310 results found
Nunn AVW, Guy GW, Bell JD, 2022, Bioelectric fields at the beginnings of life, Bioelectricity, Vol: 4, Pages: 237-247, ISSN: 2576-3105
The consensus on the origins of life is that it involved organization of prebiotic chemicals according to the underlying principles of thermodynamics to dissipate energy derived from photochemical and/or geochemical sources. Leading theories tend to be chemistry-centric, revolving around either metabolism or information-containing polymers first. However, experimental data also suggest that bioelectricity and quantum effects play an important role in biology, which might suggest that a further factor is required to explain how life began. Intriguingly, in the early part of 20th century, the concept of the “morphogenetic field” was proposed by Gurwitsch to explain how the shape of an organism was determined, while a role for quantum mechanics in biology was suggested by Bohr and Schrödinger, among others. This raises the question as to the potential of these phenomena, especially bioelectric fields, to have been involved in the origin of life. It points to the possibility that as bioelectricity is universally prevalent in biological systems today, it represents a more complex echo of an electromagnetic skeleton which helped shape life into being. It could be argued that as a flow of ions creates an electric field, this could have been pivotal in the formation of an energy dissipating structure, for instance, in deep sea thermal vents. Moreover, a field theory might also hint at the potential involvement of nontrivial quantum effects in life. Not only might this perspective help indicate the origins of morphogenetic fields, but also perhaps suggest where life may have started, and whether metabolism or information came first. It might also help to provide an insight into aging, cancer, consciousness, and, perhaps, how we might identify life beyond our planet. In short, when thinking about life, not only do we have to consider the accepted chemistry, but also the fields that must also shape it. In effect, to fully understand life, as well as the yin of
Nunn AVW, Guy GW, Brysch W, et al., 2022, Understanding Long COVID; Mitochondrial Health and Adaptation-Old Pathways, New Problems, BIOMEDICINES, Vol: 10
Thanaj M, Basty N, Cule M, et al., 2022, Liver Shape is Associated with Disease and Anthropometric Traits
<jats:title>Abstract</jats:title><jats:p>Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. 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 disease traits, including liver disease and type-2 diabetes. 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 disease groups showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variation in both type-2 diabetes and liver disease. The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic liver conditions.</jats:p>
Basty N, Sorokin EP, Thanaj M, et al., 2022, Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Fiamoncini J, Donado-Pestana CM, Duarte GBS, et al., 2022, Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test, FRONTIERS IN NUTRITION, Vol: 9, ISSN: 2296-861X
- Author Web Link
- Citations: 3
Sorokin E, Basty N, Whitcher B, et al., 2022, Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and erythrocyte morphology, American Journal of Human Genetics, Vol: 109, Pages: 1092-1104, ISSN: 0002-9297
The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank using magnetic resonance imaging, and provide the first reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1 . Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these novel common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal which co-localizes with a splicing quantitative trait locus for MS4A7 , and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to iron recycling and erythrocyte morphology.
Martin S, Tyrrell J, Thomas EL, et al., 2022, Correction: Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation., Elife, Vol: 11
Whitcher B, Thanaj M, Cule M, et al., 2022, Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts, Scientific Reports, Vol: 12, ISSN: 2045-2322
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.
Basty N, Sorokin EP, Thanaj M, et al., 2022, Abdominal Imaging Associates Body Composition with COVID-19 Severity
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Nunn AVW, Guy GW, Bell JD, 2022, Thermodynamics and inflammation: insights into quantum biology and ageing, Quantum Reports, Vol: 4, Pages: 47-74, ISSN: 2624-960X
Inflammation as a biological concept has been around a long time and derives from the Latin “to set on fire” and refers to the redness and heat, and usually swelling, which accompanies injury and infection. Chronic inflammation is also associated with ageing and is described by the term “inflammaging”. Likewise, the biological concept of hormesis, in the guise of what “does not kill you, makes you stronger”, has long been recognized, but in contrast, seems to have anti-inflammatory and age-slowing characteristics. As both phenomena act to restore homeostasis, they may share some common underlying principles. Thermodynamics describes the relationship between heat and energy, but is also intimately related to quantum mechanics. Life can be viewed as a series of self-renewing dissipative structures existing far from equilibrium as vortexes of “negentropy” that ages and dies; but, through reproduction and speciation, new robust structures are created, enabling life to adapt and continue in response to ever changing environments. In short, life can be viewed as a natural consequence of thermodynamics to dissipate energy to restore equilibrium; each component of this system is replaceable. However, at the molecular level, there is perhaps a deeper question: is life dependent on, or has it enhanced, quantum effects in space and time beyond those normally expected at the atomistic scale and temperatures that life operates at? There is some evidence it has. Certainly, the dissipative adaptive mechanism described by thermodynamics is now being extended into the quantum realm. Fascinating though this topic is, does exploring the relationship between quantum mechanics, thermodynamics, and biology give us a greater insight into ageing and, thus, medicine? It could be said that hormesis and inflammation are expressions of thermodynamic and quantum principles that control ageing via natural selection that could operate at all scales
Martin S, Tyrrell J, Thomas EL, et al., 2022, Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation, ELIFE, Vol: 11, ISSN: 2050-084X
- Author Web Link
- Citations: 5
Wesolowska-Andersen A, Brorsson CA, Bizzotto R, et al., 2022, Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study, CELL REPORTS MEDICINE, Vol: 3, ISSN: 2666-3791
- Author Web Link
- Citations: 12
Asaturyan HA, Basty N, Thanaj M, et al., 2022, Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling., PLoS One, Vol: 17
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.
Whitcher B, Thanaj M, Cule M, et al., 2021, Precision MRI Phenotyping Enables Detection of Small Changes in Body Composition for Longitudinal Cohorts, Publisher: Cold Spring Harbor Laboratory
<jats:title>ABSTRACT</jats:title><jats:p>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 3,088 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 (ASAT), 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.</jats:p>
Liu Y, Basty N, Whitcher B, et al., 2021, Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning, eLife, Vol: 10, ISSN: 2050-084X
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.
Woodley SB, Mould RR, Sahuri-Arisoylu M, et al., 2021, Mitochondrial Function as a Potential Tool for Assessing Function, Quality and Adulteration in Medicinal Herbal Teas, FRONTIERS IN PHARMACOLOGY, Vol: 12
- Author Web Link
- Citations: 1
Bizzotto R, Jennison C, Jones AG, et al., 2021, Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study, DIABETES CARE, Vol: 44, Pages: 511-518, ISSN: 0149-5992
- Author Web Link
- Citations: 10
Villarini B, Asaturyan H, Kurugol S, et al., 2021, 3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities, 34th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS), Publisher: IEEE, Pages: 166-171, ISSN: 2372-9198
- Author Web Link
- Citations: 6
Fitzpatrick JA, Basty N, Cule M, et al., 2020, Large-scale analysis of iliopsoas muscle volumes in the UK Biobank, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
- Author Web Link
- Citations: 7
Nunn AVW, Guy GW, Brysch W, et al., 2020, SARS-CoV-2 and mitochondrial health: implications of lifestyle and ageing, IMMUNITY & AGEING, Vol: 17, ISSN: 1742-4933
- Author Web Link
- Citations: 26
Alenaini W, Parkinson JRC, McCarthy JP, et al., 2020, Ethnic differences in body fat deposition and liver fat content in two UK-based cohorts, Obesity (Silver Spring, Md.), Vol: 28, Pages: 2142-2152, ISSN: 1071-7323
OBJECTIVE: Differences in the content and distribution of body fat and ectopic lipids may be responsible for ethnic variations in metabolic disease susceptibility. The aim of this study was to examine the ethnic distribution of body fat in two separate UK-based populations. METHODS: Anthropometry and body composition were assessed in two separate UK cohorts: the Hammersmith cohort and the UK Biobank, both comprising individuals of South Asian descent (SA), individuals of Afro-Caribbean descent (AC), and individuals of European descent (EUR). Regional adipose tissue stores and liver fat were measured by magnetic resonance techniques. RESULTS: The Hammersmith cohort (n = 747) had a mean (SD) age of 41.1 (14.5) years (EUR: 374 men, 240 women; SA: 68 men, 22 women; AC: 14 men, 29 women), and the UK Biobank (n = 9,533) had a mean (SD) age of 55.5 (7.5) years (EUR: 4,483 men, 4,873 women; SA: 80 men, 43 women, AC: 31 men, 25 women). Following adjustment for age and BMI, no significant differences in visceral adipose tissue or liver fat were observed between SA and EUR individuals in the either cohort. CONCLUSIONS: Our data, consistent across two independent UK-based cohorts, present a limited number of ethnic differences in the distribution of body fat depots associated with metabolic disease. These results suggest that the ethnic variation in susceptibility to features of the metabolic syndrome may not arise from differences in body fat.
Frost G, eriken R, Garcia Perez I, et al., 2020, Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study, EBioMedicine, Vol: 58, Pages: 1-9, ISSN: 2352-3964
BackgroundDietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.MethodsWe analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.FindingsA higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glu
Liu Y, Basty N, Whitcher B, et al., 2020, Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning, Publisher: eLife Sciences Publications Ltd
Cardiometabolic diseases are an increasing global health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of the drivers and mechanisms underlying these complex diseases remains incomplete. A better understanding is required to develop more effective therapeutic interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using 38,683 abdominal MRI scans in the UK Biobank, we used deep learning to systematically quantify parameters from individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots), and demonstrate that image derived phenotypes (volume, fat and iron content) reflect organ health and disease. We show that these traits have a substantial heritable component (8%-44%), and identify 93 independent genome-wide significant associations, including 3 associations with liver fat and one with liver iron that have not previously been reported, and 73 in traits that have not previously been studied. Overall our work demonstrates the utility of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues of the abdomen, and to generate new insights into the genetic architecture of complex traits.
Machann J, Stefan N, Wagner R, et al., 2020, Normalized Indices Derived from Visceral Adipose Mass Assessed by Magnetic Resonance Imaging and Their Correlation with Markers for Insulin Resistance and Prediabetes, NUTRIENTS, Vol: 12
- Author Web Link
- Citations: 8
Aldraimli M, Soria D, Parkinson J, et al., 2020, Machine learning prediction of susceptibility to visceral fat associated diseases, Health and Technology, Vol: 10, Pages: 925-944, ISSN: 2190-7188
Classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., healthy/at risk). Similar to statistical inference modelling, ML modelling is subject to the problem of class imbalance and is affected by the majority class, increasing the false-negative rate. In this study, we built and evaluated thirty-six ML models to classify approximately 4300 female and 4100 male participants from the UK Biobank into three categorical risk statuses based on discretised visceral adipose tissue (VAT) measurements from magnetic resonance imaging. We also examined the effect of sampling techniques on the models when dealing with class imbalance. The sampling techniques used had a significant impact on the classification and resulted in an improvement in risk status prediction by facilitating an increase in the information contained within each variable. Based on domain expert criteria the best three classification models for the female and male cohort visceral fat prediction were identified. The Area Under Receiver Operator Characteristic curve of the models tested (with external data) was 0.78 to 0.89 for females and 0.75 to 0.86 for males. These encouraging results will be used to guide further development of models to enable prediction of VAT value. This will be useful to identify individuals with excess VAT volume who are at risk of developing metabolic disease ensuring relevant lifestyle interventions can be appropriately targeted.
Whyte MB, Shojaee-Moradie F, Sharaf SE, et al., 2020, HDL-apoA-I kinetics in response to 16 wk of exercise training in men with nonalcoholic fatty liver disease., American Journal of Physiology: Endocrinology and Metabolism, Vol: 318, Pages: E839-E847, ISSN: 0193-1849
Nonalcoholic fatty liver disease (NAFLD) is characterized by low-circulating concentration of high-density lipoprotein cholesterol (HDL-C) and raised triacylglycerol (TAG). Exercise reduces hepatic fat content, improves insulin resistance and increases clearance of very-low-density lipoprotein-1 (VLDL1). However, the effect of exercise on TAG and HDL-C metabolism is unknown. We randomized male participants to 16 wk of supervised, moderate-intensity aerobic exercise (n = 15), or conventional lifestyle advice (n = 12). Apolipoprotein A-I (apoA-I) and VLDL-TAG and apolipoprotein B (apoB) kinetics were investigated using stable isotopes (1-[13C]-leucine and 1,1,2,3,3-2H5 glycerol) pre- and postintervention. Participants underwent MRI/spectroscopy to assess changes in visceral fat. Results are means ± SD. At baseline, there were no differences between exercise and control groups for age (52.4 ± 7.5 vs. 52.8 ± 10.3 yr), body mass index (BMI: 31.6 ± 3.2 vs. 31.7 ± 3.6 kg/m2), and waist circumference (109.3 ± 7.5 vs. 110.0 ± 13.6 cm). Percentage of liver fat was 23.8 (interquartile range 9.8-32.5%). Exercise reduced body weight (101.3 ± 10.2 to 97.9 ± 12.2 kg; P < 0.001) and hepatic fat content [from 19.6%, interquartile range (IQR) 14.6-36.1% to 8.9% (4.4-17.8%); P = 0.001] and increased the fraction HDL-C concentration (measured following ultracentrifugation) and apoA-I pool size with no change in the control group. However, plasma and VLDL1-TAG concentrations and HDL-apoA-I fractional catabolic rate (FCR) and production rate (PR) did not change significantly with exercise. Both at baseline (all participants) and after exercise there was an inverse correlation between apoA-I pool size and VLDL-TAG and -apoB pool size. The modest effect of exercise on HDL metabolism may be explained b
Atabaki-Pasdar N, Ohlsson M, Vinuela A, et al., 2020, Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts, PLoS Medicine, Vol: 17, Pages: 1-27, ISSN: 1549-1277
BackgroundNon-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.Methods and findingsWe utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of
Littlejohns TJ, Holliday J, Gibson LM, et al., 2020, The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions, Nature Communications, Vol: 11, ISSN: 2041-1723
UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.
Basty N, Liu Y, Cule M, et al., 2020, Automated Measurement of Pancreatic Fat and Iron Concentration Using Multi-Echo and T1-Weighted MRI Data, Pages: 345-348, ISSN: 1945-7928
We present an automated method for estimation of proton density fat fraction and iron concentration in the pancreas using both structural and quantitative imaging data present in the UK Biobank abdominal MRI acquisition protocol. Our method relies on automatic segmentation of 3D T1-weighted MRI data using a convolutional neural network and extracting the location of the multi -echo slice through the segmented volume. We finally estimate the fat and iron content in the pancreas using the extracted segmentation as a mask on the multi-echo data. Our segmentation model achieves a mean dice similarity coefficient of 0.842±0.071 on unseen data, which is comparable to the current state of the art for 3D segmentation of the pancreas. The proposed method is efficient and robust and enables an enhanced analysis of spatial distribution of proton density fat fraction and iron concentration over the current practice of manually placing regions of interest on often ambiguous multi-echo data.
Shinjyo N, Parkinson J, Bell J, et al., 2020, Berberine for prevention of dementia associated with diabetes and its comorbidities: A systematic review, JOURNAL OF INTEGRATIVE MEDICINE-JIM, Vol: 18, Pages: 125-151, ISSN: 2095-4964
- Author Web Link
- Citations: 23
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