66 research outputs found

    Data_Sheet_1_Reproducibility of Left Ventricular Dyssynchrony Indices by Three-Dimensional Speckle-Tracking Echocardiography: The Impact of Sub-optimal Image Quality.docx

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    Background: 3D speckle-tracking echocardiography (3D-STE) is a novel method to quantify left ventricular (LV) mechanical dyssynchrony. 3D-STE is influenced by image quality, but studies on the magnitude of its effect on 3D-STE derived LV systolic dyssynchrony indices (SDIs) and their test-retest reproducibility are limited.Methods: 3D-STE was performed in two groups, each comprising 18 healthy volunteers with good echocardiographic windows. In study 1, optimal and inferior-quality images, by intentionally poor echocardiographic technique, were acquired. In study 2, sub-optimal quality images were acquired by impairing ultrasound propagation using neoprene rubber sheets (thickness 2, 3, and 4 mm) mimicking mildly, moderately, and severely impaired images, respectively. Measures (normalized to cardiac cycle duration) were volume- and strain-based SDIs defined as the standard deviation of time to minimum segmental values, and volume- and strain-derived dispersion indices. For both studies test-retest reproducibility was assessed.Results: Test-retest reproducibility was better for most indices when restricting the analysis to good quality images; nevertheless, only volume-, circumferential strain-, and principal tangential strain-derived LV dyssynchrony indices achieved fair to good reliability. There was no evidence of systematic bias due to sub-optimal quality image. Volume-, circumferential strain-, and principal tangential strain-derived SDIs correlated closely. Radial strain- and longitudinal strain-SDI correlated moderately or weakly with volume-SDI, respectively.Conclusions: Sub-optimal image quality compromised the reliability of 3D-STE derived dyssynchrony indices but did not introduce systematic bias in healthy individuals. Even with optimal quality images, only 3D-STE indices based on volume, circumferential strain and principal tangential strain showed acceptable test-retest reliability.</p

    Data_Sheet_1_Antihypertensive Medication Use and Its Effects on Blood Pressure and Haemodynamics in a Tri-ethnic Population Cohort: Southall and Brent Revisited (SABRE).docx

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    Objectives:We characterised differences in BP control and use of antihypertensive medications in European (EA), South Asian (SA) and African-Caribbean (AC) people with hypertension and investigated the potential role of type 2 diabetes (T2DM), reduced arterial compliance (Ca), and antihypertensive medication use in any differences.Methods:Analysis was restricted to individuals with hypertension [age range 59–85 years; N = 852 (EA = 328, SA = 356, and AC =168)]. Questionnaires, anthropometry, BP measurements, echocardiography, and fasting blood assays were performed. BP control was classified according to UK guidelines operating at the time of the study. Data were analysed using generalised structural equation models, multivariable regression and treatment effect models.Results:SA and AC people were more likely to receive treatment for high BP and received a greater average number of antihypertensive agents, but despite this a smaller proportion of SA and AC achieved control of BP to target [age and sex adjusted odds ratio (95% confidence interval) = 0.52 (0.38, 0.72) and 0.64 (0.43, 0.96), respectively]. Differences in BP control were partially attenuated by controlling for the higher prevalence of T2DM and reduced Ca in SA and AC. There was little difference in choice of antihypertensive agent by ethnicity and no evidence that differences in efficacy of antihypertensive regimens contributed to ethnic differences in BP control.Conclusions:T2DM and more adverse arterial stiffness are important factors in the poorer BP control in SA and AC people. More effort is required to achieve better control of BP, particularly in UK ethnic minorities.</p

    Data_Sheet_1_Declining Levels and Bioavailability of IGF-I in Cardiovascular Aging Associate With QT Prolongation–Results From the 1946 British Birth Cohort.pdf

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    BackgroundAs people age, circulating levels of insulin-like growth factors (IGFs) and IGF binding protein 3 (IGFBP-3) decline. In rat cardiomyocytes, IGF-I has been shown to regulate sarcolemmal potassium channel activity and late sodium current thus impacting cardiac repolarization and the heart rate-corrected QT (QTc). However, the relationship between IGFs and IGFBP-3 with the QTc interval in humans, is unknown.ObjectivesTo examine the association of IGFs and IGFBP-3 with QTc interval in an older age population-based cohort.MethodsParticipants were from the 1946 Medical Research Council (MRC) National Survey of Health and Development (NSHD) British birth cohort. Biomarkers from blood samples at age 53 and 60–64 years (y, exposures) included IGF-I/II, IGFBP-3, IGF-I/IGFBP-3 ratio and the change (Δ) in marker levels between the 60–64 and 53y sampled timepoints. QTc (outcome) was recorded from electrocardiograms at the 60–64y timepoint. Generalized linear multivariable models with adjustments for relevant demographic and clinical factors, were used for complete-cases and repeated after multiple imputation.ResultsOne thousand four hundred forty-eight participants were included (48.3% men; QTc mean 414 ms interquartile range 26 ms). Univariate analysis revealed an association between low IGF-I and IGF-I/IGFBP-3 ratio at 60–64y with QTc prolongation [respectively: β −0.30 ms/nmol/L, (95% confidence intervals −0.44, −0.17), p ConclusionOver a decade, in an older age population-based cohort, declining levels and bioavailability of IGF-I associate with prolongation of the QTc interval. As QTc prolongation associates with increased risk for sudden death even in apparently healthy people, further research into the antiarrhythmic effects of IGF-I on cardiomyocytes is warranted.</p

    S3 Fig -

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    Forest plots illustrating (A) univariable and (B) multivariable MR effect estimates of childhood and adult height on measures of cardiac structure and function. The data underlying this figure can be found in S12 and S13 Tables. LV, left ventricular; MR, mendelian randomization; MRI, magnetic resonance imaging. (PNG)</p
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