, and the optimal cutoff value was 8.83, with high sensitivity and specificity. Conclusions: MiR-26b level is negatively correlated with LVH and positively correlated with left ventricular diastolic function in elderly hypertensive patients. It is a protective factor for LVH complicated with diastolic dysfunction and a potential biomarker for diagnosis. doi: https://doi.org/10.12669/pjms.37.4.4048 How to cite this:Fu J, Lin F, Pan Z, Wu C. Correlations of circulating miR-26b level with left ventricular hypertrophy and cardiac function in elderly patients with hypertension. Pak J Med Sci. 2021;37(4):---------. doi: https://doi.org/10.12669/pjms.37.4.4048 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cardiac function in systemic hypertension before and after reversal of left ventricular hypertrophy

1988 ◽  
Vol 62 (10) ◽  
pp. 745-750 ◽  
Author(s):  
Bruno Trimarco ◽  
Nicola De Luca ◽  
Bruno Ricciardelli ◽  
Giovanni Rosiello ◽  
Massimo Volpe ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Cardiac Function ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Systemic Hypertension ◽  
Before And After

scholarly journals Chronic activation of hypothalamic oxytocin neurons improves cardiac function during left ventricular hypertrophy-induced heart failure

2017 ◽  
Vol 113 (11) ◽  
pp. 1318-1328 ◽  
Author(s):  
Kara Garrott ◽  
Jhansi Dyavanapalli ◽  
Edmund Cauley ◽  
Mary Kate Dwyer ◽  
Sarah Kuzmiak-Glancy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Left Ventricular Hypertrophy ◽  
Cardiac Function ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Chronic Activation

scholarly journals Echocardiographic phenotyping of the continuum of myocardial functional remodelling in left ventricular hypertrophy - machine learning validated with cardiac magnetic resonance

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
F Loncaric ◽  
PM Marti Castellote ◽  
L Sanchiz ◽  
G Piella ◽  
A Garcia-Alvarez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Left Ventricular Hypertrophy ◽  
Cardiac Function ◽  
Ventricular Hypertrophy ◽  
Cardiac Cycle ◽  
Left Ventricular ◽  
Healthy Individuals ◽  
The Relationship ◽  
Output Space

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738) and the Clinical Research in Cardiology grant from the Spanish Cardiac Society Background Exploring phenotypes of left ventricular hypertrophy (LVH) and interpreting the relationship of genotype and phenotype are contemporary clinical challenges. Machine learning (ML) can help by integrating whole-cardiac cycle echo data and separating patients based on subtle differences of cardiac function. The aim is to investigate if an unsupervised ML approach has the potential to explore the LVH spectrum and recognize phenotypes related to distinct disease aetiologies and genotypes. Methods The cohort consisted of 342 participants: patients with hypertrophic cardiomyopathy (HCM)(n = 27), HCM relatives (n = 31), hypertensive patients (HTN) (n = 189), and healthy individuals (n = 95). All had echocardiography performed, whereas magnetic resonance (MR) and genetic testing were performed when clinically indicated. Myocardial deformation of the LV and left atrium, aortic and mitral blood-pool Doppler, as well as the septal mitral annular tissue Doppler velocity profiles were used as input for ML. Clinical data, including echo measurements, were not part of the learning, but used to validate the ML-derived phenotypes. An unsupervised ML algorithm was used to create an output space where participants were positioned based on cardiac function. Regression was used to estimate the echo and clinical characteristics of different regions in the space.  Results The ML analysis of HCM and relative data shows grouping of HCM patients in the right-most region of the output space (Fig 1B). This region was related to LV outflow tract obstruction, mitral inflow fusion, systolic impairment with septal involvement, as well as LA and LV strain impairment (Fig 1A). Clinical data concurred - showing reduced global longitudinal strain, elevated LV mass, and a pattern of systolic and diastolic impairment - defining a comprehensive phenotype of LV remodelling related to HCM. Exploration of the genotype/phenotype relationship revealed G + P- relatives grouping on the transition from the healthy to the remodelling region. Projection of the HTN and healthy individuals into the HCM space defined the LVH disease spectrum, with healthy individuals projecting in the existing healthy region and HTNs in the transition from health to extreme remodelling (Fig 1C). MR findings of late gadolinium enhancement correlated with the ML-derived functional remodelling phenotype (Fig 1C). Furthermore, 6 patients with a clinical need for septal myectomy were located in the extreme remodelling part of the output space (Fig 1C, red circles). Conclusion ML can integrate complex, whole-cardiac cycle echo data to group patients based on similarity of cardiac function. Using an interpretable ML approach, we can explore the spectrum of LV remodelling in different aetiologies and interpret the relationship between genotype and phenotype. The methodology can accommodate new patients by projecting them into the existing space to aid in clinical interpretation, risk assessment and patient management. Abstract Figure 1

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