Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Genetic evidence for distinct biological mechanisms that link adiposity to type 2 diabetes: toward precision medicine

We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We used MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites, and MRI-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size and enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population. OBJECTIVES: The goal of this study was to compare lifetime outcomes and cardiovascular phenotypes according to the presence of rare variants in sarcomere-encoding genes among middle-aged adults. METHODS: This study analyzed whole exome sequencing and cardiac magnetic resonance imaging in UK Biobank participants stratified according to sarcomere-encoding variant status. RESULTS: The prevalence of rare variants (allele frequency <0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n = 5,712; 1 in 35), and the prevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was 0.25% (n = 493; 1 in 407). SARC-HCM-P/LP variants were associated with an increased risk of death or major adverse cardiac events compared with controls (hazard ratio: 1.69; 95% confidence interval [CI]: 1.38-2.07; P < 0.001), mainly due to heart failure endpoints (hazard ratio: 4.23; 95% CI: 3.07-5.83; P < 0.001). In 21,322 participants with both cardiac magnetic resonance imaging and whole exome sequencing, SARC-HCM-P/LP variants were associated with an asymmetric increase in left ventricular maximum wall thickness (10.9 ± 2.7 mm vs 9.4 ± 1.6 mm; P < 0.001), but hypertrophy (≥13 mm) was only present in 18.4% (n = 9 of 49; 95% CI: 9%-32%). SARC-HCM-P/LP variants were still associated with heart failure after adjustment for wall thickness (hazard ratio: 6.74; 95% CI: 2.43-18.7; P < 0.001). CONCLUSIONS: In this population of middle-aged adults, SARC-HCM-P/LP variants have low aggregate penetrance for overt HCM but are associated with an increased risk of adverse cardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease

Complexity of flow motion

The analysis of complex physiologic time series has been the focus of considerable attention since simple mathematical models cannot be found to describe them. Signals derived from skin microvascular networks using Laser Doppler flowmetry (LDF) have been broadly investigated using both linear and nonlinear dynamical methods providing significant information about the microvascular function. This study aims to explore complexity methods that can quantify the changes in the complex flow motion characteristics from the human microcirculation in a range of pathophysiological states. Time and frequency domain analysis were used to define the signal values from the microvascular perfusion and their power contribution using the spectral analysis to quantify the different properties modulating the network perfusion. Nonlinear complexity methods were used to quantify the signal regularity by evaluating the presence of repeated patterns providing complexity variants at single and across multiple spatial and temporal scales. Further, a new approach, attractor reconstruction analysis, was used providing quantitative measures of the microvascular system in phase space and a visual representation in the shape and variability of the signal producing a two-dimensional attractor with features like density and symmetry. The skin blood flux (BF) and tissue oxygenation (OXY) signals obtained from a combined Laser Doppler flowmetry (LDF) and white light spectroscopy (WLS) device were investigated using time domain, frequency domain and the nonlinear methods in the skin of a healthy cohort during increased local warming. This study revealed multiple oscillatory components with a remarkable increase in the cardiac activity during thermally induced vasodilation. There was also shown a significant attenuation in the complexity across multiple scales and a significant drop in the attractor density measures during increased local warming. Subsequently, both linear and nonlinear methods were used to investigate the LDF signals obtained from groups of individuals at an increased cardiovascular disease (CVD) risk, categorised with presence or absence of type 2 diabetes and use of calcium channel blocker (CB) medication. The results showed an increase on the high frequency cardiac activity with CB treatment. There was a significant decrease in the complexity of the blood flux signals as the CVD risk increases across multiple time scales. Also, there is a decline with progression of CVD risk in the measures derived from attractor reconstruction analysis. The highest separability between these groups was achieved using the attractor and complexity measures combined. In conclusion, time and frequency domain analysis alone were insufficient to estimate the complex dynamics of the microvascular network during the application of a standard stressor. Nonlinear analysis provides a better characterisation of the flexibility of the system in a range of pathophysiological conditions. Together these mathematical approaches were able to quantify different microvascular functional states. With machine learning techniques this should allow the classification of the tissue perfusion features providing a use for clinical assessment

Dataset: Analysis of microvascular blood flow and oxygenation: discrimination between two haemodynamic steady states using nonlinear measures and multiscale analysis

Dataset supports: Thanaj, M., Chipperfield, A. J., &amp; Clough, G. F. (2018). Analysis of microvascular blood flow and oxygenation: Discrimination between two haemodynamic steady states using nonlinear measures and multiscale analysis. Computers in Biology and Medicine, 102, 157-167.</span

Analysis of microvascular blood flow and oxygenation: Discrimination between two haemodynamic steady states using nonlinear measures and multiscale analysis

Objective: This study investigates the feasibility of the use of nonlinear complexity methods as a tool to identify altered microvascular function often associated with pathological conditions. We evaluate the efficacy of multiscale nonlinear complexity methods to account for the multiple time-scales of processes modulating microvascular network perfusion. Methods: Microvascular blood flux (BF) and oxygenation (OXY: oxyHb, deoxyHb, totalHb and SO2%) signals were recorded simultaneously at the same site, from the skin of 15 healthy young male volunteers using combined laser Doppler fluximetry (LDF) and white light spectroscopy. Skin temperature was clamped at 33 °C prior to warming to 43 °C to generate a local thermal hyperaemia (LTH). Conventional and multiscale variants of sample entropy (SampEn) were used to quantify signal regularity and Lempel and Ziv (LZ) and effort to compress (ETC) to determine complexity. Results: SampEn showed a decrease in entropy during LTH in BF (p = 0.007) and oxygenated haemoglobin (oxyHb) (p = 0.029). Complexity analysis using LZ and ETC also showed a significant reduction in complexity of BF (LZ, p = 0.003; ETC, p = 0.002) and oxyHb (p &lt; 0.001, for both) with LTH. Multiscale complexity methods were better able to discriminate between haemodynamic states (p &lt; 0.001) than conventional ones over multiple time-scales. Conclusion: Our findings show that there is a good discrimination in complexity of both BF and oxyHb signals between two haemodynamic steady states which is consistent across multiple scales. Significance: Complexity-based and multiscale-based analysis of BF and OXY signals can identify different microvascular functional states and thus has potential for clinical application in the prognosis and the diagnosis of pathophysiological conditions such as microvascular dysfunction observed in non-alcoholic fatty liver disease and type 2 diabetes.</p

Attractor reconstruction analysis for blood flow signals

Attractor reconstruction analysis has been previously used to determine changes in the shape and variability of fairly periodic signals such as arterial blood pressure signals and electroencephalogram signals, providing a two-dimensional attractor with features like density and symmetry. Since BF signals are fairly periodic and quasi-stationary, we set out to investigate whether attractor reconstruction method could be applied in signals derived from the microvascular perfusion. We describe the basis and the implementation of attractor reconstruction analysis of the microvascular blood flux (BF) signals recorded from the skin of 15 healthy male volunteers, age 29.2 ± 8.1y (mean ± SD). The efficacy of attractor reconstruction analysis (ARA) as a potential method of identifying changes in the microvascular function is evaluated in two haemodynamic steady states, at 33°C, and during warming at 43°C to generate a local thermal hyperaemia (LTH). Our findings show a significant drop of the maximal density derived from the ARA, during increased flow and that there was good discrimination of the blood flow signals between the two haemodynamic steady states, having good classification accuracy (80%). This study shows that ARA of BF signals can identify different microvascular functional states and thus has a potential for the clinical assessment and diagnosis of pathophysiological condition

Multiscale analysis of microvascular blood flow and oxygenation

The purpose of this study is to investigate the feasibility of nonlinear methods for differentiating between haemodynamic steady states as a potential method of identifying microvascular dysfunction. As conventional nonlinear measures do not take into account the multiple time scales of the processes modulating microvascular function, here we evaluate the efficacy of multiscale analysis as a better discriminator of changes in microvascular health. We describe the basis and the implementation of the multiscale analysis of the microvascular blood flux (BF) and tissue oxygenation (OXY: oxyHb) signals recorded from the skin of 15 healthy male volunteers, age 29.2 ± 8.1y (mean ± SD), in two haemodynamic steady states at 33 °C and during warming at 43 °C to generate a local thermal hyperaemia (LTH). To investigate the influence of varying process time scales, multiscale analysis is employed on Sample entropy (MSE), to quantify signal regularity and Lempel and Ziv (MSLZ) and effort to compress (METC) complexity, to measure the randomness of the time series. Our findings show that there was a good discrimination in the multiscale indexes of both the BF (p = 0.001) and oxyHb (MSE, p = 0.002; METC and MSLZ, p &lt; 0.001) signals between the two haemodynamic steady states, having the highest classification accuracy in oxyHb signals (MSE: 86.67%, MSLZ: 90.00% and METC: 93.33%). This study shows that “multiscale-based” analysis of blood flow and tissue oxygenation signals can identify different microvascular functional states and thus has potential for the clinical assessment and diagnosis of pathophysiological conditions.</p

Complexity-based analysis of microvascular blood flow in human skin

The maintenance of an adequate microvascular perfusion sufficient to meet the metabolic demands of the tissue is dependent on neural, humoral and local vaso-mechanisms that determine vascular tone and blood flow patterns within a microvascular network. It has been argued that attenuation of these flow patterns may be a major contributor to disease risk. Thus, quantitative information on the in vivo spatio-temporal behaviour of microvascular perfusion is important if we are to understand network functionality and flexibility in cardiovascular disease. Time and frequency-domain analysis has been extensively used to describe the dynamic characteristics of Laser Doppler flowmetry (LDF) signals obtained from superficial microvascular networks such as that of the skin. However, neither approach has provided definitive and consistent information on the relative contribution of the oscillatory components of flowmotion (endothelial, neurogenic, myogenic, respiratory and cardiac) to a sustained and adequate microvascular perfusion; nor advance our understanding of how such processes are collectively modified in disease. More recently, non-linear complexity-based approaches have begun to yield evidence of a declining adaptability of microvascular flow patterns as disease severity increases. In this chapter we review the utility and application of these approaches for the quantitative, mechanistic exploration of microvascular (dys)function.</p