Liver shape analysis using statistical parametric maps at population scale.

BACKGROUND: Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS: Here 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. RESULTS: We 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. CONCLUSIONS: The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.

Kidney shape statistical analysis: associations with disease and anthropometric factors.

BACKGROUND: Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS: We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS: We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS: The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.

Multi-domain analysis of microvascular flow motion dynamics in NAFLD.

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.

Precision MRI phenotyping of muscle volume and quality at a population scale.

Introduction: Magnetic 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 volume measurements: total muscle volume, regional measurements, measurements of muscle quality: intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative and associate with relevant health conditions such as dynapenia and frailty. Methods: We have measured image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, derived from the neck-to-knee Dixon images in 44,520 UK Biobank participants. We further segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We defined dynapenia based on grip strength below sex-specific cut-off points and frailty based on five criteria (weight loss, exhaustion, grip strength, low physical activity and slow walking pace). We used logistic regression to investigate the association between muscle volume and quality measurements and dynapenia and frailty. Results: Muscle volumes were significantly higher in male compared with female participants, even after correcting for height while, IMAT (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants. From the overall cohort, 7.6% (N = 3,261) were identified with dynapenia, and 1.1% (N = 455) with frailty. Dynapenia and frailty were positively associated with age and negatively associated with physical activity levels. Additionally, reduced muscle volume and quality measurements were associated with both dynapenia and frailty. In dynapenia, muscle volume IDPs were most informative, particularly total muscle exhibiting odds ratios (OR) of 0.392, while for frailty, muscle quality was found to be most informative, in particular thigh IMAT volume indexed to height squared (OR = 1.396), both with p-values below the Bonferroni-corrected threshold (p<8.8×10-5). Conclusion: Our fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. For dynapenia, muscle volumes particularly those including greater body coverage such as total muscle are the most informative, whilst, for frailty, markers of muscle quality were the most informative IDPs. These results suggest that different measurements may have varying diagnostic values for different health conditions.

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.

Artifact-free fat-water separation in Dixon MRI using deep learning.

Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information: The online version contains supplementary material available at 10.1186/s40537-022-00677-1.

Abdominal imaging associates body composition with COVID-19 severity.

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.

Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling.

BACKGROUND: The fatty liver index (FLI) is frequently used as a non-invasive clinical marker for research, prognostic and diagnostic purposes. It is also used to stratify individuals with hepatic steatosis such as non-alcoholic fatty liver disease (NAFLD), and to detect the presence of type 2 diabetes or cardiovascular disease. The FLI is calculated using a combination of anthropometric and blood biochemical variables; however, it reportedly excludes 8.5-16.7% of individuals with NAFLD. Moreover, the FLI cannot quantitatively predict liver fat, which might otherwise render an improved diagnosis and assessment of fatty liver, particularly in longitudinal studies. We propose FLI+ using predictive regression modelling, an improved index reflecting liver fat content that integrates 12 routinely-measured variables, including the original FLI. METHODS AND FINDINGS: We evaluated FLI+ on a dataset from the UK Biobank containing 28,796 individual estimates of proton density fat fraction derived from magnetic resonance imaging across normal to severe levels and interpolated to align with the original FLI range. The results obtained for FLI+ outperform the original FLI by delivering a lower mean absolute error by approximately 47%, a lower standard deviation by approximately 20%, and an increased adjusted R2 statistic by approximately 49%, reflecting a more accurate representation of liver fat content. CONCLUSIONS: Our proposed model predicting FLI+ has the potential to improve diagnosis and provide a more accurate stratification than FLI between absent, mild, moderate and severe levels of hepatic steatosis.

Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts.

Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue, as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom.

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.

Dysregulated Neurovascular Control Underlies Declining Microvascular Functionality in People With Non-alcoholic Fatty Liver Disease (NAFLD) at Risk of Liver Fibrosis.

BACKGROUND/AIMS: Increasing evidence shows that non-alcoholic fatty liver disease (NAFLD) is associated with dysregulation of microvascular perfusion independently of established cardio-metabolic risk factors. We investigated whether hepatic manifestations of NAFLD such as liver fibrosis and liver fat are associated with microvascular hemodynamics through dysregulation of neurovascular control. METHODS: Microvascular dilator (post-occlusive reactive hyperemia) and sympathetically mediated constrictor (deep inspiratory breath-hold) responses were measured at the forearm and finger, respectively, using laser Doppler fluximetry. Non-linear complexity-based analysis was used to assess the information content and variability of the resting blood flux (BF) signals, attributable to oscillatory flow-motion activity, and over multiple sampling frequencies. RESULTS: Measurements were made in 189 adults (113 men) with NAFLD, with (n = 65) and without (n = 124) type 2 diabetes mellitus (T2DM), age = 50.9 ± 11.7 years (mean ± SD). Microvascular dilator and constrictor capacity were both negatively associated with age (r = -0.178, p = 0.014, and r = -0.201, p = 0.007, respectively) and enhanced liver fibrosis (ELF) score (r = -0.155, p = 0.038 and r = -0.418, p < 0.0001, respectively). There was no association with measures of liver fat, obesity or T2DM. Lempel-Ziv complexity (LZC) and sample entropy (SE) of the BF signal measured at the two skin sites were associated negatively with age (p < 0.01 and p < 0.001) and positively with ELF score (p < 0.05 and p < 0.0001). In individuals with an ELF score ≥7.8 the influence of both neurogenic and respiratory flow-motion activity on LZC was up-rated (p < 0.0001). CONCLUSION: Altered microvascular network functionality occurs in adults with NAFLD suggesting a mechanistic role for dysregulated neurovascular control in individuals at risk of severe liver fibrosis.

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.

Enhanced flow-motion complexity of skin microvascular perfusion in Sherpas and lowlanders during ascent to high altitude.

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.

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 < 0.001, for both) with LTH. Multiscale complexity methods were better able to discriminate between haemodynamic states (p < 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.