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BackgroundMagnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle measurements: total muscle volume, regional measurements or measurements of muscle quality, such as intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative to detect changes and associations with relevant health outcomes such as sarcopenia and frailty.MethodsWe developed a pipeline to automatically segment and extract image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, and applied it to the neck-to-knee Dixon images in 44,520 UK Biobank participants. We also segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We performed linear regression to assess associations with anthropometric and lifestyle factors. We further applied logistic regression to investigate the association between these IDPs and sarcopenia and frailty.ResultsAll muscle measurements were negatively associated with age, waist-to-hip ratio, and Townsend deprivation index, with p-values below the Bonferroni-corrected threshold (p<5.7 × 10−5) while they were positively associated with body mass index, alcohol intake, hand grip strength, physical activity and were significantly higher in men (p<5.7 × 10−5). Additionally, IMAT, (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants (p<5.7 × 10−5). In sarcopenia, muscle volume IDPs were most informative, particularly total muscle showing odds ratios (OR) of 0.361 and 95% confidence intervals (CI)= 0.332-0.392,p<5.7 × 10−5, while for frailty, muscle quality was most informative, particularly mid-thigh IMAT/Muscle ratio (OR = 1.328, 95% CI = 1.304-1.353,p<5.7 × 10−5).ConclusionsOur fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. While the choice of muscle measurements is important particularly when investigating associations with health conditions, most provide consistent differences relating to the detection of age, sex, body composition and lifestyle changes on muscularity.

BackgroundMorphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.MethodsHere we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three-dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes.ResultsWe found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease.ConclusionsThe results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.

Features extracted from cardiac MRI (CMR) are correlated with cardiovascular disease outcomes such as aneurysm, and have a substantial heritable component. To determine whether disease-relevant measurements are feasible in non-cardiac specific MRI, and to explore their associations with disease outcomes, and genetic and environmental risk factors. We segmented the heart, aorta, and vena cava from abdominal MRI scans using deep learning, and generated six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants. We performed genome- and phenome-wide association studies, and constructed a polygenic risk score for each phenotype. We demonstrated concordance between our IDPs and related IDPs from CMR, the current gold standard. We replicated previous findings related to sex differences and age-related changes in heart and vessel dimensions. We identified a significant association between infrarenal descending aorta CSA and incident abdominal aortic aneurysm, and between heart volume and several cardiovascular disorders. In a GWAS, we identified 72 associations at 59 loci (15 novel). We derived a polygenic risk score for each trait and demonstrated an association with TAA diagnosis, pointing to a potential screening method for individuals at high-risk of this condition. We demonstrated substantial genetic correlation with cardiovascular traits including aneurysms, varicose veins, dysrhythmia, and cardiac failure. Finally, heritability enrichment analysis implicated vascular tissue in the heritability of these traits. Our work highlights the value of non-specific MRI for exploring cardiovascular disease risk in cohort studies.

New findings What is the topic of this review? We describe a range of techniques in the time, frequency and information domains and their application alone and together for the analysis of blood flux signals acquired using laser Doppler fluximetry. What advances does it highlight? This review highlights the idea of using quantitative measures in different domains and scales to gain a better mechanistic understanding of the complex behaviours in the microcirculation. Abstract To date, time- and frequency-domain metrics of signals acquired through laser Doppler fluximetry have been unable to provide consistent and robust measures of the changes that occur in the microcirculation in healthy individuals at rest or in response to a provocation, or in patient cohorts. Recent studies have shown that in many disease states, such as metabolic and cardiovascular disease, there appears to be a reduction in the adaptive capabilities of the microvascular network and a consequent reduction in physiological information content. Here, we introduce non-linear measures for assessing the information content of fluximetry signals and demonstrate how they can yield deeper understanding of network behaviour. In addition, we show how these methods may be adapted to accommodate the multiple time scales modulating blood flow and how they can be used in combination with time- and frequency-domain metrics to discriminate more effectively between the different mechanistic influences on network properties.

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 nine 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.

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. Purpose To determine the population prevalence of HCM-associated sarcomeric variants, characterise their phenotypic manifestations, estimate penetrance, and identify associations between sarcomeric variants and clinical outcomes, we performed an observational study of 218,813 adults in the UK Biobank (UKBB), of whom 200,584 have whole exome sequencing (WES). Methods We carried out an integrated analysis of WES and cardiac magnetic resonance (CMR) imaging in UK Biobank participants stratified by sarcomere-encoding variant status. Computer vision techniques were used to automatically segment the four chambers of the heart (Figure 1). Cardiac motion analysis was used to derive strain and strain rates. Regional analysis of left ventricular wall thickness was performed using three-dimensional modelling of these segmentations. Results Median age at recruitment was 58 (IQR 50–63 years), and participants were followed up for a median of 10.8 years (IQR 9.9–11.6 years) with a total of 19,507 primary clinical events reported. The prevalence of rare variants (allele frequency SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events (MACE) compared to controls (HR 1.68, 95% CI 1.37–2.06, p While males had a higher overall risk of adverse outcomes, the incremental genetic risk from SARC-P/LP mutations was greater in females (HR for females: 2.18 CI 1.65–2.89, p In 21,322 participants with CMR, SARC-P/LP were associated with asymmetric increase in left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p Conclusions In the general population, SARC-P/LP variants have low aggregate penetrance for overt HCM but are associated with increased risk of adverse cardiovascular outcomes and a sub-clinical cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The study was supported by the Medical Research Council, UK (MC-A651-53301); National Institute for Health Research (NIHR) Imperial College Biomedical Research Centre; NIHR Royal Brompton Cardiovascular Biomedical Research Unit; British Heart Foundation (NH/17/1/32725, RG/19/6/34387, RE/18/4/34215).

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

An increased and more effective microvascular perfusion is postulated to play a key role in the physiological adaptation of Sherpa highlanders to the hypobaric hypoxia encountered at high altitude. To investigate this, we used Lempel-Ziv complexity (LZC) analysis to explore the spatiotemporal dynamics of the variability of the skin microvascular blood flux (BF) signals measured at the forearm and finger, in 32 lowlanders (LL) and 46 Sherpa highlanders (SH) during the Xtreme Everest 2 expedition. Measurements were made at baseline (BL) (LL: London 35 m; SH: Kathmandu 1300 m) and at Everest base camp (LL and SH: EBC 5,300 m). We found that BF signal content increased with ascent to EBC in both SH and LL. At both altitudes, LZC of the BF signals was significantly higher in SH, and was related to local slow-wave flow-motion activity over multiple spatial and temporal scales. In SH, BF LZC was also positively associated with LZC of the simultaneously measured tissue oxygenation signals. These data provide robust mechanistic information of microvascular network functionality and flexibility during hypoxic exposure on ascent to high altitude. They demonstrate the importance of a sustained heterogeneity of network perfusion, associated with local vaso-control mechanisms, to effective tissue oxygenation during hypobaric hypoxia.

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 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 ventricular stiffness and heart failure. Our results provide novel insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and tractable targets in heart failure.

BackgroundHypertrophic 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.MethodsWe compared outcomes and cardiovascular phenotypes in UK Biobank participants with whole exome sequencing stratified by sarcomere-encoding variant status.ResultsThe prevalence of rare variants (allele frequency ConclusionsIn the general population, SARC-P/LP variants have low aggregate penetrance for overt HCM but are associated with an increased risk of adverse cardiovascular outcomes and a sub-clinical cardiomyopathic phenotype. In contrast, rare sarcomeric variants that do not meet criteria to be classified as P/LP appear to have minimal clinical impact.

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.

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.

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 < 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.

Objective To determine whether analysis of microvascular network perfusion using complexity-based methods can discriminate between groups of individuals at an increased risk of developing CVD. Methods Data were obtained from laser Doppler recordings of skin blood flux at the forearm in 50 participants with non-alcoholic fatty liver disease grouped for absence (n = 28) or presence (n = 14) of type 2 diabetes and use of calcium channel blocker medication (n = 8). Power spectral density was evaluated and Lempel-Ziv complexity determined to quantify signal information content at single and multiple time-scales to account for the different processes modulating network perfusion. Results Complexity was associated with dilatory capacity and respiration and negatively with baseline blood flux and cardiac band power. The relationship between the modulators of flowmotion and complexity of blood flux is shown to change with time-scale improving discrimination between groups. Multiscale Lempel-Ziv achieved best classification accuracy of 86.1%. Conclusions Time and frequency domain measures alone are insufficient to discriminate between groups. As cardiovascular disease risk increases, the degree of complexity of the blood flux signal reduces, indicative of a reduced temporal activity and heterogeneous distribution of blood flow within the microvascular network sampled. Complexity-based methods, particularly multiscale variants, are shown to have good discriminatory capabilities.